Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report

BMC Public Health

Research Article

Elizabeth Muggah¹  Douglas G Manuel²  Erin Graves³  Carol Bennett³ 

[1] C.T. Lamont Primary Health Care Research Centre, Élisabeth Bruyère Research Institute, Ottawa, Ontario, Canada;Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada;C.T. Lamont Primary Health Care Research Centre, Élisabeth Bruyère Research Institute, Ottawa, Ontario, Canada;Ottawa Hospital Research Institute, Ottawa, Ontario, Canada;Institute for Clinical Evaluative Sciences, Ottawa and Toronto, Ontario, Canada;Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada;Department of Family Medicine and Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada;Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada;Ottawa Hospital Research Institute, Ottawa, Ontario, Canada;Institute for Clinical Evaluative Sciences, Ottawa and Toronto, Ontario, Canada;

关键词: Chronic disease ascertainment;    Health administrative data;    Chronic diseases;    Population health survey;   

DOI  :  10.1186/1471-2458-13-16

 received in 2012-06-26, accepted in 2012-12-27,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundHealth administrative data is increasingly being used for chronic disease surveillance. This study explored agreement between administrative and survey data for ascertainment of seven key chronic diseases, using individually linked data from a large population of individuals in Ontario, Canada.MethodsAll adults who completed any one of three cycles of the Canadian Community Health Survey (2001, 2003 or 2005) and agreed to have their responses linked to provincial health administrative data were included. The sample population included 85,549 persons. Previously validated case definitions for myocardial infarction, asthma, diabetes, chronic lung disease, stroke, hypertension and congestive heart failure based on hospital and physician billing codes were used to identify cases in health administrative data and these were compared with self-report of each disease from the survey. Concordance was measured using the Kappa statistic, percent positive and negative agreement and prevalence estimates.ResultsAgreement using the Kappa statistic was good or very good (kappa range: 0.66-0.80) for diabetes and hypertension, moderate for myocardial infarction and asthma and poor or fair (kappa range: 0.29-0.36) for stroke, congestive heart failure and COPD. Prevalence was higher in health administrative data for all diseases except stroke and myocardial infarction. Health Utilities Index scores were higher for cases identified by health administrative data compared with self-reported data for some chronic diseases (acute myocardial infarction, stroke, heart failure), suggesting that administrative data may pick up less severe cases.ConclusionsIn the general population, discordance between self-report and administrative data was large for many chronic diseases, particularly disease with low prevalence, and differences were not easily explained by individual and disease characteristics.

【 授权许可】

CC BY   
© Muggah et al.; licensee BioMed Central Ltd. 2013