Carl Walraven, MD, MSc

Hyperglycemia in hospitalized patients is frequently observed and is recognized as an important threat to the health of patients with varying levels of illness, independent of diabetes status.[1, 2, 3, 4] Previous studies have found that in‐hospital hyperglycemia is associated with higher short‐term mortality rates,[5, 6, 7] longer lengths of stay, and higher rates of admission to intensive care units.[4]

In‐hospital hyperglycemia may be the result of diabetes mellitus or stress associated with hospitalization. The mechanism of stress hyperglycemia differs from diabetic hyperglycemia and can occur independently of diabetes status.[8] Stress hyperglycemia is characterized by rapid onset of insulin resistance, which normally takes months or years in diabetes mellitus. It develops in association with or because of other stressor such as infection or inflammatory processes.[9]

To our knowledge, no long‐term follow‐up study on hospitalized patients with elevated glucose levels but without a diabetes diagnosis has ever been conducted. In this study, we measured the 1‐ and 5‐year risk of mortality and diabetes‐related diseases for hospitalized patients with a diabetes diagnosis compared with patients grouped according to their peak in‐hospital serum glucose level. Our primary comparison was between patients with diagnosed diabetes and those with peak serum glucose >200 mg/dL (11.1 mmol/L), but we also examined 4 other categories of peak serum glucose: 140 to 200 mg/dL (7.811.1 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), and <108 mg/dL (<6.0 mmol/L).

METHODS

RESULTS

Between January 1, 1996 and March 31, 2008, 194,641 nonpsychiatric and nonobstetric adults were admitted to TOH. Seventeen patients were excluded because they were ineligible for healthcare coverage and had no encounter with the Ontario healthcare system following discharge from hospital, and 11,175 of the admissions ended in death. The final cohort consisted of 114,764 unique individuals representing 183,449 encounters.

Patients had a mean age of 59.5 years (standard deviation: 18.0) and 48.9% were male. The baseline risk of dying during hospitalization was 4.8%.

Table 1 describes patients by diabetes and peak serum glucose status. Patients with diagnosed diabetes were more likely to be older and male. Patients with elevated peak serum glucose (>200 mg/dL; >11.1 mmol/L) were younger than the diagnosed diabetes group and had a higher baseline probability of in‐hospital death (9.4%, 95% CI: 9.09.7). Patients in these 2 groups had more inpatient admissions within the previous 6 months compared to the groups with other peak serum glucose values.

Of the 5082 patients classified with a peak serum glucose measurement >200 mg/dL (11.1 mmol/L), 15% had 2 and 8% had more than 2 serum glucose measurements that exceeded this threshold. For the remaining patients (with 1 peak serum glucose measurement >200 mg/dL), 52% had additional serum glucose measurements over 140 mg/dL (7.8 mmol/L).

Table 2 presents crude 1‐ and 5‐year mortality and morbidity rates by patient group. The mortality rate is the percentage of patients who died within 1 year and 5 years of their index admission, with or without developing a CV complication. Morbidity rate describes the percentage who developed the complication among patients who survived after discharge or developed the complication prior to death within a 1‐year and 5‐year period. During the 1‐year follow‐up period, the crude mortality rate among patients with elevated peak serum glucose was higher than all other groups (25.4% vs 20.6% for diagnosed diabetes and 7.4% to 22.5% for other peak serum glucose levels; all P<0.0001). For the 5‐year follow‐up, the gap between patients with elevated peak serum glucose and those with diagnosed diabetes lessened but remained significant (45.1% vs 41.7%; P<0.0001). In the 1‐year follow‐up, patients with diagnosed diabetes had significantly higher morbidity rates than all other groups except for AMI. The difference in the rate of AMI for diagnosed diabetes and patients with elevated glucose was not statistically significant.

