Original Research

Current evidence indicates that obesity may initiate psoriasis or worsen existing disease. Various factors contribute to the development of obesity, including eating disorders (EDs). The aim of this study was to screen for and identify factors associated with EDs in patients with psoriasis and their impact on the development of obesity in this population. Demographic information including body mass index (BMI), Eating Attitude Test (EAT-26), Dermatology Life Quality Index (DLQI), Attitude Scale for Healthy Nutrition (ASHN), and Depression Anxiety Stress Scale 21 (DASS-21) scores were statistically analyzed for 82 participants with psoriasis at a tertiary dermatology clinic. It is important to manage obesity and other comorbidities of psoriasis in addition to treating its cutaneous manifestations, which may require a biopsychosocial approach.

Psoriasis is a chronic multisystemic inflammatory skin disease with a worldwide prevalence of 2% to 3%.¹ Psoriasis can be accompanied by other conditions such as psoriatic arthritis, obesity, metabolic syndrome, diabetes mellitus, hypertension, dyslipidemia, atherosclerotic disease, inflammatory bowel disease, and anxiety/depression. It is important to manage comorbidities of psoriasis in addition to treating the cutaneous manifestations of the disease.¹

Obesity is a major public health concern worldwide. Numerous observational and epidemiologic studies have reported a high prevalence of obesity among patients with psoriasis.² Current evidence indicates that obesity may initiate or worsen psoriasis; furthermore, it is important to note that obesity may negatively impact the effectiveness of psoriasis-specific treatments or increase the incidence of adverse effects. Therefore, managing obesity is crucial in the treatment of psoriasis.³ Numerous studies have investigated the association between psoriasis and obesity, and they commonly conclude that both conditions share the same genetic metabolic pathways.^2-4 However, it is important to consider environmental factors such as dietary habits, smoking, alcohol consumption, and a sedentary lifestyle—all of which are associated with psoriasis and also can contribute to the development of obesity.⁵ Because of the effects of obesity in psoriasis patients, factors that impact the development of obesity have become a popular research topic.

Eating disorders (EDs) are a crucial risk factor for both developing and maintaining obesity. In particular, two EDs that are associated with obesity include binge eating disorder and bulimia nervosa.⁶ According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition,⁷ binge eating disorder can be diagnosed when a patient has at least 1 episode of binge eating per week over a 3-month period. Bulimia nervosa can be diagnosed when a patient is excessively concerned with their body weight and shape and engages in behaviors to prevent weight gain (eg, forced vomiting, excessive use of laxatives).⁷ Psychiatrists who specialize in EDs make diagnoses based on these criteria. In daily practice, there are several quick and simple questionnaires available to screen for EDs that can be used by nonpsychiatrist physicians, including the commonly used 26-item Eating Attitudes Test (EAT-26).⁸ The EAT-26 has been used to screen for EDs in patients with inflammatory disorders.⁹

The aim of this study was to screen for EDs in patients with psoriasis to identify potential risk factors for development of obesity.

Materials and Methods

This study included patients with psoriasis who were screened for EDs at a tertiary dermatology clinic in Turkey between January 2021 and December 2023. This study was approved by the local ethics committee and was in accordance with the Declaration of Helsinki (decision number E-93471371-514.99-225000079).

Study Design and Patient Inclusion Criteria—This quantitative cross-sectional study utilized EAT-26, Dermatology Life Quality Index (DLQI), Attitude Scale for Healthy Nutrition (ASHN), and Depression Anxiety Stress Scale-21 (DASS-21) scores. All the questionnaire scales used in the study were adapted and validated in Turkey.^8,10-12 The inclusion criteria consisted of being older than 18 years of age, being literate, having psoriasis for at least 1 year that was not treated topically or systemically, and having no psychiatric diseases outside an ED. The questionnaires were presented in written format following the clinical examination. Literacy was an inclusion criterion in this study due to the absence of auxiliary health personnel.

Study Variables—The study variables included age, sex, marital status (single/divorced or married), education status (primary/secondary school or high school/university), employment status (employed or unemployed/retired), body mass index (BMI), smoking status, alcohol-consumption status, Psoriasis Area Severity Index score, presence of nail psoriasis and psoriatic arthritis, duration of psoriasis, family history of psoriasis, EAT-26 score, ASHN score, DLQI score, and DASS-21 score. Body mass index was calculated by taking a participant’s weight in kilograms and dividing it by their height in meters squared. The BMI values were classified into 3 categories: normal (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥30 kg/m²).¹³

Questionnaires—The EAT-26 questionnaire includes 26 questions that are used to detect EDs. Responses to each question include Likert-type answer options (ie, “always,” “usually,” “often,” “sometimes,” “rarely,” and “never.”) Patients with scores of 20 points or higher (range, 0–78) are classified as high risk for EDs.⁸ In our study, EAT-26 scores were grouped into 2 categories: patients scoring less than 20 points and those scoring 20 points or higher.

The DLQI questionnaire includes 10 questions to measure dermatologic symptoms and qualiy of life. Responses to each question include Likert-type answer options (ie, “not at all,” “a little,” “a lot,” or “very much.”) On the DLQI scale, the higher the score, the lower the quality of life (score range, 0–30).¹⁰

The ASHN questionnaire includes 21 questions that measure attitudes toward healthy nutrition with 5 possible answer options (“strongly disagree,” “disagree,” “undecided,” “agree,” and “strongly agree”). On this scale, higher scores indicate the participant is more knowledgeable about healthy nutrition (score range, 0–78).¹¹

The DASS-21 questionnaire includes 21 questions that measure the severity of a range of symptoms common to depression, anxiety, and stress. Responses include Likert-type answer options (eg, “never,” “sometimes,” “often,” and “almost always.”) On this scale, a higher score (range of 0–21 for each) indicates higher levels of depression, anxiety, and stress.¹²

Statistical Analysis—Descriptive statistics were analyzed using SPSS software version 22.0 (IBM). The Shapiro-Wilk test was applied to determine whether the data were normally distributed. For categorical variables, frequency differences among groups were compared using the Pearson χ² test. A t test was used to compare the means of 2 independent groups with a normal distribution. One-way analysis of variance and Tukey Honest Significant Difference post hoc analysis were used to test whether there was a statistically significant difference among the normally distributed means of independent groups. Pearson correlation analysis was used to determine whether there was a linear relationship between 2 numeric measurements and, if so, to determine the direction and severity of this relationship. P<.05 indicated statistical significance in this study.

Results

Study Participant Demographics—This study included 82 participants with a mean age of 44.3 years; 52.4% (43/82) were female, and 85.4% (70/82) were married. The questionnaire took an average of 4.2 minutes for participants to complete. A total of 57.3% (47/82) of patients had completed primary/secondary education and 59.8% (49/82) were employed. The mean BMI was 28.1 kg/m². According to the BMI classification, 26.8% (22/82) participants had a normal weight, 36.6% (30/82) were overweight, and 43.9% (36/82) were obese. A total of 48.8% (40/82) of participants smoked, and 4.9% (4/82) consumed alcohol. The mean Psoriasis Area and Severity Index score was 5.4. A total of 54.9% (45/82) of participants had nail psoriasis, and 24.4% (20/82) had psoriatic arthritis. The mean duration of psoriasis was 153 months. A total of 29.3% (24/82) of participants had a positive family history of psoriasis. The mean EAT-26 score was 11.1. A total of 12.2% (10/82) of participants had an EAT-26 score of 20 points or higher and were considered at high risk for an ED. The mean ASHN score was 72.9; the mean DLQI score was 5.5; and on the DASS-21 scale, mean scores for depression, anxiety, and stress were 6.3, 8.7, and 10.0, respectively (Table).

Comparative Evaluation of the BMI Groups—The only statistically significant differences among the 3 BMI groups were related to marital status, EAT-26 score, and anxiety and stress scores (P=.02, <.01, <.01, and <.01, respectively)(eTable 1). The number of single/divorced participants in the overweight group was significantly (P=.02) greater than in the normal weight group. The mean EAT-26 score for the normal weight group was significantly (P<.01) lower than for the overweight and obese groups; there was no significant difference in mean EAT-26 scores between the overweight and obese groups. The mean anxiety score was significantly (P<.01) lower in the normal weight group compared with the overweight and obese groups. There was no significant difference between the overweight and obese groups according to the mean depression score. The mean stress and anxiety scores were significantly (P<.01) lower in the normal weight group than in the overweight and obese groups. There was no significant difference between the overweight and obese groups according to the mean anxiety score.

Comparative Evaluation of the EAT-26 Scores—There were statistically significant differences among the EAT-26 scores related to sex; BMI; and depression, anxiety, and stress scores (P=.04, .02, <.01, <.01, and <.01, respectively). The number of females in the group with a score of 20 points or higher was significantly (P=.04) less than that in the group scoring less than 20 points. The mean BMI in the group with a score of 20 points or higher was significantly (P=.02) greater than in group scoring less than 20 points. The mean depression, anxiety, and stress scores of the group scoring 20 points or higher were significantly (P<.01 for all) greater than in the group scoring less than 20 points (eTable 2).

Correlation Analysis of the Study Variables—The EAT-26 scores were positively correlated with BMI, anxiety, depression, and stress (P<.01 for all)(eTable 3).

Comment

Eating disorders are psychiatric conditions that require a multidisciplinary approach. Nonpsychiatric medical departments may be involved due to the severe consequences (eg, various skin changes¹⁴) of these disorders. Psoriasis is not known to be directly affected by the presence of an ED; however, it is possible that EDs could indirectly affect patients with psoriasis by influencing obesity. Therefore, this study aimed to examine the relationship between ED risk factors and obesity in this population.

The relationship between psoriasis and obesity has been a popular research topic in dermatology since the 1990s.¹⁵ Epidemiologic and observational studies have reported that patients with psoriasis are more likely to be overweight or have obesity, which is an independent risk factor for psoriasis.^3,16 However, the causal relationship between psoriasis and obesity remains unclear. In a comprehensive review, Barros et al¹⁷ emphasized the causal relationship between obesity and psoriasis under several headings. Firstly, a higher BMI increases the risk for psoriasis by promoting cytokine release and immune system dysregulation. Secondly, a Western diet (eg, processed foods and fast food) triggers obesity and psoriasis by increasing adipose tissue. Thirdly, the alteration of the skin and gut microbiota triggers chronic inflammation as a result of bacterial translocation in patients with obesity. Fourthly, a high-fat diet and palmitic acid disrupt the intestinal integrity of the gut and increase the risk for psoriasis and obesity by triggering chronic inflammation of bacterial fragments that pass into the blood. Finally, the decrease in the amount of adiponectin and the increase in the amount of leptin in patients with obesity may cause psoriasis by increasing proinflammatory cytokines, which are similar to those involved in the pathogenesis of psoriasis.¹⁷ Additionally, psoriatic inflammation can cause insulin resistance and metabolic dysfunction, leading to obesity.¹⁸ The relationship between psoriasis and obesity cannot be solely explained by metabolic pathways. Smoking, alcohol consumption, and a sedentary lifestyle all are associated with psoriasis and also can contribute to obesity.⁵ Our study revealed no significant difference in smoking or alcohol consumption between the normal weight and overweight/obesity groups. Based on our data, we determined that smoking and alcohol consumption did not affect obesity in our patients with psoriasis.

Observational and epidemiologic studies have shown that patients with psoriasis experience increased rates of depression, anxiety, and stress.¹⁹ In studies of pathogenesis, a connection between depression and psoriatic inflammation has been established.²⁰ It is known that inflammatory cytokines similar to those in psoriasis are involved in the development of obesity.¹⁸ In addition, depression and anxiety can lead to binge eating, unhealthy food choices, and a more sedentary lifestyle.⁵ All of these variables may contribute to the associations between depression and anxiety with psoriasis and obesity. Zafiriou et al²¹ conducted a study to investigate the relationship between psoriasis, obesity, and depression through inflammatory pathways with a focus on the importance of IL-17. Data showing that IL-17–producing Th17-cell subgroups play a considerable role in the development of obesity and depression prompted the authors to suggest that psoriasis, obesity, and ­anxiety/depression may be interconnected manifestations of immune dysregulation, potentially linked to IL-17 and its associated cells.²¹ Mrowietz et al²² also suggested that metabolic inflammation may contribute to obesity and depression in patients with psoriasis and highlighted the importance of several cytokines, including tumor necrosis factor α, IL-6, IL-8, IL-17, and IL-23. Our study revealed no significant differences in depression scores between BMI groups. Another meta-analysis reported conflicting findings on the incidence of depression in obese patients with psoriasis.²³ Some of the studies had a small number of participants. Compared to depression, anxiety has received less attention in studies of patients with obesity with psoriasis. However, these studies have shown a positive correlation between anxiety scores and BMI in patients with psoriasis.^24,25 In our study, similar to the findings of previous studies, overweight patients and those with obesitywho have psoriasis had significantly (P<.01) greater anxiety and stress scores than did normal weight patients with psoriasis.

Obesity should be assessed in patients with psoriasis via a biopsychosocial approach that takes into account genetic, behavioral, and environmental factors.²⁶ Eating disorders are considered to be one of the factors contributing to obesity. Numerous studies in the literature have demonstrated a greater incidence of EDs in patients with obesity vs those without obesity.^5,6,27 Obesity and EDs have a bidirectional relationship: individuals with obesity are at risk for EDs due to body dissatisfaction, dieting habits, and depressive states. Conversely, poor eating behaviors in individuals with a normal weight can lead to obesity.²⁸

There are few studies in the literature exploring the relationship between psoriasis and EDs. Crosta et al²⁹ demonstrated that patients with psoriasis had impaired results on ED screening tests and that these scores deteriorated further as BMI increased. Moreover, Altunay et al³⁰ demonstrated that patients with psoriasis and metabolic syndrome had higher scores on the ED screening test. In this study, patients with higher scores also exhibited high levels of anxiety.³⁰ In our study, similar to the findings of previous studies, patients with psoriasis who were overweight or had obesity had significantly (P<.01) greater EAT-26 scores than those in the normal weight group. Patients with high EAT-26 scores also exhibited elevated levels of depression, anxiety, and stress. Additionally, EAT-26 scores were positively correlated with BMI, anxiety, depression, and stress scores. Our study as well as other studies in the literature indicate that additional research is needed to determine the associations between EDs and obesity in psoriasis.

Conclusion

Managing obesity is crucial for patients with psoriasis. This study showed that EAT-26 scores were higher in patients with psoriasis who were overweight or had obesity than in those who were normal weight. Participants with high EAT-26 scores (≥20 points) were more likely to be female and have higher anxiety and stress scores. In addition, EAT-26 scores were positively correlated with BMI as well as depression, anxiety, and stress scores. Eating disorders may contribute to the development of obesity in patients with psoriasis. Although our study was limited by a small sample size, the results suggest that there is a need for large-scale multicenter studies to investigate the relationship between psoriasis and EDs.

References

1. Kalkan G. Comorbidities in psoriasis: the recognition of psoriasis as a systemic disease and current management. Turkderm-Turk Arch Dermatol Venereol. 2017;51:71-77.

2. Armstrong AW, Harskamp CT, Armstrong EJ. The association between psoriasis and obesity: a systematic review and meta-analysis of observational studies. Nutr Diabetes. 2012;2:E54.

3. Jensen P, Skov L. Psoriasis and obesity. Dermatology. 2016;232:633-639.

4. Mirghani H, Altemani AT, Altemani ST, et al. The cross talk between psoriasis, obesity, and dyslipidemia: a meta-analysis. Cureus. 2023;15:e49253.

5. Roehring M, Mashep MR, White MA, et al. The metabolic syndrome and behavioral correlates in obese patients with binge disorders. Obesity. 2009;17:481-486.

6. da Luz FQ, Hay P, Touyz S, et al. Obesity with comorbid eating disorders: associated health risks and treatment approaches. Nutrients. 2018;10:829.

7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. American Psychiatric Association; 2013.

8. Ergüney Okumus¸ FE, Sertel Berk HÖ. The psychometric properties of the Eating Attitudes Test short form (EAT-26) in a college sample. Stud Psychol. 2020;40:57-78.

9. Stoleru G, Leopold A, Auerbach A, et al. Female gender, dissatisfaction with weight, and number of IBD related surgeries as independent risk factors for eating disorders among patients with inflammatory bowel diseases. BMC Gastroenterol. 2022;22:438.

10. Öztürkcan S, Ermertcan AT, Eser E, et al. Cross validation of the Turkish version of dermatology life quality index. Int J Dermatol. 2006;45:1300-1307.

11. Demir GT, Ciciog˘lu HI˙. Attitude scale for healthy nutrition (ASHN): validity and reliability study. Gaziantep Univ J Sport Sci. 2019;4:256-274.

12. Yılmaz O, Boz H, Arslan A. The validity and reliability of depression stress and anxiety scale (DASS 21) Turkish short form. Res Financial Econ Soc Stud. 2017;2:78-91.

13. Nuttall FQ. Body mass index: obesity, BMI, and health: a critical review. Nutr Today. 2015;50:117-128.

14. Strumia R, Manzata E, Gualandi M. Is there a role for dermatologists in eating disorders? Expert Rev Dermatol. 2017; 2:109-112.

15. Henseler T, Christophers E. Disease concomitance in psoriasis. J Am Acad Dermatol. 1995;32:982-986.

16. Naldi L, Addis A, Chimenti S, et al. Impact of body mass index and obesity on clinical response to systemic treatment for psoriasis. evidence from the Psocare project. Dermatology. 2008;217:365-373.

17. Barros G, Duran P, Vera I, et al. Exploring the links between obesity and psoriasis: a comprehensive review. Int J Mol Sci. 2022;23:7499.

18. Hao Y, Zhu YJ, Zou S, et al. Metabolic syndrome and psoriasis: mechanisms and future directions. Front Immunol. 2021;12:711060.

19. Jing D, Xiao H, Shen M, et al. Association of psoriasis with anxiety and depression: a case–control study in Chinese patients. Front Med (Lausanne). 2021;8:771645.

20. Sahi FM, Masood A, Danawar NA, et al. Association between psoriasis and depression: a traditional review. Cureus. 2020;12:E9708.

21. Zafiriou E, Daponte AI, Siokas V, et al. Depression and obesity in patients with psoriasis and psoriatic arthritis: is IL-17–mediated immune dysregulation the connecting link? Front Immunol. 2021;12:699848.

22. Mrowietz U, Sümbül M, Gerdes S. Depression, a major comorbidity of psoriatic disease, is caused by metabolic inflammation. J Eur Acad Dermatol Venereol. 2023;37:1731-1738.

23. Pavlova NT, Kioskli K, Smith C, et al. Psychosocial aspects of obesity in adults with psoriasis: a systematic review. Skin Health Dis. 2021;1:E33.

24. Innamorati M, Quinto RM, Imperatori C, et al. Health-related quality of life and its association with alexithymia and difficulties in emotion regulation in patients with psoriasis. Compr Psychiatry. 2016;70:200-208.

25. Tabolli S, Naldi L, Pagliarello C, et al. Evaluation of the impact of writing exercises interventions on quality of life in patients with psoriasis undergoing systemic treatments. Br J Dermatol. 2012;167:1254‐1264.

26. Albuquerque D, Nóbrega C, Manco L, et al. The contribution of genetics and environment to obesity. Br Med Bull. 2017;123:159‐173.

27. Balantekin KN, Grammer AC, Fitzsimmons-Craft EE, et al. Overweight and obesity are associated with increased eating disorder correlates and general psychopathology in university women with eating disorders. Eat Behav. 2021;41:101482.

28. Jebeile H, Lister NB, Baur LA, et al. Eating disorder risk in adolescents with obesity. Obes Rev. 2021;22:E13173.

29. Crosta ML, Caldarola G, Fraietta S, et al. Psychopathology and eating disorders in patients with psoriasis. G Ital Dermatol Venereol. 2014;149:355-361.

30. Altunay I, Demirci GT, Ates B, et al. Do eating disorders accompany metabolic syndrome in psoriasis patients? results of a preliminary study. Clin Cosmet Investig Dermatol. 2011;4:139-143.

Current evidence indicates that obesity may initiate psoriasis or worsen existing disease. Various factors contribute to the development of obesity, including eating disorders (EDs). The aim of this study was to screen for and identify factors associated with EDs in patients with psoriasis and their impact on the development of obesity in this population. Demographic information including body mass index (BMI), Eating Attitude Test (EAT-26), Dermatology Life Quality Index (DLQI), Attitude Scale for Healthy Nutrition (ASHN), and Depression Anxiety Stress Scale 21 (DASS-21) scores were statistically analyzed for 82 participants with psoriasis at a tertiary dermatology clinic. It is important to manage obesity and other comorbidities of psoriasis in addition to treating its cutaneous manifestations, which may require a biopsychosocial approach.

Psoriasis is a chronic multisystemic inflammatory skin disease with a worldwide prevalence of 2% to 3%.¹ Psoriasis can be accompanied by other conditions such as psoriatic arthritis, obesity, metabolic syndrome, diabetes mellitus, hypertension, dyslipidemia, atherosclerotic disease, inflammatory bowel disease, and anxiety/depression. It is important to manage comorbidities of psoriasis in addition to treating the cutaneous manifestations of the disease.¹

Obesity is a major public health concern worldwide. Numerous observational and epidemiologic studies have reported a high prevalence of obesity among patients with psoriasis.² Current evidence indicates that obesity may initiate or worsen psoriasis; furthermore, it is important to note that obesity may negatively impact the effectiveness of psoriasis-specific treatments or increase the incidence of adverse effects. Therefore, managing obesity is crucial in the treatment of psoriasis.³ Numerous studies have investigated the association between psoriasis and obesity, and they commonly conclude that both conditions share the same genetic metabolic pathways.^2-4 However, it is important to consider environmental factors such as dietary habits, smoking, alcohol consumption, and a sedentary lifestyle—all of which are associated with psoriasis and also can contribute to the development of obesity.⁵ Because of the effects of obesity in psoriasis patients, factors that impact the development of obesity have become a popular research topic.

Eating disorders (EDs) are a crucial risk factor for both developing and maintaining obesity. In particular, two EDs that are associated with obesity include binge eating disorder and bulimia nervosa.⁶ According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition,⁷ binge eating disorder can be diagnosed when a patient has at least 1 episode of binge eating per week over a 3-month period. Bulimia nervosa can be diagnosed when a patient is excessively concerned with their body weight and shape and engages in behaviors to prevent weight gain (eg, forced vomiting, excessive use of laxatives).⁷ Psychiatrists who specialize in EDs make diagnoses based on these criteria. In daily practice, there are several quick and simple questionnaires available to screen for EDs that can be used by nonpsychiatrist physicians, including the commonly used 26-item Eating Attitudes Test (EAT-26).⁸ The EAT-26 has been used to screen for EDs in patients with inflammatory disorders.⁹

The aim of this study was to screen for EDs in patients with psoriasis to identify potential risk factors for development of obesity.

Materials and Methods

This study included patients with psoriasis who were screened for EDs at a tertiary dermatology clinic in Turkey between January 2021 and December 2023. This study was approved by the local ethics committee and was in accordance with the Declaration of Helsinki (decision number E-93471371-514.99-225000079).

Study Design and Patient Inclusion Criteria—This quantitative cross-sectional study utilized EAT-26, Dermatology Life Quality Index (DLQI), Attitude Scale for Healthy Nutrition (ASHN), and Depression Anxiety Stress Scale-21 (DASS-21) scores. All the questionnaire scales used in the study were adapted and validated in Turkey.^8,10-12 The inclusion criteria consisted of being older than 18 years of age, being literate, having psoriasis for at least 1 year that was not treated topically or systemically, and having no psychiatric diseases outside an ED. The questionnaires were presented in written format following the clinical examination. Literacy was an inclusion criterion in this study due to the absence of auxiliary health personnel.

Study Variables—The study variables included age, sex, marital status (single/divorced or married), education status (primary/secondary school or high school/university), employment status (employed or unemployed/retired), body mass index (BMI), smoking status, alcohol-consumption status, Psoriasis Area Severity Index score, presence of nail psoriasis and psoriatic arthritis, duration of psoriasis, family history of psoriasis, EAT-26 score, ASHN score, DLQI score, and DASS-21 score. Body mass index was calculated by taking a participant’s weight in kilograms and dividing it by their height in meters squared. The BMI values were classified into 3 categories: normal (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥30 kg/m²).¹³

Questionnaires—The EAT-26 questionnaire includes 26 questions that are used to detect EDs. Responses to each question include Likert-type answer options (ie, “always,” “usually,” “often,” “sometimes,” “rarely,” and “never.”) Patients with scores of 20 points or higher (range, 0–78) are classified as high risk for EDs.⁸ In our study, EAT-26 scores were grouped into 2 categories: patients scoring less than 20 points and those scoring 20 points or higher.

The DLQI questionnaire includes 10 questions to measure dermatologic symptoms and qualiy of life. Responses to each question include Likert-type answer options (ie, “not at all,” “a little,” “a lot,” or “very much.”) On the DLQI scale, the higher the score, the lower the quality of life (score range, 0–30).¹⁰

The ASHN questionnaire includes 21 questions that measure attitudes toward healthy nutrition with 5 possible answer options (“strongly disagree,” “disagree,” “undecided,” “agree,” and “strongly agree”). On this scale, higher scores indicate the participant is more knowledgeable about healthy nutrition (score range, 0–78).¹¹

The DASS-21 questionnaire includes 21 questions that measure the severity of a range of symptoms common to depression, anxiety, and stress. Responses include Likert-type answer options (eg, “never,” “sometimes,” “often,” and “almost always.”) On this scale, a higher score (range of 0–21 for each) indicates higher levels of depression, anxiety, and stress.¹²

Statistical Analysis—Descriptive statistics were analyzed using SPSS software version 22.0 (IBM). The Shapiro-Wilk test was applied to determine whether the data were normally distributed. For categorical variables, frequency differences among groups were compared using the Pearson χ² test. A t test was used to compare the means of 2 independent groups with a normal distribution. One-way analysis of variance and Tukey Honest Significant Difference post hoc analysis were used to test whether there was a statistically significant difference among the normally distributed means of independent groups. Pearson correlation analysis was used to determine whether there was a linear relationship between 2 numeric measurements and, if so, to determine the direction and severity of this relationship. P<.05 indicated statistical significance in this study.

Results

Study Participant Demographics—This study included 82 participants with a mean age of 44.3 years; 52.4% (43/82) were female, and 85.4% (70/82) were married. The questionnaire took an average of 4.2 minutes for participants to complete. A total of 57.3% (47/82) of patients had completed primary/secondary education and 59.8% (49/82) were employed. The mean BMI was 28.1 kg/m². According to the BMI classification, 26.8% (22/82) participants had a normal weight, 36.6% (30/82) were overweight, and 43.9% (36/82) were obese. A total of 48.8% (40/82) of participants smoked, and 4.9% (4/82) consumed alcohol. The mean Psoriasis Area and Severity Index score was 5.4. A total of 54.9% (45/82) of participants had nail psoriasis, and 24.4% (20/82) had psoriatic arthritis. The mean duration of psoriasis was 153 months. A total of 29.3% (24/82) of participants had a positive family history of psoriasis. The mean EAT-26 score was 11.1. A total of 12.2% (10/82) of participants had an EAT-26 score of 20 points or higher and were considered at high risk for an ED. The mean ASHN score was 72.9; the mean DLQI score was 5.5; and on the DASS-21 scale, mean scores for depression, anxiety, and stress were 6.3, 8.7, and 10.0, respectively (Table).

Comparative Evaluation of the BMI Groups—The only statistically significant differences among the 3 BMI groups were related to marital status, EAT-26 score, and anxiety and stress scores (P=.02, <.01, <.01, and <.01, respectively)(eTable 1). The number of single/divorced participants in the overweight group was significantly (P=.02) greater than in the normal weight group. The mean EAT-26 score for the normal weight group was significantly (P<.01) lower than for the overweight and obese groups; there was no significant difference in mean EAT-26 scores between the overweight and obese groups. The mean anxiety score was significantly (P<.01) lower in the normal weight group compared with the overweight and obese groups. There was no significant difference between the overweight and obese groups according to the mean depression score. The mean stress and anxiety scores were significantly (P<.01) lower in the normal weight group than in the overweight and obese groups. There was no significant difference between the overweight and obese groups according to the mean anxiety score.

Comparative Evaluation of the EAT-26 Scores—There were statistically significant differences among the EAT-26 scores related to sex; BMI; and depression, anxiety, and stress scores (P=.04, .02, <.01, <.01, and <.01, respectively). The number of females in the group with a score of 20 points or higher was significantly (P=.04) less than that in the group scoring less than 20 points. The mean BMI in the group with a score of 20 points or higher was significantly (P=.02) greater than in group scoring less than 20 points. The mean depression, anxiety, and stress scores of the group scoring 20 points or higher were significantly (P<.01 for all) greater than in the group scoring less than 20 points (eTable 2).

Correlation Analysis of the Study Variables—The EAT-26 scores were positively correlated with BMI, anxiety, depression, and stress (P<.01 for all)(eTable 3).

Comment

Eating disorders are psychiatric conditions that require a multidisciplinary approach. Nonpsychiatric medical departments may be involved due to the severe consequences (eg, various skin changes¹⁴) of these disorders. Psoriasis is not known to be directly affected by the presence of an ED; however, it is possible that EDs could indirectly affect patients with psoriasis by influencing obesity. Therefore, this study aimed to examine the relationship between ED risk factors and obesity in this population.

The relationship between psoriasis and obesity has been a popular research topic in dermatology since the 1990s.¹⁵ Epidemiologic and observational studies have reported that patients with psoriasis are more likely to be overweight or have obesity, which is an independent risk factor for psoriasis.^3,16 However, the causal relationship between psoriasis and obesity remains unclear. In a comprehensive review, Barros et al¹⁷ emphasized the causal relationship between obesity and psoriasis under several headings. Firstly, a higher BMI increases the risk for psoriasis by promoting cytokine release and immune system dysregulation. Secondly, a Western diet (eg, processed foods and fast food) triggers obesity and psoriasis by increasing adipose tissue. Thirdly, the alteration of the skin and gut microbiota triggers chronic inflammation as a result of bacterial translocation in patients with obesity. Fourthly, a high-fat diet and palmitic acid disrupt the intestinal integrity of the gut and increase the risk for psoriasis and obesity by triggering chronic inflammation of bacterial fragments that pass into the blood. Finally, the decrease in the amount of adiponectin and the increase in the amount of leptin in patients with obesity may cause psoriasis by increasing proinflammatory cytokines, which are similar to those involved in the pathogenesis of psoriasis.¹⁷ Additionally, psoriatic inflammation can cause insulin resistance and metabolic dysfunction, leading to obesity.¹⁸ The relationship between psoriasis and obesity cannot be solely explained by metabolic pathways. Smoking, alcohol consumption, and a sedentary lifestyle all are associated with psoriasis and also can contribute to obesity.⁵ Our study revealed no significant difference in smoking or alcohol consumption between the normal weight and overweight/obesity groups. Based on our data, we determined that smoking and alcohol consumption did not affect obesity in our patients with psoriasis.

