Presence of tuberculosis symptoms among HIV-positive men who have sex with men (MSM) in Zimbabwe.

We conducted secondary data analysis using a biobehavioral survey dataset of 1538 MSM from Zimbabwe. Survey participants were screened for the four symptoms suggestive of tuberculosis infection using the WHO TB screening algorithm. Results: All participants experienced at least one symptom suggestive of tuberculosis. 40% of HIV-positive MSM reported having had a cough in the last month and 13% of them experienced unexpected weight loss. The prevalence of experiencing any of the four TB symptoms amongst HIV-positive MSM was 23%. Contribution There is an urgent need for active TB case finding and treatment amongst HIV-positive MSM in Zimbabwe. Clinicians will need to ensure that MSM who need TB testing receive it timeously.

Cervical cancer in sub-Saharan Africa: an urgent call for improving accessibility and use of preventive services.

Sub-Saharan Africa has the highest rates of cervical cancer in the world, largely attributed to low cervical cancer screening coverage. Cervical cancer is the most common cause of death among women in 21 of the 48 countries in sub-Saharan Africa. Close to 100% of all cases of cervical cancer are attributable to Human papillomavirus (HPV). HPV types 16 and 18 cause at least 70% of all cervical cancers globally, while types 31, 33, 45, 52, and 58 cause a further 20% of the cases. Women living with HIV are six times more likely to develop cervical cancer than those without HIV. Considering that sub-Saharan Africa carries the greatest burden of cervical cancer, ways to increase accessibility and use of preventive services are urgently required. With this review, we discuss the preventive measures required to reduce the burden of cervical cancer in sub-Saharan Africa, the challenges to improving accessibility and use of the preventive services, and the recommendations to address these challenges.

Predicting sexually transmitted infections among men who have sex with men in Zimbabwe using deep learning and ensemble machine learning models.

There is a substantial increase in sexually transmitted infections (STIs) among men who have sex with men (MSM) globally. Unprotected sexual practices, multiple sex partners, criminalization, stigmatisation, fear of discrimination, substance use, poor access to care, and lack of early STI screening tools are among the contributing factors. Therefore, this study applied multilayer perceptron (MLP), extremely randomized trees (ExtraTrees) and XGBoost machine learning models to predict STIs among MSM using bio-behavioural survey (BBS) data in Zimbabwe. Data were collected from 1538 MSM in Zimbabwe. The dataset was split into training and testing sets using the ratio of 80% and 20%, respectively. The synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Using a stepwise logistic regression model, the study revealed several predictors of STIs among MSM such as age, cohabitation with sex partners, education status and employment status. The results show that MLP performed better than STI predictive models (XGBoost and ExtraTrees) and achieved accuracy of 87.54%, recall of 97.29%, precision of 89.64%, F1-Score of 93.31% and AUC of 66.78%. XGBoost also achieved an accuracy of 86.51%, recall of 96.51%, precision of 89.25%, F1-Score of 92.74% and AUC of 54.83%. ExtraTrees recorded an accuracy of 85.47%, recall of 95.35%, precision of 89.13%, F1-Score of 92.13% and AUC of 60.21%. These models can be effectively used to identify highly at-risk MSM, for STI surveillance and to further develop STI infection screening tools to improve health outcomes of MSM.

Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019 (COVID-19) pandemic in South Africa: a systematic review.

Aims: To identify virtual healthcare services and digital health technologies deployed in South Africa during coronavirus disease 2019 (COVID-19) and the challenges associated with their use. Methods: To determine the status of digital health utilization during COVID-19 in South Africa, the preferred reporting items for systematic reviews and meta-analyses model was used to perform a systematic and in-depth critical analysis of previously published studies in well-known and trusted online electronic databases using specific search keywords words that are relevant to this study. We selected published peer-reviewed articles available from the onset of COVID-19 to July 2021. Results: Total of 24 articles were included into this study. This study revealed that South Africa adopted digital technologies such as SMS-based solutions, mobile health applications, telemedicine and telehealth, WhatsApp-based systems, artificial intelligence and chatbots and robotics to provide healthcare services during COVID-19 pandemic. These innovative technologies have been used for various purposes including screening infectious and non-infectious diseases, disease surveillance and monitoring, medication and treatment compliance, creating awareness and communication. The study also revealed that teleconsultation and e-prescription, telelaboratory and telepharmacy, teleeducation and teletraining, teledermatology, teleradiology, telecardiology, teleophthalmology, teleneurology, telerehabilitation, teleoncology and telepsychiatry are among virtual healthcare services delivered through digital health technologies during COVID-19 in South Africa. However, these smart digital health technologies face several impediments such as infrastructural and technological barriers, organization and financial barriers, policy and regulatory barriers as well as cultural barriers. Conclusion: Although COVID-19 has invigorated the use of digital health technologies, there are still some shortcomings. The outbreak of pandemics like COVID-19 in the future is not inevitable. Therefore, we recommend increasing community networks in rural areas to bridge the digital divide and the modification of mHealth policy to advocate for the effective use of innovative technologies in healthcare and the development of sustainable strategies for resources mobilization through private-public partnerships as well as joining available international initiatives advocating for smart digital health.

