Real World Data Versus Probability Surveys for Estimating Health Conditions at the State Level.

IF 1.6 4区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Journal of Survey Statistics and Methodology Pub Date : 2024-11-01 DOI:10.1093/jssam/smae036
David A Marker, Charity Hilton, Jacob Zelko, Jon Duke, Deborah Rolka, Rachel Kaufmann, Richard Boyd
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Abstract

Government statistical offices worldwide are under pressure to produce statistics rapidly and for more detailed geographies, to compete with unofficial estimates available from web-based big data sources or from private companies. Commonly suggested sources of improved health information are electronic health records (EHRs) and medical claims data. These data sources are collectively known as real world data (RWD) because they are generated from routine health care processes, and they are available for millions of patients. It is clear that RWD can provide estimates that are more timely and less expensive to produce- but a key question is whether or not they are very accurate. To test this, we took advantage of a unique health data source that includes a full range of sociodemographic variables and compare estimates using all of those potential weighting variables, versus estimates derived when only age and sex are available for weighting (as is common with most RWD sources). We show that not accounting for other variables can produce misleading, and quite inaccurate, health estimates.

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真实世界数据与估计州一级健康状况的概率调查。
世界各地的政府统计部门都面临着压力,需要迅速编制更详细的地区统计数据,以与基于网络的大数据源或私营公司提供的非官方估计数据竞争。通常建议改进健康信息的来源是电子健康记录(EHRs)和医疗索赔数据。这些数据源统称为真实世界数据(RWD),因为它们是由常规医疗保健流程生成的,可供数百万患者使用。很明显,RWD可以提供更及时、成本更低的估算——但一个关键问题是它们是否非常准确。为了验证这一点,我们利用了一个独特的健康数据源,其中包括各种社会人口变量,并将使用所有这些潜在加权变量的估计值与仅使用年龄和性别进行加权的估计值进行比较(大多数RWD来源都是如此)。我们表明,不考虑其他变量可能会产生误导性的、相当不准确的健康估计。
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来源期刊
CiteScore
4.30
自引率
9.50%
发文量
40
期刊介绍: The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.
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