Multisite Electronic Health Record-Based Surveillance of the Burden of Diabetes by Type in Young Adults

在美国，目前约有300万18 - 44岁的年轻人患有糖尿病。这 预计到2060年，这一数字将增加到约580万。区分糖尿病类型至关重要，因为 糖尿病的病因、治疗和结果因类型而有很大不同。1型糖尿病（T1D） 约17%和2型糖尿病（T2D）约75%的总糖尿病在美国年轻人。糖尿病类型的分布 不断进化。我们没有一个大规模的监测系统来监测流行情况， 美国年轻人中T1D和T2D的发病率。电子设备的广泛使用和日益增加的功能 健康记录（EHR）系统大大增加了数据的数量、广度和及时性， 与基于人口的登记和调查相比，监测和降低成本。EHR算法具有 在识别糖尿病病例方面显示出巨大的潜力。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。我们将应用专家 知识，机器学习和自然语言处理，以开发最佳算法， 流行和偶发T1D和T2D病例。本研究的主要目的是建立一个基于EHR的 监测系统，用于监测美国年轻人的T1D和T2D负担。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，NY和TX）的约600万种族，民族和社会经济多样化的年轻人 3普查地区。本研究中的患者人群大致代表了 集水区。本研究的具体目的是：1）估计美国T1D和T2D的患病率 2019年按年龄、性别、种族/民族和地理区域列出的年轻人; 2）估计T1D的发病率 2019年按年龄、性别、种族/民族和地理区域划分的美国年轻人中的T2D; 3）估计10年 按年龄、性别、人种/种族列出的美国年轻成人中T1D和T2D的患病率和发病率趋势，以及 地理区域，2014 - 2023年;和4）按类型比较糖尿病的患病率和发病率，以及 时间趋势，在美国的年轻人与其他国家和地区的年轻人。本研究 创新，因为它将检测到低至0.2%的假阴性率，利用EHR进行监测（更多 效率和成本效益高于登记册和调查），使用先进的统计方法（例如，机 学习和自然语言处理），使用患者邮政编码估计分母， 监测方法根据当地临床记录的可用性，并使用两阶段抽样方法 提高病历审核效率。这项研究将促进我们对年龄，性别，种族/民族， 美国年轻人T1D和T2D负担的地理差异。获得的监测数据将 为医疗保健需求规划提供信息，优先分配医疗保健资源，并减少健康 通过识别和优先考虑亚群来预防糖尿病和相关合并症，

Approximately 3 million young adults aged 18-44 years currently have diabetes in the United States. This number is projected to increase to ~5.8 million by 2060. Differentiating diabetes types is crucial, because the etiology, treatments, and outcomes of diabetes differ substantially by type. Type 1 diabetes (T1D) accounts for ~17% and type 2 diabetes (T2D) ~75% of total diabetes in US young adults. This distribution of diabetes types continuously evolves. We do not have a large-scale surveillance system to monitor the prevalence and incidence of T1D and T2D in US young adults. The widespread use and increasing functionality of electronic health record (EHR) systems substantially increase the quantity, breadth, and timeliness of data available for surveillance and reduce costs compared with population-based registries and surveys. 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 young adults. We will collaborate with 3 EHR research networks from the National Patient-Centered Clinical Research Network (PCORnet), covering ~6 million racially, ethnically, and socioeconomically diverse young adults 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 young adults by age, sex, race/ethnicity, and geographic region in 2019; 2) to estimate the incidence of T1D and T2D in US young adults 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 young adults 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 young adults with those in young adults from other countries and regions. This study is innovative, because it will detect a false negative rate as low as 0.2%, 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. This study will advance our understanding of the age, sex, racial/ethnic, and geographic differences in the burden of T1D and T2D in US young adults. The obtained surveillance data will inform planning for healthcare needs, prioritize the allocation of healthcare resources, and reduce health disparities via identifying and prioritizing subpopulations for prevention of diabetes and related comorbidities.