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Neighborhood Socioeconomic Context and Long-Term Survival After Myocardial Infarction

and for the Israel Study Group on First Acute Myocardial Infarction
Originally published 2010;121:375–383


Background— Neighborhood of residence has been suggested to affect cardiovascular risk above and beyond personal socioeconomic status (SES). However, such data are currently lacking for patients with myocardial infarction (MI). We examined all-cause and cardiac mortality according to neighborhood SES in a cohort of MI patients.

Methods and Results— Consecutive patients ≤65 years of age discharged from 8 hospitals in central Israel after incident MI in 1992 to 1993 were followed up through 2005. Individual data were obtained at study entry, including education, income, and employment. Neighborhood SES was estimated through a composite census-derived index developed by the Israel Central Bureau of Statistics. During follow-up, 326 deaths occurred in 1179 patients. Patients residing in disadvantaged neighborhoods had higher mortality rates, with 13-year survival estimates of 61%, 74%, and 82% in increasing tertiles (Ptrend<0.001). After adjustment for sociodemographic variables, traditional risk factors, MI severity indexes, and individual SES measures, the hazard ratios for death associated with neighborhood SES were 1.47 (95% confidence interval, 1.05 to 2.06) in the lower and 1.19 (95% confidence interval, 0.86 to 1.63) in the middle tertiles compared with the upper tertile (Ptrend=0.02). The respective hazard ratios were even stronger for cardiac death (1.63; 95% confidence interval, 1.09 to 2.25; and 1.41; 95% confidence interval, 0.96 to 2.07). In the final models, neighborhood context and several individual SES measures were concurrently associated with all-cause and cardiac mortality.

Conclusions— Neighborhood SES is strongly associated with long-term survival after MI. The association is partly, but not entirely, attributable to individual SES and clinical characteristics. These data support a multidimensional relationship between SES and MI outcome.

Measures of socioeconomic status (SES) have been identified as important prognostic factors after myocardial infarction (MI).1–14 Yet, because SES is a multidimensional construct comprising various indicators acting at different levels,15,16 the mechanisms by which it affects post-MI prognosis are incompletely understood. Although SES has traditionally been treated as an intrinsic characteristic of individuals, contextual (or macro) effects on health are theoretically and empirically important.17 Indeed, a growing body of evidence indicates that a person’s health may be influenced by the socioeconomic characteristics of the neighborhood in which he or she lives, over and above his or her own SES.18–21 This suggests that characteristics of communities represent more than the aggregation of their residents’ characteristics. Furthermore, as recently pointed out in a critical review of the use of SES in health research, “one size does not fit all,” suggesting that findings from studies that have measured limited aspects of SES should be interpreted with caution.15 Accordingly, a multiple jeopardy hypothesis has been invoked to describe the complex association of SES with cardiovascular disease (CVD) risk but infrequently tested.22–25 This is particularly true after MI; most previous studies were limited to a single SES indicator or a few indicators measured at a single level. Because the effect of each socioeconomic determinant can be either explained in part by or mediated through other determinants,16 current knowledge on this topic is insufficient. This population-based cohort study was undertaken to examine the long-term survival of patients with incident MI according to the SES level of their residential neighborhood after taking into account individual SES measures and other potential confounding factors.

Editorial see p 348

Clinical Perspective on p 383


Study Design

The Israel Study of First Acute Myocardial Infarction is a longitudinal prospective investigation of the role of sociodemographic, medical, and psychosocial variables measured in patients hospitalized with incident (ie, first ever) MI in long-term clinical outcome, psychosocial adjustment, and quality of life.13,26,27 Between February 15, 1992, and February 15, 1993, a total of 1626 consecutive MI patients ≤ 65 years of age were admitted to 8 Israeli medical centers. These hospitals provide care to the entire population of central Israel (2¼ million in 1992). Of these patients, 81 (5%) died during hospitalization, leaving 1545 eligible candidates for the study, among whom 1521 (98%) consented to participate.

The diagnosis of MI was established by the presence of at least 2 of the following criteria: characteristic chest pain lasting at least 20 minutes, creatine kinase elevation ≥1.5 times the upper limit of normal or creatine kinase-MB fraction >5% when simultaneous reference creatine kinase levels exceeded the upper limit of normal, and ECG changes compatible with Q-wave or non–Q-wave MI. Subjects with prior infarction were excluded.

