1 Department of Family Medicine, Karolinska Institutet, Stockholm, Sweden.
2 Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, CA.
Received for publication March 14, 2003; accepted for publication November 4, 2003.
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ABSTRACT |
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coronary disease; follow-up studies; incidence; regression analysis; residence characteristics; social class; social environment
Abbreviations: Abbreviations: CI, confidence interval; HR, hazard ratio; ICD, International Classification of Diseases; SAMS, small-area market statistics.
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INTRODUCTION |
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In this prospective study, we used multilevel models to examine whether neighborhood socioeconomic environment, as defined by small area units, predicted incident coronary heart disease in a large random sample of the Swedish population. We used two separate measures to describe neighborhood socioeconomic environment: neighborhood education (proportion of people with less than 10 years of education in the neighborhood) and neighborhood income (proportion of people with incomes in the lowest national income quartile).
We had three primary aims in the study. First, we examined whether neighborhood education and income predicted rates of incident coronary heart disease. Second, we examined whether the relation between neighborhood education and income and coronary heart disease incidence rates remained significant after adjustment for individual-level sociodemographic characteristics. Third, we calculated the between-neighborhood variance after adjusting the results for level 1 and level 2 variables.
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MATERIALS AND METHODS |
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Neighborhoods were defined on the basis of small-area market statistics (SAMS). SAMS were originally created for commercial purposes and pertain to small geographic areas with boundaries defined by homogenous types of buildings (14). The average population in each SAMS neighborhood is approximately 2,000 people for Stockholm and 1,000 people for the rest of Sweden. SAMS neighborhoods with fewer than 50 people (1,583 areas and 58 sampled persons) were not included because of unstable statistical estimates. This yielded a final total of 6,145 SAMS neighborhoods. In our sample, an average of eight persons resided in each of the SAMS neighborhoods. The home addresses of participants in the survey had been previously geocoded, allowing us to identify the SAMS neighborhoods in which the participants lived. Participants with missing geocodes (10 percent) were not included in our sample. To estimate the duration of "exposure" to a certain neighborhood, we used a longitudinal subsample consisting of 40 percent of the participants, who were asked how long they had lived at their current home address. We found that 75 percent of the subsample had lived at their home address for at least 8 years preceding the survey. In an additional analysis, we added duration of residence in the neighborhood to our models (categorized as <8, 816, and 17 years). We also stratified the models by duration of residence in the neighborhood.
Outcome variable
Time to first hospitalization for a fatal or nonfatal coronary heart disease event was classified according to the International Classification of Diseases, Ninth Revision (ICD-9) (codes 410414) and the International Classification of Diseases, Tenth Revision (ICD-10) (codes I20I25). The ICD codes are explained below. Out-of-hospital deaths due to coronary heart disease (1.5 percent of all fatal and nonfatal heart disease events) were not included because of the low autopsy rates in Sweden, leading to possibly unreliable causes of death on the death certificates, especially for out-of-hospital deaths. To begin the study with as healthy a sample as possible, we excluded persons who had been hospitalized for a coronary heart disease event during the interview year or the 2 years preceding the interview year (413 persons). This resulted in a final sample of 25,319 persons. The In-Care Register began recording complete data on all hospital discharges in Sweden in 1986. During 1984 and 1985, the data in the In-Care Register lacked information from five counties, corresponding to 13 percent of all hospital discharges in Sweden. Therefore, for persons who were interviewed in 1986 or 1987, the exclusion of those with prior coronary heart disease during the 2 years preceding the interview was at least 87 percent complete.
The meaning of the ICD codes is as followsICD-9: code 410, acute myocardial infarction; code 411, other acute and subacute forms of coronary heart disease; code 412, old myocardial infarction; code 413, angina pectoris; and code 414, other forms of chronic coronary heart disease; ICD-10: code I20, angina pectoris; code I21, acute myocardial infarction; code I22, reinfarction (within 4 weeks); code I23, complications due to acute myocardial infarction; code I24, other acute forms of coronary heart disease; and code I25, chronic coronary heart disease.
Neighborhood-level variables
Data used to calculate neighborhood education and income were obtained from a national database for the entire Swedish adult population, containing annually collected data on income and education for each individual. Data from January 1992 (the midpoint of the study period) were released to us that included information on persons aged 2064 years. Data on persons of other ages were not available. These data were then linked to the SAMS neighborhoods using geocodes. To calculate neighborhood education and income, we used data for women and men aged 2564 years. We chose the lower age cutpoint of 25 years because, in Sweden, many people do not complete their formal education until age 25 and/or are not financially independent of their parents prior to that age.
