From the Erasmus University Rotterdam, Department of Public Health, 3000 DR Rotterdam, the Netherlands.
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ABSTRACT |
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mortality; social class; social environment; unemployment
Abbreviations: GLOBE study, Gezondheid en LevensOmstandigheden Bevolking Eindhoven en omstreken (Dutch acronym for health and living conditions of the population of Eindhoven and its surroundings)
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INTRODUCTION |
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Dutch longitudinal data (Gezondheid en Levens Omstandigheden Bevolking Eindhoven en omstreken (Dutch acronym for health and living conditions of the population of Eindhoven and its surroundings) (GLOBE) study (33)) on 8,506 men and women living in 86 neighborhoods in the city of Eindhoven (191,000 inhabitants in 1991) were used to determine whether living in a neighborhood with a low socioeconomic status is related to 6-year all-cause mortality. In the analyses, we stringently controlled for individual socioeconomic status by adjusting each neighborhood socioeconomic indicator, aggregated from individual reports (e.g., percent of blue-collar workers in neighborhood), for four individual socioeconomic indicators, including the equivalent individual report (e.g., being a blue-collar worker or not). To obtain more information on the mechanisms underlying the association between neighborhood socioeconomic status and mortality (4
, 28
), we further examined whether people living in varying socioeconomic neighborhoods differed in more specific housing, social, psychologic, and behavioral characteristics.
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MATERIALS AND METHODS |
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In Eindhoven, the major city in the study, 8,506 GLOBE participants who reported on their current or last occupation (younger people, mainly students, were thus excluded) could be attributed to 86 administrative neighborhoods (areas) with relatively homogeneous housing. On average, there were 99 respondents in a neighborhood (range, 5386). Eindhoven has a total of 106 areas, but some have only few or no inhabitants. In 1991, the 86 neighborhoods contained 191,000 inhabitants (all ages), with an average of 2,221 inhabitants per neighborhood.
Municipal population registers provided information on all-cause mortality during the follow-up period until mid-1997. These have a virtually complete coverage of the population (34). During the 6-year follow-up period, 487 men and women died (6 percent).
Individual and neighborhood socioeconomic status
Similar measures were used to indicate socioeconomic status on the individual and neighborhood levels. To indicate the socioeconomic status of the neighborhood, we aggregated individual reports on socioeconomic status to the neighborhood level. An advantage of this approach is that the effect of neighborhood socioeconomic status on mortality can be stringently controlled for equivalent measures on the individual level. This allows an accurate examination of genuine contextual effects and, to a large extent, excludes the possibility that any adverse effect of poor neighborhoods is fully based on poorer people living in poorer neighborhoods.
Four indicators of individual socioeconomic status were used: last attained educational level of respondent, current or last occupational level of household breadwinner (35), being disabled or unemployed, and presence of severe financial problems in the household. Being or not being disabled or unemployed was based on a question asking for the subjects' main activity for which they received income or special social security benefits. Disability implied long-term disability. Severe financial problems were self-reported by a single item about whether or not the household had many problems with making ends meet.
Neighborhood socioeconomic status was indexed by four equivalent indicators based upon aggregated individual GLOBE data: the percent of subjects reporting primary schooling only (range, 044 percent), the percent of subjects reporting that they were unskilled manual workers (range, 039), the percent of subjects reporting that they were unemployed or disabled (range, 028), and the percent of subjects reporting severe financial problems (range, 015). Both continuous percentage scores and quartiles of neighborhoods based on the percentage in the aggregated data were used; each quartile contained about 22 neighborhoods (86 neighborhoods total).
Disability and unemployment were combined because both groups share the characteristic of being without paid work and receiving social security benefits. Moreover, they were combined because it is likely that many disabled persons would have been assigned to the unemployed group in countries other than the Netherlands. In the Netherlands, the relatively generous disability benefit scheme has been used on a large scale by employers to give the unemployed a reasonable level of income compensation. Our findings will not be confounded by prevalent disease (as the main cause of disability) because the effect of the neighborhood percent reported as unemployed or disabled will be controlled for individual disability or unemployment and indicators of prevalent disease.
Correlates of neighborhood socioeconomic status
Twelve individual characteristics were explored for their association with neighborhood socioeconomic status. These could be classified into four groups: housing conditions, and social, psychologic, and behavioral factors. The three housing conditions were based on self-reported cold or draft in the house (yes, no), condensation (yes, no), or moisture and damp in the house (yes, no). The three social characteristics were based on self-reported perception of vandalism in neighborhood (yes, no), social difficulties with family members and neighbors (subjects in the most adverse quintile of the sum of eight five-point items asking for difficulties in contacts with eight separate groups of important others, including neighbors vs. the others), and noise pollution from neighbors (yes, no). The three psychologic factors were based on self-reported low control (subjects in the most adverse quintile of the sum of 11 five-point items asking for an external locus of control vs. the others), passive coping (subjects in the most adverse quintile of the sum of eight five-point items asking for an inactive style of coping vs. the others), and depressive coping (subjects in the most adverse quintile of the sum of seven five-point items asking for a depressive reaction pattern when faced with problems vs. the others). The three behavioral factors were based on self-reported current smoking (yes, no), physically inactivity in leisure time (subjects reporting no time spent on sports, gardening, walking, or cycling during leisure time vs. the others), and excessive alcohol consumption (subjects drinking more than six alcoholic beverages on 3 or more days a week or more than four beverages on 5 or more days a week vs. the others). More information on the measurement of these characteristics can be found elsewhere (36, 37
).
