Abstract
BACKGROUND: In Central America, Honduras experienced a significant increase in SARS-CoV-2 infections between March 11, 2020, and January 26, 2022. Although limited research has been conducted on the impact of the COVID-19 pandemic on populations in Central American countries, this study seeks to contribute to the existing body of knowledge in the region. The objective of this study was to investigate the variability of COVID-19 mortality in Honduras and the impact of sociodemographic factors. METHODS: A cross-sectional and ecological study, using data from cases collected by the National Risk Management System (SINAGER) and recorded by the Demographic Observatory of the National Autonomous University of Honduras (ODU) between March 11, 2020, and January 26, 2022. Sociodemographic variables were obtained from the 2013 XVII Population and VI Housing Census by the National Institute of Statistics (INE). Age-adjusted case and COVID-19 mortality rates by sex were calculated. To explain the potential causes of variability, multilevel logistic regression models were constructed, considering individual and contextual variables. RESULTS: A total of 513,416 COVID-19 cases were included, of which 98 % (503,176) survived and 2 % (10,240) died. The results showed differences in COVID-19 mortality rates between municipalities and departments. The multilevel model revealed that age (OR: 1.0737; 95 % CI: [1.0726; 1.0749]) and sex (OR: 0.7434; 95 % CI: [0.7027; 0.7841]) were significantly associated with COVID-19 mortality, with men being more likely to die. Among departments, the significant contextual factors were the illiteracy rate and the percentage of the rural population, both of which were associated with higher COVID-19 mortality (OR: 1.0850; 95 % CI: [1.0511; 1.1189] and OR: 1.0234; 95 % CI: [1.0146; 1.0323]), while the percentage of the active population (working age people) was associated with a decrease in COVID-19 mortality (OR: 0.9768; 95 % CI: [0.9591; 0.9944]). The intraclass correlation coefficient (ICC) showed a reduction in variability attributable to the variation between departments, with a final ICC of 0.68 % . CONCLUSIONS: Differences in COVID-19 mortality were found between the different departments, partly explained by sociodemographic factors. The results of this study show that, in addition to individual characteristics, population-level socioeconomic and educational factors influence COVID-19 mortality. Multilevel analysis is highly useful for providing evidence to improve approaches in future pandemics.