Spatial analysis of tuberculosis/HIV coinfection: its relation with socioeconomic levels in a city in south-eastern Brazil

Spatial analysis of tuberculosis/HIV coinfection: its relation with socioeconomic levels in a city in south-eastern Brazil
复制标题

DOI:
10.1590/s0037-86822010000500013
复制
发表时间:
2010-10-01
影响因子:
2
通讯作者:
Ruffino Netto, Antonio
Ruffino Netto, Antonio
中科院分区:
医学4区
文献类型:
--
作者:
Vendramini, Silvia Helena Figueiredo;Santos, Natália Sperli Geraldes Marin dos;Ruffino Netto, Antonio

文献摘要

被引文献

相似文献

简介:结核病/艾滋病毒合并感染的分布进行了空间分析,并与社会经济指标在圣何塞杜里奥普雷托,从1998年至2006年。方法:新的结核病/艾滋病病毒合并感染病例的地理参考和发病率系数计算的空间单位。Moran指数用于评估发病率的空间关联。多元回归选择的变量,可以最好地解释发病率的空间关联。空间关联的地方指标被用来确定重要的空间分组。结果:Moran指数为0.0635(p=0.0000),表明发生关联。最能解释发病率空间关联的变量是受教育程度不超过三年的户主的百分比。结核病/艾滋病毒合并感染发病率系数的丽莎聚类图显示,该市北部地区发病率高,南部和西部地区发病率低。结论:该研究阐明了结核病/艾滋病毒合并感染的空间地理分布,并确定了其与社会经济变量的关联,从而为定向规划提供了数据,优先考虑了该疾病发病率较高的社会弱势地区。
Introduction: Spatial analysis of the distribution of tuberculosis/HIV coinfection was performed and associated with socioeconomic indicators in Sao Jose do Rio Preto, from 1998 to 2006. Methods: New TB/HIV coinfection cases were georeferenced and incidence coefficients were calculated for spatial units. Moran's index was used to evaluate spatial associations of incidences. Multiple regressions selected variables that could best explain the spatial association of incidences. The local indicator of spatial association was used to identify significant spatial groupings. Results: Moran's index was 0.0635 (p=0.0000) indicating that the incidence association occurred. The variable that best explained the spatial association of incidence was the percentage of heads of families with up to three years of education. The LISA cluster map for TB/HIV coinfection incidence coefficients showed groups with high incidence rates in the North and low incidence in the South and West regions of the municipality. Conclusions: The study elucidated the spatial geographic distribution of TB/HIV coinfection and determined its association with socioeconomic variables, thus providing data for oriented planning, prioritizing socially disadvantaged regions that present a higher incidence of the disease.