Data at study entry and during follow-up were obtained through interviews, psychosocial questionnaires, and review of the entire medical record.26,27 The baseline interview was based on a detailed, structured questionnaire prepared by the principal investigator (Y.D.), which was previously examined in a pilot study. All clinical data were verified by a senior cardiologist.

Of the participants in the parent study, 1410 were assigned a census-derived SES score based on their geocoded address at time of enrollment. The remaining subjects could not be linked, mainly because they resided in small villages where such data are unavailable. Patients in these smaller villages were similar to others with regard to measures of personal SES collected from the baseline interview (eg, education, employment, and income). An addition 231 patients were excluded from analysis because of missing information on personal SES indicators (mainly income), leaving 1179 participants with complete data for this study. Nonparticipants did not differ from participants in mean age (54.0 versus 53.7 years; P=0.52), proportion of women (18% versus 19%; P=0.76), average education (11.0 versus 11.0 years; P=0.83), or cumulative survival (71% versus 73% at 13 years; P=0.55). All aspects of the study were approved by the appropriate institutional ethics committees.

Socioeconomic Measures

Individual SES data were self-reported at the index hospitalization and included the following variables: family income relative to the national average (categorized as below average, average, and above average), education (years of schooling), pre-MI employment (classified as full time, part time, and none), and living with a steady partner. The last variable was considered a socioeconomic measure on the basis of the Hollingshead 4-factor SES index.28

Neighborhood SES was estimated through a composite index developed and validated by the Israel Central Bureau of Statistics.29 The index, which is based on the 1995 National Census data, is a summary of numerous socioeconomic measures (ie, demographic, schooling and education, standard of living, employment and unemployment, and social benefits; see the online-only Data Supplement) that allows the classification of small geographical units (≈2000 inhabitants) into SES classes using a 20-point scale. Geographic Information System tools were used to assign a SES score to each patient on the basis of his/her geocoded address at the time of the index MI. For ease of presentation and interpretation, approximate tertiles of the score distribution (Figure 1) were used in the analysis (≤10, 11 to 14, and ≥15 points, including 362, 446, and 371 patients, respectively).

Figure 1. Distribution of neighborhood SES scores among 1179 study participants. The score is based on a validated composite index developed by the Israel Central Bureau of Statistics.

Neighborhood Characteristics

The study included patients residing in 512 distinct statistical units (“neighborhoods”). The neighborhoods are located within an area of ≈400 sq miles and are predominantly urban. Medical facilities (hospitals, clinics, first-aid stations) are well distributed in this area of central Israel and serve the entire population according to the National Medical Insurance Law. At any location, the distance to the nearest hospital does not exceed 7 miles. However, the availability of intensive care unit (ICU) and cardiac rehabilitation facilities is heterogeneous. For example, in 1992, the number of ICU beds in the participating hospitals ranged from 5 to 9.

Clinical Variables

The entire inpatient and outpatient medical records and data obtained through structured detailed interviews were used to ascertain CVD risk factors, MI characteristics, disease severity indexes, and acute management. Measurements recorded at the index hospitalization or at the closest time before or after the index date were considered. Obesity was defined as body mass index ≥30 kg/m2. Diabetes, hypertension, and dyslipidemia were defined clinically. Smoking was classified into (pre-MI) current or noncurrent smoking. Leisure-time physical activity was self-reported and dichotomized into active (ie, regular physical activity) or nonactive (irregular or no physical activity) in the year preceding the index MI. Comorbidity was assessed by the Charlson Index30 and analyzed categorically (no comorbidity for 0 points, moderate comorbidity for 1 to 2 points, and severe comorbidity for ≥3 points). MI characteristics and severity indexes included infarct type and location, Killip class, admission to ICU, and in-hospital complications. Reperfusion therapy and revascularization included thrombolysis, percutaneous transluminal coronary angioplasty, and coronary artery bypass grafting. Revascularization referred to early procedures performed within 45 days of the index date. Self-rated health, a 5-point scale measure, was used to assess general health.27 Patients were asked about their health in the year before the index hospitalization. Poor self-rated health was defined as a score of 1 or 2.

