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BackgroundThe use of mobile communication technologies (mHealth: mobile health) in chronic disease management has grown significantly over the years. mHealth interventions have the potential to decentralize access to health care and make it convenient, particularly in resource-constrained settings. It is against this backdrop that we aimed to codevelop (with potential users) a new generation of mobile phone–connected HIV diagnostic tests and Web-based clinical care pathways needed for optimal delivery of decentralized HIV testing, prevention, and care in low- and middle-income countries. ObjectiveThe aim of this study was to understand ways in which an mHealth intervention could be developed to overcome barriers to existing HIV testing and care services and promote HIV self-testing and linkage to prevention and care in a poor, HIV hyperendemic community in rural KwaZulu-Natal, South Africa. MethodsA total of 54 in-depth interviews and 9 focus group discussions were conducted with potential users (including health care providers) in 2 different communities. Theoretically informed by the candidacy framework, themes were identified from the interview transcripts, manually coded, and thematically analyzed. ResultsParticipants reported barriers, such as fear of HIV identity, stigma, long waiting hours, clinic space, and health care workers’ attitudes, as major impediments to effective uptake of HIV testing and care services. People continued to reassess their candidacy for HIV testing and care services on the basis of their experiences and how they or others were treated within the health systems. Despite the few concerns raised about new technology, mobile phone–linked HIV testing was broadly acceptable to potential users (particularly men and young people) and providers because of its privacy (individual control of HIV testing over health provider–initiated testing), convenience (individual time and place of choice for HIV testing versus clinic-based testing), and time saving. ConclusionsMobile phone–connected HIV testing and Web-based clinical care and prevention pathways have the potential to support access to HIV prevention and care, particularly for young people and men. Although mHealth provides a way for individuals to test their candidacy for HIV services, the barriers that can make the service unattractive at the clinic level will also need to be addressed if potential demand is to turn into actual demand.

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans--experienced nurses and newly trained community health worker staff--and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics.sup.1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections. In a pilot field study conducted in rural South Africa, deep learning algorithms can accurately classify rapid HIV tests as positive or negative, highlighting the potential of deep learning-enabled diagnostics for use in low- and middle-income countries., Author(s): Valérian Turbé [sup.1] , Carina Herbst [sup.2] , Thobeka Mngomezulu [sup.2] , Sepehr Meshkinfamfard [sup.1] , Nondumiso Dlamini [sup.2] , Thembani Mhlongo [sup.2] , Theresa Smit [sup.2] , Valeriia [...]

Background Evidence for the effectiveness of continuous quality improvement (CQI) in resource-poor settings is very limited. We aimed to establish the effects of CQI on quality of antenatal HIV care in primary care clinics in rural South Africa. Methods and findings We conducted a stepped-wedge cluster-randomised controlled trial (RCT) comparing CQI to usual standard of antenatal care (ANC) in 7 nurse-led, public-sector primary care clinics-combined into 6 clusters-over 8 steps and 19 months. Clusters randomly switched from comparator to intervention on pre-specified dates until all had rolled over to the CQI intervention. Investigators and clusters were blinded to randomisation until 2 weeks prior to each step. The intervention was delivered by trained CQI mentors and included standard CQI tools (process maps, fishbone diagrams, run charts, Plan-Do-Study-Act [PDSA] cycles, and action learning sessions). CQI mentors worked with health workers, including nurses and HIV lay counsellors. The mentors used the standard CQI tools flexibly, tailored to local clinic needs. Health workers were the direct recipients of the intervention, whereas the ultimate beneficiaries were pregnant women attending ANC. Our 2 registered primary endpoints were viral load (VL) monitoring (which is critical for elimination of mother-to-child transmission of HIV [eMTCT] and the health of pregnant women living with HIV) and repeat HIV testing (which is necessary to identify and treat women who seroconvert during pregnancy). All pregnant women who attended their first antenatal visit at one of the 7 study clinics and were [greater than or equal to]18 years old at delivery were eligible for endpoint assessment. We performed intention-to-treat (ITT) analyses using modified Poisson generalised linear mixed effects models. We estimated effect sizes with time-step fixed effects and clinic random effects (Model 1). In separate models, we added a nested random clinic-time step interaction term (Model 2) or individual random effects (Model 3). Between 15 July 2015 and 30 January 2017, 2,160 participants with 13,212 ANC visits (intervention n = 6,877, control n = 6,335) were eligible for ITT analysis. No adverse events were reported. Median age at first booking was 25 years (interquartile range [IQR] 21 to 30), and median parity was 1 (IQR 0 to 2). HIV prevalence was 47% (95% CI 42% to 53%). In Model 1, CQI significantly increased VL monitoring (relative risk [RR] 1.38, 95% CI 1.21 to 1.57, p < 0.001) but did not improve repeat HIV testing (RR 1.00, 95% CI 0.88 to 1.13, p = 0.958). These results remained essentially the same in both Model 2 and Model 3. Limitations of our study include that we did not establish impact beyond the duration of the relatively short study period of 19 months, and that transition steps may have been too short to achieve the full potential impact of the CQI intervention. Conclusions We found that CQI can be effective at increasing quality of primary care in rural Africa. Policy makers should consider CQI as a routine intervention to boost quality of primary care in rural African communities. Implementation research should accompany future CQI use to elucidate mechanisms of action and to identify factors supporting long-term success. Trial registration This trial is registered at ClinicalTrials.gov under registration number NCT02626351., Author(s): H. Manisha Yapa 1,2,*, Jan-Walter De Neve 3, Terusha Chetty 4, Carina Herbst 2, Frank A. Post 5, Awachana Jiamsakul 1, Pascal Geldsetzer 3,6, Guy Harling 2,7, Wendy Dhlomo-Mphatswe [...]

