Aim: To provide an estimate of the period prevalence of cerebral palsy (CP) in the province of Quebec. Methods: Children with CP were identified from three consecutive birth cohorts (1999–2001) from the Quebec CP Registry, covering 6 of the 17 administrative health regions of the province. Two inferential approaches were applied for period prevalence estimation, frequentist and bayesian. Results: 228 children were identified with CP. Using a frequentist approach, the overall prevalence of CP was 1.84 per 1,000 children aged 9–11 years living in those areas in 2010 (95% CI 1.60–2.08). Using a bayesian approach taking into account the uncertainty about the registry’s sensitivity in capturing all cases, the overall prevalence is higher at 2.30 per 1,000 children with a 95% CI (1.99–2.65). Conclusion: Using a bayesian approach to adjust for the registry’s known high specificity and lower sensitivity, the prevalence estimate is in concordance with worldwide estimates and estimates using administrative databases in western Canadian provinces. Future studies are needed to validate the diagnosis of CP within administrative databases and to evaluate possible regional trends across Canada in both prevalence and health service utilization, which may highlight disparities in healthcare delivery.

Keywords:
Prevalence, Registry, Cerebral palsy, Canada
1.
Hirtz D, Thurman DJ, Gwinn-Hardy K, Mohamed M, Chaudhuri AR, Zalutsky R: How common are the ‘common’ neurologic disorders? Neurology 2007;68:326–337.
2.
Strauss D, Shavelle R, Reynolds R, Rosenbloom L, Day S: Survival in cerebral palsy in the last 20 years: signs of improvement? Dev Med Child Neurol 2007;49:86–92.
3.
Hutton JL, Colver AF, Mackie PC: Effect of severity of disability on survival in north east England cerebral palsy cohort. Arch Dis Child 2000;83:468–474.
4.
Oskoui M, Shevell MI: Cerebral palsy and the transition from pediatric to adult care. Continuum Neurol 2009;15:64–77.
5.
Smith L, Kelly KD, Prkachin G, Voaklander DC: The prevalence of cerebral palsy in British Columbia, 1991–1995. Can J Neurol Sci 2008;35:342–347.
6.
Robertson CMT, Svenson LW, Joffres MR: Prevalence of cerebral palsy in Alberta. Can J Neurol Sci 1998;25(suppl 2):117–122.
7.
Robertson CMT, Watt MJ, Yasui Y: Changes in the prevalence of cerebral palsy for children born very prematurely within a population-based program over 30 years. JAMA 2007;297:2733–2740.
8.
Vincer MJ, Allen AC, Joseph KS, Stinson DA, Scott H, Wood E: Increasing prevalence of cerebral palsy among very preterm infants: a population-based study. Pediatrics 2006;118:e1621–e1626.
9.
Synnes AR, Ling EW, Whitfield MF, et al: Perinatal outcomes of a large cohort of extremely low gestational age infants (23–28 completed weeks of gestation). J Pediatr 1994;125:952–960.
10.
Lefebvre F, Glorieux J, St-Laurent-Gagnon T: Neonatal survival and disability rate at age 18 months for infants born between 23 and 28 weeks of gestation. Am J Obstet Gynecol 1996;174:833–838.
11.
Nelson KB, Ellenberg JH: Children who ‘outgrew’ cerebral palsy. Pediatrics 1982;69:529–536.
12.
Rosenbaum P, Paneth N, Leviton A, et al: A report: the definition and classification of cerebral palsy April 2006. Dev Med Child Neurol Suppl 2007;109:8–14.
13.
Palisano R, Rosenbaum P, Walter S, Russell D, Wood E, Galuppi B: Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev Med Child Neurol 1997;39:214–223.
14.
Eliasson AC, Krumlinde-Sundholm L, Rösblad B, Beckung E, Arner M, Ohrvall AM, Rosenbaum P: The Manual Ability Classification System (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability. Dev Med Child Neurol 2006;48:549–554.
15.
Lunn DJ, Thomas A, Best N, Spiegelhalter D: WinBUGS – a bayesian modelling framework: concepts, structure, and extensibility. Statist Comput 2000;10:325–337.
16.
Wyse JM, Joseph L, Barkun AN, Sewitch MJ: Accuracy of administrative claims data for polypectomy. CMAJ 2011;183:E743–E747.
17.
McV Messam LL, Branscum AJ, Collins MT, Gardner IA: Frequentist and bayesian approaches to prevalence estimation using examples from Johne’s disease. Anim Health Res Rev 2008;9:1–23.
18.
Joseph L, Gyorkos TW, Coupal L: Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. Am J Epidemiol 1995;141:263–272.
© 2013 S. Karger AG, Basel
2013
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.