ArticleBMC pediatrics2025
Child and youth chronic physical health conditions: a comparison of survey data and linked administrative health data in Ontario.
Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Using health systems evidence to study and predict long-term outcomes of childhood acute kidney injury.Pediatric nephrology (Berlin, Germany) · 2026Article
- Accessing providers for co-occurring health and mental health disorders for children and youth in Ontario: an analysis of linked survey and administrative health data.BMC primary care · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundPopulation-based studies in Canada and the United States estimate chronic physical health conditions affect between 20 to 30% of children aged 0 to 17. Challenges in measuring chronic conditions include the use of inconsistent definitions and algorithms that capture a limited number of conditions. Thus, we developed a chronic health condition (CHC) algorithm using administrative data to determine whether a child has a CHC based on (1) the diagnosis recorded for the visit, (2) the number of visits, and (3) within a specific reference period.
methodsData were from the cross-sectional 2014 Ontario Child Health Study, linked with Ontario Health Insurance Plan (OHIP) administrative health data. Unweighted prevalence estimates and agreement analyses (Cohen's Kappa, sensitivity, specificity) were used to compare the survey parent-reported and algorithm-based presence of a CHC.
results31.8% and 27.1% of children and youth had a CHC based on administrative and survey data, respectively. Agreement between administrative and survey data was poor (k = 0.17). Among a few specific conditions, agreement varied depending on the type of condition (e.g., diabetes k = 0.77 vs health conditions k = 0.21).
conclusionWe found considerable discrepancies between administrative and survey-reported data. The results highlight the importance of using algorithms developed from multiple datasets to examine complex research questions, such as the measurement of chronicity.
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