基于模型的个人健康社会决定因素估算及其在《我们大家》中的应用。

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Bo Young Kim, Rebecca Anthopolos, Hyungrok Do, Judy Zhong
{"title":"基于模型的个人健康社会决定因素估算及其在《我们大家》中的应用。","authors":"Bo Young Kim, Rebecca Anthopolos, Hyungrok Do, Judy Zhong","doi":"10.1093/jamia/ocae168","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.</p><p><strong>Materials and methods: </strong>Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.</p><p><strong>Results: </strong>Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.</p><p><strong>Discussion and conclusion: </strong>The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model-based estimation of individual-level social determinants of health and its applications in All of Us.\",\"authors\":\"Bo Young Kim, Rebecca Anthopolos, Hyungrok Do, Judy Zhong\",\"doi\":\"10.1093/jamia/ocae168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.</p><p><strong>Materials and methods: </strong>Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.</p><p><strong>Results: </strong>Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.</p><p><strong>Discussion and conclusion: </strong>The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.</p>\",\"PeriodicalId\":50016,\"journal\":{\"name\":\"Journal of the American Medical Informatics Association\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-07-14\",\"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/ocae168\",\"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/ocae168","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}
引用次数: 0

摘要

目的:我们介绍了一种广泛适用的基于模型的方法,用于估算个人层面的社会健康决定因素(SDoH),并利用 "我们所有人 "研究计划评估其有效性:我们介绍了一种广泛适用的基于模型的方法,用于估算个人层面的健康社会决定因素(SDoH),并利用 "我们所有人 "研究计划对其有效性进行了评估:我们的方法利用汇总的 SDoH 数据集来估算个人层面的 SDoH,并以无高中文凭(NOHSDP)和无医疗保险(UNINSUR)变量为例进行演示。我们使用美国社区调查数据对模型进行了估算,并将其应用于推导 "我们所有人 "参与者的个人水平估算值。我们评估了基于模型的 SDoH 估计值与 "我们所有人 "中自我报告的 SDoH 之间的一致性,并研究了与未确诊的高血压和糖尿病之间的关联:在 329074 名 All of Us 参与者中,与自我报告的 SDoHs 相比,NOHSDP 的曲线下面积为 0.727(95% CI,0.724-0.730),UNINSUR 的曲线下面积为 0.730(95% CI,0.727-0.733),均显著高于综合 SDoHs。基于模型的 NOHSDP 与未确诊高血压之间的相关性与使用自我报告的 NOHSDP 估算的相关性一致,相关系数为 0.649。同样,基于模型的 NOHSDP 与未确诊糖尿病之间的相关性与使用自我报告的 NOHSDP 估算的相关性一致,相关系数为 0.900:基于模型的 SDoH 估算方法为估算个人层面的 SDoH 提供了一种可扩展且易于标准化的方法。利用 "我们所有人 "数据集,我们证明了基于模型的 SDoH 估算值与自我报告的 SDoH 之间的合理一致性,以及与健康结果之间的一致关联。我们的研究结果还强调了地理环境在 SDoH 估算以及 SDoH 与健康结果之间关联评估中的关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-based estimation of individual-level social determinants of health and its applications in All of Us.

Objectives: We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

Materials and methods: Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.

Results: Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.

Discussion and conclusion: The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信