Neighborhood Effects on Health Correcting Bias From Neighborhood Effects on Participation

Inserm, U707, Research Unit in Epidemiology, Information Systems, and Modeling, Paris, France.
Epidemiology (Cambridge, Mass.) (Impact Factor: 6.2). 01/2011; 22(1):18-26. DOI: 10.1097/EDE.0b013e3181fd2961
Source: PubMed


Studies of neighborhood effects on health that are based on cohort data are subject to bias induced by neighborhood-related selective study participation.
We used data from the RECORD Cohort Study (REsidential Environment and CORonary heart Disease) carried out in the Paris metropolitan area, France (n = 7233). We performed separate and joint modeling of neighborhood determinants of study participation and type-2 diabetes. We sought to identify selective participation related to neighborhood, and account for any biasing effect on the associations with diabetes.
After controlling for individual characteristics, study participation was higher for people residing close to the health centers and in neighborhoods with high income, high property values, high proportion of the population looking for work, and low built surface and low building height (contextual effects adjusted for each other). After individual-level adjustment, the prevalence of diabetes was elevated in neighborhoods with the lowest levels of educational attainment (prevalence odds ratio = 1.56 [95% credible interval = 1.06-2.31]). Neighborhood effects on participation did not bias the association between neighborhood education and diabetes. However, residual geographic variations in participation weakly biased the neighborhood education-diabetes association. Bias correction through the joint modeling of neighborhood determinants of participation and diabetes resulted in an 18% decrease in the log prevalence odds ratio for low versus high neighborhood education.
Researchers should develop a comprehensive, theory-based model of neighborhood determinants of participation in their study, investigate resulting biases for the environment-health associations, and check that unexplained geographic variations in participation do not bias these environment-health relationships.

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Available from: Frédérique Thomas, Oct 10, 2014
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    • "However, a limitation is that the RECORD sample is not strictly representative of the Paris Ile-de-France population from which it was drawn, even if we a priori selected a panel of municipalities from the region to ensure the presence of people from all socioeconomic backgrounds (neighborhood-related selective participation in the sample has been investigated elsewhere [49]). Furthermore, the existence of selective residential migration processes did not allow us to demonstrate that the relationships between specific environmental factors and walking for transportation were attributable to a causal effect of these environments on walking [17]. "
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    ABSTRACT: Assessing the contextual factors that influence walking for transportation is important to develop more walkable environments and promote physical activity. To advance previous research focused on residential environments and overall walking for transportation, the present study investigates objective environmental factors assessed around the residence, the workplace, the home – work itinerary, and the home – supermarket itinerary, and considered overall walking for transportation but also walking to work and to shops.
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    • "The absence of randomization in the recruitment of the participants led to a sample that was not representative of the background population. A previous work showed that a high individual education, a high neighborhood socioeconomic status, and a low building density were associated with higher odds of participation in the RECORD Study [38]. All these factors were included in the models or considered for adjustment to minimize bias. "
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    ABSTRACT: Preliminary evidence suggests that recreational walking has different environmental determinants than utilitarian walking. However, previous studies are limited in their assessment of environmental exposures and recreational walking and in the applied modeling strategies. Accounting for individual sociodemographic profiles and weather over the walking assessment period, the study examined whether numerous street network-based neighborhood characteristics related to the sociodemographic, physical, service, social-interactional, and symbolic environments were associated with overall recreational walking and recreational walking in one's residential neighborhood and could explain their spatial distribution. Based on the RECORD Cohort Study (Paris region, France, n = 7105, 2007-2008 data), multilevel-spatial regression analyses were conducted to investigate environmental factors associated with recreational walking (evaluated by questionnaire at baseline). A risk score approach was applied to quantify the overall disparities in recreational walking that were predicted by the environmental determinants. Sixty-nine percent of the participants reported recreational walking over the past 7 days. Their mean reported recreational walking time was 3h31mn. After individual-level adjustment, a higher neighborhood education, a higher density of destinations, green and open spaces of quality, and the absence of exposure to air traffic were associated with higher odds of recreational walking and/or a higher recreational walking time in one's residential neighborhood. As the overall disparities that were predicted by these environmental factors, the odds of reporting recreational walking and the odds of a higher recreational walking time in one's neighborhood were, respectively, 1.59 [95% confidence interval (CI): 1.56, 1.62] times and 1.81 (95% CI: 1.73, 1.87) times higher in the most vs. the least supportive environments (based on the quartiles). Providing green/open spaces of quality, building communities with services accessible from the residence, and addressing environmental nuisances such as those related to air traffic may foster recreational walking in one's environment.
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    • "A limitation of this study is the heterogeneity in the source and in the quality of the original transportation noise data provided by municipalities, inter-municipalities, and government services [34]. Another limitation includes the absence of a priori sampling in the recruitment of the participants, with differences in the probability of participation according to neighborhood profiles [35]. However, it is not clear whether and how annoyance due to road traffic might influence participation in the study. "
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    ABSTRACT: Road traffic and related noise is a major source of annoyance and impairment to health in urban areas. Many areas exposed to road traffic noise are also exposed to rail and air traffic noise. The resulting annoyance may depend on individual/neighborhood socio-demographic factors. Nevertheless, few studies have taken into account the confounding or modifying factors in the relationship between transportation noise and annoyance due to road traffic. In this study, we address these issues by combining Geographic Information Systems and epidemiologic methods. Street network buffers with a radius of 500 m were defined around the place of residence of the 7290 participants of the RECORD Cohort in Ile-de-France. Estimated outdoor traffic noise levels (road, rail, and air separately) were assessed at each place of residence and in each of these buffers. Higher levels of exposure to noise were documented in low educated neighborhoods. Multilevel logistic regression models documented positive associations between road traffic noise and annoyance due to road traffic, after adjusting for individual / neighborhood socioeconomic conditions. There was no evidence that the association was of different magnitude when noise was measured at the place of residence or in the residential neighborhood. However, the strength of the association between neighborhood noise exposure and annoyance increased when considering a higher percentile in the distribution of noise in each neighborhood. Road traffic noise estimated at the place of residence and road traffic noise in the residential neighborhood (75th percentile) were independently associated with annoyance, when adjusted for each other. Interactions of effects indicated that the relationship between road traffic noise exposure in the residential neighborhood and annoyance was stronger in affluent and high educated neighborhoods. Overall, our findings suggest that it is useful to take into account (i) the exposure to transportation noise in the residential neighborhood rather than only at the residence, (ii) different percentiles of noise exposure in the residential neighborhood, and (iii) the socioeconomic characteristics of the residential neighborhood to explain variations in annoyance due to road traffic in the neighborhood.
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