Background: The Alabama Quality Management Group (AQMG), a consortium of 9 Ryan White–funded part C and D clinics, distributed statewide was established in 2006 under the guidance from the Health and Resources Services Administration with a clinical quality improvement (CQI) focus. Methods: We describe the origins and evolution of the AQMG, including requisite shifts from aggregate clinic-wide to de-identified individual-level data reporting for implementation of the Data for Care (D4C-AL) Alabama program. The D4C-AL strategy uses a clinic-wide risk stratification of all patients based on missed clinic visits in the previous 12 months. Intermediate (1–2 missed visits) and high-risk patients (>3 missed visits) receive the evidence-informed Retention through Enhanced Personal Contact intervention. We report on a pilot of the D4CAL program in 4 of 33 primary HIV care clinics at the UAB 1917 Clinic. Results: Among 3859 patients seen between April 2018 and February 2019, the missed visit rate was not significantly different between the D4C-1917 (19.2%) and non-D4C clinics (20.5%) in a preintervention period (May 2017–April 2018). However, a significantly lower missed visit rate was observed in the D4C-1917 vs. non–D4C-1917 clinics during the intervention period (April 2018–February 2019, P = 0.049). Conclusions: The AQMG has been transformed into a health service research and implementation science platform, building on a shared vision, mission, data reporting, and quality improvement focus. Moreover, CQI may be viewed as an implementation strategy that seeks to enhance uptake and sustained use of effective interventions with D4C-AL representing a prototype for future initiatives embedded within extant quality improvement consortia.
Estimating the population with undiagnosed HIV (PUHIV) is the most methodologically challenging aspect of evaluating 90-90-90 goals. The objective of this review is to discuss assumptions, strengths, and shortcomings of currently available methods of this estimation. Articles from 2000 to 2018 on methods to estimate PUHIV were reviewed. Back-calculation methods including CD4 depletion and test–retest use diagnosis CD4 count, or previous testing history to determine likely infection time thus, providing an estimate of PUHIV for previous years. Biomarker methods use immunoassays to differentiate recent from older infections. Statistical techniques treat HIV status as missing data and impute data for models of infection. Lastly, population surveys using HIV rapid testing most accurately calculates the current HIV prevalence. Although multiple methods exist to estimate the number of PUHIV, the appropriate method for future applications depends on multiple factors, namely data availability and population of interest.
Background In the U.S., 44% of people with HIV (PWH) live in the Southeastern census region; many PWH remain undiagnosed. Novel strategies to inform testing outreach in rural states with dispersed HIV-epidemics are needed. Methods Alabama state public health HIV testing surveillance data from 2013-2017 were used to estimate time from infection to HIV diagnosis using CD4 T-cell depletion modeling, mapped to county. Diagnostic HIV tests performed 2013-2021 by commercial testing entities were used to estimate HIV tests per 100,000 adults (15-65-year-old), mapped to client ZIP code tabulation area (ZCTA). We then defined testing “cold spots”: those with <10% adults tested plus either (a) within or bordering one of the 13 counties with HIV prevalence greater than 400 cases per 100,000 population or (b) within a county with average time to diagnosis greater than the state average to inform testing outreach. Results Time to HIV diagnosis is a median of 3.7 (IQR 0-9.2) years across Alabama, with a range from 0.06-12.25 years. Approximately 63% of counties (N=42) have a longer time-to-diagnosis compared to national U.S. estimates. 643 ZCTAs tested 17.3% (IQR: 10.3%,25.0%) of the adult population from 2013-2017. To prioritize areas for testing outreach, we generated maps to describe 47 areas of HIV-testing cold spots at the ZCTA level. Conclusions Combining public health surveillance with commercial testing data provides a more nuanced understanding of HIV testing gaps in a state with a rural HIV epidemic and identifies areas to prioritize for testing outreach.
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