Effect of Socioeconomic Status on Inpatient Mortality and Use of Postacute Care After Subarachnoid Hemorrhage
Background and Purpose—Studies in the United States and Canada have demonstrated socioeconomic gradients in outcomes of acute life-threatening cardiovascular and cerebrovascular diseases. The extent to which these findings are applicable to subarachnoid hemorrhage is uncertain. This study investigated socioeconomic status-related differences in risk of inpatient mortality and use of institutional postacute care after subarachnoid hemorrhage in the United States and Canada.
Methods—Subarachnoid hemorrhage patient records in the US Nationwide Inpatient Sample database (2005–2010) and the Canadian Discharge Abstract Database (2004–2010) were analyzed separately, and summative results were compared. Both databases are nationally representative and contain relevant sociodemographic, diagnostic, procedural, and administrative information. We determined socioeconomic status on the basis of estimated median household income of residents for patient’s ZIP or postal code. Multinomial logistic regression models were fitted with adjustment for relevant confounding covariates.
Results—The cohort consisted of 31 631 US patients and 16 531 Canadian patients. Mean age (58 years) and crude inpatient mortality rates (22%) were similar in both countries. A significant income–mortality association was observed among US patients (odds ratio, 0.77; 95% CI, 0.65–0.93), which was absent among Canadian patients (odds ratio, 0.97; 95% CI, 0.85–1.12). Neighborhood income status was not significantly associated with use of postacute care in the 2 countries.
Conclusions—Socioeconomic status is associated with subarachnoid hemorrhage inpatient mortality risk in the United States, but not in Canada, although it does not influence the pattern of use of institutional care among survivors in both countries.
Spontaneous subarachnoid hemorrhage (SAH) is a neurosurgical emergency that is associated with significant mortality and morbidity.1 About a quarter of patients with SAH will die within 2 weeks of hospital admission, and a similar proportion of survivors are discharged with functional disabilities that may require prolonged institutional care for recovery to premorbid lifestyle.2
At least 2 reviews have been published to synthesize the current evidence on the association with regard to stroke, but with very little or no information available on SAH.3,4 Our search found only 2 small studies that partially addressed this topic in SAH using data from >10 years ago.5,6
In undertaking this study, we aimed to provide nationally representative data on the relationship between SES and SAH outcomes, specifically inpatient mortality and overall use of institutional postacute care, comparing results across 2 different healthcare systems. The variations in outcome patterns, if any, may offer clues to unbundling some of the contributory factors to the effect of SES. The study also has policy and clinical practice implications to better outcomes for more vulnerable groups and to improve overall population health.
Patients and Methods
The following 2 nationally representative administrative databases were used for the study: the Discharge Abstract Database (DAD), managed by the Canadian Institute for Health Information, and the Nationwide Inpatient sample (NIS), managed as part of the Healthcare Cost and Utilization Project by the Agency for Healthcare Quality and Research (Rockville, MD). The databases contain patient-level sociodemographic, diagnostic, therapeutic, and administrative information on hospital discharges. Although the DAD mandatorily captures information from all hospital discharges in Canada, the NIS is designed as a representative 20% subsample of discharges from acute care hospitals in the United States. DAD data for the fiscal years 2004 to 2010 and NIS data for the fiscal years 2005 to 2010 were used in the study. In both databases, diagnoses were coded according to the International Classification of Diseases (ICD). We abstracted data for all patients with a principal diagnosis of SAH using the diagnostic code appropriate to each database (ICD, Ninth Revision, Clinical Modification [ICD-9-CM] code 430 for NIS and ICD-10-CM code I60 for DAD). The accuracy of SAH ICD coding in administrative database has been validated.7,8 To minimize the chances of including patients with traumatic SAH, we exclude patients with SAH with a secondary diagnosis of head trauma (NIS: ICD-9-CM codes 800.0–801.9, 803.0–804.9, 850.0–854.1, or 873.0–873.9 and for the DAD: ICD-10 S00–S09).