Outcome Measures

Follow-up began at the date of the index hospitalization and lasted through December 31, 2005 (median, 13.2 years; interquartile range, 12.0 to 13.5 years). Death (ie, date, location, and cause) was ascertained by a senior cardiologist (Y.D.) through various sources, including data from the Israeli Population Registry, death certificates, hospital charts, family physicians, and family members. The Israeli Population Registry has complete ascertainment of death.31 The primary study end points were all-cause and cardiac mortality. The latter was defined as death caused by coronary heart disease or other diseases of the heart.32

Statistical Analyses

Analyses were performed with SAS statistical software version 9.1 (SAS Institute Inc, Cary, NC) and SPSS version 15.0 (SPSS Inc, Chicago, Ill). Baseline characteristics across neighborhood SES tertiles were compared by the χ2 test for categorical variables and ANOVA for continuous variables.

Survival across neighborhood SES tertiles was assessed by the Kaplan-Meier method with right censoring at the time of last follow-up and compared by the log-rank test. Cox proportional-hazards models were constructed to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality and cardiac mortality associated with neighborhood SES. Because individuals may be more alike with respect to unmeasured characteristics within a given neighborhood than they are between neighborhoods, random-effects models were used, accounting for potential intraneighborhood correlation.33,34 This was done with a SAS macro.35 The proportional-hazards assumption was tested with the Schoenfeld residuals, with no violations detected. Initial adjustment was made for age, sex, and origin. Subsequently, traditional risk factors, MI characteristics, disease severity indexes, and acute management were controlled for. Finally, personal socioeconomic measures were added. There were no missing values in the models evaluated. Model discrimination was assessed by the c statistic, which represents the area under the receiver-operating characteristic curve. Calculating the c statistic from Cox proportional-hazards models was performed through a SAS macro written by W.K. Kremers.36 All P values were 2 tailed.


The analysis included 1179 incident MI patients with a mean age of 54 years (SD, 8 years); 19% were women. The percentage of patients with <12 years of education was 51%, whereas 47% reported a below-average income and 24% were unemployed before MI. As expected, CVD risk factors were highly prevalent at baseline, particularly smoking (51%), hypertension (38%), and physical inactivity (72%). Fewer than half (43%) of the patients received thrombolytic therapy on hospital admission, and 24% underwent revascularization within 45 days of hospitalization. Patient characteristics across neighborhood SES tertiles are presented in Table 1. On average, patients residing in lower-SES neighborhoods were less educated and more likely to be of Middle Eastern/North African origin, to live alone, and to report a below-average income and unemployment. They also had higher prevalence of diabetes and smoking, were less frequently engaged in physical activity, and had more severe disease, as indicated by higher Killip class, more comorbidities, and poorer self-rated health compared with patients living in higher-SES neighborhoods.

Table 1. Baseline Characteristics Across Neighborhood SES Categories

CharacteristicNeighborhood SES TertilesP
Lower (n=362), %Middle (n=446), %Upper (n=371), %
CABG indicates coronary artery bypass graft; PTCA, percutaneous transluminal coronary angioplasty.
    Age, mean±SD, y54.2±8.053.0±8.654.1±7.90.09
    Middle Eastern/North African origin463616<0.001
    Education, mean±SD, y9.1±4.810.9±3.912.9±3.6<0.001
    Relative income<0.001
        Above average82542
        Below average694726
    Pre-MI employment<0.001
        Full time567277
        Part time777
    Living with a steady partner8388880.05
Cardiovascular risk factors
    Diabetes mellitus332317<0.001
    Leisure-time physical activity212934<0.001
MI characteristics, comorbidity, and acute management
    Q-wave MI2523260.60
    Anterior MI5257600.06
    CABG w/in 45 d4680.09
    PTCA w/in 45 d141327<0.001
    Charlson comorbidity index0.008
        0 points536166
        1–2 points433431
        ≥3 points454
    Killip class >12719200.01
    Post-MI complications2425200.19
    Poor self-rated health2420140.002