Observational and epidemiologic studies have shown that patients with psoriasis experience increased rates of depression, anxiety, and stress.¹⁹ In studies of pathogenesis, a connection between depression and psoriatic inflammation has been established.²⁰ It is known that inflammatory cytokines similar to those in psoriasis are involved in the development of obesity.¹⁸ In addition, depression and anxiety can lead to binge eating, unhealthy food choices, and a more sedentary lifestyle.⁵ All of these variables may contribute to the associations between depression and anxiety with psoriasis and obesity. Zafiriou et al²¹ conducted a study to investigate the relationship between psoriasis, obesity, and depression through inflammatory pathways with a focus on the importance of IL-17. Data showing that IL-17–producing Th17-cell subgroups play a considerable role in the development of obesity and depression prompted the authors to suggest that psoriasis, obesity, and ­anxiety/depression may be interconnected manifestations of immune dysregulation, potentially linked to IL-17 and its associated cells.²¹ Mrowietz et al²² also suggested that metabolic inflammation may contribute to obesity and depression in patients with psoriasis and highlighted the importance of several cytokines, including tumor necrosis factor α, IL-6, IL-8, IL-17, and IL-23. Our study revealed no significant differences in depression scores between BMI groups. Another meta-analysis reported conflicting findings on the incidence of depression in obese patients with psoriasis.²³ Some of the studies had a small number of participants. Compared to depression, anxiety has received less attention in studies of patients with obesity with psoriasis. However, these studies have shown a positive correlation between anxiety scores and BMI in patients with psoriasis.^24,25 In our study, similar to the findings of previous studies, overweight patients and those with obesitywho have psoriasis had significantly (P<.01) greater anxiety and stress scores than did normal weight patients with psoriasis.

Obesity should be assessed in patients with psoriasis via a biopsychosocial approach that takes into account genetic, behavioral, and environmental factors.²⁶ Eating disorders are considered to be one of the factors contributing to obesity. Numerous studies in the literature have demonstrated a greater incidence of EDs in patients with obesity vs those without obesity.^5,6,27 Obesity and EDs have a bidirectional relationship: individuals with obesity are at risk for EDs due to body dissatisfaction, dieting habits, and depressive states. Conversely, poor eating behaviors in individuals with a normal weight can lead to obesity.²⁸

There are few studies in the literature exploring the relationship between psoriasis and EDs. Crosta et al²⁹ demonstrated that patients with psoriasis had impaired results on ED screening tests and that these scores deteriorated further as BMI increased. Moreover, Altunay et al³⁰ demonstrated that patients with psoriasis and metabolic syndrome had higher scores on the ED screening test. In this study, patients with higher scores also exhibited high levels of anxiety.³⁰ In our study, similar to the findings of previous studies, patients with psoriasis who were overweight or had obesity had significantly (P<.01) greater EAT-26 scores than those in the normal weight group. Patients with high EAT-26 scores also exhibited elevated levels of depression, anxiety, and stress. Additionally, EAT-26 scores were positively correlated with BMI, anxiety, depression, and stress scores. Our study as well as other studies in the literature indicate that additional research is needed to determine the associations between EDs and obesity in psoriasis.

Conclusion

Managing obesity is crucial for patients with psoriasis. This study showed that EAT-26 scores were higher in patients with psoriasis who were overweight or had obesity than in those who were normal weight. Participants with high EAT-26 scores (≥20 points) were more likely to be female and have higher anxiety and stress scores. In addition, EAT-26 scores were positively correlated with BMI as well as depression, anxiety, and stress scores. Eating disorders may contribute to the development of obesity in patients with psoriasis. Although our study was limited by a small sample size, the results suggest that there is a need for large-scale multicenter studies to investigate the relationship between psoriasis and EDs.

References

1. Kalkan G. Comorbidities in psoriasis: the recognition of psoriasis as a systemic disease and current management. Turkderm-Turk Arch Dermatol Venereol. 2017;51:71-77.

2. Armstrong AW, Harskamp CT, Armstrong EJ. The association between psoriasis and obesity: a systematic review and meta-analysis of observational studies. Nutr Diabetes. 2012;2:E54.

3. Jensen P, Skov L. Psoriasis and obesity. Dermatology. 2016;232:633-639.

4. Mirghani H, Altemani AT, Altemani ST, et al. The cross talk between psoriasis, obesity, and dyslipidemia: a meta-analysis. Cureus. 2023;15:e49253.

5. Roehring M, Mashep MR, White MA, et al. The metabolic syndrome and behavioral correlates in obese patients with binge disorders. Obesity. 2009;17:481-486.

6. da Luz FQ, Hay P, Touyz S, et al. Obesity with comorbid eating disorders: associated health risks and treatment approaches. Nutrients. 2018;10:829.

7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. American Psychiatric Association; 2013.

8. Ergüney Okumus¸ FE, Sertel Berk HÖ. The psychometric properties of the Eating Attitudes Test short form (EAT-26) in a college sample. Stud Psychol. 2020;40:57-78.

9. Stoleru G, Leopold A, Auerbach A, et al. Female gender, dissatisfaction with weight, and number of IBD related surgeries as independent risk factors for eating disorders among patients with inflammatory bowel diseases. BMC Gastroenterol. 2022;22:438.

10. Öztürkcan S, Ermertcan AT, Eser E, et al. Cross validation of the Turkish version of dermatology life quality index. Int J Dermatol. 2006;45:1300-1307.

11. Demir GT, Ciciog˘lu HI˙. Attitude scale for healthy nutrition (ASHN): validity and reliability study. Gaziantep Univ J Sport Sci. 2019;4:256-274.

12. Yılmaz O, Boz H, Arslan A. The validity and reliability of depression stress and anxiety scale (DASS 21) Turkish short form. Res Financial Econ Soc Stud. 2017;2:78-91.

13. Nuttall FQ. Body mass index: obesity, BMI, and health: a critical review. Nutr Today. 2015;50:117-128.

14. Strumia R, Manzata E, Gualandi M. Is there a role for dermatologists in eating disorders? Expert Rev Dermatol. 2017; 2:109-112.

15. Henseler T, Christophers E. Disease concomitance in psoriasis. J Am Acad Dermatol. 1995;32:982-986.

16. Naldi L, Addis A, Chimenti S, et al. Impact of body mass index and obesity on clinical response to systemic treatment for psoriasis. evidence from the Psocare project. Dermatology. 2008;217:365-373.

17. Barros G, Duran P, Vera I, et al. Exploring the links between obesity and psoriasis: a comprehensive review. Int J Mol Sci. 2022;23:7499.

18. Hao Y, Zhu YJ, Zou S, et al. Metabolic syndrome and psoriasis: mechanisms and future directions. Front Immunol. 2021;12:711060.

19. Jing D, Xiao H, Shen M, et al. Association of psoriasis with anxiety and depression: a case–control study in Chinese patients. Front Med (Lausanne). 2021;8:771645.

20. Sahi FM, Masood A, Danawar NA, et al. Association between psoriasis and depression: a traditional review. Cureus. 2020;12:E9708.

21. Zafiriou E, Daponte AI, Siokas V, et al. Depression and obesity in patients with psoriasis and psoriatic arthritis: is IL-17–mediated immune dysregulation the connecting link? Front Immunol. 2021;12:699848.

22. Mrowietz U, Sümbül M, Gerdes S. Depression, a major comorbidity of psoriatic disease, is caused by metabolic inflammation. J Eur Acad Dermatol Venereol. 2023;37:1731-1738.

23. Pavlova NT, Kioskli K, Smith C, et al. Psychosocial aspects of obesity in adults with psoriasis: a systematic review. Skin Health Dis. 2021;1:E33.

24. Innamorati M, Quinto RM, Imperatori C, et al. Health-related quality of life and its association with alexithymia and difficulties in emotion regulation in patients with psoriasis. Compr Psychiatry. 2016;70:200-208.

25. Tabolli S, Naldi L, Pagliarello C, et al. Evaluation of the impact of writing exercises interventions on quality of life in patients with psoriasis undergoing systemic treatments. Br J Dermatol. 2012;167:1254‐1264.

26. Albuquerque D, Nóbrega C, Manco L, et al. The contribution of genetics and environment to obesity. Br Med Bull. 2017;123:159‐173.

27. Balantekin KN, Grammer AC, Fitzsimmons-Craft EE, et al. Overweight and obesity are associated with increased eating disorder correlates and general psychopathology in university women with eating disorders. Eat Behav. 2021;41:101482.

28. Jebeile H, Lister NB, Baur LA, et al. Eating disorder risk in adolescents with obesity. Obes Rev. 2021;22:E13173.

29. Crosta ML, Caldarola G, Fraietta S, et al. Psychopathology and eating disorders in patients with psoriasis. G Ital Dermatol Venereol. 2014;149:355-361.

30. Altunay I, Demirci GT, Ates B, et al. Do eating disorders accompany metabolic syndrome in psoriasis patients? results of a preliminary study. Clin Cosmet Investig Dermatol. 2011;4:139-143.

Current evidence indicates that obesity may initiate psoriasis or worsen existing disease. Various factors contribute to the development of obesity, including eating disorders (EDs). The aim of this study was to screen for and identify factors associated with EDs in patients with psoriasis and their impact on the development of obesity in this population. Demographic information including body mass index (BMI), Eating Attitude Test (EAT-26), Dermatology Life Quality Index (DLQI), Attitude Scale for Healthy Nutrition (ASHN), and Depression Anxiety Stress Scale 21 (DASS-21) scores were statistically analyzed for 82 participants with psoriasis at a tertiary dermatology clinic. It is important to manage obesity and other comorbidities of psoriasis in addition to treating its cutaneous manifestations, which may require a biopsychosocial approach.

Psoriasis is a chronic multisystemic inflammatory skin disease with a worldwide prevalence of 2% to 3%.¹ Psoriasis can be accompanied by other conditions such as psoriatic arthritis, obesity, metabolic syndrome, diabetes mellitus, hypertension, dyslipidemia, atherosclerotic disease, inflammatory bowel disease, and anxiety/depression. It is important to manage comorbidities of psoriasis in addition to treating the cutaneous manifestations of the disease.¹

Obesity is a major public health concern worldwide. Numerous observational and epidemiologic studies have reported a high prevalence of obesity among patients with psoriasis.² Current evidence indicates that obesity may initiate or worsen psoriasis; furthermore, it is important to note that obesity may negatively impact the effectiveness of psoriasis-specific treatments or increase the incidence of adverse effects. Therefore, managing obesity is crucial in the treatment of psoriasis.³ Numerous studies have investigated the association between psoriasis and obesity, and they commonly conclude that both conditions share the same genetic metabolic pathways.^2-4 However, it is important to consider environmental factors such as dietary habits, smoking, alcohol consumption, and a sedentary lifestyle—all of which are associated with psoriasis and also can contribute to the development of obesity.⁵ Because of the effects of obesity in psoriasis patients, factors that impact the development of obesity have become a popular research topic.

Eating disorders (EDs) are a crucial risk factor for both developing and maintaining obesity. In particular, two EDs that are associated with obesity include binge eating disorder and bulimia nervosa.⁶ According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition,⁷ binge eating disorder can be diagnosed when a patient has at least 1 episode of binge eating per week over a 3-month period. Bulimia nervosa can be diagnosed when a patient is excessively concerned with their body weight and shape and engages in behaviors to prevent weight gain (eg, forced vomiting, excessive use of laxatives).⁷ Psychiatrists who specialize in EDs make diagnoses based on these criteria. In daily practice, there are several quick and simple questionnaires available to screen for EDs that can be used by nonpsychiatrist physicians, including the commonly used 26-item Eating Attitudes Test (EAT-26).⁸ The EAT-26 has been used to screen for EDs in patients with inflammatory disorders.⁹

The aim of this study was to screen for EDs in patients with psoriasis to identify potential risk factors for development of obesity.

Materials and Methods

This study included patients with psoriasis who were screened for EDs at a tertiary dermatology clinic in Turkey between January 2021 and December 2023. This study was approved by the local ethics committee and was in accordance with the Declaration of Helsinki (decision number E-93471371-514.99-225000079).

Study Design and Patient Inclusion Criteria—This quantitative cross-sectional study utilized EAT-26, Dermatology Life Quality Index (DLQI), Attitude Scale for Healthy Nutrition (ASHN), and Depression Anxiety Stress Scale-21 (DASS-21) scores. All the questionnaire scales used in the study were adapted and validated in Turkey.^8,10-12 The inclusion criteria consisted of being older than 18 years of age, being literate, having psoriasis for at least 1 year that was not treated topically or systemically, and having no psychiatric diseases outside an ED. The questionnaires were presented in written format following the clinical examination. Literacy was an inclusion criterion in this study due to the absence of auxiliary health personnel.

Study Variables—The study variables included age, sex, marital status (single/divorced or married), education status (primary/secondary school or high school/university), employment status (employed or unemployed/retired), body mass index (BMI), smoking status, alcohol-consumption status, Psoriasis Area Severity Index score, presence of nail psoriasis and psoriatic arthritis, duration of psoriasis, family history of psoriasis, EAT-26 score, ASHN score, DLQI score, and DASS-21 score. Body mass index was calculated by taking a participant’s weight in kilograms and dividing it by their height in meters squared. The BMI values were classified into 3 categories: normal (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥30 kg/m²).¹³

Questionnaires—The EAT-26 questionnaire includes 26 questions that are used to detect EDs. Responses to each question include Likert-type answer options (ie, “always,” “usually,” “often,” “sometimes,” “rarely,” and “never.”) Patients with scores of 20 points or higher (range, 0–78) are classified as high risk for EDs.⁸ In our study, EAT-26 scores were grouped into 2 categories: patients scoring less than 20 points and those scoring 20 points or higher.

The DLQI questionnaire includes 10 questions to measure dermatologic symptoms and qualiy of life. Responses to each question include Likert-type answer options (ie, “not at all,” “a little,” “a lot,” or “very much.”) On the DLQI scale, the higher the score, the lower the quality of life (score range, 0–30).¹⁰

The ASHN questionnaire includes 21 questions that measure attitudes toward healthy nutrition with 5 possible answer options (“strongly disagree,” “disagree,” “undecided,” “agree,” and “strongly agree”). On this scale, higher scores indicate the participant is more knowledgeable about healthy nutrition (score range, 0–78).¹¹

The DASS-21 questionnaire includes 21 questions that measure the severity of a range of symptoms common to depression, anxiety, and stress. Responses include Likert-type answer options (eg, “never,” “sometimes,” “often,” and “almost always.”) On this scale, a higher score (range of 0–21 for each) indicates higher levels of depression, anxiety, and stress.¹²

Statistical Analysis—Descriptive statistics were analyzed using SPSS software version 22.0 (IBM). The Shapiro-Wilk test was applied to determine whether the data were normally distributed. For categorical variables, frequency differences among groups were compared using the Pearson χ² test. A t test was used to compare the means of 2 independent groups with a normal distribution. One-way analysis of variance and Tukey Honest Significant Difference post hoc analysis were used to test whether there was a statistically significant difference among the normally distributed means of independent groups. Pearson correlation analysis was used to determine whether there was a linear relationship between 2 numeric measurements and, if so, to determine the direction and severity of this relationship. P<.05 indicated statistical significance in this study.

Results

Study Participant Demographics—This study included 82 participants with a mean age of 44.3 years; 52.4% (43/82) were female, and 85.4% (70/82) were married. The questionnaire took an average of 4.2 minutes for participants to complete. A total of 57.3% (47/82) of patients had completed primary/secondary education and 59.8% (49/82) were employed. The mean BMI was 28.1 kg/m². According to the BMI classification, 26.8% (22/82) participants had a normal weight, 36.6% (30/82) were overweight, and 43.9% (36/82) were obese. A total of 48.8% (40/82) of participants smoked, and 4.9% (4/82) consumed alcohol. The mean Psoriasis Area and Severity Index score was 5.4. A total of 54.9% (45/82) of participants had nail psoriasis, and 24.4% (20/82) had psoriatic arthritis. The mean duration of psoriasis was 153 months. A total of 29.3% (24/82) of participants had a positive family history of psoriasis. The mean EAT-26 score was 11.1. A total of 12.2% (10/82) of participants had an EAT-26 score of 20 points or higher and were considered at high risk for an ED. The mean ASHN score was 72.9; the mean DLQI score was 5.5; and on the DASS-21 scale, mean scores for depression, anxiety, and stress were 6.3, 8.7, and 10.0, respectively (Table).

Comparative Evaluation of the BMI Groups—The only statistically significant differences among the 3 BMI groups were related to marital status, EAT-26 score, and anxiety and stress scores (P=.02, <.01, <.01, and <.01, respectively)(eTable 1). The number of single/divorced participants in the overweight group was significantly (P=.02) greater than in the normal weight group. The mean EAT-26 score for the normal weight group was significantly (P<.01) lower than for the overweight and obese groups; there was no significant difference in mean EAT-26 scores between the overweight and obese groups. The mean anxiety score was significantly (P<.01) lower in the normal weight group compared with the overweight and obese groups. There was no significant difference between the overweight and obese groups according to the mean depression score. The mean stress and anxiety scores were significantly (P<.01) lower in the normal weight group than in the overweight and obese groups. There was no significant difference between the overweight and obese groups according to the mean anxiety score.

Comparative Evaluation of the EAT-26 Scores—There were statistically significant differences among the EAT-26 scores related to sex; BMI; and depression, anxiety, and stress scores (P=.04, .02, <.01, <.01, and <.01, respectively). The number of females in the group with a score of 20 points or higher was significantly (P=.04) less than that in the group scoring less than 20 points. The mean BMI in the group with a score of 20 points or higher was significantly (P=.02) greater than in group scoring less than 20 points. The mean depression, anxiety, and stress scores of the group scoring 20 points or higher were significantly (P<.01 for all) greater than in the group scoring less than 20 points (eTable 2).

Correlation Analysis of the Study Variables—The EAT-26 scores were positively correlated with BMI, anxiety, depression, and stress (P<.01 for all)(eTable 3).

Comment

Eating disorders are psychiatric conditions that require a multidisciplinary approach. Nonpsychiatric medical departments may be involved due to the severe consequences (eg, various skin changes¹⁴) of these disorders. Psoriasis is not known to be directly affected by the presence of an ED; however, it is possible that EDs could indirectly affect patients with psoriasis by influencing obesity. Therefore, this study aimed to examine the relationship between ED risk factors and obesity in this population.

The relationship between psoriasis and obesity has been a popular research topic in dermatology since the 1990s.¹⁵ Epidemiologic and observational studies have reported that patients with psoriasis are more likely to be overweight or have obesity, which is an independent risk factor for psoriasis.^3,16 However, the causal relationship between psoriasis and obesity remains unclear. In a comprehensive review, Barros et al¹⁷ emphasized the causal relationship between obesity and psoriasis under several headings. Firstly, a higher BMI increases the risk for psoriasis by promoting cytokine release and immune system dysregulation. Secondly, a Western diet (eg, processed foods and fast food) triggers obesity and psoriasis by increasing adipose tissue. Thirdly, the alteration of the skin and gut microbiota triggers chronic inflammation as a result of bacterial translocation in patients with obesity. Fourthly, a high-fat diet and palmitic acid disrupt the intestinal integrity of the gut and increase the risk for psoriasis and obesity by triggering chronic inflammation of bacterial fragments that pass into the blood. Finally, the decrease in the amount of adiponectin and the increase in the amount of leptin in patients with obesity may cause psoriasis by increasing proinflammatory cytokines, which are similar to those involved in the pathogenesis of psoriasis.¹⁷ Additionally, psoriatic inflammation can cause insulin resistance and metabolic dysfunction, leading to obesity.¹⁸ The relationship between psoriasis and obesity cannot be solely explained by metabolic pathways. Smoking, alcohol consumption, and a sedentary lifestyle all are associated with psoriasis and also can contribute to obesity.⁵ Our study revealed no significant difference in smoking or alcohol consumption between the normal weight and overweight/obesity groups. Based on our data, we determined that smoking and alcohol consumption did not affect obesity in our patients with psoriasis.

Observational and epidemiologic studies have shown that patients with psoriasis experience increased rates of depression, anxiety, and stress.¹⁹ In studies of pathogenesis, a connection between depression and psoriatic inflammation has been established.²⁰ It is known that inflammatory cytokines similar to those in psoriasis are involved in the development of obesity.¹⁸ In addition, depression and anxiety can lead to binge eating, unhealthy food choices, and a more sedentary lifestyle.⁵ All of these variables may contribute to the associations between depression and anxiety with psoriasis and obesity. Zafiriou et al²¹ conducted a study to investigate the relationship between psoriasis, obesity, and depression through inflammatory pathways with a focus on the importance of IL-17. Data showing that IL-17–producing Th17-cell subgroups play a considerable role in the development of obesity and depression prompted the authors to suggest that psoriasis, obesity, and ­anxiety/depression may be interconnected manifestations of immune dysregulation, potentially linked to IL-17 and its associated cells.²¹ Mrowietz et al²² also suggested that metabolic inflammation may contribute to obesity and depression in patients with psoriasis and highlighted the importance of several cytokines, including tumor necrosis factor α, IL-6, IL-8, IL-17, and IL-23. Our study revealed no significant differences in depression scores between BMI groups. Another meta-analysis reported conflicting findings on the incidence of depression in obese patients with psoriasis.²³ Some of the studies had a small number of participants. Compared to depression, anxiety has received less attention in studies of patients with obesity with psoriasis. However, these studies have shown a positive correlation between anxiety scores and BMI in patients with psoriasis.^24,25 In our study, similar to the findings of previous studies, overweight patients and those with obesitywho have psoriasis had significantly (P<.01) greater anxiety and stress scores than did normal weight patients with psoriasis.

Obesity should be assessed in patients with psoriasis via a biopsychosocial approach that takes into account genetic, behavioral, and environmental factors.²⁶ Eating disorders are considered to be one of the factors contributing to obesity. Numerous studies in the literature have demonstrated a greater incidence of EDs in patients with obesity vs those without obesity.^5,6,27 Obesity and EDs have a bidirectional relationship: individuals with obesity are at risk for EDs due to body dissatisfaction, dieting habits, and depressive states. Conversely, poor eating behaviors in individuals with a normal weight can lead to obesity.²⁸

There are few studies in the literature exploring the relationship between psoriasis and EDs. Crosta et al²⁹ demonstrated that patients with psoriasis had impaired results on ED screening tests and that these scores deteriorated further as BMI increased. Moreover, Altunay et al³⁰ demonstrated that patients with psoriasis and metabolic syndrome had higher scores on the ED screening test. In this study, patients with higher scores also exhibited high levels of anxiety.³⁰ In our study, similar to the findings of previous studies, patients with psoriasis who were overweight or had obesity had significantly (P<.01) greater EAT-26 scores than those in the normal weight group. Patients with high EAT-26 scores also exhibited elevated levels of depression, anxiety, and stress. Additionally, EAT-26 scores were positively correlated with BMI, anxiety, depression, and stress scores. Our study as well as other studies in the literature indicate that additional research is needed to determine the associations between EDs and obesity in psoriasis.

Conclusion

Managing obesity is crucial for patients with psoriasis. This study showed that EAT-26 scores were higher in patients with psoriasis who were overweight or had obesity than in those who were normal weight. Participants with high EAT-26 scores (≥20 points) were more likely to be female and have higher anxiety and stress scores. In addition, EAT-26 scores were positively correlated with BMI as well as depression, anxiety, and stress scores. Eating disorders may contribute to the development of obesity in patients with psoriasis. Although our study was limited by a small sample size, the results suggest that there is a need for large-scale multicenter studies to investigate the relationship between psoriasis and EDs.

References

1. Kalkan G. Comorbidities in psoriasis: the recognition of psoriasis as a systemic disease and current management. Turkderm-Turk Arch Dermatol Venereol. 2017;51:71-77.

2. Armstrong AW, Harskamp CT, Armstrong EJ. The association between psoriasis and obesity: a systematic review and meta-analysis of observational studies. Nutr Diabetes. 2012;2:E54.

3. Jensen P, Skov L. Psoriasis and obesity. Dermatology. 2016;232:633-639.

4. Mirghani H, Altemani AT, Altemani ST, et al. The cross talk between psoriasis, obesity, and dyslipidemia: a meta-analysis. Cureus. 2023;15:e49253.

5. Roehring M, Mashep MR, White MA, et al. The metabolic syndrome and behavioral correlates in obese patients with binge disorders. Obesity. 2009;17:481-486.

6. da Luz FQ, Hay P, Touyz S, et al. Obesity with comorbid eating disorders: associated health risks and treatment approaches. Nutrients. 2018;10:829.

7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. American Psychiatric Association; 2013.

8. Ergüney Okumus¸ FE, Sertel Berk HÖ. The psychometric properties of the Eating Attitudes Test short form (EAT-26) in a college sample. Stud Psychol. 2020;40:57-78.

9. Stoleru G, Leopold A, Auerbach A, et al. Female gender, dissatisfaction with weight, and number of IBD related surgeries as independent risk factors for eating disorders among patients with inflammatory bowel diseases. BMC Gastroenterol. 2022;22:438.

10. Öztürkcan S, Ermertcan AT, Eser E, et al. Cross validation of the Turkish version of dermatology life quality index. Int J Dermatol. 2006;45:1300-1307.

11. Demir GT, Ciciog˘lu HI˙. Attitude scale for healthy nutrition (ASHN): validity and reliability study. Gaziantep Univ J Sport Sci. 2019;4:256-274.

12. Yılmaz O, Boz H, Arslan A. The validity and reliability of depression stress and anxiety scale (DASS 21) Turkish short form. Res Financial Econ Soc Stud. 2017;2:78-91.

13. Nuttall FQ. Body mass index: obesity, BMI, and health: a critical review. Nutr Today. 2015;50:117-128.

14. Strumia R, Manzata E, Gualandi M. Is there a role for dermatologists in eating disorders? Expert Rev Dermatol. 2017; 2:109-112.

15. Henseler T, Christophers E. Disease concomitance in psoriasis. J Am Acad Dermatol. 1995;32:982-986.

16. Naldi L, Addis A, Chimenti S, et al. Impact of body mass index and obesity on clinical response to systemic treatment for psoriasis. evidence from the Psocare project. Dermatology. 2008;217:365-373.

17. Barros G, Duran P, Vera I, et al. Exploring the links between obesity and psoriasis: a comprehensive review. Int J Mol Sci. 2022;23:7499.

18. Hao Y, Zhu YJ, Zou S, et al. Metabolic syndrome and psoriasis: mechanisms and future directions. Front Immunol. 2021;12:711060.

19. Jing D, Xiao H, Shen M, et al. Association of psoriasis with anxiety and depression: a case–control study in Chinese patients. Front Med (Lausanne). 2021;8:771645.

20. Sahi FM, Masood A, Danawar NA, et al. Association between psoriasis and depression: a traditional review. Cureus. 2020;12:E9708.

21. Zafiriou E, Daponte AI, Siokas V, et al. Depression and obesity in patients with psoriasis and psoriatic arthritis: is IL-17–mediated immune dysregulation the connecting link? Front Immunol. 2021;12:699848.

22. Mrowietz U, Sümbül M, Gerdes S. Depression, a major comorbidity of psoriatic disease, is caused by metabolic inflammation. J Eur Acad Dermatol Venereol. 2023;37:1731-1738.

23. Pavlova NT, Kioskli K, Smith C, et al. Psychosocial aspects of obesity in adults with psoriasis: a systematic review. Skin Health Dis. 2021;1:E33.

24. Innamorati M, Quinto RM, Imperatori C, et al. Health-related quality of life and its association with alexithymia and difficulties in emotion regulation in patients with psoriasis. Compr Psychiatry. 2016;70:200-208.

25. Tabolli S, Naldi L, Pagliarello C, et al. Evaluation of the impact of writing exercises interventions on quality of life in patients with psoriasis undergoing systemic treatments. Br J Dermatol. 2012;167:1254‐1264.

26. Albuquerque D, Nóbrega C, Manco L, et al. The contribution of genetics and environment to obesity. Br Med Bull. 2017;123:159‐173.

27. Balantekin KN, Grammer AC, Fitzsimmons-Craft EE, et al. Overweight and obesity are associated with increased eating disorder correlates and general psychopathology in university women with eating disorders. Eat Behav. 2021;41:101482.

28. Jebeile H, Lister NB, Baur LA, et al. Eating disorder risk in adolescents with obesity. Obes Rev. 2021;22:E13173.

29. Crosta ML, Caldarola G, Fraietta S, et al. Psychopathology and eating disorders in patients with psoriasis. G Ital Dermatol Venereol. 2014;149:355-361.

30. Altunay I, Demirci GT, Ates B, et al. Do eating disorders accompany metabolic syndrome in psoriasis patients? results of a preliminary study. Clin Cosmet Investig Dermatol. 2011;4:139-143.

More than 37 million Americans have diabetes mellitus (DM), and approximately 90% have type 2 DM (T2DM), including about 25% of veterans.^1,2 The current guidelines suggest that therapy depends on a patient's comorbidities, management needs, and patient-centered treatment factors.³ About 1 in 3 adults with DM have chronic kidney disease (CKD), defined as the presence of kidney damage or an estimated glomerular filtration rate (eGFR) < 60 mL/min per 1.73 m², persisting for ≥ 3 months.⁴

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors are a class of antihyperglycemic agents acting on the SGLT-2 proteins expressed in the renal proximal convoluted tubules. They exert their effects by preventing the reabsorption of filtered glucose from the tubular lumen. There are 4 SGLT-2 inhibitors approved by the US Food and Drug Administration: canagliflozin, dapagliflozin, empagliflozin, and ertugliflozin. Empagliflozin is currently the preferred SGLT-2 inhibitor on the US Department of Veterans Affairs (VA) formulary.

According to the American Diabetes Association guidelines, empagliflozin is considered when an individual has or is at risk for atherosclerotic cardiovascular disease, heart failure, and CKD.³ SGLT-2 inhibitors are a favorable option due to their low risk for hypoglycemia while also promoting weight loss. The EMPEROR-Reduced trial demonstrated that, in addition to benefits for patients with heart failure, empagliflozin also slowed the progressive decline in kidney function in those with and without DM.⁵ The purpose of this study was to evaluate the effectiveness of empagliflozin on hemoglobin A_1c (HbA_1c) levels in patients with CKD at the Hershel “Woody” Williams VA Medical Center (HWWVAMC) in Huntington, West Virginia, along with other laboratory test markers.

Methods

The Marshall University Institutional Review Board #1 (Medical) and the HWWVAMC institutional review board and research and development committee each reviewed and approved this study. A retrospective chart review was conducted on patients diagnosed with T2DM and stage 3 CKD who were prescribed empagliflozin for DM management between January 1, 2015, and October 1, 2022, yielding 1771 patients. Data were obtained through the VHA Corporate Data Warehouse (CDW) and stored on the VA Informatics and Computing Infrastructure (VINCI) research server.

Patients were included if they were aged 18 to 89 years, prescribed empagliflozin by a VA clinician for the treatment of T2DM, had an eGFR between 30 and 59 mL/min/1.73 m², and had an initial HbA_1c between 7% and 10%. Using further random sampling, patients were either excluded or divided into, those with stage 3a CKD and those with stage 3b CKD. The primary endpoint of this study was the change in HbA_1c levels in patients with stage 3b CKD (eGFR 30-44 mL/min/1.73 m²) compared with stage 3a (eGFR 45-59 mL/min/1.73 m²) after 12 months. The secondary endpoints included effects on renal function, weight, blood pressure, incidence of adverse drug events, and cardiovascular events. Of the excluded, 38 had HbA_1c < 7%, 30 had HbA_1c ≥ 10%, 21 did not have data at 1-year mark, 15 had the medication discontinued due to decline in renal function, 14 discontinued their medication without documented reason, 10 discontinued their medication due to adverse drug reactions (ADRs), 12 had eGFR > 60 mL/ min/1.73 m², 9 died within 1 year of initiation, 4 had eGFR < 30 mL/min/1.73 m², 1 had no baseline eGFR, and 1 was the spouse of a veteran.

