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Logo of neurologyNeurologyAmerican Academy of Neurology
Neurology. 2012 September 18; 79(12): 1200–1207.
PMCID: PMC3440450

Neighborhood income and stroke care and outcomes

Moira K. Kapral, MD, MSc, FRCPC,corresponding author Jiming Fang, PhD, Crystal Chan, MD, BSc, David A. Alter, MD, PhD, FRCPC, Susan E. Bronskill, PhD, Michael D. Hill, MD, MSc, FRCPC, Douglas G. Manuel, MD, MSc, Jack V. Tu, MD, PhD, FRCPC, and Geoffrey M. Anderson, MD, PhD



To evaluate factors that may contribute to the increased stroke case fatality rates observed in individuals from low-income areas.


We conducted a cohort study on a population-based sample of all patients with stroke or TIA seen at 153 acute care hospitals in the province of Ontario, Canada, between April 1, 2002, and March 31, 2003, and April 1, 2004, and March 31, 2005. Socioeconomic status measured as income quintiles was imputed from median neighborhood income. In the study sample of 7,816 patients we determined 1-year mortality by grouped income quintile and used multivariable analyses to assess whether differences in survival were explained by cardiovascular risk factors, stroke severity, stroke management, or other prognostic factors.


There was no significant gradient across income groups for stroke severity or stroke management. However, 1-year mortality rates were higher in those from the lowest income group compared to those from the highest income group, even after adjustment for age, sex, stroke type and severity, comorbid conditions, hospital and physician characteristics, and processes of care (adjusted hazard ratio for low- vs high-income groups, 1.18; 95 confidence interval 1.03 to 1.29).


In Ontario, 1-year survival rates after an index stroke are higher for those from the richest compared to the least wealthy areas, and this is only partly explained by age, sex, comorbid conditions, and other baseline risk factors.

Many studies have demonstrated that socioeconomic status is an important predictor of overall health, disease-specific mortality, and health care utilization.14 These findings have been observed in multiple jurisdictions, and in countries with and without publicly funded universal health care. Differences in health outcomes, including stroke incidence, mortality, and case-fatality rates, appear to be driven not only by individual-level socioeconomic status, but also by community and neighborhood socioeconomic profiles.422

Theoretical explanations for the association between socioeconomic status and disease-specific case fatality rates include differences in disease severity, differential access to or use of effective treatments, differences in response to therapy, differences in baseline risk, differences in health behaviors such as exercise and diet, or differences in community resources.2325 However, the existing literature provides little information on which, if any, of these factors are most responsible for the observed effect of socioeconomic status on mortality after stroke. Identification of the relevant factors is a necessary first step for the development of interventions to address socioeconomic-related disparities in stroke outcomes.

In this study we use data from a clinical database, the Registry of the Canadian Stroke Network, to determine whether socioeconomic status, defined as neighborhood income group, is associated with differences in baseline risk factors, stroke type, stroke severity, or hospital-based processes of stroke care; and to what extent these differences in baseline factors and processes of care contribute to differences in survival across socioeconomic groups.


Data sources and patient sample.

The Registry of the Canadian Stroke Network (RCSN) performs a periodic audit of stroke care delivery on a random sample of all patients with stroke or TIA seen in the emergency department or admitted to any acute care institution in the province of Ontario, Canada.26 Stroke/TIA separations are identified from the discharge abstract database (DAD) and the National Ambulatory Care Reporting System (NACRS) databases maintained by the Canadian Institute for Health Information (CIHI), using International Classification of Diseases (ICD)–10 codes I60, I61, I63, I64, and G45 (excluding G45.4) in the primary diagnosis position. By law in Ontario, all hospitals and emergency department separations are included in the CIHI database, so the sampling frame is population-based. From all eligible cases, a simple random sample of approximately 15% is audited, through retrospective chart abstraction by centrally trained neurology research nurses. Data are collected on all aspects of acute stroke management, including patient demographics, the use of prehospital emergency medical services, stroke type and presentation, in-hospital stroke care (investigations, consultations, medications, other interventions), discharge disposition, length of stay, and in-hospital mortality. The aggregate anonymized database is managed at the coordinating center for the RCSN at the Institute for Clinical Evaluative Sciences (ICES) in Ontario, Canada. Chart validation by duplicate chart abstraction has shown excellent agreement (κ score or intraclass correlation coefficient of greater than 0.9) for key variables including age, sex, and use of thrombolysis.26 The data collection software forces chart abstraction personnel to perform complete data entry before the case record can be submitted for inclusion in the database, ensuring that there are no missing data.

