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Validation of algorithms to determine incidence of Hirschsprung disease in Ontario, Canada: a population-based study using health administrative data

Authors Nasr A, Sullivan KJ, Chan EW, Wong CA, Benchimol EI

Received 11 August 2017

Accepted for publication 3 October 2017

Published 14 November 2017 Volume 2017:9 Pages 579—590

DOI https://doi.org/10.2147/CLEP.S148890

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 2

Editor who approved publication: Professor Henrik Toft Sørensen


Ahmed Nasr,1,2 Katrina J Sullivan,1 Emily W Chan,1 Coralie A Wong,3 Eric I Benchimol2–5

1Department of Pediatric Surgery, Children’s Hospital of Eastern Ontario, 2Faculty of Medicine, University of Ottawa, 3Institute for Clinical Evaluative Science (ICES University of Ottawa), 4CHEO Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, 5School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada

Objective: Incidence rates of Hirschsprung disease (HD) vary by geographical region, yet no recent population-based estimate exists for Canada. The objective of our study was to validate and use health administrative data from Ontario, Canada to describe trends in incidence of HD between 1991 and 2013.
Study design: To identify children with HD we tested algorithms consisting of a combination of diagnostic, procedural, and intervention codes against the reference standard of abstracted clinical charts from a tertiary pediatric hospital. The algorithm with the highest positive predictive value (PPV) that could maintain high sensitivity was applied to health administrative data from April 31, 1991 to March 31, 2014 (fiscal years 1991–2013) to determine annual incidence. Temporal trends were evaluated using Poisson regression, controlling for sex as a covariate.
Results: The selected algorithm was highly sensitive (93.5%) and specific (>99.9%) with excellent predictive abilities (PPV 89.6% and negative predictive value >99.9%). Using the algorithm, a total of 679 patients diagnosed with HD were identified in Ontario between 1991 and 2013. The overall incidence during this time was 2.05 per 10,000 live births (or 1 in 4,868 live births). The incidence did not change significantly over time (odds ratio 0.998, 95% confidence interval 0.983–1.013, p = 0.80).
Conclusion: Ontario health administrative data can be used to accurately identify cases of HD and describe trends in incidence. There has not been a significant change in HD incidence over time in Ontario between 1991 and 2013.

Keywords: Hirschsprung disease, algorithm validation, incidence, health administrative data

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