Impact of supportive housing health homes program on medicaid utilization for persons diagnosed with HIV (PDWH).

ABSTRACTUnstable housing among persons diagnosed with HIV (PDWH) has been consistently linked to poor HIV-related care engagement. We examined the relationship between enrollment in a supportive housing program and health care utilization (use of outpatient services, emergency department (ED) visits, and hospitalizations) for a group of unstably housed, Medicaid and Health Homes (HH)-enrolled PDWH in New York State. We analyzed monthly longitudinal data consisting of linked supportive housing data, HH data, and Medicaid claims from New York State (excluding New York City) between 2012 and 2017 using time series models. Participants who had at least six consecutive months of supportive housing at month t had 20% higher odds of using an outpatient service, 19% lower odds of visiting the ED, and 24% lower odds of being hospitalized compared to those with less than six consecutive months of supportive housing after adjusting for covariates. Supportive housing may promote better medical management by increasing outpatient visits among chronically homeless PDWH.

Validation of an Optimized Algorithm for Identifying Persons Living With Diagnosed HIV From New York State Medicaid Data, 2006-2014.

Algorithms are regularly used to identify persons living with diagnosed human immunodeficiency virus (HIV) (PLWDH) in Medicaid data. To our knowledge, there are no published reports of an HIV algorithm from Medicaid claims codes that have been compared with an HIV surveillance system to assess its sensitivity, specificity, positive predictive value, and negative predictive value in identifying PLWDH. Therefore, our aims in this study were to 1) develop an algorithm that could identify PLWDH in New York State Medicaid data from 2006-2014 and 2) validate this algorithm using the New York State HIV surveillance system. Classification and regression tree analysis identified 16 nodes that we combined to create a case-finding algorithm with 5 criteria. This algorithm identified 86,930 presumed PLWDH, 88.0% of which were verified by matching to the surveillance system. The algorithm yielded a sensitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predictive value of 97.6%. This validated algorithm has the potential to improve the utility of Medicaid data for assessing health outcomes and programmatic interventions.