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.

哥伦比亚的死亡率模式正在发生重大变化。以前已经进行了国家和部门级特定原因的分析，但是尽管对流行病学感兴趣，但对市政级趋势知之甚少。我们首先根据空间自相关指标Global Moran's I和Local Moran's I分析七个死亡原因组的标准化死亡率，以获得高死亡率和低死亡率。。然后，使用Mann-Whitney非参数检验确定高和低死亡率群与已知健康决定因素之间的统计关联。随后，我们应用空间滞后和Durbin（当存在空间自相关时）和OLS模型（当不存在时）来解释特定原因死亡率中的整体空间格局。从国家统计局（DANE）获得了按年龄和性别划分的死亡原因死亡率和人口数据。由于注册不足，每个城市的死亡人数均得到纠正。结果表明，除男性循环系统疾病和围产期死亡率外，所有死亡原因类别的空间自相关都随时间下降。在外部原因中，尤其是在男性中，这一比例最高，死亡热点从安第斯中部地区转移到Orinoquia和亚马逊雨林。男性死亡率也比女性死亡率（尤其是肿瘤）在空间上更加集中，并且外部原因的死亡率也受到邻近城市条件的间接影响。市政表面积，种族和公共卫生教育支出是解释死亡率地区差异最常见的背景变量。确定哥伦比亚的地理死亡率群将使决策者能够优先考虑那些死亡率较高的地区。市政表面积，种族和公共卫生教育支出是解释死亡率地区差异最常见的背景变量。确定哥伦比亚的地理死亡率群将使决策者能够优先考虑那些死亡率较高的地区。市政表面积，种族和公共卫生教育支出是解释死亡率地区差异最常见的背景变量。确定哥伦比亚的地理死亡率群将使决策者能够优先考虑那些死亡率较高的地区。