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Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program. 全民研究计划中乳腺癌队列的心血管疾病多模式风险预测。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae199
Han Yang, Sicheng Zhou, Zexi Rao, Chen Zhao, Erjia Cui, Chetan Shenoy, Anne H Blaes, Nishitha Paidimukkala, Jinhua Wang, Jue Hou, Rui Zhang

Objective: This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables.

Materials and methods: We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period.

Results: The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors.

Discussion: The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness.

Conclusion: This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

研究目的本研究利用 "我们所有人研究计划"(All of Us)数据集的丰富多样性,设计出乳腺癌(BC)幸存者心血管疾病(CVD)的预测模型。这项工作的核心是创建一个强大的数据集成管道,该管道可综合电子健康记录(EHR)、患者调查和基因组数据,同时维护不同人口统计学变量之间的公平性:我们开发了一个通用数据处理管道,用于处理和合并 "我们所有人 "数据集的异构数据源,解决数据缺失和数据差异问题,并将不同的数据模式整合到一个连贯的分析框架中。利用包括电子病历、生活方式和健康的社会决定因素 (SDoH) 数据在内的复合特征集,我们采用自适应拉索和随机森林回归模型来预测 6 种心血管疾病的结果。在 10 年的时间里,我们使用 c 指数和随时间变化的接收者工作特征曲线下面积对模型进行了评估:结果:自适应套索模型在大多数心血管疾病结果中表现出一致的性能,而随机森林模型在预测短暂性脑缺血发作等结果时表现尤为突出,因为它结合了完整的多模型特征集。特征重要性分析表明,年龄和既往冠心病事件是预测心血管疾病结果的主要因素,而SDoH聚类标签则突出了社会因素的细微影响:基于 Cox 的预测模型和随机森林回归模型的开发代表了 "我们所有人 "在整合电子病历和患者调查以提高精准医疗方面的广泛应用。SDoH聚类标签的加入揭示了社会行为因素对患者预后的重大影响,强调了预测模型中综合健康决定因素的重要性。尽管取得了这些进步,但仍存在一些局限性,包括未纳入基因数据、心血管疾病分类过宽,以及需要进行公平性分析以确保模型在不同人群中的公平表现。未来的工作应完善临床和社会变量测量,采用先进的估算技术,并探索更多的预测算法,以提高模型的精确性和公平性:本研究证明了 "我们所有人 "的多样化数据集在开发多模式预测模型以预测不列颠哥伦比亚省幸存者心血管疾病方面的作用。数据整合管道和后续预测模型为未来个性化医疗保健研究奠定了方法论基础。
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引用次数: 0
All of whom? Limitations encountered using All of Us Researcher Workbench in a Primary Care residents secondary data analysis research training block. 所有人?在初级保健住院医师二次数据分析研究培训模块中使用 "我们所有人 "研究员工作台遇到的限制。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae162
Fred Willie Zametkin LaPolla, Marco Barber Grossi, Sharon Chen, Tai Wei Guo, Kathryn Havranek, Olivia Jebb, Minh Thu Nguyen, Sneha Panganamamula, Noah Smith, Shree Sundaresh, Jonathan Yu, Gabrielle Mayer

Objectives: The goal of this case report is to detail experiences and challenges experienced in the training of Primary Care residents in secondary analysis using All of Us Researcher Workbench. At our large, urban safety net hospital, Primary Care/Internal Medicine residents in their third year undergo a research intensive block, the Research Practicum, where they work as a team to conduct secondary data analysis on a dataset with faculty facilitation. In 2023, this research block focused on use of the All of Us Researcher Workbench for secondary data analysis.

Materials and methods: Two groups of 5 residents underwent training to access the All of Us Researcher Workbench, and each group explored available data with a faculty facilitator and generated original research questions. Two blocks of residents successfully completed their research blocks and created original presentations on "social isolation and A1C" levels and "medical discrimination and diabetes management."

Results: Departmental faculty were satisfied with the depth of learning and data exploration. In focus groups, some residents noted that for those without interest in performing research, the activity felt extraneous to their career goals, while others were glad for the opportunity to publish. In both blocks, residents highlighted dissatisfaction with the degree to which the All of Us Researcher Workbench was representative of patients they encounter in a large safety net hospital.

