{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:21:37Z","timestamp":1789104097772,"version":"build-2803163510"},"reference-count":42,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Services Research"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop easy to use and validated predictive models to identify beneficiaries experiencing homelessness from administrative data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Sources<\/jats:title>\n                    <jats:p>We pooled enrollment and claims data from enrollees of the California Whole Person Care (WPC) Medicaid demonstration program that coordinated the care of a subset of Medicaid beneficiaries identified as high utilizers in 26 California counties (25 WPC Pilots). We also used public directories of social service and health care facilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Using WPC Pilot\u2010reported homelessness status, we trained seven supervised learning algorithms with different specifications to identify beneficiaries experiencing homelessness. The list of predictors included address\u2010 and claims\u2010based indicators, demographics, health status, health care utilization, and county\u2010level homelessness rate. We then assessed model performance using measures of balanced accuracy (BA), sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (area under the curve [AUC]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Collection\/Extraction Methods<\/jats:title>\n                    <jats:p>We included 93,656 WPC enrollees from 2017 to 2018, 37,441 of whom had a WPC Pilot\u2010reported homelessness indicator.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Principal Findings<\/jats:title>\n                    <jats:p>The random forest algorithm with all available indicators had the best performance (87% BA and 0.95 AUC), but a simpler Generalized Linear Model (GLM) also performed well (74% BA and 0.83 AUC). Reducing predictors to the top 20 and top five most important indicators in a GLM model yields only slightly lower performance (86% BA and 0.94 AUC for the top 20 and 86% BA and 0.91 AUC for the top five).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Large samples can be used to accurately predict homelessness in Medicaid administrative data if a validated homelessness indicator for a small subset can be obtained. In the absence of a validated indicator, the likelihood of homelessness can be calculated using county rate of homelessness, address\u2010 and claim\u2010based indicators, and beneficiary age using a prediction model presented here. These approaches are needed given the rising prevalence of homelessness and the focus of Medicaid and other payers on addressing homelessness and its outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1475-6773.14143","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:16:11Z","timestamp":1675901771000},"page":"882-893","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Easy to use and validated predictive models to identify beneficiaries experiencing homelessness in Medicaid administrative data"],"prefix":"10.1111","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-1188","authenticated-orcid":false,"given":"Nadereh","family":"Pourat","sequence":"first","affiliation":[{"name":"Health Economics and Evaluation Research Program UCLA Center for Health Policy Research  Los Angeles California USA"},{"name":"Department of Health Policy and Management UCLA Fielding School of Public Health  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1525-6776","authenticated-orcid":false,"given":"Dahai","family":"Yue","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management University of Maryland School of Public Health  College Park Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Health Economics and Evaluation Research Program UCLA Center for Health Policy Research  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Health Economics and Evaluation Research Program UCLA Center for Health Policy Research  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenna","family":"O'Masta","sequence":"additional","affiliation":[{"name":"Health Economics and Evaluation Research Program UCLA Center for Health Policy Research  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"e_1_2_7_2_1","volume-title":"Permanent Supportive Housing: Evaluating the Evidence for Improving Health Outcomes among People Experiencing Chronic Homelessness","author":"National Academies of Sciences E, Medicine","year":"2018"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2019.6010"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2018.05308"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2019.112585"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0b013e31829fafbb"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(14)61132-6"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199806113382406"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.puhe.2017.03.002"},{"issue":"2","key":"e_1_2_7_10_1","first-page":"161","article-title":"Barriers to appropriate diabetes management among homeless people in Toronto","volume":"163","author":"Hwang SW","year":"2000","journal-title":"Can Med Assoc J"},{"key":"e_1_2_7_11_1","volume-title":"Supportive Housing for Chronically Homeless Medicaid Enrollees: State Strategies","author":"Moses K","year":"2016"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2015.1277"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2016.2615"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11414-009-9206-y"},{"key":"e_1_2_7_15_1","volume-title":"Medicaid\u2010Financed Services in Supportive Housing for High\u2010Need Homeless Beneficiaries: the Business Case","author":"Nardone M","year":"2012"},{"key":"e_1_2_7_16_1","unstructured":"GundlapalliAV CarterME PalmerM et al.Using Natural Language Processing on the Free Text of Clinical Documents to Screen for Evidence of Homelessness among US Veterans. 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