Socioeconomic Status and Mortality after Acute Myocardial Infarction

Context Many studies show worse cardiovascular outcomes among poor persons than among affluent persons, but the factors that mediate this relationship are unknown. Contribution In this prospective study of 3407 Canadians who were hospitalized for myocardial infarction, 7.1% of the patients in the high-income group died within 2 years compared with 15.3% of the patients in the low-income group. Adjustment for age, preexisting cardiovascular disease, and risk factors greatly attenuated the relationship between mortality rates and income. Adjustment for other factors had little effect. Implications A history of 1 or more cardiovascular events and worse cardiovascular risk factors may explain why poor people have worse outcomes than affluent people after myocardial infarction. The Editors For many decades (1, 2) and across multiple nations (3-6), differences in socioeconomic status have been consistently associated with variations in cardiovascular disease and mortality rates (6, 7). This wealthhealth gradient (8) is independent of the socioeconomic indicator used (9), persists even after such cardiovascular events as acute myocardial infarction (MI) (10), and has been observed in countries with publicly funded universal health care (10-13). The causes of these incomeoutcome gradients are debatable (14-17). Poorer patients are more likely to smoke or have diabetes and hypertension, all of which lead to accelerated atherosclerosis and higher subsequent mortality rates (18, 19). However, incomeoutcome gradients persist after adjustment for cardiovascular events and traditional cardiac risk factors. These residual effects of income or education have led to speculation about differences in behaviors after MI, psychosocial stressors, and variations in access to medical care (10, 20-24). Although the mechanisms whereby psychosocial factors affect cardiovascular health are still incompletely delineated, ordinary risk factors (such as cigarette use) and health service intensity are potentially modifiable among the poor and those with less education. Therefore, our study focused on delineating the extent to which the association between socioeconomic factors and increased mortality rates can be explained by traditional risk factors and variations in service use. We hypothesized that cardiovascular risk factors remain the central intermediary pathway by which socioeconomic status is linked to increased mortality rates. We tested this hypothesis by using a cohort of patients who were hospitalized after an acute MI. By evaluating medium- term all-cause mortality in this sample, we increased the likelihood that death would be the result of a vascular event and reduced the risk for confounding by other causes of death (25). We aggregated traditional risk factors with previous vascular disease to obtain a powerful proxy for cumulative atherosclerotic burden, thereby enabling us to focus on assessing the incremental prognostic effect of socioeconomic status. Methods Data Source We obtained data from the Socio-Economic and Acute Myocardial Infarction (SESAMI) study, a prospective observational study of patients who were hospitalized because of acute MI throughout Ontario, Canada (19, 24). Of these data, we included a 13-item patient-completed questionnaire that addressed risk factors for atherosclerosis and socioeconomic status. By using encrypted health card numbers, we linked survey data to administrative databases for additional clinical information. We tracked each patient's hospitalization history by using computerized abstracts that were assembled by the Canadian Institute for Health Information from 1 April 1988 to the date of the patient's admission for the index event. We ascertained the number and types of cardiac procedures performed during and following the index admission by using the institute's data and physician billing claims from the Ontario Health Insurance Plan databases (10, 24). Procedure use reported in administrative databases was compared with patient self-reports; agreement levels ranged from 74% (coronary angiography) to 98% (coronary artery bypass surgery) and were similar across socioeconomic strata (24). We calculated patient deaths by acquiring data from the Ontario Registered Persons Data Base. Study Sample The SESAMI investigators recruited English-speaking patients who were admitted through the emergency departments in 53 of 57 large-volume (defined as having 100 or more patient admissions for MI per year) Ontario hospitals between 1 December 1999 and 26 February 2003. Trained nurses identified eligible patients through chart surveillance while patients were hospitalized in coronary or intensive care units. The diagnosis of MI was confirmed if at least 2 of 3 criteria were met: presence of symptoms, abnormal electrocardiographic findings, or elevated serum levels of cardiac enzymes. Of all eligible patients, 96% had acute MI confirmed by chart audits (19). We