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Social determinants of tuberculosis via a zero-inflated model in small areas of a city in Southeastern Brazil

机译:通过零膨胀模型在巴西东南部城市的小区域中结核病的社会决定因素

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INTRODUCTION: This study aimed to analyze social factors involved in the spatial distribution and under-reporting of tuberculosis (TB) in the city of Vitória, Espírito Santo State, Brazil. METHODS: This was an ecological study of the reported cases of TB between 2009 and 2011, according to census tracts. The outcome was TB incidence for the study period and the variables of exposure were proportions of literacy, inhabitants with an income of up to half the minimum monthly wage (MMW), and inhabitants associated with sewer mains or with access to safe drinking water. We used a zero-inflated process, zero-inflated negative binomial regression (ZINB), and selected an explanatory model based on the Akaike Information Criterion (AIC). RESULTS: A total of 588 cases of tuberculosis were reported in Vitória during the study period, distributed among 223 census tracts (38.6%), with 354 (61.4%) tracts presenting zero cases. In the ZINB model, the mean value of p i was 0.93, indicating that there is a 93% chance that an observed false zero could be due to sub-notification. CONCLUSIONS: It is important to prioritize areas exhibiting determinants that influence the occurrence of TB in the municipality of Vitória. The zero-inflated model can be useful to the public health sector since it identifies the percentage of false zeros, generating an estimate of the real epidemiological condition of TB in Vitória.
机译:简介:本研究旨在分析巴西圣埃斯皮里图州维托里亚市结核病(TB)空间分布和报告不足的社会因素。方法:根据人口普查数据,这是对2009年至2011年间报告的结核病例的生态研究。结果是研究期间的结核病发病率,暴露水平的变量是识字率,收入不超过最低月工资(MMW)一半的居民以及与下水道主管或获得安全饮用水相关的居民。我们使用零膨胀过程,零膨胀负二项式回归(ZINB),并根据赤池信息准则(AIC)选择了一种解释模型。结果:在研究期间,Vitória总共报告了588例结核病,分布在223个人口普查区(占38.6%),其中354例(61.4%)占了零例。在ZINB模型中,p i的平均值为0.93,表明观察到的假零可能是由于子通知引起的93%的机会。结论:重要的是要优先考虑那些具有决定因素的地区,这些地区会影响维托里亚市的结核病发生。零膨胀模型对公共卫生部门有用,因为它可以识别错误零的百分比,从而估算出维托利亚结核病的实际流行病学状况。

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