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BackgroundLimited information exists on the impact of mobile health (mHealth) use by community health workers (CHWs) on improving the use of maternal health services in sub-Saharan Africa (SSA). ObjectiveThis systematic review addresses 2 objectives: evaluating the impact of mHealth use by CHWs on antenatal care (ANC) use, facility-based births, and postnatal care (PNC) use in SSA; and identifying facilitators and barriers to mHealth use by CHWs in programs designed to increase ANC use, facility-based births, and PNC use in SSA using a sociotechnical system approach. MethodsWe searched for articles in 6 databases (MEDLINE, CINAHL, Web of Science, Embase, Scopus, and Africa Index Medicus) from inception up to September 2022, with additional articles identified from Google Scholar. After article selection, 2 independent reviewers performed title and abstract screening, full-text screening, and data extraction using Covidence software (Veritas Health Innovation Ltd). In addition, we manually screened the references lists of the included articles. Finally, we performed a narrative synthesis of the outcomes. ResultsAmong the 2594 records retrieved, 10 (0.39%) studies (n=22, 0.85% articles) met the inclusion criteria and underwent data extraction. The studies were published between 2012 and 2022 in 6 countries. Of the studies reporting on ANC outcomes, 43% (3/7) reported that mHealth use by CHWs increased ANC use. Similarly, of the studies reporting on facility-based births, 89% (8/9) demonstrated an increase due to mHealth use by CHWs. In addition, in the PNC studies, 75% (3/4) showed increased PNC use associated with mHealth use by CHWs. Many of the studies reported on the importance of addressing factors related to the social environment of mHealth-enabled CHWs, including the perception of CHWs by the community, trust, relationships, digital literacy, training, mentorship and supervision, skills, CHW program ownership, and the provision of incentives. Very few studies reported on how program goals and culture influenced mHealth use by CHWs. Providing free equipment, accessories, and internet connectivity while addressing ongoing challenges with connectivity, power, the ease of using mHealth software, and equipment maintenance support allowed mHealth-enabled CHW programs to thrive. ConclusionsmHealth use by CHWs was associated with an increase in ANC use, facility-based births, and PNC use in SSA. Identifying and addressing social and technical barriers to the use of mHealth is essential to ensure the success of mHealth programs. Trial RegistrationPROSPERO CRD42022346364; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346364

To determine early COVID-19 burden in Malawi, we conducted a multistage cluster survey in 5 districts. During October–December 2020, we recruited 5,010 community members (median age 32 years, interquartile range 21–43 years) and 1,021 health facility staff (HFS) (median age 35 years, interquartile range 28–43 years). Real-time PCR–confirmed SARS-CoV-2 infection prevalence was 0.3% (95% CI 0.2%–0.5%) among community and 0.5% (95% CI 0.1%–1.2%) among HFS participants; seroprevalence was 7.8% (95% CI 6.3%–9.6%) among community and 9.7% (95% CI 6.4%–14.5%) among HFS participants. Most seropositive community (84.7%) and HFS (76.0%) participants were asymptomatic. Seroprevalence was higher among urban community (12.6% vs. 3.1%) and HFS (14.5% vs. 7.4%) than among rural community participants. Cumulative infection findings 113-fold higher from this survey than national statistics (486,771 vs. 4,319) and predominantly asymptomatic infections highlight a need to identify alternative surveillance approaches and predictors of severe disease to inform national response.

The challenge for Product Recall Insurance companies and their policyholders to manually explore their customer product’s defects from online customer reviews (OCR) delays product risk analysis and product recall recovery processes. In today's product life cycle, product recall events happen almost every day and there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as defect information, performance issue, and serviceability feedback, to the Product Recall Insurance team as well as their policyholders’ engineers to analyze the product risk and evaluate their premium. This lack of early risk analysis and defect detection mechanism often increases the risks of a product recall and cost of claims for both the insurers and policyholder, potentially causing billions of dollars in economic loss, liability resulting from the bodily injury, and loss of company credibility. This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide the insurers and the policyholders with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews. Results of this research show that both the insurers and the policyholders can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach can locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the insurers and the policyholders to take required mitigation actions earlier, proactively stop the diffusion of the detective products, and hence lower the cost of claim and premium.

IntroductionOver 40% of global tuberculosis case notifications are diagnosed clinically without mycobacteriological confirmation. Standard diagnostic algorithms include ‘trial-of-antibiotics’—empirical antibiotic treatment given to mycobacteriology-negative individuals to treat infectious causes of symptoms other than tuberculosis, as a ‘rule-out’ diagnostic test for tuberculosis. Potentially 26.5 million such antibiotic courses/year are prescribed globally for the 5.3 million/year mycobacteriology-negative patients, making trial-of-antibiotics the most common tuberculosis diagnostic, and a global-scale risk for antimicrobial resistance (AMR). Our systematic review found no randomised controlled trial (RCT) to support use of trial-of-antibiotic. The RCT aims to determine the diagnostic and clinical value and AMR consequences of trial-of-antibiotics.Methods and analysisA three-arm, open-label, RCT randomising (1:1:1) Malawian adults (≥18 years) seeking primary care for cough into: (a) azithromycin 500 mg one time per day for 3 days or (b) amoxicillin 1 g three times per day for 5 days or (c) standard-of-care (no immediate antibiotic). We will perform mycobacteriology tests (microscopy, Xpert MTB/RIF (Mycobacterium tuberculosis/rifampicin) and Mycobacterium tuberculosis culture) at baseline. We will use audiocomputer-assisted self-interview to assess clinical improvement at day 8. First primary outcome will be proportion of patients reporting day 8 improvement out of those with negative mycobacteriology (specificity). Second primary outcome will be day 29 incidence of a composite endpoint of either death or hospitalisation or missed tuberculosis diagnosis. To determine AMR impact we compare proportion of resistant nasopharyngeal Streptococcus pneumoniae isolates on day 29. 400 mycobacteriology-negative participants/arm will be required to detect a ≥10% absolute difference in diagnostic specificity with 80% power. We will estimate measures of effect by comparing outcomes in antibiotic arms (combined and individually) to standard-of-care.Ethics and disseminationThe study has been reviewed and approved by Malawi College of Medicine Research and Ethics Committee, London School of Hygiene & Tropical Medicine (LSHTM) Research Ethics Committee and Regional Committee for Health and Research Ethics – Norway, and Malawi Pharmacy, Medicines and Poisons Board. We will present abstracts at relevant conferences, and prepare a manuscript for publication in a peer-reviewed journal.Trial registration numberThe clinical trial is registered with ClinicalTrials.gov, NCT03545373