excluded patients younger than 19 years of age or older than 101 years of age, those lacking a valid health card number issued by the province of Ontario, and those who were transferred to the recruiting hospital. To be eligible, patients were required to complete a self-administered baseline survey at study entry. This requirement rendered ineligible those dying within 24 hours, those who had very severe illness (for example, patients receiving mechanical ventilation), those who had language barriers, or those undergoing early discharge or transfer (19, 24). Among 4668 consecutive eligible patients approached for consent, 3504 agreed to participate in baseline surveys and subsequent data linkage. For purposes of this study, 97 patients could not be linked to administrative data because of invalid or inaccurately documented health card numbers; 3407 patients remained available for participation. Socioeconomic Status, Ethnicity, and Demographic Factors We assessed self-reported household annual income (from all sources) as a 7-level categorical variable ranging from less than $15000 to greater than $80000 Canadian. Self-reported educational attainment was analyzed as a 5-level categorical variable ranging from an incomplete high school education to a university degree. Income and education levels were self-reported by 92% and 98% of participants, respectively. To mitigate the confounding effect of retirement from the labor force, the cohort was stratified into persons younger than 65 years of age and those 65 years of age and older (12, 26). To ensure similar sample sizes across socioeconomic subgroups, we reaggregated income categories into a 3-level ordinal variable for each age group. For the younger age group, income categories were less than $30000, $30000 to $59999, and at least $60000 Canadian; for the older age group, the categories were less than $20000, $20000 to $39999, and at least $40000 Canadian. These cut-points corresponded to the low-income, middle-income, and high-income taxation thresholds for Canadian citizens in the labor force and have been used previously (24). In analyses of all patients combined, we kept income stratified according to age-specific relative income rankings. There was no incomesexmortality rate interaction (P= 0.89); therefore, income was not stratified by sex. Information on ethnicity was also obtained through self-report. Patients selected 1 or more categories from 12 possible ethnic and racial subgroups as defined by the Canadian Census (27). For the purposes of this study, we reaggregated ethnic and racial data a priori into 5 variables: white, black, South Asian, First Nations/aboriginal, and other (for example, East Asian and Chinese). Our statistical models did not change when we analyzed ethnoracial factors as a binary (white vs. nonwhite) or as a 5-level categorical variable. Psychosocial Factors Some have hypothesized that socioeconomic-related outcome differences may be partially mediated by variations in psychosocial factors and social support (28). Consequently, we asked respondents whether they lived alone and had someone to talk to about private feelings and personal decisions. Preexisting Cardiovascular Risk Profile Factors considered in the global cardiovascular risk profile were diabetes, hypertension, hyperlipidemia, current or former cigarette use, family history, and evidence of preexisting cardiovascular disease or previous events (for example, previous MI, angina, congestive heart failure, stroke or transient ischemic attack, or peripheral vascular disease). Smoking and family history were ascertained by patient questionnaire. We determined preexisting cardiovascular disease and major vascular risk factors by reviewing primary and secondary diagnostic fields in computerized hospital discharge abstracts from 1 April 1988 to the date of index hospitalization. To avoid confusion with diagnoses made after admission, we isolated cardiovascular conditions that were present at the time of admission by including only those fields identified as preexisting conditions. We compared coding of administrative data for preexisting cardiovascular disease and vascular risk factors with self-reported information. Agreement between self-report and administrative data ranged from 65.8% ( = 0.29) for hyperlipidemia to 94.3% ( = 0.85) for diabetes. Because details regarding each patient's noncardiovascular preexisting conditions were available only through administrative data, we used this information rather than self-reported information for our primary analysis. In a sensitivity analysis, we compared these results to those obtained with data collected through self-report. Preexisting Noncardiovascular Comorbid Conditions Noncardiovascular risk factors consisted of all comorbid diseases that were captured through primary and secondary diagnostic fields of hospital discharge abstracts (Canadian Institute for Health Information) from 1 April 1988 to the presenting hospitalization. Only preexisting condi