The Longitudinal Associations of Perceived Neighborhood Disorder and Lack of Social Cohesion With Depression Among Adults Aged 50 Years or Older: An Individual-Participant-Data Meta-Analysis From 16 High-Income Countries
Year: 2019
Type: review
Abstract: Although residential environment might be an important predictor of depression among older adults, systematic reviews point to a lack of longitudinal investigations, and the generalizability of the findings is limited to a few countries. We used longitudinal data collected between 2012 and 2017 in 3 surveys including 15 European countries and the United States and comprising 32,531 adults aged 50 years or older. The risk of depression according to perceived neighborhood disorder and lack of social cohesion was estimated using 2-stage individual-participant-data meta-analysis; country-specific parameters were analyzed by meta-regression. We conducted additional analyses on retired individuals. Neighborhood disorder (odds ratio (OR) = 1.25) and lack of social cohesion (OR = 1.76) were significantly associated with depression in the fully adjusted models. In retirement, the risk of depression was even higher (neighborhood disorder: OR = 1.35; lack of social cohesion: OR = 1.93). Heterogeneity across countries was low and was significantly reduced by the addition of country-level data on income inequality and population density. Perceived neighborhood problems increased the overall risk of depression among adults aged 50 years or older. Policies, especially in countries with stronger links between neighborhood and depression, should focus on improving the physical environment and supporting social ties in communities, which can reduce depression and contribute to healthy aging.
Source: American Journal of Epidemiology
DOI: 10.1093/aje/kwz209
Institutions: University of Edinburgh, University of Geneva
Language: English
Subfield: Health
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,856
Funders: National Science Foundation, University College London, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Université de Genève, Economic and Social Research Council, Horizon 2020, National Institute on Aging, H2020 Marie Skłodowska-Curie Actions
Awards: G1579762323, G1708035608, Methodologies and Data mining techniques for the analysis of Big Data based on Longitudinal Population and Epidemiological Registers, Extension for the Longitudinal Studies Centre - Scotland (LSCS) from 2018 to 2020, G2874390097, G3188738659, Methodologies and Data mining techniques for the analysis of Big Data based on Longitudinal Population and Epidemiological Registers, G5849176554, NCCR LIVES: Overcoming vulnerability - life course perspectives, Health and Retirement Study: Yrs 35-40
OpenAlex ID: W2978359490