Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Jun 28, 2024)
Date Submitted: Jun 26, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Estimation of HIV Prevalence at the ZIP Code-Level Using Passive Surveillance Data and Social Determinants of Disease Spreading in Atlanta, Georgia: Bayesian Prediction Modeling.
ABSTRACT
Background:
Better information at the ZIP Code-level has the potential to enhance interventions targeting, identify treatment gaps, and optimize resources utilization. Currently there are no methods designed to estimate undiagnosed HIV cases at jurisdictions smaller than counties.
Objective:
This study aims to predict the number of undiagnosed HIV cases at the ZIP Code-level in Atlanta, Georgia, based on publicly available information.
Methods:
The CDC reports both passive surveillance (PS) and estimated total (MS) HIV cases for selected counties as part of the Ending of the HIV Epidemic initiative. We employed a Bayesian hierarchical model to: 1) Model MS as random draws from a Poisson distribution with mean equal to the true total HIV cases in the county. 2) A Binomial model for PS arising from the true denominator, with mean P, known as the ascertainment probability. 3) Use a logistic fractional model to allow P to be dependent on socio-economic determinants of HIV extracted from the American Community Survey. These determinants were chosen through a feature selection algorithm. The prediction model was tested out-of-sample on Georgia counties. Finally, we combined zip-code-level covariate data with the posterior predictive distribution of the logit coefficients to predict the mean P at zip-code-level. Final estimates were spatially-smoothed and aggregated to county-level for secondary validations.
Results:
The county-level model showed good mixing properties and predictive accuracy. The mean ascertainment probability calibrated to the ZIP Code-level varied from 78.4% (95% credibility interval: 24.4%-99.3%) to 93.8% (95%CI: 80.6%-99.8%). Further, the predicted undiagnosed HIV cases ranged between 12 (95%CI: 6-19; ZIP Code 30322) to 1,603 (95%CI 1,209-1,968; ZIP Code 30318).
Conclusions:
Our findings provide a more detailed understanding of the risk profile of the city, in particular regarding the heterogeneity and concentration of cases within the city, and therefore a more complete picture of the transmission risk. This information could be leveraged to better identify underserved communities, better targeting the delivery of prevention and treatment services, and overall increase the efficiency in the control of the HIV epidemic. Furthermore, our methodological approach can be applied to other cities in the country, to obtain a more detailed depictions of its HIV risk-profile and complement passive surveillance efforts. Clinical Trial: NA
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