Statistical Analysis

All statistical analyses were performed using STATA v.15. We used t tests to examine changes within each group, along with paired t tests to compare the 2 groups. Two-sample t tests were used to analyze the continuous data at both the primary and secondary endpoints.

Results

Of the 1771 patients included in the initial data set, a randomized sample of 255 charts were reviewed, 155 were excluded, and 100 were included. Fifty patients, had stage 3a CKD and 50 had stage 3b CKD. Baseline demographics were similar between the stage 3a and 3b groups (Table 1). Both groups were predominantly White and male, with mean age > 70 years.

The primary endpoint was the differences in HbA_1c levels over time and between groups for patients with stage 3a and stage 3b CKD 1 year after initiation of empagliflozin. The starting doses of empagliflozin were either 12.5 mg or 25.0 mg. For both groups, the changes in HbA_1c levels were statistically significant (Table 2). HbA_1c levels dropped 0.65% for the stage 3a group and 0.48% for the 3b group. When compared to one another, the results were not statistically significant (P = .51).

Secondary Endpoint

There was no statistically significant difference in serum creatinine levels within each group between baselines and 1 year later for the stage 3a (P = .21) and stage 3b (P = .22) groups, or when compared to each other (P = .67). There were statistically significant changes in weight for patients in the stage 3a group (P < .05), but not for stage 3b group (P = .06) or when compared to each other (P = .41). A statistically significant change in systolic blood pressure was observed for the stage 3a group (P = .003), but not the stage 3b group (P = .16) or when compared to each other (P = .27). There were statistically significant changes in diastolic blood pressure within the stage 3a group (P = .04), but not within the stage 3b group (P = .61) or when compared to each other (P = .31).

Ten patients discontinued empagliflozin before the 1-year mark due to ADRs, including dizziness, increased incidence of urinary tract infections, rash, and tachycardia (Table 3). Additionally, 3 ADRs resulted in the empagliflozin discontinuation after 1 year (Table 3).

Discussion

This study showed a statistically significant change in HbA_1c levels for patients with stage 3a and stage 3b CKD. With eGFR levels in these 2 groups > 30 mL/min/1.73 m², patients were able to achieve glycemic benefits. There were no significant changes to the serum creatinine levels. Both groups saw statistically significant changes in weight loss within their own group; however, there were no statistically significant changes when compared to each other. With both systolic and diastolic blood pressure, the stage 3a group had statistically significant changes.

The EMPA-REG BP study demonstrated that empagliflozin was associated with significant and clinically meaningful reductions in blood pressure and HbA_1c levels compared with placebo and was well tolerated in patients with T2DM and hypertension.^6,7,8

Limitations

This study had a retrospective study design, which resulted in missing information for many patients and higher rates of exclusion. The population was predominantly older, White, and male and may not reflect other populations. The starting doses of empagliflozin varied between the groups. The VA employs tablet splitting for some patients, and the available doses were either 10.0 mg, 12.5 mg, or 25.0 mg. Some prescribers start veterans at lower doses and gradually increase to the higher dose of 25.0 mg, adding to the variability in starting doses.

Patients with eGFR < 30 mL/min/1.73 m² make it difficult to determine any potential benefit in this population. The EMPA-KIDNEY trial demonstrated that the benefits of empagliflozin treatment were consistent among patients with or without DM and regardless of eGFR at randomization.⁹ Furthermore, many veterans had an initial HbA_1c levels outside the inclusion criteria range, which was a factor in the smaller sample size.

Conclusions

While the reduction in HbA_1c levels was less in patients with stage 3b CKD compared to patients stage 3a CKD, all patients experienced a benefit. The overall incidence of ADRs was low in the study population, showing empagliflozin as a favorable choice for those with T2DM and CKD. Based on the findings of this study, empagliflozin is a potentially beneficial option for reducing HbA_1c levels in patients with CKD.

More than 37 million Americans have diabetes mellitus (DM), and approximately 90% have type 2 DM (T2DM), including about 25% of veterans.^1,2 The current guidelines suggest that therapy depends on a patient's comorbidities, management needs, and patient-centered treatment factors.³ About 1 in 3 adults with DM have chronic kidney disease (CKD), defined as the presence of kidney damage or an estimated glomerular filtration rate (eGFR) < 60 mL/min per 1.73 m², persisting for ≥ 3 months.⁴

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors are a class of antihyperglycemic agents acting on the SGLT-2 proteins expressed in the renal proximal convoluted tubules. They exert their effects by preventing the reabsorption of filtered glucose from the tubular lumen. There are 4 SGLT-2 inhibitors approved by the US Food and Drug Administration: canagliflozin, dapagliflozin, empagliflozin, and ertugliflozin. Empagliflozin is currently the preferred SGLT-2 inhibitor on the US Department of Veterans Affairs (VA) formulary.

According to the American Diabetes Association guidelines, empagliflozin is considered when an individual has or is at risk for atherosclerotic cardiovascular disease, heart failure, and CKD.³ SGLT-2 inhibitors are a favorable option due to their low risk for hypoglycemia while also promoting weight loss. The EMPEROR-Reduced trial demonstrated that, in addition to benefits for patients with heart failure, empagliflozin also slowed the progressive decline in kidney function in those with and without DM.⁵ The purpose of this study was to evaluate the effectiveness of empagliflozin on hemoglobin A_1c (HbA_1c) levels in patients with CKD at the Hershel “Woody” Williams VA Medical Center (HWWVAMC) in Huntington, West Virginia, along with other laboratory test markers.

Methods

The Marshall University Institutional Review Board #1 (Medical) and the HWWVAMC institutional review board and research and development committee each reviewed and approved this study. A retrospective chart review was conducted on patients diagnosed with T2DM and stage 3 CKD who were prescribed empagliflozin for DM management between January 1, 2015, and October 1, 2022, yielding 1771 patients. Data were obtained through the VHA Corporate Data Warehouse (CDW) and stored on the VA Informatics and Computing Infrastructure (VINCI) research server.

Patients were included if they were aged 18 to 89 years, prescribed empagliflozin by a VA clinician for the treatment of T2DM, had an eGFR between 30 and 59 mL/min/1.73 m², and had an initial HbA_1c between 7% and 10%. Using further random sampling, patients were either excluded or divided into, those with stage 3a CKD and those with stage 3b CKD. The primary endpoint of this study was the change in HbA_1c levels in patients with stage 3b CKD (eGFR 30-44 mL/min/1.73 m²) compared with stage 3a (eGFR 45-59 mL/min/1.73 m²) after 12 months. The secondary endpoints included effects on renal function, weight, blood pressure, incidence of adverse drug events, and cardiovascular events. Of the excluded, 38 had HbA_1c < 7%, 30 had HbA_1c ≥ 10%, 21 did not have data at 1-year mark, 15 had the medication discontinued due to decline in renal function, 14 discontinued their medication without documented reason, 10 discontinued their medication due to adverse drug reactions (ADRs), 12 had eGFR > 60 mL/ min/1.73 m², 9 died within 1 year of initiation, 4 had eGFR < 30 mL/min/1.73 m², 1 had no baseline eGFR, and 1 was the spouse of a veteran.

Statistical Analysis

All statistical analyses were performed using STATA v.15. We used t tests to examine changes within each group, along with paired t tests to compare the 2 groups. Two-sample t tests were used to analyze the continuous data at both the primary and secondary endpoints.

Results

Of the 1771 patients included in the initial data set, a randomized sample of 255 charts were reviewed, 155 were excluded, and 100 were included. Fifty patients, had stage 3a CKD and 50 had stage 3b CKD. Baseline demographics were similar between the stage 3a and 3b groups (Table 1). Both groups were predominantly White and male, with mean age > 70 years.

The primary endpoint was the differences in HbA_1c levels over time and between groups for patients with stage 3a and stage 3b CKD 1 year after initiation of empagliflozin. The starting doses of empagliflozin were either 12.5 mg or 25.0 mg. For both groups, the changes in HbA_1c levels were statistically significant (Table 2). HbA_1c levels dropped 0.65% for the stage 3a group and 0.48% for the 3b group. When compared to one another, the results were not statistically significant (P = .51).

Secondary Endpoint

There was no statistically significant difference in serum creatinine levels within each group between baselines and 1 year later for the stage 3a (P = .21) and stage 3b (P = .22) groups, or when compared to each other (P = .67). There were statistically significant changes in weight for patients in the stage 3a group (P < .05), but not for stage 3b group (P = .06) or when compared to each other (P = .41). A statistically significant change in systolic blood pressure was observed for the stage 3a group (P = .003), but not the stage 3b group (P = .16) or when compared to each other (P = .27). There were statistically significant changes in diastolic blood pressure within the stage 3a group (P = .04), but not within the stage 3b group (P = .61) or when compared to each other (P = .31).

Ten patients discontinued empagliflozin before the 1-year mark due to ADRs, including dizziness, increased incidence of urinary tract infections, rash, and tachycardia (Table 3). Additionally, 3 ADRs resulted in the empagliflozin discontinuation after 1 year (Table 3).

Discussion

This study showed a statistically significant change in HbA_1c levels for patients with stage 3a and stage 3b CKD. With eGFR levels in these 2 groups > 30 mL/min/1.73 m², patients were able to achieve glycemic benefits. There were no significant changes to the serum creatinine levels. Both groups saw statistically significant changes in weight loss within their own group; however, there were no statistically significant changes when compared to each other. With both systolic and diastolic blood pressure, the stage 3a group had statistically significant changes.

The EMPA-REG BP study demonstrated that empagliflozin was associated with significant and clinically meaningful reductions in blood pressure and HbA_1c levels compared with placebo and was well tolerated in patients with T2DM and hypertension.^6,7,8

Limitations

This study had a retrospective study design, which resulted in missing information for many patients and higher rates of exclusion. The population was predominantly older, White, and male and may not reflect other populations. The starting doses of empagliflozin varied between the groups. The VA employs tablet splitting for some patients, and the available doses were either 10.0 mg, 12.5 mg, or 25.0 mg. Some prescribers start veterans at lower doses and gradually increase to the higher dose of 25.0 mg, adding to the variability in starting doses.

Patients with eGFR < 30 mL/min/1.73 m² make it difficult to determine any potential benefit in this population. The EMPA-KIDNEY trial demonstrated that the benefits of empagliflozin treatment were consistent among patients with or without DM and regardless of eGFR at randomization.⁹ Furthermore, many veterans had an initial HbA_1c levels outside the inclusion criteria range, which was a factor in the smaller sample size.

Conclusions

While the reduction in HbA_1c levels was less in patients with stage 3b CKD compared to patients stage 3a CKD, all patients experienced a benefit. The overall incidence of ADRs was low in the study population, showing empagliflozin as a favorable choice for those with T2DM and CKD. Based on the findings of this study, empagliflozin is a potentially beneficial option for reducing HbA_1c levels in patients with CKD.

More than 37 million Americans have diabetes mellitus (DM), and approximately 90% have type 2 DM (T2DM), including about 25% of veterans.^1,2 The current guidelines suggest that therapy depends on a patient's comorbidities, management needs, and patient-centered treatment factors.³ About 1 in 3 adults with DM have chronic kidney disease (CKD), defined as the presence of kidney damage or an estimated glomerular filtration rate (eGFR) < 60 mL/min per 1.73 m², persisting for ≥ 3 months.⁴

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors are a class of antihyperglycemic agents acting on the SGLT-2 proteins expressed in the renal proximal convoluted tubules. They exert their effects by preventing the reabsorption of filtered glucose from the tubular lumen. There are 4 SGLT-2 inhibitors approved by the US Food and Drug Administration: canagliflozin, dapagliflozin, empagliflozin, and ertugliflozin. Empagliflozin is currently the preferred SGLT-2 inhibitor on the US Department of Veterans Affairs (VA) formulary.

According to the American Diabetes Association guidelines, empagliflozin is considered when an individual has or is at risk for atherosclerotic cardiovascular disease, heart failure, and CKD.³ SGLT-2 inhibitors are a favorable option due to their low risk for hypoglycemia while also promoting weight loss. The EMPEROR-Reduced trial demonstrated that, in addition to benefits for patients with heart failure, empagliflozin also slowed the progressive decline in kidney function in those with and without DM.⁵ The purpose of this study was to evaluate the effectiveness of empagliflozin on hemoglobin A_1c (HbA_1c) levels in patients with CKD at the Hershel “Woody” Williams VA Medical Center (HWWVAMC) in Huntington, West Virginia, along with other laboratory test markers.

Methods

The Marshall University Institutional Review Board #1 (Medical) and the HWWVAMC institutional review board and research and development committee each reviewed and approved this study. A retrospective chart review was conducted on patients diagnosed with T2DM and stage 3 CKD who were prescribed empagliflozin for DM management between January 1, 2015, and October 1, 2022, yielding 1771 patients. Data were obtained through the VHA Corporate Data Warehouse (CDW) and stored on the VA Informatics and Computing Infrastructure (VINCI) research server.

Patients were included if they were aged 18 to 89 years, prescribed empagliflozin by a VA clinician for the treatment of T2DM, had an eGFR between 30 and 59 mL/min/1.73 m², and had an initial HbA_1c between 7% and 10%. Using further random sampling, patients were either excluded or divided into, those with stage 3a CKD and those with stage 3b CKD. The primary endpoint of this study was the change in HbA_1c levels in patients with stage 3b CKD (eGFR 30-44 mL/min/1.73 m²) compared with stage 3a (eGFR 45-59 mL/min/1.73 m²) after 12 months. The secondary endpoints included effects on renal function, weight, blood pressure, incidence of adverse drug events, and cardiovascular events. Of the excluded, 38 had HbA_1c < 7%, 30 had HbA_1c ≥ 10%, 21 did not have data at 1-year mark, 15 had the medication discontinued due to decline in renal function, 14 discontinued their medication without documented reason, 10 discontinued their medication due to adverse drug reactions (ADRs), 12 had eGFR > 60 mL/ min/1.73 m², 9 died within 1 year of initiation, 4 had eGFR < 30 mL/min/1.73 m², 1 had no baseline eGFR, and 1 was the spouse of a veteran.

Statistical Analysis

All statistical analyses were performed using STATA v.15. We used t tests to examine changes within each group, along with paired t tests to compare the 2 groups. Two-sample t tests were used to analyze the continuous data at both the primary and secondary endpoints.

Results

Of the 1771 patients included in the initial data set, a randomized sample of 255 charts were reviewed, 155 were excluded, and 100 were included. Fifty patients, had stage 3a CKD and 50 had stage 3b CKD. Baseline demographics were similar between the stage 3a and 3b groups (Table 1). Both groups were predominantly White and male, with mean age > 70 years.

The primary endpoint was the differences in HbA_1c levels over time and between groups for patients with stage 3a and stage 3b CKD 1 year after initiation of empagliflozin. The starting doses of empagliflozin were either 12.5 mg or 25.0 mg. For both groups, the changes in HbA_1c levels were statistically significant (Table 2). HbA_1c levels dropped 0.65% for the stage 3a group and 0.48% for the 3b group. When compared to one another, the results were not statistically significant (P = .51).

Secondary Endpoint

There was no statistically significant difference in serum creatinine levels within each group between baselines and 1 year later for the stage 3a (P = .21) and stage 3b (P = .22) groups, or when compared to each other (P = .67). There were statistically significant changes in weight for patients in the stage 3a group (P < .05), but not for stage 3b group (P = .06) or when compared to each other (P = .41). A statistically significant change in systolic blood pressure was observed for the stage 3a group (P = .003), but not the stage 3b group (P = .16) or when compared to each other (P = .27). There were statistically significant changes in diastolic blood pressure within the stage 3a group (P = .04), but not within the stage 3b group (P = .61) or when compared to each other (P = .31).

Ten patients discontinued empagliflozin before the 1-year mark due to ADRs, including dizziness, increased incidence of urinary tract infections, rash, and tachycardia (Table 3). Additionally, 3 ADRs resulted in the empagliflozin discontinuation after 1 year (Table 3).

Discussion

This study showed a statistically significant change in HbA_1c levels for patients with stage 3a and stage 3b CKD. With eGFR levels in these 2 groups > 30 mL/min/1.73 m², patients were able to achieve glycemic benefits. There were no significant changes to the serum creatinine levels. Both groups saw statistically significant changes in weight loss within their own group; however, there were no statistically significant changes when compared to each other. With both systolic and diastolic blood pressure, the stage 3a group had statistically significant changes.

The EMPA-REG BP study demonstrated that empagliflozin was associated with significant and clinically meaningful reductions in blood pressure and HbA_1c levels compared with placebo and was well tolerated in patients with T2DM and hypertension.^6,7,8

Limitations

This study had a retrospective study design, which resulted in missing information for many patients and higher rates of exclusion. The population was predominantly older, White, and male and may not reflect other populations. The starting doses of empagliflozin varied between the groups. The VA employs tablet splitting for some patients, and the available doses were either 10.0 mg, 12.5 mg, or 25.0 mg. Some prescribers start veterans at lower doses and gradually increase to the higher dose of 25.0 mg, adding to the variability in starting doses.

Patients with eGFR < 30 mL/min/1.73 m² make it difficult to determine any potential benefit in this population. The EMPA-KIDNEY trial demonstrated that the benefits of empagliflozin treatment were consistent among patients with or without DM and regardless of eGFR at randomization.⁹ Furthermore, many veterans had an initial HbA_1c levels outside the inclusion criteria range, which was a factor in the smaller sample size.

Conclusions

While the reduction in HbA_1c levels was less in patients with stage 3b CKD compared to patients stage 3a CKD, all patients experienced a benefit. The overall incidence of ADRs was low in the study population, showing empagliflozin as a favorable choice for those with T2DM and CKD. Based on the findings of this study, empagliflozin is a potentially beneficial option for reducing HbA_1c levels in patients with CKD.

As large-scale natural disasters become more common, health care coalitions and the engagement of health systems with local, state, and federal public health departments have effectively bolstered communities’ resilience via collective sharing and distribution of resources.¹ These resources may include supplies and the dissemination of emergency information, education, and training.² The COVID-19 pandemic demonstrated that larger health care systems including hospital networks and nursing homes are better connected to health care coalition resources than smaller, independent systems, such as community home health agencies.³ This leaves some organizations on their own to meet requirements that maintain continuity of care and support their patients and staff throughout a natural disaster.

Home health care workers play important roles in the care of older adults.⁴ Older adults experience high levels of disability and comorbidities that put them at risk during emergencies; they often require support from paid, family, and neighborhood caregivers to live independently.⁵ More than 9.3 million US adults receive paid care from 2.6 million home health care workers (eg, home health aides and personal care assistants).⁶ Many of these individuals are hired through small independent home health agencies (HHAs), while others may work directly for an individual. When neighborhood resources and family caregiving are disrupted during emergencies, the critical services these workers administer become even more essential to ensuring continued access to medical care and social services.

The importance of these services was underscored by the Centers for Medicare and Medicaid Services 2017 inclusion of HHAs in federal emergency preparedness guidelines.^7,8 The fractured and decentralized nature of the home health care industry means many HHAs struggle to maintain continuous care during emergencies and protect their staff. HHAs, and health care workers in the home, are often isolated, under-resourced, and disconnected from broader emergency planning efforts. Additionally, home care jobs are largely part-time, unstable, and low paying, making the workers themselves vulnerable during emergencies.^3,9-13

This is a significant issue for the Veterans Health Administration (VHA), which annually purchases 10.5 million home health care worker visits for 150,000 veterans from community-based HHAs to enable those individuals to live independently. Figure 1 illustrates the existing structure of directly provided and contracted VHA services for community-dwelling veterans, highlighting the circle of care around the veteran.^8,9 Home health care workers anchored health care teams during the COVID-19 pandemic, observing and reporting on patients’ well-being to family caregivers, primary care practitioners, and HHAs. They also provided critical emotional support and companionship to patients isolated from family and friends.⁹ These workers also exposed themselves and their families to considerable risk and often lacked the protection afforded by personal protective equipment (PPE) in accordance with infection prevention guidance.^3,12

Through a combination of its national and local health care networks, the VHA has a robust and well-positioned emergency infrastructure to supportcommunity-dwelling older adults during disasters.¹⁴ This network is supported by the VHA Office of Emergency Management, which shares resources and guidance with local emergency managers at each facility as well as individual programs such as the VHA Home Based Primary Care (HBPC) program, which provides 38,000 seriously ill veterans with home medical visits.¹⁵ Working closely with their local and national hospital networks and emergency managers, individual VHA HBPC programs were able to maintain the safety of staff and continuity of care for patients enrolled in HBPC by rapidly administering COVID-19 vaccines to patients, caregivers, and staff, and providing emergency assistance during the 2017 hurricane season.^16,17 These efforts were successful because HBPC practitioners and their patients, had access to a level of emergency-related information, resources, and technology that are often out of reach for individual community-based health care practitioners (HCPs). The US Department of Veterans Affairs (VA) also supports local communities through its Fourth Mission, which provides emergency resources to non-VHA health care facilities (ie, hospitals and nursing homes) during national emergencies and natural disasters.¹⁷ Although there has been an expansion in the definition of shared resources, such as extending behavioral health support to local communities, the VHA has not historically provided these resources to HHAs.¹⁴

This study examines opportunities to leverage VHA emergency management resources to support contracted HHAs and inform other large health system emergency planning efforts. The findings from the exploratory phase are described in this article. We interviewed VHA emergency managers, HBPC and VA staff who coordinate home health care worker services, as well as administrators at contracted HHAs within a Veterans Integrated Services Network (VISN). These findings will inform the second (single-site pilot study) and third (feasibility study) phases. Our intent was to (1) better understand the relationships between VA medical centers (VAMCs) and their contracted HHAs; (2) identify existing VHA emergency protocols to support community-dwelling older adults; and (3) determine opportunities to build on existing infrastructure and relationships to better support contracted HHAs and their staff in emergencies.

Methods

The 18 VISNs act as regional systems of care that are loosely connected to better meet local health needs and maximize access to care. This study was conducted at 6 of 9 VAMCs within VISN 2, the New York/New Jersey VHA Health Care Network.¹⁸ VAMCs that serve urban, rural, and mixed urban/rural catchment areas were included.

Each VAMC has an emergency management program led by an emergency manager, an HBPC program led by a program director and medical director, and a community care or purchased care office that has a liaison who manages contracted home health care worker services. The studyfocused on HBPC programs because they are most likely to interact with veterans’ home health care workers in the home and care for community-dwelling veterans during emergencies. Each VHA also contracts with a series of local HHAs that generally have a dedicated staff member who interfaces with the VHA liaison. Our goal was to interview ≥ 1 emergency manager, ≥ 1 HBPC team member, ≥ 1 community care staff person, and ≥ 1 contracted home health agency administrator at each site to gain multiple perspectives from the range of HCPs serving veterans in the community.

Recruitment and Data Collection

The 6 sites were selected in consultation with VISN 2 leadership for their strong HBPC and emergency management programs. To recruit respondents, we contacted VISN and VAMC leads and used our professional networks to identify a sample of multidisciplinary individuals who represent both community care and HBPC programs who were contacted via email.

Since each VAMC is organized differently, we utilized a snowball sampling approach to identify the appropriate contacts.¹⁹ At the completion of each interview, we asked the participant to suggest additional contacts and introduce us to any remaining stakeholders (eg, the emergency manager) at that site or colleagues at other VISN facilities. Because roles vary among VAMCs, we contacted the person who most closely resembled the identified role and asked them to direct us to a more appropriate contact, if necessary. We asked community care managers to identify 1 to 2 agencies serving the highest volume of patients who are veterans at their site and requested interviews with those liaisons. This resulted in the recruitment of key stakeholders from 4 teams across the 6 sites (Table).

A semistructured interview guide was jointly developed based on constructs of interest, including relationships within VAMCs and between VAMCs and HHAs; existing emergency protocols and experience during disasters; and suggestions and opportunities for supporting agencies during emergencies and potential barriers. Two researchers (TWL and EF) who were trained in qualitative methods jointly conducted interviews using the interview guide, with 1 researcher leading and another taking notes and asking clarifying questions.

Interviews were conducted virtually via Microsoft Teams with respondents at their work locations between September 2022 and January 2023. Interviews were audio recorded and transcribed and 2 authors (TWL and ESO) reviewed transcripts for accuracy. Interviews averaged 47 minutes in length (range, 20-59).

The study was reviewed and determined to be exempt by institutional review boards at the James J. Peters VAMC and Greater Los Angeles VAMC. We asked participants for verbal consent to participate and preserved their confidentiality.

Analysis

Data were analyzed via an inductive approach, which involves drawing salient themes rather than imposing preconceived theories.²⁰ Three researchers (TWL, EF, and ES) listened to and discussed 2 staff interviews and tagged text with specific codes (eg, communication between the VHA and HHA, internal communication, and barriers to case fulfillment) so the team could selectively return to the interview text for deeper analysis, allowing for the development of a final codebook. The project team synthesized the findings to identify higher-level themes, drawing comparisons across and within the respondent groups, including within and between health care systems. Throughout the analysis, we maintained analytic memos, documented discussions, and engaged in analyst triangulation to ensure trustworthiness.^21,22 To ensure the analysis accurately reflected the participants’ understanding, we held 2 virtual member-checking sessions with participants to share preliminary findings and conclusions and solicit feedback. Analysis was conducted using ATLAS.ti version 20.

Results

VHA-based participants described internal emergency management systems that are deployed during a disaster to support patients and staff. Agency participants described their own internal emergency management protocols. Respondents discussed how and when the 2 intersected, as well as opportunities for future mutual support. The analysis identified several themes: (1) relationships between VAMC teams; (2) relationships between VHA and HHAs; (3) VHA and agencies responses during emergencies; (4) receptivity and opportunities for extending VHA resources into the community; and (5) barriers and facilitators to deeper engagement.

Relationships Within VHA (n = 17)

Staff at all VHA sites described close relationships between the internal emergency management and HBPC teams. HBPC teams identified patients who were most at risk during emergencies to triage those with the highest medical needs (eg, patients dependent on home infusion, oxygen, or electronic medical devices) and worked alongside emergency managers to develop plans to continue care during an emergency. HBPC representatives were part of their facilities’ local emergency response committees. Due to this close collaboration, VHA emergency managers were familiar with the needs of homebound veterans and caregivers. “I invite our [HBPC] program manager to attend [committee] meetings and … they’re part of the EOC [emergency operations center]," an emergency manager said. “We work together and I’m constantly in contact with that individual, especially during natural disasters and so forth, to ensure that everybody’s prepared in the community.”

On the other hand, community caremanagers—who described frequent interactions with HBPC teams, largely around coordinating and managing non-VHA home care services—were less likely to have direct relationships with their facility emergency managers. For example, when asked if they had a relationship with their emergency manager, a community care manager admitted, “I [only] know who he is.” They also did not report having structured protocols for veteran outreach during emergencies, “because all those veterans who are receiving [home health care worker] services also belong to a primary care team,” and considered the outreach to be the responsibility of the primary care team and HHA.

Relationships Between the VHA and HHAs (n = 17)

Communication between VAMCs and contracted agencies primarily went through community care managers, who described established long-term relationships with agency administrators. Communication was commonly restricted to operational activities, such as processing referrals and occasional troubleshooting. According to a community care manager most communication is “why haven’t you signed my orders?” There was a general sense from participants that communication was promptly answered, problems were addressed, and professional collegiality existed between the agencies as patients were referred and placed for services. One community care manager reported meeting with agencies regularly, noting, “I talk to them pretty much daily.”

If problems arose, community care managers described themselves as “the liaison” between agencies and VHA HCPs who ordered the referrals. This is particularly the case if the agency needed help finding a VHA clinician or addressing differences in care delivery protocols.

Responding During Emergencies (n = 19)

During emergencies, VHA and agency staff described following their own organization’s protocols and communicating with each other only on a case-by-case basis rather than through formal or systematic channels and had little knowledge of their counterpart’s emergency protocols. Beyond patient care, there was no evidence of information sharing between VHA and agency staff. Regarding sharing information with their local community, an HBPC Program Director said, “it’s almost like the VHA had become siloed” and operated on its own without engaging with community health systems or emergency managers.

Beyond the guidance provided by state departments of public health, HHAs described collaborating with other agencies in their network and relying on their informal professional network to manage the volume of information and updates they followed during emergencies like the COVID-19 pandemic. One agency administrator did not frequently communicate with VHA partners during the pandemic but explained that the local public health department helped work through challenges. However, “we realized pretty quickly they were overloaded and there was only so much they could do.” The agency administrator turned to a “sister agency” and local hospitals, noting, “Wherever you have connections in the field or in the industry, you know you’re going to reach out to people for guidance on policies and… protocol.”

Opportunities for Extending VHA Resources to the Community (n = 16)

All VHA emergency managers were receptive to extending support to community-based HCPS and, in some cases, felt strongly that they were an essential part of veterans’ care networks. Emergency managers offered examples for how they supportedcommunity-based HCPs, such as helping those in the VAMC medical foster home program develop and evaluate emergency plans. Many said they had not explicitly considered HHAs before (Appendix).

Emergency managers also described how supporting community-based HCPs could be considered within the scope of the VHA role and mission, specifically the Fourth Mission. “I think that we should be making our best effort to make sure that we’re also providing that same level [of protection] to the people taking care of the veteran [as our VHA staff],” an emergency manager said. “It’s our responsibility to provide the best for the staff that are going into those homes to take care of that patient.”

In many cases, emergency managers had already developed practical tools that could be easily shared outside the VHA, including weather alerts, trainings, emergency plan templates, and lists of community resources and shelters (Figure 2). A number of these examples built on existing communication channels. One emergency manager said that the extension of resources could be an opportunity to decrease the perceived isolation of home health care workers through regular training for agencies that are providing health care aides, so that they know that “some bigger folks are keeping an eye on it.”

On the agency side, participants noted that some HHAs could benefit more from support than others. While some agencies are well staffed and have good protocols and keep up to date, “There are smaller agencies, agencies that are starting up that may not have the resources to just disseminate all the information. Those are the agencies [that] could well benefit from the VHA,” an HBPC medical director explained. Agency administrators suggested several areas where they would welcome support, including a deeper understanding of available community resources and access to PPE for staff. Regarding informational resources, an administrator said, “Anytime we can get information, it’s good to have it come to you and not always have to go out searching for it.”

Barriers and Facilitators to Partnering With Community Agencies (n = 16)

A primary barrier regarding resource sharing was potential misalignment between each organization’s policies. HHAs followed state and federal public health guidelines, which sometimes differed from VHA policies. Given that agencies care for both VHA and non-VHA clients, questions also arose around how agencies would prioritize information from the VHA, if they were already receiving information from other sources. When asked about information sharing, both VHA staff and agencies agreed staff time to support any additional activities should be weighed against the value of the information gained.

Six participants also shared that education around emergency preparedness could be an opportunity to bridge gaps between VAMCs and their surrounding communities. One local Chief of Community Care noted, “Any opportunity to just give information is going to make it a lot better for the veteran patient … to have something that’s a little more robust.”

Two emergency managers noted the need to be sensitive in the way they engaged with partners, respecting and building on the work that agencies were already doing in this area to ensure VHA was seen as a trusted partner and resource rather than trying to impose new policies or rules on community-based HCPs. “I know that like all leadership in various organizations, there’s a little bit of bristling going on when other people try and tell them what to do,” an HBPC medical director said. “However, if it is established that as a sort of greater level like a state level or a federal level, that VHA can be a resource. I think that as long as that’s recognized by their own professional organizations within each state, then I think that that would be a tremendous advantage to many agencies.”