Neighborhood Socioeconomic Context and Outcomes

Over a total of 13 535 person-years of follow-up, 326 patients died; 233 of these deaths (71%) were cardiac deaths. Overall survival differed substantially by SES category, with a dose-response pattern of a relationship between neighborhood SES and long-term survival after MI (Figure 2). The 13-year survival estimates in the lower through upper neighborhood SES tertiles were 61%, 74%, and 82%, respectively (P for trend <0.001). In random-effects Cox analyses accounting for possible spatial autocorrelation, the HRs for death in the lower versus upper SES tertiles were 2.37 (95% CI, 1.75 to 3.20) after adjustment for age, sex, and origin; 1.75 (95% CI, 1.28 to 2.40) after the addition of classic risk factors, MI characteristics, disease severity indexes, and acute management; and 1.47 (95% CI, 1.05 to 2.06) after further adjustment for individual SES measures (Table 2). An even stronger association was noted for cardiac death, with respective estimates of 2.91 (95% CI, 2.02 to 4.21), 2.09 (95% CI, 1.42 to 3.05), and 1.63 (95% CI, 1.09 to 2.45). Tests for linear trend revealed statistically significant decreases in all-cause (χ2 change, 5.51; P=0.02) and cardiac mortality (χ2 change, 5.45; P=0.02) with increasing neighborhood SES tertiles in the final models. The c statistics for the final models were 0.75 for all-cause mortality and 0.76 for cardiac mortality, which indicate a fair to good discriminatory ability.

Figure 2. Kaplan-Meier survival curves for neighborhood socioeconomic tertiles (based on 326 deaths occurring in 1179 incident MI patients).

Table 2. HRs of All-Cause and Cardiac Mortality Associated With Neighborhood SES Categories

AdjustmentNeighborhood SES, HR (95% CI)P for Trend
First (Lower) Tertile (n=362)Second Tertile (n=446)Third Tertile (n=371)
Model 1: adjusted for age, sex, origin, CVD risk factors (hypertension, diabetes mellitus, dyslipidemia, smoking, and physical activity), and disease severity indexes (admission to ICU, anterior MI, comorbidity index, Killip class, coronary artery bypass graft within 45 days, percutaneous coronary angioplasty within 45 days, and self-rated health). Model 2: model 1 plus individual-level socioeconomic measures (education, income, pre-MI employment, and living with a steady partner).
All-cause mortality
    Deaths, n14111570
    Unadjusted2.41 (1.80–3.23)1.40 (1.04–1.90)1 (Reference)<0.001
    Age, sex, and origin2.37 (1.75–3.20)1.45 (1.07–1.98)1 (Reference)<0.001
    Model 11.75 (1.28–2.40)1.28 (0.93–1.75)1 (Reference)<0.001
    Model 21.47 (1.05–2.06)1.19 (0.86–1.63)1 (Reference)0.02
Cardiac mortality
    Deaths, n1068542
    Unadjusted2.98 (2.09–4.26)1.78 (1.23–2.58)1 (Reference)<0.001
    Age, sex, and origin2.91 (2.02–4.21)1.81 (1.25–2.63)1 (Reference)<0.001
    Model 12.09 (1.42–3.05)1.56 (1.07–2.28)1 (Reference)<0.001
    Model 21.63 (1.09–2.45)1.41 (0.96–2.07)1 (Reference)0.02

Multiple Socioeconomic Measures and MI Prognosis

The multivariable-adjusted associations of various SES determinants with all-cause and cardiac mortality are shown in Table 3. Education, employment, income, living with a steady partner, and neighborhood SES were all strongly associated with both end points (model 1). Furthermore, the association of most of these measures persisted even on simultaneous adjustment for all other SES indicators (model 2). These data demonstrate the multidimensional importance of SES, and neighborhood context as an integral part of it, in long-term survival after MI.

Table 3. Multivariable-Adjusted Associations With All-Cause Mortality and Cardiac Mortality for Various Socioeconomic Measures