Discussion: Using the All of Us Researcher Workbench provided residents with an opportunity to explore novel questions in a massive data source. Many residents however noted that because the population described in the All of Us Researcher Workbench appeared to be more highly educated and less racially diverse than patients they encounter in their practice, research may be hard to generalize in a community health context. Additionally, given that the data required knowledge of 1 of 2 code-based data analysis languages (R or Python) and work within an idiosyncratic coding environment, residents were heavily reliant on a faculty facilitator to assist with analysis.

Conclusion: Using the All of Us Researcher Workbench for research training allowed residents to explore novel questions and gain first-hand exposure to opportunities and challenges in secondary data analysis.

目的:本病例报告旨在详细介绍使用 "我们所有人 "研究员工作台对初级保健住院医师进行二次分析培训的经验和挑战。在我们这家大型城市安全网医院,初级保健/内科住院医师在第三年要接受研究实习这一研究强化阶段的培训,在这一阶段,他们以团队的形式在教师的协助下对数据集进行二次数据分析。2023 年,该研究单元的重点是使用 "我们所有人 "研究员工作台进行二级数据分析:两组共 5 名住院医师接受了访问 All of Us Researcher Workbench 的培训,每组在教师的协助下探索可用数据,并提出原创研究问题。两组住院医师成功完成了他们的研究模块,并就 "社会隔离与 A1C "水平和 "医疗歧视与糖尿病管理 "发表了原创演讲:部门教师对学习和数据探索的深度表示满意。在焦点小组中,一些住院医师指出,对于那些没有兴趣从事研究的住院医师来说,这项活动感觉与他们的职业目标无关,而另一些住院医师则为有机会发表论文而感到高兴。在这两个讨论组中,住院医师们都强调了对 "我们所有人 "研究人员工作台在多大程度上代表了他们在大型安全网医院中遇到的病人的不满:讨论:使用 "我们所有人 "研究人员工作台为住院医师提供了一个在海量数据源中探索新问题的机会。然而,许多居民指出,由于 "我们所有人 "研究人员工作台中描述的人群与他们在实践中遇到的患者相比,受教育程度更高,种族多样性更少,因此研究可能难以在社区卫生环境中推广。此外,鉴于数据需要掌握 2 种基于代码的数据分析语言(R 或 Python)中的一种,并且需要在特殊的编码环境中工作,因此居民在很大程度上依赖于教师协助分析:使用 "我们所有人 "研究人员工作台进行研究培训,使住院医师能够探索新问题,并亲身体验二手数据分析的机遇和挑战。
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引用次数: 0
User guide for Social Determinants of Health Survey data in the All of Us Research Program. 全民研究计划中的社会决定因素健康调查数据用户指南。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae214
Theresa A Koleck, Caitlin Dreisbach, Chen Zhang, Susan Grayson, Maichou Lor, Zhirui Deng, Alex Conway, Peter D R Higgins, Suzanne Bakken

Objectives: Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.

Target audience: This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.

Scope: We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.

目标:将健康的社会决定因素纳入健康结果研究将使研究人员能够研究健康不平等问题。我们所有人研究计划有可能成为丰富的健康社会决定因素数据来源。然而,我们需要用户友好型的建议来对 "我们所有人的社会决定因素健康调查 "进行评分和解释,以便通过提高研究人员使用 "我们所有人的研究中心 "研究人员工作台的能力来为社区创造价值。我们创建了一份用户指南,旨在为研究人员提供健康状况社会决定因素调查的概述、对参与者回复进行评分和解释的建议,以及易于执行的 R 和 Python 函数:本用户指南的目标受众是 "我们所有人 "研究中心(All of Us Research Hub)研究人员工作台(Researcher Workbench)的注册用户,该工作台是一个支持 "我们所有人 "数据分析的云平台,目前正在使用或计划使用健康社会决定因素调查进行分析:我们介绍了作为健康社会决定因素调查一部分而评估的 14 个构造,并总结了构造的可操作性。我们提供了 30 项参考文献的建议,用于对参与者的回答进行评分和解释分数,其中 8 个构像有多个选项。然后,我们将通过 R 和 Python 函数示例来重新标注回答和结构式评分,这些函数可直接在研究者工作台的 Jupyter Notebook 或 RStudio 中实现。完整的源代码可在补充文件和 GitHub 中获取。最后,我们将讨论与研究人员健康社会决定因素调查相关的心理测量注意事项。
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引用次数: 0
Returning value to communities from the All of Us Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building. 通过数据使用、分析、传播和研究能力建设方面的创新方法,将“我们所有人”研究项目的价值回馈给社区。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae276
Suzanne Bakken, Elaine Sang, Berry de Brujin
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引用次数: 0
Sex-based disparities with cost-related medication adherence issues in patients with hypertension, ischemic heart disease, and heart failure. 高血压、缺血性心脏病和心力衰竭患者在坚持服药方面与成本相关的性别差异。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae203
Ivann Agapito, Tu Hoang, Michael Sayer, Ali Naqvi, Pranav M Patel, Aya F Ozaki