The 1‐year adjusted hazard ratios for mortality and CV complications are presented in Figure 1. After adjustment for baseline demographics and clinical and hospital factors, having a peak serum glucose level above 200 mg/dL (11.1 mmol/L) was an independent predictor of death. The mortality risk for this group in the year following discharge was 31% higher than patients with diagnosed diabetes (adjusted HR: 1.31, 95% CI: 1.20‐1.43). There was no mortality risk difference between the group with diagnosed diabetes and the lower peak serum glucose levels (adjusted HR: 1.05, 95% CI: 0.99‐1.11), 108160 mg/dL (6.07.8 mmol/L) (adjusted HR: 1.02, 95% CI: 0.971.07); and <108 mg/dL (<6.0 mmol/L) (adjusted HR: 0.97, 95% CI: 0.921.03). Adjusted HRs, with 95% CIs and P values for 5‐year follow‐up are presented in Figure 2.

Figure 1

Figure 2

Adjusted hazard ratios for morbidity showed a different pattern. Patients with diagnosed diabetes had a higher risk of developing complications in the year following discharge from the hospital (Figure 1). After adjusting for potential confounders, we found that patients with diagnosed diabetes had the same risk of AMI and CVD as patients with peak serum glucose levels above 200 mg/dL (11.1 mmol/L) (AMI, adjusted HR: 0.96; 95% CI: 0.78‐1.18; CVD, adjusted HR: 0.79; 95% CI: 0.61‐1.00) but a 36% to 57% higher AMI risk and 29% to 34% higher CVD risk than other peak serum glucose level groups. Compared to all peak serum glucose groups, patients with diagnosed diabetes had a significantly higher risk of developing CHF, PVD, and ESRD (Figure 1). Similarly, the 5‐year risk of AMI, CVD, CHF, PVD, and ESRD was higher for patients with diagnosed diabetes compared to all peak serum glucose groups (Figure 2).

CONCLUSION/DISCUSSION

The study found that in‐hospital hyperglycemia was a strong predictor of mortality at 1‐ and 5‐years follow‐up, even after adjustment using well‐established and discriminating comorbidity measures. Extreme in‐hospital hyperglycemia was a stronger predictor of mortality than diagnosed diabetes.

Previous studies have shown that diabetes and stress hyperglycemia among hospitalized patients are important markers for poor clinical outcomes and in‐hospital mortality.[1, 2, 4] This study indicates that hospitalized patients with extreme hyperglycemia (peak serum glucose >200 mg/dL) were at a high risk of death for at least 5 years following discharge.

These findings, based on a large general sample of hospitalized patients, indicate that the extreme elevations in peak serum glucose convey a risk for all hospitalized patients, not only those with critical illness.[3, 18] Hyperglycemia appears to be an independent indicator of mortality risk and should be evaluated as a potential component within risk prediction tools. Further study is required to determine mechanisms for this risk association to identify what therapies, if any, might be used to minimize this risk.

The diagnosed diabetes group, which likely included some patients who also had extreme in‐hospital hyperglycemia, had a lower 1‐year risk of death than patients with hyperglycemia who did not have diabetes diagnosis. This may be an indication of the protective effect of blood glucose control, because patients with diabetes are more likely to receive therapy for hyperglycemia during and after hospitalization.

Classification of patients by several serum glucose levels (as opposed to a dichotomous classification, where hyperglycemia is either present or absent), showed that hyperglycemia constitutes a graded risk[5] for almost all outcomes examined, particularly mortality, AMI, and CVD.

Previous studies indicate that diabetes is observed in only 23% to 35% of hospitalized patients with hyperglycemia.[18, 19] We would expect higher risk for CV complications for patients with elevated glucose if the proportion of these patients who had undiagnosed diabetes was higher than the proportion estimated in the literature. However, we observed lower risk of CV complications (especially PVD and ESRD) for the elevated glucose group in the 1 and 5 years following discharge. In‐hospital hyperglycemia is not equivalent to undiagnosed diabetes.

