Nigeria began administering COVID-19 vaccines on 5 March 2021 and is working towards the WHO’s African regional goal to fully vaccinate 70% of their eligible population by December 2022. Nigeria’s ...COVID-19 vaccination information system includes a surveillance system for COVID-19 adverse events following immunisation (AEFI), but as of April 2021, AEFI data were being collected and managed by multiple groups and lacked routine analysis and use for action. To fill this gap in COVID-19 vaccine safety monitoring, between April 2021 and June 2022, the US Centers for Disease Control and Prevention, in collaboration with other implementing partners led by the Institute of Human Virology Nigeria, supported the Government of Nigeria to triangulate existing COVID-19 AEFI data. This paper describes the process of implementing published draft guidelines for data triangulation for COVID-19 AEFI data in Nigeria. Here, we focus on the process of implementing data triangulation rather than analysing the results and impacts of triangulation. Work began by mapping the flow of COVID-19 AEFI data, engaging stakeholders and building a data management system to intake and store all shared data. These datasets were used to create an online dashboard with key indicators selected based on existing WHO guidelines and national guidance. The dashboard went through an iterative review before dissemination to stakeholders. This case study highlights a successful example of implementing data triangulation for rapid use of AEFI data for decision-making and emphasises the importance of stakeholder engagement and strong data governance structures to make data triangulation successful.
Obesity is associated with detrimental changes in cardiovascular and metabolic parameters, including blood pressure, dyslipidemia, markers of systemic inflammation, and insulin resistance. In the ...elderly living with the human immunodeficiency virus (EPLHIV), and being treated with antiretroviral medications, the obesity complications escalate and expose the elderly to the risk of noncommunicable diseases. Given that over 3 million EPLHIV in sub-Sahara Africa, we assessed the prevalence of obesity and its associated factors among EPLHIV in a low-resource setting.This was a cross sectional study of EPLHIV aged 50 years and older, being treated with antiretroviral medications from 2004 to 2018. HIV treatment data collected from multiple treatment sites were analyzed. Baseline characteristics of the participants were described, and multivariable relative risk model was applied to assess the associations between obesity (body mass index BMI ≥30 kg/m2) and the prespecified potential risk factors.Of the 134,652 in HIV cohort, 19,566 (14.5%) were EPLHIV: 12,967 (66.3%) were normal weight (18.5 ≤ BMI < 25), 4548 (23.2%) were overweight (25 ≤ BMI < 30), while 2,051 (10.5%) were obese (BMI ≥30). The average age the normal weight (57.1; standard deviation 6.6) and the obese (56.5; standard deviation 5.5) was similar. We observed that being an employed (relative risk RR 1.71; 95% confidence interval CI 1.48-2.00; P < .001), educated (RR 1.93; 95% CI 1.54-2.41; P < .001), and presence of hypertension (RR 1.78; 95% CI 1.44-2.20; P < .001), increased the risk of obesity. Also, being male (RR 0.38; 95% CI 0.33-0.44; P < .001), stages III/IV of the World Health Organization clinical stages of HIV (RR 0.58; 95% CI 0.50-0.68; P < .001), tenofovir-based regimen (RR 0.84; 95% CI 0.73-0.96, P < .001), and low CD4 count (RR 0.56; 95% CI 0.44-0.71; P < .001) were inversely associated with obesity.This study demonstrates that multiple factors are driving obesity prevalence in EPLHIV. The study provides vital information for policy-makers and HIV program implementers in implementing targeted-interventions to address obesity in EPLHIV. Its findings would assist in the implementation of a one-stop-shop model for the management of HIV and other comorbid medical conditions in EPLHIV.
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