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MORE DISEASE: HOW MAJOR A FACTOR IN HIGHER UTILIZATION

更多疾病：高利用率的主要因素

基本信息

批准号：
6154438
负责人：
Michael Shwartz
金额：
$ 1.05万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
1998
资助国家：
美国
起止时间：
1998-09-30 至 2000-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6154438
关键词：
Medicare /Medicaid clinical research epidemiology geographic site health care service evaluation health services research tag hospital utilization human data human old age (65+) outpatient care statistics /biometry

项目摘要

Many studies have reported large differences in age/sex adjusted rates of hospital utilization across small areas, often leading to the conclusion that hospitalization rates in the higher rate areas can be reduced to those in lower "benchmark" areas. An assumption underlying these recommendations is that age and sex adjustment has fully accounted for differences in underlying disease in the different areas. The hypothesis that we will examine is that there is more underlying disease in higher rate areas: As a proxy for underlying disease, we will use rates of treatment of an "outpatient only" basis. We will study Medicare admission in Massachusetts in 1995 for 22 medical conditions, 17 of which have shown in earlier work to be in the top 50% in terms of small area variations. From our earlier work, we known that the drive of variations in inpatient admissions are variations in a number of individuals admitted (as opposed to re-admissions). Thus, we will focus on individuals treated in two categories: as inpatients and as outpatients only. We will use the 70 small geographic areas in Massachusetts previously created. For each are and each condition, we will calculate relative rates of individual inpatient admissions, individuals treated as outpatients only, and individuals treated as inpatients or outpatients only as follows: (observed number-expected number)/expected number. Empirical Bayes (EB) techniques will be used to estimate "true" relative rates in order to guard against the impact that extreme rates from small areas might have on results. In the analysis, we will focus on the correlation of inpatient and outpatient EB-estimated rates (a high positive correlation would support the "more disease" hypothesis; a high negative correlation the "practice style" hypothesis); R-squared, indicating the percent of variation in inpatient rates explained by variation in outpatient only rates; and a comparison of EB estimates of systematic variation in individuals admitted to systematic variation in inpatients plus outpatients. Empirical support for the "more disease" hypothesis might shift some research focus from ways to reduce inpatient utilization to study of the causes of more disease.

许多研究报告了年龄/性别调整后的发病率的巨大差异