Contextual effects of social fragmentation and material deprivation on risk of myocardial infarction—results from the Stockholm Heart Epidemiology Program (SHEEP)

Maria K Stjärne1,2, Antonio Ponce de Leon3, Johan Hallqvist1,2 and the SHEEP Study Group*

1 Karolinska Institute, Centre for Health Equity Studies (CHESS)
2 Departments of Epidemiology and 3 Social Medicine, Stockholm County Council, Stockholm, Sweden

Correspondence: Maria Kölegård Stjärne, CHESS—Centre for Health Equity Studies, Stockholms Universitet/Karolinska Institutet, SE 106 91 Stockholm, Sweden. E-mail: maria.k.stjarne{at}chess.su.se, www.chess.su.se


    Abstract
 Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Background Socioeconomic deprivation has been suggested as a contextual feature of importance for cardiovascular disease and mortality, whereas the effect of social fragmentation has largely been studied in relation to suicide. In this study we examine the contextual effects of social fragmentation and material deprivation on the incidence of myocardial infarction (MI).

Methods A population-based case-control study (SHEEP). The study base included all Swedish citizens aged 45–70 living in the Stockholm metropolitan area. Cases (n = 1631) were all first events of MI during 1992–1994. Exposure information on individual risk factors was obtained from a questionnaire. Areas (n = 862) were classified according to the Townsend index, measuring material deprivation, and the Congdon index, measuring social fragmentation.

Results We found increased incidence of MI in both materially deprived and socially fragmented contexts that were not due to confounding from individual social risk factors being more prevalent among subjects in deprived settings. The adjusted relative risk of MI was 2.0 (95% CI: 1.3, 3.1) for women living in the top quartile of materially deprived areas. For men, the adjusted relative risk (RR) was 1.6 (95% CI: 1.2, 2.1).Women living in the top quartile of socially fragmented areas had an RR of MI of 1.6 (95% CI: 1.0, 2.5) after adjustment, while the corresponding figure for men was 1.4 (95% CI: 1.0, 1.8).

Conclusion Our findings support the notion that the social context in which people live has an impact on the risk of coronary heart disease. We could not determine which of the contextual aspects under study made the most substantial contribution. Mutual adjustment of the two indices suggests that material deprivation is the dominating factor, especially for women. However, the indices were highly correlated (r = 0.87), and it cannot be ruled out that they partly measure the same underlying phenomenon.


Keywords Material deprivation, myocardial infarction, social context, social fragmentation, social position

The socioeconomic context of neighbourhoods has been associated with specific morbidity and mortality,1–5 as well as with health-related behaviours.6–8 There is also some recent evidence suggesting an influence of neighbourhood socioeconomic context on incidence of myocardial infarction (MI).9,10 Earlier studies have shown associations between level of neighbourhood deprivation and prevalence of coronary heart disease,11 prevalence of cardiovascular risk factors,11–13 mortality due to coronary heart disease,4,14,15 and survival after MI.16 A wide variety of contextual measures has been used in these studies, and most capture socioeconomic and material aspects.

In 1993 Macintyre et al. presented a conceptual framework for organizing features of local areas into what they call ‘opportunity structures’.17 They suggested that socially constructed and socially patterned features of the physical and social environment may promote or damage health, either directly, or through the opportunities they provide for people to live healthy lives. Later, they proposed that these features are part either of material and infrastructural resources, or of collective social functioning.18

In similar fashion, Townsend et al. distinguished between the concepts of material deprivation and social deprivation. Material deprivation entails a lack of goods, services, resources and amenities, i.e. an absence of the physical environment that is customary in the society in question. Social deprivation, on the other hand, involves non-participation in roles, relationships, customs and functions, and a lack of the rights and responsibilities implied by membership of a society.19 The authors also constructed a small-area measure of material deprivation for exploration of how far health inequality might be explained by variation in material conditions. Since then, the Townsend index of relative material deprivation has been widely employed in relation to various health outcomes.