Primary Predictor Variable
Information about patients’ SES was determined on the basis of estimated median household income of residents for patient’s ZIP or postal code. This information is available in the NIS and DAD data sets but computed and categorized somewhat differently in the 2 data sets. In the NIS, neighborhood income status was computed relative to the 2000 distribution of the US population, with annual corrections to account for inflation and change in income distribution.9 In the DAD, neighborhood income status was computed relative to the 2006 distribution of Canadian population.10 The NIS applied quartile cut points with quartile 1 representing the lowest income neighborhoods, and quartile 4 representing the wealthiest neighborhoods. The DAD applied quintile cut points with quintile 1 representing the lowest income neighborhoods, and quintile 5 representing the wealthiest neighborhoods. Both processes of determining SES have been validated and used in previous studies.11,12
We recategorized patient discharge disposition into a 3-level categorical outcome variable that was similar for both data sets: (1) Routine discharge, comprising discharged home or alive, destination unknown or signed against medical advice; (2) In-hospital mortality; (3) Discharge to institutional care comprising transfer to short-term hospital, home healthcare, other transfers, including skilled nursing facility, intermediate care, and another type of facility.
Potential confounding variables that were accounted for in the analysis included the following: (1) Demographic covariates such as age, sex, race (categorized in the NIS as white, black, Hispanic, Asian/pacific Islander, Native American or Others), and insurance status (Medicare, Medicaid, Private, including Health Maintenance Organization, self-pay, or no charges). Information on race was not provided in the DAD, and insurance status was not relevant in the Canadian context because healthcare services in Canada are publicly funded. (2) Clinical covariates such as admission type (elective versus urgent/emergency) and modified Charlson–Deyo comorbid index score, a measure of the number and severity of patients’ comorbid illness.13 (3) Hospital covariates such as geographic region of hospital location (Northeast, Midwest, South, or West) as in the NIS; bed size (small, medium, or large), and teaching status (teaching or nonteaching hospital). The hospital covariate provided in the DAD was hospital status, which was categorized into small, medium, or large community hospitals or teaching hospitals on the basis of bed size and academic status.
Handling of Missing Data
There were no missing data in the DAD. The proportion of missing data in the NIS was <0.5% for each variable, except for median neighborhood income quartile (2.7% missing) and race (23.8% missing) for which we performed multiple imputations by chained equations, generating 20 imputed data sets for analysis. The imputation model was specified on the primary predictor variable, outcome variable, and explanatory covariates as well as NIS hospital and discharge weights for variance estimation.
Considering that the NIS and DAD data sets differ in sampling and coding designs, we analyzed the 2 data sets separately and made comparison at the level of aggregate results. We first computed descriptive statistics to provide information on patient demographic, clinical, and hospital characteristics as well as crude outcomes according to neighborhood income status. Trends across categorical data were tested with a Mantel–Haenszel χ2 test and across continuous variables by ANOVA. Multinomial logistic regression models were thereafter fitted to examine the association between neighborhood income and in-hospital mortality or discharge to institutional postacute care, with routine discharge as base outcome. Where a significant SES-outcome association was present, sequential adjustment for demographic, clinical, and hospital factors was performed to assess whether these factors had a mediation effect on the association. In addition, we performed tests of overall interaction effects of neighborhood income and sex, race, or insurance status. Plots of predicted probabilities were obtained to visually examine how any observed income–mortality association changes with age. Given the single-stage stratified cluster sampling design of the NIS, we applied discharge and hospital weights provided for variance estimation to obtain robust confidence intervals, using Stata svyset suite of commands. We further performed sensitivity analysis to examine the impact of imputing data. We compared results obtained after imputation with results of complete case analysis and results obtained after repeating the analysis with race excluded. Results were comparable in all cases (see the online-only Data Supplement). The level of statistical significance for hypothesis testing was set at P≤0.05. All data analyses were performed using Stata (version 12.1; Stata Corporation, Texas).
The study cohort consisted of 31 631 US patients and 16 531 Canadian patients. Baseline characteristics according to neighborhood income levels are shown in Table 1 for US patients and in Table 2 for Canadian patients. Average age of patients in both countries was similar, at 58 years. Average age or sex did not differ by neighborhood income status in the United States or Canada. US patients living in the lowest income neighborhoods were less likely than those in wealthy neighborhoods to be white (52% versus 70%) or Asian/Pacific Islanders (1.95% versus 9.0%) and more likely to be black (25% versus 7%) or Hispanics (17% versus 9%), P<0.001. Low-income patients were more likely to be seen in hospitals in the south region (54% versus 24%; P<0.001), to be on Medicaid or opt for self-pay, or have greater comorbid burden. US patients were more likely to receive urgent/emergency admission than Canadian patients (91% versus 83%) and presented with greater comorbidity compared with their Canadian counterparts (40% versus 25%). However, crude mortality rate among US patients (22%) was similar to that of Canadian patients (21%). The percentage of patients who experienced routine discharge was higher in Canada (55%) compared with the United States (36%) where the proportion differed by SES (lowest income neighborhood, 35% versus highest income neighborhood, 38%).
