AIDS Drug Assistance Program disenrollment is associated with loss of viral suppression beyond differences in homelessness, mental health, and substance use disorders: An evaluation in Washington state 2017-2019.

AIDS Drug Assistance Programs (ADAPs) are state-administered programs that pay for medical care for people living with HIV in the US. Maintaining enrollment in the programs is challenging, and a large proportion of clients in Washington state (WA) fail to recertify and are disenrolled. In this study we sought to quantify the impact of disenrollment from ADAPs on viral suppression. We conducted a retrospective cohort study of the 5238 clients in WA ADAP from 2017 to 2019 and estimated the risk difference (RD) of viral suppression before and after disenrollment. We performed a quantitative bias analysis (QBA) to assess the effect of unmeasured confounders, as the factors that contribute to disenrollment and medication discontinuation may overlap. Of the 1336 ADAP clients who disenrolled ≥1 time, 83% were virally suppressed before disenrollment versus 69% after (RD 12%, 95%CI 9-15%). The RD was highest among clients with dual Medicaid-Medicare insurance (RD 22%, 95%CI 9-35%) and lowest among privately insured individuals (RD 8%, 95%CI 5-12%). The results of the QBA suggest that unmeasured confounders do not negate the overall RD. The ADAP recertification procedures negatively impact the care of clients who struggle to stay in the program; alternative procedures may reduce this impact.

Direct and Indirect Effects of a Project ECHO Longitudinal Clinical Tele-Mentoring Program on Viral Suppression for Persons With HIV: A Population-Based Analysis.

BACKGROUND: Project Extension for Community Health Outcomes (ECHO) aims to connect community providers to academic specialists, deliver longitudinal clinical mentorship and case consultations, plus encourage dissemination of knowledge and resources. The impact on outcomes for persons with HIV (PWH) is uncertain. SETTING: PWH in Washington and Oregon outside of the Seattle and Portland metro areas, January 2011 to March 2018. METHODS: Using viral load (VL) surveillance data, we assessed difference in the percentage of PWH who were virally suppressed among PWH whose providers participated versus did not participate in Project ECHO. Analyses included multiple mixed-effects regression models, adjusting for time and for patient, provider, and clinic characteristics. RESULTS: Based on 65,623 VL results, Project ECHO participation was associated with an increase in the percentage of patients with VL suppression (13.7 percentage points greater; P < 0.0001), although the effect varied by estimated provider PWH patient volume. The difference was 14.7 percentage points ( P < 0.0001) among patients of providers who order <20 VL's/quarter and 2.3 and -0.6 percentage points among patients of providers who order 20-40 or >40 VL's/quarter, respectively ( P > 0.5). The magnitude of difference in VL suppression was associated with the number of sessions attended. Among patients of lower-volume providers who did not participate, VL suppression was 6.2 percentage points higher if providers worked in a clinic where another provider did participate ( P < 0.0001). CONCLUSION: Project ECHO is associated with improvement in VL suppression for PWH whose providers participate or work in the same clinic system as a provider who participates, primarily because of benefits for patients of lower-volume providers.

Contraceptive Use Among Women in the United States Aged 18-44 Years with Selected Medical Contraindications to Estrogen.

Objective: Medical contraindications to estrogen limit women's contraceptive options. This study assessed the association between selected medical contraindications to estrogen on contraceptive use and examined whether contraindications serve as a barrier to the prevention of unintended pregnancy. Materials and Methods: We analyzed women aged 18-44 at risk of unintended pregnancy participating in the 2017 Behavioral Risk Factor Surveillance System. Survey questions queried women regarding contraceptive use and contraindications to estrogen use. We assessed the most recently used contraceptive method and compared the odds of women using each category of contraception (no methods, less effective methods, pill/patch/ring, injection, intrauterine device, implant, permanent contraception) between those with and without potential contraindications to estrogen using multinomial logistic regression models. Results: This study included 32,098 women, of whom 16% had one or more potential contraindications to estrogen. There were significant differences in contraceptive choice by potential contraindication status (p < 0.01). Fifteen percent of women with potential contraindications reported using estrogen-containing methods (pill, patch, or ring) compared with 20% of women with no potential contraindication. Women with potential contraindications to estrogen more frequently used permanent contraception (odds ratio [OR] vs. pill/patch/ring: 1.48 95% confidence interval [CI]: 1.17-1.88) or no contraceptive method (OR vs. pill/patch/ring: 1.37 95% CI: 1.07-1.75) after adjustment for race, age, marital status, and income. Conclusions: Potential medical contraindications to estrogen are associated with permanent contraception and the use of no contraception. These results portray a complicated relationship but could suggest a lack of access to other contraceptive options.