There are several potential limitations in this study. The first is our method for ascertaining CV complications. Diverse disease definitions are used in medical literature, and similar studies using different definitions may yield different results, though we would not expect to find a wide range of variation. Second, our study did not include information about severity of diabetes and the persistence of elevated glucose; if available, this knowledge may provide better insight into patient experiences, especially in long‐term follow‐up. Third, results are also limited by the absence of data on cause of death, a potentially helpful means of identifying posthospitalization difficulties experienced by patients with hyperglycemia. Fourth, this study mainly compares experiences of patients with elevated peak serum glucose level to diabetes patients; it would be worthwhile to explore the impact of lower gradations of glucose levels.

We would like to emphasize that we did not confirm diabetes diagnosis following discharge for patients with hyperglycemia. However, we did not observe a high rate of complications at 5‐year follow‐up, particularly for ESRD. This may be because most patients with in‐hospital elevated glucose had early diabetes or transient hyperglycemia and therefore lower risk of long‐term diabetes‐specific consultations.

Hyperglycemia is an important independent indicator, carrying a greater risk for 1‐ and 5‐year mortality than diagnosed diabetes. However, it is unclear whether hospitalized patients with elevated peak serum glucose have early diabetes or their hyperglycemia reflects hospital stress or another comorbidity concept.

Hyperglycemia in hospitalized patients is frequently observed and is recognized as an important threat to the health of patients with varying levels of illness, independent of diabetes status.[1, 2, 3, 4] Previous studies have found that in‐hospital hyperglycemia is associated with higher short‐term mortality rates,[5, 6, 7] longer lengths of stay, and higher rates of admission to intensive care units.[4]

In‐hospital hyperglycemia may be the result of diabetes mellitus or stress associated with hospitalization. The mechanism of stress hyperglycemia differs from diabetic hyperglycemia and can occur independently of diabetes status.[8] Stress hyperglycemia is characterized by rapid onset of insulin resistance, which normally takes months or years in diabetes mellitus. It develops in association with or because of other stressor such as infection or inflammatory processes.[9]

To our knowledge, no long‐term follow‐up study on hospitalized patients with elevated glucose levels but without a diabetes diagnosis has ever been conducted. In this study, we measured the 1‐ and 5‐year risk of mortality and diabetes‐related diseases for hospitalized patients with a diabetes diagnosis compared with patients grouped according to their peak in‐hospital serum glucose level. Our primary comparison was between patients with diagnosed diabetes and those with peak serum glucose >200 mg/dL (11.1 mmol/L), but we also examined 4 other categories of peak serum glucose: 140 to 200 mg/dL (7.811.1 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), and <108 mg/dL (<6.0 mmol/L).

METHODS

RESULTS

Between January 1, 1996 and March 31, 2008, 194,641 nonpsychiatric and nonobstetric adults were admitted to TOH. Seventeen patients were excluded because they were ineligible for healthcare coverage and had no encounter with the Ontario healthcare system following discharge from hospital, and 11,175 of the admissions ended in death. The final cohort consisted of 114,764 unique individuals representing 183,449 encounters.

Patients had a mean age of 59.5 years (standard deviation: 18.0) and 48.9% were male. The baseline risk of dying during hospitalization was 4.8%.

Table 1 describes patients by diabetes and peak serum glucose status. Patients with diagnosed diabetes were more likely to be older and male. Patients with elevated peak serum glucose (>200 mg/dL; >11.1 mmol/L) were younger than the diagnosed diabetes group and had a higher baseline probability of in‐hospital death (9.4%, 95% CI: 9.09.7). Patients in these 2 groups had more inpatient admissions within the previous 6 months compared to the groups with other peak serum glucose values.

Of the 5082 patients classified with a peak serum glucose measurement >200 mg/dL (11.1 mmol/L), 15% had 2 and 8% had more than 2 serum glucose measurements that exceeded this threshold. For the remaining patients (with 1 peak serum glucose measurement >200 mg/dL), 52% had additional serum glucose measurements over 140 mg/dL (7.8 mmol/L).

