Table 1.

The multivariable regression models used to assess time trends in health services and surgical outcomes among children with inflammatory bowel disease in Ontario.

Type of data	Regression model	Effect estimate	Reported measure	Population	Exposure	Outcome
Study 1: Trends in health service use in IBD patients
Count [number of events per person per year]	Negative binomiala	IRR	APC	IBD, CD, UC	Year of diagnosis [linear]	IBD-specific hospitalisations, IBD-related hospitalisations
			APC			IBD-specific ED visits, IBD-related ED visits
			APC		Year of diagnosis [knot at 2005]	IBD-specific outpatient visits, IBD-related outpatient visits
Time to first event	Cox proportional hazarda	HR	APC	IBD, CD, UC	Year of diagnosis [linear]	IBD-specific hospitalisations, IBD-related hospitalisations
			APC			IBD-specific ED visits, IBD–related ED visits
			APC	CD	Year of diagnosis [linear]	Intestinal resection
			APC	UC	Year of diagnosis [linear]	Colectomy
Study 2: Comparison of trends in health service use among children with and without IBD
Count [number of events per person per year]	Negative binomialb	IRR	APC	With and without IBD	Year of diagnosis or index date [linear]	All-cause hospitalisations
						All-cause ED visits
					Year of diagnosis or index date [knot at 2005]	All-cause outpatient visits

APC, average annual percentage change; CD, Crohn’s disease; ED, emergency department visits; HR, hazard ratio; IBD, inflammatory bowel disease; IRR, incidence rate ratio; OR, odds ratio; UC, ulcerative colitis; y, years’

^aAll models adjusted for age [10 y or older versus <10 y], sex [female versus male], rural/urban residency, and mean neighbourhood income quintile.

^bAll models adjusted for IBD diagnosis [Yes/no] and included an interaction term between year of diagnosis and IBD diagnosis to compare trends in children with and without IBD. Autocorrelation was accounted for using an unstructured covariance.