Colombia is undergoing major changes in mortality patterns. National- and department-level cause-specific analyses have previously been carried out, but very little is known about municipal-level trends, despite their epidemiological interest. We first analyze standardized mortality rates for seven cause-of-death groups to obtain high and low mortality clusters based on the spatial autocorrelation indicators Global Moran’s I and Local Moran’s I. The Mann–Whitney nonparametric test is then used to ascertain statistical associations between the high and low mortality clusters and known health determinants. We subsequently apply spatial lag and Durbin (when spatial autocorrelation was present) and OLS models (when not) to explain overall spatial patterns in cause-specific mortality. Age- and sex-specific cause-of-death mortality and population data were obtained from the National Administrative Department of Statistics (DANE). Deaths were corrected for each municipality due to under-registration. Results show that spatial autocorrelation declined over time for all cause-of-death categories, except male circulatory system diseases and perinatal mortality. It is highest in external causes, especially among men, with mortality hotspots moving from the central Andean area to Orinoquia and the Amazon rainforest. Male mortality is also more spatially clustered than female mortality and especially neoplasms, and external-cause mortality is also indirectly affected by the conditions of neighboring municipalities. Municipal surface area, ethnicity and public expenditure on health and education are the most frequent contextual variables explaining territorial differences in mortality. The identification of geographical mortality clusters in Colombia will allow decision makers to prioritize those regions with higher mortality.
|Journal||Journal of Geographical Systems|
|Publication status||Accepted in press - 2020|
- Causes of death
- Spatial Durbin model
- Spatial cluster analysis