Importance and objective: Identifying sources of sex-based disparities is the first step in improving clinical outcomes for female patients. Using All of Us data, we examined the association of biological sex with cost-related medication adherence (CRMA) issues in patients with cardiovascular comorbidities.

Materials and methods: Retrospective data collection identified the following patients: 18 and older, completing personal medical history surveys, having hypertension (HTN), ischemic heart disease (IHD), or heart failure (HF) with medication use history consistent with these diagnoses. Implementing univariable and adjusted logistic regression, we assessed the influence of biological sex on 7 different patient-reported CRMA outcomes within HTN, IHD, and HF patients.

Results: Our study created cohorts of HTN (n = 3891), IHD (n = 5373), and HF (n = 2151) patients having CRMA outcomes data. Within each cohort, females were significantly more likely to report various cost-related medication issues: being unable to afford medications (HTN hazards ratio [HR]: 1.68, confidence interval [CI]: 1.33-2.13; IHD HR: 2.33, CI: 1.72-3.16; HF HR: 1.82, CI: 1.22-2.71), skipping doses (HTN HR: 1.76, CI: 1.30-2.39; IHD HR: 2.37, CI: 1.69-3.64; HF HR: 3.15, CI: 1.87-5.31), taking less medication (HTN HR: 1.86, CI: 1.37-2.45; IHD HR: 2.22, CI: 1.53-3.22; HF HR: 2.99, CI: 1.78-5.02), delaying filling prescriptions (HTN HR: 1.83, CI: 1.43-2.39; IHD HR: 2.02, CI: 1.48-2.77; HF HR: 2.99, CI: 1.79-5.03), and asking for lower cost medications (HTN HR: 1.41, CI: 1.16-1.72; IHD HR: 1.75, CI: 1.37-2.22; HF HR: 1.61, CI: 1.14-2.27).

Discussion and conclusion: Our results clearly demonstrate CRMA issues disproportionately affect female patients with cardiovascular comorbidities, which may contribute to the larger sex-based disparities in cardiovascular care. These findings call for targeted interventions and strategies to address these disparities and ensure equitable access to cardiovascular medications and care for all patients.

