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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.

项目摘要在美国，哮喘影响超过2500万成人和儿童，具有高相关发病率，结果的社会经济差异。因为有有效的药物可以治疗和预防哮喘恶化和循证干预措施存在，以减轻有害的影响，由于社会经济因素的影响，早期识别那些处于最高风险的人至关重要。然而，预测未来的努力哮喘急性发作产生了适度的结果，很少纳入全面的信息社会困难，如粮食无保障和住房不稳定，或经济困难，如提供控制药物的费用，这对那些拥有私人健康的人来说尤其重要。保险确定社会和经济困难需要基础广泛的筛查，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关于重要的社会，临床和环境因素。机器学习是一种先进的风险预测计算方法，有很大的潜力来改善传统的方法来预测哮喘急性发作的风险通过间接估计社会困难和纳入时间风险因素。执行在健康计划设置中增强的哮喘风险预测模型提供了明显的优势，投资于哮喘护理管理，并在整个护理过程中及时获得索赔数据。因此，SPACER研究的目的（预测哮喘控制和急诊室的社会标志物）访问）是1）描述私人保险的成人和儿童哮喘患者的社会和经济困难，以及与药物依从性和病情加重的相关性，2）间接估计自我报告的社会和财政困难，使用定期收集的健康计划和空间数据，和3）开发和验证一个机器学习网络模型，结合时间社会标记，临床和环境数据，在健康计划中预测哮喘恶化。这项研究利用了独特的研究人口医学系是哈佛医学院的一个学术研究部门，学校，坐落在一个区域性的非营利健康计划，哈佛朝圣者医疗保健。指导的职业生涯发展奖将支持阿隆佩尔茨博士，医生和卫生服务研究员，在发展机器学习建模和使用社会数据的专业知识，以改善不良临床预测结果。

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）

Measuring health disparities using a continuous social risk factor.

使用连续的社会风险因素衡量健康差异。

DOI：
10.1111/1475-6773.14048
发表时间：
2023
期刊：
Health services research
影响因子：
3.4
作者：
Herrin,Jeph;Barthel,Andrea;Goutos,Demetri;Du,Chengan;Zhou,Sheng;Peltz,Alon;Poyer,James;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：
10.1016/j.acap.2022.12.010
发表时间：
2023
期刊：
Academic pediatrics
影响因子：
3.1
作者：
Bucholz,EmilyM;Hall,Matt;Harris,Mitch;Teufel2nd,RonaldJ;Auger,KatherineA;Morse,Rustin;Neuman,MarkI;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.

DOI：
10.1016/j.hpopen.2023.100112
发表时间：
2023-12-15
期刊：
HEALTH POLICY OPEN
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.

DOI：
10.1001/jamahealthforum.2021.4611
发表时间：
2022-01
期刊：
JAMA health forum
影响因子：
0
作者：
Silvestri D;Goutos D;Lloren A;Zhou S;Zhou G;Farietta T;Charania S;Herrin J;Peltz A;Lin Z;Bernheim S
通讯作者：
Bernheim S

Socioeconomic and Health Characteristics of Families at Risk for Losing Supplemental Nutrition Assistance Program Benefits.

面临失去补充营养援助计划福利风险的家庭的社会经济和健康特征。