Pinheiro Pedro Cisalpino, Queiroz Bernardo Lanza
Centro de Desenvolvimento e Planejamento Regional de Minas Gerais, Universidade Federal de Minas Gerais. Av. Antônio Carlos 6627, Pampulha. 31270-901, Belo Horizonte, MG, Brasil.
Cien Saude Colet. 2020 Feb;25(2):683-692. doi: 10.1590/1413-81232020252.14472018. Epub 2018 Jun 16.
Analysis of the distribution of motorcycle-related mortality rates in Brazilian municipalities is fundamental to understand and seek to minimize the occurrence of this growing phenomenon. The main objective of this work is to analyze the spatial distribution of motorcycle rider mortality rates in Brazil, based on more robust and reliable estimates. An attempt was also made to identify the presence of spatial clusters in the distribution of such mortality rates in given municipalities. The rates were estimated based on the average number of motorcyclist deaths recorded in the years 2014, 2015 and 2016. These rates were then directly standardized and graduated based on the local empirical Bayesian estimator. A Local Indicator of Spatial Autocorrelation (LISA) indicated the presence of spatial patterns. The Northeast and Mid-West regions concentrated most of the municipalities with high mortality rates as well and most of the clusters of municipalities with a high-high distribution pattern. Graduated Bayesian estimation was effective to deal with the occurrence of extreme values, thereby improving the reliability of the estimates and enhancing the visualization of the rates on the map.
分析巴西各城市与摩托车相关的死亡率分布情况,对于理解并努力减少这一日益增长的现象的发生至关重要。这项工作的主要目标是基于更稳健可靠的估计,分析巴西摩托车骑手死亡率的空间分布。同时还试图确定特定城市中此类死亡率分布的空间聚集情况。死亡率是根据2014年、2015年和2016年记录的摩托车手死亡平均人数估算得出的。然后基于局部经验贝叶斯估计量对这些死亡率进行直接标准化和分级。局部空间自相关指标(LISA)表明存在空间格局。东北部和中西部地区集中了大部分高死亡率城市以及大部分呈现高高分布模式的城市集群。分级贝叶斯估计对于处理极值的出现很有效,从而提高了估计的可靠性,并增强了地图上死亡率的可视化效果。