Identifying cases of chronic pain using health administrative data: A validation study

Autor: Shabnam Asghari, Michelle Ploughman, John Knight, Heather E Foley, Rick Audas
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Canadian Journal of Pain = Revue canadienne de la douleur
article-version (VoR) Version of Record
Canadian Journal of Pain, Vol 4, Iss 1, Pp 252-267 (2020)
ISSN: 2474-0527
Popis: Background: Chronic pain is a pervasive and challenging public health issue. Prevalence estimates vary widely, both globally (2-54%) and in Canada (6.5-44%). Most estimates are derived from surveys, which are expensive and labour intensive. Health administrative data is a potential easily-accessed, low-cost method to obtain epidemiological and cost estimates of chronic pain, but its validity as a source to ascertain cases of this complex and multi-faceted condition is unknown. The purpose of this study was to derive and validate an algorithm to identify cases of chronic pain using health administrative data.Methods: A reference standard was developed and applied to the electronic medical records data of a general population sample participating in the Canadian Primary Care Sentinel Surveillance Network. Preliminary chronic pain algorithms were then created and refined through health administrative data analysis of four populations with known chronic pain diagnoses. Classification performance of the chronic pain administrative data algorithms was compared to that of the reference standard, and statistical tests of selection accuracy (sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio positive, likelihood ratio negative, diagnostic odds ratio, Kappa agreement, and area under the Receiver Operating Characteristic curve) were calculated. Results: The optimal algorithm to ascertain cases of chronic pain from health administrative data was a combination of one chronic pain clinic procedure code OR five physician claims with a pain-related diagnostic code in five years with more than 183 days separating at least two claims. The sensitivity was 0.703 (0.684-0.721 95% confidence interval), the specificity was 0.668 (0.657-0.679 95% confidence interval), and the positive predictive value was 0.408(0.398-0.418 95% confidence interval). The Chronic Pain Algorithm identified 37.6% of a Newfoundland and Labrador provincial cohort. Conclusions: Health administrative data is a valid source for information on chronic pain in Canada. The optimal chronic pain algorithm can be used to determine epidemiology and health care utilization statistics on people seeking physician care for chronic pain, and to evaluate cost effectiveness of any change to policy or health service delivery.
Databáze: OpenAIRE
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