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Neighbourhood characteristics and prevalence and severity of depression: pooled analysis of eight Dutch cohort studies

  • Ellen Generaal (a1), Emiel O. Hoogendijk (a2), Mariska Stam (a3), Celina E. Henke (a4), Femke Rutters (a5), Mirjam Oosterman (a6), Martijn Huisman (a7), Sophia E. Kramer (a8), Petra J. M. Elders (a9), Erik J. Timmermans (a2), Jeroen Lakerveld (a10), Eric Koomen (a11), Margreet ten Have (a12), Ron de Graaf (a12), Marieke B. Snijder (a13), Karien Stronks (a14), Gonneke Willemsen (a15), Dorret I. Boomsma (a16), Johannes H. Smit (a17) and Brenda W. J. H. Penninx (a17)...

Abstract

Background

Studies on neighbourhood characteristics and depression show equivocal results.

Aims

This large-scale pooled analysis examines whether urbanisation, socioeconomic, physical and social neighbourhood characteristics are associated with the prevalence and severity of depression.

Method

Cross-sectional design including data are from eight Dutch cohort studies (n= 32 487). Prevalence of depression, either DSM-IV diagnosis of depressive disorder or scoring for moderately severe depression on symptom scales, and continuous depression severity scores were analysed. Neighbourhood characteristics were linked using postal codes and included (a) urbanisation grade, (b) socioeconomic characteristics: socioeconomic status, home value, social security beneficiaries and non-Dutch ancestry, (c) physical characteristics: air pollution, traffic noise and availability of green space and water, and (d) social characteristics: social cohesion and safety. Multilevel regression analyses were adjusted for the individual's age, gender, educational level and income. Cohort-specific estimates were pooled using random-effects analysis.

Results

The pooled analysis showed that higher urbanisation grade (odds ratio (OR) = 1.05, 95% CI 1.01–1.10), lower socioeconomic status (OR = 0.90, 95% CI 0.87–0.95), higher number of social security beneficiaries (OR = 1.12, 95% CI 1.06–1.19), higher percentage of non-Dutch residents (OR = 1.08, 95% CI 1.02–1.14), higher levels of air pollution (OR = 1.07, 95% CI 1.01–1.12), less green space (OR = 0.94, 95% CI 0.88–0.99) and less social safety (OR = 0.92, 95% CI 0.88–0.97) were associated with higher prevalence of depression. All four socioeconomic neighbourhood characteristics and social safety were also consistently associated with continuous depression severity scores.

Conclusions

This large-scale pooled analysis across eight Dutch cohort studies shows that urbanisation and various socioeconomic, physical and social neighbourhood characteristics are associated with depression, indicating that a wide range of environmental aspects may relate to poor mental health.

Declaration of interest

None.

Copyright

Corresponding author

Correspondence: Brenda W. J. H. Penninx, Oldenaller 1, 1081 HJ Amsterdam, the Netherlands. Email: b.penninx@vumc.nl

References

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Neighbourhood characteristics and prevalence and severity of depression: pooled analysis of eight Dutch cohort studies

  • Ellen Generaal (a1), Emiel O. Hoogendijk (a2), Mariska Stam (a3), Celina E. Henke (a4), Femke Rutters (a5), Mirjam Oosterman (a6), Martijn Huisman (a7), Sophia E. Kramer (a8), Petra J. M. Elders (a9), Erik J. Timmermans (a2), Jeroen Lakerveld (a10), Eric Koomen (a11), Margreet ten Have (a12), Ron de Graaf (a12), Marieke B. Snijder (a13), Karien Stronks (a14), Gonneke Willemsen (a15), Dorret I. Boomsma (a16), Johannes H. Smit (a17) and Brenda W. J. H. Penninx (a17)...
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eLetters

The association between the neighbourhood characteristics and depression: was the regression model satisfactory?

abhishek ghosh, Assistant Professor, Department of Psychiatry , PGIMER, Chandigarh
Abhishek Ghosh, Assistant Professor, Department of Psychiatry, POST GRADUATE INSTITUTE OF MED EDUCATION AND RESEARCH, CHANDIGARH
Natarajan Varadharajan, Senior Resident, Department of Psychiatry, POST GRADUATE INSTITUTE OF MED EDUCATION AND RESEARCH, CHANDIGARH
16 August 2019

The study by Generaal and colleagues is noteworthy because of its large sample size, objective and intelligent measurements of the myriad neighbourhood characteristics.

However, we would like to draw the readers’ attention to the regression models. The Supplementary Table 1, with the bivariate correlations among the independent variables (IV), showed modest to strong correlations between large numbers of variables. Therefore multicollinearity was present. Under such circumstances, it is advisable to do Variance Inflation Factor (VIF) estimation. VIF of more than 10 suggests multicollinearity is a significant problem. IVs with VIF more than ten should have been removed from the model.1 The other option is to carry out Principal Component Analysis of highly correlated IVs. As the authors have not done either of these corrections, significant multicollinearity might have affected the magnitude of the standardized regression coefficients, their standard errors, and the p values. These could potentially result in unreliable interpretations.2 The authors could have added the proportion of variance (R2) in the dependent variable (depression prevalence/ severity) explained by the independent variables (neighbourhood characteristics) because R2 is not affected by multicollinearity. Additionally, R2 would have given an idea about the goodness of fit of the regression models.

The severity of depression (i.e., the dependent variable) had skewed distributions in five out of the seven cohorts. We agree, with a large sample size linear regression analysis could be done even with a non-parametric dependent variable. However, the Ordinary Least Square (OLS) estimations should have been carried out to demonstrate statistical robustness of the regression analysis. In the case of non-normality of the OLS, bootstrapping is an alternative.3

Because of these limitations, we would be cautious while interpreting the results of the regression analysis, done to examine the association between the neighbourhood characteristics and severity of depression.

References

1. Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in RegressionAnalyses Conducted in Epidemiologic Studies.Epidemiology (Sunnyvale). 2016;6: 227-46.

2. Kim JH. Multicollinearity and misleading statistical results. Korean JAnesthesiol. 2019 Jul 15. [Epub ahead of print]

3. Hubert M, Rousseeuw PJ, Aelst SV. Inconsistency of resampling algorithms for high-breakdown regression estimators and a new algorithm. J Amer Stat Assoc. 2002; 97:151–153.

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Conflict of interest: None declared

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