Table 2 presents crude 1‐ and 5‐year mortality and morbidity rates by patient group. The mortality rate is the percentage of patients who died within 1 year and 5 years of their index admission, with or without developing a CV complication. Morbidity rate describes the percentage who developed the complication among patients who survived after discharge or developed the complication prior to death within a 1‐year and 5‐year period. During the 1‐year follow‐up period, the crude mortality rate among patients with elevated peak serum glucose was higher than all other groups (25.4% vs 20.6% for diagnosed diabetes and 7.4% to 22.5% for other peak serum glucose levels; all P<0.0001). For the 5‐year follow‐up, the gap between patients with elevated peak serum glucose and those with diagnosed diabetes lessened but remained significant (45.1% vs 41.7%; P<0.0001). In the 1‐year follow‐up, patients with diagnosed diabetes had significantly higher morbidity rates than all other groups except for AMI. The difference in the rate of AMI for diagnosed diabetes and patients with elevated glucose was not statistically significant.

The 1‐year adjusted hazard ratios for mortality and CV complications are presented in Figure 1. After adjustment for baseline demographics and clinical and hospital factors, having a peak serum glucose level above 200 mg/dL (11.1 mmol/L) was an independent predictor of death. The mortality risk for this group in the year following discharge was 31% higher than patients with diagnosed diabetes (adjusted HR: 1.31, 95% CI: 1.20‐1.43). There was no mortality risk difference between the group with diagnosed diabetes and the lower peak serum glucose levels (adjusted HR: 1.05, 95% CI: 0.99‐1.11), 108160 mg/dL (6.07.8 mmol/L) (adjusted HR: 1.02, 95% CI: 0.971.07); and <108 mg/dL (<6.0 mmol/L) (adjusted HR: 0.97, 95% CI: 0.921.03). Adjusted HRs, with 95% CIs and P values for 5‐year follow‐up are presented in Figure 2.

Figure 1

Figure 2

Adjusted hazard ratios for morbidity showed a different pattern. Patients with diagnosed diabetes had a higher risk of developing complications in the year following discharge from the hospital (Figure 1). After adjusting for potential confounders, we found that patients with diagnosed diabetes had the same risk of AMI and CVD as patients with peak serum glucose levels above 200 mg/dL (11.1 mmol/L) (AMI, adjusted HR: 0.96; 95% CI: 0.78‐1.18; CVD, adjusted HR: 0.79; 95% CI: 0.61‐1.00) but a 36% to 57% higher AMI risk and 29% to 34% higher CVD risk than other peak serum glucose level groups. Compared to all peak serum glucose groups, patients with diagnosed diabetes had a significantly higher risk of developing CHF, PVD, and ESRD (Figure 1). Similarly, the 5‐year risk of AMI, CVD, CHF, PVD, and ESRD was higher for patients with diagnosed diabetes compared to all peak serum glucose groups (Figure 2).

CONCLUSION/DISCUSSION

The study found that in‐hospital hyperglycemia was a strong predictor of mortality at 1‐ and 5‐years follow‐up, even after adjustment using well‐established and discriminating comorbidity measures. Extreme in‐hospital hyperglycemia was a stronger predictor of mortality than diagnosed diabetes.

Previous studies have shown that diabetes and stress hyperglycemia among hospitalized patients are important markers for poor clinical outcomes and in‐hospital mortality.[1, 2, 4] This study indicates that hospitalized patients with extreme hyperglycemia (peak serum glucose >200 mg/dL) were at a high risk of death for at least 5 years following discharge.

These findings, based on a large general sample of hospitalized patients, indicate that the extreme elevations in peak serum glucose convey a risk for all hospitalized patients, not only those with critical illness.[3, 18] Hyperglycemia appears to be an independent indicator of mortality risk and should be evaluated as a potential component within risk prediction tools. Further study is required to determine mechanisms for this risk association to identify what therapies, if any, might be used to minimize this risk.

The diagnosed diabetes group, which likely included some patients who also had extreme in‐hospital hyperglycemia, had a lower 1‐year risk of death than patients with hyperglycemia who did not have diabetes diagnosis. This may be an indication of the protective effect of blood glucose control, because patients with diabetes are more likely to receive therapy for hyperglycemia during and after hospitalization.

