Proximate and Contextual Socioeconomic Determinants of Mortality: Multilevel Approaches in a Setting with Universal Health Care Coverage

Paul J. Veugelers1, Alexandra M. Yip1 and George Kephart1

From the Dalhousie University Department of Community Health and Epidemiology, Faculty of Medicine, 5849 University Avenue, Halifax, Nova Scotia, B3H 4H7 Canada.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Investigations of contextual factors (income inequality, cultural disruption, access to health and social services, safety and crime rate, and others) have received little emphasis by epidemiologists, although a few have demonstrated the importance of such factors for mortality, particularly in the United States. To expand current understanding of the importance of contextual factors, the authors evaluated mortality in a longitudinal study in Nova Scotia, Canada, where all residents have greater access to health and social services and where income inequalities are smaller than in the United States. A total of 2,116 participants were followed from 1990 through December 1999, linked to the 1991 Canada Census as a source of neighborhood characteristics, and analyzed using individual-level and multilevel logistic regression. Well-educated and high-earning persons fared better. Neighborhood socioeconomic characteristics (neighborhood income, educational level, unemployment rate), in contrast, were not significantly associated with mortality. However, within advantaged neighborhoods, the importance of individual income and education for mortality was increased relative to disadvantaged neighborhoods. The latter findings may direct health policy aimed at reducing health inequalities.

health status; income; mortality; socioeconomic factors

Abbreviations: BMI, body mass index; CI, confidence interval


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Epidemiologic investigations have successfully identified many important proximate determinants of health but have given little emphasis to the importance of poverty, income inequality, cultural disruption, social environment, and socioeconomic neighborhood characteristics (1Go, 2Go). Investigations of such contextual factors are important for the identification of populations and subgroups with particular health concerns to provide clues and directions for targeted interventions and health policy. Their potential to benefit health may exceed that of further investigations of proximate risk factors with currently "seemingly fewer large effects remaining to be discovered" (2Go, p. 890). In this respect, we have demonstrated that alleviating the health deficiencies of disadvantaged communities would improve life expectancy more than the hypothetical elimination of cancer or cardiovascular diseases would benefit life expectancy at the national level (3Go).

Contextual factors affect health at a supraindividual level and require multilevel analytic approaches that seek to account for both individual and contextual factors in explaining individual health outcomes (4Go, 5Go). Mackenbach (6Go) recently called for more studies using these nontraditional analytic approaches to build on our understanding of the importance of contextual factors. A growing number of contributions have now applied multilevel approaches. However, the majority are cross-sectional studies evaluating disease prevalence or self-rated health (7GoGoGo–10Go). Longitudinal studies evaluating morbidity and mortality and applying multilevel approaches have better inferential potential but are rare (11Go, 12Go).

Lochner et al. (13Go) used multilevel analysis of longitudinal observations and demonstrated that state-level income inequality exerts a contextual effect on mortality, independent of individual income: Various income subgroups in states with high income inequality had higher mortality rates than did their income equivalents in states with low income inequality. Yen and Kaplan (14Go) reported an inverse relation between mortality and the quality of neighborhood social environment in Alameda County, California. In particular, poor persons living in "rich" neighborhoods had elevated mortality rates relative to their income equivalents living in "poor" neighborhoods. The authors suggested "differential access to resources" (14Go, p. 905) as an explanation for these findings. It is important to confirm the above findings in other contextual settings, specifically, settings that differ with respect to access to resources.

To build on our understanding of the importance of contextual factors, here we use multilevel approaches to evaluate mortality in a longitudinal study in Nova Scotia, Canada, where access to public resources such as health, education, and social services is markedly different than in the United States. Canada has publicly funded universal access to hospital and physician services and provincially funded public schools, and the Canadian tax system provides much greater redistribution of income, resulting in smaller income inequalities than in the United States (15Go).


