Background <p>Population-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.</p> Methods <p>Data 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.</p> Results <p>31.8% 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 (<i>k</i> = 0.17). Among a few specific conditions, agreement varied depending on the type of condition (e.g., diabetes <i>k</i> = 0.77 vs health conditions <i>k</i> = 0.21).</p> Conclusion <p>We 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.</p>