Are we there yet? Unbundling the potential adoption and integration of telemedicine to improve virtual healthcare services in African health systems.

Since the outbreak of COVID-19, the attention has now shifted towards universal vaccination to gracefully lift strict COVID-19 restrictions previously imposed to contain the spread of the disease. Sub-Saharan Africa is experiencing an exponential increase of infections and deaths coupled with vaccines shortages, personal protective equipment, weak health systems and COVID-19 emerging variants. Some developed countries integrated telemedicine to reduce the impacts of the shortage of healthcare professionals and potentially reduce the risk of exposure, ensuring easy delivery of quality health services while limiting regular physical contact and direct hospitalization. However, the adoption of telemedicine and telehealth is still nascent in many sub-Saharan Africa countries. Therefore, this study reflects on progress made towards the use of telemedicine, virtual health care services, challenges encountered, and proffers ways to address them. We conducted a systematic literature review to synthesise literature on telemedicine in sub-Saharan Africa. The study revealed that telemedicine provides unprecedented benefits such as improving efficiency, effective utilization of healthcare resources, forward triaging, prevention of medical personnel infection, aiding medical students' clinical observation and participation, and assurance of social support for patients. However, the absence of policy on virtual care and political will, cost of sustenance of virtual health care services, inadequate funding, technological and infrastructural barriers, patient and healthcare personnel bias on virtual care and cultural barriers are identified as limiting factors to the adoption of virtual health care in many African health systems. To alleviate some of these barriers, we recommend the development of robust policies and frameworks for virtual health care, the inclusion of virtual care in the medical school curriculum, supporting virtual care research and development, increasing health funding, removing monopolisation of telecommunication services, developing of virtual health solutions that address eccentricities of African health systems.

The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.

Human identification of unknown samples following disaster and mass casualty events is essential, especially to bring closure to family and friends of the deceased. Unfortunately, victim identification is often challenging for forensic investigators as analysis becomes complicated when biological samples are degraded or of poor quality as a result of exposure to harsh environmental factors. Mitochondrial DNA becomes the ideal option for analysis, particularly for determining the origin of the samples. In such events, the estimation of genetic parameters plays an important role in modelling and predicting genetic relatedness and is useful in assigning unknown individuals to an ethnic group. Various techniques exist for the estimation of genetic relatedness, but the use of Machine learning (ML) algorithms are novel and presently the least used in forensic genetic studies. In this study, we investigated the ability of ML algorithms to predict genetic relatedness using hypervariable region I sequences; that were retrieved from the GenBank database for three race groups, namely African, Asian and Caucasian. Four ML classification algorithms; Support vector machines (SVM), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Random Forest (RF) were hybridised with one-hot encoding, Principal component analysis (PCA) and Bags of Words (BoW), and were compared for inferring genetic relatedness. The findings from this study on WEKA showed that genetic inferences based on PCA-SVM achieved an overall accuracy of 80-90% and consistently outperformed PCA-LDA, PCA-RF and PCA-QDA, while in Python BoW-PCA-RF achieved 94.4% accuracy which outperformed BoW-PCA-SVM, BoW-PCA-LDA and BoW-PCA-QDA respectively. ML results from the use of WEKA and Python software tools displayed higher accuracies as compared to the Analysis of molecular variance results. Given the results, SVM and RF algorithms are likely to also be useful in other sequence classification applications, making it a promising tool in genetics and forensic science. The study provides evidence that ML can be utilized as a supplementary tool for forensic genetics casework analysis.

Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques.

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.

Risk Factors for COVID-19 Infection Among Healthcare Workers. A First Report From a Living Systematic Review and meta-Analysis.