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Individual characteristics
Individual characteristics were taken from the 1990 Nova Scotia Nutrition Survey. This survey among noninstitutionalized residents aged 18–75 years involved face-to-face interviews regarding dietary intake, lifestyle, and other individual characteristics. The sampling frame of this survey consisted of the Nova Scotia medical insurance register covering all provincial residents. Nova Scotia is divided into 18 counties, the two largest of which were automatically included and six others of which were randomly selected. These eight counties formed the first stage of sampling, and 12 age-gender segments formed the second stage. From the 3,684 sampled persons, 2,777 (75 percent) were located, 2,198 (79 percent) of whom completed the survey. Individual characteristics included age, gender, smoking status, body mass index (BMI), diabetes, household income, and education. Complete information was available on all of the above individual characteristics except household income, which was an elective question completed by 1,886 participants (86 percent). Responders to this question were categorized into three groups according to annual gross household income: 1) less than $20,000; 2) $20,000–$40,000; and 3) and more than $40,000 (all in Canadian dollars). Nonresponders were included in a "missing household income" category. We categorized education on the basis of the highest level of schooling completed: 1) less than high school; 2) high school or vocational school; and 3) college or university. Age was considered as a continuous variable in all analyses. To allow the risk adjustment both of persons with low BMI (<20) and obese persons (those with a BMI >=27), we categorized participants into three groups. The low number of Black (n = 15) and Chinese (n = 1) participants prevented meaningful analysis of racial differences.

Contextual characteristics
For the neighborhoods, household income, dwelling value, education, unemployment, and percentage of single mothers are contextual characteristics representing averages and percentages measured at the enumeration area level in the 1991 Canada Census. The advantage of using Census information is that it summarizes characteristics of all residents rather than of a selected number of study participants. Census enumeration areas in Nova Scotia comprise 40–2,200 residents. For 98 participants (4.6 percent), enumeration area information on household income or dwelling value was suppressed because of small population counts and consequent privacy issues. In these cases, we imputed information from a randomly assigned adjacent enumeration area. Neighborhood household income, dwelling value, and unemployment rate were categorized into three strata to allow the examination of nonlinear associations with mortality. For the choice of the cutpoints, we considered both balanced numbers of participants in each of the categories as well as rounded and meaningful cutoff values. Neighbor-hood household income was categorized into three groups according to average annual gross household income: 1) less than $30,000; 2) $30,000–$40,000; and 3) more than $40,000 (all in Canadian dollars). Average dwelling value was categorized as less than $60,000, $60,000–$80,000, and more than $80,000. Unemployment rate was categorized as less than 10, 10–15, and more than 15 percent. Disad-vantaged neighborhoods were also characterized as having more than 15 percent of their residents with an education of less than ninth grade or having more than 10 percent of the families headed by single mothers.

Data linkage
Data linkage and analysis for this study received ethical approval by the Health Sciences Human Research Ethics Board, Dalhousie University, Halifax, Nova Scotia, Canada. Records of participants were first linked with the 1991 Canada Census by using established procedures based on residential postal code (16Go). This linkage could not be established for 82 participants (3.8 percent) because of incomplete address information. These participants were excluded in this study. The remaining 2,116 participants resided in 705 of the 1,442 enumeration areas in Nova Scotia. They were then linked to provincial administrative databases and Nova Scotia Vital Statistics to reveal out-of-province migration and death status through December 1999 (17Go). The linkage between participants and the administrative databases is through encrypted health card numbers. The linkage with vital statistics is through health card numbers and requires the involvement of a third party to decode the encrypted health card numbers and to secure privacy. Nova Scotia Vital Statistics records all death reports within the province. Recordings of out-of-province deaths of Nova Scotia residents depend on voluntary reporting and should be considered delayed and incomplete. The above linkages revealed that 104 participants (4.9 percent) moved out of the province during follow-up. Their observations, up to the day of departure, were included in the statistical analysis.