Neighborhood education
The proportion of people with less than 10 years of education was calculated for each neighborhood. The distribution was then divided into quintiles. Quintile 1 represents neighborhoods with the lowest proportion of people with low education (range, 021 percent), and quintile 5 represents neighborhoods with the highest proportion of people with low education (range, 4278 percent).
Neighborhood income
The proportion of people with incomes in the lowest national income quartile was calculated for each neighborhood. Quintile 1 represents neighborhoods with the lowest proportion of low-income residents (range, 016 percent), and quintile 5 represents neighborhoods with the highest proportion of low-income residents (range, 34100 percent). The neighborhood income measure was based on annual family income divided by the number of people in the family. The family income measure also took into consideration the ages of people in the family, and it used a weighted system whereby small children were given lower weights than adolescents and adults. Women and men under age 25 or over age 64 years were included in the neighborhood income measure if they lived in homes with one or more persons aged 2564 years.
The correlation between neighborhood education and neighborhood income was 0.65 (Pearsons correlation coefficient for the proportions of people with low education and low income at the SAMS level).
Individual-level variables
Individual-level variables included gender, age, education, income, cigarette smoking, and duration of residence in the neighborhood. Age was used as a continuous variable and was centered at the mean age of all respondents in order to facilitate interpretation of the regression coefficients (15). Educational level was classified into three categories: <10 years (no high school), 1012 years (some high school or completion of high school), and >12 years (more than high school). Income level was defined as individual income and was divided into five groups. Cigarette smoking was defined as daily smoking; it was used in an additional analysis that examined the influence of adding smoking to the full models. Data on duration of residence in the neighborhood were divided into three groups (<8, 816, and 17 years) according to how long the participant had lived at his or her current address.
Statistical analysis
A multilevel Cox proportional hazards model reflecting the hierarchical structure of our data was used to calculate hazard ratios for incident coronary heart disease (16). In separate models, we tested for effects of neighborhood education and income (level 2) on the incidence of heart disease after adjustment for individual-level factors (level 1).
The formula used was
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where hij(t) represents the hazard function, 00(t) represents the intercept, and h0(t) represents the baseline hazard function of time t. Time t is defined as the number of months from the participants entry into the study to a coronary heart disease event or censoring. Nkj represents each set of level 2 neighborhood variables, and Iij represents the set of level 1 individual variables. U0j represents the intercept random effect, and Rij represents the individual residual. The subscript j represents neighborhoods, and the subscript i represents individuals.
We first analyzed the age- and sex-adjusted associations (i.e., hazard ratios) between neighborhood education and income and coronary heart disease. We then added individual-level socioeconomic characteristics (i.e., income and education). We tested the models for cross-level interactionsthat is, age x neighborhood education, age x neighborhood income, sex x neighborhood education, and sex x neighborhood income. We also tested for the interactions age x individual education, age x individual income, sex x individual education, and sex x individual income. No significant interactions were found. Therefore, no interaction terms were included in the final models.
All multilevel analyses were performed with MLwiN (17), using the macro for the survival and event duration models (18). Preliminary estimates of the effects were made by means of a first-order marginal quasilikelihood estimation procedure. To obtain more accurate estimates, we then performed reestimation in the final models using a predictive quasilikelihood procedure combined with a second-order Taylor expansion series (17). The results are presented as hazard ratios with 95 percent confidence intervals. All estimates should be interpreted as being conditional on area random effects.
The incidence rates for coronary heart disease among all persons aged 3574 years who were interviewed between 1986 and 1993 were calculated from separate multilevel models for neighborhood education and neighborhood income, standardized for age and gender, and presented as number of cases per 10,000 person-years.
Ethical considerations
This study was approved by the Ethics Committee of the Karolinska Institutet, Stockholm, Sweden.
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RESULTS |
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DISCUSSION |
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Our findings are in agreement with those of a US study that used a summary score including neighborhood indicators of education and income (10). After adjustment for individual-level socioeconomic characteristics, the authors found that neighborhood socioeconomic environment was associated with coronary heart disease incidence rates (10). In another study, the same authors used multilevel logistic regression models and found that neighborhood socioeconomic environment was associated with both increased prevalence of coronary heart disease and increased levels of heart disease risk factors (12). The Renfrew and Paisley Study in Scotland found that a high level of neighborhood deprivation was associated with higher cardiovascular disease mortality and less favorable heart disease risk factor profiles (19). Other studies, some of them using multilevel models, have also demonstrated a neighborhood effect on coronary heart disease risk factors, including diastolic blood pressure, cholesterol levels, dietary habits, smoking, physical inactivity, and obesity (2025).
