Prevalence and incidence of treatment-range adolescent idiopathic scoliosis diagnosed in Ontario between 2012 and 2021: Validating a population-based health administrative algorithm for case identification in youth aged 10-17 years

Purpose — The purpose of this study is to estimate the prevalence and incidence of treatment-range adolescent idiopathic scoliosis (≥ 20°) over 10-years in Ontario youth 10– 17 years of age, by validating a population-based health administrative data algorithm for case ascertainment.

Patients and methods — Algorithms were developed using a combination of health administrative data: diagnostic, fee and/or specialty codes from physician billing data over various look-back periods. Algorithms’ ability to distinguish between youth with scoliosis, confirmed by a tertiary-care spine specialist (AIS+; n = 2732), and a provincially derived comparator group without (AIS-; n = 49,049) were evaluated using sensitivity, specificity, positive and negative predictive values with their 95% confidence intervals. The top performing algorithm was used to estimate sex- and age-standardized prevalence and incidence between 2012 and 21. Annual rate ratios were calculated using a negative binomial regression model, adjusted for age, sex, and age–sex interaction. Significance was accepted at p < 0.05.

Results — The AIS+ cohort had a median curve magnitude of 35° (interquartile range: 25.5– 45.5). Of the 93 algorithms tested, the top was “ 2 physician billing codes for scoliosis in 2 years” with sensitivity: 83.1% (95% CI, 81.6– 84.5%), specificity: 99.3% (95% CI, 99.2– 99.3%), positive predictive value: 86.3% (95% CI, 85.0– 87.6%), negative predictive value: 99.1% (95% CI, 99.0– 99.1%). Annual prevalence estimates averaged 513.3/100 000 and incidence 128.2/100 000. There was a modest annual increase in the adjusted rate ratios: 1% for prevalence, 2% for incidence. Rates were highest for females at 13-years and males at 15-years of age, with rates 65% lower for males.

Conclusion — The selected health administrative data algorithm demonstrated excellent diagnostic accuracy in identifying radiographically confirmed, treatment-range adolescent idiopathic scoliosis in 10– 17-year-old youth. This is an efficient and scalable method for clinically meaningful population-level cohort creation that will facilitate surveillance of scoliosis diagnostic and treatment trends and longitudinal outcome research.