The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.

Background: Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. Methods: We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data. Results: Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health. Conclusions: Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Responding to Africa's burden of disease: accelerating progress.

Although Africa is home to about 14% of the global population (1.14 billion people), it is growing three times faster than the global average [1]. The continent carries a high burden of disease, but there has been real progress in eradication, elimination, and control since 2015. Examples are the eradication of wild polio in 2020 [2] and the eradication or elimination of neglected tropical diseases, such as dracunculiasis in Kenya in 2018; Human African trypanosomiasis in Togo in 2022; and trachoma in Togo, Gambia, Ghana, and Malawi in 2022 [3]. New HIV infections reduced by 44% in 2021 compared to 2010 [4], and in 2021 the African region passed the 2020 milestone of the End TB Strategy, with a 22% reduction in new infections compared with 2015 [5].

Towards a Healthcare Innovation Scaling Framework-The Voice of the Innovator.

This paper investigates the systemic challenges that African healthcare innovators experience in the quest to scale their innovations. The aim is to aggregate insights and to conceptualize a foundation towards building a framework that can be used as a guide by intermediary organizations and global partners to support collaborative innovation in African countries. These insights were gained from analyzing a dataset of survey responses obtained from a follow-up on 230 innovators who took part in the inaugural WHO Africa Innovation Challenge that was held in 2018. The insights led to the identification of 10 key foundational blocks that assist in ecosystem management in a bid to strengthen national health innovation ecosystems and to improve the sustainability and integration of innovations in the health system.

COVID-19 vaccination rollout in the World Health Organization African region: status at end June 2022 and way forward.

In October 2021, the WHO published an ambitious strategy to ensure that all countries had vaccinated 40% of their population by the end of 2021 and 70% by mid-2022. The end of June 2022 marks 18 months of implementation of coronavirus disease 2019 (COVID-19) vaccination in the African region and provides an opportunity to look back and think ahead about COVID-19 vaccine set targets, demand and delivery strategies. As of 26 June 2022 two countries in the WHO African region have achieved this target (Mauritius and Seychelles) and seven are on track, having vaccinated between 40% and 69% of their population. By the 26 June 2022, seven among the 20 countries that had less than 10% of people fully vaccinated at the end of January 2022, have surpassed 15% of people fully vaccinated at the end of June 2022. This includes five targeted countries, which are being supported by the WHO Regional Office for Africa through the Multi-Partners' Country Support Team Initiative. As we enter the second semester of 2022, a window of opportunity has opened to provide new impetus to COVID-19 vaccination rollout in the African region guided by the four principles: Scale-up, Transition, Consolidation and Communication. Member States need to build on progress made to ensure that this impetus is not lost and that the African region does not remain the least vaccinated global region, as economies open up and world priorities change.

COVID-19 in the 47 countries of the WHO African region: a modelling analysis of past trends and future patterns.

BACKGROUND: COVID-19 has affected the African region in many ways. We aimed to generate robust information on the transmission dynamics of COVID-19 in this region since the beginning of the pandemic and throughout 2022. METHODS: For each of the 47 countries of the WHO African region, we consolidated COVID-19 data from reported infections and deaths (from WHO statistics); published literature on socioecological, biophysical, and public health interventions; and immunity status and variants of concern, to build a dynamic and comprehensive picture of COVID-19 burden. The model is consolidated through a partially observed Markov decision process, with a Fourier series to produce observed patterns over time based on the SEIRD (denoting susceptible, exposed, infected, recovered, and dead) modelling framework. The model was set up to run weekly, by country, from the date the first infection was reported in each country until Dec 31, 2021. New variants were introduced into the model based on sequenced data reported by countries. The models were then extrapolated until the end of 2022 and included three scenarios based on possible new variants with varying transmissibility, severity, or immunogenicity. FINDINGS: Between Jan 1, 2020, and Dec 31, 2021, our model estimates the number of SARS-CoV-2 infections in the African region to be 505·6 million (95% CI 476·0-536·2), inferring that only 1·4% (one in 71) of SARS-CoV-2 infections in the region were reported. Deaths are estimated at 439 500 (95% CI 344 374-574 785), with 35·3% (one in three) of these reported as COVID-19-related deaths. Although the number of infections were similar between 2020 and 2021, 81% of the deaths were in 2021. 52·3% (95% CI 43·5-95·2) of the region's population is estimated to have some SARS-CoV-2 immunity, given vaccination coverage of 14·7% as of Dec 31, 2021. By the end of 2022, we estimate that infections will remain high, at around 166·2 million (95% CI 157·5-174·9) infections, but deaths will substantially reduce to 22 563 (14 970-38 831). INTERPRETATION: The African region is estimated to have had a similar number of COVID-19 infections to that of the rest of the world, but with fewer deaths. Our model suggests that the current approach to SARS-CoV-2 testing is missing most infections. These results are consistent with findings from representative seroprevalence studies. There is, therefore, a need for surveillance of hospitalisations, comorbidities, and the emergence of new variants of concern, and scale-up of representative seroprevalence studies, as core response strategies. FUNDING: None.

