Abstract
OBJECTIVES: Our primary objectives were (1) to develop and validate an administrative data algorithm for the identification of hand trauma cases using clinical diagnoses documented in medical records as the reference standard and (2) to estimate the incidence of hand trauma in a universal public healthcare system from 1993 to 2023 using a population-based research cohort constructed using a validated case identification algorithm. DESIGN: A population-based retrospective validation study. SETTING: Ontario, Canada, from 2022 to 2023 (validation) and from 1993 to 2023 (estimation). PARTICIPANTS: Our reference standard was the known hand trauma status of 301 patients (N=147 with hand trauma) who presented to an urban tertiary-care hand trauma centre in Toronto, Ontario. PRIMARY AND SECONDARY OUTCOME MEASURES: (1) The sensitivity, specificity, positive and negative predictive values of the optimal algorithm to identify hand trauma using provincial health administrative data and (2) age-standardised and sex-standardised incidence rates of hand trauma among men and women, by age, and by area of patient residence. RESULTS: The optimal algorithm had a sensitivity of 73.8% (95% CI 66.6% to 81.0%), specificity of 80.1% (95% CI 73.8% to 86.5%), positive predictive value of 78.1% (95% CI 71.2% to 85.0%) and negative predictive value of 76.1% (95% CI 69.5% to 82.7%). Over the study period, the age-standardised and sex-standardised incidence of hand trauma increased from 384 to 530 per 100 000. The greatest increase was observed in males and individuals aged 0-19 and 80+, with higher incidence rates in Southern compared with Northern Ontario. CONCLUSIONS: Our algorithm enabled identification of hand trauma cases using health administrative data suitable for population-level surveillance and health services research, revealing a rising burden of hand trauma from 1993 to 2023. These findings can support improved surveillance, resource allocation and care delivery for this public health problem.