In terms of sharing physical resources, emergency managers raised concerns around potential liability, although they also acknowledged this issue was important enough to think about potential workarounds. As one emergency manager said, “I want to know that my PPE is not compromised in any way shape or form and that I am in charge of that PPE, so to rely upon going to a home and hoping that [the PPE] wasn’t compromised … would kind of make me a little uneasy.” This emergency manager suggested possible solutions, such as creating a sealed PPE package to give directly to an aide.

Discussion

As the prevalence of climate-related disasters increases, the need to ensure the safety and independence of older adults during emergencies grows more urgent. Health systems must think beyond the direct services they provide and consider the community resources upon which their patients rely. While relationships did not formally exist between VHA emergency managers and community home health HCPs in the sample analyzed in this article, there is precedent and interest in supporting contracted home health agencies caring for veterans in the community. Although not historically part of the VA Fourth Mission, creating a pipeline of support for contracted HHAs by leveraging existing relationships and resources can potentially strengthen its mission to protect older veterans in emergencies, help them age safely in place, and provide a model for health systems to collaborate with community-based HCPs around emergency planning and response (Figure 3).²³

Existing research on the value of health care coalitions highlights the need for established and growing partnerships with a focus on ensuring they are value-added, which echoes concerns we heard in interviews.²⁴ Investment in community partnerships not only includes sharing supplies but also relying on bidirectional support that can be a trusted form of timely information.^1,25 The findings in this study exhibit strong communication practices within the VHA during periods of nonemergency and underscore the untapped value of the pre-existing relationship between VAMCs and their contracted HHAs as an area of potential growth for health care coalitions.

Sharing resources in a way that does not put new demands on partners contributes to the sustainability and value-added nature of coalitions. Examples include establishing new low-investment practices (ie, information sharing) that support capacity and compliance with existing requirements rather than create new responsibilities for either member of the coalition. The relationship between the VHA emergency managers and the VHA HBPC program can act as a guide. The emergency managers interviewed for this study are currently engaged with HBPC programs and therefore understand the needs of homebound older adults and their caregivers. Extending the information already available to the HBPC teams via existing channels strengthens workforce practices and increased security for the shared patient, even without direct relationships between emergency managers and agencies. It is important to understand the limitations of these practices, including concerns around conflicting federal and state mandates, legal concerns around the liability of sharing physical resources (such as PPE), and awareness that the objective is not for the VHA to increase burdens (eg, increasing compliance requirements) but rather to serve as a resource for a mutual population in a shared community.

Offering training and practical resources to HHA home health care workers can help them meet disaster preparedness requirements. This is particularly important considering the growing home care workforce shortages, a topic mentioned by all HBPC and community care participants interviewed for this study.^26,27 Home health care workers report feeling underprepared and isolated while on the job in normal conditions, a sentiment exacerbated by the COVID-19 pandemic.^3,10 Supporting these individuals may help them feel more prepared and connected to their work, improving stability and quality of care.

While these issues are priorities within the VHA, there is growing recognition at the state and federal level of the importance of including older adults and their HCPs in disaster preparedness and response.^5,28 The US Department of Health and Human Services, for example, includes older adults and organizations that serve them on its National Advisory Committee on Seniors and Disasters. The Senate version of the 2023 reauthorization of the Pandemic and All-Hazards Preparedness and Response Act included specific provisions to support community-dwelling older adults and people with disabilities, incorporating funding for community organizations to support continuity of services and avoid institutionalization in an emergency.²⁹ Other proposed legislation includes the Real Emergency Access for Aging and Disability Inclusion for Disasters Act, which would ensure the needs of older adults and people with disabilities are explicitly included in all phases of emergency planning and response.³⁰

The VHA expansion of the its VEText program to include disaster response is an effort to more efficiently extend outreach to older and vulnerable patients who are veterans.³¹ Given these growing efforts, the VHA and other health systems have an opportunity to expand internal emergency preparedness efforts to ensure the health and safety of individuals living in the community.

Limitations

VISN 2 has been a target of terrorism and other disasters. In addition to the sites being initially recruited for their strong emergency management protocols, this context may have biased respondents who are favorable to extending their resources into the community. At the time of recruitment, contracted HHAs were still experiencing staff shortages due to the COVID-19 pandemic, which limited the ability of agency staff to participate in interviews. Additionally, while the comprehensive exploration of VISN 2 facilities allows for confidence of the organizational structures described, the qualitative research design and small study sample, the study findings cannot be immediately generalized to all VISNs.

Conclusions

Many older veterans increasingly rely on home health care workers to age safely. The VHA, as a large national health care system and leader in emergency preparedness, could play an important role in supporting home health care workers and ameliorating their sense of isolation during emergencies and natural disasters. Leveraging existing resources and relationships may be a low-cost, low-effort opportunity to build higher-level interventions that support the needs of patients. Future research and work in this field, including the authors’ ongoing work, will expand agency participation and engage agency staff in conceptualizing pilot projects to ensure they are viable and feasible for the field.

As large-scale natural disasters become more common, health care coalitions and the engagement of health systems with local, state, and federal public health departments have effectively bolstered communities’ resilience via collective sharing and distribution of resources.¹ These resources may include supplies and the dissemination of emergency information, education, and training.² The COVID-19 pandemic demonstrated that larger health care systems including hospital networks and nursing homes are better connected to health care coalition resources than smaller, independent systems, such as community home health agencies.³ This leaves some organizations on their own to meet requirements that maintain continuity of care and support their patients and staff throughout a natural disaster.

Home health care workers play important roles in the care of older adults.⁴ Older adults experience high levels of disability and comorbidities that put them at risk during emergencies; they often require support from paid, family, and neighborhood caregivers to live independently.⁵ More than 9.3 million US adults receive paid care from 2.6 million home health care workers (eg, home health aides and personal care assistants).⁶ Many of these individuals are hired through small independent home health agencies (HHAs), while others may work directly for an individual. When neighborhood resources and family caregiving are disrupted during emergencies, the critical services these workers administer become even more essential to ensuring continued access to medical care and social services.

The importance of these services was underscored by the Centers for Medicare and Medicaid Services 2017 inclusion of HHAs in federal emergency preparedness guidelines.^7,8 The fractured and decentralized nature of the home health care industry means many HHAs struggle to maintain continuous care during emergencies and protect their staff. HHAs, and health care workers in the home, are often isolated, under-resourced, and disconnected from broader emergency planning efforts. Additionally, home care jobs are largely part-time, unstable, and low paying, making the workers themselves vulnerable during emergencies.^3,9-13

This is a significant issue for the Veterans Health Administration (VHA), which annually purchases 10.5 million home health care worker visits for 150,000 veterans from community-based HHAs to enable those individuals to live independently. Figure 1 illustrates the existing structure of directly provided and contracted VHA services for community-dwelling veterans, highlighting the circle of care around the veteran.^8,9 Home health care workers anchored health care teams during the COVID-19 pandemic, observing and reporting on patients’ well-being to family caregivers, primary care practitioners, and HHAs. They also provided critical emotional support and companionship to patients isolated from family and friends.⁹ These workers also exposed themselves and their families to considerable risk and often lacked the protection afforded by personal protective equipment (PPE) in accordance with infection prevention guidance.^3,12

Through a combination of its national and local health care networks, the VHA has a robust and well-positioned emergency infrastructure to supportcommunity-dwelling older adults during disasters.¹⁴ This network is supported by the VHA Office of Emergency Management, which shares resources and guidance with local emergency managers at each facility as well as individual programs such as the VHA Home Based Primary Care (HBPC) program, which provides 38,000 seriously ill veterans with home medical visits.¹⁵ Working closely with their local and national hospital networks and emergency managers, individual VHA HBPC programs were able to maintain the safety of staff and continuity of care for patients enrolled in HBPC by rapidly administering COVID-19 vaccines to patients, caregivers, and staff, and providing emergency assistance during the 2017 hurricane season.^16,17 These efforts were successful because HBPC practitioners and their patients, had access to a level of emergency-related information, resources, and technology that are often out of reach for individual community-based health care practitioners (HCPs). The US Department of Veterans Affairs (VA) also supports local communities through its Fourth Mission, which provides emergency resources to non-VHA health care facilities (ie, hospitals and nursing homes) during national emergencies and natural disasters.¹⁷ Although there has been an expansion in the definition of shared resources, such as extending behavioral health support to local communities, the VHA has not historically provided these resources to HHAs.¹⁴

This study examines opportunities to leverage VHA emergency management resources to support contracted HHAs and inform other large health system emergency planning efforts. The findings from the exploratory phase are described in this article. We interviewed VHA emergency managers, HBPC and VA staff who coordinate home health care worker services, as well as administrators at contracted HHAs within a Veterans Integrated Services Network (VISN). These findings will inform the second (single-site pilot study) and third (feasibility study) phases. Our intent was to (1) better understand the relationships between VA medical centers (VAMCs) and their contracted HHAs; (2) identify existing VHA emergency protocols to support community-dwelling older adults; and (3) determine opportunities to build on existing infrastructure and relationships to better support contracted HHAs and their staff in emergencies.

Methods

The 18 VISNs act as regional systems of care that are loosely connected to better meet local health needs and maximize access to care. This study was conducted at 6 of 9 VAMCs within VISN 2, the New York/New Jersey VHA Health Care Network.¹⁸ VAMCs that serve urban, rural, and mixed urban/rural catchment areas were included.

Each VAMC has an emergency management program led by an emergency manager, an HBPC program led by a program director and medical director, and a community care or purchased care office that has a liaison who manages contracted home health care worker services. The studyfocused on HBPC programs because they are most likely to interact with veterans’ home health care workers in the home and care for community-dwelling veterans during emergencies. Each VHA also contracts with a series of local HHAs that generally have a dedicated staff member who interfaces with the VHA liaison. Our goal was to interview ≥ 1 emergency manager, ≥ 1 HBPC team member, ≥ 1 community care staff person, and ≥ 1 contracted home health agency administrator at each site to gain multiple perspectives from the range of HCPs serving veterans in the community.

Recruitment and Data Collection

The 6 sites were selected in consultation with VISN 2 leadership for their strong HBPC and emergency management programs. To recruit respondents, we contacted VISN and VAMC leads and used our professional networks to identify a sample of multidisciplinary individuals who represent both community care and HBPC programs who were contacted via email.

Since each VAMC is organized differently, we utilized a snowball sampling approach to identify the appropriate contacts.¹⁹ At the completion of each interview, we asked the participant to suggest additional contacts and introduce us to any remaining stakeholders (eg, the emergency manager) at that site or colleagues at other VISN facilities. Because roles vary among VAMCs, we contacted the person who most closely resembled the identified role and asked them to direct us to a more appropriate contact, if necessary. We asked community care managers to identify 1 to 2 agencies serving the highest volume of patients who are veterans at their site and requested interviews with those liaisons. This resulted in the recruitment of key stakeholders from 4 teams across the 6 sites (Table).

A semistructured interview guide was jointly developed based on constructs of interest, including relationships within VAMCs and between VAMCs and HHAs; existing emergency protocols and experience during disasters; and suggestions and opportunities for supporting agencies during emergencies and potential barriers. Two researchers (TWL and EF) who were trained in qualitative methods jointly conducted interviews using the interview guide, with 1 researcher leading and another taking notes and asking clarifying questions.

Interviews were conducted virtually via Microsoft Teams with respondents at their work locations between September 2022 and January 2023. Interviews were audio recorded and transcribed and 2 authors (TWL and ESO) reviewed transcripts for accuracy. Interviews averaged 47 minutes in length (range, 20-59).

The study was reviewed and determined to be exempt by institutional review boards at the James J. Peters VAMC and Greater Los Angeles VAMC. We asked participants for verbal consent to participate and preserved their confidentiality.

Analysis

Data were analyzed via an inductive approach, which involves drawing salient themes rather than imposing preconceived theories.²⁰ Three researchers (TWL, EF, and ES) listened to and discussed 2 staff interviews and tagged text with specific codes (eg, communication between the VHA and HHA, internal communication, and barriers to case fulfillment) so the team could selectively return to the interview text for deeper analysis, allowing for the development of a final codebook. The project team synthesized the findings to identify higher-level themes, drawing comparisons across and within the respondent groups, including within and between health care systems. Throughout the analysis, we maintained analytic memos, documented discussions, and engaged in analyst triangulation to ensure trustworthiness.^21,22 To ensure the analysis accurately reflected the participants’ understanding, we held 2 virtual member-checking sessions with participants to share preliminary findings and conclusions and solicit feedback. Analysis was conducted using ATLAS.ti version 20.

Results

VHA-based participants described internal emergency management systems that are deployed during a disaster to support patients and staff. Agency participants described their own internal emergency management protocols. Respondents discussed how and when the 2 intersected, as well as opportunities for future mutual support. The analysis identified several themes: (1) relationships between VAMC teams; (2) relationships between VHA and HHAs; (3) VHA and agencies responses during emergencies; (4) receptivity and opportunities for extending VHA resources into the community; and (5) barriers and facilitators to deeper engagement.

Relationships Within VHA (n = 17)

Staff at all VHA sites described close relationships between the internal emergency management and HBPC teams. HBPC teams identified patients who were most at risk during emergencies to triage those with the highest medical needs (eg, patients dependent on home infusion, oxygen, or electronic medical devices) and worked alongside emergency managers to develop plans to continue care during an emergency. HBPC representatives were part of their facilities’ local emergency response committees. Due to this close collaboration, VHA emergency managers were familiar with the needs of homebound veterans and caregivers. “I invite our [HBPC] program manager to attend [committee] meetings and … they’re part of the EOC [emergency operations center]," an emergency manager said. “We work together and I’m constantly in contact with that individual, especially during natural disasters and so forth, to ensure that everybody’s prepared in the community.”

On the other hand, community caremanagers—who described frequent interactions with HBPC teams, largely around coordinating and managing non-VHA home care services—were less likely to have direct relationships with their facility emergency managers. For example, when asked if they had a relationship with their emergency manager, a community care manager admitted, “I [only] know who he is.” They also did not report having structured protocols for veteran outreach during emergencies, “because all those veterans who are receiving [home health care worker] services also belong to a primary care team,” and considered the outreach to be the responsibility of the primary care team and HHA.

Relationships Between the VHA and HHAs (n = 17)

Communication between VAMCs and contracted agencies primarily went through community care managers, who described established long-term relationships with agency administrators. Communication was commonly restricted to operational activities, such as processing referrals and occasional troubleshooting. According to a community care manager most communication is “why haven’t you signed my orders?” There was a general sense from participants that communication was promptly answered, problems were addressed, and professional collegiality existed between the agencies as patients were referred and placed for services. One community care manager reported meeting with agencies regularly, noting, “I talk to them pretty much daily.”

If problems arose, community care managers described themselves as “the liaison” between agencies and VHA HCPs who ordered the referrals. This is particularly the case if the agency needed help finding a VHA clinician or addressing differences in care delivery protocols.

Responding During Emergencies (n = 19)

During emergencies, VHA and agency staff described following their own organization’s protocols and communicating with each other only on a case-by-case basis rather than through formal or systematic channels and had little knowledge of their counterpart’s emergency protocols. Beyond patient care, there was no evidence of information sharing between VHA and agency staff. Regarding sharing information with their local community, an HBPC Program Director said, “it’s almost like the VHA had become siloed” and operated on its own without engaging with community health systems or emergency managers.

Beyond the guidance provided by state departments of public health, HHAs described collaborating with other agencies in their network and relying on their informal professional network to manage the volume of information and updates they followed during emergencies like the COVID-19 pandemic. One agency administrator did not frequently communicate with VHA partners during the pandemic but explained that the local public health department helped work through challenges. However, “we realized pretty quickly they were overloaded and there was only so much they could do.” The agency administrator turned to a “sister agency” and local hospitals, noting, “Wherever you have connections in the field or in the industry, you know you’re going to reach out to people for guidance on policies and… protocol.”

Opportunities for Extending VHA Resources to the Community (n = 16)

All VHA emergency managers were receptive to extending support to community-based HCPS and, in some cases, felt strongly that they were an essential part of veterans’ care networks. Emergency managers offered examples for how they supportedcommunity-based HCPs, such as helping those in the VAMC medical foster home program develop and evaluate emergency plans. Many said they had not explicitly considered HHAs before (Appendix).

Emergency managers also described how supporting community-based HCPs could be considered within the scope of the VHA role and mission, specifically the Fourth Mission. “I think that we should be making our best effort to make sure that we’re also providing that same level [of protection] to the people taking care of the veteran [as our VHA staff],” an emergency manager said. “It’s our responsibility to provide the best for the staff that are going into those homes to take care of that patient.”

In many cases, emergency managers had already developed practical tools that could be easily shared outside the VHA, including weather alerts, trainings, emergency plan templates, and lists of community resources and shelters (Figure 2). A number of these examples built on existing communication channels. One emergency manager said that the extension of resources could be an opportunity to decrease the perceived isolation of home health care workers through regular training for agencies that are providing health care aides, so that they know that “some bigger folks are keeping an eye on it.”

On the agency side, participants noted that some HHAs could benefit more from support than others. While some agencies are well staffed and have good protocols and keep up to date, “There are smaller agencies, agencies that are starting up that may not have the resources to just disseminate all the information. Those are the agencies [that] could well benefit from the VHA,” an HBPC medical director explained. Agency administrators suggested several areas where they would welcome support, including a deeper understanding of available community resources and access to PPE for staff. Regarding informational resources, an administrator said, “Anytime we can get information, it’s good to have it come to you and not always have to go out searching for it.”

Barriers and Facilitators to Partnering With Community Agencies (n = 16)

A primary barrier regarding resource sharing was potential misalignment between each organization’s policies. HHAs followed state and federal public health guidelines, which sometimes differed from VHA policies. Given that agencies care for both VHA and non-VHA clients, questions also arose around how agencies would prioritize information from the VHA, if they were already receiving information from other sources. When asked about information sharing, both VHA staff and agencies agreed staff time to support any additional activities should be weighed against the value of the information gained.

Six participants also shared that education around emergency preparedness could be an opportunity to bridge gaps between VAMCs and their surrounding communities. One local Chief of Community Care noted, “Any opportunity to just give information is going to make it a lot better for the veteran patient … to have something that’s a little more robust.”

Two emergency managers noted the need to be sensitive in the way they engaged with partners, respecting and building on the work that agencies were already doing in this area to ensure VHA was seen as a trusted partner and resource rather than trying to impose new policies or rules on community-based HCPs. “I know that like all leadership in various organizations, there’s a little bit of bristling going on when other people try and tell them what to do,” an HBPC medical director said. “However, if it is established that as a sort of greater level like a state level or a federal level, that VHA can be a resource. I think that as long as that’s recognized by their own professional organizations within each state, then I think that that would be a tremendous advantage to many agencies.”

In terms of sharing physical resources, emergency managers raised concerns around potential liability, although they also acknowledged this issue was important enough to think about potential workarounds. As one emergency manager said, “I want to know that my PPE is not compromised in any way shape or form and that I am in charge of that PPE, so to rely upon going to a home and hoping that [the PPE] wasn’t compromised … would kind of make me a little uneasy.” This emergency manager suggested possible solutions, such as creating a sealed PPE package to give directly to an aide.

Discussion

As the prevalence of climate-related disasters increases, the need to ensure the safety and independence of older adults during emergencies grows more urgent. Health systems must think beyond the direct services they provide and consider the community resources upon which their patients rely. While relationships did not formally exist between VHA emergency managers and community home health HCPs in the sample analyzed in this article, there is precedent and interest in supporting contracted home health agencies caring for veterans in the community. Although not historically part of the VA Fourth Mission, creating a pipeline of support for contracted HHAs by leveraging existing relationships and resources can potentially strengthen its mission to protect older veterans in emergencies, help them age safely in place, and provide a model for health systems to collaborate with community-based HCPs around emergency planning and response (Figure 3).²³

Existing research on the value of health care coalitions highlights the need for established and growing partnerships with a focus on ensuring they are value-added, which echoes concerns we heard in interviews.²⁴ Investment in community partnerships not only includes sharing supplies but also relying on bidirectional support that can be a trusted form of timely information.^1,25 The findings in this study exhibit strong communication practices within the VHA during periods of nonemergency and underscore the untapped value of the pre-existing relationship between VAMCs and their contracted HHAs as an area of potential growth for health care coalitions.

Sharing resources in a way that does not put new demands on partners contributes to the sustainability and value-added nature of coalitions. Examples include establishing new low-investment practices (ie, information sharing) that support capacity and compliance with existing requirements rather than create new responsibilities for either member of the coalition. The relationship between the VHA emergency managers and the VHA HBPC program can act as a guide. The emergency managers interviewed for this study are currently engaged with HBPC programs and therefore understand the needs of homebound older adults and their caregivers. Extending the information already available to the HBPC teams via existing channels strengthens workforce practices and increased security for the shared patient, even without direct relationships between emergency managers and agencies. It is important to understand the limitations of these practices, including concerns around conflicting federal and state mandates, legal concerns around the liability of sharing physical resources (such as PPE), and awareness that the objective is not for the VHA to increase burdens (eg, increasing compliance requirements) but rather to serve as a resource for a mutual population in a shared community.

Offering training and practical resources to HHA home health care workers can help them meet disaster preparedness requirements. This is particularly important considering the growing home care workforce shortages, a topic mentioned by all HBPC and community care participants interviewed for this study.^26,27 Home health care workers report feeling underprepared and isolated while on the job in normal conditions, a sentiment exacerbated by the COVID-19 pandemic.^3,10 Supporting these individuals may help them feel more prepared and connected to their work, improving stability and quality of care.

While these issues are priorities within the VHA, there is growing recognition at the state and federal level of the importance of including older adults and their HCPs in disaster preparedness and response.^5,28 The US Department of Health and Human Services, for example, includes older adults and organizations that serve them on its National Advisory Committee on Seniors and Disasters. The Senate version of the 2023 reauthorization of the Pandemic and All-Hazards Preparedness and Response Act included specific provisions to support community-dwelling older adults and people with disabilities, incorporating funding for community organizations to support continuity of services and avoid institutionalization in an emergency.²⁹ Other proposed legislation includes the Real Emergency Access for Aging and Disability Inclusion for Disasters Act, which would ensure the needs of older adults and people with disabilities are explicitly included in all phases of emergency planning and response.³⁰

The VHA expansion of the its VEText program to include disaster response is an effort to more efficiently extend outreach to older and vulnerable patients who are veterans.³¹ Given these growing efforts, the VHA and other health systems have an opportunity to expand internal emergency preparedness efforts to ensure the health and safety of individuals living in the community.

Limitations

VISN 2 has been a target of terrorism and other disasters. In addition to the sites being initially recruited for their strong emergency management protocols, this context may have biased respondents who are favorable to extending their resources into the community. At the time of recruitment, contracted HHAs were still experiencing staff shortages due to the COVID-19 pandemic, which limited the ability of agency staff to participate in interviews. Additionally, while the comprehensive exploration of VISN 2 facilities allows for confidence of the organizational structures described, the qualitative research design and small study sample, the study findings cannot be immediately generalized to all VISNs.

Conclusions

Many older veterans increasingly rely on home health care workers to age safely. The VHA, as a large national health care system and leader in emergency preparedness, could play an important role in supporting home health care workers and ameliorating their sense of isolation during emergencies and natural disasters. Leveraging existing resources and relationships may be a low-cost, low-effort opportunity to build higher-level interventions that support the needs of patients. Future research and work in this field, including the authors’ ongoing work, will expand agency participation and engage agency staff in conceptualizing pilot projects to ensure they are viable and feasible for the field.

As large-scale natural disasters become more common, health care coalitions and the engagement of health systems with local, state, and federal public health departments have effectively bolstered communities’ resilience via collective sharing and distribution of resources.¹ These resources may include supplies and the dissemination of emergency information, education, and training.² The COVID-19 pandemic demonstrated that larger health care systems including hospital networks and nursing homes are better connected to health care coalition resources than smaller, independent systems, such as community home health agencies.³ This leaves some organizations on their own to meet requirements that maintain continuity of care and support their patients and staff throughout a natural disaster.

Home health care workers play important roles in the care of older adults.⁴ Older adults experience high levels of disability and comorbidities that put them at risk during emergencies; they often require support from paid, family, and neighborhood caregivers to live independently.⁵ More than 9.3 million US adults receive paid care from 2.6 million home health care workers (eg, home health aides and personal care assistants).⁶ Many of these individuals are hired through small independent home health agencies (HHAs), while others may work directly for an individual. When neighborhood resources and family caregiving are disrupted during emergencies, the critical services these workers administer become even more essential to ensuring continued access to medical care and social services.

The importance of these services was underscored by the Centers for Medicare and Medicaid Services 2017 inclusion of HHAs in federal emergency preparedness guidelines.^7,8 The fractured and decentralized nature of the home health care industry means many HHAs struggle to maintain continuous care during emergencies and protect their staff. HHAs, and health care workers in the home, are often isolated, under-resourced, and disconnected from broader emergency planning efforts. Additionally, home care jobs are largely part-time, unstable, and low paying, making the workers themselves vulnerable during emergencies.^3,9-13

This is a significant issue for the Veterans Health Administration (VHA), which annually purchases 10.5 million home health care worker visits for 150,000 veterans from community-based HHAs to enable those individuals to live independently. Figure 1 illustrates the existing structure of directly provided and contracted VHA services for community-dwelling veterans, highlighting the circle of care around the veteran.^8,9 Home health care workers anchored health care teams during the COVID-19 pandemic, observing and reporting on patients’ well-being to family caregivers, primary care practitioners, and HHAs. They also provided critical emotional support and companionship to patients isolated from family and friends.⁹ These workers also exposed themselves and their families to considerable risk and often lacked the protection afforded by personal protective equipment (PPE) in accordance with infection prevention guidance.^3,12

Through a combination of its national and local health care networks, the VHA has a robust and well-positioned emergency infrastructure to supportcommunity-dwelling older adults during disasters.¹⁴ This network is supported by the VHA Office of Emergency Management, which shares resources and guidance with local emergency managers at each facility as well as individual programs such as the VHA Home Based Primary Care (HBPC) program, which provides 38,000 seriously ill veterans with home medical visits.¹⁵ Working closely with their local and national hospital networks and emergency managers, individual VHA HBPC programs were able to maintain the safety of staff and continuity of care for patients enrolled in HBPC by rapidly administering COVID-19 vaccines to patients, caregivers, and staff, and providing emergency assistance during the 2017 hurricane season.^16,17 These efforts were successful because HBPC practitioners and their patients, had access to a level of emergency-related information, resources, and technology that are often out of reach for individual community-based health care practitioners (HCPs). The US Department of Veterans Affairs (VA) also supports local communities through its Fourth Mission, which provides emergency resources to non-VHA health care facilities (ie, hospitals and nursing homes) during national emergencies and natural disasters.¹⁷ Although there has been an expansion in the definition of shared resources, such as extending behavioral health support to local communities, the VHA has not historically provided these resources to HHAs.¹⁴

This study examines opportunities to leverage VHA emergency management resources to support contracted HHAs and inform other large health system emergency planning efforts. The findings from the exploratory phase are described in this article. We interviewed VHA emergency managers, HBPC and VA staff who coordinate home health care worker services, as well as administrators at contracted HHAs within a Veterans Integrated Services Network (VISN). These findings will inform the second (single-site pilot study) and third (feasibility study) phases. Our intent was to (1) better understand the relationships between VA medical centers (VAMCs) and their contracted HHAs; (2) identify existing VHA emergency protocols to support community-dwelling older adults; and (3) determine opportunities to build on existing infrastructure and relationships to better support contracted HHAs and their staff in emergencies.

Methods

The 18 VISNs act as regional systems of care that are loosely connected to better meet local health needs and maximize access to care. This study was conducted at 6 of 9 VAMCs within VISN 2, the New York/New Jersey VHA Health Care Network.¹⁸ VAMCs that serve urban, rural, and mixed urban/rural catchment areas were included.

Each VAMC has an emergency management program led by an emergency manager, an HBPC program led by a program director and medical director, and a community care or purchased care office that has a liaison who manages contracted home health care worker services. The studyfocused on HBPC programs because they are most likely to interact with veterans’ home health care workers in the home and care for community-dwelling veterans during emergencies. Each VHA also contracts with a series of local HHAs that generally have a dedicated staff member who interfaces with the VHA liaison. Our goal was to interview ≥ 1 emergency manager, ≥ 1 HBPC team member, ≥ 1 community care staff person, and ≥ 1 contracted home health agency administrator at each site to gain multiple perspectives from the range of HCPs serving veterans in the community.

Recruitment and Data Collection

The 6 sites were selected in consultation with VISN 2 leadership for their strong HBPC and emergency management programs. To recruit respondents, we contacted VISN and VAMC leads and used our professional networks to identify a sample of multidisciplinary individuals who represent both community care and HBPC programs who were contacted via email.

Since each VAMC is organized differently, we utilized a snowball sampling approach to identify the appropriate contacts.¹⁹ At the completion of each interview, we asked the participant to suggest additional contacts and introduce us to any remaining stakeholders (eg, the emergency manager) at that site or colleagues at other VISN facilities. Because roles vary among VAMCs, we contacted the person who most closely resembled the identified role and asked them to direct us to a more appropriate contact, if necessary. We asked community care managers to identify 1 to 2 agencies serving the highest volume of patients who are veterans at their site and requested interviews with those liaisons. This resulted in the recruitment of key stakeholders from 4 teams across the 6 sites (Table).

A semistructured interview guide was jointly developed based on constructs of interest, including relationships within VAMCs and between VAMCs and HHAs; existing emergency protocols and experience during disasters; and suggestions and opportunities for supporting agencies during emergencies and potential barriers. Two researchers (TWL and EF) who were trained in qualitative methods jointly conducted interviews using the interview guide, with 1 researcher leading and another taking notes and asking clarifying questions.

Interviews were conducted virtually via Microsoft Teams with respondents at their work locations between September 2022 and January 2023. Interviews were audio recorded and transcribed and 2 authors (TWL and ESO) reviewed transcripts for accuracy. Interviews averaged 47 minutes in length (range, 20-59).

The study was reviewed and determined to be exempt by institutional review boards at the James J. Peters VAMC and Greater Los Angeles VAMC. We asked participants for verbal consent to participate and preserved their confidentiality.

Analysis

Data were analyzed via an inductive approach, which involves drawing salient themes rather than imposing preconceived theories.²⁰ Three researchers (TWL, EF, and ES) listened to and discussed 2 staff interviews and tagged text with specific codes (eg, communication between the VHA and HHA, internal communication, and barriers to case fulfillment) so the team could selectively return to the interview text for deeper analysis, allowing for the development of a final codebook. The project team synthesized the findings to identify higher-level themes, drawing comparisons across and within the respondent groups, including within and between health care systems. Throughout the analysis, we maintained analytic memos, documented discussions, and engaged in analyst triangulation to ensure trustworthiness.^21,22 To ensure the analysis accurately reflected the participants’ understanding, we held 2 virtual member-checking sessions with participants to share preliminary findings and conclusions and solicit feedback. Analysis was conducted using ATLAS.ti version 20.

Results

VHA-based participants described internal emergency management systems that are deployed during a disaster to support patients and staff. Agency participants described their own internal emergency management protocols. Respondents discussed how and when the 2 intersected, as well as opportunities for future mutual support. The analysis identified several themes: (1) relationships between VAMC teams; (2) relationships between VHA and HHAs; (3) VHA and agencies responses during emergencies; (4) receptivity and opportunities for extending VHA resources into the community; and (5) barriers and facilitators to deeper engagement.

