Multisite Electronic Health Record-based Surveillance of the Burden of Diabetes by Type in Children and Adolescents

糖尿病是18岁以下儿童和青少年常见的慢性疾病。最近二 近几十年来，1型糖尿病（T1 D）和2型糖尿病（T2 D）的患病率和发病率均已降低， 在美国的儿童和青少年中有很大的增长。T2 D现在占糖尿病病例的约15% 在童年和青春期被诊断出来。青少年糖尿病研究是唯一正在进行的 美国儿童和青少年T1 D和T2 D的多中心监测研究。这项研究是一项 基于人口的登记，这是一种更昂贵，更劳动密集型和更慢的监测方法 与使用电子健康记录（EHR）的监测系统相比。EHR算法显示 在识别糖尿病病例方面的巨大潜力。本研究将分析结构化EHR数据（例如，诊断 代码、药物和实验室结果）和非结构化临床记录。我们将运用专业知识， 机器学习和自然语言处理，以开发最佳算法， T1 D和T2 D病例。本研究的主要目的是建立一个基于电子健康档案的监测 监测美国儿童和青少年T1 D和T2 D负担的系统。我们将与3 国家以患者为中心的临床研究网络（PCORnet）的EHR研究网络，包括 来自4个州（IL，LA， 纽约州和德克萨斯州）在3个人口普查地区。本研究中的患者人群大致代表了 集水区的人口。本研究的具体目的是：1）估计T1 D的患病率 2019年按年龄、性别、种族/民族和地理区域划分的美国儿童和青少年中的T2 D; 2） 按年龄、性别、人种/种族估计美国儿童和青少年中T1 D和T2 D的发病率，以及 2019年的地理区域; 3）估计T1 D和T2 D患病率和发病率的10年趋势， 2014-2023年按年龄、性别、种族/民族和地理区域划分的美国儿童和青少年;以及4）比较 美国儿童和青少年糖尿病的患病率和发病率（按类型）以及时间趋势 与其他国家和地区的儿童和青少年进行比较。这项研究是创新的，因为它将 利用EHR进行监测（比登记和调查更有效，更经济），使用先进的 统计方法（例如，机器学习和自然语言处理），使用 患者邮政编码，根据当地临床记录的可用性，在监测方法中建立灵活性， 并采用两阶段抽样方法，以提高病历审查效率。本研究中的所有4个州都没有 为儿童和青少年提供积极的糖尿病监测系统， study.这项研究将促进我们对地理，年龄，性别和种族/民族差异的理解， 美国儿童和青少年的T1 D和T2 D负担。获得的监测数据将有助于确定 并优先考虑预防糖尿病和相关合并症的亚人群，以减少健康差距。

Diabetes is a common chronic disease in children and adolescents under age of 18 years. In the recent two decades, both the prevalence and incidence of type 1 diabetes (T1D) and type 2 diabetes (T2D) have been increasing considerably among US children and adolescents. T2D now accounts for ~15% of diabetes cases diagnosed in childhood and adolescence. The SEARCH for Diabetes in Youth study is the only ongoing multisite surveillance study of T1D and T2D in US children and adolescents. The SEARCH study is a population-based registry, which is a more expensive, more labor-intensive, and slower surveillance method compared with a surveillance system using electronic health records (EHRs). EHR algorithms have shown great potential in identifying diabetes cases. This study will analyze both structured EHR data (e.g., diagnosis codes, medications, and laboratory results) and unstructured clinical notes. We will apply expert knowledge, machine learning, and natural language processing to develop the best algorithms for identifying prevalent and incident T1D and T2D cases. The primary objective of this study is to establish an EHR-based surveillance system for monitoring the burden of T1D and T2D in US children and adolescents. We will collaborate with 3 EHR research networks from the National Patient-Centered Clinical Research Network (PCORnet), covering ~3 million racially, ethnically, and socioeconomically diverse children and adolescents from 4 states (IL, LA, NY, and TX) in 3 Census regions. The patient populations in this study are roughly representative of the source populations in the catchment areas. The specific aims of this study are 1) to estimate the prevalence of T1D and T2D in US children and adolescents by age, sex, race/ethnicity, and geographic region in 2019; 2) to estimate the incidence of T1D and T2D in US children and adolescents by age, sex, race/ethnicity, and geographic region in 2019; 3) to estimate 10-year trends in the prevalence and incidence of T1D and T2D in US children and adolescents by age, sex, race/ethnicity, and geographic region, 2014-2023; and 4) to compare the prevalence and incidence of diabetes by type, as well as temporal trends, in US children and adolescents with those in children and adolescents from other countries and regions. This study is innovative because it will leverage EHRs for surveillance (more efficient and cost-effective than registries and surveys), use advanced statistical approaches (e.g., machine learning and natural language processing), estimate a denominator using patient zip codes, build flexibility into the surveillance methods according to local availability of clinical notes, and use a 2-staged sampling approach to improve chart review efficiency. All 4 states in this study do not have an active diabetes surveillance system for children and adolescents and are not covered by the SEARCH study. This study will advance our understanding of the geographic, age, sex, and racial/ethnic differences in the burden of T1D and T2D in US children and adolescents. The obtained surveillance data will help identify and prioritize subpopulations for prevention of diabetes and related comorbidities to reduce health disparities.