Health and Health Services Use in the HRS/AHEAD

描述（由申请人提供）：本研究的目的是深入检查基线时70岁或以上的7，447名HRS/AHEAD受访者的健康和健康服务使用情况。我们将分析基线（1993年）和1995年，1998年，2000年，2002年和2004年后续访谈的公共使用数据，并将其与三个限制访问文件的数据合并：医疗保险索赔，国家死亡指数和地理编码。通过地理编码文件，我们将使用区域资源文件链接州和县一级的数据，并在本地访问级别使用GIS。我们的四个具体目标是：（1）使用自我报告，使用医疗保险索赔及其信件检查全因住院的风险;（2）评估八种特定主要疾病事件及其后遗症的风险;（3）评估可预防或门诊护理敏感的医院事件的风险;以及（4）使用调查小组开发的非常有前途的类型学来模拟住院模式的晚期生命过程轨迹。我们的理论框架包括社会人口学特征，社会经济和其他访问因素，健康信念和生活方式，健康状况和卫生服务使用。在这个框架中嵌入了四个明确的假设。首先，在对全因住院进行建模时，我们预计市场结构和实践模式因素将是最显着的预测因素，其次是当地准入措施。其次，我们预计，8种常见疾病的流行病学风险因素将是建模时最突出的因素，其次是市场结构和实践模式的措施，以及人际初级保健的连续性。第三，在对可预防或门诊护理敏感的住院进行建模时，我们预计最突出的预测因素将是教育、人际初级保健的连续性以及当地可及区域的初级保健医疗服务的供应。最后，在对医院利用模式的基线后晚期生命历程轨迹进行建模时，我们预计基线前模式将是最突出的预测因素，其次是市场结构和实践模式，当地访问因素以及功能和社会经济地位。估计技术将主要依赖于多水平事件历史模型，如二项和多项逻辑回归和比例风险模型。在适当情况下，还将使用线性和非线性回归以及随机效应模型。

DESCRIPTION (provided by applicant): The objective of this study is an in-depth examination of the health and health services use of the 7,447 HRS/AHEAD respondents aged 70 years old or older at baseline. We will analyze the public use data from the baseline (1993) and the 1995, 1998, 2000, 2002, and 2004 follow-up interviews merged with data from three of the restricted access files: Medicare claims, National Death Index, and geocodes. With the geocode files we will link State and county level data using the Area Resource File, and at the local access level using GIS. Our four specific aims are to: (1) examine the risk of all-cause hospitalization using self reports, using Medicare claims, and their correspondence; (2) assess the risk for eight specific major morbid events and their sequelae; (3) evaluate the risks for preventable or ambulatory care sensitive hospital episodes; and (4) model the late life course trajectories of hospitalization patterns using a very promising typology developed by the investigative team. Our theoretical framework includes sociodemographic characteristics, socioeconomic and other access factors, health beliefs and lifestyle, health status, and health services use. Embedded in this framework are four explicit hypotheses. First, when modeling all-cause hospitalization, we expect that the market structure and practice pattern factors will be the most salient predictors, followed by the local access measures. Second, we expect that the epidemiologic risk factors for each of the 8 common, morbid conditions will be the most salient factors when modeling those conditions, followed by the market structure and practice pattern measures, and the continuity of interpersonal primary care. Third, when modeling preventable or ambulatory care sensitive hospitalizations, we expect the most salient predictors will be education, continuity of interpersonal primary care, and the supply of primary care health care services in the local access area. Finally, when modeling post-baseline late life course trajectories of hospital utilization patterns, we expect that pre-baseline patterns will be the most salient predictors, followed by market structure and practice patterns, local access factors, and functional and socioeconomic status. Estimation techniques will principally rely on multilevel event history models, such as binomial and multinomial logistic regression, and proportional hazards models. Linear and non-linear regression and random effects models will also be used where appropriate.