Leveraging artificial intelligence to optimize COVID-19 robust spread and vaccination roll-out strategies in Southern Africa.

The outbreak of coronavirus in the year 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted widespread illness, death, and extended economic devastation worldwide. In response, numerous countries, including Botswana and South Africa, instituted various clinical public health (CPH) strategies to mitigate and control the disease. However, the emergence of variants of concern (VOC), vaccine hesitancy, morbidity, inadequate and inequitable vaccine supply, and ineffective vaccine roll-out strategies caused continuous disruption of essential services. Based on Botswana and South Africa hospitalization and mortality data, we studied the impact of age and gender on disease severity. Comparative analysis was performed between the two countries to establish a vaccination strategy that could complement the existing CPH strategies. To optimize the vaccination roll-out strategy, artificial intelligence was used to identify the population groups in need of insufficient vaccines. We found that COVID-19 was associated with several comorbidities. However, hypertension and diabetes were more severe and common in both countries. The elderly population aged ≥60 years had 70% of major COVID-19 comorbidities; thus, they should be prioritized for vaccination. Moreover, we found that the Botswana and South Africa populations had similar COVID-19 mortality rates. Hence, our findings should be extended to the rest of Southern African countries since the population in this region have similar demographic and disease characteristics.

Determinants of health-related quality of life in young adults living with perinatally acquired HIV infection in Botswana.

Background: Due to the successful antiretroviral therapy (ART) programme in Botswana, large numbers of perinatally HIV-infected adolescents are emerging into young adulthood. Young adulthood is a critical period of human development. However, there is lack of information on the factors affecting the health-related quality of life (HRQOL) of young adults living with perinatally acquired HIV (YALPH) in Botswana. Objectives: The objective of this study was to assess the HRQOL and its determinants among YALPH who were enrolled on ART at Botswana-Baylor Children's Clinical Centre of Excellence in Gaborone, Botswana. Method: A cross-sectional study assessed the HRQOL of 509 YALPH aged 18-30 years using the WHOQOL-HIV BREF. Data about other variables of interest were abstracted from medical records. Bivariate analyses were performed using t and Chi-square tests to determine the associations between demographic and clinical variables and general HRQOL. The variables that were associated with the general HRQOL at P-value < 0.1 were included in the multivariable analysis using the logistic regression approach. Results: The majority of participants had good general HRQOL (78.4%). The highest mean HRQOL score was in the Physical domain (5.4 [± 2.9]) and the lowest in the Environment domain (13.8 [± 2.7]). The factors that were significantly associated with the general HRQOL included: level of education attained (P = 0.012), employment status (P = 0.069), viral load suppression (P = 0.073) and self-reported illness (P = 0.001). Conclusion: Interventions that effectively increase educational attainment, employment opportunities, ART adherence, and prevention or management of illness are needed to promote good HRQOL among YALPH in Botswana.

Assessing the reporting of categorised quantitative variables in observational epidemiological studies.

BACKGROUND: One aspect to consider when reporting results of observational studies in epidemiology is how quantitative risk factors are analysed. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines recommend that researchers describe how they handle quantitative variables when analysing data. For categorised quantitative variables, the authors are required to provide reasons and justifications informing their practice. We investigated and assessed the practices and reporting of categorised quantitative variables in epidemiology. METHODS: The assessment was based on five medical journals that publish epidemiological research. Observational studies published between April and June 2015 and investigating the relationships between quantitative exposures (or risk factors) and the outcomes were considered for assessment. A standard form was used to collect the data, and the reporting patterns amongst eligible studies were quantified and described. RESULTS: Out of 61 articles assessed for eligibility, 23 observational studies were included in the assessment. Categorisation of quantitative exposures occurred in 61% of these studies and reasons informing the practice were rarely provided. Only one article explained the choice of categorisation in the analysis. Transformation of quantitative exposures into four or five groups was common and dominant amongst studies using equally spaced categories. Dichotomisation was not popular; the practice featured in one article. Overall, the majority (86%) of the studies preferred ordered or arbitrary group categories. Other criterions used to decide categorical boundaries were based on established guidelines such as consensus statements and WHO standards. CONCLUSION: Categorisation of continuous variables remains a dominant practice in epidemiological studies. The reasons informing the practice of categorisation within published work are limited and remain unknown in most articles. The existing STROBE guidelines could provide stronger recommendations on reporting quantitative risk factors in epidemiology.