Optimizing Screening for HIV

Abstract

Background: The HIV epidemic is unevenly distributed throughout the United States, even within neighborhoods. This study evaluated how effectively current testing approaches reached persons at risk for HIV infection across San Diego (SD) County, California.

Methods: HIV case and testing data, sexually transmitted infection (STI) data, and sociodemographic data for SD County were collected from the SD Health and Human Services Agency and the "Early Test" community-based HIV screening program between 1998 and 2016. Relationships between HIV diagnoses, HIV prevalence, and STI diagnoses with screening at the ZIP code level were evaluated.

Results: Overall, 379 074 HIV tests were performed. The numbers of HIV tests performed on persons residing in a ZIP code or region overall strongly correlated with prevalent HIV cases (R ² = .714), new HIV diagnoses (R ² = .798), and STI diagnoses (R ² = .768 [chlamydia], .836 [gonorrhea], .655 [syphilis]) in those regions. ZIP codes with the highest HIV prevalence had the highest number of tests per resident and fewest number of tests per diagnosis. Even though most screening tests occurred at fixed venues located in high-prevalence areas, screening of residents from lower-prevalence areas was mostly proportional to the prevalence of HIV and rates of new HIV and STI diagnoses in those locales.

Conclusions: This study supported the ability of a small number of standalone testing centers to reach at-risk populations dispersed across SD County. These methods can also be used to highlight geographic areas or demographic segments that may benefit from more intensive screening.

Keywords: HIV; prevalence; screening; sexually transmitted infections.

Figure 1.

HIV testing in San Diego County (by Health and Human Services Agency [HHSA] and SD Primary Infection Resource Consortium [SDPIRC]), 1998–2016. A, Maps of San Diego County depicting the number of HIV tests administered in each ZIP code over 3 time periods. B, Map of the HHSA Regions of San Diego County with the 5 testing venues marked by a red ribbon. C, Annual number of HIV tests administered by HHSA and SDPIRC by HHSA region from 1998 to 2016. A notable drop in the number of tests performed occurred in 2014 due to programmatic changes.

Figure 2.

2016 map of HIV prevalence in San Diego County by ZIP code. Heat map is color-coded according the number of prevalent cases residing in that ZIP code (data from the Health and Human Services Agency).

Figure 3.

New HIV diagnoses in San Diego County. This figure presents the annual number of new HIV diagnoses in each Health and Human Services Agency (HHSA) region reported to the HHSA from 1996 to 2017. When examining the years 2009–2017, we found a significant decrease in new diagnoses in the Central region (P = .008) but no change in the other regions.

Figure 4.

HIV testing by prevalence and new diagnoses in San Diego County. This figure presents the HIV tests administered by San Diego County Public Health and SD Primary Infection Resource Consortium (SDPIRC) in 2012 (A) and 2016 (B) for each ZIP code vs the estimated HIV prevalence of those ZIP codes. The estimated prevalence from 2010 was used for comparisons with 2012 testing data, and the estimated prevalence from 2016 was used for comparisons with 2016 testing data. Tight correlations were observed, with R² values of .64 for 2012 and .71 for 2016. C, The relationship between HIV testing and future incident diagnosis HIV tests administered by the Health and Human Services Agency (HHSA) and SDPIRC by HHSA region vs HIV incident diagnoses for those regions demonstrates a strong association (R² = .80).

Figure 5.

HIV testing by sexually transmitted infection (STI) incident diagnoses in San Diego County. The 2016 STI diagnoses in each ZIP are plotted against the number of HIV tests performed by the Health and Human Services Agency and SD Primary Infection Resource Consortium. A tight correlation was observed, with an R² of .77 for chlamydia (A), .84 for gonorrhea (B), and .66 for syphilis (C). However, a number of high–STI incidence ZIP codes are noted (particularly for chlamydia) that have fewer tests than expected based on regression lines utilizing data from all ZIP codes in the county.