For example, such studies are important complements to randomized clinical trials that study the benefits and risks of pain pharmacotherapy (e.g., larger sample sizes, possibility to study long-term effects, greater external validity). In the field of chronic noncancer pain (CNCP), longitudinal population-based studies representative of the real-world clinical context are clearly needed.
Using validated algorithms to identify and characterize specific patient populations is thus a priority in order to reduce bias in administrative database studies. Moreover, the validity of a given algorithm may vary from one data source to another due to variability of database completeness across jurisdictions (e.g., remuneration methods, diagnostic code types and specificity, number of diagnostic fields). Such codes are sometimes misclassified, and it has been shown that their validity varies according to the characteristics of patients, clinical conditions, health care encounters, physicians, and their billing practices.
In fact, many administrative databases used for research purposes are by-products of physician billing claims in the context of which it is diagnostic codes are entered, for example, according to the International Classification of Diseases (ICD codes). However, the validity of studies conducted with these data sources relies greatly on the accuracy of the diagnostic information used to create or characterize cohorts of patients suffering from particular health disorders. Health administrative databases are commonly used to conduct epidemiologic, pharmacoepidemiologic, and pharmacoeconomic research and are indispensable to enrich our understanding of health trajectories, health care utilization, and real-world risks and benefits of drugs among large populations. Chronic Pain, Algorithms, Diagnostic Codes, Validity, Accuracy, Sensitivity, Specificity, Administrative Databases, Claims, Back Pain, Neck Pain, Neuropathic Pain, Complex Regional Pain Syndrome, Fibromyalgia, Headache, Migraine Introduction
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