We used data from patients seen between April 1, 2002, and March 31, 2003, and between April 1, 2004, and March 31, 2005. The audits captured both incident and recurrent stroke but, for patients with more than 1 stroke event during the study period, only the first event was included in the present analyses. Patients with a final diagnosis of nonstroke or subarachnoid hemorrhage were excluded, as were those with strokes that occurred as an in-hospital complication, those with invalid postal codes, and those where income quintile could not be determined. Using unique encrypted patient identifiers, we linked the RCSN database to the Ontario Registered Persons Database to obtain information on 30-day and 1-year mortality after stroke, regardless of place of death.

Assessment of socioeconomic status.

We aimed to explore the relationship between community rather than individual-level socioeconomic status on stroke mortality. Therefore, we used the 2001 and 2006 Canada Census database to impute socioeconomic status using aggregate level measures of income for audit data from 2002–2003 and 2004–2005, respectively. Within the Census data, we calculated the median income in each dissemination area (which contains 400 to 700 persons), and divided neighborhoods into income quintiles, with quintiles 1 and 5 having the lowest and highest median incomes, respectively.2 The dissemination area is the smallest geographical unit used for reporting census data, and has been shown to provide a better estimate of individual-level socioeconomic status than larger reporting areas.27 Each patient from the RCSN was linked to the dissemination area of his or her principal residence using the Statistics Canada Postal Code Conversion File. Following initial analyses using all 5 income quintiles, in order to simplify presentation of the results, the 2 lowest income quintiles were combined to create a “low-income” group, quintiles 3 and 4 were combined to create a “middle-income” group, and quintile 5 was designated as the “high-income” group.

Severity of illness and comorbid illness.

Stroke severity was recorded in the RCSN database using the Canadian Neurological Scale, which measures level of consciousness, orientation, language, and motor function, and ranges from 0 (most severe deficit) to 11.5 (no deficit), and has been shown to be valid and reliable even when done by retrospective chart abstraction.28 Many individual comorbid conditions and stroke risk factors were recorded in the database, however, certain risk factors, such as obesity, diet, exercise, and quantification of alcohol consumption, were not available. Comorbid illness was summarized according to the Charlson-Deyo comorbidity index score, which is a weighted summary score based on the presence or absence of 17 medical conditions, where a score of zero indicates that no comorbid illness is present and higher scores indicate a greater burden of comorbidity.29

Statistical analysis.

Baseline patient characteristics and use of stroke care interventions were compared across income groups using χ2 tests for categorical variables and analysis of variance and Kruskal-Wallis tests for continuous variables. Kaplan-Meier curves were created for survival after stroke by income group. Cox proportional hazards models were developed to determine the relationship of neighborhood income to 30-day and 1-year mortality, with adjustment for multiple prognostic factors, and with the low- and medium-income groups compared to the high-income group (reference category). Covariates were sequentially modeled to examine changes in the income-mortality gradient after adjusting for 1) demographic factors (age and sex); 2) comorbid conditions (diabetes, hypertension, atrial fibrillation, smoking, and previous myocardial infarction); 3) stroke severity (Canadian Neurological Scale score categorized as greater than or equal to 8, 5 to 7, or less than or equal to 4); 4) stroke type (ischemic, intracerebral hemorrhage, undetermined) or TIA; 5) processes of care, including type of hospital (teaching, large community, small community), most responsible physician (neurologist vs other), and care on an acute stroke unit; and 6) rural vs urban patient residence. Income group was treated as a categorical variable (“low,” “middle,” or “high” income). The assumptions of the proportional hazards model were verified using the no time-varying effect. The protective effect of neighborhood income on mortality was calculated using the formula [(adjusted β − unadjusted β)/unadjusted β × 100%], where adjusted β refers to the β coefficient obtained from multivariable Cox proportional hazards modeling, and unadjusted β refers to the β coefficient obtained from univariate Cox proportional hazards modeling.30 Bootstrapping methods (1,000 bootstrap samples) were used to estimate 95% confidence intervals around the relative changes in the protective effect of income on mortality rates. SAS version 9.2 was used for all analyses.

