Effects of the 2014-2016 Ebola Epidemic on Infectious Disease Prevention in Guinea

Abstract

From 2014-2016, Ebola ravaged the three West African countries of Guinea, Sierra Leone, and Liberia. There were more than 28,000 cases and 11,000 deaths, with more than 10,600 additional deaths due to other medical conditions. We aimed to examine how utilization of infectious disease prevention services changed in Guinea over the course of the Ebola epidemic, using Andersen’s model of healthcare utilization as a theoretical framework. The specific aims of this study were: 1) to examine differences in measles vaccination rates among children ages 12-35 months before and after the Ebola epidemic by wealth quintile; 2) to estimate mosquito net possession at two time points post-epidemic compared to pre-epidemic among households with at least one child under five, nationally and regionally; and 3) to quantify how much of the association between exposure to the Ebola epidemic and HIV testing prevalence can be explained by changes in urban/rural residence rates among adults in Guinea. The studies use data from the 2012 and 2018 Guinea DHS, and the 2016 MICS. Studies 1 and 2 use quasi-Poisson regression models to estimate prevalence ratios, and study 3 uses log-binomial regression models in a mediation analysis. In study 1 (n = 2,573 children ages 12-35 months), the poorest children were 54% (95% CI = 58%-67%) as likely to be vaccinated for measles in 2018 compared to 2012, and the wealthiest children were 78% (95% CI = 69%-90%) as likely. In study 2 (n = 14,756 households with at least one child under five), mosquito net possession in 2016 was 72% (95% CI = 56%-90%) higher and in 2018 was 12% (95% CI = 8%-15%) higher than in 2012. In study 3 (n = 27,809 adults), of the 4.59% (95% CI = 4%-6%) increase in the log-likelihood of ever having been tested for HIV due to being in the 2018 cohort, an estimated 0.269% of the effect (95% CI = 0.04%-1%) could be attributed to differences in urban/rural residence. Understanding these changes gives a more complete picture of the effects of epidemics on infectious disease prevention and can help public health officials plan for future epidemics.