重要性和目的:确定性别差异的根源是改善女性患者临床治疗效果的第一步。利用 "我们所有人 "的数据,我们研究了心血管合并症患者的生理性别与成本相关用药依从性(CRMA)问题的关联:回顾性数据收集确定了以下患者:18岁及以上,完成个人病史调查,患有高血压(HTN)、缺血性心脏病(IHD)或心力衰竭(HF),且用药史与这些诊断一致。通过单变量和调整后的逻辑回归,我们评估了生理性别对高血压、缺血性心脏病和心力衰竭患者的 7 种不同的患者报告 CRMA 结果的影响:我们的研究建立了具有 CRMA 结果数据的高血压、心肌缺血和高血脂患者队列,分别为 3891 人、5373 人和 2151 人。在每个队列中,女性更有可能报告各种与费用相关的用药问题:无法负担药物费用(高血压危险比 [HR]:1.68,置信区间 [CR]:1.68,置信区间 [CR]:1.68):1.68,置信区间 [CI]:1.33-2.13;IHD HR:2.33,CI:1.72-3.16;HF HR:1.82,CI:1.22-2.71)、漏服药(HTN HR:1.76,CI:1.30-2.39;IHD HR:2.37,CI:1.69-3.02)、延迟开处方(高血压 HR:1.83,CI:1.43-2.39;高血脂 HR:2.02,CI:1.48-2.77;高血脂 HR:2.99,CI:1.79-5.03)、要求低价药物(高血压 HR:1.41,CI:1.16-1.72;高血脂 HR:1.75,CI:1.37-2.22;高血脂 HR:1.61,CI:1.14-2.27):我们的研究结果清楚地表明,CRMA 问题对患有心血管合并症的女性患者的影响尤为严重,这可能会导致心血管护理中更大的性别差异。这些发现要求采取有针对性的干预措施和策略来解决这些差异,并确保所有患者都能公平地获得心血管药物和护理。
{"title":"Sex-based disparities with cost-related medication adherence issues in patients with hypertension, ischemic heart disease, and heart failure.","authors":"Ivann Agapito, Tu Hoang, Michael Sayer, Ali Naqvi, Pranav M Patel, Aya F Ozaki","doi":"10.1093/jamia/ocae203","DOIUrl":"10.1093/jamia/ocae203","url":null,"abstract":"<p><strong>Importance and objective: </strong>Identifying sources of sex-based disparities is the first step in improving clinical outcomes for female patients. Using All of Us data, we examined the association of biological sex with cost-related medication adherence (CRMA) issues in patients with cardiovascular comorbidities.</p><p><strong>Materials and methods: </strong>Retrospective data collection identified the following patients: 18 and older, completing personal medical history surveys, having hypertension (HTN), ischemic heart disease (IHD), or heart failure (HF) with medication use history consistent with these diagnoses. Implementing univariable and adjusted logistic regression, we assessed the influence of biological sex on 7 different patient-reported CRMA outcomes within HTN, IHD, and HF patients.</p><p><strong>Results: </strong>Our study created cohorts of HTN (n = 3891), IHD (n = 5373), and HF (n = 2151) patients having CRMA outcomes data. Within each cohort, females were significantly more likely to report various cost-related medication issues: being unable to afford medications (HTN hazards ratio [HR]: 1.68, confidence interval [CI]: 1.33-2.13; IHD HR: 2.33, CI: 1.72-3.16; HF HR: 1.82, CI: 1.22-2.71), skipping doses (HTN HR: 1.76, CI: 1.30-2.39; IHD HR: 2.37, CI: 1.69-3.64; HF HR: 3.15, CI: 1.87-5.31), taking less medication (HTN HR: 1.86, CI: 1.37-2.45; IHD HR: 2.22, CI: 1.53-3.22; HF HR: 2.99, CI: 1.78-5.02), delaying filling prescriptions (HTN HR: 1.83, CI: 1.43-2.39; IHD HR: 2.02, CI: 1.48-2.77; HF HR: 2.99, CI: 1.79-5.03), and asking for lower cost medications (HTN HR: 1.41, CI: 1.16-1.72; IHD HR: 1.75, CI: 1.37-2.22; HF HR: 1.61, CI: 1.14-2.27).</p><p><strong>Discussion and conclusion: </strong>Our results clearly demonstrate CRMA issues disproportionately affect female patients with cardiovascular comorbidities, which may contribute to the larger sex-based disparities in cardiovascular care. These findings call for targeted interventions and strategies to address these disparities and ensure equitable access to cardiovascular medications and care for all patients.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2924-2931"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Communicating research findings as a return of value to All of Us Research Program participants: insights from staff at Federally Qualified Health Centers. 将研究成果作为对 "全民研究计划 "参与者的价值回报进行宣传:联邦合格卫生中心工作人员的见解。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae207
Kathryn P Smith, Jenn Holmes, Jennifer Shelley

Objectives: Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools.

Materials and methods: Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers.

Results: Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied.

Discussion: Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools.

Conclusion: The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.

