Predictors of Medication Utilization for Opioid Use Disorder Among Medicaid-Insured HIV Patients in New York.

BACKGROUND AND OBJECTIVES: This paper investigates the prevalence and predictors for opioid use disorder (OUD) pharmacotherapy utilization for Medicaid-insured patients with human immunodeficiency virus (HIV) in New York. METHODS: We identified patients with HIV and OUD in 2014 in the New York State Medicaid claims data (n = 5621). The claims were used to identify individual client medication for addiction treatment (MAT) utilization, demographic information, and other medical and psychiatric health conditions. The logistic regression analyses were performed to explore the potential predictors of MAT service utilization among people with HIV and OUD. RESULTS: Of 5621 identified patients with HIV and OUD, 3647 (65%) received some type of MAT. Eighty-seven percent of treated patients received methadone while 10% received buprenorphine and 3% utilized both the therapies. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: A substantial number of patients with HIV and OUD did not receive MAT. Findings suggest that there are opportunities to improve OUD care for patients with HIV and OUD, particularly among the younger generation, blacks, individuals living outside of New York City, and among those with serious psychiatric conditions. This initial study suggests that an additional research is needed to better understand how the gap in care affects this population. (Am J Addict 2020;29:151-154).

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.