Detection and Response to an HIV Cluster: People Living Homeless and Using Drugs in Seattle, Washington.

INTRODUCTION: The HIV epidemic in King County, Washington has traditionally been highly concentrated among men who have sex with men, and incidence has gradually declined over 2 decades. In 2018, King County experienced a geographically concentrated outbreak of HIV among heterosexual people who inject drugs. METHODS: Data sources to describe the 2018 outbreak and King County's response were partner services interview data, HIV case reports, syringe service program client surveys, hospital data, and data from a rapid needs assessment of homeless individuals and people who inject drugs. In 2020, the authors examined the impact of delays in molecular sequence analyses and cluster member size thresholds, for identifying genetically similar clusters, on the timing of outbreak identification. RESULTS: In 2018, the health department identified a North Seattle cluster, growing to 30 people with related HIV infections diagnosed in 2008-2019. In total, 70% of cluster members were female, 77% were people who inject drugs, 87% were homeless, and 27% reported exchanging sex. Intervention activities included a rapid needs assessment, 2,485 HIV screening tests in a jail and other outreach settings, provision of 87,488 clean syringes in the outbreak area, and public communications. A lower cluster size threshold and more rapid receipt and analyses of data would have identified this outbreak 4-16 months earlier. CONCLUSIONS: This outbreak shows the vulnerability of people who inject drugs to HIV infection, even in areas with robust syringe service programs and declining HIV epidemics. Although molecular HIV surveillance did not identify this outbreak, it may have done so with a lower threshold for defining clusters and more rapid receipt and analyses of HIV genetic sequences.

Predictive Value of Time-Space Clusters for HIV Transmission in Washington State, 2017-2019.

BACKGROUND: Pillar 4 of the United States' End the HIV Epidemic plan is to respond quickly to HIV outbreaks, but the utility of CDC's tool for identifying HIV outbreaks through time-space cluster detection has not been evaluated. The objective of this evaluation is to quantify the ability of the CDC time-space cluster criterion to predict future HIV diagnoses and to compare it to a space-time permutation statistic implemented in SaTScan software. SETTING: Washington State from 2017 to 2019. METHODS: We applied both cluster criteria to incident HIV cases in Washington State to identify clusters. Using a repeated-measures Poisson model, we calculated a rate ratio comparing the 6 months after cluster detection with a baseline rate from 24 to 12 months before the cluster was detected. We also compared the demographics of cases within clusters with all other incident cases. RESULTS: The CDC criteria identified 17 clusters containing 192 cases in the 6 months after cluster detection, corresponding to a rate ratio of 1.25 (95% confidence interval: 0.95 to 1.65) relative to baseline. The time-space permutation statistic identified 5 clusters containing 25 cases with a rate ratio of 2.27 (95% confidence interval: 1.28 to 4.03). Individuals in clusters identified by the new criteria were more likely to be of Hispanic origin (61% vs 20%) and in rural areas (51% vs 12%). CONCLUSIONS: The space-time permutation cluster analysis is a promising tool for identification of clusters with the largest growth potential for whom interruption may prove most beneficial.

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State.

Molecular cluster detection can be used to interrupt HIV transmission but is dependent on identifying clusters where transmission is likely. We characterized molecular cluster detection in Washington State, evaluated the current cluster investigation criteria, and developed a criterion using machine learning. The population living with HIV (PLWH) in Washington State, those with an analyzable genotype sequences, and those in clusters were described across demographic characteristics from 2015 to2018. The relationship between 3- and 12-month cluster growth and demographic, clinical, and temporal predictors were described, and a random forest model was fit using data from 2016 to 2017. The ability of this model to identify clusters with future transmission was compared to Centers for Disease Control and Prevention (CDC) and the Washington state criteria in 2018. The population with a genotype was similar to all PLWH, but people in a cluster were disproportionately white, male, and men who have sex with men. The clusters selected for investigation by the random forest model grew on average 2.3 cases (95% CI 1.1-1.4) in 3 months, which was not significantly larger than the CDC criteria (2.0 cases, 95% CI 0.5-3.4). Disparities in the cases analyzed suggest that molecular cluster detection may not benefit all populations. Jurisdictions should use auxiliary data sources for prediction or continue using established investigation criteria.