A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus

Abstract The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the b...

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Autores principales:	Sayanti Mukherjee, Emmanuel Frimpong Boamah, Prasangsha Ganguly, Nisha Botchwey
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0bd2b9e77f9147dba6423126fb198203
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spelling	oai:doaj.org-article:0bd2b9e77f9147dba6423126fb1982032021-12-02T19:09:19ZA multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus10.1038/s41598-021-96801-x2045-2322https://doaj.org/article/0bd2b9e77f9147dba6423126fb1982032021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96801-xhttps://doaj.org/toc/2045-2322Abstract The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the built environment conditions. The MSPAF combines rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analysis to test various hypotheses on how the built environment impacts community mental health outcomes across the largest metropolitan areas in the US. Among other findings, our results suggest that declining socio-economic conditions of the built environment (e.g., poverty, low income, unemployment, decreased access to public health insurance) are significantly associated with increased reported mental health disorders. Similarly, physical conditions of the built environment (e.g., increased housing vacancies and increased travel costs) are significantly associated with increased reported mental health disorders. However, this positive relationship between the physical conditions of the built environment and mental health outcomes does not hold across all the metropolitan areas, suggesting a mixed effect of the built environment’s physical conditions on community mental health. We conclude by highlighting future opportunities of incorporating other variables and datasets into the MSPAF framework to test additional hypotheses on how the built environment impacts community mental health.Sayanti MukherjeeEmmanuel Frimpong BoamahPrasangsha GangulyNisha BotchweyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sayanti Mukherjee Emmanuel Frimpong Boamah Prasangsha Ganguly Nisha Botchwey A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
description	Abstract The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the built environment conditions. The MSPAF combines rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analysis to test various hypotheses on how the built environment impacts community mental health outcomes across the largest metropolitan areas in the US. Among other findings, our results suggest that declining socio-economic conditions of the built environment (e.g., poverty, low income, unemployment, decreased access to public health insurance) are significantly associated with increased reported mental health disorders. Similarly, physical conditions of the built environment (e.g., increased housing vacancies and increased travel costs) are significantly associated with increased reported mental health disorders. However, this positive relationship between the physical conditions of the built environment and mental health outcomes does not hold across all the metropolitan areas, suggesting a mixed effect of the built environment’s physical conditions on community mental health. We conclude by highlighting future opportunities of incorporating other variables and datasets into the MSPAF framework to test additional hypotheses on how the built environment impacts community mental health.
format	article
author	Sayanti Mukherjee Emmanuel Frimpong Boamah Prasangsha Ganguly Nisha Botchwey
author_facet	Sayanti Mukherjee Emmanuel Frimpong Boamah Prasangsha Ganguly Nisha Botchwey
author_sort	Sayanti Mukherjee
title	A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
title_short	A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
title_full	A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
title_fullStr	A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
title_full_unstemmed	A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
title_sort	multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0bd2b9e77f9147dba6423126fb198203
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A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus

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