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

项目摘要 在美国，哮喘影响着2500多万成年人和儿童，与哮喘相关的发病率和 结果方面的社会经济差距。因为有有效的药物可以治疗和预防 存在哮喘加重和循证干预措施，以减轻有害因素的影响 考虑到社会经济因素，及早确定高危人群至关重要。然而，预测未来的努力 哮喘的恶化产生了温和的结果，很少包括全面的信息。 关于社会困难，如粮食无保障和住房不稳定，或财政困难，如困难 负担控制者药物的费用，这对那些拥有私人健康的人特别相关 保险公司。识别社会和经济困难需要广泛的筛选，这是资源 密集、难以在临床环境中实施，而且往往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将时间变量(时间)数据纳入重要社会、 临床和环境因素。机器学习，一种先进的风险预测计算方法， 有很大潜力改进传统的哮喘恶化风险预测方法 通过间接估计社会困难和纳入临时风险因素。实施 在健康计划环境中增强的哮喘风险预测模型由于现有的 在哮喘护理管理方面的投资，以及在整个护理过程中获得及时的索赔数据。 因此，间隔研究(预测哮喘控制和急诊室的社会标志物)的目的 访问)是1)描述私人保险的成年人和患有哮喘的儿童的社会和经济困难， 以及与服药依从性和病情恶化的关联，2)间接估计自我报告的社会 和财务困难，使用常规收集的健康计划和空间数据，以及3)开发和验证 机器学习网络模型，整合了时间社会标记、临床和环境数据，以 在健康计划设置中预测哮喘恶化。这项研究利用了独特的研究 哈佛大学医学院人口医学系的环境 这所学校位于一个地区性的非营利性医疗计划哈佛朝圣者医疗保健计划中。被指导的职业生涯 发展奖将支持医生和卫生服务研究员阿隆·佩尔茨博士开发 擅长机器学习、建模和使用社交数据来改进对临床不良反应的预测 结果。

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.