As to social deprivation or collective social functioning, there are a variety of related concepts such as social cohesion, social integration, and social capital, and some empirical evidence connecting those collective features to health or mortality,20 but it is still an open question whether such effect on MI exists.21

While there are several measures of material deprivation available, measures of social collective functioning based on administrative register are scarce. For an ecological study of suicide in London, Congdon developed an index to define anomic areas, based foremost on indicators of population turnover and instability.22 Later, this has been referred to as an index of social fragmentation, measuring lack of opportunities for social integration.21,23,24 The Townsend and Congdon indices have recently been used in ecological studies to analyse whether material deprivation and social fragmentation affect different types of diseases, and to establish whether it is possible to disentangle the effects of the two factors.22–25

The aim of this study is to analyse the effect of the economic and social environment on incidence of MI, which is a well-defined disease outcome related to specific pathogenic mechanisms, with the help of some available and internationally frequently used measures of exposure. In an earlier study of contextual impacts on MI, we measured context at parish level.10 Some parishes were large and comprised several small areas that differed contextually, which resulted in non-differential misclassification of exposure. For the analyses reported here, however, we used a refined geographical classification, allowing us to operationalize neighbourhoods as smaller, more homogenous residential areas. Our specific questions were:

Does material deprivation, as measured by the Townsend index, increase the risk of MI? Are there persisting contextual effects, after adjustment for indicators of individual social position that operates in segregation processes?

Does social fragmentation, as measured by the Congdon index, increase the risk of MI, and, if so, does the excess risk persist after adjustment for individual factors?

Is it possible to disentangle the effect of material deprivation, according to Townsend, and the effect of social fragmentation, according to Congdon, on MI? Which is the most important, and to what extent are they mutual confounders?


    Materials and Methods
 Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
The Stockholm Heart Epidemiology Program (SHEEP) is a population-based case-control study of the causes of MI.26,27 The study comprised all non-fatal (n = 1643) and fatal (n = 603) first events of MI among Swedish citizens aged 45–70, resident in Stockholm County during 1992–1993 (1992–1994 for women). Cases were identified from either the coronary and intensive care units at emergency hospitals in Stockholm County, or the Hospital Discharge Register for the county, or death certificates from the National Cause of Death Register maintained by Statistics Sweden. Standardized diagnostic criteria for MI were applied.28 Cases were included at time of disease onset. Simultaneously, one control per case was randomly selected from the corresponding study base after stratification for age, sex, and hospital catchment area (10 areas). All controls were initially checked for previous MI and were alive when recruited, regardless of the vital status of the corresponding case. More referents than cases were finally included, because sometimes the referent was already included when the case chose not to participate. In addition, if a referent at first did not choose to participate another one was sampled, but sometimes they both ended up participating. All subjects received a postal questionnaire covering a large set of potential risk factors for MI, including residence history, occupational history, education and other social factors, lifestyle factors, and physical and psychosocial work environment. Telephone interviews were used to fill in any missing questionnaire information. Information on fatal cases was obtained from close relatives. For non-fatal cases and their controls a health examination was carried out 3 months after disease onset in order to collect data on various biological parameters related to cardiovascular disease.

In total, the SHEEP study encompassed 2246 cases and 3206 controls. The non-participation rate among cases was 28% for females and 19% for males, while the corresponding figures for controls were 30% and 25% respectively. Subjects responded to the same extent in different age groups, and were equally inclined to participate regardless of catchment area.27 Due to insufficient address information, 7% of subjects were not included in the analysis. After excluding rural inhabitants and subjects in sparsely populated small urban areas (4%), 3610 individuals were included—485 female and 1061 male cases, and 698 female and 1366 male controls.

Exposures
The geographical unit for social context was ‘small residential area’. The areas were originally used as census areas and defined according to homogeneity criteria regarding type of buildings and land use. These areas are now continually updated by Office of Regional Planning and Urban Transportation, but no longer used for census purposes.29 All SHEEP participants‘ addresses were Geographic Information Systems (GIS) coded and assigned a small residential area code based on latest address before inclusion. At this time, Stockholm County comprised 1132 small areas. Rural areas were excluded from the analyses, since the processes of economic and residential segregation are different in rural and urban areas30 (urban area ≥200 inhabitants or <200 m between buildings). We also excluded areas with less than 20 inhabitants (mostly industrial sites) because of unavoidable lack of precision in calculating the contextual measures. In total, 862 small residential areas with a median population of 1142 (SD = 1459) were included in the contextual analyses.