Relationships Within VHA (n = 17)

Staff at all VHA sites described close relationships between the internal emergency management and HBPC teams. HBPC teams identified patients who were most at risk during emergencies to triage those with the highest medical needs (eg, patients dependent on home infusion, oxygen, or electronic medical devices) and worked alongside emergency managers to develop plans to continue care during an emergency. HBPC representatives were part of their facilities’ local emergency response committees. Due to this close collaboration, VHA emergency managers were familiar with the needs of homebound veterans and caregivers. “I invite our [HBPC] program manager to attend [committee] meetings and … they’re part of the EOC [emergency operations center]," an emergency manager said. “We work together and I’m constantly in contact with that individual, especially during natural disasters and so forth, to ensure that everybody’s prepared in the community.”

On the other hand, community caremanagers—who described frequent interactions with HBPC teams, largely around coordinating and managing non-VHA home care services—were less likely to have direct relationships with their facility emergency managers. For example, when asked if they had a relationship with their emergency manager, a community care manager admitted, “I [only] know who he is.” They also did not report having structured protocols for veteran outreach during emergencies, “because all those veterans who are receiving [home health care worker] services also belong to a primary care team,” and considered the outreach to be the responsibility of the primary care team and HHA.

Relationships Between the VHA and HHAs (n = 17)

Communication between VAMCs and contracted agencies primarily went through community care managers, who described established long-term relationships with agency administrators. Communication was commonly restricted to operational activities, such as processing referrals and occasional troubleshooting. According to a community care manager most communication is “why haven’t you signed my orders?” There was a general sense from participants that communication was promptly answered, problems were addressed, and professional collegiality existed between the agencies as patients were referred and placed for services. One community care manager reported meeting with agencies regularly, noting, “I talk to them pretty much daily.”

If problems arose, community care managers described themselves as “the liaison” between agencies and VHA HCPs who ordered the referrals. This is particularly the case if the agency needed help finding a VHA clinician or addressing differences in care delivery protocols.

Responding During Emergencies (n = 19)

During emergencies, VHA and agency staff described following their own organization’s protocols and communicating with each other only on a case-by-case basis rather than through formal or systematic channels and had little knowledge of their counterpart’s emergency protocols. Beyond patient care, there was no evidence of information sharing between VHA and agency staff. Regarding sharing information with their local community, an HBPC Program Director said, “it’s almost like the VHA had become siloed” and operated on its own without engaging with community health systems or emergency managers.

Beyond the guidance provided by state departments of public health, HHAs described collaborating with other agencies in their network and relying on their informal professional network to manage the volume of information and updates they followed during emergencies like the COVID-19 pandemic. One agency administrator did not frequently communicate with VHA partners during the pandemic but explained that the local public health department helped work through challenges. However, “we realized pretty quickly they were overloaded and there was only so much they could do.” The agency administrator turned to a “sister agency” and local hospitals, noting, “Wherever you have connections in the field or in the industry, you know you’re going to reach out to people for guidance on policies and… protocol.”

Opportunities for Extending VHA Resources to the Community (n = 16)

All VHA emergency managers were receptive to extending support to community-based HCPS and, in some cases, felt strongly that they were an essential part of veterans’ care networks. Emergency managers offered examples for how they supportedcommunity-based HCPs, such as helping those in the VAMC medical foster home program develop and evaluate emergency plans. Many said they had not explicitly considered HHAs before (Appendix).

Emergency managers also described how supporting community-based HCPs could be considered within the scope of the VHA role and mission, specifically the Fourth Mission. “I think that we should be making our best effort to make sure that we’re also providing that same level [of protection] to the people taking care of the veteran [as our VHA staff],” an emergency manager said. “It’s our responsibility to provide the best for the staff that are going into those homes to take care of that patient.”

In many cases, emergency managers had already developed practical tools that could be easily shared outside the VHA, including weather alerts, trainings, emergency plan templates, and lists of community resources and shelters (Figure 2). A number of these examples built on existing communication channels. One emergency manager said that the extension of resources could be an opportunity to decrease the perceived isolation of home health care workers through regular training for agencies that are providing health care aides, so that they know that “some bigger folks are keeping an eye on it.”

On the agency side, participants noted that some HHAs could benefit more from support than others. While some agencies are well staffed and have good protocols and keep up to date, “There are smaller agencies, agencies that are starting up that may not have the resources to just disseminate all the information. Those are the agencies [that] could well benefit from the VHA,” an HBPC medical director explained. Agency administrators suggested several areas where they would welcome support, including a deeper understanding of available community resources and access to PPE for staff. Regarding informational resources, an administrator said, “Anytime we can get information, it’s good to have it come to you and not always have to go out searching for it.”

Barriers and Facilitators to Partnering With Community Agencies (n = 16)

A primary barrier regarding resource sharing was potential misalignment between each organization’s policies. HHAs followed state and federal public health guidelines, which sometimes differed from VHA policies. Given that agencies care for both VHA and non-VHA clients, questions also arose around how agencies would prioritize information from the VHA, if they were already receiving information from other sources. When asked about information sharing, both VHA staff and agencies agreed staff time to support any additional activities should be weighed against the value of the information gained.

Six participants also shared that education around emergency preparedness could be an opportunity to bridge gaps between VAMCs and their surrounding communities. One local Chief of Community Care noted, “Any opportunity to just give information is going to make it a lot better for the veteran patient … to have something that’s a little more robust.”

Two emergency managers noted the need to be sensitive in the way they engaged with partners, respecting and building on the work that agencies were already doing in this area to ensure VHA was seen as a trusted partner and resource rather than trying to impose new policies or rules on community-based HCPs. “I know that like all leadership in various organizations, there’s a little bit of bristling going on when other people try and tell them what to do,” an HBPC medical director said. “However, if it is established that as a sort of greater level like a state level or a federal level, that VHA can be a resource. I think that as long as that’s recognized by their own professional organizations within each state, then I think that that would be a tremendous advantage to many agencies.”

In terms of sharing physical resources, emergency managers raised concerns around potential liability, although they also acknowledged this issue was important enough to think about potential workarounds. As one emergency manager said, “I want to know that my PPE is not compromised in any way shape or form and that I am in charge of that PPE, so to rely upon going to a home and hoping that [the PPE] wasn’t compromised … would kind of make me a little uneasy.” This emergency manager suggested possible solutions, such as creating a sealed PPE package to give directly to an aide.

Discussion

As the prevalence of climate-related disasters increases, the need to ensure the safety and independence of older adults during emergencies grows more urgent. Health systems must think beyond the direct services they provide and consider the community resources upon which their patients rely. While relationships did not formally exist between VHA emergency managers and community home health HCPs in the sample analyzed in this article, there is precedent and interest in supporting contracted home health agencies caring for veterans in the community. Although not historically part of the VA Fourth Mission, creating a pipeline of support for contracted HHAs by leveraging existing relationships and resources can potentially strengthen its mission to protect older veterans in emergencies, help them age safely in place, and provide a model for health systems to collaborate with community-based HCPs around emergency planning and response (Figure 3).²³

Existing research on the value of health care coalitions highlights the need for established and growing partnerships with a focus on ensuring they are value-added, which echoes concerns we heard in interviews.²⁴ Investment in community partnerships not only includes sharing supplies but also relying on bidirectional support that can be a trusted form of timely information.^1,25 The findings in this study exhibit strong communication practices within the VHA during periods of nonemergency and underscore the untapped value of the pre-existing relationship between VAMCs and their contracted HHAs as an area of potential growth for health care coalitions.

Sharing resources in a way that does not put new demands on partners contributes to the sustainability and value-added nature of coalitions. Examples include establishing new low-investment practices (ie, information sharing) that support capacity and compliance with existing requirements rather than create new responsibilities for either member of the coalition. The relationship between the VHA emergency managers and the VHA HBPC program can act as a guide. The emergency managers interviewed for this study are currently engaged with HBPC programs and therefore understand the needs of homebound older adults and their caregivers. Extending the information already available to the HBPC teams via existing channels strengthens workforce practices and increased security for the shared patient, even without direct relationships between emergency managers and agencies. It is important to understand the limitations of these practices, including concerns around conflicting federal and state mandates, legal concerns around the liability of sharing physical resources (such as PPE), and awareness that the objective is not for the VHA to increase burdens (eg, increasing compliance requirements) but rather to serve as a resource for a mutual population in a shared community.

Offering training and practical resources to HHA home health care workers can help them meet disaster preparedness requirements. This is particularly important considering the growing home care workforce shortages, a topic mentioned by all HBPC and community care participants interviewed for this study.^26,27 Home health care workers report feeling underprepared and isolated while on the job in normal conditions, a sentiment exacerbated by the COVID-19 pandemic.^3,10 Supporting these individuals may help them feel more prepared and connected to their work, improving stability and quality of care.

While these issues are priorities within the VHA, there is growing recognition at the state and federal level of the importance of including older adults and their HCPs in disaster preparedness and response.^5,28 The US Department of Health and Human Services, for example, includes older adults and organizations that serve them on its National Advisory Committee on Seniors and Disasters. The Senate version of the 2023 reauthorization of the Pandemic and All-Hazards Preparedness and Response Act included specific provisions to support community-dwelling older adults and people with disabilities, incorporating funding for community organizations to support continuity of services and avoid institutionalization in an emergency.²⁹ Other proposed legislation includes the Real Emergency Access for Aging and Disability Inclusion for Disasters Act, which would ensure the needs of older adults and people with disabilities are explicitly included in all phases of emergency planning and response.³⁰

The VHA expansion of the its VEText program to include disaster response is an effort to more efficiently extend outreach to older and vulnerable patients who are veterans.³¹ Given these growing efforts, the VHA and other health systems have an opportunity to expand internal emergency preparedness efforts to ensure the health and safety of individuals living in the community.

Limitations

VISN 2 has been a target of terrorism and other disasters. In addition to the sites being initially recruited for their strong emergency management protocols, this context may have biased respondents who are favorable to extending their resources into the community. At the time of recruitment, contracted HHAs were still experiencing staff shortages due to the COVID-19 pandemic, which limited the ability of agency staff to participate in interviews. Additionally, while the comprehensive exploration of VISN 2 facilities allows for confidence of the organizational structures described, the qualitative research design and small study sample, the study findings cannot be immediately generalized to all VISNs.

Conclusions

Many older veterans increasingly rely on home health care workers to age safely. The VHA, as a large national health care system and leader in emergency preparedness, could play an important role in supporting home health care workers and ameliorating their sense of isolation during emergencies and natural disasters. Leveraging existing resources and relationships may be a low-cost, low-effort opportunity to build higher-level interventions that support the needs of patients. Future research and work in this field, including the authors’ ongoing work, will expand agency participation and engage agency staff in conceptualizing pilot projects to ensure they are viable and feasible for the field.

About 1 in 10 Americans have diabetes mellitus (DM), of which about 90% to 95% are diagnosed with type 2 DM (T2DM) and veterans are disproportionately affected.^1,2 About 25% enrolled in the Veterans Health Administration (VHA) have T2DM, which has been attributed to exposure to herbicides (eg, Agent Orange), decreased physical activity resulting from past physical strain, chronic pain, and other physical limitations resulting from military service.^3-5

Pharmacologic management of DM is guided by the effectiveness of lifestyle interventions and comorbid diagnoses. Current DM management guidelines recommend patients with comorbid atherosclerotic cardiovascular disease, chronic kidney disease, or congestive heart failure receive first-line diabetes therapy with a sodium-glucose cotransporter-2 (SGLT-2) inhibitor or glucagon-like peptide-1 receptor (GLP-1) agonist.

Metformin remains a first-line pharmacologic option for the treatment of T2DM with the goal of achieving glycemic management when lifestyle interventions are insufficient.^6,7 Newer antihyperglycemic therapies have been studied as adjunct therapy to metformin. However, there is limited literature comparing metformin directly to other medication classes for the treatment of T2DM.^8-13 A systematic review of treatment-naive patients found HbA_1c reductions were similar whether patients received metformin vs an SGLT-2 inhibitor, GLP-1 agonist, sulfonylurea, or thiazolidinedione monotherapy.¹⁰ The analysis found dipeptidyl-peptidase-4 (DPP-4) inhibitors had inferior HbA_1c reduction compared to metformin.¹⁰ A Japanese systematic review compared metformin to thiazolidinediones, sulfonylureas, glinides, DPP-4 inhibitors, α-glucosidase inhibitors, or SGLT-2 inhibitors for ≥ 12 weeks but found no statistically significant differences in HbA_1c reduction.¹¹ The AWARD-3 trial compared once-weekly dulaglutide to metformin in treatment-experienced patients and found greater improvement in HbA_1c and achievement of HbA_1c goal with dulaglutide.¹³ While these studies show some comparisons of metformin to alternative pharmacologic therapy, researchers have not looked at what happens to patients’ HbA_1c levels when an event, such as a recall, prompts a rapid change to a different antihyperglycemic agent.

On May 28, 2020, the US Food and Drug Administration (FDA) asked 5 pharmaceutical companies to voluntarily recall certain formulations of metformin. This action was taken when FDA testing revealed unacceptably high levels of N-Nitrosodimethylamine, a probable carcinogen.¹⁴ This FDA recall of metformin extended-release, referred to as metformin sustained-action (SA) within the VHA electronic medication file but the same type of formulation, prompted clinicians to revisit and revise the pharmacologic regimens of patients taking the drug. Because of the paucity of head-to-head trials comparing metformin with newer alternative antihyperglycemic therapies, the effect of treatment change was unknown. In response, we aimed to establish a data registry within Veterans Integrated Service Network (VISN) 6.

Registry Development

The VISN 6 registry was established to gather long-term, observational, head-to-head data that would allow review of HbA_1c levels before and after the recall, as well as HbA_1c levels broken down by the agent that patients were switched to after the recall. Another goal was to explore prescribing trends following the recall.

Data Access Request Tracker approval was obtained and a US Department of Veterans Affairs (VA) Information and Computing Infrastructure workspace was developed to host the registry data. The research cohort was established from this data, and the registry framework was finalized using Structured Query Language (SQL). The SQL coding allows for recurring data updates for all individuals within the cohort including date of birth, race, sex, ethnicity, VHA facility visited, weight, body mass index, HbA_1c level, creatinine clearance, serum creatinine, antihyperglycemic medication prescriptions, adverse drug reactions, medication adherence (as defined by ≥ 80% refill history), and hospitalizations related to diabetes. For the purposes of this initial analysis, registry data included demographics, diabetes medications, and HbA_1c results.

METHODS

This study was a concurrent, observational, multicenter, registry-based study conducted at the Western North Carolina VA Health Care System (WNCVAHCS). The study was approved by the WNCVAHCS institutional review board and research and development committees.

All patients aged ≥ 18 years with T2DM and receiving health care from VISN 6 facilities who had an active metformin SA prescription on, and 1 year prior to, June 1, 2020 (the initial date VHA began implementing the FDA metformin recall) were entered into the registry. Data from 1 year prior were collected to provide a baseline. Veterans were excluded if they received metformin SA for any indication other than T2DM, there was no pre- or postrecall HbA_1c measurement, or death. We included 15,594 VISN 6 veterans.

Registry data were analyzed to determine whether a significant change in HbA_1c level occurred after the metformin recall and in response to alternative agents being prescribed. Data from veterans who met all inclusion criteria were assessed during the year before and after June 1, 2020. Demographic data were analyzed using frequency and descriptive statistics. The Shapiro Wilkes test was performed, and data were found to be nonparametric; therefore the Wilcoxon signed-rank test was used to evaluate the hypothesis that HbA_1c levels were not impacted by the recall.

Our sample size allowed us to create exact matched pairs of 9130 individuals and utilize rank-biserial correlation to establish effect size. Following this initial population-level test, we constructed 2 models. The first, a linear mixed-effects model, focused solely on the interaction effects between the pre- and postrecall periods and various medication classes on HbA_1c levels. Second, we constructed a random-effects within-between model (REWB) to evaluate the impact ofmedication classes and demographic variables. Statistical significance was measured at P < .05 with conservative power at .90. The effect size was set to 1.0, reflecting a minimum clinically important difference. Literature establishes 0.5 as a modest level of HbA_1c improvement and 1.0 as a clinically significant improvement.

RESULTS

Preliminary results included 15,594 veterans who received a metformin SA prescription as of June 1, 2020 from VISN 6 facilities; 15,392 veterans had a drug exposure end on June 1, 2020, indicating their standard therapy of metformin SA was discontinued following the FDA recall. Two hundred and two veterans were excluded from the registry because they continued to receive metformin SA from existing stock at a VISN6 facility. After identifying veterans with data for 1 year prior (June 1, 2019) to the index date and 1 year after (June 1, 2021) the study population was adjusted to 9130. The population was predominantly males aged> 60 years. Roughly 55% of the registry identified as White and nearly 40% as Black, and 2% indentified as Hispanic (Table 1).

Wilcoxon Signed-Rank Test

We created exact pairs by iterating the data and finding the closest measurements for each patient before and after the recall. This has the advantage over averaging a patient’s pre- and post-HbA_1c levels, as it allows for a rank-biserial correlation. Using the nonparametric Wilcoxon signed-rank test, V was 20,100,707 (P < .001), indicating a significant effect. The –0.29 rank-biserial correlation, which was computed to assess the effect size of the recall, suggests that the median HbA_1c level was lower postrecall vs prerecall. The magnitude of the correlation suggests a moderate effect size, and while the recall had a noticeable impact at a population level, it was not extreme (Table 2).

Linear Mixed-Effects Model

The binary variable for medication class exposure suggests the use of a logit link function for binary outcomes within the multilevel modeling framework.¹⁵ We employed a linear mixed-effects model to investigate the impact that switching from metformin SA to other T2DM medications had on HbA_1c levels. The model was adjusted for patient-specific random effects and included interaction terms between the recall period (before and after) and the usage of different T2DM medications.

Model Fit and Random Effects

The model demonstrated a residual maximum likelihood criterion of 100,219.7, indicating its fit to the data. Notably, the random effects analysis revealed a substantial variability in baseline HbA_1c levels across patients (SD, 0.94), highlighting the importance of individual differences in DM management. Medication classes with zero or near-zero exposure rate were removed. Due to demographic homogeneity, the model did not converge on demographic variables. Veterans were taking a mean of 1.8 T2DM medications and metformin SA was most common (Table 3).

During the postrecall period, metformin SA remained the most frequently prescribed medication class. This may be attributed to the existence of multiple manufacturers of metformin SA, some of which may not have been impacted by the recall. VISN 6 medical centers could have sought metformin SA outside of the usual procurement path following the recall.

Complex Random Effects Model

We employed a complex REWB model that evaluated the impact of medication classes on HbA_1c levels, accounting for both within and between subject effects of these medications, along with demographic variables (sex, race, and ethnicity) (eAppendix). This model accounts for individual-level changes over time (within-patient effects) and between groups of patients (between-patient effects). This is a more comprehensive model aimed at understanding the broader impact of medications on HbA_1c levels across diverse patient groups.

Most demographic categories did not demonstrate significant effects in this model. Black individuals experienced a slight increase in HbA_1c levels compared with other racial categories that was not statistically significant. However, this model confirms the findings from the linear mixed-effects model that GLP-1 agonists showed a substantial decrease in HbA_1c levels within patients (coefficient –0.5; 95% CI, –0.56 to –0.44; P < .001) and a moderate increase between patients (coefficient, 0.21; 95% CI, 0.12-0.31; P < .001). Additionally, SGLT-2 inhibitors had a notable decrease within patients (coefficient, –0.27; 95% CI, –0.32 to –0.22; P < .001).Another notable finding with our REWB model is insulin usage was associated with high HbA_1c levels, but only between subjects. Long-acting insulin (coefficient, 0.96; 95% CI, 0.90-1.01; P <. 001) and mixed insulin (coefficient, 1.09; 95% CI, 0.94-1.24; P < .001) both displayed marked increases between patients, suggesting future analysis may benefit from stratifying across insulin users and nonusers.

Fixed Effect Analysis

The fixed effects analysis yielded several notable findings. The intercept, representing the mean baseline HbA_1c level, was estimated at 7.8% (58 mmol/mol). The coefficient for the period (postrecall) was not statistically significant, indicating no overall change in HbA_1c levels from before to after the recall when specific medication classes were not considered (Table 4). Among medication classes examined, several showed significant associations with HbA_1c levels. DPP-4 inhibitors and GLP-1 agonists were associated with a decrease in HbA_1c levels, with coefficients of −0.08 and −0.24, respectively. Long-acting insulin and metformin immediate-release (IR) were associated with an increase in HbA_1c levels, as indicated by their positive coefficients of 0.38 and 0.16, respectively. Mixed insulin formulations and sulfonylureas showed an association with decreased HbA_1c levels.

Interaction Effects

The interaction terms between the recall period and the medication classes provided insights into the differential impact of the medication switch postrecall. Notably, the interaction term for long-acting insulin (coefficient, −0.10) was significant, suggesting a differential effect on HbA_1c levels postrecall. Other medications, like metformin IR, also exhibited significant interaction effects, indicating changes in the impact on HbA_1c levels in the postrecall period. The binary variable for medication class exposure suggests the use of a logit link function for binary outcomes within the multilevel modeling framework.¹⁵ We did not address the potential for cross cluster heterogeneity due to different medication classes.

DISCUSSION

This study is an ongoing, concurrent, observational, multicenter, registry-based study consisting of VISN 6 veterans who have T2DM and were prescribed metformin SA on June 1, 2020. This initial aim was to evaluate change in HbA_1c levels following the FDA metformin recall. While there was substantial variability in baseline HbA_1c levels across the patients, the mean baseline HbA_1c level at 7.5% (58 mmol/mol). Patients taking GLP-1 agonists showed substantial decrease in HbA_1c levels (coefficient; –0.5; 95% CI, –0.56 to –0.44; P <. 001). Patients taking SGLT-2 inhibitors had a notable decrease in HbA_1c (coefficient, –0.27; 95% CI, –0.32 to –0.22; P < .001). Despite this, the coefficient for the postrecall period was not statistically significant, indicating no overall change in HbA_1c levels from pre- to postrecall when specific medication classes were not considered.

Further analysis included assessment of prescribing trends postrecall. There was an increase in SGLT-2 inhibitor, GLP-1 agonist, and DPP-4 inhibitor prescribing. Considering the growing evidence of the cardiovascular and renal benefits of these medication classes, specifically the GLP-1 agonists and SGLT-2 inhibitors, this trend would be expected.

Limitations

This study cohort did not capture veterans with T2DM who transferred their health care to VISN 6 after June 1, 2020, and continued to receive metformin SA from the prior facility. Inclusion of these veterans would have increased the registry population. Additionally, the cohort did not identify veterans who continued to receive metformin SA through a source other than the VA. Without that information, the registry cohort may include veterans thought to have either transitioned to a different therapy or to no other T2DM therapy after the recall.

Given that DM can progress over time, it is possible the transition to a new medication after the recall was the result of suboptimal management, or in response to an adverse effect from a previous medication, and not solely due to the metformin SA recall. In addition, there are several factors that could impact HbA_1c level over time that were not accounted for in this study, such as medication adherence and lifestyle modifications.

The notable level of metformin SA prescriptions, despite the recall, may be attributed to several factors. First, not all patients stopped metformin completely. Review of the prescription data indicated that some veterans were provided with limited refills at select VA medical centers that had supplies (medication lots not recalled). Access to a safe supply of metformin SA after the recall may have varied among VISN 6 facilities. It is also possible that as new supplies of metformin SA became available, veterans restarted metformin SA. This may have been resumed while continuing a new medication prescribed at the beginning of the recall. As the year progressed after the recall, an increase in metformin SA prescriptions likely occurred as supplies became available and clinicians/veterans chose to resume this medication therapy.

Conclusions

Results of this initial registry study found no difference in HbA_1c levels across the study population after the metformin SA recall. However, there was clinical difference in the HbA_1c within veterans prescribed SGLT-2 inhibitors and GLP-1 agonists. As expected, prescribing trends showed an increase in these agents after the recall. With the known benefits of these medications beyond glucose lowering, it is anticipated the cohort of veterans prescribed these medications will continue to grow.

The VISN 6 research registry allowed this study to gain an important snapshot in time following the metformin SA recall, and will serve as an important resource for future DM research endeavors. It will allow for ongoing evaluation of the impact of the transition to alternative T2DM medications after the metformin SA recall. Future exploration will include evaluation of adverse drug reactions, DM-related hospitalizations, emergency department visits related to T2DM, changes in renal function, and cardiovascular events among all diabetes medication classes.

Acknowledgments

The study team thanks the Veterans Affairs Informatics and Computing Infrastructure for their help and expertise throughout this project. The authors acknowledge the contributions of Philip Nelson, PharmD, and Brian Peek, PharmD.

About 1 in 10 Americans have diabetes mellitus (DM), of which about 90% to 95% are diagnosed with type 2 DM (T2DM) and veterans are disproportionately affected.^1,2 About 25% enrolled in the Veterans Health Administration (VHA) have T2DM, which has been attributed to exposure to herbicides (eg, Agent Orange), decreased physical activity resulting from past physical strain, chronic pain, and other physical limitations resulting from military service.^3-5

Pharmacologic management of DM is guided by the effectiveness of lifestyle interventions and comorbid diagnoses. Current DM management guidelines recommend patients with comorbid atherosclerotic cardiovascular disease, chronic kidney disease, or congestive heart failure receive first-line diabetes therapy with a sodium-glucose cotransporter-2 (SGLT-2) inhibitor or glucagon-like peptide-1 receptor (GLP-1) agonist.

Metformin remains a first-line pharmacologic option for the treatment of T2DM with the goal of achieving glycemic management when lifestyle interventions are insufficient.^6,7 Newer antihyperglycemic therapies have been studied as adjunct therapy to metformin. However, there is limited literature comparing metformin directly to other medication classes for the treatment of T2DM.^8-13 A systematic review of treatment-naive patients found HbA_1c reductions were similar whether patients received metformin vs an SGLT-2 inhibitor, GLP-1 agonist, sulfonylurea, or thiazolidinedione monotherapy.¹⁰ The analysis found dipeptidyl-peptidase-4 (DPP-4) inhibitors had inferior HbA_1c reduction compared to metformin.¹⁰ A Japanese systematic review compared metformin to thiazolidinediones, sulfonylureas, glinides, DPP-4 inhibitors, α-glucosidase inhibitors, or SGLT-2 inhibitors for ≥ 12 weeks but found no statistically significant differences in HbA_1c reduction.¹¹ The AWARD-3 trial compared once-weekly dulaglutide to metformin in treatment-experienced patients and found greater improvement in HbA_1c and achievement of HbA_1c goal with dulaglutide.¹³ While these studies show some comparisons of metformin to alternative pharmacologic therapy, researchers have not looked at what happens to patients’ HbA_1c levels when an event, such as a recall, prompts a rapid change to a different antihyperglycemic agent.

On May 28, 2020, the US Food and Drug Administration (FDA) asked 5 pharmaceutical companies to voluntarily recall certain formulations of metformin. This action was taken when FDA testing revealed unacceptably high levels of N-Nitrosodimethylamine, a probable carcinogen.¹⁴ This FDA recall of metformin extended-release, referred to as metformin sustained-action (SA) within the VHA electronic medication file but the same type of formulation, prompted clinicians to revisit and revise the pharmacologic regimens of patients taking the drug. Because of the paucity of head-to-head trials comparing metformin with newer alternative antihyperglycemic therapies, the effect of treatment change was unknown. In response, we aimed to establish a data registry within Veterans Integrated Service Network (VISN) 6.

Registry Development

The VISN 6 registry was established to gather long-term, observational, head-to-head data that would allow review of HbA_1c levels before and after the recall, as well as HbA_1c levels broken down by the agent that patients were switched to after the recall. Another goal was to explore prescribing trends following the recall.

Data Access Request Tracker approval was obtained and a US Department of Veterans Affairs (VA) Information and Computing Infrastructure workspace was developed to host the registry data. The research cohort was established from this data, and the registry framework was finalized using Structured Query Language (SQL). The SQL coding allows for recurring data updates for all individuals within the cohort including date of birth, race, sex, ethnicity, VHA facility visited, weight, body mass index, HbA_1c level, creatinine clearance, serum creatinine, antihyperglycemic medication prescriptions, adverse drug reactions, medication adherence (as defined by ≥ 80% refill history), and hospitalizations related to diabetes. For the purposes of this initial analysis, registry data included demographics, diabetes medications, and HbA_1c results.

METHODS

This study was a concurrent, observational, multicenter, registry-based study conducted at the Western North Carolina VA Health Care System (WNCVAHCS). The study was approved by the WNCVAHCS institutional review board and research and development committees.

All patients aged ≥ 18 years with T2DM and receiving health care from VISN 6 facilities who had an active metformin SA prescription on, and 1 year prior to, June 1, 2020 (the initial date VHA began implementing the FDA metformin recall) were entered into the registry. Data from 1 year prior were collected to provide a baseline. Veterans were excluded if they received metformin SA for any indication other than T2DM, there was no pre- or postrecall HbA_1c measurement, or death. We included 15,594 VISN 6 veterans.

Registry data were analyzed to determine whether a significant change in HbA_1c level occurred after the metformin recall and in response to alternative agents being prescribed. Data from veterans who met all inclusion criteria were assessed during the year before and after June 1, 2020. Demographic data were analyzed using frequency and descriptive statistics. The Shapiro Wilkes test was performed, and data were found to be nonparametric; therefore the Wilcoxon signed-rank test was used to evaluate the hypothesis that HbA_1c levels were not impacted by the recall.

Our sample size allowed us to create exact matched pairs of 9130 individuals and utilize rank-biserial correlation to establish effect size. Following this initial population-level test, we constructed 2 models. The first, a linear mixed-effects model, focused solely on the interaction effects between the pre- and postrecall periods and various medication classes on HbA_1c levels. Second, we constructed a random-effects within-between model (REWB) to evaluate the impact ofmedication classes and demographic variables. Statistical significance was measured at P < .05 with conservative power at .90. The effect size was set to 1.0, reflecting a minimum clinically important difference. Literature establishes 0.5 as a modest level of HbA_1c improvement and 1.0 as a clinically significant improvement.

RESULTS

Preliminary results included 15,594 veterans who received a metformin SA prescription as of June 1, 2020 from VISN 6 facilities; 15,392 veterans had a drug exposure end on June 1, 2020, indicating their standard therapy of metformin SA was discontinued following the FDA recall. Two hundred and two veterans were excluded from the registry because they continued to receive metformin SA from existing stock at a VISN6 facility. After identifying veterans with data for 1 year prior (June 1, 2019) to the index date and 1 year after (June 1, 2021) the study population was adjusted to 9130. The population was predominantly males aged> 60 years. Roughly 55% of the registry identified as White and nearly 40% as Black, and 2% indentified as Hispanic (Table 1).

Wilcoxon Signed-Rank Test

We created exact pairs by iterating the data and finding the closest measurements for each patient before and after the recall. This has the advantage over averaging a patient’s pre- and post-HbA_1c levels, as it allows for a rank-biserial correlation. Using the nonparametric Wilcoxon signed-rank test, V was 20,100,707 (P < .001), indicating a significant effect. The –0.29 rank-biserial correlation, which was computed to assess the effect size of the recall, suggests that the median HbA_1c level was lower postrecall vs prerecall. The magnitude of the correlation suggests a moderate effect size, and while the recall had a noticeable impact at a population level, it was not extreme (Table 2).

