Louisa H Smith, Wanjiang Wang, Brianna Keefe-Oates
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We aimed to identify pregnancy episodes with high-quality electronic health record (EHR) data in All of Us Research Program data and evaluate the program's utility for pregnancy-related research.</p><p><strong>Materials and methods: </strong>We used a previously published algorithm to identify pregnancy episodes in All of Us EHR data. We described these pregnancies, validated them with All of Us survey data, and compared them to national statistics.</p><p><strong>Results: </strong>Our study identified 18 970 pregnancy episodes from 14 234 participants; other possible pregnancy episodes had low-quality or insufficient data. Validation against people who reported a current pregnancy on an All of Us survey found low false positive and negative rates. Demographics were similar in some respects to national data; however, Asian-Americans were underrepresented, and older, highly educated pregnant people were overrepresented.</p><p><strong>Discussion: </strong>Our approach demonstrates the capacity of All of Us to support pregnancy research and reveals the diversity of the pregnancy cohort. However, we noted an underrepresentation among some demographics. Other limitations include measurement error in gestational age and limited data on non-live births.</p><p><strong>Conclusion: </strong>The wide variety of data in the All of Us program, encompassing EHR, survey, genomic, and fitness tracker data, offers a valuable resource for studying pregnancy, yet care must be taken to avoid biases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research.\",\"authors\":\"Louisa H Smith, Wanjiang Wang, Brianna Keefe-Oates\",\"doi\":\"10.1093/jamia/ocae195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. 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引用次数: 0
摘要
目标:美国国立卫生研究院的 "我们所有人研究计划 "通过收集不同人群的健康数据来弥补生物医学研究的不足。孕妇在生物医学研究中的代表性历来不足,与怀孕相关的研究往往受到数据可用性、样本大小以及孕妇多样性代表性不足等因素的限制。我们所有人》整合了丰富的健康相关数据,为开展全面的孕期相关研究提供了独特的机会。我们的目的是在 "我们所有人 "研究计划的数据中识别具有高质量电子健康记录(EHR)数据的妊娠事件,并评估该计划在妊娠相关研究中的实用性:我们使用之前发布的算法来识别 All of Us 电子病历数据中的妊娠事件。我们对这些妊娠进行了描述,用 All of Us 调查数据对其进行了验证,并将其与国家统计数据进行了比较:我们的研究从 14 234 名参与者中识别出了 18 970 次怀孕事件;其他可能的怀孕事件数据质量较低或不足。与在 "我们所有人 "调查中报告当前怀孕的人进行验证后发现,假阳性率和假阴性率都很低。人口统计学在某些方面与全国数据相似;但是,亚裔美国人的比例偏低,年龄较大、受过高等教育的孕妇比例偏高:讨论:我们的方法展示了 "我们所有人 "支持孕期研究的能力,并揭示了孕期人群的多样性。然而,我们也注意到某些人口统计中存在代表性不足的情况。其他限制还包括胎龄测量误差和非活产数据有限:结论:All of Us 计划中的数据种类繁多,包括电子病历、调查、基因组和健身追踪器数据,为研究妊娠提供了宝贵的资源,但必须注意避免偏差。
Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research.
Objectives: The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. Pregnant individuals have historically been underrepresented in biomedical research, and pregnancy-related research is often limited by data availability, sample size, and inadequate representation of the diversity of pregnant people. All of Us integrates a wealth of health-related data, providing a unique opportunity to conduct comprehensive pregnancy-related research. We aimed to identify pregnancy episodes with high-quality electronic health record (EHR) data in All of Us Research Program data and evaluate the program's utility for pregnancy-related research.
Materials and methods: We used a previously published algorithm to identify pregnancy episodes in All of Us EHR data. We described these pregnancies, validated them with All of Us survey data, and compared them to national statistics.
Results: Our study identified 18 970 pregnancy episodes from 14 234 participants; other possible pregnancy episodes had low-quality or insufficient data. Validation against people who reported a current pregnancy on an All of Us survey found low false positive and negative rates. Demographics were similar in some respects to national data; however, Asian-Americans were underrepresented, and older, highly educated pregnant people were overrepresented.
Discussion: Our approach demonstrates the capacity of All of Us to support pregnancy research and reveals the diversity of the pregnancy cohort. However, we noted an underrepresentation among some demographics. Other limitations include measurement error in gestational age and limited data on non-live births.
Conclusion: The wide variety of data in the All of Us program, encompassing EHR, survey, genomic, and fitness tracker data, offers a valuable resource for studying pregnancy, yet care must be taken to avoid biases.
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.