» Articles » PMID: 34315817

Evidence and Theory for Lower Rates of Depression in Larger US Urban Areas

Overview
Specialty Science
Date 2021 Jul 28
PMID 34315817
Citations 17
Authors
Affiliations
Soon will be listed here.
Abstract

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual's accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.

Citing Articles

The influence of credits and stigmas in volunteering on depression, the modulating effects of volunteer personality and motivation.

Chen J, Zhang Y, Zhou S, Yang C, Li L, Ma L BMC Public Health. 2025; 25(1):460.

PMID: 39910525 PMC: 11796000. DOI: 10.1186/s12889-025-21727-2.


Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.

Park J, Ko C, Lee D, Kim J Sci Rep. 2025; 15(1):1196.

PMID: 39775165 PMC: 11707018. DOI: 10.1038/s41598-025-85157-1.


Urban and Rural Differences in the Efficacy of a Mobile Health Depression Treatment for Young Adults.

Mennis J, Coatsworth J, Russell M, Zaharakis N, Brown A, Mason M Int J Environ Res Public Health. 2025; 21(12.

PMID: 39767414 PMC: 11675546. DOI: 10.3390/ijerph21121572.


The U-shape Association between Population Agglomeration and Individual Depression: the Role of Dialect Diversity.

Han J, Zhang K, Lin H, Chang L, Tu J, Mai Q J Urban Health. 2024; 101(4):740-751.

PMID: 38987523 PMC: 11329481. DOI: 10.1007/s11524-024-00890-8.


Built Environment, Natural Environment, and Mental Health.

Wei Y, Wang Y, Curtis D, Shin S, Wen M Geohealth. 2024; 8(6):e2024GH001047.

PMID: 38912227 PMC: 11193151. DOI: 10.1029/2024GH001047.


References
1.
Mirowsky J, Ross C . Age and the effect of economic hardship on depression. J Health Soc Behav. 2001; 42(2):132-50. View

2.
Bettencourt L, Lobo J, Helbing D, Kuhnert C, West G . Growth, innovation, scaling, and the pace of life in cities. Proc Natl Acad Sci U S A. 2007; 104(17):7301-6. PMC: 1852329. DOI: 10.1073/pnas.0610172104. View

3.
Arthur R, Williams H . Scaling laws in geo-located Twitter data. PLoS One. 2019; 14(7):e0218454. PMC: 6655604. DOI: 10.1371/journal.pone.0218454. View

4.
Greenberg P, Kessler R, Birnbaum H, Leong S, Lowe S, Berglund P . The economic burden of depression in the United States: how did it change between 1990 and 2000?. J Clin Psychiatry. 2004; 64(12):1465-75. DOI: 10.4088/jcp.v64n1211. View

5.
Vinkers C, Joels M, Milaneschi Y, Kahn R, Penninx B, Boks M . Stress exposure across the life span cumulatively increases depression risk and is moderated by neuroticism. Depress Anxiety. 2014; 31(9):737-45. DOI: 10.1002/da.22262. View