Linear Mixed-Effects Model

The binary variable for medication class exposure suggests the use of a logit link function for binary outcomes within the multilevel modeling framework.¹⁵ We employed a linear mixed-effects model to investigate the impact that switching from metformin SA to other T2DM medications had on HbA_1c levels. The model was adjusted for patient-specific random effects and included interaction terms between the recall period (before and after) and the usage of different T2DM medications.

Model Fit and Random Effects

The model demonstrated a residual maximum likelihood criterion of 100,219.7, indicating its fit to the data. Notably, the random effects analysis revealed a substantial variability in baseline HbA_1c levels across patients (SD, 0.94), highlighting the importance of individual differences in DM management. Medication classes with zero or near-zero exposure rate were removed. Due to demographic homogeneity, the model did not converge on demographic variables. Veterans were taking a mean of 1.8 T2DM medications and metformin SA was most common (Table 3).

During the postrecall period, metformin SA remained the most frequently prescribed medication class. This may be attributed to the existence of multiple manufacturers of metformin SA, some of which may not have been impacted by the recall. VISN 6 medical centers could have sought metformin SA outside of the usual procurement path following the recall.

Complex Random Effects Model

We employed a complex REWB model that evaluated the impact of medication classes on HbA_1c levels, accounting for both within and between subject effects of these medications, along with demographic variables (sex, race, and ethnicity) (eAppendix). This model accounts for individual-level changes over time (within-patient effects) and between groups of patients (between-patient effects). This is a more comprehensive model aimed at understanding the broader impact of medications on HbA_1c levels across diverse patient groups.

Most demographic categories did not demonstrate significant effects in this model. Black individuals experienced a slight increase in HbA_1c levels compared with other racial categories that was not statistically significant. However, this model confirms the findings from the linear mixed-effects model that GLP-1 agonists showed a substantial decrease in HbA_1c levels within patients (coefficient –0.5; 95% CI, –0.56 to –0.44; P < .001) and a moderate increase between patients (coefficient, 0.21; 95% CI, 0.12-0.31; P < .001). Additionally, SGLT-2 inhibitors had a notable decrease within patients (coefficient, –0.27; 95% CI, –0.32 to –0.22; P < .001).Another notable finding with our REWB model is insulin usage was associated with high HbA_1c levels, but only between subjects. Long-acting insulin (coefficient, 0.96; 95% CI, 0.90-1.01; P <. 001) and mixed insulin (coefficient, 1.09; 95% CI, 0.94-1.24; P < .001) both displayed marked increases between patients, suggesting future analysis may benefit from stratifying across insulin users and nonusers.

Fixed Effect Analysis

The fixed effects analysis yielded several notable findings. The intercept, representing the mean baseline HbA_1c level, was estimated at 7.8% (58 mmol/mol). The coefficient for the period (postrecall) was not statistically significant, indicating no overall change in HbA_1c levels from before to after the recall when specific medication classes were not considered (Table 4). Among medication classes examined, several showed significant associations with HbA_1c levels. DPP-4 inhibitors and GLP-1 agonists were associated with a decrease in HbA_1c levels, with coefficients of −0.08 and −0.24, respectively. Long-acting insulin and metformin immediate-release (IR) were associated with an increase in HbA_1c levels, as indicated by their positive coefficients of 0.38 and 0.16, respectively. Mixed insulin formulations and sulfonylureas showed an association with decreased HbA_1c levels.

Interaction Effects

The interaction terms between the recall period and the medication classes provided insights into the differential impact of the medication switch postrecall. Notably, the interaction term for long-acting insulin (coefficient, −0.10) was significant, suggesting a differential effect on HbA_1c levels postrecall. Other medications, like metformin IR, also exhibited significant interaction effects, indicating changes in the impact on HbA_1c levels in the postrecall period. The binary variable for medication class exposure suggests the use of a logit link function for binary outcomes within the multilevel modeling framework.¹⁵ We did not address the potential for cross cluster heterogeneity due to different medication classes.

DISCUSSION

This study is an ongoing, concurrent, observational, multicenter, registry-based study consisting of VISN 6 veterans who have T2DM and were prescribed metformin SA on June 1, 2020. This initial aim was to evaluate change in HbA_1c levels following the FDA metformin recall. While there was substantial variability in baseline HbA_1c levels across the patients, the mean baseline HbA_1c level at 7.5% (58 mmol/mol). Patients taking GLP-1 agonists showed substantial decrease in HbA_1c levels (coefficient; –0.5; 95% CI, –0.56 to –0.44; P <. 001). Patients taking SGLT-2 inhibitors had a notable decrease in HbA_1c (coefficient, –0.27; 95% CI, –0.32 to –0.22; P < .001). Despite this, the coefficient for the postrecall period was not statistically significant, indicating no overall change in HbA_1c levels from pre- to postrecall when specific medication classes were not considered.

Further analysis included assessment of prescribing trends postrecall. There was an increase in SGLT-2 inhibitor, GLP-1 agonist, and DPP-4 inhibitor prescribing. Considering the growing evidence of the cardiovascular and renal benefits of these medication classes, specifically the GLP-1 agonists and SGLT-2 inhibitors, this trend would be expected.

Limitations

This study cohort did not capture veterans with T2DM who transferred their health care to VISN 6 after June 1, 2020, and continued to receive metformin SA from the prior facility. Inclusion of these veterans would have increased the registry population. Additionally, the cohort did not identify veterans who continued to receive metformin SA through a source other than the VA. Without that information, the registry cohort may include veterans thought to have either transitioned to a different therapy or to no other T2DM therapy after the recall.

Given that DM can progress over time, it is possible the transition to a new medication after the recall was the result of suboptimal management, or in response to an adverse effect from a previous medication, and not solely due to the metformin SA recall. In addition, there are several factors that could impact HbA_1c level over time that were not accounted for in this study, such as medication adherence and lifestyle modifications.

The notable level of metformin SA prescriptions, despite the recall, may be attributed to several factors. First, not all patients stopped metformin completely. Review of the prescription data indicated that some veterans were provided with limited refills at select VA medical centers that had supplies (medication lots not recalled). Access to a safe supply of metformin SA after the recall may have varied among VISN 6 facilities. It is also possible that as new supplies of metformin SA became available, veterans restarted metformin SA. This may have been resumed while continuing a new medication prescribed at the beginning of the recall. As the year progressed after the recall, an increase in metformin SA prescriptions likely occurred as supplies became available and clinicians/veterans chose to resume this medication therapy.

Conclusions

Results of this initial registry study found no difference in HbA_1c levels across the study population after the metformin SA recall. However, there was clinical difference in the HbA_1c within veterans prescribed SGLT-2 inhibitors and GLP-1 agonists. As expected, prescribing trends showed an increase in these agents after the recall. With the known benefits of these medications beyond glucose lowering, it is anticipated the cohort of veterans prescribed these medications will continue to grow.

The VISN 6 research registry allowed this study to gain an important snapshot in time following the metformin SA recall, and will serve as an important resource for future DM research endeavors. It will allow for ongoing evaluation of the impact of the transition to alternative T2DM medications after the metformin SA recall. Future exploration will include evaluation of adverse drug reactions, DM-related hospitalizations, emergency department visits related to T2DM, changes in renal function, and cardiovascular events among all diabetes medication classes.

Acknowledgments

The study team thanks the Veterans Affairs Informatics and Computing Infrastructure for their help and expertise throughout this project. The authors acknowledge the contributions of Philip Nelson, PharmD, and Brian Peek, PharmD.

About 1 in 10 Americans have diabetes mellitus (DM), of which about 90% to 95% are diagnosed with type 2 DM (T2DM) and veterans are disproportionately affected.^1,2 About 25% enrolled in the Veterans Health Administration (VHA) have T2DM, which has been attributed to exposure to herbicides (eg, Agent Orange), decreased physical activity resulting from past physical strain, chronic pain, and other physical limitations resulting from military service.^3-5

Pharmacologic management of DM is guided by the effectiveness of lifestyle interventions and comorbid diagnoses. Current DM management guidelines recommend patients with comorbid atherosclerotic cardiovascular disease, chronic kidney disease, or congestive heart failure receive first-line diabetes therapy with a sodium-glucose cotransporter-2 (SGLT-2) inhibitor or glucagon-like peptide-1 receptor (GLP-1) agonist.

Metformin remains a first-line pharmacologic option for the treatment of T2DM with the goal of achieving glycemic management when lifestyle interventions are insufficient.^6,7 Newer antihyperglycemic therapies have been studied as adjunct therapy to metformin. However, there is limited literature comparing metformin directly to other medication classes for the treatment of T2DM.^8-13 A systematic review of treatment-naive patients found HbA_1c reductions were similar whether patients received metformin vs an SGLT-2 inhibitor, GLP-1 agonist, sulfonylurea, or thiazolidinedione monotherapy.¹⁰ The analysis found dipeptidyl-peptidase-4 (DPP-4) inhibitors had inferior HbA_1c reduction compared to metformin.¹⁰ A Japanese systematic review compared metformin to thiazolidinediones, sulfonylureas, glinides, DPP-4 inhibitors, α-glucosidase inhibitors, or SGLT-2 inhibitors for ≥ 12 weeks but found no statistically significant differences in HbA_1c reduction.¹¹ The AWARD-3 trial compared once-weekly dulaglutide to metformin in treatment-experienced patients and found greater improvement in HbA_1c and achievement of HbA_1c goal with dulaglutide.¹³ While these studies show some comparisons of metformin to alternative pharmacologic therapy, researchers have not looked at what happens to patients’ HbA_1c levels when an event, such as a recall, prompts a rapid change to a different antihyperglycemic agent.

On May 28, 2020, the US Food and Drug Administration (FDA) asked 5 pharmaceutical companies to voluntarily recall certain formulations of metformin. This action was taken when FDA testing revealed unacceptably high levels of N-Nitrosodimethylamine, a probable carcinogen.¹⁴ This FDA recall of metformin extended-release, referred to as metformin sustained-action (SA) within the VHA electronic medication file but the same type of formulation, prompted clinicians to revisit and revise the pharmacologic regimens of patients taking the drug. Because of the paucity of head-to-head trials comparing metformin with newer alternative antihyperglycemic therapies, the effect of treatment change was unknown. In response, we aimed to establish a data registry within Veterans Integrated Service Network (VISN) 6.

Registry Development

The VISN 6 registry was established to gather long-term, observational, head-to-head data that would allow review of HbA_1c levels before and after the recall, as well as HbA_1c levels broken down by the agent that patients were switched to after the recall. Another goal was to explore prescribing trends following the recall.

Data Access Request Tracker approval was obtained and a US Department of Veterans Affairs (VA) Information and Computing Infrastructure workspace was developed to host the registry data. The research cohort was established from this data, and the registry framework was finalized using Structured Query Language (SQL). The SQL coding allows for recurring data updates for all individuals within the cohort including date of birth, race, sex, ethnicity, VHA facility visited, weight, body mass index, HbA_1c level, creatinine clearance, serum creatinine, antihyperglycemic medication prescriptions, adverse drug reactions, medication adherence (as defined by ≥ 80% refill history), and hospitalizations related to diabetes. For the purposes of this initial analysis, registry data included demographics, diabetes medications, and HbA_1c results.

METHODS

This study was a concurrent, observational, multicenter, registry-based study conducted at the Western North Carolina VA Health Care System (WNCVAHCS). The study was approved by the WNCVAHCS institutional review board and research and development committees.

All patients aged ≥ 18 years with T2DM and receiving health care from VISN 6 facilities who had an active metformin SA prescription on, and 1 year prior to, June 1, 2020 (the initial date VHA began implementing the FDA metformin recall) were entered into the registry. Data from 1 year prior were collected to provide a baseline. Veterans were excluded if they received metformin SA for any indication other than T2DM, there was no pre- or postrecall HbA_1c measurement, or death. We included 15,594 VISN 6 veterans.

Registry data were analyzed to determine whether a significant change in HbA_1c level occurred after the metformin recall and in response to alternative agents being prescribed. Data from veterans who met all inclusion criteria were assessed during the year before and after June 1, 2020. Demographic data were analyzed using frequency and descriptive statistics. The Shapiro Wilkes test was performed, and data were found to be nonparametric; therefore the Wilcoxon signed-rank test was used to evaluate the hypothesis that HbA_1c levels were not impacted by the recall.

Our sample size allowed us to create exact matched pairs of 9130 individuals and utilize rank-biserial correlation to establish effect size. Following this initial population-level test, we constructed 2 models. The first, a linear mixed-effects model, focused solely on the interaction effects between the pre- and postrecall periods and various medication classes on HbA_1c levels. Second, we constructed a random-effects within-between model (REWB) to evaluate the impact ofmedication classes and demographic variables. Statistical significance was measured at P < .05 with conservative power at .90. The effect size was set to 1.0, reflecting a minimum clinically important difference. Literature establishes 0.5 as a modest level of HbA_1c improvement and 1.0 as a clinically significant improvement.

RESULTS

Preliminary results included 15,594 veterans who received a metformin SA prescription as of June 1, 2020 from VISN 6 facilities; 15,392 veterans had a drug exposure end on June 1, 2020, indicating their standard therapy of metformin SA was discontinued following the FDA recall. Two hundred and two veterans were excluded from the registry because they continued to receive metformin SA from existing stock at a VISN6 facility. After identifying veterans with data for 1 year prior (June 1, 2019) to the index date and 1 year after (June 1, 2021) the study population was adjusted to 9130. The population was predominantly males aged> 60 years. Roughly 55% of the registry identified as White and nearly 40% as Black, and 2% indentified as Hispanic (Table 1).

Wilcoxon Signed-Rank Test

We created exact pairs by iterating the data and finding the closest measurements for each patient before and after the recall. This has the advantage over averaging a patient’s pre- and post-HbA_1c levels, as it allows for a rank-biserial correlation. Using the nonparametric Wilcoxon signed-rank test, V was 20,100,707 (P < .001), indicating a significant effect. The –0.29 rank-biserial correlation, which was computed to assess the effect size of the recall, suggests that the median HbA_1c level was lower postrecall vs prerecall. The magnitude of the correlation suggests a moderate effect size, and while the recall had a noticeable impact at a population level, it was not extreme (Table 2).

Linear Mixed-Effects Model

The binary variable for medication class exposure suggests the use of a logit link function for binary outcomes within the multilevel modeling framework.¹⁵ We employed a linear mixed-effects model to investigate the impact that switching from metformin SA to other T2DM medications had on HbA_1c levels. The model was adjusted for patient-specific random effects and included interaction terms between the recall period (before and after) and the usage of different T2DM medications.

Model Fit and Random Effects

The model demonstrated a residual maximum likelihood criterion of 100,219.7, indicating its fit to the data. Notably, the random effects analysis revealed a substantial variability in baseline HbA_1c levels across patients (SD, 0.94), highlighting the importance of individual differences in DM management. Medication classes with zero or near-zero exposure rate were removed. Due to demographic homogeneity, the model did not converge on demographic variables. Veterans were taking a mean of 1.8 T2DM medications and metformin SA was most common (Table 3).

During the postrecall period, metformin SA remained the most frequently prescribed medication class. This may be attributed to the existence of multiple manufacturers of metformin SA, some of which may not have been impacted by the recall. VISN 6 medical centers could have sought metformin SA outside of the usual procurement path following the recall.

Complex Random Effects Model

We employed a complex REWB model that evaluated the impact of medication classes on HbA_1c levels, accounting for both within and between subject effects of these medications, along with demographic variables (sex, race, and ethnicity) (eAppendix). This model accounts for individual-level changes over time (within-patient effects) and between groups of patients (between-patient effects). This is a more comprehensive model aimed at understanding the broader impact of medications on HbA_1c levels across diverse patient groups.

Most demographic categories did not demonstrate significant effects in this model. Black individuals experienced a slight increase in HbA_1c levels compared with other racial categories that was not statistically significant. However, this model confirms the findings from the linear mixed-effects model that GLP-1 agonists showed a substantial decrease in HbA_1c levels within patients (coefficient –0.5; 95% CI, –0.56 to –0.44; P < .001) and a moderate increase between patients (coefficient, 0.21; 95% CI, 0.12-0.31; P < .001). Additionally, SGLT-2 inhibitors had a notable decrease within patients (coefficient, –0.27; 95% CI, –0.32 to –0.22; P < .001).Another notable finding with our REWB model is insulin usage was associated with high HbA_1c levels, but only between subjects. Long-acting insulin (coefficient, 0.96; 95% CI, 0.90-1.01; P <. 001) and mixed insulin (coefficient, 1.09; 95% CI, 0.94-1.24; P < .001) both displayed marked increases between patients, suggesting future analysis may benefit from stratifying across insulin users and nonusers.

Fixed Effect Analysis

The fixed effects analysis yielded several notable findings. The intercept, representing the mean baseline HbA_1c level, was estimated at 7.8% (58 mmol/mol). The coefficient for the period (postrecall) was not statistically significant, indicating no overall change in HbA_1c levels from before to after the recall when specific medication classes were not considered (Table 4). Among medication classes examined, several showed significant associations with HbA_1c levels. DPP-4 inhibitors and GLP-1 agonists were associated with a decrease in HbA_1c levels, with coefficients of −0.08 and −0.24, respectively. Long-acting insulin and metformin immediate-release (IR) were associated with an increase in HbA_1c levels, as indicated by their positive coefficients of 0.38 and 0.16, respectively. Mixed insulin formulations and sulfonylureas showed an association with decreased HbA_1c levels.

Interaction Effects

The interaction terms between the recall period and the medication classes provided insights into the differential impact of the medication switch postrecall. Notably, the interaction term for long-acting insulin (coefficient, −0.10) was significant, suggesting a differential effect on HbA_1c levels postrecall. Other medications, like metformin IR, also exhibited significant interaction effects, indicating changes in the impact on HbA_1c levels in the postrecall period. The binary variable for medication class exposure suggests the use of a logit link function for binary outcomes within the multilevel modeling framework.¹⁵ We did not address the potential for cross cluster heterogeneity due to different medication classes.

DISCUSSION

This study is an ongoing, concurrent, observational, multicenter, registry-based study consisting of VISN 6 veterans who have T2DM and were prescribed metformin SA on June 1, 2020. This initial aim was to evaluate change in HbA_1c levels following the FDA metformin recall. While there was substantial variability in baseline HbA_1c levels across the patients, the mean baseline HbA_1c level at 7.5% (58 mmol/mol). Patients taking GLP-1 agonists showed substantial decrease in HbA_1c levels (coefficient; –0.5; 95% CI, –0.56 to –0.44; P <. 001). Patients taking SGLT-2 inhibitors had a notable decrease in HbA_1c (coefficient, –0.27; 95% CI, –0.32 to –0.22; P < .001). Despite this, the coefficient for the postrecall period was not statistically significant, indicating no overall change in HbA_1c levels from pre- to postrecall when specific medication classes were not considered.

Further analysis included assessment of prescribing trends postrecall. There was an increase in SGLT-2 inhibitor, GLP-1 agonist, and DPP-4 inhibitor prescribing. Considering the growing evidence of the cardiovascular and renal benefits of these medication classes, specifically the GLP-1 agonists and SGLT-2 inhibitors, this trend would be expected.

Limitations

This study cohort did not capture veterans with T2DM who transferred their health care to VISN 6 after June 1, 2020, and continued to receive metformin SA from the prior facility. Inclusion of these veterans would have increased the registry population. Additionally, the cohort did not identify veterans who continued to receive metformin SA through a source other than the VA. Without that information, the registry cohort may include veterans thought to have either transitioned to a different therapy or to no other T2DM therapy after the recall.

Given that DM can progress over time, it is possible the transition to a new medication after the recall was the result of suboptimal management, or in response to an adverse effect from a previous medication, and not solely due to the metformin SA recall. In addition, there are several factors that could impact HbA_1c level over time that were not accounted for in this study, such as medication adherence and lifestyle modifications.

The notable level of metformin SA prescriptions, despite the recall, may be attributed to several factors. First, not all patients stopped metformin completely. Review of the prescription data indicated that some veterans were provided with limited refills at select VA medical centers that had supplies (medication lots not recalled). Access to a safe supply of metformin SA after the recall may have varied among VISN 6 facilities. It is also possible that as new supplies of metformin SA became available, veterans restarted metformin SA. This may have been resumed while continuing a new medication prescribed at the beginning of the recall. As the year progressed after the recall, an increase in metformin SA prescriptions likely occurred as supplies became available and clinicians/veterans chose to resume this medication therapy.

Conclusions

Results of this initial registry study found no difference in HbA_1c levels across the study population after the metformin SA recall. However, there was clinical difference in the HbA_1c within veterans prescribed SGLT-2 inhibitors and GLP-1 agonists. As expected, prescribing trends showed an increase in these agents after the recall. With the known benefits of these medications beyond glucose lowering, it is anticipated the cohort of veterans prescribed these medications will continue to grow.

The VISN 6 research registry allowed this study to gain an important snapshot in time following the metformin SA recall, and will serve as an important resource for future DM research endeavors. It will allow for ongoing evaluation of the impact of the transition to alternative T2DM medications after the metformin SA recall. Future exploration will include evaluation of adverse drug reactions, DM-related hospitalizations, emergency department visits related to T2DM, changes in renal function, and cardiovascular events among all diabetes medication classes.

Acknowledgments

The study team thanks the Veterans Affairs Informatics and Computing Infrastructure for their help and expertise throughout this project. The authors acknowledge the contributions of Philip Nelson, PharmD, and Brian Peek, PharmD.

To the Editor:

Although biologics provide major therapeutic benefits for dermatologic conditions, they also come with a substantial cost, making them among the most expensive medications available. Medicare and Medicaid spending on biologics for dermatologic conditions increased by 320% from 2012 to 2018, reaching a staggering $10.6 billion in 2018 alone.¹ Biosimilars show promise in reducing health care spending for dermatologic conditions; however, their utilization has been limited due to multiple factors, including delayed market entry from patent thickets, exclusionary formulary contracts, and prescriber skepticism regarding their safety and efficacy.² For instance, a national survey of 1201 US physicians in specialties that are high prescribers of biologics reported that 55% doubted the safety and appropriateness of biosimilars.³

US Food and Drug Administration approval of biosimilars for adalimumab and etanercept offers the potential to reduce health care spending for dermatologic conditions. However, this cost reduction is dependent on utilization rates among dermatologists. In this national cross-sectional review of Medicare data, we predicted the impact of these biosimilars on dermatologic Medicare costs and demonstrated how differing utilization rates among dermatologists can influence potential savings.

To model 2023 utilization and cost reduction from biosimilars, we analyzed Medicare Part D data from 2020 on existing biosimilars, including granulocyte colony–stimulating factors, erythropoiesis-stimulating agents, and tumor necrosis factor α inhibitors.⁴ Methods in line with a 2021 report from the US Department of Health and Human Services⁵ as well as those of Yazdany et al⁶ were used. For each class, we calculated the 2020 distribution of biosimilar and originator drug claims as well as biosimilar cost reduction per 30-day claim. We utilized 2018-2021 annual growth rates for branded adalimumab and etanercept to estimate 30-day claims for 2023 and the cost of these branded agents in the absence of biosimilars. The hypothetical 2023 cost reduction from adalimumab and etanercept biosimilars was estimated by assuming 2020 biosimilar utilization rates and mean cost reduction per claim. This study utilized publicly available or aggregate summary data (not attributable to specific patients) and did not qualify as human subject research; therefore, institutional review board approval was not required.

In 2020, biosimilar utilization proportions ranged from 6.4% (tumor necrosis factor α inhibitors) to 82.7% (granulocyte colony–stimulating factors), with a mean across all classes of 35.7%. On average, the cost per 30-day claim of biosimilars was 66.8% of originator agents (Table 1). In 2021, we identified 57,868 30-day claims for branded adalimumab and etanercept submitted by dermatologists. From 2018 to 2021, 30-day branded adalimumab claims increased by 1.27% annually (cost + 10.62% annually), while claims for branded etanercept decreased by 13.0% annually (cost + 5.68% annually). Assuming these trends, the cost of branded adalimumab and etanercept was estimated to be $539 million in 2023. Applying the aforementioned 35.7% utilization, the introduction of biosimilars in dermatology would yield a cost reduction of approximately $118 million (21.9%). A high utilization rate (82.7%) of biosimilars among dermatologists would increase cost savings to $199 million (36.9%)(Table 2).

We found that the extent of savings from biosimilars was dependent on the utilization rates among dermatologists, with the highest utilization rate almost doubling the total savings of average utilization rates. Given the impact of high utilization and the wide variation observed, understanding the factors that have influenced uptake of biosimilars is important to increasing utilization as these medications become integrated into dermatology. For instance, limited uptake of infliximab initially may have been influenced by concerns about efficacy and increased adverse events.^8,9 In contrast, the high utilization of filgrastim biosimilars (82.7%) may be attributed to its longevity in the market and familiarity to prescribers, as filgrastim was the first biosimilar to be approved in the United States.¹⁰

Promoting reasonable utilization of biosimilars may require prescriber education on their safety and approval processes, which could foster increased utilization and reduce skepticism.⁴ Under the Biologics Price Competition and Innovation Act, the US Food and Drug Administration approves biosimilars only when they exhibit “high similarity” and show no “clinically meaningful differences” compared to the reference biologic, with no added safety risks or reduced efficacy.¹¹ Moreover, a 2023 systematic review of 17 studies found no major difference in efficacy and safety between biosimilars and originators of etanercept, infliximab, and other biologics.¹² Understanding these findings may reassure dermatologists and patients about the reliability and safety of biosimilars.

A limitation of our study is that it solely assesses Medicare data and estimates derived from existing (separate) biologic classes. It also does not account for potential expenditure shifts to newer biologic agents (eg, IL-12/17/23 inhibitors) or changes in manufacturer behavior or promotions. Nevertheless, it indicates notable financial savings from new biosimilar agents in dermatology; along with their compelling efficacy and safety profiles, this could represent a substantial benefit to patients and the health care system.

To the Editor:

Although biologics provide major therapeutic benefits for dermatologic conditions, they also come with a substantial cost, making them among the most expensive medications available. Medicare and Medicaid spending on biologics for dermatologic conditions increased by 320% from 2012 to 2018, reaching a staggering $10.6 billion in 2018 alone.¹ Biosimilars show promise in reducing health care spending for dermatologic conditions; however, their utilization has been limited due to multiple factors, including delayed market entry from patent thickets, exclusionary formulary contracts, and prescriber skepticism regarding their safety and efficacy.² For instance, a national survey of 1201 US physicians in specialties that are high prescribers of biologics reported that 55% doubted the safety and appropriateness of biosimilars.³

US Food and Drug Administration approval of biosimilars for adalimumab and etanercept offers the potential to reduce health care spending for dermatologic conditions. However, this cost reduction is dependent on utilization rates among dermatologists. In this national cross-sectional review of Medicare data, we predicted the impact of these biosimilars on dermatologic Medicare costs and demonstrated how differing utilization rates among dermatologists can influence potential savings.

To model 2023 utilization and cost reduction from biosimilars, we analyzed Medicare Part D data from 2020 on existing biosimilars, including granulocyte colony–stimulating factors, erythropoiesis-stimulating agents, and tumor necrosis factor α inhibitors.⁴ Methods in line with a 2021 report from the US Department of Health and Human Services⁵ as well as those of Yazdany et al⁶ were used. For each class, we calculated the 2020 distribution of biosimilar and originator drug claims as well as biosimilar cost reduction per 30-day claim. We utilized 2018-2021 annual growth rates for branded adalimumab and etanercept to estimate 30-day claims for 2023 and the cost of these branded agents in the absence of biosimilars. The hypothetical 2023 cost reduction from adalimumab and etanercept biosimilars was estimated by assuming 2020 biosimilar utilization rates and mean cost reduction per claim. This study utilized publicly available or aggregate summary data (not attributable to specific patients) and did not qualify as human subject research; therefore, institutional review board approval was not required.

In 2020, biosimilar utilization proportions ranged from 6.4% (tumor necrosis factor α inhibitors) to 82.7% (granulocyte colony–stimulating factors), with a mean across all classes of 35.7%. On average, the cost per 30-day claim of biosimilars was 66.8% of originator agents (Table 1). In 2021, we identified 57,868 30-day claims for branded adalimumab and etanercept submitted by dermatologists. From 2018 to 2021, 30-day branded adalimumab claims increased by 1.27% annually (cost + 10.62% annually), while claims for branded etanercept decreased by 13.0% annually (cost + 5.68% annually). Assuming these trends, the cost of branded adalimumab and etanercept was estimated to be $539 million in 2023. Applying the aforementioned 35.7% utilization, the introduction of biosimilars in dermatology would yield a cost reduction of approximately $118 million (21.9%). A high utilization rate (82.7%) of biosimilars among dermatologists would increase cost savings to $199 million (36.9%)(Table 2).

We found that the extent of savings from biosimilars was dependent on the utilization rates among dermatologists, with the highest utilization rate almost doubling the total savings of average utilization rates. Given the impact of high utilization and the wide variation observed, understanding the factors that have influenced uptake of biosimilars is important to increasing utilization as these medications become integrated into dermatology. For instance, limited uptake of infliximab initially may have been influenced by concerns about efficacy and increased adverse events.^8,9 In contrast, the high utilization of filgrastim biosimilars (82.7%) may be attributed to its longevity in the market and familiarity to prescribers, as filgrastim was the first biosimilar to be approved in the United States.¹⁰

Promoting reasonable utilization of biosimilars may require prescriber education on their safety and approval processes, which could foster increased utilization and reduce skepticism.⁴ Under the Biologics Price Competition and Innovation Act, the US Food and Drug Administration approves biosimilars only when they exhibit “high similarity” and show no “clinically meaningful differences” compared to the reference biologic, with no added safety risks or reduced efficacy.¹¹ Moreover, a 2023 systematic review of 17 studies found no major difference in efficacy and safety between biosimilars and originators of etanercept, infliximab, and other biologics.¹² Understanding these findings may reassure dermatologists and patients about the reliability and safety of biosimilars.

A limitation of our study is that it solely assesses Medicare data and estimates derived from existing (separate) biologic classes. It also does not account for potential expenditure shifts to newer biologic agents (eg, IL-12/17/23 inhibitors) or changes in manufacturer behavior or promotions. Nevertheless, it indicates notable financial savings from new biosimilar agents in dermatology; along with their compelling efficacy and safety profiles, this could represent a substantial benefit to patients and the health care system.

To the Editor:

Although biologics provide major therapeutic benefits for dermatologic conditions, they also come with a substantial cost, making them among the most expensive medications available. Medicare and Medicaid spending on biologics for dermatologic conditions increased by 320% from 2012 to 2018, reaching a staggering $10.6 billion in 2018 alone.¹ Biosimilars show promise in reducing health care spending for dermatologic conditions; however, their utilization has been limited due to multiple factors, including delayed market entry from patent thickets, exclusionary formulary contracts, and prescriber skepticism regarding their safety and efficacy.² For instance, a national survey of 1201 US physicians in specialties that are high prescribers of biologics reported that 55% doubted the safety and appropriateness of biosimilars.³

US Food and Drug Administration approval of biosimilars for adalimumab and etanercept offers the potential to reduce health care spending for dermatologic conditions. However, this cost reduction is dependent on utilization rates among dermatologists. In this national cross-sectional review of Medicare data, we predicted the impact of these biosimilars on dermatologic Medicare costs and demonstrated how differing utilization rates among dermatologists can influence potential savings.

