Accuracy of administrative claims data for cerebral palsy diagnosis: a retrospective cohort study.

نویسندگان

  • Maryam Oskoui
  • Pamela Ng
  • Marc Dorais
  • Nicole Pigeon
  • Louise Koclas
  • Céline Lamarre
  • Francine Malouin
  • Carol L Richards
  • Michael Shevell
  • Lawrence Joseph
چکیده

BACKGROUND Cerebral palsy is the most common cause of childhood physical disability, with multiple associated comorbidities. Administrative claims data provide population-level prevalence estimates for cerebral palsy surveillance; however, their diagnostic accuracy has never been validated in Quebec. This study aimed to assess the accuracy of administrative claims data for the diagnosis of cerebral palsy. METHODS We conducted a retrospective cohort study of children with cerebral palsy born between 1999 and 2002 within 6 health administrative regions of Quebec. Provincial cerebral palsy registry data (reference standard) and administrative physician claims were linked. We explored differences between true-positive and false-negative cases using subgroup sensitivity analysis. RESULTS A total of 301 children were identified with confirmed cerebral palsy from the provincial registry, for an estimated prevalence of 1.8 (95% confidence interval [CI] 1.6-2.1) per 1000 children 5 years of age. The sensitivity and specificity of administrative claims data for cerebral palsy were 65.5% (95% CI 59.8%-70.8%) and 99.9% (95% CI 99.9%-99.9%), respectively, yielding a prevalence of 2.0 (95% CI 1.9-2.3) per 1000 children 5 years of age. The positive and negative predictive values were 58.8% (95% CI 53.3%-64.1%) and 99.9% (95% CI 99.9%-99.9%), respectively. The κ value was 0.62 (95% CI 0.57-0.67). Administrative claims data were more sensitive for children from rural regions, born preterm, with spastic quadriparesis and with higher levels of motor impairment. INTERPRETATION Administrative claims data do not capture the full spectrum of children with cerebral palsy. This suggests the need for a more sensitive case definition and caution when using such data without validation.

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عنوان ژورنال:
  • CMAJ open

دوره 5 3  شماره 

صفحات  -

تاریخ انتشار 2017