Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results.

Introduction: HIV self-testing (HIVST) is highly sensitive and specific, addresses known barriers to HIV testing (such as stigma), and is recommended by the World Health Organization as a testing option for the delivery of HIV pre-exposure prophylaxis (PrEP). Nevertheless, HIVST remains underutilized as a diagnostic tool in community-based, differentiated HIV service delivery models, possibly due to concerns about result misinterpretation, which could lead to inadvertent onward transmission of HIV, delays in antiretroviral therapy (ART) initiation, and incorrect initiation on PrEP. Ensuring that HIVST results are accurately interpreted for correct clinical decisions will be critical to maximizing HIVST's potential. Early evidence from a few small pilot studies suggests that artificial intelligence (AI) computer vision and machine learning could potentially assist with this task. As part of a broader study that task-shifted HIV testing to a new setting and cadre of healthcare provider (pharmaceutical technologists at private pharmacies) in Kenya, we sought to understand how well AI technology performed at interpreting HIVST results. Methods: At 20 private pharmacies in Kisumu, Kenya, we offered free blood-based HIVST to clients ≥18 years purchasing products indicative of sexual activity (e.g., condoms). Trained pharmacy providers assisted clients with HIVST (as needed), photographed the completed HIVST, and uploaded the photo to a web-based platform. In real time, each self-test was interpreted independently by the (1) client and (2) pharmacy provider, with the HIVST images subsequently interpreted by (3) an AI algorithm (trained on lab-captured images of HIVST results) and (4) an expert panel of three HIVST readers. Using the expert panel's determination as the ground truth, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HIVST result interpretation for the AI algorithm as well as for pharmacy clients and providers, for comparison. Results: From March to June 2022, we screened 1,691 pharmacy clients and enrolled 1,500 in the study. All clients completed HIVST. Among 854 clients whose HIVST images were of sufficient quality to be interpretable by the AI algorithm, 63% (540/854) were female, median age was 26 years (interquartile range: 22-31), and 39% (335/855) reported casual sexual partners. The expert panel identified 94.9% (808/854) of HIVST images as HIV-negative, 5.1% (44/854) as HIV-positive, and 0.2% (2/854) as indeterminant. The AI algorithm demonstrated perfect sensitivity (100%), perfect NPV (100%), and 98.8% specificity, and 81.5% PPV (81.5%) due to seven false-positive results. By comparison, pharmacy clients and providers demonstrated lower sensitivity (93.2% and 97.7% respectively) and NPV (99.6% and 99.9% respectively) but perfect specificity (100%) and perfect PPV (100%). Conclusions: AI computer vision technology shows promise as a tool for providing additional quality assurance of HIV testing, particularly for catching Type II error (false-negative test interpretations) committed by human end-users. We discuss possible use cases for this technology to support differentiated HIV service delivery and identify areas for future research that is needed to assess the potential impacts-both positive and negative-of deploying this technology in real-world HIV service delivery settings.

The Acceptability of Pharmacy-Based HPV Vaccination in Western Kenya among Pharmacy Clients and Providers.

Vaccine coverage for the human papillomavirus (HPV) remains low globally, and differentiated models of vaccine delivery are needed to expand access. Pharmacy-based models of the HPV vaccination may engage women who could benefit. We assessed the acceptability of such a model among pharmacy clients and providers at 20 private pharmacies in Kisumu County, Kenya. In questionnaires, participants (≥18 years) were asked the extent they agreed (5-point scale) with statements that assessed different acceptability component constructs outlined in the Theoretical Framework of Acceptability (TFA). From March to June 2022, 1500 pharmacy clients and 40 providers were enrolled and completed questionnaires. Most clients liked the intervention (TFA: affective attitude; 96%, 1435/1500) and did not think it would be hard to obtain (TFA: burden; 93%, 1399/1500). All providers agreed the intervention could reduce HPV infection (TFA: perceived effectiveness) and felt confident they could deliver it (TFA: self-efficacy). Among the clients who had received or were planning to receive the HPV vaccine in the future, half (50%, 178/358) preferred a pharmacy-based HPV vaccination. In this study, most Kenyan pharmacy clients and providers perceived a pharmacy-delivered HPV vaccination as highly acceptable; however, more research is needed to test the feasibility and effectiveness of this novel vaccine delivery model in Africa.

Measuring the performance of HIV self-testing at private pharmacies in Kenya: a cross-sectional study.

