How Do We Identify Homelessness in Large Health Care Data? Measuring Variation in Composition and Comorbidities by Definition (307832)*Wyatt P Bensken, Case Western Reserve University
Keywords: Homelessness, Social Determinants of Health,
Limited research exists on the health of the homeless population, and often relies on large health care datasets and varied methods, with little work to understand the accuracy of existing identification methods. Using state-wide hospital discharge data, we identified homeless records three ways: via ICD-10 Z59.0, an administrative indicator, and those with both. From 2,832,180 discharges, 28,529 were identified via Z-code, 17,591 via the indicator, and only 5,742 had both. Only 19.2% of Z-code records had the indicator and only 32.6% of the indicator had the Z-code. There were statistically significant differences in the race, sex, payer, and admission type between the groups. Similarly, comorbidity profiles were significantly different, with the Z-code cohort nearly always having higher rates of comorbidities such as hypertension (9% higher), chronic pulmonary disease (5.7% higher), and alcohol abuse (5% higher). The difference in the size, composition, and comorbidities between these groups suggests that researchers, clinicians, and policy experts should be wary of current methods to identify homeless individuals and continue to improve capture of this determinant of health.