Sociomarkers to Predict Asthma Control and Emergency Room Visits (SPACER)

预测哮喘控制和急诊室就诊的社会标记 (SPACER)

基本信息

批准号：
10534672
负责人：
Alon Peltz
金额：
$ 16.93万
依托单位：
HARVARD PILGRIM HEALTH CARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-15 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10534672
关键词：
Adult Asthma Award Child Classification Clinical Computer Models Continuity of Patient Care Data Data Set Demographic Factors Development Early identification Emergency department visit Enrollment Environment Environmental Risk Factor Evidence based intervention Family Financial Hardship Future Goals Health Insurance Health Services Healthcare Incentives Individual Intervention Investments K-Series Research Career Programs Learning Link Low income Machine Learning Managed Care Managed Care Programs Measures Mediating Medicaid Medicine Mentors Methods Modality Modeling Morbidity - disease rate Outcome Patient Self-Report Patients Persons Pharmaceutical Preparations Physicians Population Positioning Attribute Privatization Public Policy Research Research Personnel Resources Risk Risk Factors Severity of illness Socioeconomic Factors Surveys Time asthma exacerbation care seeking clinical risk cost experience food insecurity health plan health record high risk housing instability improved machine learning model medical schools medication compliance network models predictive modeling prevent programs random forest relative cost risk prediction risk prediction model screening skills social socioeconomic disparity

项目摘要

PROJECT ABSTRACT Asthma impacts more than 25 million adults and children in the U.S. with high associated morbidity and socioeconomic disparities in outcomes. Because effective medications are available to treat and prevent exacerbations of asthma and evidence-based interventions exist to mitigate the impact of harmful socioeconomic factors, early identification of those at highest risk is crucial. However, efforts to predict future exacerbations of asthma have yielded modest results with infrequent inclusion of comprehensive information on social hardships, such as food insecurity and housing instability, or financial hardships, such as difficulty affording the costs of controller medications which is particularly relevant for those with private health insurance. Identifying social and financial hardships requires broad-based screenings which are resource intensive, difficult to implement in clinical settings and often incomplete or limited to care seeking populations. Further, few asthma risk prediction modalities incorporate time-variable (temporal) data on important social, clinical, and environmental factors. Machine learning, an advanced computational approach to risk prediction, has great potential to improve upon conventional approaches to risk prediction of asthma exacerbations through indirect estimation of social hardships and inclusion of temporal risk factors. Implementation of enhanced asthma risk-prediction models in a health plan setting offers distinct advantages due to existing investments in asthma care management and access to timely claims data across the full care continuum. Accordingly, the aims of the SPACER study (Sociomarkers to Predict Asthma Control and Emergency Room visits) are 1) To describe social and financial hardships in privately insured adults and children with asthma, and association with medication adherence and exacerbations, 2) To indirectly estimate self-reported social and financial hardships using routinely collected health plan and spatial data, and 3) To develop and validate a machine learning network model, incorporating temporal sociomarker, clinical, and environmental data, to predict asthma exacerbations in a health plan setting. The research leverages the unique research environment of the Department of Population Medicine, an academic research department of Harvard Medical School, situated in a regional non-profit health plan, Harvard Pilgrim Health Care. The mentored career development award will support Dr. Alon Peltz, a physician and health services researcher, in developing expertise in machine learning modeling and use of social data to improve prediction of adverse clinical outcomes.

