{"title":"利用校准提高从 \"我们所有人 \"数据中获得的估计值的精确度。","authors":"Vivian Hsing-Chun Wang, Julie Holm, José A Pagán","doi":"10.1093/jamia/ocae181","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program.</p><p><strong>Materials and methods: </strong>We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.</p><p><strong>Results: </strong>About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.</p><p><strong>Conclusions: </strong>Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Use of calibration to improve the precision of estimates obtained from All of Us data.\",\"authors\":\"Vivian Hsing-Chun Wang, Julie Holm, José A Pagán\",\"doi\":\"10.1093/jamia/ocae181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program.</p><p><strong>Materials and methods: </strong>We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.</p><p><strong>Results: </strong>About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.</p><p><strong>Conclusions: </strong>Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.</p>\",\"PeriodicalId\":50016,\"journal\":{\"name\":\"Journal of the American Medical Informatics Association\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Medical Informatics Association\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1093/jamia/ocae181\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocae181","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Use of calibration to improve the precision of estimates obtained from All of Us data.
Objectives: To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program.
Materials and methods: We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.
Results: About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.
Conclusions: Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.
期刊介绍：
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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