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Validation of Canadian health administrative data algorithms for estimating trends in the incidence and prevalence of osteoarthritis


Objective — To estimate the 1) accuracy of algorithms for identifying osteoarthritis (OA) using health administrative data; and 2) population-level OA prevalence and incidence over time in Ontario, Canada.

Method — We performed a retrospective chart abstraction study to identify OA patients in a random sample of 7500 primary care patients from electronic medical records. The validation sample was linked with several administrative data sources. Accuracy of administrative data algorithms for identifying OA was tested against two reference standard definitions by estimating the sensitivity, specificity and predictive values. The validated algorithms were then applied to the Ontario population to estimate and compare population-level prevalence and incidence from 2000 to 2017.

Results — OA prevalence within the validation sample ranged from 10% to 23% across the two reference standards. Algorithms varied in accuracy depending on the reference standard, with the sensitivity highest (77%) for patients with OA documented in medical problem lists. Using the top performing administrative data algorithms, the crude population-level OA prevalence ranged from 11% to 25% and standardized prevalence ranged from 9 to 22% in 2017. Over time, prevalence increased whereas incidence remained stable (∼1% annually).

Conclusion — Health administrative data have limited sensitivity in adequately identifying all OA patients and appear to be more sensitive at detecting OA patients for whom their physician formally documented their diagnosis in medical problem lists than individuals who have their diagnosis documented outside of problem lists. Irrespective of the algorithm used, OA prevalence has increased over the past decade while annual incidence has been stable.



Widdifield J, Jaakkimainen RL, Gatley JM, Hawker GA, Lix LM, Bernatsky S, Ravi B, Wasserstein D, Yu B, Tu K. Osteoarthr Cartil Open. 2020; 2(4):100115. Epub 2020 Nov 4.

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