目标:研究参与者重视了解他们的数据贡献是如何推动健康研究的(即数据故事)。我们所有人研究项目收集了项目员工的意见,以了解他们认为参与者感兴趣的研究课题、员工在传播数据故事时需要哪些支持,以及员工如何使用数据故事传播工具:我们使用 25 个项目的在线评估,向 7 个联邦合格医疗中心的 "我们所有人 "项目员工收集信息:最感兴趣或最相关的主题包括收入无保障(83%)、糖尿病(78%)和心理健康(78%)。受访者优先选择在社区(70%)进行面对面宣传,以分享数据故事。对现有传播工具的熟悉程度各不相同:讨论:受访者支持优先使用面对面宣传材料,并培训员工如何使用传播工具:结论:调查结果将为 "我们所有人 "的传播战略、内容、材料和员工培训资源提供参考,从而有效地传播数据故事,为参与者带来价值回报。
{"title":"Communicating research findings as a return of value to All of Us Research Program participants: insights from staff at Federally Qualified Health Centers.","authors":"Kathryn P Smith, Jenn Holmes, Jennifer Shelley","doi":"10.1093/jamia/ocae207","DOIUrl":"10.1093/jamia/ocae207","url":null,"abstract":"<p><strong>Objectives: </strong>Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools.</p><p><strong>Materials and methods: </strong>Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers.</p><p><strong>Results: </strong>Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied.</p><p><strong>Discussion: </strong>Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools.</p><p><strong>Conclusion: </strong>The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2962-2967"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Equitable community-based participatory research engagement with communities of color drives All of Us Wisconsin genomic research priorities. 与有色人种社区开展以社区为基础的公平参与式研究,推动了 "我们威斯康星人 "基因组研究的优先事项。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae265
Suma K Thareja, Xin Yang, Paramita Basak Upama, Aziz Abdullah, Shary Pérez Torres, Linda Jackson Cocroft, Michael Bubolz, Kari McGaughey, Xuelin Lou, Sailaja Kamaraju, Sheikh Iqbal Ahamed, Praveen Madiraju, Anne E Kwitek, Jeffrey Whittle, Zeno Franco

Objective: The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities.

Materials and methods: We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG). An expert community engagement scientist facilitated 6 SIG meetings over the year, explicitly fostering openness and flexibility during conversations. We qualitatively analyzed discussions using a social movement framework tailored for community-based participatory research (CBPR) mobilization.

Results: The SIG evolved through CBPR stages of emergence, coalescence, momentum, and maintenance/integration. Researchers prioritized community needs above personal academic interests while community members kept discussions focused on tangible return of value to communities. One key outcome includes SIG-driven shifts in programmatic and research priorities of the All of Us Research Program in Southeastern Wisconsin. One major challenge was building equitable conversations that balanced scientific rigor and community understanding.

Discussion: Our approach allowed for a rich dialogue to emerge. Points of connection and disconnection between community members and scientists offered important guidance for emerging areas of genomic inquiry.

Conclusion: Our study presents a robust foundation for future efforts to engage diverse communities in CBPR, particularly on healthcare concerns affecting UBR communities.

目标:美国国立卫生研究院(NIH)的 "我们所有人研究计划"(All of Us Research Program)旨在推动个性化医疗的发展,该计划不仅要将患者记录、调查和基因组数据联系起来,还要让参与者参与进来,尤其是那些传统上在生物医学研究领域代表性不足的群体(UBR)。本研究详细介绍了科学家与社区成员(包括许多来自有色人种社区的成员)之间的对话是如何影响当地研究重点的:我们从社区和参与者咨询委员会中招募了地区定量、基础和临床科学家以及对 "我们所有人 "研究有兴趣的社区成员,作为特别兴趣小组(SIG)的成员。在这一年中,一位社区参与科学家专家主持了 6 次 SIG 会议,明确提出要在对话中培养开放性和灵活性。我们使用为社区参与式研究(CBPR)动员量身定制的社会运动框架对讨论进行了定性分析:结果:SIG 经历了 CBPR 的兴起、凝聚、动力和维持/整合阶段。研究人员将社区需求置于个人学术利益之上,而社区成员则将讨论重点放在对社区的实际价值回报上。其中一项重要成果包括,在 SIG 的推动下,威斯康星州东南部的 "我们大家 "研究计划的计划和研究重点发生了变化。一个主要挑战是建立公平的对话,平衡科学的严谨性和社区的理解:我们的方法使丰富的对话得以出现。社区成员与科学家之间的联系点和脱节点为基因组研究的新兴领域提供了重要指导:我们的研究为今后让不同社区参与 CBPR,特别是影响 UBR 社区的医疗保健问题奠定了坚实的基础。
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引用次数: 0
Informatics innovation to provide return of value to participant communities in the All of Us Research Program. 信息学创新为参与我们所有研究项目的社区提供价值回报。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae264
Brandy M Mapes, Rachele S Peterson, Karriem Watson, Melissa Basford, Elizabeth Cohn, Paul A Harris, Joshua C Denny

Objectives: The All of Us Research Program harnesses advances in technology, science, and engagement for precision medicine research. We describe informatics innovations which support that goal and return value to the participant cohort and community.