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. PROSPERO registration number: CRD42020193508 available for public comments via the link below https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020193508).

Is malaria elimination a distant dream? Reconsidering malaria elimination strategies in Zimbabwe.

Malaria remains a public health problem decimating vulnerable populace especially in resource-constrained areas in Zimbabwe. Significant progress towards malaria elimination has beenik made in the previous decades through intensified and improved malaria control measures such as indoor residual spraying (IRS), distribution of long-lasting insecticidal nets (LLIN), artemisinin-based combination therapy and administration of intermittent preventive treatment in pregnancy. However, the outbreak of pandemics like coronavirus disease (COVID-19), cyclones and tropical storms, lack of funding, porous political environment, dearth of resources for vector control, changes in vector behaviour, vector resistance to insecticides, community behavioural change and lack of feasible and sustainable digital technologies for managing malaria control interventions retards progress made towards malaria elimination. Also, arbitrary political environment and unstable economic situation often interfere with health programmes which subsequently lead to malaria outbreaks. Most recently, the country recorded a sharp increase in malaria incidences in malaria-endemic areas especially during the pandemic due to some factors such as movement restrictions, temporary cancellation of IRS activities, delayed delivery of IRS chemicals and recursive lockdown. Therefore, we propose ways to mitigate future malaria outbreaks and advocate for reconsidering malaria elimination strategies to addresses emerging challenges in eradicating malaria in Zimbabwe.

Digital technologies and COVID-19: reconsidering lockdown exit strategies for Africa.

Widespread vaccination provides a means for countries to lift strict COVID-19 restrictions previously imposed to contain the spread of the disease. However, to date, Africa has secured enough COVID-19 vaccine doses for less than 5% of its population. With widespread vaccination not on the horizon for Africa, there is a strong emphasis on non-pharmaceutical interventions which include movement restrictions (lockdowns). This general COVID-19 pandemic response of imposing lockdowns, however, neglects to factor in non-fatal consequences leading to disruption socio-economic wellbeing of the society at large. The economy in most African countries can no longer sustain lockdown restrictions. Some studies have indicated that a hard lockdown statistical value of the extra lives saved would be dwarfed by its long-term cost. At the same time not responding to the threat of the pandemic will cost lives and disrupts the social fabric. This paper proffers ways to mitigate the both and advocate for better policymaking that addresses specific challenges in defined communities thus yield higher population welfare.

A critical review of emerging technologies for tackling COVID-19 pandemic.

COVID-19 pandemic affects people in various ways and continues to spread globally. Researches are ongoing to develop vaccines and traditional methods of Medicine and Biology have been applied in diagnosis and treatment. Though there are success stories of recovered cases as of November 10, 2020, there are no approved treatments and vaccines for COVID-19. As the pandemic continues to spread, current measures rely on prevention, surveillance, and containment. In light of this, emerging technologies for tackling COVID-19 become inevitable. Emerging technologies including geospatial technology, artificial intelligence (AI), big data, telemedicine, blockchain, 5G technology, smart applications, Internet of Medical Things (IoMT), robotics, and additive manufacturing are substantially important for COVID-19 detecting, monitoring, diagnosing, screening, surveillance, mapping, tracking, and creating awareness. Therefore, this study aimed at providing a comprehensive review of these technologies for tackling COVID-19 with emphasis on the features, challenges, and country of domiciliation. Our results show that performance of the emerging technologies is not yet stable due to nonavailability of enough COVID-19 dataset, inconsistency in some of the dataset available, nonaggregation of the dataset due to contrasting data format, missing data, and noise. Moreover, the security and privacy of people's health information is not totally guaranteed. Thus, further research is required to strengthen the current technologies and there is a strong need for the emergence of a robust computationally intelligent model for early differential diagnosis of COVID-19.

Utility of telemedicine in sub-Saharan Africa during the COVID-19 pandemic. A rapid review.

Telemedicine is the use of technology to achieve remote care. This review looks at the utility of telemedicine during the pandemic, period March 2020 to February 2021. Eleven articles met inclusion criteria. There was moderate use of telemedicine in sub-Sahara Africa during the pandemic, however, there were also some limitations. Benefits of telemedicine include continuing medical service provision, connecting relatives with loved ones in quarantine, education, and awareness of mental health issues, and toxicovigilance and infection control. Challenges to the implementation of telemedicine on the continent were lack of supporting telemedicine framework and policies, digital barriers, and patient and healthcare personnel biases. To address these challenges, this article proposes the development of policy frameworks that fosters telemedicine use by all stakeholders, including medical insurance organizations, the introduction of telemedicine training of medical workers, educational awareness programs for the public, and improvement of digital platforms access and affordability.

