ArticleAnnals of epidemiology2025
Concordance of number of chronic conditions estimated from electronic health record or self-report.
Article in Annals of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Phenomic profiles and disease patterns of 1.9 million participants from Our Future Health.Nature medicine · 2026Article
- Chronic Health Conditions and Substance Use Among Canadian Youth: A Nationally Representative Study: Problèmes de santé chroniques et usage de substances chez les jeunes Canadiens : étude représentative à l'échelle nationale.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2026Article
- Youth stress, happiness, and life satisfaction across morbidity status: a gender-stratified analysis.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeSelf-report or electronic health records can be used to calculate number of chronic conditions for study participants. Although agreement for specific conditions can be found in the literature, there is a lack of information on how the total number of conditions compares between the two sources.
methodsUsing a long-standing cohort study, the number of chronic conditions was estimated for 351 participants using self-report and data from their electronic health record. Agreement for each condition, and for total number of conditions was estimated using a weighted kappa with 95 % Confidence Interval (CI), and using Lin's concordance coefficient, and a Bland-Altman plot. Predictors of discord were identified using generalized linear models with concordant pairs as reference.
resultsDiscord varied by condition but ranged from 3.7 % (diabetes, kappa=0.86) to 33.2 % (hyperlipidemia, kappa=0.31). While the mean number of chronic conditions was similar, it showed poor agreement [weighted kappa= 0.46 (95 % CI: 0.41, 0.52)], and did not show pattern of discrepancy as the average number of chronic conditions increased. Discrepancies were related to condition, male sex, and lower education.
conclusionThere are opportunities to ensure that electronic health records are complete, and that patients receive adequate health education to become aware of the conditions that they have.
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Registered trials
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