SES CharacteristicHR (95% CI)
All-Cause Death (n=326)Cardiac Death (n=233)
Model 1Model 2Model 1Model 2
Model 1: adjusted for age, sex, origin, hypertension, diabetes, dyslipidemia, smoking, physical activity, ICU, anterior MI, comorbidity index, Killip class, coronary artery bypass graft within 45 days, percutaneous coronary angioplasty within 45 days, and self-rated health. Model 2: model 1 plus all other SES measures shown in the table.
Education, 3 y0.85 (0.78–0.92)0.91 (0.84–0.99)0.79 (0.72–0.87)0.86 (0.78–0.96)
Relative income
    Below average1.59 (1.14–2.20)1.07 (0.75–1.54)1.84 (1.23–2.75)1.15 (0.74–1.78)
    Average0.98 (0.67–1.42)0.87 (0.59–1.27)1.14 (0.72–1.79)0.96 (0.60–1.53)
    Above average1 (Reference)1 (Reference)1 (Reference)1 (Reference)
Pre-MI employment
    Unemployment2.04 (1.56–2.68)1.74 (1.31–2.31)2.08 (1.51–2.87)1.71 (1.22–2.39)
    Part time1.51 (0.98–2.32)1.35 (0.87–2.09)1.48 (0.87–2.50)1.33 (0.78–2.26)
    Full time1 (Reference)1 (Reference)1 (Reference)1 (Reference)
Living with a partner0.59 (0.44–0.79)0.66 (0.49–0.88)0.61 (0.44–0.87)0.70 (0.49–0.99)
Neighborhood SES tertile
    1 (Lower)1.75 (1.28–2.40)1.47 (1.05–2.06)2.09 (1.42–3.05)1.63 (1.09–2.45)
    21.28 (0.93–1.75)1.19 (0.86–1.63)1.56 (1.07–2.28)1.41 (0.96–2.07)
    31 (Reference)1 (Reference)1 (Reference)1 (Reference)


Principal Findings

In this prospective cohort study of community-dwelling patients <65 years of age discharged from hospital after incident MI and followed up for a median of 13 years, neighborhood SES was strongly related to survival in a dose-response manner. Adjustment for various individual socioeconomic measures and established prognostic factors attenuated the association, yet excess risk persisted for living in a disadvantaged neighborhood. The prognostic importance of neighborhood SES was even stronger with regard to cardiac mortality. In the final models, several SES measures, including neighborhood context, were concurrently associated with all-cause and cardiac mortality, supporting a multiple jeopardy phenomenon.

Comparison With Other Studies

Primary Prevention Studies

A number of primary prevention studies have shown an increased CVD risk associated with poor area-level SES after controlling for individual-level measures.22–25 For example, Diez Roux et al22 have used a summary neighborhood score based on US Census data and found an association between residence in a disadvantaged neighborhood and increased incidence of coronary heart disease after adjustment for personal income, education, and occupation. Comparably, Sundquist et al,23 using follow-up data of 25 319 subjects in Sweden, have shown that low levels of both neighborhood education and neighborhood income predicted incident coronary heart disease after adjustment for age, sex, and individual income and education (HRs of 1.25 and 1.23 for the lowest and highest quintiles, respectively).

Post-MI Research

Socioeconomic position in post-MI research has traditionally been treated as an intrinsic characteristic of individuals. Several studies have shown that low-SES patients, usually defined on the basis of personal income, educational attainment, and occupational status, typically present with more severe disease as indicated by longer history of CVD, higher prevalence of risk factors and comorbidity, and more complicated MI.5,8–10,13 Some,5,10,11,13 but not all,14,37 studies have further revealed that despite their higher-risk profiles, low-SES patients are less likely to receive reperfusion therapy and evidence-based medications (ie, aspirin, statins, and β-blockers) and to be treated with invasive cardiac procedures, particularly angiography and percutaneous coronary interventions. Nearly all previous studies eventually found an increased mortality risk associated with low personal SES.4,5,8–11,13

Many studies have used area-based SES measures as proxies for individual measures.3,6,7,12,38–40 These studies generally reported findings similar to those of studies that used personal SES measures, although the observed effect sizes were usually smaller. The weaker regression coefficients may be due in part to the inherent exposure misclassification resulting from using aggregate geographic data to proxy individual SES.41