We calculated Townsend deprivation scores19 to determine material deprivation, using 1990 census and other registry data on proportion of unemployment, car ownership, home ownership, and overcrowding. Congdon's index22 was used to measure social fragmentation; scores were computed from census data on proportions of private rented accommodation, single person households, unmarried people, and population turnover (Table 1). Both indices are summary measures. By subtracting the overall mean and dividing by the standard deviation, a z score for each indicator was obtained. All areas were assigned this value, reflecting their deviation from the mean. Z-scores for all items in the indices were then summed. The Townsend scores ranged from –5.2 to 21.2, and the Congdon scores from –5.3 to 20.0. Exposure categories were based on quartiles of areas ranked by deprivation or fragmentation score.


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Table 1 Indicators in Townsend's deprivation index and Congdon's index of social fragmentation

 
Information on individual social position was derived from the questionnaire and consisted in the following: socioeconomic group determined by latest occupation before inclusion and classified according to a system developed by Statistics Sweden,31 educational level, labour-market position at inclusion, and cohabiting/non-cohabiting.

Information on individual health behaviour: smoking status from the questionnaire, body mass index (BMI) from the health examination, and as a second choice self-reported height and weight from the questionnaire, hypertension from the health examination, and/or information from the questionnaire stating medically treated hypertension.

Further, a summary measure of individual social network was constructed, composed of items concerning social contacts, job support, social participation, and cohabiting/non-cohabiting.

Statistical analyses
The strategy was to fit multilevel random intercept logistic regression models with small residential areas treated as second-level units. The Townsend and Congdon indices were regarded as second-level variables, and individual attributes as first-level ones. We used second-order penalized quasilikelihood as estimation method, since it is regarded as giving the least biased variance parameter estimates.32 The presence of extra binomial variation was judged from fitting an overdispersion parameter which ideally should be approximately 1. All the fitted models met this requirement.

The sample of controls was stratified by age, gender, and hospital catchment area. All analyses were adjusted for age (in 5-year age groups) and stratified by gender. Since hospital catchment area is correlated with residential area characteristics, the stratified sampling of controls would introduce confounding, biasing relative risks towards unity. Accordingly, all models included control weights to eliminate the effect of stratified sampling according to catchment area. Three dummy variables accounted for four contextual exposure levels at small-area level. We included indicators of individual social position that operate in the segregation process as confounders—i.e. as determinants of the selection of individuals into specific types of neighbourhoods, such as socioeconomic group, educational level, labour-market position, and marital status.33 In the final models plausible mediators such as: smoking status, BMI, and hypertension was included together with the indicators of social position. As social fragmentation might imply less opportunity for well-developed social networks on the individual level, social network was included in the final social fragmentation model. Odds ratios (OR) as estimators of incidence-rate ratios with 95% CI were computed from the multilevel models. The percentage of the excess risk in deprived areas explained by individual social factors was computed as follows: (OR(age adjusted) – 1) – (OR(adjusted by individual social indicators) – 1)/ (OR(age-adjusted)–1). The total attributable fraction was computed as suggested by Rothman34 and the variance partition coefficient as suggested by Goldstein.35

The multilevel analyses were carried out using MlwiN version1.1 while SAS version 8.02 was used for the other analyses.


    Results
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 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Table 2 shows the distribution of SHEEP controls according to social position for each area quartile of material deprivation and social fragmentation. Women in materially deprived areas are to a greater extent old age pensioners, manual workers and lone dwellers. For several of the items the trend levels out—especially for the social fragmentation quartiles, where the trend peaks in the third quartile.


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Table 2 Percentage of Stockholm Heart Epidemiology Program (SHEEP) controls in different social positions in each area quartile of material deprivation, as measured by Townsend' deprivation index, and social fragmentation, as measured by Congdon's index. The first quartile refers to areas with the lowest level of deprivation or fragmentation and the fourth quartile to areas with the highest

 
Among men, there is a higher frequency of manual workers, pensioners, the least educated and lone dwellers in deprived areas. The composition is similar across quartiles of social fragmentation, with the exception of a break in the trend for manual workers and non-manual employees (in conformity with the pattern among women).

