Examining neighborhood effects on mental health utilizing a novel two-stage modeling approach

N W Boderie, H van Kippersluis, J V Been, F J van Lenthe, J Oude Groeniger*

*Corresponding author for this work

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Purpose: Neighborhood conditions may affect health, but health may also determine a preference for where to live. This study estimates the effect of neighborhood characteristics on mental health while aiming to adjust for this residential self-selection. Methods: A two-step method was implemented using register data from Statistics Netherlands from all residents of the city of Rotterdam relocating within the city in 2013 (N = 12,456). First, using a conditional logit model, we estimated for each individual the probability of relocating to a neighborhood over all other neighborhoods in Rotterdam, based on personal and neighborhood characteristics in 2013. Second, we corrected this selection process in a model investigating the effects of neighborhood characteristics in 2014 on reimbursed anti-depressant or anti-psychotic medication in 2016. Results: Personal and neighborhood characteristics predicted neighborhood choice, indicating strong patterns of selection into neighborhoods. Unadjusted for selection log neighborhood income was associated with reimbursed medication (β = −0.040, 95% CI = −0.060, −0.020), but the association strongly attenuated after controlling for self-selection into neighborhoods (β = −0.010, 95% CI = −0.030, 0.011). The opposite was observed for contact with neighbors; unadjusted for self-selection there was no association (β = −0.020, 95% CI = −0.073, 0.033), but after adjustment increased neighborhood contact was associated with an 8.5% relative reduction in reimbursed medication (β = −0.075, 95% CI = −0.126, −0.025). Conclusions: The method illustrated in this study offers new opportunities to disentangle selection from causation in neighborhood health research.

Original languageEnglish
Pages (from-to)60-70.e7
JournalAnnals of Epidemiology
Early online date24 Apr 2023
Publication statusPublished - Jul 2023

Bibliographical note

Funding Information: The project was funded by ODISSEI Microdata Access Grant and the Erasmus Initiative “Smarter Choices for Better Health”. The funders had no role in analysis or interpretation of data.

Publisher Copyright: © 2023 The Authors


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