Only a few studies have attempted to evaluate the combined impact of personal-level and neighborhood-level SES on mortality after MI. Winkleby et al42 have assessed the independent association of neighborhood deprivation with coronary heart disease incidence and case fatality rate in Sweden. In multivariable models, neighborhood deprivation was significantly associated with both coronary heart disease incidence and 1-year case fatality after adjustment for 7 personal socioeconomic factors. However, no clinical data were available in this study, which leaves substantial room for confounding. The roles of personal-level education and neighborhood-level income in survival after MI were evaluated in a recent cohort study (median follow-up, 13 months) conducted in Olmsted County, Minnesota.14 Both poor individual education and low neighborhood income were associated with a worse clinical presentation, and the latter was shown to be a powerful predictor of mortality even after adjustment for personal education and a variety of potential confounding factors. However, because education was the sole personal SES characteristic measured in the study, it is likely that neighborhood income has acted, at least partly, as a proxy for unmeasured characteristics of individual SES. In summary, previous post-MI research was generally restricted to the prognostic role of either individual-level or neighborhood-level SES measures. The few studies that attempted to evaluate the combined effects of personal- and neighborhood-level SES lacked essential data on either personal SES determinants or clinical characteristics and were limited by a relatively short follow-up period.

What This Study Adds

To the best of our knowledge, this work is the first to evaluate the role of neighborhood socioeconomic context in long-term post-MI prognosis after accounting for multiple personal measures of SES and clinical confounders. A positive relationship was found between individual and neighborhood SES, which supports previous reports.41,43,44 Furthermore, the results suggest an additional contribution of residential neighborhood to mortality risk beyond individual SES. Risk of death, particularly cardiac death, was significantly higher in patients living in disadvantaged neighborhoods compared with their more advantaged counterparts in a dose-response manner. Notably, repetition of findings in a new context adds support to a causal relationship.45 Our finding of a role for neighborhood SES in long-term survival after MI supports previous reports from primary prevention studies and raises the possibility of a causal relationship. Furthermore, our study population has the distinct advantage of being ethnically, culturally, and socioeconomically diverse, an ideal setting for such research.

Possible Mechanisms

The strong association shown in this study between neighborhood SES and post-MI prognosis is in accordance with a contextual effect. The alternative explanation would be that neighborhood SES merely reflected unmeasured characteristics of individual SES. However, it is unlikely that this is the main explanation for the findings because we minimized the possibility of residual confounding by personal SES by simultaneously adjusting for education, income, employment, and living with a spouse, all of which are known prognostic factors after MI.9,10,13,46

Theoretically, neighborhood features that may affect MI outcome can be classified into characteristics of the physical environment and characteristics of the social environment.47 In our study, patients with higher neighborhood SES were more likely to have been hospitalized in an ICU and to have undergone revascularization, even though they were typically healthier to begin with and suffered less severe MI. This might be related to unequal hospital resources; the availability of ICU facilities has been higher in our study for hospitals serving more advantaged populations (around Tel Aviv) compared with those serving the less affluent. Similarly, cardiac rehabilitation facilities have been more available in more advantaged areas. The observed inequality in the provision of health services supports previous reports showing pronounced socioeconomic effects on access to specialized cardiac services, use of invasive procedures, and treatment with evidence-based therapies after MI.3,39,40

Neighborhood effects can also be related to characteristics of the social environment, which include social norms and prevailing attitudes toward health and health-related behavior (eg, smoking, diet, and physical activity) and features of the social connections within neighborhoods such as social cohesion and support.47 Such connections may lead to better sharing of health-related information and expertise gained from experience with the healthcare system. Hypothetically, patients from high-SES neighborhoods and their families may have been more vigilant in their responses to symptoms, more knowledgeable regarding various treatment options, and more assertive at obtaining them. Alternatively, it cannot be ruled out that among neighborhoods with lower SES, there might be additional sources of stress (eg, crime) and consequent anxiety among patients in these areas that might further aggravate risk. These mechanisms should be the focus of future research.

Methodological Considerations

Although personal SES was measured more directly and more comprehensively than in many previous studies, the information was self-reported and sometimes included a limited number of categories, both of which may result in some misclassification. In addition, socioeconomic changes occurring after hospital discharge, including residential mobility, were not accounted for. Yet, baseline SES measures were recently shown to predict long-term mortality in this cohort effectively.13 By nature of the length of follow-up, the participants were enrolled in the early 1990s. Because changes have taken place both in the definition of MI48 and in coronary intervention practice,49 confirmation of our findings in more recent cohorts is needed. Seventy-eight percent of the original enrollees were included in the analysis. The primary reason for exclusion was missing values on SES measures, the exposure of interest. However, nonparticipants did not differ from participants in any of the sociodemographic or clinical variables evaluated, including long-term survival. This argues against substantial selection bias. Finally, although causes of death were carefully ascertained through multiple sources of information, misclassification of cardiac deaths is possible.