On the resilience of health systems: A methodological exploration across countries in the WHO African Region.

The need for resilient health systems is recognized as important for the attainment of health outcomes, given the current shocks to health services. Resilience has been defined as the capacity to "prepare and effectively respond to crises; maintain core functions; and, informed by lessons learnt, reorganize if conditions require it". There is however a recognized dichotomy between its conceptualization in literature, and its application in practice. We propose two mutually reinforcing categories of resilience, representing resilience targeted at potentially known shocks, and the inherent health system resilience, needed to respond to unpredictable shock events. We determined capacities for each of these categories, and explored this methodological proposition by computing country-specific scores against each capacity, for the 47 Member States of the WHO African Region. We assessed face validity of the computed index, to ensure derived values were representative of the different elements of resilience, and were predictive of health outcomes, and computed bias-corrected non-parametric confidence intervals of the emergency preparedness and response (EPR) and inherent system resilience (ISR) sub-indices, as well as the overall resilience index, using 1000 bootstrap replicates. We also explored the internal consistency and scale reliability of the index, by calculating Cronbach alphas for the various proposed capacities and their corresponding attributes. We computed overall resilience to be 48.4 out of a possible 100 in the 47 assessed countries, with generally lower levels of ISR. For ISR, the capacities were weakest for transformation capacity, followed by mobilization of resources, awareness of own capacities, self-regulation and finally diversity of services respectively. This paper aims to contribute to the growing body of empirical evidence on health systems and service resilience, which is of great importance to the functionality and performance of health systems, particularly in the context of COVID-19. It provides a methodological reflection for monitoring health system resilience, revealing areas of improvement in the provision of essential health services during shock events, and builds a case for the need for mechanisms, at country level, that address both specific and non-specific shocks to the health system, ultimately for the attainment of improved health outcomes.

Data revolution, health status transformation and the role of artificial intelligence for health and pandemic preparedness in the African context.

BACKGROUND: Artificial Intelligence (AI) platforms, increasingly deployed in public health, utilize robust data systems as a critical component for health emergency preparedness. Yet, Africa faces numerous challenges in the availability, analyses, and use of data to inform health decision-making. Countries have limited access to their population data. Those with access, struggle to utilize these data for program improvements. Owing to the rapid growth of mobile phone ownership and use in the region, Africa is poised to leverage AI technologies to increase the adoption, access and use of data for health. To discuss and propose solutions for responsible development and adoption of innovations like AI in Africa, a virtual workshop was organized from the 21st to 24th June, 2021. This report highlights critical policy dimensions of strengthening digital health ecosystems by high-level policymakers, technical experts, academia, public and private sector partners. METHOD: The four days' workshop focused on nine sessions, with each session focusing on three themes. Discussions during the sessions concentrated on public and private sectors, the academia and multilateral organizations' deployment of AI. These discussions expanded participants' understanding of AI, the opportunities and challenges that exist during adoption, including the future of AI for health in the African region. Approximately 250 participants attended the workshop, including countries representatives from ministries of Health, Information and Technology, Developmental Organizations, Private Sector, Academia and Research Institutions among others. RESULTS: The workshop resolved that governments and relevant stakeholders should collaborate to ensure that AI and digital health receive critical attention. Government ownership and leadership were identified as critical for sustainable financing and effective scale-up of AI-enabled applications in Africa. Thus, government is to ensure that key recommendations from the workshop are implemented to improve health sector development in Africa. CONCLUSIONS: The AI workshop was a good forum to deliberate important issues regarding AI for health in the African context. It was concluded that there is a need to focus on vital priorities in deploying AI in Africa: Data protection, privacy and sharing protocols; training and creating platforms for researchers; funding and business models; developing frameworks for assessing and implementing AI; organizing forums and conferences on AI; and instituting regulations, governance and ethical guidelines for AI. There is a need to adopt a health systems approach in planning for AI to reduce inefficiencies, redundancies while increasing effectiveness in the use of AI. Thus, robust collaborations and partnerships among governments and various stakeholders were identified as key.

The effect of educational attainment and other factors on HIV risk in South African women: results from antenatal surveillance, 2000-2005.

