Identifying Homeless Medicaid Enrollees Using Enrollment Addresses

Vickery, Katherine; Shippee, Nathan D.; Bodurtha, Peter; Guzman-Corrales, Laura; Reamer, Elyse; Soderlund, Dana; Abel, Stephanie L; Robertshaw, Danielle; Gelberg, Lillian

doi:10.1111/1475-6773.12738

Cited by 28 publications

(24 citation statements)

References 17 publications

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“…Most prior work50–57 identified homelessness using homeless indicators or shelter addresses given during healthcare encounters, assuming these data represented true housing status. Recently, Vickery et al validated addresses indicative of homelessness during healthcare encounters against self-reported housing status in a sample of Medicaid recipients, finding sensitivities between 30% and 76% and specificities between 79% and 97% 58. However, this study required the use of location and time-specific shelter address registries, making the methodology challenging to scale or generalise.…”

Section: Discussionmentioning

confidence: 99%

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada

Richard¹,

Hwang

Forchuk

et al. 2019

BMJ Open

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ObjectivesTo validate case ascertainment algorithms for identifying individuals experiencing homelessness in health administrative databases between 2007 and 2014; and to estimate homelessness prevalence trends in Ontario, Canada, between 2007 and 2016.DesignA population-based retrospective validation study.SettingOntario, Canada, from 2007 to 2014 (validation) and 2007 to 2016 (estimation).ParticipantsOur reference standard was the known housing status of a longitudinal cohort of housed (n=137 200) and homeless or vulnerably housed (n=686) individuals. Two reference standard definitions of homelessness were adopted: the housing episode and the annual housing experience (any homelessness within a calendar year).Main outcome measuresSensitivity, specificity, positive and negative predictive values and positive likelihood ratios of 30 case ascertainment algorithms for detecting homelessness using up to eight health service databases.ResultsSensitivity estimates ranged from 10.8% to 28.9% (housing episode definition) and 18.5% to 35.6% (annual housing experience definition). Specificities exceeded 99% and positive likelihood ratios were high using both definitions. The most optimal algorithm estimates that 59 974 (95% CI 55 231 to 65 208) Ontarians (0.53% of the adult population) experienced homelessness in 2016, a 67.3% increase from 2007.ConclusionsIn Ontario, case ascertainment algorithms for identifying homelessness had low sensitivity but very high specificity and positive likelihood ratio. The use of health administrative databases may offer opportunities to track individuals experiencing homelessness over time and inform efforts to improve housing and health status in this vulnerable population.

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Section: Discussionmentioning

confidence: 99%

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada

Richard¹,

Hwang

Forchuk

et al. 2019

BMJ Open

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show abstract

“…Recognition of this shortcoming has led to increased interest in developing predictive models to identify persons experiencing homelessness using available data in administrative records. Much of this work has been conducted in health care systems where studies have used indicators obtained from medical records, including diagnosis codes [ 8 ], address information [ 9 , 10 ], and free text notes [ 11 – 13 ], to develop models identifying persons experiencing homelessness. Yet, these studies are limited by their exclusive reliance on data obtained from medical records and thus are based on a limited set of predictor variables and apply to non-representative samples of individuals.…”

Section: Introductionmentioning

confidence: 99%

A classification model of homelessness using integrated administrative data: Implications for targeting interventions to improve the housing status, health and well-being of a highly vulnerable population

et al. 2020

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Homelessness is poorly captured in most administrative data sets making it difficult to understand how, when, and where this population can be better served. This study sought to develop and validate a classification model of homelessness. Our sample included 5,050,639 individuals aged 11 years and older who were included in a linked dataset of administrative records from multiple state-maintained databases in Massachusetts for the period from 2011-2015. We used logistic regression to develop a classification model with 94 predictors and subsequently tested its performance. The model had high specificity (95.4%), moderate sensitivity (77.8%) for predicting known cases of homelessness, and excellent classification properties (area under the receiver operating curve 0.94; balanced accuracy 86.4%). To demonstrate the potential opportunity that exists for using such a modeling approach to target interventions to mitigate the risk of an adverse health outcome, we also estimated the association between model predicted homeless status and fatal opioid overdoses, finding that model predicted homeless status was associated with a nearly 23-fold increase in the risk of fatal opioid overdose. This study provides a novel approach for identifying homelessness using integrated administrative data. The strong performance of our model underscores the potential value of linking data from multiple service systems to improve the identification of housing instability and to assist government in developing programs that seek to improve health and other outcomes for homeless individuals.

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“…Accurate identification can lead to more efficient targeting of interventions to prevent housing instability or to mitigate the potential ill effects of continued housing instability. The development and testing of approaches to achieve this goal is a growing area of inquiry: several recent studies have tested methods that rely on data obtained from patients’ medical records to identify persons at risk of or experiencing homelessness using a variety of indicators including diagnosis codes, address information, and free text notes …”

Section: Introductionmentioning

confidence: 99%

“…9From a practical standpoint, efforts to address housing instability within the health care system rely on the accurate identification of those who are, or are at risk of, experiencing housing instability.Accurate identification can lead to more efficient targeting of interventions to prevent housing instability or to mitigate the potential ill effects of continued housing instability. The development and testing of approaches to achieve this goal is a growing area of inquiry: several recent studies have tested methods that rely on data obtained from patients' medical records to identify persons at risk of or experiencing homelessness using a variety of indicators including diagnosis codes, 10 address information, 11,12 and free text notes. [13][14][15] An alternative approach relies on self-reported information about housing status collected through the use of screening tools administered to patients in clinical settings.…”

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confidence: 99%

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

Byrne

Montgomery

Fargo

2018

Health Services Research

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Objective To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). Data Sources/Study Setting Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. Study Design We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. Data Collection/Extraction Methods Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. Principal Findings Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. Conclusions Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.

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Identifying Homeless Medicaid Enrollees Using Enrollment Addresses

Abstract: An address-based indicator can identify a large proportion of Medicaid enrollees who are experiencing homelessness. This approach may be of interest to researchers, states, and health systems attempting to identify homeless populations.

Cited by 28 publications

References 17 publications

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada

Validation study of health administrative data algorithms to identify individuals experiencing homelessness and estimate population prevalence of homelessness in Ontario, Canada

A classification model of homelessness using integrated administrative data: Implications for targeting interventions to improve the housing status, health and well-being of a highly vulnerable population

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

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