Using a machine learning approach to explore predictors of healthcare visits as missed opportunities for HIV diagnosis.

OBJECTIVES

A significant number of individuals with a new HIV diagnosis are still late presenters despite numerous healthcare encounters prior to HIV diagnosis. We employed a machine learning approach to identify the predictors for the missed opportunities for earlier HIV diagnosis.

METHODS

The cohort comprised of individuals who were diagnosed with HIV in South Carolina from January 2008 to December 2016. Late presenters (LPs) (initial CD4 ≤200 cells/mm3 within one month of HIV diagnosis) with any healthcare visit during three years prior to HIV diagnosis were defined as patients with a missed opportunity. Using least absolute shrinkage and selection operator (LASSO) regression, two prediction models were developed to capture the impact of facility type (model 1) and physician specialty (model 2) of healthcare visits on missed opportunities.

RESULTS

Among 4,725 eligible participants, 72.2% had at least one healthcare visit prior to their HIV diagnosis, with most of the healthcare visits (78.5%) happening in the emergency departments (ED). A total of 1,148 individuals were LPs, resulting in an overall prevalence of 24.3% for the missed opportunities for earlier HIV diagnosis. Common predictors in both models included ED visit, older age, male gender, and alcohol use.

CONCLUSIONS

The findings underscored the need to reinforce the universal HIV testing strategy ED remains an important venue for HIV screening, especially for medically underserved or elder population. An improved and timely HIV screening strategy in clinical settings can be a key for early HIV diagnosis and play an increasingly important role in ending HIV epidemic.