Classification of patients by several serum glucose levels (as opposed to a dichotomous classification, where hyperglycemia is either present or absent), showed that hyperglycemia constitutes a graded risk[5] for almost all outcomes examined, particularly mortality, AMI, and CVD.

Previous studies indicate that diabetes is observed in only 23% to 35% of hospitalized patients with hyperglycemia.[18, 19] We would expect higher risk for CV complications for patients with elevated glucose if the proportion of these patients who had undiagnosed diabetes was higher than the proportion estimated in the literature. However, we observed lower risk of CV complications (especially PVD and ESRD) for the elevated glucose group in the 1 and 5 years following discharge. In‐hospital hyperglycemia is not equivalent to undiagnosed diabetes.

There are several potential limitations in this study. The first is our method for ascertaining CV complications. Diverse disease definitions are used in medical literature, and similar studies using different definitions may yield different results, though we would not expect to find a wide range of variation. Second, our study did not include information about severity of diabetes and the persistence of elevated glucose; if available, this knowledge may provide better insight into patient experiences, especially in long‐term follow‐up. Third, results are also limited by the absence of data on cause of death, a potentially helpful means of identifying posthospitalization difficulties experienced by patients with hyperglycemia. Fourth, this study mainly compares experiences of patients with elevated peak serum glucose level to diabetes patients; it would be worthwhile to explore the impact of lower gradations of glucose levels.

We would like to emphasize that we did not confirm diabetes diagnosis following discharge for patients with hyperglycemia. However, we did not observe a high rate of complications at 5‐year follow‐up, particularly for ESRD. This may be because most patients with in‐hospital elevated glucose had early diabetes or transient hyperglycemia and therefore lower risk of long‐term diabetes‐specific consultations.

Hyperglycemia is an important independent indicator, carrying a greater risk for 1‐ and 5‐year mortality than diagnosed diabetes. However, it is unclear whether hospitalized patients with elevated peak serum glucose have early diabetes or their hyperglycemia reflects hospital stress or another comorbidity concept.

Hyperglycemia in hospitalized patients is frequently observed and is recognized as an important threat to the health of patients with varying levels of illness, independent of diabetes status.[1, 2, 3, 4] Previous studies have found that in‐hospital hyperglycemia is associated with higher short‐term mortality rates,[5, 6, 7] longer lengths of stay, and higher rates of admission to intensive care units.[4]

In‐hospital hyperglycemia may be the result of diabetes mellitus or stress associated with hospitalization. The mechanism of stress hyperglycemia differs from diabetic hyperglycemia and can occur independently of diabetes status.[8] Stress hyperglycemia is characterized by rapid onset of insulin resistance, which normally takes months or years in diabetes mellitus. It develops in association with or because of other stressor such as infection or inflammatory processes.[9]

To our knowledge, no long‐term follow‐up study on hospitalized patients with elevated glucose levels but without a diabetes diagnosis has ever been conducted. In this study, we measured the 1‐ and 5‐year risk of mortality and diabetes‐related diseases for hospitalized patients with a diabetes diagnosis compared with patients grouped according to their peak in‐hospital serum glucose level. Our primary comparison was between patients with diagnosed diabetes and those with peak serum glucose >200 mg/dL (11.1 mmol/L), but we also examined 4 other categories of peak serum glucose: 140 to 200 mg/dL (7.811.1 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), and <108 mg/dL (<6.0 mmol/L).

METHODS

RESULTS

Between January 1, 1996 and March 31, 2008, 194,641 nonpsychiatric and nonobstetric adults were admitted to TOH. Seventeen patients were excluded because they were ineligible for healthcare coverage and had no encounter with the Ontario healthcare system following discharge from hospital, and 11,175 of the admissions ended in death. The final cohort consisted of 114,764 unique individuals representing 183,449 encounters.

Patients had a mean age of 59.5 years (standard deviation: 18.0) and 48.9% were male. The baseline risk of dying during hospitalization was 4.8%.