INTRODUCTION: HIV self-testing (HIVST) has the potential to support daily oral pre-exposure prophylaxis (PrEP) delivery in private pharmacies, but many national guidelines have not approved HIVST for PrEP dispensing. In Kenya, pharmacy providers are permitted to deliver HIVST, but often do not have the required certification to deliver rapid diagnostic testing (RDT). We estimated the performance of provider-delivered HIVST compared to RDT, the standard of care for PrEP delivery, at private pharmacies in Kenya to inform decisions on the use of HIVST for PrEP scale-up. METHODS: At 20 pharmacies in Kisumu County, we trained pharmacy providers (pharmacists and pharmaceutical technologists) on blood-based HIVST use and client assistance (if requested). We recruited pharmacy clients purchasing sexual and reproductive health-related products (e.g. condoms) and enrolled those ≥18 years with self-reported behaviours associated with HIV risk. Enrolled clients received HIVST with associated provider counselling, followed by RDT by a certified HIV testing services (HTS) counsellor. Pharmacy providers and clients independently interpreted HIVST results prior to RDT (results interpreted only by the HTS counsellor). We calculated the sensitivity and specificity of pharmacy provider-delivered HIVST compared to HTS counsellor-administered RDT. RESULTS: Between March and June 2022, we screened 1691 clients and enrolled 1500; 64% (954/1500) were female and the median age was 26 years (IQR 22-31). We additionally enrolled 40 providers; 42% (17/40) were pharmacy owners and their median years of experience was 6 (IQR 4-10). The majority (79%, 1190/1500) of clients requested provider assistance with HIVST and providers spent a median of 20 minutes (IQR 15-43) with each HIVST client. The sensitivity of provider-delivered HIVST at the pharmacy was high when interpreted by providers (98.5%, 95% CI 97.8%, 99.1%) and clients (98.8%, 95% CI 98.0%, 99.3%), as was the specificity of HIVST in this setting (provider-interpretation: 96.9%, 95% CI 89.2%, 99.6%; client-interpretation: 93.8%, 95% CI 84.8%, 98.3%). CONCLUSIONS: When compared to the national HIV testing algorithm, provider-delivered blood-based HIVST at private pharmacies in Kenya performed well. These findings suggest that blood-based HIVST may be a useful tool to support PrEP initiation and continuation at private pharmacies and potentially other community-based delivery settings.

The Association Between Adverse Childhood Experiences and Human Papillomavirus Vaccination Coverage in US Young Adults: A Cross-Sectional Study.

BACKGROUND: Adverse childhood experiences (ACEs) contribute to adverse health outcomes in adulthood. Access to preventive health care services, including genital human papillomavirus (HPV) vaccinations, may mitigate the impact of ACEs on adverse health outcomes. Our objective was to assess associations between ACEs and HPV vaccination coverage among young adults. METHODS: We included 3415 respondents aged 18 to 29 years to the 2019-2020 Behavioral Risk Factor Surveillance System ACE and HPV vaccination modules. Adverse childhood experiences included emotional, physical, and sexual abuse; household intimate partner violence, substance abuse, and mental illness; and parental separation/divorce and incarcerated household member. We used log-binomial regression models to calculate prevalence ratios (PRs) with 95% confidence intervals (CI) for associations between ACEs and self-reported HPV vaccination and completion. Secondary outcomes included influenza vaccination uptake, time since routine checkup, HIV testing history, and HIV-related risk behavior. RESULTS: Several ACEs were positively associated with HPV vaccination initiation, including emotional abuse (PR, 1.29; 95% CI, 1.17-1.43), intimate partner violence (PR, 1.14; 95% CI, 1.00-1.30), substance abuse (PR, 1.20; 95% CI, 1.08-1.33), and mental illness (PR, 1.35; 95% CI, 1.22-1.50). Similar associations were observed for completion. Conversely, most ACEs were negatively associated with influenza vaccination (PRs from 0.72 to 1.00) and with recent checkup (PRs from 0.92 to 1.00). Adverse childhood experiences were positively associated with having had an HIV test (PRs from 1.19 to 1.56) and HIV-related risk behavior (PRs from 1.19 to 2.07). CONCLUSIONS: The unexpected positive associations between ACEs and HPV vaccination coverage could be due to opportunities to receive HPV vaccination in late adolescence or early adulthood while accessing STI/HIV prevention or treatment services. Future studies should evaluate associations between ACEs and timely HPV vaccination in early adolescence.

Prediction models of all-cause mortality among older adults in nursing home setting: A systematic review and meta-analysis.

Background and Aims: Few studies have meta-analyzed different prognostic models developed for older adults, especially nursing home residents. We aimed to systematically review and meta-analyze the performance of all published models that predicted all-cause mortality among older nursing home residents. Methods: We systematically searched PubMed and EMBASE from the databases' inception to January 1, 2020 to capture studies developing and/or validating a prognostic/prediction model for all-cause mortality among nursing home residents. We then carried out both qualitative and quantitative analyses evaluating these models' risks of bias and applicability. Results: The systematic search yielded 23,975 articles. We identified 28 indices that predicted the risk of all-cause mortality from 14 days to 39 months among older adults in nursing homes. The most used predictors were age, sex, body weight, swallowing problem, congestive heart failure, shortness of breath, body mass index, and activities of daily living. Of the 28 indices, 8 (29%) and 3 (11%) were internally and externally validated, respectively. None of the indices was validated in more than one cohort. Of the 28 indices, 22 (79%) reported the C-statistic, while only 6 (6%) reported the 95% confidence interval for the C statistic in the development cohorts. In the validation cohorts, 11 (39%) reported the C-statistic and 8 (29%) reported the 95% confidence interval. The meta-analyzed C statistic for all indices is 0.733 (95% prediction interval: 0.669-0.797). All studies/indices had high risks of bias and high concern for applicability according to PROBAST. Conclusion: We identified 28 indices for predicting all-cause mortality among older nursing home residents. The overall quality of evidence was low due to a high degree of bias and poor reporting of model performance statistics. Before any prediction model could be recommended in routine care, future research is needed to rigorously validate existing prediction models and evaluate their applicability and develop new prediction models.