The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records

被引:1
作者
Li, Piaopiao [1 ,2 ]
Lyu, Tianchen [3 ]
Alkhuzam, Khalid [1 ]
Spector, Eliot [3 ]
Donahoo, William T. [4 ]
Bost, Sarah [3 ]
Wu, Yonghui [3 ]
Hogan, William R. [3 ]
Prosperi, Mattia [5 ]
Schatz, Desmond A. [6 ]
Atkinson, Mark A. [7 ]
Haller, Michael J. [6 ]
Shenkman, Elizabeth A. [3 ]
Guo, Yi [3 ]
Bian, Jiang [3 ]
Shao, Hui [1 ,2 ,8 ,9 ,10 ]
机构
[1] Univ Florida, Coll Pharm, Dept Pharmaceut Outcomes & Policy, Gainesville, FL USA
[2] Emory Univ, Rollins Sch Publ Hlth, Hubert Dept Global Hlth, Atlanta, GA 30322 USA
[3] Univ Florida, Coll Med, Dept Hlth Outcomes & Biomed Informat, Gainesville, FL 32609 USA
[4] Univ Florida, Coll Med, Div Endocrinol Diabet & Metab, Jacksonville, FL USA
[5] Univ Florida, Coll Publ Hlth & Hlth Profess, Dept Epidemiol, Gainesville, FL USA
[6] Univ Florida, Coll Med, Dept Pediat, Gainesville, FL USA
[7] Univ Florida, Diabet Inst, Gainesville, FL USA
[8] Univ Florida, Ctr Drug Evaluat & Safety, Gainesville, FL USA
[9] Emory Univ, Sch Med, Dept Family & Prevent Med, Atlanta, GA USA
[10] Emory Rollins Sch Publ Hlth, Hubert Dept Global Hlth, 1518 Clifton Rd,NE,CNR Room 7041, Atlanta, GA 30322 USA
关键词
electronic health records; health system penetration rate; type; 1; diabetes; prevalence; disease surveillance; children and adolescent; NATIONAL-HEALTH; UNITED-STATES; RISK-FACTORS; TRENDS; CARE; US; DISPARITIES; HISPANICS; NUTRITION; ETHNICITY;
D O I
10.1093/jamia/ocad194
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Objective Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence.Materials and methods We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study.Results Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019.Conclusion Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance.
引用
收藏
页码:165 / 173
页数:9
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