Potential Implications

Our findings indicate the importance of enhancing preventive/rehabilitative healthcare services for post-MI patients residing in deprived neighborhoods. These services should be applied at the community level. Cardiac rehabilitation programs, previously shown to be cost-effective both from a community perspective and from the patients’ perspective,50 need to be modified to meet patients’ and community needs and to overcome the barriers for participation. Further research is warranted to identify the most relevant features of residential environments that affect MI outcome. These studies should include the elderly MI population and incorporate psychosocial measures such as depression, social support, and sense of coherence.


This study suggests a role for neighborhood context in long-term survival after MI beyond individual SES, thereby supporting a multidimensional association between SES and post-MI outcome.

The following investigators and institutions took part in the Israel Study Group on First Acute Myocardial Infarction: Yaacov Drory, MD, principal investigator, Department of Rehabilitation, Sackler Medical School, Tel Aviv University, Tel Aviv; Yeheskiel Kishon, MD, Michael Kriwisky, MD, and Yoseph Rosenman, MD, Wolfson Medical Center, Holon; Uri Goldbourt, PhD, Hanoch Hod, MD, Eliezer Kaplinsky, MD, and Michael Eldar, MD, Sheba Medical Center, Tel Hashomer; Itzhak Shapira, MD, Amos Pines, MD, Margalit Drory, MSW, Arie Roth, MD, Shlomo Laniado, MD, and Gad Keren, MD, Tel-Aviv Sourasky Medical Center, Tel-Aviv; Daniel David, MD, Morton Leibowitz, MD, and Hana Pausner, MD, Meir Medical Center, Kfar Sava; Zvi Schlesinger, MD, and Zvi Vered, MD, Assaf Harofeh Medical Center, Zerifin; Alexander Battler, MD, Alejandro Solodky, MD, and Samuel Sclarovsky, MD, Beilinson Medical Center, Petach Tikvah; Izhar Zehavi, MD, and Rachel Marom-Klibansky, MD, Hasharon Medical Center, Petah Tikvah; and Ron Leor, MD, Laniado Medical Center, Netanya. The authors are also indebted to Zalman Kaufman, MSc, for assistance with the Geographic Information System analysis.

Source of Funding

This work was supported in part by the Israel National Institute for Health Policy and Health Services Research.




Correspondence to Dr Yariv Gerber, Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, 69978, Israel. E-mail


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circulationahaCirculationCirculationCirculation0009-73221524-4539Lippincott Williams & Wilkins

Neighborhood of residence has been suggested to affect cardiovascular risk over and above personal socioeconomic status (SES). However, such data are currently lacking for patients with myocardial infarction (MI). We examined long-term survival according to neighborhood SES in a cohort of myocardial infarction patients. Consecutive patients (n=1179) ≤65 years of age discharged from 8 hospitals in central Israel after incident (ie, first ever) myocardial infarction in 1992 to 1993 were followed up for a median of 13 years. Personal socioeconomic and clinical data were obtained at study entry. Neighborhood SES was estimated through a composite census-derived index developed and validated by the Israel Central Bureau of Statistics. Neighborhood SES was strongly related to survival in a dose-response manner; patients residing in disadvantaged neighborhoods had higher mortality rates. Adjustment for personal SES measures and established prognostic factors attenuated the association, yet excess risk persisted for living in a disadvantaged neighborhood. The association was even stronger with regard to cardiac mortality. In the final fully adjusted models, several personal SES measures, including education, employment, and living with a steady partner, as well as neighborhood socioeconomic context, were concurrently associated with all-cause and cardiac mortality. Thus, our data support a multidimensional relationship between SES and myocardial infarction survival and indicate the importance of enhancing preventive/rehabilitative healthcare services for post–myocardial infarction patients residing in deprived neighborhoods. Further research is needed to identify the most relevant features of residential environments that affect cardiovascular incidence and prognosis.

See the Acknowledgments for a complete list of participating medical centers and investigators.

The online-only Data Supplement is available with this article at