Literature DB >> 29936285

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

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

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.
Copyright © 2018 Elsevier Inc. All rights reserved.

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

Mesh:

Substances:

Year:  2018        PMID: 29936285     DOI: 10.1016/j.envres.2018.06.010

Source DB:  PubMed          Journal:  Environ Res        ISSN: 0013-9351            Impact factor:   6.498


  21 in total

1.  Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach.

Authors:  Marco Helbich; Julian Hagenauer; Hannah Roberts
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2019-11-14       Impact factor: 4.328

2.  Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure.

Authors:  Yi Sun; Xingzhi Wang; Jiayin Zhu; Liangjian Chen; Yuhang Jia; Jean M Lawrence; Luo-Hua Jiang; Xiaohui Xie; Jun Wu
Journal:  Sci Total Environ       Date:  2021-05-08       Impact factor: 10.753

3.  The effects of greenness exposure on hypertension incidence among Chinese oldest-old: a prospective cohort study.

Authors:  Zhou Wensu; Wang Wenjuan; Zhou Fenfen; Chen Wen; Ling Li
Journal:  Environ Health       Date:  2022-07-11       Impact factor: 7.123

Review 4.  Moving Beyond Disciplinary Silos Towards a Transdisciplinary Model of Wellbeing: An Invited Review.

Authors:  Jessica Mead; Zoe Fisher; Andrew H Kemp
Journal:  Front Psychol       Date:  2021-05-14

5.  Associations between Urban Green Spaces and Health are Dependent on the Analytical Scale and How Urban Green Spaces are Measured.

Authors:  Liqing Zhang; Puay Yok Tan
Journal:  Int J Environ Res Public Health       Date:  2019-02-16       Impact factor: 3.390

6.  Spatiotemporal Contextual Uncertainties in Green Space Exposure Measures: Exploring a Time Series of the Normalized Difference Vegetation Indices.

Authors:  Marco Helbich
Journal:  Int J Environ Res Public Health       Date:  2019-03-08       Impact factor: 3.390

7.  Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China.

Authors:  Marco Helbich; Yao Yao; Ye Liu; Jinbao Zhang; Penghua Liu; Ruoyu Wang
Journal:  Environ Int       Date:  2019-02-20       Impact factor: 9.621

8.  Dynamic Urban Environmental Exposures on Depression and Suicide (NEEDS) in the Netherlands: a protocol for a cross-sectional smartphone tracking study and a longitudinal population register study.

Authors:  Marco Helbich
Journal:  BMJ Open       Date:  2019-08-10       Impact factor: 2.692

Review 9.  Assessing the role of urban green spaces for human well-being: a systematic review.

Authors:  Muhammad Jabbar; Mariney Mohd Yusoff; Aziz Shafie
Journal:  GeoJournal       Date:  2021-07-20

10.  Does Residential Green and Blue Space Promote Recovery in Psychotic Disorders? A Cross-Sectional Study in the Province of Utrecht, The Netherlands.

Authors:  Susanne Boers; Karin Hagoort; Floortje Scheepers; Marco Helbich
Journal:  Int J Environ Res Public Health       Date:  2018-10-08       Impact factor: 3.390

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