Material deprivation
Figure 1 shows a gradually increasing risk of MI over quartiles of material deprivation for both women and men. Generally, occupational class effected the largest change to the contextual risk estimate of MI. Material deprivation doubles the risk of MI (OR = 2.1, 95% CI: 1.6, 3.0) for women in the most deprived areas, and there was virtually no confounding from individual social position (i.e. socioeconomic group, education, marital status, and labour market position). Some 26% of the adjusted contextual effect was potentially mediated through smoking, obesity, and hypertension. The OR for men living in the most deprived areas was 1.8 (95% CI: 1.4, 2.4), with 26% of the excess risk being explained by individual social position. Hence, the remaining contextual effect was OR 1.6 (95% CI: 1.2, 2.1) of which smoking, obesity, and hypertension mediates 39%.



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Figure 1 Relative risk of myocardial infarction according to level of material deprivation in small residential areas, as measured by Townsend's index.

aThe socioeconomic indicator had the following categories: 1) unskilled and 2) skilled manual workers; 3) low-level, 4) intermediate and 5) high-level non-manual employees, 6) self-employed manual workers; and 7) self-employed non-manual workers. Educational level was categorised as: 1) compulsory school and vocational training; 2) upper-secondary school and university. Information on labour-market position at inclusion was grouped into: 1) employed and self-employed, 2) old-age pensioners, 3) unemployed and housewives, and 4) long-term ill and early-retirement pensioners. Cohabiting was a dichotomy: married/cohabiting and non-cohabiting.

bSmoking status was defined as: Never smokers, ex-smokers, and current smokers, where ex-smoker was defined as those who stopped smoking more than 2 years previously. BMI was dichotomized: those with a body mass index >27 and those <27. Hypertension was defined as a systolic pressure >170 mmHg, and/or a diastolic pressure >95 mmHg.

 
Social fragmentation
For women the relative risk of MI increases with increasing levels of social fragmentation, but the effect is not as strong as that of material deprivation. The excess risk in the top quartile of fragmented areas was OR = 1.8 (95% CI: 1.2, 2.6) and individual social position explained 21% of the excess risk. Thus the contextual effect was OR 1.6 (95% CI: 1.1, 2.5), of which 21% was potentially mediated through smoking, obesity, hypertension, and individual social network. For men the effect of social fragmentation was weaker and did not show the same clear gradient as the effect of material deprivation; the OR was 1.6 (95% CI: 1.2, 2.0) in the top quartile of fragmented areas. Following adjustment for the individual indicators of social position, the contextual effect was OR 1.4 (95% CI: 1.1, 1.8). Further adjustment by smoking, obesity, hypertension and individual social network reduced the excess risk with 40% (Figure 2).



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Figure 2 Relative risk of myocardial infarction according to levels of social fragmentation in small residential areas, as measured by Congdon's index.

aThe socioeconomic indicator had the following categories: 1) unskilled and 2) skilled manual workers; 3) low-level, 4) intermediate and 5) high-level non-manual employees, 6) self-employed manual workers; and 7) self-employed non-manual workers. Educational level was categorised as: 1) compulsory school and vocational training; 2) upper-secondary school and university. Information on labour-market position at inclusion was grouped into: 1) employed and self-employed, 2) old-age pensioners, 3) unemployed and housewives, and 4) long-term ill and early-retirement pensioners. Cohabiting was a dichotomy: married/cohabiting and non-cohabiting.

bSmoking status was defined as: Never smokers, ex-smokers and current smokers, where ex-smoker was defined as those who stopped smoking more than 2 years previously. BMI was dichotomized: those with a body mass index >27 and those <27. Hypertension was defined as a systolic pressure >170 mmHg, and/or a diastolic pressure >95 mmHg. A summary measure of individual social network was constructed from four items concerning social contacts, three items concerning job support, three items concerning social participation, and three items concerning marital status and cohabiting/non-cohabiting.