Standard protocol approvals, registrations, and patient consents.

The RCSN is “prescribed” under Ontario's Personal Health Information Protection Act, and patient data are collected without consent for the purpose of facilitating the provision of stroke care in the province of Ontario. The overall project is approved by the Research Ethics Board of Sunnybrook Health Sciences Centre.


The 2002–2003 and 2004–2005 RCSN Ontario Stroke Audits included 8,463 patients with stroke or TIA. After exclusions (241 with nonstroke, 263 with subarachnoid hemorrhage, 96 with in-hospital strokes, 7 with invalid postal codes, and 40 with missing income data), 7,816 were retained for the current analyses. Overall, 51% were women and the mean age was 73 years (table 1). Higher income was associated with being male, married, English or French-speaking, nonsmoking, and having a lower prevalence of diabetes mellitus (table 1). Stroke type was similar across income groups, except that those in the low-income group were less likely than those in the higher income groups to present with TIA (table 2). There were no significant differences in stroke severity by income groups, based on either mean Canadian Neurological Scale scores or the proportion of patients with Canadian Neurological Scale scores greater than or equal to 8 (table 2). Income was not associated with rates of use of ambulance services, and, among those arriving by ambulance, transport times were similar across income groups. Despite this, higher socioeconomic status was associated with a shorter duration between stroke onset and hospital arrival (table 2).

Table 1
Baseline characteristics of patients by income group
Table 2
Stroke presentation, management, and outcomes by income quintile

Patients in the high-income group were more likely than those in the lower income groups to be seen at academic institutions (21%, 18%, and 16% for the high-, middle-, and low-income groups, respectively; p < 0.001) and to be cared for by a neurologist during hospital admission (15%, 14%, and 10% for the high-, middle-, and low-income groups, respectively; p < 0.001) (table 2). There was no difference based on income group in the use of neuroimaging, admission to a stroke unit, thrombolysis, antithrombotic therapy for ischemic stroke, or warfarin for ischemic stroke with atrial fibrillation (table 2).

The overall crude 30-day and 1-year stroke mortality rates were 11% and 21%, respectively (table 2). There was no income-mortality gradient in survival at 30 days, however, at 1 year there was a survival advantage for those in the high-income group compared to the 2 lower income groups (figure). The difference in survival at 1 year persisted in the low- compared to the high-income group, even after adjustment for age, sex, comorbid conditions, and stroke type and severity (adjusted hazard ratio [HR] 1.18; 95% confidence interval [CI] 1.03 to 1.36), but was no longer significant for the middle- compared to the high-income group (adjusted HR 1.11; 95% CI 0.96 to 1.29) (table 3).

Kaplan-Meier survival curves by income group
Table 3
Multivariate analysis of predictors of 1-year mortality after stroke

Table 4shows the effect of sequential risk adjustment. Age, sex, and comorbid conditions accounted for only 0.7% (95% CI 0.0% to 27.6%) of the difference in mortality rates after stroke between the low- and high-income groups, while stroke severity accounted for an additional 3.5% (95% CI 0.0% to 36.9%), stroke type for 2.9% (95% CI 0.0% to 42.8%), processes of care and hospital and physician characteristics for 5.6% (95% CI 0.0% to 28.4%), and rural residence for 1.9% (95% CI, 0.0% to 15.0%). Collectively, all factors under consideration explained only 13.9% (95% CI 0.0% to 78.3%) of the disparity in stroke mortality between the low- and high-income groups.

