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Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California
INTRODUCTION: Homelessness contributes to worsening health and increased health care costs. There is little published research that leverages rich electronic health record (EHR) data to predict future homelessness risk and inform interventions to address it. The authors’ objective was to develop a m...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Permanente Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013725/ https://www.ncbi.nlm.nih.gov/pubmed/36911893 http://dx.doi.org/10.7812/TPP/22.096 |
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author | Rodriguez, Luis A Thomas, Tainayah W Finertie, Holly Wiley, Deanne Dyer, Wendy T Sanchez, Perla E Yassin, Maher Banerjee, Somalee Adams, Alyce Schmittdiel, Julie A |
author_facet | Rodriguez, Luis A Thomas, Tainayah W Finertie, Holly Wiley, Deanne Dyer, Wendy T Sanchez, Perla E Yassin, Maher Banerjee, Somalee Adams, Alyce Schmittdiel, Julie A |
author_sort | Rodriguez, Luis A |
collection | PubMed |
description | INTRODUCTION: Homelessness contributes to worsening health and increased health care costs. There is little published research that leverages rich electronic health record (EHR) data to predict future homelessness risk and inform interventions to address it. The authors’ objective was to develop a model for predicting future homelessness using individual EHR and geographic data covariates. METHODS: This retrospective cohort study included 2,543,504 adult members (≥ 18 years old) from Kaiser Permanente Northern California and evaluated which covariates predicted a composite outcome of homelessness status (hospital discharge documentation of a homeless patient, medical diagnosis of homelessness, approved medical financial assistance application for homelessness, and/or “homeless/shelter” in address name). The predictors were measured in 2018–2019 and included prior diagnoses and demographic and geographic data. The outcome was measured in 2020. The cohort was split (70:30) into a derivation and validation set, and logistic regression was used to model the outcome. RESULTS: Homelessness prevalence was 0.35% in the overall sample. The final logistic regression model included 26 prior diagnoses, demographic, and geographic-level predictors. The regression model using the validation set had moderate sensitivity (80.4%) and specificity (83.2%) for predicting future cases of homelessness and achieved excellent classification properties (area under the curve of 0.891 [95% confidence interval = 0.884-0.897]). DISCUSSION: This prediction model can be used as an initial triage step to enhance screening and referral tools for identifying and addressing homelessness, which can improve health and reduce health care costs. CONCLUSIONS: EHR data can be used to predict chance of homelessness at a population health level. |
format | Online Article Text |
id | pubmed-10013725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Permanente Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-100137252023-03-15 Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California Rodriguez, Luis A Thomas, Tainayah W Finertie, Holly Wiley, Deanne Dyer, Wendy T Sanchez, Perla E Yassin, Maher Banerjee, Somalee Adams, Alyce Schmittdiel, Julie A Perm J Original Research INTRODUCTION: Homelessness contributes to worsening health and increased health care costs. There is little published research that leverages rich electronic health record (EHR) data to predict future homelessness risk and inform interventions to address it. The authors’ objective was to develop a model for predicting future homelessness using individual EHR and geographic data covariates. METHODS: This retrospective cohort study included 2,543,504 adult members (≥ 18 years old) from Kaiser Permanente Northern California and evaluated which covariates predicted a composite outcome of homelessness status (hospital discharge documentation of a homeless patient, medical diagnosis of homelessness, approved medical financial assistance application for homelessness, and/or “homeless/shelter” in address name). The predictors were measured in 2018–2019 and included prior diagnoses and demographic and geographic data. The outcome was measured in 2020. The cohort was split (70:30) into a derivation and validation set, and logistic regression was used to model the outcome. RESULTS: Homelessness prevalence was 0.35% in the overall sample. The final logistic regression model included 26 prior diagnoses, demographic, and geographic-level predictors. The regression model using the validation set had moderate sensitivity (80.4%) and specificity (83.2%) for predicting future cases of homelessness and achieved excellent classification properties (area under the curve of 0.891 [95% confidence interval = 0.884-0.897]). DISCUSSION: This prediction model can be used as an initial triage step to enhance screening and referral tools for identifying and addressing homelessness, which can improve health and reduce health care costs. CONCLUSIONS: EHR data can be used to predict chance of homelessness at a population health level. The Permanente Press 2023-03-13 /pmc/articles/PMC10013725/ /pubmed/36911893 http://dx.doi.org/10.7812/TPP/22.096 Text en © 2023 The Authors. https://creativecommons.org/licenses/by-nc-nd/4.0/Published by The Permanente Federation LLC under the terms of the CC BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. |
spellingShingle | Original Research Rodriguez, Luis A Thomas, Tainayah W Finertie, Holly Wiley, Deanne Dyer, Wendy T Sanchez, Perla E Yassin, Maher Banerjee, Somalee Adams, Alyce Schmittdiel, Julie A Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California |
title | Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California |
title_full | Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California |
title_fullStr | Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California |
title_full_unstemmed | Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California |
title_short | Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California |
title_sort | identifying predictors of homelessness among adults in a large integrated health system in northern california |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013725/ https://www.ncbi.nlm.nih.gov/pubmed/36911893 http://dx.doi.org/10.7812/TPP/22.096 |
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