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The results show the need to include in the curricula of health programs some fundamental aspects for the proper implementation of MIAS.

Objective: To determine the association between chronic exposure to fine particulate matter (PM2.5), sociodemographic aspects, and health conditions and COVID-19 mortality in Colombia. Methods: Ecological study using data at the municipality level, as units of analysis. COVID-19 data were obtained from official reports up to and including July 17th, 2020. PM2.5 long-term exposure was defined as the 2014-2018 average of the estimated concentrations at municipalities obtained from the Copernicus Atmospheric Monitoring Service Reanalysis (CAMSRA) model. We fit a logit-negative binomial hurdle model for the mortality rate adjusting for sociodemographic and health conditions. Results: Estimated mortality rate ratios (MRR) for long-term average PM2.5 were not statistically significant in either of the two components of the hurdle model (i.e., the likelihood of reporting at least one death or the count of fatal cases). We found that having 10% or more of the population over 65 years of age (MRR=3.91 95%CI 2.24-6.81), the poverty index (MRR=1.03 95%CI 1.01-1.05), and the prevalence of hypertension over 6% (MRR=1.32 95%CI1.03-1.68) are the main factors associated with death rate at the municipality level. Having a higher hospital beds capacity is inversely correlated to mortality. Conclusions: There was no evidence of an association between long-term exposure to PM2.5 and mortality rate at the municipality level in Colombia. Demographics, health system capacity, and social conditions did have evidence of an ecological effect on COVID-19 mortality.

Mortality inequalities have been described across Latin American countries, but less is known about inequalities within cities, where most populations live. We aimed to identify geographic and socioeconomic inequalities in mortality within the urban areas of four main cities in Colombia. We analyzed mortality due to non-violent causes of diseases in adults between 2015 and 2019 using census sectors as unit of analysis in Barranquilla, Bogotá, Cali, and Medellín. We calculated smoothed Bayesian mortality rates as main health outcomes and used concentration indexes (CInd) for assessing inequalities using the multidimensional poverty index (MPI) as the socioeconomic measure. Moran eigenvector spatial filters were calculated to capture the spatial patterns of mortality and then used in multivariable models of the association between mortality rates and quintiles of MPI. Social inequalities were evident but not consistent across cities. The most disadvantaged groups showed the highest mortality rates in Cali. Geographic inequalities in mortality rates, regardless of the adults and poverty distribution, were identified in each city, suggesting that other social, environmental, or individual conditions are impacting the spatial distribution of mortality rates within the four cities.