Reducing disparities in clinical preventive services

在一些临床预防服务（常规儿童免疫接种、破伤风和流感/肺炎免疫接种、乳房x光检查、胆固醇筛查、定期护理来源、常规口腔检查、乙状窦镜检查和结肠镜检查）方面存在种族差异，科学证据证明，这些服务的使用改善了发病率和死亡率。为了提高我们设计具有成本效益的干预措施的能力，以增加少数种族和族裔群体成员适当使用临床预防服务，我们将：1。分析种族和少数民族使用临床预防服务的个体和环境预测因素，以确定差异存在的程度。2. 使用逻辑回归在(a)不同种族和民族群体中比较这些预测因子的效果，以确定差异是如何引起的(i)人口特征的差异和/或（ii）预测因子在人口中的影响差异；(b)比较这些预测因子在不同临床预防服务中的影响，以确定共同的可变因素。3. 利用预防性服务改善健康状况文献中的现有估计、上述参数估计以及修改每个因素的“最佳估计”成本，对不同干预措施的相对优点进行蒙特卡洛模拟。4. 为至少一项干预试验设计并争取额外资金，该试验将解决上述临床预防服务差异的交叉决定因素。将使用MEPS、MCBS、NHIS和加州健康访谈调查进行分析。前两个数据集以较小的样本量和较少的因变量提供了大量关于自变量的详细信息。后者的数据集包含了大量的因变量细节，而对自变量的细节有一些损失。

Racial disparities exist for a number of clinical preventive services for which scientific evidence documents improved morbidity and mortality outcomes as a result of their use (routine childhood immunizations, tetanus and flu/pneumonia immunizations, mammography, cholesterol screening, having a regular source of care, routine oral exams, sigmoidoscopy, and colonoscopy). To improve our ability to design cost- effective interventions to increase the appropriate use of clinical preventive services by members of racial and ethnic minority groups, we will: 1. Analyze the individual and contextual predictors of clinical preventive service use by racial and ethnic minorities to determine the extent to which disparities exist. 2. Compare the effects of these predictors using logistic regression across (a) different racial and ethnic groups to identify how differences result from (i) differences in population characteristics and/or (ii) differences in the effects of predictors across population AND (b) compare effects of these predictors across different clinical preventive services to identify common mutable factors. 3. Drawing on existing estimates from the literature of preventive services' health status improvements, the parameter estimates developed above, and "best estimate" costs of modifying each factor, conduct Monte Carlo simulations of the relative merits of different interventions. 4. Design and solicit additional funding for at least one intervention trial that would address a cross-cutting determinant of clinical preventive service disparities identified above. Analyses will be conducted using the MEPS, MCBS, NHIS, and California Health Interview Survey. The first two datasets provide extensive detail on independent variables with smaller sample sizes and fewer dependent variables. The latter datasets contain extensive detail on dependent variables with some loss of detail on independent variables.