AbuKoura R, Checchi F, Abdalla O, Ibrahim O, Hemeadan AT, Eldirdiri AAA, Mohamed DI, Ahmed A, Ahmed AE, Abdelmagid N, Pepe P, Dahab M. Population mortality before and during the COVID-19 epidemic in two Sudanese settings: a key informant study.
BMC Public Health 2024;
24:701. [PMID:
38443885 PMCID:
PMC10916139 DOI:
10.1186/s12889-023-17298-9]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 11/22/2023] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND
Population mortality is an important metric that sums information from different public health risk factors into a single indicator of health. However, the impact of COVID-19 on population mortality in low-income and crisis-affected countries like Sudan remains difficult to measure. Using a community-led approach, we estimated excess mortality during the COVID-19 epidemic in two Sudanese communities.
METHODS
Three sets of key informants in two study locations, identified by community-based research teams, were administered a standardised questionnaire to list all known decedents from January 2017 to February 2021. Based on key variables, we linked the records before analysing the data using a capture-recapture statistical technique that models the overlap among lists to estimate the true number of deaths.
RESULTS
We estimated that deaths per day were 5.5 times higher between March 2020 and February 2021 compared to the pre-pandemic period in East Gezira, while in El Obeid City, the rate was 1.6 times higher.
CONCLUSION
This study suggests that using a community-led capture-recapture methodology to measure excess mortality is a feasible approach in Sudan and similar settings. Deploying similar community-led estimation methodologies should be considered wherever crises and weak health infrastructure prevent an accurate and timely real-time understanding of epidemics' mortality impact in real-time.
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