Statistical approaches
The importance of individual and contextual characteristics to mortality was evaluated by using individual-level and multilevel logistic regression (4Go, 5Go). Individual characteristics were considered to be first-level covariates, and their effect on mortality was assumed to be similar across contextual characteristics, which were considered to be second-level covariates. We quantified the effect of individual and contextual characteristics on mortality initially in terms of age- and gender-adjusted odds ratios. Subsequently, we also adjusted for smoking status, BMI, and diabetes to quantify the importance of socioeconomic characteristics independent of these proximate health determinants. Simultaneous adjustment for socioeconomic characteristics resulted in multicollinearity and consequent broad confidence intervals surrounding the estimated odds ratios. This also hampered the analysis of cross-level interaction terms. We therefore analyzed the importance of individual socio-economic characteristics on mortality within socioeconomically disadvantaged and advantaged neighborhoods separately. Disadvantaged and advantaged neighborhoods were distinguished on the basis of the average values for neighborhood characteristics, with disadvantaged neighborhoods having less-than-average neighborhood income, less-than-average dwelling value, more-than-average proportion of residents with little education, more-than-average unemployment, or a higher-than-average proportion of single mothers. To investigate the significance of the differential association in disadvantaged and advantaged neighborhoods further, we followed the recommendations of Backlund et al. (18Go) and modeled the logarithm of individual household income as a determinant of mortality, while adjusting for age, gender, smoking status, BMI, and diabetes. We then tested the odds ratios of the logarithm of individual household income within disadvantaged neighborhoods against those within advantaged neighborhoods.

For longitudinal analysis of survey data with follow-up, Korn and Graubard (19Go) recommend adjusting for the variables used in defining the sample weights (age, gender, and county) and not incorporating sample weights. We applied these recommendations in all analyses. However, since county did not substantially affect any of our estimates of interest, we excluded this covariate from our analyses.

We conducted various sensitivity analyses to examine potential sources of bias (11Go, 12Go). We repeated all of the above analyses, excluding participants who moved out of the province during the study period and those for whom we imputed neighborhood characteristics, to reveal potential bias resulting from migration or misclassification resulting from the imputation procedure. Poor health of participants may determine lifestyle factors (e.g., smoking cessation, weight loss resulting from disease) and socioeconomic factors (e.g., inability to work may reduce income), whereas here we aim to investigate the importance of these factors for mortality. To reveal the potential importance of such associations, we repeated the above analyses, excluding participants who died in the first 5 years after enrollment.

The analyses were conducted by using HLM5 and S-PLUS 2000.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Individual and neighborhood characteristics of the 2,116 participants of the Nova Scotia Nutrition Survey are presented in table 1. Between the interview held in 1990 and December 1999, 211 participants (10.0 percent) died. Among these, 61 (29 percent) died of cardiovascular diseases, 86 (41 percent) of cancer, and 1 (0.47 percent) of human immunodeficiency virus infection, while for 19 deaths (9.0 percent) the cause was unspecified. None of these deaths were reported to have resulted from motor vehicle accidents, homicide, or suicide. The age- and gender-adjusted mortality risk was significantly higher among smokers; persons with a BMI of less than 20; and persons with diabetes, low household income, and limited education (table 1). None of the neighborhood socioeconomic characteristics (average household income, average dwelling value, percentage of residents with less than a ninth grade education, unemployment rate, and percentage of families headed by a single mother) was significantly associated with mortality (table 1).


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TABLE 1. Individual and neighborhood characteristics and their age- and gender-adjusted odds ratios for mortality among 2,116 participants in the 1990 Nova Scotia Nutrition Survey

 
Table 2 presents the associations between socioeconomic characteristics and mortality, adjusted for proximate health effects of age, gender, smoking, BMI, and diabetes. Relative to the estimates not adjusted for smoking, BMI, and diabetes, the associations between individual socioeconomic characteristics and mortality were somewhat less pronounced, of which the association of education remained statistically significant. Neighborhood characteristics adjusted for the proximate health effects were not significantly associated with mortality (table 2).