Multivariable analysis of US data revealed a significant income–mortality association (P=0.001) with patients with SAH in the highest neighborhood income quartile at significantly lower risk of mortality than patients in the lowest income quartile (Table 3). This income–mortality association was not attenuated by sequential adjustment for demographic (model 2), clinical (model 3), and hospital (model 4) covariates. Canadian patients in the highest income quintile experienced a marginal reduction in the risk of in-hospital mortality compared with those in the lowest income quintile (Table 4). The risk reduction, however, did not reach statistically significant levels (P=0.51).
Use of institutional postacute care did not differ significantly by income status in the United States or Canada (Tables 3 and 4). Among US patients, the effects of income status on inpatient mortality or discharge to postacute institutional care varied with insurance status (interaction; P=0.001 for both outcomes) but not with sex (mortality, P=0.401; institutional outcome, P=0.414) or race (mortality, P=0.464; institutional outcome, P=0.184). Plots of predicted probabilities indicated that the income–mortality association was present across the age spectrum, and that it increased with age (Figure). A distinction was observed between patients with SAH of the poorest and wealthiest neighborhoods in the chances of routine discharge. The effect of neighborhood income status on use of institutional postacute care was dependent on age (see the online-only Data Supplement).
Using nationally representative data sets with comparable variables and covering recent similar time periods, we examined the association between SES and inpatient mortality after SAH in the United States and Canada. SES was significantly associated with inpatient mortality in the United States with patients with SAH living in wealthy neighborhoods experiencing a modest reduction in risk of mortality compared with patients living in low-income neighborhoods. The magnitude of the income–mortality association was not influenced by nonmodifiable risk factors of age, sex, race, and comorbidity or hospital status. Unlike in the United States, no significant effect was demonstrated in Canada.
The results agree somewhat with those of previous studies in SAH that reported higher mortality rates among lower SES groups compared with higher SES groups.5,6 The Finnish contribution to the World Health Organization Multinational Monitoring of trends and determinants of Cardiovascular disease (FINMONICA) study used personal income as a proxy for SES and investigated 956 SAH cases for socioeconomic differences in case fatality at 7 days, 28 days, and 1 year postadmission across age groups and sex.6 The study reported significant income–mortality association in only young adult males of ages 25 to 44 years. The present study indicated that income–mortality association tends to widen with increasing age in the United States. The FINMONICA study might have been underpowered to detect significant associations at older age. A study of annual SAH mortality across ethnic/racial groups by household income in Los Angeles5 found an inverse income–mortality association among minority populations only. Our more inclusive analysis did not indicate that ethnicity/race significantly alters the effect of SES on inpatient mortality. Much of the income–mortality gradient seen among US patients was not explained by demographic, comorbid, and hospital factors, which suggests that these factors are not primary mediators in the link between SES and SAH mortality. However, our analyses showed that among US patients the effect of SES cannot be interpreted independent of patient insurance status. Insurance status has been shown to significantly impact postoperative outcomes in neurosurgical patients in the United States with worse outcomes more likely to occur among patients who are inadequately insured.14 Better insured patients are more likely to live in wealthy neighborhoods and have better access to timely, high quality specialized care,15 which has been shown to be critical to improved outcomes after SAH.16
The present study provides some evidence in support of the concept that access to care inclusive of prevention and management of comorbid conditions for lower SES groups is relatively better in a public healthcare system than in a private healthcare system.17,18 Patients with SAH in the United States presented with greater comorbid burden compared with Canadian patients. The effect of SES was more pronounced in the United States than in Canada; a finding consistent with previous comparative studies that demonstrated stronger link between SES and overall population health19,20 and disease-specific outcomes21 in the United States compared with Canada. It is possible that Canada’s more inclusive publicly funded health insurance coverage facilitated a relatively better access to treatment for comorbid conditions and improved chances of better outcomes after SAH among lower SES groups.
It is also possible that our analysis potentially underestimated the effect of SES in Canada. When median household income is used as SES indicator, the choice of census geographic unit and number of subgroups has been identified as potential confounders of the strength of income–mortality associations.22 Furthermore, because this community-level SES indicator was computed relative to a fixed time point in the Canadian data set, it might have inadequately accounted for widening income disparities with time, hence less reflective of socioeconomic inequalities in Canada.
