COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98–57∙73)] than in the second wave [19∙19 (17∙60–20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35–2∙02) and second wave 1∙52 (1∙11–2∙06)], being symptomatic [aIRR 3∙17 (2∙59–3∙89) and 3∙04 (2∙20–4∙21)], and being hospitalised [aIRR 4∙19 (3∙26–5∙39) and 7∙84 (4∙90–12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country’s surveillance system during the study

Epidemiology, diagnostics and factors associated with mortality during a cholera epidemic in Nigeria, October 2020-October 2021: a retrospective analysis of national surveillance data.

OBJECTIVES: Nigeria reported an upsurge in cholera cases in October 2020, which then transitioned into a large, disseminated epidemic for most of 2021. This study aimed to describe the epidemiology, diagnostic performance of rapid diagnostic test (RDT) kits and the factors associated with mortality during the epidemic. DESIGN: A retrospective analysis of national surveillance data. SETTING: 33 of 37 states (including the Federal Capital Territory) in Nigeria. PARTICIPANTS: Persons who met cholera case definition (a person of any age with acute watery diarrhoea, with or without vomiting) between October 2020 and October 2021 within the Nigeria Centre for Disease Control surveillance data. OUTCOME MEASURES: Attack rate (AR; per 100 000 persons), case fatality rate (CFR; %) and accuracy of RDT performance compared with culture using area under the receiver operating characteristic curve (AUROC). Additionally, individual factors associated with cholera deaths and hospitalisation were presented as adjusted OR with 95% CIs. RESULTS: Overall, 93 598 cholera cases and 3298 deaths (CFR: 3.5%) were reported across 33 of 37 states in Nigeria within the study period. The proportions of cholera cases were higher in men aged 5-14 years and women aged 25-44 years. The overall AR was 46.5 per 100 000 persons. The North-West region recorded the highest AR with 102 per 100 000. Older age, male gender, residency in the North-Central region and severe dehydration significantly increased the odds of cholera deaths. The cholera RDT had excellent diagnostic accuracy (AUROC=0.91; 95% CI 0.87 to 0.96). CONCLUSIONS: Cholera remains a serious public health threat in Nigeria with a high mortality rate. Thus, we recommend making RDT kits more widely accessible for improved surveillance and prompt case management across the country

Measuring and evaluating the impact of policies on sexual and gender minority youth populations: A scoping review protocol

BACKGROUND: Studies aiming to examine the relationship between policy and sexual orientation and gender identity (SOGI) health inequity have grown exponentially in recent years. With this rapid expansion, there have also been myriad ways in which researchers conceptualize and operationalize the policy in the context of LGBTQ+ health. These idiosyncrasies make comparisons across studies difficult and a muddy translation of the work to policy and practice. To the best of our knowledge, there is no existing review summarizing the evidence linking to measuring and evaluating the impact of policies on sexual and gender minority youth populations. Therefore, the scoping review aims to identify and assess existing literature that discusses and measures associations between United States federal and state policies, including their implementation and impact on the health of SGMY populations. METHODS: We will follow the five steps for scoping review as defined by Arksey and O’Malley’s (2005) methodological framework: 1) identify the research question; 2) identify relevant studies; 3) select studies; 4) extract, map, and chart the data; 5) summarize, synthesize, and report the results. The review process will be guided by the JBI Manual for Evidence Synthesis (Chapter 11; Aromataris & Munn, 2020) and the PRISMA Extension for Scoping Reviews (PRISMA-ScR) Checklist and Explanation (Tricco et al., 2018). Key search terms will be identified based on the Population, Intervention, Comparator, Outcomes (PICO) framework, and seven databases will be searched for relevant studies: EBSCO databases - Academic Search Ultimate, CINAHL, LGBTQ+ Source, MEDLINE, PsycINFO; GenderWatch (ProQuest); and Scopus (Elsevier). To be considered for inclusion in this scoping review, studies must be peer-reviewed, published 2000-2023 in English language academic journals, and related to the measurement of policies affecting the health outcomes of LGBTQ+ youth aged 26 or younger and living in the United States. CONCLUSION: Findings will provide an overview of methods that have been utilized to operationalize policy and examine the relationship between policy and LGBTQ+ population health. Following a review of article methods, we will make recommendations for how to standardize the operationalization of policy in studies testing its association with LGBTQ+ population health in the hope that this may strengthen comparisons across studies and the implications for policy interventions that support LGBTQ+ population health