Table 4
Effect of sequential risk adjustment on income-mortality gradients


We found that individuals from low-income neighborhoods were less likely to present with TIA, had longer times from stroke onset to hospital arrival, and had slightly lower rates of neurologist care compared to those from higher income neighborhoods. However, all other measured aspects of stroke care delivery were similar across income groups. At 1 year after stroke there was a persistent survival advantage for those in the highest compared to the lowest income areas, even after adjustment for age, sex, cardiovascular risk factors, stroke type and severity, in-hospital processes of care, and hospital and physician characteristics. These identifiable risk factors only accounted for a small proportion (13.9%) of the observed disparity in survival between the low- and high-income groups.

An important finding is that mortality at 30 days was similar among socioeconomic groups, and mortality differences were not observed until after the acute care period. This may be due to lower event rates at 30 days with consequent reduced power to observe smaller differences, or due to the fact that stroke severity—rather than processes of care or baseline risk factors—is the principal determinant of early stroke mortality.31 However, this observation also suggests that stroke aftercare may be an important determinant of late mortality after stroke, and is consistent with the structure of our health care system where, in a broad sense, acute care is similar for all.

The finding of lower stroke case fatality rates with higher socioeconomic status has been previously documented, even in Canada and in other countries with universal access to health care.6, 9, 13, 15 Our results suggest that better outcomes among those from high-income areas are not due to marked differences in acute stroke care delivery, and are only partially explained by baseline risk factors and stroke characteristics. This contrasts with findings from a Canadian study of patients with acute myocardial infarction, where age and vascular risk factors appeared to account for the majority of the income-mortality gradient.30 We note that within the CIs, a larger effect of baseline risk factors is possible, and risk factor medication should therefore be a target for interventions designed to improve outcomes in low socioeconomic groups. It is also likely that unmeasured risk factors, including important stroke risk factors such as blood pressure control, physical activity, and waist-to-hip ratios, could account for some of the difference in survival between those from low- and high-income areas, as could differences in adherence to medications and access to follow-up care.32 Of note, in our cohort, higher socioeconomic status was associated with a clustering of risk factors likely to be associated with better outcomes, including low smoking levels and less diabetes, as well as a clustering of sociodemographic factors which could affect access and adherence to care, such as urban residence, higher education, male sex, and fluency in English or French.

Although socioeconomic status has been shown to be an important predictor of the use of certain medical services, such as angiography after myocardial infarction, previous studies have found no consistent association between socioeconomic status and stroke care delivery.2,13,3336 Given the association between lower socioeconomic status and rural residence, with the potential for limited access to larger hospitals with more stroke-specific resources, one might have anticipated lower rates of specialized interventions such as carotid imaging, neuroimaging, and stroke unit care among patients from low-income neighborhoods. However, Ontario has developed a provincial stroke system aimed at optimizing and coordinating care across the province, and although the system had not been fully implemented during the study period, this might have permitted some patients from rural areas to have been transferred for care and evaluation at regional stroke centers.37 Thus, our results may not be generalizable to jurisdictions without a coordinated stroke care strategy, or to countries without universal access to health care. Of note, the finding of a longer time from stroke onset to hospital arrival in those from lower compared to higher income areas—despite similar rates of ambulance use and similar transport times—suggests that those from lower income neighborhoods may have delays in symptom recognition or in activation of emergency medical services. Knowledge of stroke symptoms and the need for rapid assessment of transient symptoms could also explain the more frequent presentation with TIA in the higher income groups.

