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Defining a low-risk birth cohort: a cohort study comparing two perinatal data sets in Ontario, Canada


Introduction — There are two main data sources for perinatal data in Ontario, Canada: the BORN BIS and CIHI-DAD. Such databases are used for perinatal health surveillance and research, and to guide health care related decisions.

Objectives — Our primary objective was to examine the level of agreement between the BIS and CIHI-DAD. Our secondary objectives were to identify the differences between the data sources when identifying a low-risk birth (LRB) cohort and to understand their implications.

Methods — We conducted a population-based cohort study comparing characteristics and clinical outcomes of all linkable births in BIS and CIHI-DAD between 1$^{\rm st}$ April 2012 and 31$^{\rm st}$ March 2018. We excluded out-of-hospital births, those with invalid healthcare numbers, non-Ontario residents and gestational age < 20 weeks. We compared the portion of the cohort that met the criteria of a provincial definition of LRB based on each data source and compared clinical outcomes between the groups.

Results — During the study period, 779,979 eligible births were linkable between the two data sources. After applying the LRB exclusions, there were 129,908 cases in the BIS and 136,184 cases in CIHI-DAD. Most exclusion criteria had almost perfect, substantial or moderate agreement. The agreement for non-cephalic presentation and BMI ≥ 40 kg/m2 (kappa coefficients 0.409 and 0.256, respectively) was fair. Comparison between the two LRB cohorts identified differences in the prevalence of cesarean (14.3% BIS versus 12.0% CIHI-DAD) and NICU admission (8.7% BIS versus 7.5% CIHI-DAD) and only 0.01% difference in the prevalence of ICU admission.

Conclusions — Overall, we found high levels of agreement between the BIS and CIHI-DAD. Identifying a LRB cohort in either database may be appropriate, with the caveat of appropriate understanding of the collection, coding and definition of certain outcomes. The decision for selecting a database may depend on which variables are most important in a particular analysis.



Darling EK, Marquez O, Park AL. Int J Popul Data Sci. 2024; 9(1):2364. Epub 2024 Mar 18.

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