Young people’s life chances can be predicted by characteristics of their neighbourhood1. Children growing up in disadvantaged neighbourhoods exhibit worse physical and mental health and suffer poorer educational and economic outcomes than children growing up in advantaged neighbourhoods. Increasing recognition that aspects of social inequalities tend, in fact, to be geographical inequalities2,3,4,5 is stimulating research and focusing policy interest on the role of place in shaping health, behaviour and social outcomes. Where neighbourhood effects are causal, neighbourhood-level interventions can be effective. Where neighbourhood effects reflect selection of families with different characteristics into different neighbourhoods, interventions should instead target families or individuals directly. To test how selection may affect different neighbourhood-linked problems, we linked neighbourhood data with genetic, health and social outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health studies. We tested selection/concentration of genetic risks for obesity, schizophrenia, teen pregnancy and poor educational outcomes in high-risk neighbourhoods, including genetic analysis of neighbourhood mobility. Findings argue against genetic selection/concentration as an explanation for neighbourhood gradients in obesity and mental health problems. By contrast, modest genetic selection/concentration was evident for teen pregnancy and poor educational outcomes, suggesting that neighbourhood effects for these outcomes should be interpreted with care.
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The E-Risk data set reported in the current article is not publicly available owing to a lack of informed consent and ethical approval, but is available on request by qualified scientists. Requests require a concept paper describing the purpose of data access, ethical approval at the applicant’s institution and provision for secure data access. We offer secure access on the Duke University and King’s College London campuses. All data analysis scripts and results files are available for review. The Add Health data can be accessed through the Add Health study. Details are available through the Carolina Population Center as described here: https://www.cpc.unc.edu/projects/addhealth/documentation. Genotype data are available through dbGaP.
All data analysis scripts and results files are available for review.
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The E-Risk Study is funded by the Medical Research Council (UKMRC grant G1002190). Additional support was provided by NICHD grant HD077482, Google and by the Jacobs Foundation. The Add Health study is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grants P01HD31921, R01HD073342 and R01HD060726, with cooperative funding from 23 other federal agencies and foundations. D.W.B. and C.L.O. were supported by fellowships from the Jacobs Foundation. C.L.O. is supported by the Canadian Institute for Advanced Research. B.W.D. is supported by the Russell Sage Foundation award 961704. We are grateful to the E-Risk study mothers and fathers, the twins and the twins’ teachers, and the Add Health study participants and their parents for their participation. Our thanks to CACI, Google Street View and to members of the E-Risk team for their dedication, hard work and insights. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Social Science & Medicine (2019)