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TABLE 2. Odds ratios for mortality adjusted for age, gender, smoking status, body mass index, and diabetes among 2,116 participants in the 1990 Nova Scotia Nutrition Survey

 
Table 3 presents the differential associations of household income with mortality for socioeconomically disadvantaged and advantaged neighborhoods. For all neighborhood characteristics, the effect of household income is larger within advantaged neighborhoods than within disadvantaged neighborhoods. This increased importance of household income for mortality within advantaged neighborhoods is reflected in statistically significant differences between persons with low and high income for four of the five neighborhood characteristics, whereas no statistically significant differences are observed within disadvantaged neighborhoods. Similarly, the change in mortality associated with a log change in individual household income was greater in advantaged neighborhoods for all five neighborhood characteristics. Distinguishing neighborhoods on the basis of income, education, and the proportion of families headed by single mothers resulted in significantly different associations for mortality between disadvantaged and advantaged neighborhoods with respect to the importance of individual household income (p < 0.05, table 3). The differential associations of individual education with mortality within socioeconomically disadvantaged and advantaged neighborhoods were similar to those of individual income with mortality (data not shown in table 3). For example, among participants living in neighborhoods with more than an average household income, relative to those with less than a high school education, the odds ratio for participants with a high school or vocational education was 0.99 (95 percent confidence interval (CI): 0.52, 1.88), and that for participants with a college or university education was 0.31 (95 percent CI: 0.14, 0.69). With the same reference group (participants with less than a high school education and living in advantaged neighborhoods), the odds ratios of persons living in disadvantaged neighborhoods who had less than a high school education, a high school or vocational school education, and a college or university education were 0.94 (95 percent CI: 0.61, 1.44), 0.92 (95 percent CI: 0.53, 1.61), and 0.60 (95 percent CI: 0.31, 1.16), respectively.


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TABLE 3. Odds ratios of individual household income for mortality in disadvantaged and advantaged neighborhoods of 2,116 participants in the 1990 Nova Scotia Nutrition Survey{dagger}

 
The above analysis included 104 participants who moved out of the province during follow-up and 98 participants with imputed neighborhood characteristics from adjacent enumeration areas. Repeating the analyses with these participants excluded did not substantially change the results presented above. In addition, repeating the analyses and excluding the 87 persons who died in the first 5 years after their interview revealed no substantially different estimates, although wider confidence intervals resulting from less statistical power.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Individual socioeconomic characteristics, such as income and education, are strongly associated with mortality. Well-educated and high-earning persons fare better, even after adjustment for proximate health determinants, including smoking, BMI, and diabetes. In contrast, neighborhood socioeconomic characteristics are not significantly associated with mortality. However, within advantaged neighborhoods, the importance of individual socioeconomic characteristics for mortality is increased relative to disadvantaged neighborhoods.

The importance of socioeconomic factors for health has been the topic of decades of research. Most of this research has focused on individual socioeconomic characteristics and has demonstrated positive associations between socioeconomic status and health (20Go, 21Go). Our study confirms this in the setting of Nova Scotia, Canada, where all residents have greater access to health care and where income inequalities are smaller than in the United States.

Relative to individual socioeconomic characteristics, neighborhood socioeconomic characteristics have been less investigated. In particular, longitudinal studies applying multilevel approaches are rare, whereas such a design has better inferential potential. Using such a study design, we failed to demonstrate substantial and statistically significant health differences between disadvantaged and advantaged neighborhoods. This seems to contrast with studies demonstrating the importance of income inequality (13Go) and social environment (14Go) for mortality in the United States. This suggests that contextual factors (income inequality, cultural disruption, access to health and social services and to public schools, safety, and crime rate, etc.) are more important determinants of health in the United States than they are in Canada. This interpretation concurs with results from state and metropolitan comparisons by Wolfson et al. (22Go), who suggested the importance of social milieu as a health determinant in the United States, and by Ross et al. (15Go), who found apparent relations between health and income inequality within the United States but not within Canada.

