Geospatial clustering and correlates of deaths during the Ebola outbreak in Liberia: a Bayesian geoadditive semiparametric analysis of nationally representative cross-sectional survey data

Abstract

ObjectiveTo investigate the extent of geospatial clustering of reported deaths during the Ebola outbreak in Liberia and the covariates associated with the observed clustering.DesignCross-sectional study.ParticipantsMale and female respondents from the 2019–2020 Liberia Demographic and Health Survey. The analysis covered 11 928 (women=7854 and men=4074) respondents for whom complete data were available.Outcome measuresThe outcome variable was the death of a household member or relative during the Ebola outbreak in Liberia, coded 1 if the respondent reported death and 0 otherwise.MethodsWe applied the Bayesian geoadditive semiparametric regression to examine the extent of geospatial clustering of deaths at the district-level and community-level development and socioeconomic factors associated with the observed clustering.ResultsAlmost a quarter (24.8%) of all respondents reported the death of a household member or relative during the Ebola outbreak. The results show that deaths were clustered within districts in six (Grand Cape Mount, Bomi, Monsterrado, Margibi, Gbarpolu and Lofa) of the 15 counties in Liberia. Districts with high death clustering were all near or shared borders with Sierra Leone and Guinea. The community-level development indicators (global human footprint, gross cell production and population density) had a non-linear associative effect with the observed spatial clustering. Also, respondents’ characteristics (respondent’s age (non-linear effect), educational attainment and urban-rural place of residence) were associated with the observed clustering. The results show that death clustering during outbreaks was constrained to poor settings and impacts areas of moderate and high socioeconomic development.ConclusionReported deaths during the Ebola outbreak in Liberia were not randomly distributed at the district level but clustered. The findings highlight the need to identify at-risk populations during epidemics and respond with the needed interventions to save lives.