{"id":23466,"date":"2026-03-19T23:12:11","date_gmt":"2026-03-20T03:12:11","guid":{"rendered":"https:\/\/www.ices.on.ca\/?post_type=journal_article&#038;p=23466"},"modified":"2026-03-31T23:19:13","modified_gmt":"2026-04-01T03:19:13","slug":"prevalence-and-incidence-of-treatment-range-adolescent-idiopathic-scoliosis-diagnosed-in-ontario-between-2012-and-2021","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/prevalence-and-incidence-of-treatment-range-adolescent-idiopathic-scoliosis-diagnosed-in-ontario-between-2012-and-2021\/","title":{"rendered":"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"},"content":{"rendered":"<p><strong>Purpose<\/strong> \u2014 The purpose of this study is to estimate the prevalence and incidence of treatment-range adolescent idiopathic scoliosis (\u2265 20\u00b0) over 10-years in Ontario youth 10\u2013 17 years of age, by validating a population-based health administrative data algorithm for case ascertainment.<\/p>\n<p><strong>Patients and methods<\/strong> \u2014 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\u2019 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\u2013sex interaction. Significance was accepted at p &lt; 0.05.<\/p>\n<p><strong>Results<\/strong> \u2014 The AIS+ cohort had a median curve magnitude of 35\u00b0 (interquartile range: 25.5\u2013 45.5). Of the 93 algorithms tested, the top was \u201c 2 physician billing codes for scoliosis in 2 years\u201d with sensitivity: 83.1% (95% CI, 81.6\u2013 84.5%), specificity: 99.3% (95% CI, 99.2\u2013 99.3%), positive predictive value: 86.3% (95% CI, 85.0\u2013 87.6%), negative predictive value: 99.1% (95% CI, 99.0\u2013 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.<\/p>\n<p><strong>Conclusion<\/strong> \u2014 The selected health administrative data algorithm demonstrated excellent diagnostic accuracy in identifying radiographically confirmed, treatment-range adolescent idiopathic scoliosis in 10\u2013 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Purpose \u2014 The purpose of this study is to estimate the prevalence and incidence of treatment-range adolescent idiopathic scoliosis (\u2265 20\u00b0) over 10-years in Ontario youth 10\u2013 17 years of age, by validating a population-based health administrative data algorithm for case ascertainment. Patients and methods \u2014 Algorithms were developed using a combination of health administrative [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[16,59,51],"migration-helper-qa-sample-set":[],"class_list":["post-23466","journal_article","type-journal_article","status-publish","hentry","topic-children-and-young-people","topic-data-science","topic-musculoskeletal-diseases-and-disorders"],"acf":{"citation":"Dermott JA, To T, Jaakkimainen L, Bouchard M, Howard A, Lebel DE. <em>Clin Epidemiol<\/em>. 2026; 18:578144.","source_url":"https:\/\/doi.org\/10.2147\/clep.s578144","ices_scientist":[1111,1283,1262],"site":[6735],"research_program":[6745],"news_release":"","journal_article":"","atlas":"","research_report":"","infographic":"","video":"","downloads":null,"links":null,"sitecore_item_id":"","sitecore_item_name":"","sitecore_field_values":"","previous_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO 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