A population-level analysis of armed conflict and diphtheria at the subnational level in the WHO African Region 2017–2024 - Tierney O’Sullivan; Lindsay T. Keegan (BMC Global and Public Health)
Abstract Background Diphtheria has been re-emerging around the world at alarming rates, raising concerns about emergency preparedness, especially when global supplies of life-saving diphtheria antitoxin are insufficient. Outbreaks have occurred in areas with suboptimal coverage of the three-dose diphtheria tetanus and pertussis (DTP3) vaccine and regions experiencing conflict, but systematic studies assessing the association between these variables and the risk of diphtheria emergence are limited. This population-level study investigated the relationship between fatalities from armed conflict, childhood DTP3 vaccination coverage, and the presence of reported diphtheria cases in countries in the World Health Organization’s (WHO) African region from 2017 to 2024. Methods The analysis was conducted at a subnational geographic scale (I countries = 35, N subnational regions = 541). Data sources include DTP3 coverage from the Demographic Health Surveys (DHS), conflict-related fatalities from the Armed Conflict Location and Event Database (ACLED), and diphtheria cases from the WHO. We first assessed whether a history of fatalities from armed conflict is a predictor of childhood DTP3 coverage using mixed-effects beta regression. To assess the relationship between conflict and diphtheria emergence, we fit a crude logistic regression model to assess their overall association in the study period, as well as repeated measures mixed-effects models to estimate the relationship between time-varying rates of conflict-related fatalities and diphtheria status, adjusting for diphtheria vaccine coverage estimates. Results Conflict and subsequent childhood DTP3 vaccine coverage were negatively associated (odds ratio [OR] = 0.93, 95% CI 0.88–0.98). Conflict is also a significant predictor of diphtheria presence, both in the crude (OR = 1.41, 95% CI 1.17–1.68) and best-fitting repeated measures model (OR = 30.30, 95% CI 23.30–39.39), though risk varied by location. The best-fit model also associated lower estimates of diphtheria risk in areas with high (> 80%) and low (< 25%) vaccine coverage, though this is possibly due to underreporting of the true burden of disease in low-resource settings. Conclusions This exploratory analysis indicates that conflict-related fatalities are potentially helpful indicators of subnational diphtheria risk in countries in the WHO African region from 2017 to 2024. Further, it may be especially useful in cases where estimates of population-level diphtheria immunity are limited.