Understanding the burden of SARS-CoV-2 infections among healthcare workers is a critical component to inform occupational health policy and strategy. We conducted a systematic review and meta-analysis to map and analayse the available global evidence on the prevalence of SARS-CoV-2 infections among healthcare workers. The random-effects adjusted pooled prevalence of COVID-19 among those studies that conducted the test using the antibody (Ab) method was 7% [95% CI: 3 to 17%]. The random-effects adjusted pooled prevalence of COVID-19 among those studies that conducted the test using the PCR method was 11% [95% CI: 7 to 16%]. We found the burden of COVID-19 among healthcare workers to be quite significant and therefore a cause for global health concern. Furthermore, COVID-19 infections among healthcare workers affect service delivery through workers’ sick leave, the isolation of confirmed cases and quarantine of contacts, all of which place significant strain on an already shrunken health workforce.

Health care workers (HCWs) are more than ten times more likely to be infected with coronavirus infectious disease 2019 (COVID-19) than the general population, thus demonstrating the burden of COVID-19 among HCWs. Factors that expose HCWs to a differentially high-risk of COVID-19 acquisition are important to elucidate, enable appropriate public health interventions to mitigate against high risk and reduce adverse outcomes from the infection. We conducted a systematic review and meta-analysis to summarize and critically analyze the existing evidence on SARS-CoV-2 risk factors among HCWs. With no geographical limitation, we included studies, in any country, that reported (i) the PCR laboratory diagnosis of COVID-19 as an independent variable (ii) one or more COVID-19 risk factors among HCWs with risk estimates (relative risk, odds ratio, or hazard ratio) (iii) original, quantitative study design, and published in English or Mandarian. Our initial search resulted in 470 articles overall, however, only 10 studies met the inclusion criteria for this review. Out of the 10 studies included in the review, inadequate/lack of protective personal equipment, performing tracheal intubation, and gender were the most common risk factors of COVID-19. Based on the random effects adjusted pooled relative risk, HCWs who reported the use of protective personal equipment were 29% (95% CI: 16% to 41%) less likely to test positive for COVID-19. The study also revealed that HCWs who performed tracheal intubations were 34% (95% CI: 14% to 57%) more likely to test positive for COVID-19. Interestingly, this study showed that female HCWs are at 11% higher risk (RR 1.11 95% CI 1.01–1.21) of COVID-19 than their male counterparts. This article presents initial findings from a living systematic review and meta-analysis, therefore, did not yield many studies; however, it revealed a significant insight into better understanding COVID-19 risk factors among HCWs; insights important for devising preventive strategies that protect them from this infection. CRD42020193508 available for public comments via the link below https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020193508).

Sexually transmitted infections (STIs) and Reproductive tract infections (RTIs) are responsible for high morbidity among women. We aim to quantify the magnitude of the burden and risk factors of STI/RTI s among pregnant women in Zimbabwe.

Zimbabwe is scaling up HIV differentiated service delivery (DSD) to improve treatment outcomes and health system efficiencies. Shifting stable patients into less-intensive DSD models is a high priority in order to accommodate the large numbers of newly-diagnosed people living with HIV (PLHIV) needing treatment and to provide healthcare workers with the time and space needed to treat people with advanced HIV disease. DSD is also seen as a way to improve service quality and enhance retention in care. National guidelines support five differentiated antiretroviral treatment models (DART) for stable HIV-positive adults, but little is known about patient preferences, a critical element needed to guide DART scale-up and ensure person-centered care. We designed a mixed-methods study to explore treatment preferences of PLHIV in urban Zimbabwe. The study was conducted in Harare, and included 35 health care worker (HCW) key informant interviews (KII); 8 focus group discussions (FGD) with 54 PLHIV; a discrete choice experiment (DCE) in which 500 adult DART-eligible PLHIV selected their preferences for health facility (HF) vs. community location, individual vs. group meetings, provider cadre and attitude, clinic operation times, visit frequency, visit duration and cost to patient; and a survey with the 500 DCE participants exploring DART knowledge and preferences. Patient preferences were consistent in the FGDs, DCE and survey. Participants strongly preferred respectful HCWs, HF-based services, individual DART models, and less costly services. Patients also preferred less frequent visits and shorter wait times. They were indifferent to variations in HCW cadre and distances from home to HF. These preferences were mostly homogenous, with only minor differences between male vs. female and older vs. younger patients. HCWs in the KII correctly characterized facility-based individual models as the one most favored by patients; HCWs also preferred this model, which they felt decongested HFs and reduced their workload. DART-eligible PLHIV in Harare found it relatively easy to access HFs, and preferred attributes associated with facility-based individual models. Prioritizing these for scale-up in urban areas may be the most efficient way to sustain positive patient outcomes and increase health system performance.

HIV and AIDS continue to be major public health concerns globally. Despite significant progress in addressing their impact on the general population and achieving epidemic control, there is a need to improve HIV testing, particularly among men who have sex with men (MSM). This study applied deep and machine learning algorithms such as recurrent neural networks (RNNs), the bagging classifier, gradient boosting classifier, support vector machines, and Naïve Bayes classifier to predict HIV status among MSM using the dataset from the Zimbabwe Ministry of Health and Child Care. RNNs performed better than the bagging classifier, gradient boosting classifier, support vector machines, and Gaussian Naïve Bayes classifier in predicting HIV status. RNNs recorded a high prediction accuracy of 0.98 as compared to the Gaussian Naïve Bayes classifier (0.84), bagging classifier (0.91), support vector machine (0.91), and gradient boosting classifier (0.91). In addition, RNNs achieved a high precision of 0.98 for predicting both HIV-positive and -negative cases, a recall of 1.00 for HIV-negative cases and 0.94 for HIV-positive cases, and an F1-score of 0.99 for HIV-negative cases and 0.96 for positive cases. HIV status prediction models can significantly improve early HIV screening and assist healthcare professionals in effectively providing healthcare services to the MSM community. The results show that integrating HIV status prediction models into clinical software systems can complement indicator condition-guided HIV testing strategies and identify individuals that may require healthcare services, particularly for hard-to-reach vulnerable populations like MSM. Future studies are necessary to optimize machine learning models further to integrate them into primary care. The significance of this manuscript is that it presents results from a study population where very little information is available in Zimbabwe due to the criminalization of MSM activities in the country. For this reason, MSM tends to be a hidden sector of the population, frequently harassed and arrested. In almost all communities in Zimbabwe, MSM issues have remained taboo, and stigma exists in all sectors of society.

The ongoing COVID-19 pandemic brought unprecedented challenges for the population. The advent of national COVID-19 vaccination programmes was therefore welcome as a key control strategy for the COVID-19 pandemic, as evidence has shown that vaccination is the best strategy to reduce the adverse individual and population level adverse outcomes associated with infectious diseases such as COVID-19. Zimbabwe rolled out its vaccination programme in February 2021 with an ambitious target to vaccinate at least 60% of its eligible population by December 2021. However, by that time, the country was still to reach that target. To move the vaccination programme towards achieving this target, it is crucial to understand the strengths, weaknesses, opportunities and threats to the programme. We, therefore, with this narrative review, discuss some of the strengths, weaknesses, opportunities and threats to the programme since its rollout in February 2021. Though the programme has several strengths and opportunities to leverage on, we argue that among other challenges, the emergence of new variants of concern poses one of the biggest threats to local, regional and international vaccination programmes and requires concerted multistakeholder efforts to deal with. Additionally, addressing vaccine hesitancy remains as important as availing the vaccines to the population, to obtain the most benefits out of the programme.