Table 1 describes patients by diabetes and peak serum glucose status. Patients with diagnosed diabetes were more likely to be older and male. Patients with elevated peak serum glucose (>200 mg/dL; >11.1 mmol/L) were younger than the diagnosed diabetes group and had a higher baseline probability of in‐hospital death (9.4%, 95% CI: 9.09.7). Patients in these 2 groups had more inpatient admissions within the previous 6 months compared to the groups with other peak serum glucose values.

Of the 5082 patients classified with a peak serum glucose measurement >200 mg/dL (11.1 mmol/L), 15% had 2 and 8% had more than 2 serum glucose measurements that exceeded this threshold. For the remaining patients (with 1 peak serum glucose measurement >200 mg/dL), 52% had additional serum glucose measurements over 140 mg/dL (7.8 mmol/L).

Table 2 presents crude 1‐ and 5‐year mortality and morbidity rates by patient group. The mortality rate is the percentage of patients who died within 1 year and 5 years of their index admission, with or without developing a CV complication. Morbidity rate describes the percentage who developed the complication among patients who survived after discharge or developed the complication prior to death within a 1‐year and 5‐year period. During the 1‐year follow‐up period, the crude mortality rate among patients with elevated peak serum glucose was higher than all other groups (25.4% vs 20.6% for diagnosed diabetes and 7.4% to 22.5% for other peak serum glucose levels; all P<0.0001). For the 5‐year follow‐up, the gap between patients with elevated peak serum glucose and those with diagnosed diabetes lessened but remained significant (45.1% vs 41.7%; P<0.0001). In the 1‐year follow‐up, patients with diagnosed diabetes had significantly higher morbidity rates than all other groups except for AMI. The difference in the rate of AMI for diagnosed diabetes and patients with elevated glucose was not statistically significant.

The 1‐year adjusted hazard ratios for mortality and CV complications are presented in Figure 1. After adjustment for baseline demographics and clinical and hospital factors, having a peak serum glucose level above 200 mg/dL (11.1 mmol/L) was an independent predictor of death. The mortality risk for this group in the year following discharge was 31% higher than patients with diagnosed diabetes (adjusted HR: 1.31, 95% CI: 1.20‐1.43). There was no mortality risk difference between the group with diagnosed diabetes and the lower peak serum glucose levels (adjusted HR: 1.05, 95% CI: 0.99‐1.11), 108160 mg/dL (6.07.8 mmol/L) (adjusted HR: 1.02, 95% CI: 0.971.07); and <108 mg/dL (<6.0 mmol/L) (adjusted HR: 0.97, 95% CI: 0.921.03). Adjusted HRs, with 95% CIs and P values for 5‐year follow‐up are presented in Figure 2.

Figure 1

Figure 2

Adjusted hazard ratios for morbidity showed a different pattern. Patients with diagnosed diabetes had a higher risk of developing complications in the year following discharge from the hospital (Figure 1). After adjusting for potential confounders, we found that patients with diagnosed diabetes had the same risk of AMI and CVD as patients with peak serum glucose levels above 200 mg/dL (11.1 mmol/L) (AMI, adjusted HR: 0.96; 95% CI: 0.78‐1.18; CVD, adjusted HR: 0.79; 95% CI: 0.61‐1.00) but a 36% to 57% higher AMI risk and 29% to 34% higher CVD risk than other peak serum glucose level groups. Compared to all peak serum glucose groups, patients with diagnosed diabetes had a significantly higher risk of developing CHF, PVD, and ESRD (Figure 1). Similarly, the 5‐year risk of AMI, CVD, CHF, PVD, and ESRD was higher for patients with diagnosed diabetes compared to all peak serum glucose groups (Figure 2).

CONCLUSION/DISCUSSION

The study found that in‐hospital hyperglycemia was a strong predictor of mortality at 1‐ and 5‐years follow‐up, even after adjustment using well‐established and discriminating comorbidity measures. Extreme in‐hospital hyperglycemia was a stronger predictor of mortality than diagnosed diabetes.

Previous studies have shown that diabetes and stress hyperglycemia among hospitalized patients are important markers for poor clinical outcomes and in‐hospital mortality.[1, 2, 4] This study indicates that hospitalized patients with extreme hyperglycemia (peak serum glucose >200 mg/dL) were at a high risk of death for at least 5 years following discharge.

