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Master of Public Health

Development and Validation of a Multivariable Prediction Model for Missed HIV Health Care Provider Visits in a Large US Clinical Cohort

AUTHORS

Pettit AC , Bian A , Schember CO , Rebeiro PF , Keruly JC , Mayer KH , Mathews WC , Moore RD , Crane HM , Geng E , Napravnik S , Shepherd BE , Mugavero MJ , . Open forum infectious diseases. 2021 4 8; 8(7). ofab130

ABSTRACT

Background: Identifying individuals at high risk of missing HIV care provider visits could support proactive intervention. Previous prediction models for missed visits have not incorporated data beyond the individual level.

Methods: We developed prediction models for missed visits among people with HIV (PWH) with ≥1 follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems from 2010 to 2016. Individual-level (medical record data and patient-reported outcomes), community-level (American Community Survey), HIV care site-level (standardized clinic leadership survey), and structural-level (HIV criminalization laws, Medicaid expansion, and state AIDS Drug Assistance Program budget) predictors were included. Models were developed using random forests with 10-fold cross-validation; candidate models with the highest area under the curve (AUC) were identified.

Results: Data from 382 432 visits among 20 807 PWH followed for a median of 3.8 years were included; the median age was 44 years, 81% were male, 37% were Black, 15% reported injection drug use, and 57% reported male-to-male sexual contact. The highest AUC was 0.76, and the strongest predictors were at the individual level (prior visit adherence, age, CD4+ count) and community level (proportion living in poverty, unemployed, and of Black race). A simplified model, including readily accessible variables available in a web-based calculator, had a slightly lower AUC of .700.

Conclusions: Prediction models validated using multilevel data had a similar AUC to previous models developed using only individual-level data. The strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive. Absent additional data, PWH with previous missed visits should be prioritized by interventions to improve visit adherence.

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