Background: We evaluated continuous quality improvement (CQI) targeting antenatal HIV care quality in rural South Africa using a stepped-wedge cluster-randomised controlled trial (Management and Optimisation of Nutrition, Antenatal, Reproductive, Child health, MONARCH) and an embedded process evaluation. Here, we present results of the process evaluation examining determinants of CQI practice and 'normalisation.' Methods: A team of CQI mentors supported public-sector health workers in seven primary care clinics to (1) identify root causes of poor HIV viral load (VL) monitoring among pregnant women living with HIV and repeat HIV testing among pregnant women not living with HIV, and (2) design and iteratively test their own solutions. We used a mixed methods evaluation with field notes from CQI mentors ('dose' and 'reach' of CQI, causes of poor HIV care testing rates, implemented change ideas); patient medical records (HIV care testing by clinic and time step); and semi-structured interviews with available health workers. We analysed field notes and semi-structured interviews for determinants of CQI implementation and 'normalisation' using Normalisation Process Theory (NPT) and Tailored Implementation of Chronic Diseases (TICD) frameworks. Results: All interviewed health workers found the CQI mentors and methodology helpful for quality improvement. Total administered 'dose' was higher than planned but 'reach' was limited by resource constraints, particularly staffing shortages. Simple workable improvements to identified root causes were implemented, such as a patient tracking notebook and results filing system. VL monitoring improved over time, but not repeat HIV testing. Besides resource constraints, gaps in knowledge of guidelines, lack of leadership, poor clinical documentation, and data quality gaps reduced CQI implementation fidelity and normalisation. Conclusion: While CQI holds promise, we identified several health system challenges. Priorities for policy makers include improving staffing and strategies to improve clinical documentation. Additional support with implementing clinical guidelines and improving routine data quality are needed. Normalising CQI may be challenging without concurrent health system improvements. [ABSTRACT FROM AUTHOR]

Background: Recent population-representative estimates of sexually transmitted infection (STI) prevalence in high HIV burden areas in southern Africa are limited. We estimated the prevalence and associated factors of 3 STIs among adolescents and young adults (AYA) in rural South Africa., Methods: Between March 2020 and May 2021, a population-representative sample of AYA aged 16 to 29 years were randomly selected from a Health and Demographic Surveillance Site in rural KwaZulu-Natal, South Africa, for a 2 × 2 factorial randomized controlled trial. Participants in 2 intervention arms were offered baseline testing for gonorrhea, chlamydia, and trichomoniasis using GeneXpert. Prevalence estimates were weighted for participation bias, and logistic regression models were used to assess factors associated with STIs., Results: Of 2323 eligible AYA, 1743 (75%) enrolled in the trial. Among 863 eligible for STI testing, 814 (94%) provided specimens (median age of 21.8 years, 52% female, and 71% residing in rural areas). Population-weighted prevalence estimates were 5.0% (95% confidence interval [CI], 4.2%-5.8%) for gonorrhea, 17.9% (16.5%-19.3%) for chlamydia, 5.4% (4.6%-6.3%) for trichomoniasis, and 23.7% (22.2%-25.3%) for any STI. In multivariable models, female sex (adjusted odds ratio [aOR], 2.24; 95% CI, 1.48-3.09) and urban/periurban (vs. rural) residence (aOR, 1.48; 95% CI, 1.02-2.15) were associated with STIs; recent migration was associated with lower odds of STI (aOR, 0.37; 95% CI, 0.15-0.89). Among those with an STI, 53 (31.0%) were treated within 7 days; median time to treatment was 11 days (interquartile range, 6-77 days)., Conclusions: We identified a high prevalence of curable STIs among AYA in rural South Africa. Improved access to STI testing to enable etiologic diagnosis and rapid treatment is needed., Competing Interests: Conflict of Interest and Sources of Funding: J.J. has received in-kind research support from Binx Health. All other authors declare no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Sexually Transmitted Diseases Association.)