Clinical epidemiology · 2026
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.
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Dermott Jennifer A, To Teresa, Jaakkimainen Liisa, Bouchard Maryse, Howard Andrew, Lebel David E
Аннотация
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.
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.