It is plausible that unhealthy behaviors and coronary heart disease risk factors lie in the causal pathway between neighborhoods and coronary heart disease, thus acting as mediating variables rather than confounding variables. For example, neighborhoods may have different social norms that might in turn differentially influence health behaviors, such as smoking habits, diet, and physical activity. In some neighborhoods, a poor physical environmentfor example, a lack of access to smoke-free areas, healthy food stores, and safe places to exercisemay deny people the opportunity to develop and maintain heart-healthy behaviors. Moreover, access to health care resources and information about the advantages of obtaining treatment for hypertension and diabetes may differ between neighborhoods. Finally, relative neighborhood deprivation may influence feelings of hopelessness and alienation and mediate the neighborhood effect on coronary heart disease.
Our findings are also in agreement with the US study (10) in that the addition of several cardiovascular disease risk factors to regression models already containing individual socioeconomic characteristics had little effect on the relation between neighborhood characteristics and coronary heart disease incidence. As in that study, when we added smoking to the full model, the regression coefficients and the variance did not change significantly, although there was a strong association between smoking and coronary heart disease.
Strengths and limitations
The key strengths of our study were the large sample size, the prospective design, the small-area neighborhood units, the multilevel analytical technique, and the replication of past work in a new context. Repetition of findings in a new context adds support to past research showing that the observed relations are causal (26). The prospective nature of our study design allowed us to calculate incidence rates for coronary heart disease rather than prevalence rates; incidence is a much stronger outcome measure for determination of causal relations. The use of SAMS neighborhoods, which are relatively small (approximately 1,0002,000 people) and homogenous in terms of building type, was another strength. Moreover, our neighborhood measures were almost 100 percent complete and were based on the entire Swedish population. Furthermore, the validity of the diagnosis of myocardial infarction (54.4 percent of our coronary heart disease events) was high in an evaluation carried out by the National Board of Health and Welfare for the years 1987 and 1995 (27). The reliability of the survey questions, with data being collected by well-trained interviewers in face-to-face interviews, was high. Reinterview of a sample of the participants (the test-retest method) yielded kappa coefficients of 0.710.78 for level of education and 0.960.99 for cigarette smoking (28, 29). There was little or no loss to follow-up, since the Swedish registration system provides a personal identification number for each individual, which we used to follow each individual throughout the entire study period.
However, our study also had several limitations. First, response bias may have occurred if the nonrespondents (21.8 percent) differed from the respondents with respect to coronary heart disease incidence rates. Of the nonrespondents, approximately 70 percent refused participation, 20 percent could not be located, and 10 percent were too ill to participate. We examined this possible bias by including both nonresponders and responders in a proportional hazards model that adjusted for age, sex, marital status, and region, with all-cause mortality as the outcome. The 70 percent who refused participation had the same mortality risk as the respondents, but the other two groups had significantly higher mortality risks. Second, of the many individual-level risk factors for coronary heart disease, we only had data on smoking. Third, residual confounding probably exists, because individual education and income cannot be measured precisely and completely (30, 31). For example, years of education does not capture information on the quality of schooling or literacy levels. Fourth, the In-Care Register began recording complete data on all hospital discharges in Sweden in 1986. During 1984 and 1985, the data in the In-Care Register lacked information from five counties, corresponding to 13 percent of all hospital discharges in Sweden. Therefore, for persons who were interviewed in 1986 or 1987, the exclusion of those with prior coronary heart disease during the 2 years preceding the interview was not 100 percent complete; this probably resulted in a modest bias.
Implications and recommendations
Currently, health care resources are directed primarily toward the individual, with a focus on providing people with the knowledge and resources needed to help them achieve and maintain healthy behavior. This approach has had disappointing results in both Sweden (32) and the United States (3335). Our findings suggest that programs for coronary heart disease prevention should combine both individual- and neighborhood-level approaches in order to give all people the opportunity to achieve and maintain better health.
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ACKNOWLEDGMENTS |
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The authors thank Dr. Dave Ahn, Dr. Catherine Cubbin, and Alana Koehler of the Stanford Prevention Research Center, Stanford University School of Medicine (Palo Alto, California), and Sanna Sundquist of Foothill College (Los Altos Hills, California).
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NOTES |
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REFERENCES |
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