LINC: Leveraging IT for Neighborhoods in Childhood

摘要 尽管确定了预防儿童肥胖的可改变的行为目标， 在美国，肥胖症仍然是历史上最高的，最严重的形式在年轻人中正在增加 孩子最近的流行病学证据表明，肥胖的种族/民族和社会经济差异 患病率正在扩大，到24个月时已经很明显。因此，儿童肥胖预防 努力必须解决个人水平行为风险因素之外的因素，并应在最初几个月开始 生命健康的社会决定因素（SDoH）越来越被认为在健康的发展中发挥着关键的上游作用。 肥胖的病因，特别是在负担不成比例的人群。因此，SDoH可能是质数 在少数种族/族裔和低收入人口中预防儿童肥胖的干预措施目标。 最近，创新的医疗保健模式激励了SDoH筛查和社会服务的整合 电子健康记录（EHR）。虽然技术进步允许个人实施 和社区措施SDoH到EHR，一个关键的差距，了解哪些因素最强烈 预测肥胖-因此应优先作为临床措施和干预目标-排除 实施这些技术进步的肥胖预防干预措施。此外，少数研究 研究了从出生到24个月的SDoH测量与生长参数的关系，这是一个关键的 可塑性和发育期，在此期间，压力源可能导致长期的健康后果。 本研究的总体目标是确定多个层面（个人，家庭，社区， 和环境），显示出采用健康IT系统以预防儿童肥胖的前景。我们 此外，我们亦会研究社会服务转介及使用对社会服务署与婴儿之间关系的影响 体重结果。为达致这些目标，我们会善用现有以环境健康纪录为本的自定义健康检查系统， 定向社区社会服务转介系统。患者在常规良好的儿童护理， 为大量种族/少数民族和低收入家庭服务的多地点学术实践，将 形成从出生到24个月大的1300名婴儿的纵向队列的基础。此外，我们将执行 深入访谈，探讨患者、提供者和社区利益相关者对基于EHR的SDoH的看法 筛查和社会服务转介。这项研究的结果可以加强我们对这一作用的理解。 儿童肥胖的多层次决定因素。它还将提供有关影响的新信息， 关于SDoH和婴儿生长轨迹的临床-社区资源联系。这项研究将填补关键 知识差距，以加速整合SDoH措施，最有力地预测不健康的婴儿体重 卫生信息技术系统。拟议研究的结果将构成以下基础： 未来的比较有效性研究，以防止儿童肥胖不成比例地负担 人口。 1

ABSTRACT Despite identification of modifiable behavioral targets for childhood obesity prevention, prevalence of obesity remains historically high in the United States and the most severe forms are increasing among young children. Recent epidemiologic evidence shows that racial/ethnic and socioeconomic disparities in obesity prevalence are widening, and already are apparent by age 24 months. Thus, childhood obesity prevention efforts must address factors beyond individual-level behavioral risk factors and should start in the first months of life. Social determinants of health (SDoH) are increasingly recognized as playing key upstream roles in etiologies of obesity, particularly in disproportionately burdened populations. Therefore, SDoH may be prime targets for interventions to prevent childhood obesity among racial/ethnic minority and low-income populations. Recently, innovative health care models have incentivized integration of SDoH screening and social service referrals into electronic health records (EHRs). While technologic advances allow implementation of individual and neighborhood measures of SDoH into EHRs, a critical gap in understanding which factors most strongly predict obesity – and thus should be prioritized as clinical measures and intervention targets – precludes implementation of these technologic advances for obesity prevention interventions. Furthermore, few studies have examined relationships of SDoH measures with growth parameters from birth to age 24 months, a critical period of plasticity and development in which stressors can lead to long-term health consequences. The overall goal of this study is to identify SDoH measures at multiple levels (individual, family, community, and environmental) that show promise for adoption into health IT systems to prevent childhood obesity. We also will examine effects of social services referrals and utilization on associations between SDoH and infant weight outcomes. To achieve these aims, we will leverage an existing EHR-based SDoH screening and bi- directional community-based social services referral system. Patients presenting for routine well child care at a multi-site academic practice that serves a large number of racial/ethnic minority and low-income families, will form the basis of a longitudinal cohort of 1300 infants from birth to age 24 months. Additionally, we will perform in-depth interviews to explore patient, provider, and community stakeholder perceptions of EHR-based SDoH screening and social services referrals. The results of this study could strengthen our understanding of the role of specific multi-level determinants of childhood obesity. It also will provide new information about the effects of clinical-community resource linkages on SDoH and infant growth trajectories. This research will fill critical knowledge gaps to accelerate integration of SDoH measures that most strongly predict unhealthy infant weight gain into health information technology systems. The results of the proposed research will form the basis of future comparative effectiveness research to prevent childhood obesity in disproportionately burdened populations. 1