These findings, based on a large general sample of hospitalized patients, indicate that the extreme elevations in peak serum glucose convey a risk for all hospitalized patients, not only those with critical illness.[3, 18] Hyperglycemia appears to be an independent indicator of mortality risk and should be evaluated as a potential component within risk prediction tools. Further study is required to determine mechanisms for this risk association to identify what therapies, if any, might be used to minimize this risk.

The diagnosed diabetes group, which likely included some patients who also had extreme in‐hospital hyperglycemia, had a lower 1‐year risk of death than patients with hyperglycemia who did not have diabetes diagnosis. This may be an indication of the protective effect of blood glucose control, because patients with diabetes are more likely to receive therapy for hyperglycemia during and after hospitalization.

Classification of patients by several serum glucose levels (as opposed to a dichotomous classification, where hyperglycemia is either present or absent), showed that hyperglycemia constitutes a graded risk[5] for almost all outcomes examined, particularly mortality, AMI, and CVD.

Previous studies indicate that diabetes is observed in only 23% to 35% of hospitalized patients with hyperglycemia.[18, 19] We would expect higher risk for CV complications for patients with elevated glucose if the proportion of these patients who had undiagnosed diabetes was higher than the proportion estimated in the literature. However, we observed lower risk of CV complications (especially PVD and ESRD) for the elevated glucose group in the 1 and 5 years following discharge. In‐hospital hyperglycemia is not equivalent to undiagnosed diabetes.

There are several potential limitations in this study. The first is our method for ascertaining CV complications. Diverse disease definitions are used in medical literature, and similar studies using different definitions may yield different results, though we would not expect to find a wide range of variation. Second, our study did not include information about severity of diabetes and the persistence of elevated glucose; if available, this knowledge may provide better insight into patient experiences, especially in long‐term follow‐up. Third, results are also limited by the absence of data on cause of death, a potentially helpful means of identifying posthospitalization difficulties experienced by patients with hyperglycemia. Fourth, this study mainly compares experiences of patients with elevated peak serum glucose level to diabetes patients; it would be worthwhile to explore the impact of lower gradations of glucose levels.

We would like to emphasize that we did not confirm diabetes diagnosis following discharge for patients with hyperglycemia. However, we did not observe a high rate of complications at 5‐year follow‐up, particularly for ESRD. This may be because most patients with in‐hospital elevated glucose had early diabetes or transient hyperglycemia and therefore lower risk of long‐term diabetes‐specific consultations.

Hyperglycemia is an important independent indicator, carrying a greater risk for 1‐ and 5‐year mortality than diagnosed diabetes. However, it is unclear whether hospitalized patients with elevated peak serum glucose have early diabetes or their hyperglycemia reflects hospital stress or another comorbidity concept.

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, ²=27.4, P<0.0001; Mantel‐Haenszel trend ²=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (²=19.8, df=1, P<0.0001).

Table 2.Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk

	Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range)
	1 (1416) [A]	2 (1727)	3 (2839)	4 (4056)	5 (57114) [B]	Income Quintile Overall [C]
NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).
Income quintile
1 (poorest)	18/1,485 (1.2%)	42/1,667 (2.5%)	65/1,635 (4.0%)	117/1,722 (6.8%)	379/1,910 (19.8%)	621/8,419 (7.4%)
2	21/1,627 (1.3%)	39/1,665 (2.3%)	65/1,598 (4.1%)	130/1,808 (5.2%)	331/1,700 (19.5%)	586/8,398 (7.0%)
3	18/1,761 (1.0%)	33/1,665 (2.0%)	63/1,568 (4.0%)	96/1,499 (6.4%)	274/1,548 (17.7%)	484/8,041 (6.0%)
4	27/1,851 (1.5%)	42/1,698 (2.4%)	57/1,585 (3.6%)	110/1,548 (6.1%)	264/1,379 (19.1%)	500/8,061 (6.2%)
5 (richest)	20/1,864 (1.1%)	32/1,736 (1.8%)	60/1,468 (4.1%)	107/1,525 (7.0%)	228/1,315 (17.3%)	447/7,908 (5.6%)
Risk quintile overall [D]	104/8,588 (1.2%)	188/8,431 (2.2%)	310/7,854 (4.0%)	560/8,102 (6.9%)	1476/7,852 (18.8%)	2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (²=240, P<0.0001; Mantel‐Haenszel trend ²=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (²=2703, P<0.0001; Mantel‐Haenszel trend ²=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel ²=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, ²=27.4, P<0.0001; Mantel‐Haenszel trend ²=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (²=19.8, df=1, P<0.0001).

