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Community- and data-driven homelessness prevention and service delivery: optimizing for equity

Community- and data-driven homelessness prevention and service delivery: optimizing for equity ObjectiveThe study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources.Materials and MethodsSystem-wide administrative records capture the delivery of an array of homeless services (prevention, shelter, short-term housing, supportive housing) and whether households reenter the system within 2 years. Counterfactual machine learning identifies which service most likely prevents reentry for each household. Based on community input, predictions are aggregated for subpopulations of interest (race/ethnicity, gender, families, youth, and health conditions) to generate transparent prioritization rules for whom to serve first. Simulations of households entering the system during the study period evaluate whether reallocating services based on prioritization rules compared with services-as-usual.ResultsHomelessness prevention benefited households who could access it, while differential effects exist for homeless households that partially align with community interests. Households with comorbid health conditions avoid homelessness most when provided longer-term supportive housing, and families with children fare best in short-term rentals. No additional differential effects existed for intersectional subgroups. Prioritization rules reduce community-wide homelessness in simulations. Moreover, prioritization mitigated observed reentry disparities for female and unaccompanied youth without excluding Black and families with children.DiscussionLeveraging administrative records with machine learning supplements local decision-making and enables ongoing evaluation of data- and equity-driven homeless services.ConclusionsCommunity- and data-driven prioritization rules more equitably target scarce homeless resources. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Community- and data-driven homelessness prevention and service delivery: optimizing for equity

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References (44)

Publisher
Oxford University Press
Copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1093/jamia/ocad052
Publisher site
See Article on Publisher Site

Abstract

ObjectiveThe study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources.Materials and MethodsSystem-wide administrative records capture the delivery of an array of homeless services (prevention, shelter, short-term housing, supportive housing) and whether households reenter the system within 2 years. Counterfactual machine learning identifies which service most likely prevents reentry for each household. Based on community input, predictions are aggregated for subpopulations of interest (race/ethnicity, gender, families, youth, and health conditions) to generate transparent prioritization rules for whom to serve first. Simulations of households entering the system during the study period evaluate whether reallocating services based on prioritization rules compared with services-as-usual.ResultsHomelessness prevention benefited households who could access it, while differential effects exist for homeless households that partially align with community interests. Households with comorbid health conditions avoid homelessness most when provided longer-term supportive housing, and families with children fare best in short-term rentals. No additional differential effects existed for intersectional subgroups. Prioritization rules reduce community-wide homelessness in simulations. Moreover, prioritization mitigated observed reentry disparities for female and unaccompanied youth without excluding Black and families with children.DiscussionLeveraging administrative records with machine learning supplements local decision-making and enables ongoing evaluation of data- and equity-driven homeless services.ConclusionsCommunity- and data-driven prioritization rules more equitably target scarce homeless resources.

Journal

Journal of the American Medical Informatics Association – Oxford University Press

Published: Apr 8, 2023

Keywords: child; public housing; homeless persons; machine learning; policy

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