That the use of institutional postacute care was not influenced by SES among SAH survivors in the United States or Canada is not unexpected, considering the need for institutional care could be influenced by multiple factors, including the severity of postacute residual disability, availability of rehabilitation services, differences in referral patterns and sociocultural behaviors related to family preferences and support. In contrast, some studies in ischemic stroke have reported differing patterns of discharge to and use of institutional postacute care by insurance status or personal income in the United States23,24 or by neighborhood income status in Canada.11
This study has multiple potential limitations. It is cross-sectional and focused on the inpatient course; hence, the results are not generalizable to patients with SAH who did not survive to hospital admission or who died after discharge from acute care. Inpatient mortality and discharge disposition are rather crude outcome measures, although validated. However, it is plausible that more nuanced outcomes would have demonstrated stronger effect of SES in the United States than in Canada.19 The limitations of using large administrative data sets for health services research have been highlighted elsewhere.25,26 Of particular relevance is our inability to adjust for case severity. Although low SES groups have been shown to present with more severe stroke in comparison with higher SES groups,27 there is inconsistent evidence in support of a causal effect of stroke severity on income–mortality associations after ischemic stroke.28,29 The effects of SES on SAH severity, if any, and on mortality could be causally unrelated. Despite the use of multiple imputation and sensitivity analysis, we cannot rule out the remote possibility that 20% of missing race/ethnicity might have influenced our results. Another limitation relates to the known disadvantages of using ecological measures of SES.30 Finally, subtle differences in the way variables were defined and categorized between the 2 databases, and adjusted for in the analysis requires that the results be viewed as indication of overall trends rather than an estimation of the magnitude of SES–mortality associations after SAH in both countries.
In conclusion, the findings and limitations of the present study indicate the need for further research to understand what motivating or mitigating factors relate to the observed effect of SES on SAH. Research should focus on exploring the role of systematic differences in access to care, provision of the processes of care, and classic behavioral vascular risk factors, using more robust study designs, preferably prospective population-based studies, and outcome measures. Future research may, in addition to capturing the effect of contextual factors, interrogate the influence of individual level measures of SES, such as personal income, education, among others.
We acknowledge the Canadian Institute for Health Information for providing free access to the DAD data set. We are grateful to Prof Muhammed Mamdani, Department of Health Policy, Management, and Evaluation, University of Toronto for his editorial inputs.
R.L. Macdonald receives grant support from the Physicians Services Incorporated Foundation, Brain Aneurysm Foundation, Canadian Stroke Network and the Heart and Stroke Foundation of Ontario. R.L. Macdonald is a consultant for Actelion Pharmaceuticals and Chief Scientific Officer of Edge Therapeutics, Inc. The other authors have no conflicts to report.
The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.113.001368/-/DC1.
- Received March 11, 2013.
- Accepted June 14, 2013.
- © 2013 American Heart Association, Inc.
- Addo J,
- Ayerbe L,
- Mohan KM,
- Crichton S,
- Sheldenkar A,
- Chen R,
- et al
- Tirschwell DL,
- Longstreth WT Jr
- Kokotailo RA,
- Hill MD
- 9.↵Claritas. Demographic Update Methodology. http://www.gc.maricopa.edu/crs/ServiceArea/SiteReports%20(Claritas)/2004_execsummary.pdf. Accessed January 16, 2013.
- 10.↵Canadian Institute for Health Information. Reducing Gaps in Health: A Focus on Socioeconomic Status in Urban Canada. Ottawa, Canada: Canadian Institute for Health Information; 2008.
- Kapral MK,
- Wang H,
- Mamdani M,
- Tu JV
- Saposnik G,
- Jeerakathil T,
- Selchen D,
- Baibergenova A,
- Hachinski V,
- Kapral MK
- Gorey KM
- Reinier K,
- Thomas E,
- Andrusiek DL,
- Aufderheide TP,
- Brooks SC,
- Callaway CW,
- et al
- Chan L,
- Wang H,
- Terdiman J,
- Hoffman J,
- Ciol MA,
- Lattimore BF,
- et al
- Woodworth GF,
- Baird CJ,
- Garces-Ambrossi G,
- Tonascia J,
- Tamargo RJ
- Kleindorfer D,
- Lindsell C,
- Alwell KA,
- Moomaw CJ,
- Woo D,
- Flaherty ML,
- et al
- Arrich J,
- Lalouschek W,
- Müllner M