Table 2.Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk

	Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range)
	1 (1416) [A]	2 (1727)	3 (2839)	4 (4056)	5 (57114) [B]	Income Quintile Overall [C]
NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).
Income quintile
1 (poorest)	18/1,485 (1.2%)	42/1,667 (2.5%)	65/1,635 (4.0%)	117/1,722 (6.8%)	379/1,910 (19.8%)	621/8,419 (7.4%)
2	21/1,627 (1.3%)	39/1,665 (2.3%)	65/1,598 (4.1%)	130/1,808 (5.2%)	331/1,700 (19.5%)	586/8,398 (7.0%)
3	18/1,761 (1.0%)	33/1,665 (2.0%)	63/1,568 (4.0%)	96/1,499 (6.4%)	274/1,548 (17.7%)	484/8,041 (6.0%)
4	27/1,851 (1.5%)	42/1,698 (2.4%)	57/1,585 (3.6%)	110/1,548 (6.1%)	264/1,379 (19.1%)	500/8,061 (6.2%)
5 (richest)	20/1,864 (1.1%)	32/1,736 (1.8%)	60/1,468 (4.1%)	107/1,525 (7.0%)	228/1,315 (17.3%)	447/7,908 (5.6%)
Risk quintile overall [D]	104/8,588 (1.2%)	188/8,431 (2.2%)	310/7,854 (4.0%)	560/8,102 (6.9%)	1476/7,852 (18.8%)	2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (²=240, P<0.0001; Mantel‐Haenszel trend ²=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (²=2703, P<0.0001; Mantel‐Haenszel trend ²=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel ²=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, ²=27.4, P<0.0001; Mantel‐Haenszel trend ²=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (²=19.8, df=1, P<0.0001).

Table 2.Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk

	Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range)
	1 (1416) [A]	2 (1727)	3 (2839)	4 (4056)	5 (57114) [B]	Income Quintile Overall [C]
NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).
Income quintile
1 (poorest)	18/1,485 (1.2%)	42/1,667 (2.5%)	65/1,635 (4.0%)	117/1,722 (6.8%)	379/1,910 (19.8%)	621/8,419 (7.4%)
2	21/1,627 (1.3%)	39/1,665 (2.3%)	65/1,598 (4.1%)	130/1,808 (5.2%)	331/1,700 (19.5%)	586/8,398 (7.0%)
3	18/1,761 (1.0%)	33/1,665 (2.0%)	63/1,568 (4.0%)	96/1,499 (6.4%)	274/1,548 (17.7%)	484/8,041 (6.0%)
4	27/1,851 (1.5%)	42/1,698 (2.4%)	57/1,585 (3.6%)	110/1,548 (6.1%)	264/1,379 (19.1%)	500/8,061 (6.2%)
5 (richest)	20/1,864 (1.1%)	32/1,736 (1.8%)	60/1,468 (4.1%)	107/1,525 (7.0%)	228/1,315 (17.3%)	447/7,908 (5.6%)
Risk quintile overall [D]	104/8,588 (1.2%)	188/8,431 (2.2%)	310/7,854 (4.0%)	560/8,102 (6.9%)	1476/7,852 (18.8%)	2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (²=240, P<0.0001; Mantel‐Haenszel trend ²=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (²=2703, P<0.0001; Mantel‐Haenszel trend ²=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel ²=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.