Ethics for integrating emerging technologies to contain COVID-19 in Zimbabwe.

Zimbabwe is among the countries affected with the coronavirus disease (COVID-19) and implemented several infection control and measures such as social distancing, contact tracing, regular temperature checking in strategic entry and exit points, face masking among others. The country also implemented recursive national lockdowns and curfews to reduce the virus transmission rate and its catastrophic impact. These large-scale measures are not easy to implement, adhere to and subsequently difficult to practice and maintain which lead to imperfect public compliance, especially if there is a significant impact on social and political norms, economy, and psychological wellbeing of the affected population. Also, emerging COVID-19 variants, porous borders, regular movement of informal traders and sale of fake vaccination certificates continue to threaten impressive progress made towards virus containment. Therefore, several emerging technologies have been adopted to strengthen the health system and health services delivery, improve compliance, adherence and maintain social distancing. These technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation, and quarantine compliance. However, the use of emerging technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which impact their efficacy, adoption and ultimately influence public trust. Therefore, the study proposes ethical framework for using emerging technologies to contain the COVID-19 pandemic. The framework is centered on ethical practices such as security, privacy, justice, human dignity, autonomy, solidarity, beneficence, and non-maleficence.

COVID-19 Prevalence among Healthcare Workers. A Systematic Review and Meta-Analysis.

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.

Effects of COVID-19 in South African health system and society: An explanatory study.

BACKGROUND AND AIMS: The underestimation of the severity of COVID-19 by the South African government resulted in delayed action against the pandemic. Ever since WHO declared COVID-19 a pandemic preventive action was comprehensively upgraded worldwide. This prompted South African authorities to implement physical distancing, self-isolation, closure of non-essential services, schools, travelling restrictions and recursive national lockdowns to mitigate the impact of COVID-19. This explanatory study sought to review the effects of COVID-19 in the South African health system and society. METHODS: The study applied literature research of COVID-19 reports, policies from the National Department of Health, WHO, Africa CDC, and articles from Google Scholar, Science Direct, Web of Science, Scopus and PubMed. RESULTS: The South African health system is affected by the lack of PPEs, increased mortality rates, mental health problems, substance abuse, resurgent of NCDs. The closure of international borders, global demand meltdown, supply disruptions, dramatic scaling down of human and industrial activities during lockdown cause socio-economic problems. The prolonged effects of lockdown on psychosocial support services resulted in the outbursts of uncertainties, acute panic, fear, depression, obsessive behaviours, social unrests, stigmatization, anxiety, increased gender-based violence cases and discrimination in the distribution of relief food aid. CONCLUSION: To slow down the spread of COVID-19, massive testing must be adopted, contact tracing, isolation, and home quarantine guidelines for asymptomatic cases which promote behavioural change and reviewing of policy on food relief.

Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls.

BACKGROUND AND AIMS: With no approved vaccines for treating COVID-19 as of August 2020, many health systems and governments rely on contact tracing as one of the prevention and containment methods. However, there have been instances when the infected person forgets his/her contact-persons and does not have their contact details. Therefore, this study aimed at analyzing possible opportunities and challenges of integrating emerging technologies into COVID-19 contact tracing. METHODS: The study applied literature search from Google Scholar, Science Direct, PubMed, Web of Science, IEEE and WHO COVID-19 reports and guidelines analyzed. RESULTS: While the integration of technology-based contact tracing applications to combat COVID-19 and break transmission chains promise to yield better results, these technologies face challenges such as technical limitations, dealing with asymptomatic individuals, lack of supporting ICT infrastructure and electronic health policy, socio-economic inequalities, deactivation of mobile devices' WIFI, GPS services, interoperability and standardization issues, security risks, privacy issues, political and structural responses, ethical and legal risks, consent and voluntariness, abuse of contact tracing apps, and discrimination. CONCLUSION: Integrating emerging technologies into COVID-19 contact tracing is seen as a viable option that policymakers, health practitioners and IT technocrats need to seriously consider in mitigating the spread of coronavirus. Further research is also required on how best to improve efficiency and effectiveness in the utilisation of emerging technologies in contact tracing while observing the security and privacy of people in fighting the COVID-19 pandemic.