 
Material deprivation versus social fragmentation
In an attempt to disentangle the separate effects of material and social deprivation we mutually adjusted the two indices by fitting age-adjusted multilevel models, where both indices were included as continuous variables. Table 3 shows an increase in the relative risk of MI of 9% for women and 7% for men for every unit increase in the Townsend score, and an excess risk of 8% for women and 7% for men for each unit increase in the Congdon score. After mutual adjustment, only the effect of material deprivation on MI among women remained significant. However, because of the small number of individuals living in materially deprived areas with low levels of social fragmentation and vice versa there is an inherent problem in the adjustment procedure. The cross-tabulation of SHEEP participants by area quartiles shows (Table 4) that the indices were highly correlated and that stratified analyses are impossible. Further, mutual adjustment using exposure categories based on tertiles, quartiles, or quintiles gave very different results depending on the choice of cut-off points.


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Table 3 The age-adjusted odds ratio (OR) of myocardial infarction (MI) for a one-unit increase in Townsend and Congdon score, each separately and adjusted by one another

 

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Table 4 Number of Stockholm Heart Epidemiology Program (SHEEP) participants in each area quartile

 
To further examine this issue, and to make our results comparable with earlier ecological studies we adopted an ecological approach by conducting a partial correlation analysis between occurrence of MI and the indices of deprivation and social fragmentation at the small-area level. Partial correlation coefficients provide the correlations between MI and the level of deprivation (Townsend) free from fragmentation (Congdon) and vice versa. A two-level model was used to regress MI on each of the indices, and a single-level model to regress the indices on each other. Second-level residuals from the former and ordinary residuals from the latter model were correlated. Table 5 shows that the partial correlations are very small, with a tendency of greater importance of material deprivation for MI among women. The indices are also highly correlated at small-area level (r = 0.87).


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Table 5 Partial correlations between myocardial infarction (MI) and the Townsend and Congdon indices

 

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Table 6 Variance partition coefficients describing the variation in myocardial infarction attributable to the higher level source of variation, in this case residential areas and parishes

 
Choice of the most appropriate spatial scale is not straightforward. In this study we used small residential areas with a high degree of homogeneity regarding land use and building structure. We calculated the variance partition coefficient, which describes the percentage of variation that is attributable to the higher-level source of variation.35 Table 5 shows that the clustering of MI is much larger in residential areas than in parishes; 15% of the variation in MI among women and 7% among men is attributable to the residential area level, while the corresponding figures for variation attributable to the parish level were 5% and 0.7%.


    Discussion
 Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
We found increased incidence of MI in materially deprived contexts for both women and men that were not due to confounding from individual social risk factors being more prevalent among subjects in deprived settings. We also found contextual effects, but somewhat weaker, of social fragmentation for both women and men.

Our findings support the only study so far published on contextual risk factors and incidence of MI. From a cohort study conducted in four US communities, Diez-Roux et al. report hazard ratios for coronary heart disease of 1.9 among white women and 1.7 among white men living in socioeconomically deprived areas, after adjustment for individual social position. In this case, the socioeconomic context was measured by a summary score of wealth, income, and occupation, and the neighbourhoods were of similar size to those in our study.9

What is most important—material deprivation or social fragmentation? Davey Smith and colleagues compared the effects of material deprivation and social fragmentation in parliamentary constituencies in Great Britain with regard to cause-specific mortality. Their findings suggest that material deprivation is related to overall mortality and cardiovascular and stomach cancer mortality, whereas social fragmentation is associated with suicide. Lung cancer and cirrhosis mortality were equally associated with both types of contexts.21 In another study, with the same setting and approach, they suggest that both socially fragmented areas and areas with increasing social fragmentation are associated with higher suicide rates independent of material deprivation.23 However, the independent impact of social fragmentation on suicide was not replicated in a study of electoral wards in Oxfordshire.25 These studies all had an ecological design and reported correlation coefficients of r = 0.7 between the indices. Partial correlations were used to distinguish the effects of the two indices.

