Diabetes and Tuberculosis Pilot

在临床结核病(TB)的高危人群中筛查结核分枝杆菌感染是美国疾病控制和预防中心(CDC)控制结核病传播的关键战略。这在美国/墨西哥边境尤其重要，在那里，与美国全国平均水平相比，聚合的社会经济和人口因素导致了更高的结核病负担。该地区II型糖尿病(DB)的发病率也很高，长期以来一直被认为是活动性结核病的易感因素。根据这一信息，我们假设DB对墨西哥-美国边境四个德克萨斯州县的拉美裔人口中的结核病负担起到了作用。这一试点项目将分析格兰德河谷下部的四个县：伊达尔戈、卡梅伦、威拉西和 斯塔尔。2001年期间德克萨斯州医院出院情况的二次分析将使用ICD 9代码进行病例和对照选择。采用非条件多元Logistic回归分析评价结核病与暂定相关因素之间的关系。卫生生产函数也将使用无条件多元Logistic回归进行估计。该项目的具体目标是： 目标1：确定LRGV中西班牙裔DB和TB的关联度 目标2：确定结核病与其相关危险因素(除DB外)之间的关联强度，重点是潜在的医疗条件。 目标3：估计卫生生产函数，以确定结核病和结核病的相关社会经济指标。

Screening for Mycobacterium tuberculosis infection in those at high risk for developing clinical tuberculosis (TB) is a key strategy of the Centers for Disease Control and Prevention (CDC) to control the spread of TB in the US. This is particularly important at the US/Mexico border, where converging socioeconomic and demographic factors contribute to the higher burden of TB when compared to the US national average. This region also has high prevalence of type II diabetes (DB), which has long been known to predispose to active tuberculosis. Based on this information, we hypothesize that DB contributes to the burden of TB in the Hispanic population from four Texan counties along the Mexican-American border. Four counties in the Lower Rio Grande Valley (LRGV) will be analyzed in this pilot project: Hidalgo, Cameron, WilIacy and Starr. A secondary analysis of the Texas hospital discharges during 2001 will be implemented using ICD 9 codes for case and control selection. Unconditional multiple logistic regression analysis will be used to evaluate the relationship between tuberculosis and tentative associated factors. Health production functions will be estimated, using unconditional multiple logistic regression as well. The specific aims of the project are: Aim 1: Determine the degree of association of DB and TB in Hispanics in the LRGV Aim 2: Determine the strength of the association between TB and its associated risk factors (besides DB), with emphasis on underlying medical conditions. Aim 3: Estimate health production function that will identify relevant socio-economic indicators of TB and DB.