Materials and methods: Research data from the All of Us Research Program are available to authorized users on the All of Us Researcher Workbench. We describe the technical infrastructure that enables data access and usage for researchers. Participants are considered partners. To ensure return of value, we outline participant access to information.

Results: The All of Us Research Hub allows broad access to data, regardless of background. The innovations described are rooted in the program's core values: participation is open and reflects the diversity of the United States; participants are partners and have access to their information; transparency, security, and privacy are of the highest importance; data are broadly accessible; and the program promotes positive change. We assess research impact and reflect on how All of Us can increase existing return of value to participant communities through future informatics advancements.

Discussion: The program will continue to support efforts to ensure equitable access to data and return of value to participants. Looking ahead, we invite the community to join us.

Conclusion: All of Us research findings can change clinical care, inform guidelines, and set a new bar for data sharing. The ultimate return of value is better care for all.

目标:我们所有人的研究计划利用先进的技术,科学和参与精密医学研究。我们描述了支持这一目标的信息学创新,并将价值回报给参与者群体和社区。材料和方法:所有我们研究计划的研究数据可供授权用户在所有我们研究工作台上使用。我们描述了使研究人员能够访问和使用数据的技术基础设施。参与者被视为合作伙伴。为了确保价值回报,我们概述了参与者对信息的访问权限。结果:我们所有人的研究中心允许广泛访问数据,而不考虑背景。所描述的创新植根于该项目的核心价值:参与是开放的,反映了美国的多样性;参与者是合作伙伴,可以获得他们的信息;透明、安全和隐私是最重要的;数据可广泛获取;这个项目促进了积极的改变。我们评估研究的影响,并反思我们所有人如何通过未来信息学的进步来增加参与者社区的现有价值回报。讨论:本项目将继续支持确保公平获取数据和向参与者回报价值的努力。展望未来,我们邀请社会各界加入我们。结论:我们所有的研究结果都可以改变临床护理,为指导方针提供信息,并为数据共享设定新的标准。价值的最终回报是更好地照顾所有人。
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引用次数: 0
Model-based estimation of individual-level social determinants of health and its applications in All of Us. 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 Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae168
Bo Young Kim, Rebecca Anthopolos, Hyungrok Do, Judy Zhong

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.

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。
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引用次数: 0
On the utility of using the All of Us Research Program as a resource to study military service members and veterans. 关于利用 "我们大家 "研究计划作为研究军人和退伍军人的资源的实用性。
IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1093/jamia/ocae153
Ben Porter

Objectives: To illustrate the utility of the All of Us Research Program for studying military and veteran health.

Materials and methods: Results were derived from the All of Us Researcher Workbench Controlled Tier v7. Specific variables examined were family history of post-traumatic stress disorder (PTSD), medical encounters, and body mass index/body size.

Results: There are 37 363 military and veteran participants enrolled in the All of Us Research Program. The population is older (M = 63.3 years), White (71.3%), and male (83.2%), consistent with military and veteran populations. Participants reported a high prevalence of PTSD (13.4%), obesity (40.2%), and abdominal obesity (77.1%).

Discussion and conclusion: The breadth and depth of health data from service members and veterans enrolled in the All of Us Research Program allow researchers to address pressing health questions in these populations. Future enrollment and data releases will make this an increasingly powerful and useful study for understanding military and veteran health.

目的说明 "我们所有人 "研究计划在研究军人和退伍军人健康方面的实用性:研究的具体变量包括创伤后应激障碍(PTSD)家族史、医疗遭遇和体重指数/体型:共有 37 363 名军人和退伍军人参加了 "我们所有人 "研究计划。参与者年龄较大(M = 63.3 岁)、白人(71.3%)、男性(83.2%),与军人和退伍军人群体一致。参与者报告了创伤后应激障碍(13.4%)、肥胖(40.2%)和腹部肥胖(77.1%)的高发病率:参加 "我们所有人 "研究计划的军人和退伍军人的健康数据的广度和深度使研究人员能够解决这些人群中迫切的健康问题。未来的注册和数据发布将使这项研究在了解军人和退伍军人健康状况方面变得越来越强大和有用。
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引用次数: 0
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Journal of the American Medical Informatics Association
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