Accepted for/Published in: Online Journal of Public Health Informatics
Date Submitted: Mar 4, 2024
Date Accepted: May 23, 2024
Warning: This is an author submission that is not peer-reviewed or edited.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.
Inferring population HIV viral load from a single HIV clinic’s electronic health record
ABSTRACT
Background:
Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV.
Objective:
A given HIV clinic’s electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure.
Methods:
We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a logarithmic mean of 3.6. Three hypothetical EHRs sampled from A) the source population, B) those diagnosed, and C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using patient data from an HIV clinic EHR in Delaware.
Results:
Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows, clinic A: 3.64 (3.29, 4.05), clinic B: 3.65 (3.28, 4.01), and clinic C: 2.38 (2.05, 2.75). Bayesian adjusted weighting further improved the estimate.
Conclusions:
These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic’s EHR without the resource-intensive elucidation of an informative prior.
Citation
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