Email updates

Keep up to date with the latest news and content from BMC Public Health and BioMed Central.

Open Access Research article

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

Elizabeth Muggah12*, Erin Graves34, Carol Bennett34 and Douglas G Manuel134567

Author affiliations

1 C.T. Lamont Primary Health Care Research Centre, Élisabeth Bruyère Research Institute, Ottawa, Ontario, Canada

2 Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada

3 Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

4 Institute for Clinical Evaluative Sciences, Ottawa and Toronto, Ontario, Canada

5 Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada

6 Department of Family Medicine and Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada

7 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

For all author emails, please log on.

Citation and License

BMC Public Health 2013, 13:16  doi:10.1186/1471-2458-13-16

Published: 9 January 2013

Abstract

Background

Health 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.

Methods

All 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.

Results

Agreement 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.

Conclusions

In 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.

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