HIV / STI
Hidden Over Time: Rising “No Risk Reported” in State HIV Surveillance and Its Treatment in CDC Data Cindy Tran* Angel Algarin Algarin Algarin Algarin Algarin Arizona State University
Background:
Accurate HIV transmission risk classification is central to U.S. surveillance and prevention planning. Yet a substantial proportion of new HIV diagnoses are reported with no reported risk (NRR). To address missingness, the Centers for Disease Control and Prevention (CDC) applies multiple imputation (MI) to reassign cases to existing transmission categories. However, the accuracy of MI depends on both the extent of missing data and the strength of associations between missing and observed data. How CDC-imputed classifications compare with state-reported data over time remains poorly characterized.
Methods:
We compared state-reported HIV transmission data abstracted from publicly available surveillance reports and direct health department requests against CDC-imputed data abstracted from AIDSVu for 2021–2023. Analysis was limited to 42 states and jurisdictions with usable data across all years. Transmission categories were harmonized into five groups: men who have sex with men (MSM), injection drug use (IDU), MSM/IDU, heterosexual contact (HET), and NRR/Other/Unknown. These distributions were visualized using multiple-line plots.
Results:
From 2021–2023, the proportion of new HIV diagnoses classified as NRR differed greatly between state-reported and CDC data. In state reports, the proportion of cases classified as NRR increased steadily from 17.4% in 2021 to 22.9% in 2023. In contrast, CDC classified less than 0.2% of cases as NRR in all three years. Visual comparisons indicate that this divergence reflects a growing share of cases classified as NRR in state data rather than single-year fluctuations.
Conclusions:
Systematic differences exist between state-reported and CDC-imputed HIV transmission data. While MI addresses missing data, reliance on MI without data transparency may obscure accurate interpretation. Upstream interventions improving transmission data collection may reduce missingness and enhance the accuracy of HIV surveillance data.