OBJECTIVES: To assess the effect of educational attainment and other factors on the risk of HIV in pregnant South African women. DESIGN: Repeated cross-sectional surveys. METHODS: Pregnant women attending public antenatal clinics were tested for HIV annually between 2000 and 2005, and provided demographic information. Logistic regression models were applied separately to the data collected in each year, to identify factors associated with HIV infection. Data from all years were combined in a logistic regression model that tested for trends in HIV prevalence. RESULTS: Amongst women aged 15-24 years, HIV risk in those who had completed secondary education was significantly lower than in those who had only primary education, in all years except 2000. HIV risk increased by 8% per annum (odds ratio 1.08, 95% confidence interval 1.04-1.12) in young women with no secondary education but did not increase in young women with secondary education. In women aged 25-49 years, HIV risk increased over the 2000-2005 period, at all levels of educational attainment, and did not differ between women with completed secondary education and women with only primary education. CONCLUSION: Together with other evidence, this study suggests that higher educational attainment did not protect against HIV in the early stages of the South African HIV/AIDS epidemic. In recent years, the risk of HIV infection in young South African women with completed secondary education has reduced significantly relative to that in young women with primary education, suggesting that HIV prevention strategies may have been more effective in more educated women.

Antiretroviral drug resistance surveillance among drug-naive HIV-1-infected individuals in Gauteng Province, South Africa in 2002 and 2004.

BACKGROUND: Surveillance for transmitted HIV-1 drug resistance was conducted among drug-naive HIV-1-infected pregnant women in South Africa, where single-dose nevirapine has been in use since 2001 and a national antiretroviral treatment programme started in 2004. METHODS: All subjects were from the Gauteng Province and were part of the 2002 and 2004 annual antenatal HIV seroprevalence survey conducted by the South African National Department of Health. All subjects met the inclusion criteria as set out by the World Health Organisation guidelines for HIV-1 transmitted drug resistance surveillance (women <22 years of age and in first pregnancy). Genotyping was performed on viral RNA by sequencing the protease and reverse transcriptase genes. Samples were also tested for the K103N mutation using a highly sensitive allele-specific real-time PCR assay (AS-PCR). RESULTS: Of 128 eligible participants from 2002, 65 (51%) samples were successfully amplified. None of them had evidence of resistance mutations by genotyping or by AS-PCR. Of 117 eligible participants from 2004, 48 (41%) samples were successfully amplified. Of these, one had T69D and one had the K70R resistance mutation, to give a total of 2/48 (4.2%) participants with evidence of resistance mutations by genotyping. One sample that was wild-type by genotyping was positive for K103N by AS-PCR. All samples clustered phylogenetically with HIV-1 subtype C, the predominant subtype circulating in South Africa. CONCLUSIONS: Using the threshold survey, resistance prevalence overall and for each drug class in 2002 and 2004 was <5% for the Gauteng province of South Africa. The detection of a low frequency of resistance mutations in the 2004 survey suggests that surveillance should be conducted annually among untreated populations to determine if this increases with time.

Masking through averages--intraprovincial heterogeneity in HIV prevalence within the Western Cape.

OBJECTIVE: To measure HIV prevalence at health-district level in the Western Cape (WC) and to compare these findings with those of the National HIV Antenatal Surveys (NHASs). This investigation aimed to estimate the degree of heterogeneity of HIV prevalence within the province in order to inform the design of appropriate and targeted HIV interventions. METHOD: Annual cross-sectional, unlinked district HIV antenatal surveys were implemented in all 25 health districts of the WC for the years 2001 - 2004, concurrently with the NHAS. A stratified proportional sample was drawn for each district, involving all 344 antenatal clinics in the province, and the anonymous screening method as described by the World Health Organization (WHO) was applied. RESULTS: The NHAS revealed a significant increase in HIV prevalence in the WC from 8.6% (95% confidence interval (CI): 5.6 - 11.6) in 2001 to 15.4% (95% CI: 12.5 - 18.2) in 2004. The district-level HIV surveys showed wide variation in HIV prevalence across the health districts, which increased progressively during this period (a range of 0.6 - 22% for the year 2001 increased to 1 - 33% in 2004). Spatial analysis of HIV prevalence by health district for this period also revealed progressive spatial growth of the sub-epidemics, with the highest prevalence observed in districts located in the Cape metropole region. CONCLUSIONS: These concurrent surveys highlight the fact that examining a provincial estimate of HIV prevalence alone has the potential to mask epicentres within the province. This underscores the importance of expanding the surveillance systems to detect heterogeneity sub-provincially, in order to link with local-level planning and resource allocation.