To model 2023 utilization and cost reduction from biosimilars, we analyzed Medicare Part D data from 2020 on existing biosimilars, including granulocyte colony–stimulating factors, erythropoiesis-stimulating agents, and tumor necrosis factor α inhibitors.⁴ Methods in line with a 2021 report from the US Department of Health and Human Services⁵ as well as those of Yazdany et al⁶ were used. For each class, we calculated the 2020 distribution of biosimilar and originator drug claims as well as biosimilar cost reduction per 30-day claim. We utilized 2018-2021 annual growth rates for branded adalimumab and etanercept to estimate 30-day claims for 2023 and the cost of these branded agents in the absence of biosimilars. The hypothetical 2023 cost reduction from adalimumab and etanercept biosimilars was estimated by assuming 2020 biosimilar utilization rates and mean cost reduction per claim. This study utilized publicly available or aggregate summary data (not attributable to specific patients) and did not qualify as human subject research; therefore, institutional review board approval was not required.

In 2020, biosimilar utilization proportions ranged from 6.4% (tumor necrosis factor α inhibitors) to 82.7% (granulocyte colony–stimulating factors), with a mean across all classes of 35.7%. On average, the cost per 30-day claim of biosimilars was 66.8% of originator agents (Table 1). In 2021, we identified 57,868 30-day claims for branded adalimumab and etanercept submitted by dermatologists. From 2018 to 2021, 30-day branded adalimumab claims increased by 1.27% annually (cost + 10.62% annually), while claims for branded etanercept decreased by 13.0% annually (cost + 5.68% annually). Assuming these trends, the cost of branded adalimumab and etanercept was estimated to be $539 million in 2023. Applying the aforementioned 35.7% utilization, the introduction of biosimilars in dermatology would yield a cost reduction of approximately $118 million (21.9%). A high utilization rate (82.7%) of biosimilars among dermatologists would increase cost savings to $199 million (36.9%)(Table 2).

We found that the extent of savings from biosimilars was dependent on the utilization rates among dermatologists, with the highest utilization rate almost doubling the total savings of average utilization rates. Given the impact of high utilization and the wide variation observed, understanding the factors that have influenced uptake of biosimilars is important to increasing utilization as these medications become integrated into dermatology. For instance, limited uptake of infliximab initially may have been influenced by concerns about efficacy and increased adverse events.^8,9 In contrast, the high utilization of filgrastim biosimilars (82.7%) may be attributed to its longevity in the market and familiarity to prescribers, as filgrastim was the first biosimilar to be approved in the United States.¹⁰

Promoting reasonable utilization of biosimilars may require prescriber education on their safety and approval processes, which could foster increased utilization and reduce skepticism.⁴ Under the Biologics Price Competition and Innovation Act, the US Food and Drug Administration approves biosimilars only when they exhibit “high similarity” and show no “clinically meaningful differences” compared to the reference biologic, with no added safety risks or reduced efficacy.¹¹ Moreover, a 2023 systematic review of 17 studies found no major difference in efficacy and safety between biosimilars and originators of etanercept, infliximab, and other biologics.¹² Understanding these findings may reassure dermatologists and patients about the reliability and safety of biosimilars.

A limitation of our study is that it solely assesses Medicare data and estimates derived from existing (separate) biologic classes. It also does not account for potential expenditure shifts to newer biologic agents (eg, IL-12/17/23 inhibitors) or changes in manufacturer behavior or promotions. Nevertheless, it indicates notable financial savings from new biosimilar agents in dermatology; along with their compelling efficacy and safety profiles, this could represent a substantial benefit to patients and the health care system.

To the Editor:

Patients with primary psychiatric disorders with dermatologic manifestations often seek treatment from dermatologists instead of psychiatrists.¹ For example, patients with delusions of parasitosis may lack insight into the underlying etiology of their disease and instead fixate on establishing an organic cause for their symptoms. As a result, it is an increasingly common practice for dermatologists to diagnose and treat psychiatric conditions.¹ The goal of this study was to evaluate trends for the top 5 antipsychotics most frequently prescribed by dermatologists in the Medicare Part D database.

In this retrospective analysis, we consulted the Medicare Provider Utilization and Payment Data for January 2013 through December 2020, which is provided to the public by the Centers for Medicare & Medicaid Services.² Only prescribing data from dermatologists were included in this study by using the built-in filter on the website to select “dermatology” as the prescriber type. All other provider types were excluded. We chose the top 5 most prescribed antipsychotics based on the number of supply days reported. Supply days—defined by Medicare as the number of days’ worth of medication that is prescribed—were used as a metric for ­utilization; therefore, each drug’s total supply days prescribed by dermatologists were calculated using this combined filter of drug name and total supply days using the database.

To analyze utilization over time, the annual average growth rate (AAGR) was calculated by determining the growth rate in total supply days annually from 2013 to 2020 and then averaging those rates to determine the overall AAGR. For greater clinical relevance, we calculated the average growth in supply days for the entire study period by determining the difference in the number of supply days for each year and then averaging these values. This was done to consider overall trends across dermatology rather than individual dermatologist prescribing patterns.

Based on our analysis, the antipsychotics most frequently prescribed by dermatologists for Medicare patients from January 2013 to December 2020 were pimozide, quetiapine, risperidone, olanzapine, and aripiprazole. The AAGR for each drug was 2.35%, 4.89%, 5.59%, 9.48%, and 20.72%, respectively, which is consistent with increased utilization over the study period for all 5 drugs (Table 1). The change in cost per supply day for the same period was 1.3%, –66.1%, –60.2%, –81.7%, and –84.3%, respectively. The net difference in cost per supply day over this entire period was $0.02, –$2.79, –$1.06, –$5.37, and –$21.22, respectively (Table 2).

To the Editor:

Patients with primary psychiatric disorders with dermatologic manifestations often seek treatment from dermatologists instead of psychiatrists.¹ For example, patients with delusions of parasitosis may lack insight into the underlying etiology of their disease and instead fixate on establishing an organic cause for their symptoms. As a result, it is an increasingly common practice for dermatologists to diagnose and treat psychiatric conditions.¹ The goal of this study was to evaluate trends for the top 5 antipsychotics most frequently prescribed by dermatologists in the Medicare Part D database.

In this retrospective analysis, we consulted the Medicare Provider Utilization and Payment Data for January 2013 through December 2020, which is provided to the public by the Centers for Medicare & Medicaid Services.² Only prescribing data from dermatologists were included in this study by using the built-in filter on the website to select “dermatology” as the prescriber type. All other provider types were excluded. We chose the top 5 most prescribed antipsychotics based on the number of supply days reported. Supply days—defined by Medicare as the number of days’ worth of medication that is prescribed—were used as a metric for ­utilization; therefore, each drug’s total supply days prescribed by dermatologists were calculated using this combined filter of drug name and total supply days using the database.

To analyze utilization over time, the annual average growth rate (AAGR) was calculated by determining the growth rate in total supply days annually from 2013 to 2020 and then averaging those rates to determine the overall AAGR. For greater clinical relevance, we calculated the average growth in supply days for the entire study period by determining the difference in the number of supply days for each year and then averaging these values. This was done to consider overall trends across dermatology rather than individual dermatologist prescribing patterns.

Based on our analysis, the antipsychotics most frequently prescribed by dermatologists for Medicare patients from January 2013 to December 2020 were pimozide, quetiapine, risperidone, olanzapine, and aripiprazole. The AAGR for each drug was 2.35%, 4.89%, 5.59%, 9.48%, and 20.72%, respectively, which is consistent with increased utilization over the study period for all 5 drugs (Table 1). The change in cost per supply day for the same period was 1.3%, –66.1%, –60.2%, –81.7%, and –84.3%, respectively. The net difference in cost per supply day over this entire period was $0.02, –$2.79, –$1.06, –$5.37, and –$21.22, respectively (Table 2).

To the Editor:

Patients with primary psychiatric disorders with dermatologic manifestations often seek treatment from dermatologists instead of psychiatrists.¹ For example, patients with delusions of parasitosis may lack insight into the underlying etiology of their disease and instead fixate on establishing an organic cause for their symptoms. As a result, it is an increasingly common practice for dermatologists to diagnose and treat psychiatric conditions.¹ The goal of this study was to evaluate trends for the top 5 antipsychotics most frequently prescribed by dermatologists in the Medicare Part D database.

In this retrospective analysis, we consulted the Medicare Provider Utilization and Payment Data for January 2013 through December 2020, which is provided to the public by the Centers for Medicare & Medicaid Services.² Only prescribing data from dermatologists were included in this study by using the built-in filter on the website to select “dermatology” as the prescriber type. All other provider types were excluded. We chose the top 5 most prescribed antipsychotics based on the number of supply days reported. Supply days—defined by Medicare as the number of days’ worth of medication that is prescribed—were used as a metric for ­utilization; therefore, each drug’s total supply days prescribed by dermatologists were calculated using this combined filter of drug name and total supply days using the database.

To analyze utilization over time, the annual average growth rate (AAGR) was calculated by determining the growth rate in total supply days annually from 2013 to 2020 and then averaging those rates to determine the overall AAGR. For greater clinical relevance, we calculated the average growth in supply days for the entire study period by determining the difference in the number of supply days for each year and then averaging these values. This was done to consider overall trends across dermatology rather than individual dermatologist prescribing patterns.

Based on our analysis, the antipsychotics most frequently prescribed by dermatologists for Medicare patients from January 2013 to December 2020 were pimozide, quetiapine, risperidone, olanzapine, and aripiprazole. The AAGR for each drug was 2.35%, 4.89%, 5.59%, 9.48%, and 20.72%, respectively, which is consistent with increased utilization over the study period for all 5 drugs (Table 1). The change in cost per supply day for the same period was 1.3%, –66.1%, –60.2%, –81.7%, and –84.3%, respectively. The net difference in cost per supply day over this entire period was $0.02, –$2.79, –$1.06, –$5.37, and –$21.22, respectively (Table 2).

Psoriasis is a chronic inflammatory disease affecting approximately 8 million adults in the United States and 2% of the global population.^1,2 Psoriasis causes pain, itching, and disfigurement and is associated with a physical, psychological, and economic burden that substantially affects health-related quality of life.^3-5

Setting treatment goals and treating to target are evidence-based approaches that have been successfully applied to several chronic diseases to improve patient outcomes, including diabetes, hypertension, and rheumatoid arthritis.^6-9 Treat-to-target strategies generally set low disease activity (or remission) as an overall goal and seek to achieve this using available therapeutic options as necessary. Introduced following the availability of biologics and targeted systemic therapies, treat-to-target strategies generally provide guidance on expectations of treatment but not specific treatments, as personalized treatment decisions depend on an assessment of individual patients and consider clinical and demographic features as well as preferences for available therapeutic options. If targets are not achieved in the assigned time span, adjustments can be made to the treatment approach in close consultation with the patient. If the target is reached, follow-up visits can be scheduled to ensure improvement is maintained or to establish if more aggressive goals could be selected.

Treat-to-target strategies for the management of psoriasis developed by the National Psoriasis Foundation (NPF) Medical Board include reducing the extent of psoriasis to 1% or lower total body surface area (BSA) after 3 months of treatment.¹⁰ Treatment targets endorsed by the European Academy of Dermatology and Venereology (EADV) in guidelines on the use of systemic therapies in psoriasis include achieving a 75% or greater reduction in Psoriasis Area and Severity Index (PASI) score within 3 to 4 months of treatment.¹¹

In clinical practice, many patients do not achieve these treatment targets, and topical treatments alone generally are insufficient in achieving treatment goals for psoriasis.^12,13 Moreover, conventional topical treatments (eg, topical corticosteroids) used by most patients with psoriasis regardless of disease severity are associated with adverse events that can limit their use. Most topical corticosteroids have US Food and Drug Administration label restrictions relating to sites of application, duration and extent of use, and frequency of administration.^14,15

Tapinarof cream 1% (VTAMA [Dermavant Sciences, Inc]) is a first-in-class topical nonsteroidal aryl hydrocarbon receptor agonist that was approved by the US Food and Drug Administration for the treatment of plaque psoriasis in adults¹⁶ and is being studied for the treatment of plaque psoriasis in children 2 years and older as well as for atopic dermatitis in adults and children 2 years and older. In PSOARING 1 (ClinicalTrials .gov identifier NCT03956355) and PSOARING 2 (NCT03983980)—identical 12-week pivotal phase 3 trials—monotherapy with tapinarof cream 1% once daily (QD) demonstrated statistically significant efficacy vs vehicle cream and was well tolerated in adults with mild to severe plaque psoriasis (Supplementary Figure S1).¹⁷ Lebwohl et al¹⁷ reported that significantly higher PASI75 responses were observed at week 12 with tapinarof cream vs vehicle in PSOARING 1 and PSOARING 2 (36% and 48% vs 10% and 7%, respectively; both P<.0001). A significantly higher PASI90 response of 19% and 21% at week 12 also was observed with tapinarof cream vs 2% and 3% with vehicle in PSOARING 1 and PSOARING 2, respectively (P=.0005 and P<.0001).¹⁷

In PSOARING 3 (NCT04053387)—the long-term extension trial (Supplementary Figure S1)—efficacy continued to improve or was maintained beyond the two 12-week trials, with improvements in total BSA affected and PASI scores for up to 52 weeks.¹⁸ Tapinarof cream 1% QD demonstrated positive, rapid, and durable outcomes in PSOARING 3, including high rates of complete disease clearance (Physician Global Assessment [PGA] score=0 [clear])(40.9% [312/763]), durability of response on treatment with no evidence of tachyphylaxis, and a remittive effect of approximately 4 months when off therapy (defined as maintenance of a PGA score of 0 [clear] or 1 [almost clear] after first achieving a PGA score of 0).¹⁸

Herein, we report absolute treatment targets for patients with plaque psoriasis who received tapinarof cream 1% QD in the PSOARING trials that are at least as stringent as the corresponding NPF and EADV targets of achieving a total BSA affected of 1% or lower or a PASI75 response within 3 to 4 months, respectively.

METHODS

Study Design

The pooled efficacy analyses included all patients with a baseline PGA score of 2 or higher (mild or worse) before treatment with tapinarof cream 1% QD in the PSOARING trials. This included patients who received tapinarof cream 1% in PSOARING 1 and PSOARING 2 who may or may not have continued into PSOARING 3, as well as those who received the vehicle in PSOARING 1 and PSOARING 2 who enrolled in PSOARING 3 and had a PGA score of 2 or higher before receiving tapinarof cream 1%.

Trial Participants

Full methods, including inclusion and exclusion criteria, for the PSOARING trials have been previously reported.^17,18 Patients were aged 18 to 75 years and had chronic plaque psoriasis that was stable for at least 6 months before randomization; 3% to 20% total BSA affected (excluding the scalp, palms, fingernails, toenails, and soles); and a PGA score of 2 (mild), 3 (moderate), or 4 (severe) at baseline.

The clinical trials were conducted in compliance with the guidelines for Good Clinical Practice and the Declaration of Helsinki. Approval was obtained from local ethics committees or institutional review boards at each center. All patients provided written informed consent.

Trial Treatment

In PSOARING 1 and PSOARING 2, patients were randomized (2:1) to receive tapinarof cream 1% or vehicle QD for 12 weeks. In PSOARING 3 (the long-term extension trial), patients received up to 40 weeks of open-label tapinarof, followed by 4 weeks of follow-up off treatment. Patients received intermittent or continuous treatment with tapinarof cream 1% in PSOARING 3 based on PGA score: those entering the trial with a PGA score of 1 or higher received tapinarof cream 1% until complete disease clearance was achieved (defined as a PGA score of 0 [clear]). Those entering PSOARING 3 with or achieving a PGA score of 0 (clear) discontinued treatment and were observed for the duration of maintenance of a PGA score of 0 (clear) or 1 (almost clear) while off therapy (the protocol-defined “duration of remittive effect”). If disease worsening (defined as a PGA score 2 or higher) occurred, tapinarof cream 1% was restarted and continued until a PGA score of 0 (clear) was achieved. This pattern of treatment, discontinuation on achieving a PGA score of 0 (clear), and retreatment on disease worsening continued until the end of the trial. As a result, patients in PSOARING 3 could receive tapinarof cream 1% continuously or intermittently for 40 weeks.

Outcome Measures and Statistical Analyses

The assessment of total BSA affected by plaque psoriasis is an estimate of the total extent of disease as a percentage of total skin area. In the PSOARING trials, the skin surface of one hand (palm and digits) was assumed to be approximately equivalent to 1% BSA. The total BSA affected by psoriasis was evaluated from 0% to 100%, with greater total BSA affected being an indication of more extensive disease. The BSA efficacy outcomes used in these analyses were based post hoc on the proportion of patients who achieved a 1% or lower or 0.5% or lower total BSA affected. The smallest BSA affected increment that investigators were trained to measure and could record was 0.1%.

Psoriasis Area and Severity Index scores assess both the severity and extent of psoriasis. A PASI score lower than 5 often is considered indicative of mild psoriasis, a score of 5 to 10 indicates moderate disease, and a score higher than 10 indicates severe disease.¹⁹ The maximum PASI score is 72. The PASI efficacy outcomes used in these analyses were based post hoc on the proportion of patients who achieved an absolute total PASI score of 3 or lower, 2 or lower, and 1 or lower.

Efficacy analyses were based on pooled data for all patients in the PSOARING trials who had a PGA score of 2 to 4 (mild to severe) before treatment with tapinarof cream 1% in the intention-to-treat population using observed cases. Time-to-target analyses were based on Kaplan-Meier (KM) estimates using observed cases.

Safety analyses included the incidence and frequency of adverse events and were based on all patients who received tapinarof cream 1% in the PSOARING trials.

RESULTS

Baseline Patient Demographics and Disease Characteristics

The pooled efficacy analyses included 915 eligible patients (Table). At baseline, the mean (SD) age was 50.2 (13.25) years, 58.7% were male, the mean (SD) weight was 92.2 (23.67) kg, and the mean (SD) body mass index was 31.6 (7.53) kg/m². The percentage of patients with a PGA score of 2 (mild), 3 (moderate), or 4 (severe) was 13.9%, 78.1%, and 8.0%, respectively. The mean (SD) PASI score was 8.7 (4.23) and mean (SD) total BSA affected was 7.8% (4.98).

Efficacy

Achievement of BSA-Affected Targets—The NPF-recommended target of 1% or lower total BSA affected within 3 months was achieved by 40% of patients (KM estimate [95% CI, 37%-43%])(Figure 1). Across the total trial period of up to 52 weeks, a total BSA affected of 1% or lower was achieved by 61% of patients (561/915), with the median time to target of approximately 4 months (KM estimate: 120 days [95% CI, 113-141])(Supplementary Figure S2a). Approximately 50% of patients (455/915) achieved a total BSA affected of 0.5% or lower, with a median time to target of 199 days (KM estimate [95% CI, 172-228)(Figure 1; Supplementary Figure S2b).

Achievement of Absolute PASI Targets—Across the total trial period (up to 52 weeks), an absolute total PASI score of 3 or lower was achieved by 75% of patients (686/915), with a median time to achieve this of 2 months (KM estimate: 58 days [95% CI, 57-63]); approximately 67% of patients (612/915) achieved a total PASI score of 2 or lower, with a median time to achieve of 3 months (KM estimate: 87 days [95% CI, 85-110])(Figure 2; Supplementary Figures S3a and S3b). A PASI score of 1 or lower was achieved by approximately 50% of patients (460/915), with a median time to achieve of approximately 6 months (KM estimate: 185 days [95% CI, 169-218])(Figure 2, Supplementary Figure S3c).

Illustrative Case—Case photography showing the clinical response in a 63-year-old man with moderate plaque psoriasis in PSOARING 2 is shown in Figure 3. After 12 weeks of treatment with tapinarof cream 1% QD, the patient achieved all primary and secondary efficacy end points. In addition to achieving the regulatory end point of a PGA score of 0 (clear) or 1 (almost clear) and a decrease from baseline of at least 2 points, achievement of 0% total BSA affected and a total PASI score of 0 at week 12 exceeded the NPF and EADV consensus treatment targets.^10,11 Targets were achieved as early as week 4, with a total BSA affected of 0.5% or lower and a total PASI score of 1 or lower, illustrated by marked skin clearing and only faint residual erythema that completely resolved at week 12, with the absence of postinflammatory hyperpigmentation.

Safety

Safety data for the PSOARING trials have been previously reported.^17,18 The most common treatment-emergent adverse events were folliculitis, contact dermatitis, upper respiratory tract infection, and nasopharyngitis. Treatment-emergent adverse events generally were mild or moderate in severity and did not lead to trial discontinuation.^17,18

COMMENT

Treat-to-target management approaches aim to improve patient outcomes by striving to achieve optimal goals. The treat-to-target approach supports shared decision-making between clinicians and patients based on common expectations of what constitutes treatment success.

The findings of this analysis based on pooled data from a large cohort of patients demonstrate that a high proportion of patients can achieve or exceed recommended treatment targets with tapinarof cream 1% QD and maintain improvements long-term. The NPF-recommended treatment target of 1% or lower BSA affected within approximately 3 months (90 days) of treatment was achieved by 40% of tapinarof-treated patients. In addition, 1% or lower BSA affected at any time during the trials was achieved by 61% of patients (median, approximately 4 months). The analyses also indicated that PASI total scores of 3 or lower and 2 or lower were achieved by 75% and 67% of tapinarof-treated patients, respectively, within 2 to 3 months.

These findings support the previously reported efficacy of tapinarof cream, including high rates of complete disease clearance (40.9% [312/763]), durable response following treatment interruption, an off-therapy remittive effect of approximately 4 months, and good disease control on therapy with no evidence of tachyphylaxis.^17,18

CONCLUSION

Taken together with previously reported tapinarof efficacy and safety results, our findings demonstrate that a high proportion of patients treated with tapinarof cream as monotherapy can achieve aggressive treatment targets set by both US and European guidelines developed for systemic and biologic therapies. Tapinarof cream 1% QD is an effective topical treatment option for patients with plaque psoriasis that has been approved without restrictions relating to severity or extent of disease treated, duration of use, or application sites, including application to sensitive and intertriginous skin.

Acknowledgments—Editorial and medical writing support under the guidance of the authors was provided by Melanie Govender, MSc (Med), ApotheCom (United Kingdom), and was funded by Dermavant Sciences, Inc, in accordance with Good Publication Practice (GPP) guidelines.

Psoriasis is a chronic inflammatory disease affecting approximately 8 million adults in the United States and 2% of the global population.^1,2 Psoriasis causes pain, itching, and disfigurement and is associated with a physical, psychological, and economic burden that substantially affects health-related quality of life.^3-5

Setting treatment goals and treating to target are evidence-based approaches that have been successfully applied to several chronic diseases to improve patient outcomes, including diabetes, hypertension, and rheumatoid arthritis.^6-9 Treat-to-target strategies generally set low disease activity (or remission) as an overall goal and seek to achieve this using available therapeutic options as necessary. Introduced following the availability of biologics and targeted systemic therapies, treat-to-target strategies generally provide guidance on expectations of treatment but not specific treatments, as personalized treatment decisions depend on an assessment of individual patients and consider clinical and demographic features as well as preferences for available therapeutic options. If targets are not achieved in the assigned time span, adjustments can be made to the treatment approach in close consultation with the patient. If the target is reached, follow-up visits can be scheduled to ensure improvement is maintained or to establish if more aggressive goals could be selected.

Treat-to-target strategies for the management of psoriasis developed by the National Psoriasis Foundation (NPF) Medical Board include reducing the extent of psoriasis to 1% or lower total body surface area (BSA) after 3 months of treatment.¹⁰ Treatment targets endorsed by the European Academy of Dermatology and Venereology (EADV) in guidelines on the use of systemic therapies in psoriasis include achieving a 75% or greater reduction in Psoriasis Area and Severity Index (PASI) score within 3 to 4 months of treatment.¹¹

In clinical practice, many patients do not achieve these treatment targets, and topical treatments alone generally are insufficient in achieving treatment goals for psoriasis.^12,13 Moreover, conventional topical treatments (eg, topical corticosteroids) used by most patients with psoriasis regardless of disease severity are associated with adverse events that can limit their use. Most topical corticosteroids have US Food and Drug Administration label restrictions relating to sites of application, duration and extent of use, and frequency of administration.^14,15

Tapinarof cream 1% (VTAMA [Dermavant Sciences, Inc]) is a first-in-class topical nonsteroidal aryl hydrocarbon receptor agonist that was approved by the US Food and Drug Administration for the treatment of plaque psoriasis in adults¹⁶ and is being studied for the treatment of plaque psoriasis in children 2 years and older as well as for atopic dermatitis in adults and children 2 years and older. In PSOARING 1 (ClinicalTrials .gov identifier NCT03956355) and PSOARING 2 (NCT03983980)—identical 12-week pivotal phase 3 trials—monotherapy with tapinarof cream 1% once daily (QD) demonstrated statistically significant efficacy vs vehicle cream and was well tolerated in adults with mild to severe plaque psoriasis (Supplementary Figure S1).¹⁷ Lebwohl et al¹⁷ reported that significantly higher PASI75 responses were observed at week 12 with tapinarof cream vs vehicle in PSOARING 1 and PSOARING 2 (36% and 48% vs 10% and 7%, respectively; both P<.0001). A significantly higher PASI90 response of 19% and 21% at week 12 also was observed with tapinarof cream vs 2% and 3% with vehicle in PSOARING 1 and PSOARING 2, respectively (P=.0005 and P<.0001).¹⁷

In PSOARING 3 (NCT04053387)—the long-term extension trial (Supplementary Figure S1)—efficacy continued to improve or was maintained beyond the two 12-week trials, with improvements in total BSA affected and PASI scores for up to 52 weeks.¹⁸ Tapinarof cream 1% QD demonstrated positive, rapid, and durable outcomes in PSOARING 3, including high rates of complete disease clearance (Physician Global Assessment [PGA] score=0 [clear])(40.9% [312/763]), durability of response on treatment with no evidence of tachyphylaxis, and a remittive effect of approximately 4 months when off therapy (defined as maintenance of a PGA score of 0 [clear] or 1 [almost clear] after first achieving a PGA score of 0).¹⁸

Herein, we report absolute treatment targets for patients with plaque psoriasis who received tapinarof cream 1% QD in the PSOARING trials that are at least as stringent as the corresponding NPF and EADV targets of achieving a total BSA affected of 1% or lower or a PASI75 response within 3 to 4 months, respectively.

METHODS

Study Design

The pooled efficacy analyses included all patients with a baseline PGA score of 2 or higher (mild or worse) before treatment with tapinarof cream 1% QD in the PSOARING trials. This included patients who received tapinarof cream 1% in PSOARING 1 and PSOARING 2 who may or may not have continued into PSOARING 3, as well as those who received the vehicle in PSOARING 1 and PSOARING 2 who enrolled in PSOARING 3 and had a PGA score of 2 or higher before receiving tapinarof cream 1%.

Trial Participants

Full methods, including inclusion and exclusion criteria, for the PSOARING trials have been previously reported.^17,18 Patients were aged 18 to 75 years and had chronic plaque psoriasis that was stable for at least 6 months before randomization; 3% to 20% total BSA affected (excluding the scalp, palms, fingernails, toenails, and soles); and a PGA score of 2 (mild), 3 (moderate), or 4 (severe) at baseline.

The clinical trials were conducted in compliance with the guidelines for Good Clinical Practice and the Declaration of Helsinki. Approval was obtained from local ethics committees or institutional review boards at each center. All patients provided written informed consent.

Trial Treatment

In PSOARING 1 and PSOARING 2, patients were randomized (2:1) to receive tapinarof cream 1% or vehicle QD for 12 weeks. In PSOARING 3 (the long-term extension trial), patients received up to 40 weeks of open-label tapinarof, followed by 4 weeks of follow-up off treatment. Patients received intermittent or continuous treatment with tapinarof cream 1% in PSOARING 3 based on PGA score: those entering the trial with a PGA score of 1 or higher received tapinarof cream 1% until complete disease clearance was achieved (defined as a PGA score of 0 [clear]). Those entering PSOARING 3 with or achieving a PGA score of 0 (clear) discontinued treatment and were observed for the duration of maintenance of a PGA score of 0 (clear) or 1 (almost clear) while off therapy (the protocol-defined “duration of remittive effect”). If disease worsening (defined as a PGA score 2 or higher) occurred, tapinarof cream 1% was restarted and continued until a PGA score of 0 (clear) was achieved. This pattern of treatment, discontinuation on achieving a PGA score of 0 (clear), and retreatment on disease worsening continued until the end of the trial. As a result, patients in PSOARING 3 could receive tapinarof cream 1% continuously or intermittently for 40 weeks.

Outcome Measures and Statistical Analyses

The assessment of total BSA affected by plaque psoriasis is an estimate of the total extent of disease as a percentage of total skin area. In the PSOARING trials, the skin surface of one hand (palm and digits) was assumed to be approximately equivalent to 1% BSA. The total BSA affected by psoriasis was evaluated from 0% to 100%, with greater total BSA affected being an indication of more extensive disease. The BSA efficacy outcomes used in these analyses were based post hoc on the proportion of patients who achieved a 1% or lower or 0.5% or lower total BSA affected. The smallest BSA affected increment that investigators were trained to measure and could record was 0.1%.

Psoriasis Area and Severity Index scores assess both the severity and extent of psoriasis. A PASI score lower than 5 often is considered indicative of mild psoriasis, a score of 5 to 10 indicates moderate disease, and a score higher than 10 indicates severe disease.¹⁹ The maximum PASI score is 72. The PASI efficacy outcomes used in these analyses were based post hoc on the proportion of patients who achieved an absolute total PASI score of 3 or lower, 2 or lower, and 1 or lower.

Efficacy analyses were based on pooled data for all patients in the PSOARING trials who had a PGA score of 2 to 4 (mild to severe) before treatment with tapinarof cream 1% in the intention-to-treat population using observed cases. Time-to-target analyses were based on Kaplan-Meier (KM) estimates using observed cases.

Safety analyses included the incidence and frequency of adverse events and were based on all patients who received tapinarof cream 1% in the PSOARING trials.

RESULTS

Baseline Patient Demographics and Disease Characteristics

The pooled efficacy analyses included 915 eligible patients (Table). At baseline, the mean (SD) age was 50.2 (13.25) years, 58.7% were male, the mean (SD) weight was 92.2 (23.67) kg, and the mean (SD) body mass index was 31.6 (7.53) kg/m². The percentage of patients with a PGA score of 2 (mild), 3 (moderate), or 4 (severe) was 13.9%, 78.1%, and 8.0%, respectively. The mean (SD) PASI score was 8.7 (4.23) and mean (SD) total BSA affected was 7.8% (4.98).

Efficacy

Achievement of BSA-Affected Targets—The NPF-recommended target of 1% or lower total BSA affected within 3 months was achieved by 40% of patients (KM estimate [95% CI, 37%-43%])(Figure 1). Across the total trial period of up to 52 weeks, a total BSA affected of 1% or lower was achieved by 61% of patients (561/915), with the median time to target of approximately 4 months (KM estimate: 120 days [95% CI, 113-141])(Supplementary Figure S2a). Approximately 50% of patients (455/915) achieved a total BSA affected of 0.5% or lower, with a median time to target of 199 days (KM estimate [95% CI, 172-228)(Figure 1; Supplementary Figure S2b).

Achievement of Absolute PASI Targets—Across the total trial period (up to 52 weeks), an absolute total PASI score of 3 or lower was achieved by 75% of patients (686/915), with a median time to achieve this of 2 months (KM estimate: 58 days [95% CI, 57-63]); approximately 67% of patients (612/915) achieved a total PASI score of 2 or lower, with a median time to achieve of 3 months (KM estimate: 87 days [95% CI, 85-110])(Figure 2; Supplementary Figures S3a and S3b). A PASI score of 1 or lower was achieved by approximately 50% of patients (460/915), with a median time to achieve of approximately 6 months (KM estimate: 185 days [95% CI, 169-218])(Figure 2, Supplementary Figure S3c).