Our study was unable to disentangle the effect of material deprivation from that of social fragmentation; i.e. to measure the effect of one contextual concept not confounded by the other. One probable reason for this is that the Townsend and the Congdon indices are not specific enough in measuring the two theoretically different aspects of context, which implies non-differential misclassification of both exposures. The Townsend index was created to measure deprivation in different areas in Britain (as distinct from the victims of the conditions). Unemployment was used as an indicator of a general lack of material resources and the insecurity to which this gives rise. Car ownership and housing tenure were included as substitutes for income data, car ownership reflecting current economic situation, and owner occupancy reflecting wealth and income in a longer term sense (although housing tenure might in fact reflect more aspects of wealth and stability than income). Overcrowding represents living circumstances and housing conditions. We compared all indicators in the Townsend index with another single indicator of relative affluence/deprivation (i.e. per cent of people in the highest income quintile of Stockholm County in each area), and found that unemployment, overcrowding and owner occupancy correlated reasonably well, whereas car ownership showed a very weak association. As Whitehead has commented in a British context, the incorporation of car access means that the index is a reasonably reliable indicator of deprivation in most parts of the country, but not in London—where lacking access to a car is not necessarily a sign of material deprivation.36 The finding regarding car ownership in a Swedish urban setting might have a similar explanation to that which has been suggested for London. Transposing indices to different settings might also be problematic, both from an urban/rural as well as an international perspective. For example, owner occupation does not always represent substantial command of resources partly due to different traditions in providing public housing. Especially in a Swedish urban context, renting is an accepted alternative in an extensive housing market. Originally the inclusion of overcrowding was thought to balance some of this effect.

The Congdon index comprises percentage of residents moving in or out as an indicator of population turnover and prevalence of single person households (<65 years) as an indicator of non-family areas and instability; proportion of unmarried people might be a similar indicator. However, it has earlier been questioned whether it should be updated to reflect social trends towards increasing cohabitation,23 a trend which is strongest in urban areas.37 Moreover, the fourth item in the index ’proportion of rented accommodation‘ might not be a good indicator of instability, as private rental differs according to traditions mentioned above. Additionally, it is highly correlated with ’owner occupation‘ in the Townsend index, which also impairs the possibilities to disentangle the two constructs.

Another reason for our inability to separate the effects of material deprivation from those of social deprivation (on the risk of MI) could be that especially the Congdon index is not a theoretically valid construct for hypotheses on the aetiology of MI. The Congdon index was designed to capture areas characterized by high population turnover, instability, and transience, and to be used in studies of suicidal behaviour. It has also been related to theories of social disorganization and social fragmentation.22 However, aspects of social integration and social cohesion20 are highly relevant when aiming at testing whether material or social contextual deprivation is the most important risk factor. Therefore, we suggest that the problem with the Congdon index is mainly dependent on the choice of empirical indicators and how distinctly they operationalize the theoretical construct. Unfortunately, we had no other measure of social deprivation available for use as comparison in this study.

The gender differences support earlier findings that contextual effects are stronger among women. Part of the higher relative risk among women might also be due to the lower incidence of MI: the incidence of MI in Stockholm 1993–1995 were 49.1 for men and 29.7 for women per 10 000 person-years, in the age group 30–89). Moreover, we used individual occupation to classify socioeconomic group, which omits potential influences from the family unit. In this age cohort this might particularly bias the socioeconomic information for women since the situation white-collar husband/blue-collar wife was 3.5 times more common than the opposite situation.38 This is also indicated by the minor change of risk estimate among women after adjustment for individual socioeconomic position. The dominance method,39 (considers information on occupation from both spouses, and uses the superior) is suggested as a better discriminator in terms of class variation and should have been used if the information was available. It could also be the case that women are more sensitive to shortcomings in their social surroundings than men, a question that needs to be analysed in further studies.

Strength and weaknesses of the study
Evidence of increased mortality risk in deprived neighbourhoods has grown during the last decade,40 but there are still few studies of specific disease incidence. Contextual exposures might be expected to have stronger effects on mortality due to coronary heart disease than on incidence of MI, since case fatality rates have been shown to be related to deprivation levels.15 Further, the risk of health selection bias is more pronounced in both mortality studies and studies with prevalent outcomes. The use of first event of MI as outcome makes this study less sensitive to these kinds of biases. To further reduce health selection bias we controlled for confounding from employment status, including categories of long-term illness and early retirement.