Data analysis
Each separate neighborhood socioeconomic indicator was related to all-cause mortality. The effect of individual socioeconomic status was also determined. The effect of neighborhood socioeconomic status (e.g., percent of subjects reporting to be unskilled manual workers) was estimated as both unadjusted and adjusted for its individual-level equivalent socioeconomic measure (e.g., occupational level). Any residual confounding by socioeconomic status on the individual level was taken into account by controlling for all four individual-level indicators of socioeconomic status. Age, sex, and baseline health status were controlled for in these analyses. Neighborhood socioeconomic status had similar effects on mortality in men and women and in the young and old (there were no statistically significant interactions with sex and age). Therefore, no sex- or age-specific analyses were performed. Baseline health status was indicated by two dummy variables indicating whether or not the respondent reported any less (e.g., hypertension, migraine) or more (e.g., heart disease, cancer) severe chronic conditions in a 23-item checklist.
We further explored the associations between neighborhood socioeconomic status and the adverse housing, social, psychologic, and behavioral conditions. Neighborhood socioeconomic status was therefore related to individual reports of any of these conditions, adjusting for all four individual-level indicators of socioeconomic status, age, sex, and baseline health status. Since the information on these conditions was available for only a subsample that was extensively interviewed (n = 2,726) (33), detailed multivariate analyses determining the contribution of reported adverse conditions to the neighborhood-mortality association were precluded.
Because individuals (level one) were nested within neighborhoods (level two), the analyses with death or individual reports of problems as the outcome were done with multilevel logistic regression using the MLN program (38, 39
). The probability of dying of the ith individual in the jth neighborhood was modeled as follows:
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RESULTS |
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The independence of neighborhood socioeconomic status and individual socioeconomic status is shown in figure 1 for the educational indicators. Living in a poorly educated neighborhood increased probabilities of dying for both highly and poorly educated individuals. Similarly, a low individual educational level increased probabilities of dying within both highly and poorly educated neighborhoods. For example, 3.6 percent of the subjects with a low educational level living in a neighborhood with many respondents with only primary schooling were estimated to die within the follow-up period compared with 2.4 percent of their poorly educated counterparts living in highly educated neighborhoods. The 1.7 percent deaths in highly educated subjects living in highly educated neighborhoods compared with the 3.6 percent deaths in poorly educated subjects living in poorly educated neighborhoods indicates the cumulative effects of individual and neighborhood socioeconomic statuses.
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The odds ratios in table 3 show that neighborhood socioeconomic status (percent reporting that they were unemployed or disabled, in quartiles) was related in the predicted direction to all housing, social, psychologic, and behavioral factors. For example, individuals living in neighborhoods with a high percent of subjects reporting that they were unemployed or disabled more often reported that they experienced cold or draft in their houses (odds ratio = 2.29), that they experienced vandalism in the neighborhood (odds ratio = 2.05), that they used more passive instead of active coping (odds ratio = 1.46), and that they more often did not engage in physical activity (odds ratio = 1.48). The associations were independent of whether or not the subjects themselves had a high educational or occupational level, whether or not they were unemployed or disabled, and whether or not they had severe financial problems. There was no clear association between neighborhood socioeconomic status and excessive alcohol consumption. Similar associations were found with the other indicators of neighborhood socioeconomic status.
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DISCUSSION |
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Adverse conditions in neighborhoods with a low socioeconomic status
Subjects living in the neighborhoods with a low socioeconomic status reported adverse conditions more often than did their counterparts in better-off neighborhoods. This again holds both for subjects with a high and those with a low socioeconomic status. The varying adverse conditions may be causally related and may be contributing to the pathway between socioeconomic context and individual risk of mortality. The poor physical environment (poor quality housing, e.g., cold, drafty, or damp houses) in neighborhoods with a low socioeconomic status may adversely affect the social environment by increasing social disintegration and decreasing social cohesion and public commitment (e.g., vandalism and noise pollution neighbors) (40, 41
). Our finding that particularly the percentage of subjects in a neighborhood who reported that they were unemployed or disabled was independently related to mortality may also be interpreted as a contextual effect of social exclusion, i.e., poor social cohesion between the unemployed and employed (42
). The disintegration of the social fabric may further generate an environment of hopelessness and powerlessness that may become manifest in unhealthy individual psychologic profiles (e.g., low control, passive coping). Such profiles are likely to negatively affect the ability to quit unhealthy lifestyles (e.g., smoking and no physical activity). These varying gloomy characteristics may well contribute to a higher general susceptibility in areas with a low socioeconomic status. This may underlie the association with all-cause mortality in our study and a range of causes of death in another study (18
). The hypothesized causal pathways between neighborhood socioeconomic status and mortality remain to be tested in further studies, however.
