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Using a machine learning approach to explore predictors of healthcare visits as missed opportunities for HIV
Sharon Weissman1,2, Xueying Yang2,3, Jiajia Zhang2,4
1Department of Internal Medicine, School of Medicine.
Insights
Many individuals diagnosed with HIV are late presenters, missing opportunities for earlier detection. Machine learning identified emergency department visits, older age, male gender, and alcohol use as key predictors for these missed opportunities.
Area of Science:
- Public Health
- Epidemiology
- Machine Learning in Healthcare
Background:
- Late HIV diagnosis remains a significant public health challenge, despite prior healthcare interactions.
- Identifying missed opportunities for earlier HIV diagnosis is crucial for improving patient outcomes.
Purpose of the Study:
- To utilize a machine learning approach to identify predictors of missed opportunities for earlier HIV diagnosis.
- To develop prediction models based on healthcare visit characteristics to understand factors contributing to late HIV presentation.
Main Methods:
- A cohort of individuals diagnosed with HIV in South Carolina (2008-2016) was analyzed.
- Late presenters were defined as those with CD4 count ≤200 cells/mm³ within one month of diagnosis and prior healthcare visits.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to build two prediction models focusing on facility type and physician specialty.
Main Results:
- Out of 4,725 participants, 24.3% were late presenters with missed opportunities.
- Emergency department (ED) visits were common (78.5% of pre-diagnosis visits).
- Predictors for missed opportunities included ED visits, older age, male gender, and alcohol use.
Conclusions:
- Reinforcing universal HIV testing strategies is essential, with the ED being a key venue for screening, particularly for underserved or elderly populations.
- Improving timely HIV screening in clinical settings is vital for early diagnosis and combating the HIV epidemic.
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.
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