项目摘要哮喘影响着美国超过 2500 万成人和儿童，其发病率和发病率都很高结果的社会经济差异。因为可以使用有效的药物来治疗和预防哮喘恶化和基于证据的干预措施可以减轻有害物质的影响社会经济因素，及早识别风险最高的人至关重要。然而，努力预测未来哮喘恶化的结果有限，很少包含全面的信息社会困难，例如粮食不安全和住房不稳定，或经济困难，例如困难负担控制药物的费用，这对于那些有私人健康状况的人尤其重要保险。识别社会和经济困难需要进行广泛的筛查，这是资源密集，难以在临床环境中实施，并且通常不完整或仅限于寻求护理的人群。此外，很少有哮喘风险预测模式包含重要的社会、临床和环境因素。机器学习，一种先进的风险预测计算方法，具有改进哮喘急性发作风险预测的传统方法的巨大潜力通过间接估计社会困难并纳入时间风险因素。实施在健康计划环境中增强的哮喘风险预测模型具有明显的优势，因为现有的对哮喘护理管理的投资以及在整个护理过程中及时获取索赔数据。因此，SPACER 研究（预测哮喘控制和急诊室的社会标志物）的目标访问）是 1) 描述患有哮喘的私人保险成人和儿童的社会和经济困难，以及与药物依从性和病情加重的关联，2) 间接估计自我报告的社会使用常规收集的健康计划和空间数据来应对经济困难，以及 3) 开发和验证机器学习网络模型，结合时间社会标记、临床和环境数据，在健康计划设置中预测哮喘恶化。该研究利用了独特的研究哈佛医学院学术研究部门人口医学系环境学校坐落在一个地区性非营利健康计划——哈佛朝圣者医疗保健中心内。受指导的职业生涯开发奖将支持医生和卫生服务研究员 Alon Peltz 博士开发机器学习建模和使用社交数据的专业知识来改善不良临床的预测结果。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Association of the Childhood Opportunity Index on Pediatric Readmissions and Emergency Department Revisits.

儿童再入院和急诊科复诊儿童机会指数协会。

DOI：
发表时间：
2022
期刊：
影响因子：
3.1
作者：
Bettenhausen, Jessica L;Noelke, Clemens;Ressler, Robert W;Hall, Matthew;Harris, Mitch;Peltz, Alon;Auger, Katherine A;Teufel 2nd, Ronald J;Lutmer, Jeffrey E;Krager, Molly K;Simon, Harold K;Neuman, Mark I;Pavuluri, Padmaja;Morse, Rustin B;Eght
通讯作者：
Eght

Clinical and public policy interventions to address food insecurity among children.

解决儿童粮食不安全问题的临床和公共政策干预措施。

DOI：
发表时间：
2022-02-01
期刊：
影响因子：
3.6
作者：
Brochier, Annelise;Garg, Arvin;Peltz, Alon
通讯作者：
Peltz, Alon

"Pick a Plan and Roll the Dice": A qualitative study of consumer experiences selecting a health plan in the non-group market.

“选择一个计划并掷骰子”：对非团体市场中选择健康计划的消费者体验的定性研究。

DOI：
发表时间：
2023-12-15
期刊：
影响因子：
0
作者：
Faugno, Elena;Gilkey, Melissa B;Cripps, Lauren A;Sinaiko, Anna;Peltz, Alon;Kingsdale, Jon;Galbraith, Alison A
通讯作者：
Galbraith, Alison A

Factors Associated With Disparities in Hospital Readmission Rates Among US Adults Dually Eligible for Medicare and Medicaid.

与具有医疗保险和医疗补助双重资格的美国成年人再入院率差异相关的因素。

DOI：
发表时间：
2022-01
期刊：
影响因子：
0
作者：
Silvestri, David;Goutos, Demetri;Lloren, Anouk;Zhou, Sheng;Zhou, Guohai;Farietta, Thalia;Charania, Sana;Herrin, Jeph;Peltz, Alon;Lin, Zhenqiu;Bernheim, Susannah
通讯作者：
Bernheim, Susannah

Annual Variation in 30-Day Risk-Adjusted Readmission Rates in U.S. Children's Hospitals.

美国儿童医院 30 天风险调整再入院率的年度变化。

DOI：
发表时间：
2023-08
期刊：
影响因子：
3.1
作者：
Bucholz, Emily M;Hall, Matt;Harris, Mitch;Teufel 2nd, Ronald J;Auger, Katherine A;Morse, Rustin;Neuman, Mark I;Peltz, Alon
通讯作者：
Peltz, Alon