OBJECTIVE: In large cohort studies comorbidities are usually self-reported by the patients. This way to collect health information only represents conditions known, memorized and openly reported by the patients. Several studies addressed the relationship between self-reported comorbidities and medical records or pharmacy data, but none of them provided a structured, documented method of evaluation. We thus developed a detailed procedure to compare self-reported comorbidities with information on comorbidities derived from medication inspection. This was applied to the data of the German COPD cohort COSYCONET. METHODS: Approach I was based solely on ICD10-Codes for the diseases and the indications of medications. To overcome the limitations due to potential non-specificity of medications, Approach II was developed using more detailed information, such as ATC-Codes specific for one disease. The relationship between reported comorbidities and medication was expressed by a four-level concordance score. RESULTS: Approaches I and II demonstrated that the patterns of concordance scores markedly differed between comorbidities in the COSYCONET data. On average, Approach I resulted in more than 50% concordance of all reported diseases to at least one medication. The more specific Approach II showed larger differences in the matching with medications, due to large differences in the disease-specificity of drugs. The highest concordance was achieved for diabetes and three combined cardiovascular disorders, while it was substantial for dyslipidemia and hyperuricemia, and low for asthma. CONCLUSION: Both approaches represent feasible strategies to confirm self-reported diagnoses via medication. Approach I covers a broad spectrum of diseases and medications but is limited regarding disease-specificity. Approach II uses the information from medications specific for a single disease and therefore can reach higher concordance scores. The strategies described in a detailed and reproducible manner are generally applicable in large studies and might be useful to extract as much information as possible from the available data.
- Lucke, T.
- Herrera, R.
- Wacker, M.
- Holle, R.
- Biertz, F.
- Nowak, D.
- Huber, R. M.
- Sohler, S.
- Vogelmeier, C.
- Ficker, J. H.
- Muckter, H.
- Jorres, R. A.
- Cosyconet Consortium
Keywords
- Aged
- Cohort Studies
- *Comorbidity
- Data Collection
- Drug Therapy/statistics & numerical data
- Humans
- International Classification of Diseases
- Middle Aged
- Pulmonary Disease, Chronic Obstructive/drug therapy/epidemiology
- *Self Report