Our study has a number of limitations which merit comment. First, we used area-level rather than individual-level measures of socioeconomic status. This may result in nondifferential misclassification of socioeconomic status and lead to underestimates of social gradients in mortality. However, neighborhood-level income measures have the advantage of capturing aspects of neighborhoods, such as the availability of parks, schools, and hospitals, that may affect health, and have been found to provide results that are complementary to those found using individual level data.38,39 Second, we used income as our primary measure of socioeconomic status rather than wealth, occupation, social class, or other composite measures. Although most measures of socioeconomic status tend to be highly correlated and to predict mortality in a similar direction, they may not be consistent in direction and magnitude for all outcomes.14,17,40 In addition, although the socioeconomic status of both an individual and an area may fluctuate over time, the cross-sectional nature of our study meant that we were only able to capture neighborhood income at a single time point. Finally, our data were collected through hospital chart audits rather than prospective data collection or patient or provider interviews. Thus, we were unable to capture information on stroke symptom awareness and potential barriers to stroke care delivery, and we were unable to evaluate risk factor modification and treatment adherence after hospital discharge. In addition, we do not have information on potentially important explanatory variables such as obesity, social isolation, and mental illness. Despite these limitations, our database contains high-quality clinical data on a population-based patient sample, and is strengthened by its linkage to administrative databases to provide long-term follow-up outcomes.

In Ontario's universal health care system, we found higher survival rates after stroke for individuals from the richest compared to the least wealthy neighborhoods. This was not fully explained by differences in stroke type, stroke severity, or processes of stroke care delivery, and may be related in part to a lower burden of unmeasured baseline risk factors for poor health. The superior health outcomes seen for the wealthiest members of society may provide a benchmark for what could be achieved for all through targeted interventions to reduce socioeconomic disparities in health.

Supplementary Material

Accompanying Editorial:


The authors thank the hospitals, investigators, and research coordinators who participated in the Ontario Stroke Audit of the Registry of the Canadian Stroke Network, as well as the staff at the Institute for Clinical Evaluative Sciences and the Canadian Stroke Network who assisted in the coordination of the Registry.


confidence interval
Canadian Institute for Health Information
discharge abstract database
hazard ratio
International Classification of Diseases
Institute for Clinical Evaluative Sciences
National Ambulatory Care Reporting System
Registry of the Canadian Stroke Network


Editorial, page 1190.


Drs. Kapral, Chan, Alter, Bronskill, Hill, Manuel, Tu, and Anderson contributed to the study concept and design, interpretation of the data, and drafting and revisions to the manuscript. Dr. Fang contributed to the study design, performed data analyses, and revised the manuscript.

Study funding

This work was funded by an operating grant from the Canadian Stroke Network. The Registry of the Canadian Stroke Network is funded by the Canadian Stroke Network and the Ontario Ministry of Health and Long-Term Care. The Institute for Clinical Evaluative Sciences is supported by an operating grant from the Ontario Ministry of Health and Long-Term Care. Dr. Kapral is supported by a Career Investigator Award from the Heart and Stroke Foundation of Canada. Dr. Bronskill is supported by a New Investigator Award in the Area of Aging from CIHR. Dr. Hill is supported by a scholarship from the CIHR and Heart & Stroke Foundation of Alberta/NWT/Nunavut. Dr. Tu holds a Canada Research Chair in Health Services Research and a Career Investigator Award from the Heart and Stroke Foundation of Ontario. Dr. Manuel holds a Chair in Applied Public Health from CIHR. Dr. Anderson holds the Chair in Health Management Strategies in the Faculty of Medicine, University of Toronto. The results and conclusions are those of the authors, and should not be attributed to any of the sponsoring or funding agencies. The funding agencies had no role in the design or conduct of the study or the collection, management, analysis, and interpretation of the data. The manuscript was reviewed and approved by the publications committee of the Registry of the Canadian Stroke Network.


The authors report no disclosures relevant to the manuscript. Go to for full disclosures.


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