More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach

Marco Helbich; Nadja Klein; Hannah Roberts; Paulien Hagedoorn; Peter P Groenewegen

doi:10.1016/j.envres.2018.06.010

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach

Environ Res. 2018 Oct:166:290-297. doi: 10.1016/j.envres.2018.06.010. Epub 2018 Jun 21.

Authors

Marco Helbich¹, Nadja Klein², Hannah Roberts³, Paulien Hagedoorn³, Peter P Groenewegen⁴

Affiliations

¹ Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands. Electronic address: m.helbich@uu.nl.
² Chair of Statistics, University of Cologne, Germany.
³ Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands.
⁴ Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands; Netherlands Institute for Health Services Research, Utrecht, The Netherlands.

PMID: 29936285
DOI: 10.1016/j.envres.2018.06.010

Abstract

Background: Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings.

Objective: Our aim was: a) to assess antidepressant prescription rates in relation to green space, and b) to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates.

Methods: We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15%, 50%, and 85% quantiles to estimate green space-prescription rate correlations, controlling for physical activity levels, socio-demographics, urbanicity, etc. RESULTS: The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one.

Conclusion: Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28% needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79%. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies.

Keywords: Antidepressants; Exposures; Green space; Mental health; Quantile regression; Spatial epidemiology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Antidepressive Agents / therapeutic use*
Bayes Theorem
Cross-Sectional Studies
Environment*
Humans
Mental Health*
Netherlands

Substances

Antidepressive Agents