Validating ICD coding algorithms for diabetes mellitus from administrative data

被引:130
作者
Chen, Guanmin [1 ]
Khan, Nadia [2 ]
Walker, Robin [1 ]
Quan, Hude [1 ]
机构
[1] Univ Calgary, Dept Community Hlth Sci, Calgary, AB T2N 4N1, Canada
[2] Univ British Columbia, Dept Med, Vancouver, BC V5Z 1M9, Canada
基金
加拿大健康研究院;
关键词
Validity; Administrative data; Diabetes; ICD; Surveillance; PUBLIC-HEALTH; POPULATION; PREVALENCE; CARE;
D O I
10.1016/j.diabres.2010.03.007
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Aim: To assess validity of diabetes International Classification of Disease (ICD) 9 and 10 coding algorithms from administrative data using physicians' charts as the 'gold standard' across time periods and geographic regions. Methods: From 48 urban and 16 rural general practitioners' clinics in Alberta and British Columbia, Canada, we randomly selected 50 patient charts/clinic for those who visited the clinic in either 2001 or 2004. Reviewed chart data were linked with inpatient discharge abstract and physician claims administrative data. We identified patients with diabetes in the administrative databases using ICD-9 code 250.xx and ICD-10 codes E10.x-E14.x. Results: The prevalence of diabetes was 8.1% among clinic charts. The coding algorithm of "2 physician claims within 2 years or 1 hospitalization with the relevant diabetes ICD codes" had higher validity than other 7 algorithms assessed (sensitivity 92.3%, specificity 96.9%, positive predictive value 77.2%, and negative predictive value 99.3%). After adjustment for age, sex, and comorbid conditions, sensitivity and positive predictive values were not significantly different between time periods and regions. Conclusion: Diabetes could be accurately identified in administrative data using the following case definition "2 physician claims within 2 years or 1 hospital discharge abstract record with diagnosis codes 250.xx or E10.x-E14.x". (C) 2010 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:189 / 195
页数:7
相关论文
共 15 条
[1]   REVISITING THE BEHAVIORAL-MODEL AND ACCESS TO MEDICAL-CARE - DOES IT MATTER [J].
ANDERSEN, RM .
JOURNAL OF HEALTH AND SOCIAL BEHAVIOR, 1995, 36 (01) :1-10
[2]  
[Anonymous], 2003, The Statistical Evaluation of Medical Tests for Classification and Prediction
[3]   The accuracy of population health data for monitoring trends and outcomes among women with diabetes in pregnancy [J].
Bell, Jane C. ;
Ford, Jane B. ;
Cameron, Carolyn A. ;
Roberts, Christine L. .
DIABETES RESEARCH AND CLINICAL PRACTICE, 2008, 81 (01) :105-109
[4]   Full Accounting of Diabetes and Pre-Diabetes in the US Population in 1988-1994 and 2005-2006 [J].
Cowie, Catherine C. ;
Rust, Keith F. ;
Ford, Earl. S. ;
Eberhardt, Mark S. ;
Byrd-Holt, Danita D. ;
Li, Chaoyang ;
Williams, Desmond E. ;
Gregg, Edward W. ;
Bainbridge, Kathleen E. ;
Saydah, Sharon H. ;
Geiss, Linda S. .
DIABETES CARE, 2009, 32 (02) :287-294
[5]   Public health - Diabetes and disease surveillance [J].
Fairchild, Amy L. .
SCIENCE, 2006, 313 (5784) :175-176
[6]   New York city's initiatives on diabetes and HIV/AIDS: Implications for patient care, public health, and medical professionalism [J].
Goldman, Janlori ;
Kinnear, Sydrey ;
Chung, Jeannie ;
Rothman, David J. .
AMERICAN JOURNAL OF PUBLIC HEALTH, 2008, 98 (05) :807-813
[7]   Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children [J].
Guttmann, Astrid ;
Nakhla, Meranda ;
Henderson, Melanie ;
To, Teresa ;
Daneman, Denis ;
Cauch-Dudek, Karen ;
Wang, Xuesong ;
Lam, Kelvin ;
Hux, Jan .
PEDIATRIC DIABETES, 2010, 11 (02) :122-128
[8]   Diabetes in Ontario - Determination of prevalence and incidence using a validated administrative data algorithm [J].
Hux, JE ;
Flintoft, V ;
Ivis, F ;
Bica, A .
DIABETES CARE, 2002, 25 (03) :512-516
[9]   Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995-2005: a population-based study [J].
Lipscombe, Lorraine L. ;
Hux, Janet E. .
LANCET, 2007, 369 (9563) :750-756
[10]  
LIX L, 2004, POLICY MCFH