{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:45:36Z","timestamp":1782485136382,"version":"3.54.5"},"reference-count":13,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Epidemiol."],"abstract":"<jats:p>Population-based studies of non-cancer chronic disease often rely on self-reported data for disease diagnosis, which may be incomplete, unreliable and suffer from bias. Recently, the British Columbia Generations Project (BCGP; <jats:italic>n<\/jats:italic>\u2009=\u200929,736) linked self-reported chronic disease history data to a Chronic Disease Registry (CDR) that applied algorithms to administrative health data to ascertain diagnoses of multiple chronic diseases in the Province of British Columbia. For the 10 diseases captured by both self-report and the CDR, including asthma, chronic obstructive pulmonary disease (COPD), diabetes, hypertension, multiple sclerosis, myocardial infarction, osteoarthritis, osteoporosis, rheumatoid arthritis, and stroke, we calculated Cohen's kappa coefficient to examine concordance of chronic disease status (i.e., ever\/never diagnosed) between the data sources. Using CDR data as the gold standard, we also calculated sensitivity, specificity, and positive-predictive value (PPV) for self-reported chronic disease occurrence. The prevalence of each chronic disease was similar across both data sources. Substantial levels of concordance (0.66\u20130.73) and moderate to high sensitivities (0.64\u20130.92), specificities (0.98\u20130.99) and PPVs (0.55\u20130.84) were observed for diabetes, hypertension, multiple sclerosis, and myocardial infarction. We did observe degree of concordance to vary by age, sex, body mass index (BMI), health perception, and ethnicity across most of the chronic diseases that were evaluated. While administrative health data are imperfect, they are less likely to suffer from bias, making them a reasonable gold standard. Our results demonstrate that for at least some chronic diseases, self-report may be a reasonable method for case ascertainment. However, characteristics of the study population will likely have impacts on the quality of the data.<\/jats:p>","DOI":"10.3389\/fepid.2022.1054485","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T05:28:46Z","timestamp":1671600526000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Agreement between self-report and administrative health data on occurrence of non-cancer chronic disease among participants of the BC generations project"],"prefix":"10.3389","volume":"2","author":[{"given":"Maryam","family":"Darvishian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Simkin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Woods","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parveen","family":"Bhatti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyy160","article-title":"Cohort profile: the British columbia generations project (BCGP)","year":"2018","journal-title":"Int J Epidemiol"},{"key":"B2","year":""},{"key":"B3","year":""},{"key":"B4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"B5","doi-asserted-by":"publisher","first-page":"A126","DOI":"10.1002\/art.20827","article-title":"Accuracy of veterans affairs databases for diagnoses of chronic diseases","volume":"6","author":"Singh","year":"2009","journal-title":"Prev Chronic Dis"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.23889\/ijpds.v5i1.1155","article-title":"Is there an agreement between self-reported medical diagnosis in the CARTaGENE cohort and the Qu\u00e9bec administrative health databases?","author":"Payette","year":"","journal-title":"Int J Popul Data Sci"},{"key":"B7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.24095\/hpcdp.29.1.04","article-title":"Population-based data sources for chronic disease surveillance","volume":"29","year":"2008","journal-title":"Chronic Dis Can"},{"key":"B8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1471-2458-13-16","article-title":"Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report","volume":"13","author":"Muggah","year":"2013","journal-title":"BMC Public Health"},{"key":"B9","doi-asserted-by":"publisher","first-page":"e61","DOI":"10.1097\/MLR.0000000000000837","article-title":"Predicting high health care resource utilization in a single-payer public health care system: development and validation of the high resource user population risk tool","volume":"56","year":"2018","journal-title":"Med Care"},{"key":"B10","doi-asserted-by":"publisher","first-page":"3527","DOI":"10.1038\/s41598-019-40072-0","article-title":"Comparison of self-reported and register-based hospital medical data on comorbidities in women","volume":"9","year":"2019","journal-title":"Sci. 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