Are neighborhood effects overestimated?
Some methodological aspects may have caused overestimated effects of neighborhood socioeconomic status. First, we may have forgotten important confounders on the neighborhood level (16, 26
). Neighborhoods do not only differ in their level of socioeconomic status. Neighborhood income inequality may be considered in this context, since area income inequality has been found to be related to mortality independent of mean area income level (43
, 44
). In further analyses of GLOBE data, we will use integral information from the municipal authorities on a wide range of neighborhood characteristics, possibly allowing us to examine potential neighborhood confounders.
Second, we examined whether social class during upbringing contributed to the neighborhood socioeconomic status-mortality association (4). Low background social class could be related to living in poorer areas, independent of adult social class, and low background social class has been found to be related to adult health (45
, 46
), also independent of adult social class. In our study, however, social class during upbringing did not affect the association between neighborhood socioeconomic status and mortality (not tabulated).
Third, our measures of baseline health status were self-reported only. Because of particular diseases, people may move to specific neighborhoods. To control for this confounding effect of disease, baseline health status should ideally have been measured through medical screenings. In our data, self-reported diseases were particularly underreported by the lower socioeconomic groups, causing underestimated socioeconomic differences in prevalent disease (47). In a longitudinal design in which prevalent disease is controlled for, this may, however, result in overestimated socioeconomic differences in mortality risks. This argument holds only when prevalent disease is considered a confounder. We think, however, that it is more likely that neighborhood socioeconomic status causes health to deteriorate, i.e., prevalent disease is an intermediate factor. In this perspective, our results may even be underestimated.
Are neighborhood effects underestimated?
Some methodological concerns that are likely to have caused underestimated effects of neighborhood socioeconomic status should also be discussed. First, the neighborhood boundaries were derived from the neighborhood classification scheme used by the municipal authorities. It is based upon areas that have relatively homogeneous types of houses (e.g., predominantly rented or private). It is possible that particular parts of neighborhoods are not correctly defined by this classification or that people perceive different boundaries to define their neighborhood. Neighborhoods, so defined, could be of similar, smaller, or larger magnitude. One of the strengths of the GLOBE study is the availability of many neighborhoods (relative to the population). Although this increases the socioeconomic homogeneity within neighborhoods, the incorrect classification of neighborhoods may still have resulted in misclassification, possibly leading to underestimated effects of neighborhood socioeconomic status (5, 16
, 26
). Furthermore, the socioeconomic status of some neighborhoods was described by few respondents only, and students were excluded from the analyses. This may also have led to misclassification of the neighborhood socioeconomic status. It is not completely clear how this may have affected our findings. The results were, however, similar in subjects living in neighborhoods with many and those with few respondents.
Second, neighborhood socioeconomic status was stringently controlled for individual socioeconomic status, as if the latter were a confounder. In this perspective, people with a low socioeconomic status are thought to search for neighborhoods with a low socioeconomic status; because of their low income, they buy or rent cheaper houses in particular areas. From another perspective, a neighborhood with a low socioeconomic status is thought to result in less of a tendency among its inhabitants to study or to move up the social ladder (16). Here, the individual socioeconomic status should be considered as an intermediate factor. To the extent that the latter viewpoint is applicable, the adjustment for individual socioeconomic status has resulted in underestimated contextual effects, particularly because one neighborhood socioeconomic indicator was controlled for four individual socioeconomic indicators.
Third, because of a longer exposure to adverse socioeconomic neighborhood conditions, neighborhood socioeconomic effects may be stronger in persons who lived longer in particular neighborhoods (16, 18
). We could not explore this effect specifically because there was no individual-level information on how long persons lived in their neighborhood. Municipal integral information on the neighborhood percent of persons living in the neighborhood for less than 3 years, however, did not interact with neighborhood socioeconomic status, i.e., neighborhood socioeconomic effects were not stronger when most people moved in a longer time ago (not tabulated).
Low neighborhood socioeconomic status was a consistent and substantial predictor of premature mortality in Dutch men and women. Since persons from lower and those from higher socioeconomic groups were equally adversely affected by living in such neighborhoods, neighborhood socioeconomic status had genuine contextual effects on mortality. Although more research is needed to determine the specific mechanisms involved, our findings pointed to a higher prevalence of poor housing conditions, social disintegration, adverse psychologic profiles, and unhealthy behaviors in neighborhoods with a low socioeconomic status. Both individual and neighborhood socioeconomic statuses were related to longevity. The independent effect of the latter suggests potential public health benefits of modifying socioeconomic characteristics of areas.
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ACKNOWLEDGMENTS |
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The study is conducted in close collaboration with the Public Health Services of the Dutch city of Eindhoven and the region of South-East Brabant.
The authors thank Roel Faber, Ilse Oonk, and Michel Provoost for carefully constructing the database and Dr. Carola Schrijvers for providing comments on previous drafts of the paper.
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NOTES |
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REFERENCES |
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