These analyses assume that the measured exposure of neighbourhood context at inclusion affects risk of MI, but neither length of residence nor residential mobility was considered. This might lead to a misclassification bias that would also dilute the effect. However, 76% of the controls in the study had lived at the same address for 10 years or more.

Confounding
Individuals exposed to different levels of contextual deprivation were made comparable with regard to individual risk factors for MI that also select people into areas with different levels of contextual deprivation. The problem of residual confounding from individual-level risk factors is considerable in this type of study.41–43 It is possible that there is some residual confounding from socioeconomic circumstances at individual level, since we lack individual and family income information. Further, there are a number of potential confounders that we would regard foremost as downstream mediating factors in the contextual mechanism. Several studies report higher frequencies of negative health behaviours in deprived areas,6–8 and also that residence influences preferences concerning health behaviours during adolescence.44,45 To explore how much of the contextual effect was mediated through hypertension, BMI, and smoking, those terms were included in a multilevel model together with indicators of social position (Figures 1 and 2). The contextual effect of material deprivation decreased from OR 2.0 to 1.8 for women and from 1.6 to 1.4 for men, while the corresponding reductions for the social fragmentation effect were from 1.6 to 1.5 for women and from 1.4 to 1.3 for men. This gives some indication of the magnitude of the effects, but the causal chains involved are complex (not analysed in this study).

Public health implications
Comparing contextual effects is problematic due to the wide variety of geographical levels, exposure measures, and outcomes that can be used. The sizes of the contextual effects found were quite moderate, but the deleterious health effects of contextual exposures have in earlier studies also consistently been shown to be modest.40 However, the public health implica- tion can still be regarded as quite extensive. If the excess risk found in deprived areas reflects a causal effect, the total attributable fraction can be estimated. If all areas had the same level of material resources as the least deprived quartile of areas, there would be a 21% reduction in cases of MI (for both women and men). The corresponding figures for social fragmentation are an 18% reduction in cases among women, and 19% among men.


    Conclusion
 Top
 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Our findings support the notion that the social context in which people live has an impact on the risk of coronary heart disease. We could not draw any conclusion concerning whether material deprivation or social fragmentation makes the greater contribution because the indices were highly correlated and we caution against an uncritical use of both indices, especially Congdon's, when measuring these contextual aspects.


KEY MESSAGES

  • There is a contextual effect on the first event of myocardial infarction of material deprivation and social fragmentation as measured by Townsend and Congdon indices after adjustment for individual social position.
  • The effect of material deprivation and social fragmentation in neighbourhoods was not possible to empirically disentangle.
  • We caution against an uncritical use of both indices, especially Congdon's when measuring these contextual concepts.

 


    Acknowledgments
 
We would like to thank following people who all willingly contributed to this study: Ulla Moberg for her support in the area level data collection process, Niklas Berglind for his contribution in the GIS coding procedure, Tomas Andersson for programming the calculation of referent weights and Wagner de Souza Tassinari for help with the variance partition coefficient simulations. This study was supported by the Swedish Council for Social Research, Sweden's National Institute of Public health, and the Swedish Council for Working Life and Social Research.


    Notes
 
* The Sheep Study Group: Karolinska Institute—Institute of Environmental Medicine, Department of Public Health Sciences, units of Social Medicine and Occupational Health, and Department of Medical Epidemiology; National Institute for Working Life—Department of Occupational Health; National Institute for Psychosocial Factors and Health; Stockholm County Council—Departments of Environmental Medicine, Epidemiology, Occupational Health, and Social Medicine; the Departments of Medicine at Danderyd, Huddinge, Löwenströmska, Nacka, Norrtälje, Sabbatsberg, St Görans, Söder, and Södertälje hospitals, and the Departments of Cardiovascular Medicine and Clinical Chemistry, Karolinska Hospital (all at hospitals in the County of Stockholm, Sweden). Back


    References
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 Abstract
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
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