While our study and that of Yen and Kaplan (14Go) differ in the importance of contextual factors for health, both studies found that the relation between health and income is differential for disadvantaged and advantaged neighborhoods. Poor persons living in rich neighborhoods in Alameda County, California, were reported to have an estimated 5.5 increased mortality risk relative to rich persons in these neighborhoods, whereas mortality among poor and rich persons in poor neighborhoods was approximately the same (14Go, p. 905). In our study, persons with low income living in advantaged neighborhoods were estimated to have a 2.2 (table 3: 1/0.45) increased mortality risk relative to persons with high income living in advantaged neighborhoods. Mortality differences were also observed in disadvantaged neighborhoods, although they were smaller than those in advantaged neighborhoods. Yen and Kaplan suggested "differential access to resources" (14Go, p. 905) as an explanation for the above-mentioned differences. However, Hook (23Go) suggested lower "effective income" of poor persons living in expensive neighborhoods as a mechanism for less "access to resources." The setting of our study is characterized by free access to basic health care and more equal access to other public goods such as education and social services, suggesting that financial barriers are reduced and that access to resources is a less pronounced determinant of health. This is consistent with the smaller health differences between persons with low versus high income observed in our study relative to the larger health disparities in Alameda County, California.

The 1990 Nova Scotia Nutrition Survey aimed to collect information regarding the health status of Nova Scotians and their risk for chronic diseases. The way in which this investigation was conducted permits generalization to the eight counties where the survey was held. In investigations of risk factors for chronic diseases and mortality, a follow-up of 9–10 years should be considered limited, and one should therefore remain cautious with respect to the interpretation of the presented risk estimates. More specifically, the statistical power to detect differences in individual characteristics appears sufficient, but the statistical power to detect differences between contextual characteristics is difficult to judge since sample size and power calculations for multilevel approaches are complex and no applicable format has yet been developed (24Go). In this respect, however, it is important to note that the study by Yen and Kaplan is similar in design and statistical power (996 participants and 228 deaths) and that this US study did demonstrate statistically significant effects of contextual factors, whereas our study did not. Statistical power limitations also apply to cross-level interactions and to the analyses stratified by disadvantaged and advantaged neighborhoods. With respect to the latter, we presented three sets of analyses: those with categories of individual household income, those of the logarithm of individual household income, and those of individual education. All of these analyses suggest a stronger association of individual socioeconomic characteristics in advantaged neighborhoods relative to disadvantaged neighborhoods.

Strengths of our study are the longitudinal design and the high percentage (96.2 percent: 100 - 3.8) of original participants included in the analysis. In addition, we confirmed that our results were not substantially affected by migration, data linkage procedures, or bias associated with income loss resulting from morbidity. Use of secondary data, as utilized here, also has the advantage of immediate results but limits the research to information collected for other purposes. In this respect, we were limited to neighborhood characteristics available through the 1991 Canada Census, whereas we would have preferred to evaluate a broader spectrum of community characteristics. Reliance on the Census also limited us with respect to boundary specifications that distinguish neighborhoods and contextual conditions in a more meaningful manner. Future prospective studies should overcome these limitations.

In conclusion, this study adds to our understanding of individual and contextual socioeconomic determinants in health. The significance of individual socioeconomic characteristics and their differential importance within disadvantaged and advantaged neighborhoods have been demonstrated in the United States and seem also to be present, although less pronounced, in a setting with universal access to basic health and social services. Further research elaborating on the mechanisms of the combined importance of individual and contextual socioeconomic factors will facilitate policy aimed at reducing health inequality (1Go, 2Go, 6Go, 25Go).


    ACKNOWLEDGMENTS
 
This study uses data of the Nova Scotia Nutrition Survey, conducted by Dr. D. R. MacLean and financed by the National Health Research and Development Program and the Nova Scotia Department of Health. Support for the present analysis is provided through funding by the Canada Foundation for Innovation, the Dalhousie Medical Research Foundation, and a Canadian Institutes of Health Research Career Award to Dr. Paul J. Veugelers and through a Nova Scotia Clinical Research Scholar Award to Dr. George Kephart.

The authors thank Angela Fitzgerald, Shane Hornibrook and, from the Population Health Research Unit, Michael Pennock, Chris Skedgel, and Mark Smith for their helpful assistance.


    NOTES
 
Correspondence to Dr. Paul J. Veugelers at this address (e-mail: paul.veugelers{at}dal.ca).


    REFERENCES
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication October 26, 2000. Accepted for publication June 26, 2001.