外部验证和应用糖尿病人口风险工具 (DPoRT) 预测美国人口中 2 型糖尿病的发病情况

IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM BMJ Open Diabetes Research & Care Pub Date : 2024-03-01 DOI:10.1136/bmjdrc-2023-003905
Kathy Kornas, Christopher Tait, Ednah Negatu, Laura C Rosella
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引用次数: 0

摘要

导言:描述人群中的糖尿病风险对于人群健康评估和糖尿病预防规划非常重要。我们旨在从外部验证美国现有的 2 型糖尿病 10 年人口风险模型,并利用人口调查数据建立糖尿病预防方法的人口受益模型。研究设计和方法 糖尿病人群风险工具(DPoRT)最初是在加拿大衍生和验证的,我们将其应用于 2009 年全国健康访谈调查(NHIS)中的 23 477 名成年人组成的外部验证队列。我们根据 2009-2018 年国家健康访谈调查周期中发现的糖尿病病例,评估了分辨力(C 统计量)和校准图的预测性能。我们将 DPoRT 应用于 2018 年 NHIS 队列(n=21 187),以生成 10 年风险预测估计值,并描述三种糖尿病预防方案的预防效益:(1)全社区策略;(2)高风险策略;(3)综合方法。结果 DPoRT 在整个风险范围内表现出良好的区分度(C 统计量=0.778(男性);0.787(女性))和校准性。我们预测 2018 年至 2028 年美国糖尿病的基线风险为 10.2%,新增病例为 21 076 000 例。全社区策略和高风险策略估计糖尿病风险分别降低了 0.2% 和 0.3%。综合方法估计可降低 0.4% 的风险,并在 10 年内避免 843 000 例糖尿病病例。结论 DPoRT 具有可移植性,可利用日常收集的调查数据预测美国人群的糖尿病风险。我们展示了该模型在人口健康评估和糖尿病预防规划方面的适用性。我们的模型预测,要减轻美国的糖尿病负担,需要将整个社区和针对高危人群的针对性预防方法结合起来。数据可在公开、开放的资源库中获取。NHIS 数据可从 www.cdc.gov/nchs/nhis/index.htm 获取。
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External validation and application of the Diabetes Population Risk Tool (DPoRT) for prediction of type 2 diabetes onset in the US population
Introduction Characterizing diabetes risk in the population is important for population health assessment and diabetes prevention planning. We aimed to externally validate an existing 10-year population risk model for type 2 diabetes in the USA and model the population benefit of diabetes prevention approaches using population survey data. Research design and methods The Diabetes Population Risk Tool (DPoRT), originally derived and validated in Canada, was applied to an external validation cohort of 23 477 adults from the 2009 National Health Interview Survey (NHIS). We assessed predictive performance for discrimination (C-statistic) and calibration plots against observed incident diabetes cases identified from the NHIS 2009–2018 cycles. We applied DPoRT to the 2018 NHIS cohort (n=21 187) to generate 10-year risk prediction estimates and characterize the preventive benefit of three diabetes prevention scenarios: (1) community-wide strategy; (2) high-risk strategy and (3) combined approach. Results DPoRT demonstrated good discrimination (C-statistic=0.778 (males); 0.787 (females)) and good calibration across the range of risk. We predicted a baseline risk of 10.2% and 21 076 000 new cases of diabetes in the USA from 2018 to 2028. The community-wide strategy and high-risk strategy estimated diabetes risk reductions of 0.2% and 0.3%, respectively. The combined approach estimated a 0.4% risk reduction and 843 000 diabetes cases averted in 10 years. Conclusions DPoRT has transportability for predicting population-level diabetes risk in the USA using routinely collected survey data. We demonstrate the model’s applicability for population health assessment and diabetes prevention planning. Our modeling predicted that the combination of community-wide and targeted prevention approaches for those at highest risk are needed to reduce diabetes burden in the USA. Data are available in a public, open access repository. The NHIS data are available from www.cdc.gov/nchs/nhis/index.htm.
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来源期刊
BMJ Open Diabetes Research & Care
BMJ Open Diabetes Research & Care Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
9.30
自引率
2.40%
发文量
123
审稿时长
18 weeks
期刊介绍: BMJ Open Diabetes Research & Care is an open access journal committed to publishing high-quality, basic and clinical research articles regarding type 1 and type 2 diabetes, and associated complications. Only original content will be accepted, and submissions are subject to rigorous peer review to ensure the publication of high-quality — and evidence-based — original research articles.
期刊最新文献
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