Illustrative Case—Case photography showing the clinical response in a 63-year-old man with moderate plaque psoriasis in PSOARING 2 is shown in Figure 3. After 12 weeks of treatment with tapinarof cream 1% QD, the patient achieved all primary and secondary efficacy end points. In addition to achieving the regulatory end point of a PGA score of 0 (clear) or 1 (almost clear) and a decrease from baseline of at least 2 points, achievement of 0% total BSA affected and a total PASI score of 0 at week 12 exceeded the NPF and EADV consensus treatment targets.^10,11 Targets were achieved as early as week 4, with a total BSA affected of 0.5% or lower and a total PASI score of 1 or lower, illustrated by marked skin clearing and only faint residual erythema that completely resolved at week 12, with the absence of postinflammatory hyperpigmentation.

Safety

Safety data for the PSOARING trials have been previously reported.^17,18 The most common treatment-emergent adverse events were folliculitis, contact dermatitis, upper respiratory tract infection, and nasopharyngitis. Treatment-emergent adverse events generally were mild or moderate in severity and did not lead to trial discontinuation.^17,18

COMMENT

Treat-to-target management approaches aim to improve patient outcomes by striving to achieve optimal goals. The treat-to-target approach supports shared decision-making between clinicians and patients based on common expectations of what constitutes treatment success.

The findings of this analysis based on pooled data from a large cohort of patients demonstrate that a high proportion of patients can achieve or exceed recommended treatment targets with tapinarof cream 1% QD and maintain improvements long-term. The NPF-recommended treatment target of 1% or lower BSA affected within approximately 3 months (90 days) of treatment was achieved by 40% of tapinarof-treated patients. In addition, 1% or lower BSA affected at any time during the trials was achieved by 61% of patients (median, approximately 4 months). The analyses also indicated that PASI total scores of 3 or lower and 2 or lower were achieved by 75% and 67% of tapinarof-treated patients, respectively, within 2 to 3 months.

These findings support the previously reported efficacy of tapinarof cream, including high rates of complete disease clearance (40.9% [312/763]), durable response following treatment interruption, an off-therapy remittive effect of approximately 4 months, and good disease control on therapy with no evidence of tachyphylaxis.^17,18

CONCLUSION

Taken together with previously reported tapinarof efficacy and safety results, our findings demonstrate that a high proportion of patients treated with tapinarof cream as monotherapy can achieve aggressive treatment targets set by both US and European guidelines developed for systemic and biologic therapies. Tapinarof cream 1% QD is an effective topical treatment option for patients with plaque psoriasis that has been approved without restrictions relating to severity or extent of disease treated, duration of use, or application sites, including application to sensitive and intertriginous skin.

Acknowledgments—Editorial and medical writing support under the guidance of the authors was provided by Melanie Govender, MSc (Med), ApotheCom (United Kingdom), and was funded by Dermavant Sciences, Inc, in accordance with Good Publication Practice (GPP) guidelines.

Psoriasis is a chronic inflammatory disease affecting approximately 8 million adults in the United States and 2% of the global population.^1,2 Psoriasis causes pain, itching, and disfigurement and is associated with a physical, psychological, and economic burden that substantially affects health-related quality of life.^3-5

Setting treatment goals and treating to target are evidence-based approaches that have been successfully applied to several chronic diseases to improve patient outcomes, including diabetes, hypertension, and rheumatoid arthritis.^6-9 Treat-to-target strategies generally set low disease activity (or remission) as an overall goal and seek to achieve this using available therapeutic options as necessary. Introduced following the availability of biologics and targeted systemic therapies, treat-to-target strategies generally provide guidance on expectations of treatment but not specific treatments, as personalized treatment decisions depend on an assessment of individual patients and consider clinical and demographic features as well as preferences for available therapeutic options. If targets are not achieved in the assigned time span, adjustments can be made to the treatment approach in close consultation with the patient. If the target is reached, follow-up visits can be scheduled to ensure improvement is maintained or to establish if more aggressive goals could be selected.

Treat-to-target strategies for the management of psoriasis developed by the National Psoriasis Foundation (NPF) Medical Board include reducing the extent of psoriasis to 1% or lower total body surface area (BSA) after 3 months of treatment.¹⁰ Treatment targets endorsed by the European Academy of Dermatology and Venereology (EADV) in guidelines on the use of systemic therapies in psoriasis include achieving a 75% or greater reduction in Psoriasis Area and Severity Index (PASI) score within 3 to 4 months of treatment.¹¹

In clinical practice, many patients do not achieve these treatment targets, and topical treatments alone generally are insufficient in achieving treatment goals for psoriasis.^12,13 Moreover, conventional topical treatments (eg, topical corticosteroids) used by most patients with psoriasis regardless of disease severity are associated with adverse events that can limit their use. Most topical corticosteroids have US Food and Drug Administration label restrictions relating to sites of application, duration and extent of use, and frequency of administration.^14,15

Tapinarof cream 1% (VTAMA [Dermavant Sciences, Inc]) is a first-in-class topical nonsteroidal aryl hydrocarbon receptor agonist that was approved by the US Food and Drug Administration for the treatment of plaque psoriasis in adults¹⁶ and is being studied for the treatment of plaque psoriasis in children 2 years and older as well as for atopic dermatitis in adults and children 2 years and older. In PSOARING 1 (ClinicalTrials .gov identifier NCT03956355) and PSOARING 2 (NCT03983980)—identical 12-week pivotal phase 3 trials—monotherapy with tapinarof cream 1% once daily (QD) demonstrated statistically significant efficacy vs vehicle cream and was well tolerated in adults with mild to severe plaque psoriasis (Supplementary Figure S1).¹⁷ Lebwohl et al¹⁷ reported that significantly higher PASI75 responses were observed at week 12 with tapinarof cream vs vehicle in PSOARING 1 and PSOARING 2 (36% and 48% vs 10% and 7%, respectively; both P<.0001). A significantly higher PASI90 response of 19% and 21% at week 12 also was observed with tapinarof cream vs 2% and 3% with vehicle in PSOARING 1 and PSOARING 2, respectively (P=.0005 and P<.0001).¹⁷

In PSOARING 3 (NCT04053387)—the long-term extension trial (Supplementary Figure S1)—efficacy continued to improve or was maintained beyond the two 12-week trials, with improvements in total BSA affected and PASI scores for up to 52 weeks.¹⁸ Tapinarof cream 1% QD demonstrated positive, rapid, and durable outcomes in PSOARING 3, including high rates of complete disease clearance (Physician Global Assessment [PGA] score=0 [clear])(40.9% [312/763]), durability of response on treatment with no evidence of tachyphylaxis, and a remittive effect of approximately 4 months when off therapy (defined as maintenance of a PGA score of 0 [clear] or 1 [almost clear] after first achieving a PGA score of 0).¹⁸

Herein, we report absolute treatment targets for patients with plaque psoriasis who received tapinarof cream 1% QD in the PSOARING trials that are at least as stringent as the corresponding NPF and EADV targets of achieving a total BSA affected of 1% or lower or a PASI75 response within 3 to 4 months, respectively.

METHODS

Study Design

The pooled efficacy analyses included all patients with a baseline PGA score of 2 or higher (mild or worse) before treatment with tapinarof cream 1% QD in the PSOARING trials. This included patients who received tapinarof cream 1% in PSOARING 1 and PSOARING 2 who may or may not have continued into PSOARING 3, as well as those who received the vehicle in PSOARING 1 and PSOARING 2 who enrolled in PSOARING 3 and had a PGA score of 2 or higher before receiving tapinarof cream 1%.

Trial Participants

Full methods, including inclusion and exclusion criteria, for the PSOARING trials have been previously reported.^17,18 Patients were aged 18 to 75 years and had chronic plaque psoriasis that was stable for at least 6 months before randomization; 3% to 20% total BSA affected (excluding the scalp, palms, fingernails, toenails, and soles); and a PGA score of 2 (mild), 3 (moderate), or 4 (severe) at baseline.

The clinical trials were conducted in compliance with the guidelines for Good Clinical Practice and the Declaration of Helsinki. Approval was obtained from local ethics committees or institutional review boards at each center. All patients provided written informed consent.

Trial Treatment

In PSOARING 1 and PSOARING 2, patients were randomized (2:1) to receive tapinarof cream 1% or vehicle QD for 12 weeks. In PSOARING 3 (the long-term extension trial), patients received up to 40 weeks of open-label tapinarof, followed by 4 weeks of follow-up off treatment. Patients received intermittent or continuous treatment with tapinarof cream 1% in PSOARING 3 based on PGA score: those entering the trial with a PGA score of 1 or higher received tapinarof cream 1% until complete disease clearance was achieved (defined as a PGA score of 0 [clear]). Those entering PSOARING 3 with or achieving a PGA score of 0 (clear) discontinued treatment and were observed for the duration of maintenance of a PGA score of 0 (clear) or 1 (almost clear) while off therapy (the protocol-defined “duration of remittive effect”). If disease worsening (defined as a PGA score 2 or higher) occurred, tapinarof cream 1% was restarted and continued until a PGA score of 0 (clear) was achieved. This pattern of treatment, discontinuation on achieving a PGA score of 0 (clear), and retreatment on disease worsening continued until the end of the trial. As a result, patients in PSOARING 3 could receive tapinarof cream 1% continuously or intermittently for 40 weeks.

Outcome Measures and Statistical Analyses

The assessment of total BSA affected by plaque psoriasis is an estimate of the total extent of disease as a percentage of total skin area. In the PSOARING trials, the skin surface of one hand (palm and digits) was assumed to be approximately equivalent to 1% BSA. The total BSA affected by psoriasis was evaluated from 0% to 100%, with greater total BSA affected being an indication of more extensive disease. The BSA efficacy outcomes used in these analyses were based post hoc on the proportion of patients who achieved a 1% or lower or 0.5% or lower total BSA affected. The smallest BSA affected increment that investigators were trained to measure and could record was 0.1%.

Psoriasis Area and Severity Index scores assess both the severity and extent of psoriasis. A PASI score lower than 5 often is considered indicative of mild psoriasis, a score of 5 to 10 indicates moderate disease, and a score higher than 10 indicates severe disease.¹⁹ The maximum PASI score is 72. The PASI efficacy outcomes used in these analyses were based post hoc on the proportion of patients who achieved an absolute total PASI score of 3 or lower, 2 or lower, and 1 or lower.

Efficacy analyses were based on pooled data for all patients in the PSOARING trials who had a PGA score of 2 to 4 (mild to severe) before treatment with tapinarof cream 1% in the intention-to-treat population using observed cases. Time-to-target analyses were based on Kaplan-Meier (KM) estimates using observed cases.

Safety analyses included the incidence and frequency of adverse events and were based on all patients who received tapinarof cream 1% in the PSOARING trials.

RESULTS

Baseline Patient Demographics and Disease Characteristics

The pooled efficacy analyses included 915 eligible patients (Table). At baseline, the mean (SD) age was 50.2 (13.25) years, 58.7% were male, the mean (SD) weight was 92.2 (23.67) kg, and the mean (SD) body mass index was 31.6 (7.53) kg/m². The percentage of patients with a PGA score of 2 (mild), 3 (moderate), or 4 (severe) was 13.9%, 78.1%, and 8.0%, respectively. The mean (SD) PASI score was 8.7 (4.23) and mean (SD) total BSA affected was 7.8% (4.98).

Efficacy

Achievement of BSA-Affected Targets—The NPF-recommended target of 1% or lower total BSA affected within 3 months was achieved by 40% of patients (KM estimate [95% CI, 37%-43%])(Figure 1). Across the total trial period of up to 52 weeks, a total BSA affected of 1% or lower was achieved by 61% of patients (561/915), with the median time to target of approximately 4 months (KM estimate: 120 days [95% CI, 113-141])(Supplementary Figure S2a). Approximately 50% of patients (455/915) achieved a total BSA affected of 0.5% or lower, with a median time to target of 199 days (KM estimate [95% CI, 172-228)(Figure 1; Supplementary Figure S2b).

Achievement of Absolute PASI Targets—Across the total trial period (up to 52 weeks), an absolute total PASI score of 3 or lower was achieved by 75% of patients (686/915), with a median time to achieve this of 2 months (KM estimate: 58 days [95% CI, 57-63]); approximately 67% of patients (612/915) achieved a total PASI score of 2 or lower, with a median time to achieve of 3 months (KM estimate: 87 days [95% CI, 85-110])(Figure 2; Supplementary Figures S3a and S3b). A PASI score of 1 or lower was achieved by approximately 50% of patients (460/915), with a median time to achieve of approximately 6 months (KM estimate: 185 days [95% CI, 169-218])(Figure 2, Supplementary Figure S3c).

Illustrative Case—Case photography showing the clinical response in a 63-year-old man with moderate plaque psoriasis in PSOARING 2 is shown in Figure 3. After 12 weeks of treatment with tapinarof cream 1% QD, the patient achieved all primary and secondary efficacy end points. In addition to achieving the regulatory end point of a PGA score of 0 (clear) or 1 (almost clear) and a decrease from baseline of at least 2 points, achievement of 0% total BSA affected and a total PASI score of 0 at week 12 exceeded the NPF and EADV consensus treatment targets.^10,11 Targets were achieved as early as week 4, with a total BSA affected of 0.5% or lower and a total PASI score of 1 or lower, illustrated by marked skin clearing and only faint residual erythema that completely resolved at week 12, with the absence of postinflammatory hyperpigmentation.

Safety

Safety data for the PSOARING trials have been previously reported.^17,18 The most common treatment-emergent adverse events were folliculitis, contact dermatitis, upper respiratory tract infection, and nasopharyngitis. Treatment-emergent adverse events generally were mild or moderate in severity and did not lead to trial discontinuation.^17,18

COMMENT

Treat-to-target management approaches aim to improve patient outcomes by striving to achieve optimal goals. The treat-to-target approach supports shared decision-making between clinicians and patients based on common expectations of what constitutes treatment success.

The findings of this analysis based on pooled data from a large cohort of patients demonstrate that a high proportion of patients can achieve or exceed recommended treatment targets with tapinarof cream 1% QD and maintain improvements long-term. The NPF-recommended treatment target of 1% or lower BSA affected within approximately 3 months (90 days) of treatment was achieved by 40% of tapinarof-treated patients. In addition, 1% or lower BSA affected at any time during the trials was achieved by 61% of patients (median, approximately 4 months). The analyses also indicated that PASI total scores of 3 or lower and 2 or lower were achieved by 75% and 67% of tapinarof-treated patients, respectively, within 2 to 3 months.

These findings support the previously reported efficacy of tapinarof cream, including high rates of complete disease clearance (40.9% [312/763]), durable response following treatment interruption, an off-therapy remittive effect of approximately 4 months, and good disease control on therapy with no evidence of tachyphylaxis.^17,18

CONCLUSION

Taken together with previously reported tapinarof efficacy and safety results, our findings demonstrate that a high proportion of patients treated with tapinarof cream as monotherapy can achieve aggressive treatment targets set by both US and European guidelines developed for systemic and biologic therapies. Tapinarof cream 1% QD is an effective topical treatment option for patients with plaque psoriasis that has been approved without restrictions relating to severity or extent of disease treated, duration of use, or application sites, including application to sensitive and intertriginous skin.

Acknowledgments—Editorial and medical writing support under the guidance of the authors was provided by Melanie Govender, MSc (Med), ApotheCom (United Kingdom), and was funded by Dermavant Sciences, Inc, in accordance with Good Publication Practice (GPP) guidelines.

To the Editor:

Skin cancers are extremely common worldwide. Malignant melanomas comprise approximately 1 in 5 of these cancers. Exposure to UV radiation is postulated to be responsible for a global rise in melanoma cases over the past 50 years.¹ Pediatric melanoma is a particularly rare condition that affects approximately 6 in every 1 million children.² Melanoma incidence in children ranges by age, increasing by approximately 10-fold from age 1 to 4 years to age 15 to 19 years. Tumor ulceration is a feature more commonly seen among children younger than 10 years and is associated with worse outcomes. Tumor thickness and ulceration strongly predict sentinel lymph node metastases among children, which also is associated with a poor prognosis.³

A recent study evaluating stage IV melanoma survival rates in adolescents and young adults (AYAs) vs older adults found that survival is much worse among AYAs. Thicker tumors and public health insurance also were associated with worse survival rates for AYAs, while early detection was associated with better survival rates.⁴

Health disparities and their role in the prognosis of pediatric melanoma is another important factor. One study analyzed this relationship at the state level using Texas Cancer Registry data (1995-2009).⁵ Patients’ socioeconomic status (SES) and driving distance to the nearest pediatric cancer care center were included in the analysis. Hispanic children were found to be 3 times more likely to present with advanced disease than non-Hispanic White children. Although SES and distance to the nearest treatment center were not found to affect the melanoma stage at presentation, Hispanic ethnicity or being in the lowest SES quartile were correlated with a higher mortality risk.⁵

When considering specific subtypes of melanoma, acral lentiginous melanoma (ALM) is known to develop in patients with skin of color. A 2023 study by Holman et al⁶ reported that the percentage of melanomas that were ALMs ranged from 0.8% in non-Hispanic White individuals to 19.1% in Hispanic Black, American Indian/Alaska Native, and Asian/Pacific Islander individuals. However, ALM is rare in children. In a pooled cohort study with patient information retrieved from the nationwide Dutch Pathology Registry, only 1 child and 1 adolescent were found to have ALM across a total of 514 patients.⁷ We sought to analyze pediatric melanoma outcomes based on race and other barriers to appropriate care.

We conducted a search of the Surveillance, Epidemiology, and End Results (SEER) database from January 1995 to December 2016 for patients aged 21 years and younger with a primary melanoma diagnosis. The primary outcome was the 5-year survival rate. County-level SES variables were used to calculate a prosperity index. Kaplan-Meier analysis and Cox proportional hazards model were used to compare 5-year survival rates among the different racial/ethnic groups.

A sample of 2742 patients was identified during the study period and followed for 5 years. Eighty-two percent were White, 6% Hispanic, 2% Asian, 1% Black, and 5% classified as other/unknown race (data were missing for 4%). The cohort was predominantly female (61%). White patients were more likely to present with localized disease than any other race/ethnicity (83% vs 65% in Hispanic, 60% in Asian/Pacific Islander, and 45% in Black patients [P<.05]).

Black and Hispanic patients had the worst 5-year survival rates on bivariate analysis. On multivariate analysis, this finding remained significant for Hispanic patients when compared with White patients (hazard ratio, 2.37 [P<.05]). Increasing age, male sex, advanced stage at diagnosis, and failure to receive surgery were associated with increased odds of mortality.

Patients with regionalized and disseminated disease had increased odds of mortality (6.16 and 64.45, respectively; P<.05) compared with patients with localized disease. Socioeconomic status and urbanization were not found to influence 5-year survival rates.

Pediatric melanoma often presents a clinical challenge with special considerations. Pediatric-specific predisposing risk factors for melanoma and an atypical clinical presentation are some of the major concerns that necessitate a tailored approach to this malignancy, especially among different age groups, skin types, and racial and socioeconomic groups.⁵

Standard ABCDE criteria often are inadequate for accurate detection of pediatric melanomas. Initial lesions often manifest as raised, red, amelanotic lesions mimicking pyogenic granulomas. Lesions tend to be very small (<6 mm in diameter) and can be uniform in color, thereby making the melanoma more difficult to detect compared to the characteristic findings in adults.⁵ Bleeding or ulceration often can be a warning sign during physical examination.

With regard to incidence, pediatric melanoma is relatively rare. Since the 1970s, the incidence of pediatric melanoma has been increasing; however, a recent analysis of the SEER database showed a decreasing trend from 2000 to 2010.⁴

Our analysis of the SEER data showed an increased risk for pediatric melanoma in older adolescents. In addition, the incidence of pediatric melanoma was higher in females of all racial groups except Asian/Pacific Islander individuals. However, SES was not found to significantly influence the 5-year survival rate in pediatric melanoma.

White pediatric patients were more likely to present with localized disease compared with other races. Pediatric melanoma patients with regional disease had a 6-fold increase in mortality rate vs those with localized disease; those with disseminated disease had a 65-fold higher risk. Consistent with this, Black and Hispanic patients had the worst 5-year survival rates on bivariate analysis.

These findings suggest a relationship between race, melanoma spread, and disease severity. Patient education programs need to be directed specifically to minority groups to improve their knowledge on evolving skin lesions and sun protection practices. Physicians also need to have heightened suspicion and better knowledge of the unique traits of pediatric melanoma.⁵

Given the considerable influence these disparities can have on melanoma outcomes, further research is needed to characterize outcomes based on race and determine obstacles to appropriate care. Improved public outreach initiatives that accommodate specific cultural barriers (eg, language, traditional patterns of behavior) also are required to improve current circumstances.

To the Editor:

Skin cancers are extremely common worldwide. Malignant melanomas comprise approximately 1 in 5 of these cancers. Exposure to UV radiation is postulated to be responsible for a global rise in melanoma cases over the past 50 years.¹ Pediatric melanoma is a particularly rare condition that affects approximately 6 in every 1 million children.² Melanoma incidence in children ranges by age, increasing by approximately 10-fold from age 1 to 4 years to age 15 to 19 years. Tumor ulceration is a feature more commonly seen among children younger than 10 years and is associated with worse outcomes. Tumor thickness and ulceration strongly predict sentinel lymph node metastases among children, which also is associated with a poor prognosis.³

A recent study evaluating stage IV melanoma survival rates in adolescents and young adults (AYAs) vs older adults found that survival is much worse among AYAs. Thicker tumors and public health insurance also were associated with worse survival rates for AYAs, while early detection was associated with better survival rates.⁴

Health disparities and their role in the prognosis of pediatric melanoma is another important factor. One study analyzed this relationship at the state level using Texas Cancer Registry data (1995-2009).⁵ Patients’ socioeconomic status (SES) and driving distance to the nearest pediatric cancer care center were included in the analysis. Hispanic children were found to be 3 times more likely to present with advanced disease than non-Hispanic White children. Although SES and distance to the nearest treatment center were not found to affect the melanoma stage at presentation, Hispanic ethnicity or being in the lowest SES quartile were correlated with a higher mortality risk.⁵

When considering specific subtypes of melanoma, acral lentiginous melanoma (ALM) is known to develop in patients with skin of color. A 2023 study by Holman et al⁶ reported that the percentage of melanomas that were ALMs ranged from 0.8% in non-Hispanic White individuals to 19.1% in Hispanic Black, American Indian/Alaska Native, and Asian/Pacific Islander individuals. However, ALM is rare in children. In a pooled cohort study with patient information retrieved from the nationwide Dutch Pathology Registry, only 1 child and 1 adolescent were found to have ALM across a total of 514 patients.⁷ We sought to analyze pediatric melanoma outcomes based on race and other barriers to appropriate care.

We conducted a search of the Surveillance, Epidemiology, and End Results (SEER) database from January 1995 to December 2016 for patients aged 21 years and younger with a primary melanoma diagnosis. The primary outcome was the 5-year survival rate. County-level SES variables were used to calculate a prosperity index. Kaplan-Meier analysis and Cox proportional hazards model were used to compare 5-year survival rates among the different racial/ethnic groups.

A sample of 2742 patients was identified during the study period and followed for 5 years. Eighty-two percent were White, 6% Hispanic, 2% Asian, 1% Black, and 5% classified as other/unknown race (data were missing for 4%). The cohort was predominantly female (61%). White patients were more likely to present with localized disease than any other race/ethnicity (83% vs 65% in Hispanic, 60% in Asian/Pacific Islander, and 45% in Black patients [P<.05]).

Black and Hispanic patients had the worst 5-year survival rates on bivariate analysis. On multivariate analysis, this finding remained significant for Hispanic patients when compared with White patients (hazard ratio, 2.37 [P<.05]). Increasing age, male sex, advanced stage at diagnosis, and failure to receive surgery were associated with increased odds of mortality.

Patients with regionalized and disseminated disease had increased odds of mortality (6.16 and 64.45, respectively; P<.05) compared with patients with localized disease. Socioeconomic status and urbanization were not found to influence 5-year survival rates.

Pediatric melanoma often presents a clinical challenge with special considerations. Pediatric-specific predisposing risk factors for melanoma and an atypical clinical presentation are some of the major concerns that necessitate a tailored approach to this malignancy, especially among different age groups, skin types, and racial and socioeconomic groups.⁵

Standard ABCDE criteria often are inadequate for accurate detection of pediatric melanomas. Initial lesions often manifest as raised, red, amelanotic lesions mimicking pyogenic granulomas. Lesions tend to be very small (<6 mm in diameter) and can be uniform in color, thereby making the melanoma more difficult to detect compared to the characteristic findings in adults.⁵ Bleeding or ulceration often can be a warning sign during physical examination.

With regard to incidence, pediatric melanoma is relatively rare. Since the 1970s, the incidence of pediatric melanoma has been increasing; however, a recent analysis of the SEER database showed a decreasing trend from 2000 to 2010.⁴

Our analysis of the SEER data showed an increased risk for pediatric melanoma in older adolescents. In addition, the incidence of pediatric melanoma was higher in females of all racial groups except Asian/Pacific Islander individuals. However, SES was not found to significantly influence the 5-year survival rate in pediatric melanoma.

White pediatric patients were more likely to present with localized disease compared with other races. Pediatric melanoma patients with regional disease had a 6-fold increase in mortality rate vs those with localized disease; those with disseminated disease had a 65-fold higher risk. Consistent with this, Black and Hispanic patients had the worst 5-year survival rates on bivariate analysis.

These findings suggest a relationship between race, melanoma spread, and disease severity. Patient education programs need to be directed specifically to minority groups to improve their knowledge on evolving skin lesions and sun protection practices. Physicians also need to have heightened suspicion and better knowledge of the unique traits of pediatric melanoma.⁵

Given the considerable influence these disparities can have on melanoma outcomes, further research is needed to characterize outcomes based on race and determine obstacles to appropriate care. Improved public outreach initiatives that accommodate specific cultural barriers (eg, language, traditional patterns of behavior) also are required to improve current circumstances.

To the Editor:

Skin cancers are extremely common worldwide. Malignant melanomas comprise approximately 1 in 5 of these cancers. Exposure to UV radiation is postulated to be responsible for a global rise in melanoma cases over the past 50 years.¹ Pediatric melanoma is a particularly rare condition that affects approximately 6 in every 1 million children.² Melanoma incidence in children ranges by age, increasing by approximately 10-fold from age 1 to 4 years to age 15 to 19 years. Tumor ulceration is a feature more commonly seen among children younger than 10 years and is associated with worse outcomes. Tumor thickness and ulceration strongly predict sentinel lymph node metastases among children, which also is associated with a poor prognosis.³

A recent study evaluating stage IV melanoma survival rates in adolescents and young adults (AYAs) vs older adults found that survival is much worse among AYAs. Thicker tumors and public health insurance also were associated with worse survival rates for AYAs, while early detection was associated with better survival rates.⁴

Health disparities and their role in the prognosis of pediatric melanoma is another important factor. One study analyzed this relationship at the state level using Texas Cancer Registry data (1995-2009).⁵ Patients’ socioeconomic status (SES) and driving distance to the nearest pediatric cancer care center were included in the analysis. Hispanic children were found to be 3 times more likely to present with advanced disease than non-Hispanic White children. Although SES and distance to the nearest treatment center were not found to affect the melanoma stage at presentation, Hispanic ethnicity or being in the lowest SES quartile were correlated with a higher mortality risk.⁵

When considering specific subtypes of melanoma, acral lentiginous melanoma (ALM) is known to develop in patients with skin of color. A 2023 study by Holman et al⁶ reported that the percentage of melanomas that were ALMs ranged from 0.8% in non-Hispanic White individuals to 19.1% in Hispanic Black, American Indian/Alaska Native, and Asian/Pacific Islander individuals. However, ALM is rare in children. In a pooled cohort study with patient information retrieved from the nationwide Dutch Pathology Registry, only 1 child and 1 adolescent were found to have ALM across a total of 514 patients.⁷ We sought to analyze pediatric melanoma outcomes based on race and other barriers to appropriate care.

We conducted a search of the Surveillance, Epidemiology, and End Results (SEER) database from January 1995 to December 2016 for patients aged 21 years and younger with a primary melanoma diagnosis. The primary outcome was the 5-year survival rate. County-level SES variables were used to calculate a prosperity index. Kaplan-Meier analysis and Cox proportional hazards model were used to compare 5-year survival rates among the different racial/ethnic groups.

A sample of 2742 patients was identified during the study period and followed for 5 years. Eighty-two percent were White, 6% Hispanic, 2% Asian, 1% Black, and 5% classified as other/unknown race (data were missing for 4%). The cohort was predominantly female (61%). White patients were more likely to present with localized disease than any other race/ethnicity (83% vs 65% in Hispanic, 60% in Asian/Pacific Islander, and 45% in Black patients [P<.05]).

Black and Hispanic patients had the worst 5-year survival rates on bivariate analysis. On multivariate analysis, this finding remained significant for Hispanic patients when compared with White patients (hazard ratio, 2.37 [P<.05]). Increasing age, male sex, advanced stage at diagnosis, and failure to receive surgery were associated with increased odds of mortality.

Patients with regionalized and disseminated disease had increased odds of mortality (6.16 and 64.45, respectively; P<.05) compared with patients with localized disease. Socioeconomic status and urbanization were not found to influence 5-year survival rates.

Pediatric melanoma often presents a clinical challenge with special considerations. Pediatric-specific predisposing risk factors for melanoma and an atypical clinical presentation are some of the major concerns that necessitate a tailored approach to this malignancy, especially among different age groups, skin types, and racial and socioeconomic groups.⁵

Standard ABCDE criteria often are inadequate for accurate detection of pediatric melanomas. Initial lesions often manifest as raised, red, amelanotic lesions mimicking pyogenic granulomas. Lesions tend to be very small (<6 mm in diameter) and can be uniform in color, thereby making the melanoma more difficult to detect compared to the characteristic findings in adults.⁵ Bleeding or ulceration often can be a warning sign during physical examination.

With regard to incidence, pediatric melanoma is relatively rare. Since the 1970s, the incidence of pediatric melanoma has been increasing; however, a recent analysis of the SEER database showed a decreasing trend from 2000 to 2010.⁴

Our analysis of the SEER data showed an increased risk for pediatric melanoma in older adolescents. In addition, the incidence of pediatric melanoma was higher in females of all racial groups except Asian/Pacific Islander individuals. However, SES was not found to significantly influence the 5-year survival rate in pediatric melanoma.

White pediatric patients were more likely to present with localized disease compared with other races. Pediatric melanoma patients with regional disease had a 6-fold increase in mortality rate vs those with localized disease; those with disseminated disease had a 65-fold higher risk. Consistent with this, Black and Hispanic patients had the worst 5-year survival rates on bivariate analysis.

These findings suggest a relationship between race, melanoma spread, and disease severity. Patient education programs need to be directed specifically to minority groups to improve their knowledge on evolving skin lesions and sun protection practices. Physicians also need to have heightened suspicion and better knowledge of the unique traits of pediatric melanoma.⁵

Given the considerable influence these disparities can have on melanoma outcomes, further research is needed to characterize outcomes based on race and determine obstacles to appropriate care. Improved public outreach initiatives that accommodate specific cultural barriers (eg, language, traditional patterns of behavior) also are required to improve current circumstances.