ArticlePloS one2026
Concordance between self-report and six commonly used clinical estimates or serological measures: Insights from a Canadian healthy aging study.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo assess the concordance between self-reported and clinically assessed prevalence of selected chronic conditions and latent viral infections among women living with and without HIV.
methodsWomen (aged ≥ 16 years residing in British Columbia) enrolled in the BCC3 Study, a prospective cohort, between 2020 and 2024 were included in the cross-sectional analysis. Self-reported prevalence of six conditions/viruses (chronic kidney disease, liver disease, depression, post-traumatic stress disorder, and hepatitis B and C viruses (HBV, HCV)), were compared to clinical estimates based on screening tools and serology. Agreement was assessed via Cohen's kappa.
resultsIn both women with (n = 220) and without HIV (n = 309), clinical estimate-based prevalence of depression and PTSD was higher than self-reported prevalence (all p < 0.001). Among women with HIV, clinical estimate-based prevalence of HBV was higher than self-report-based prevalence (p < 0.001). For both groups, there was no difference between the two prevalence estimates for chronic kidney disease. Among women without HIV, clinical estimate-based prevalence of liver disease was lower than self-report-based prevalence (p < 0.001), but this was not the case for women with HIV. In both groups, agreement between self-report and clinical estimate of prevalence was fair to poor for all conditions/viruses (all κ < 0.4), except for HCV, for which the agreement was near perfect (κ > 0.8).
conclusionsSelf-reported HCV history shows high concordance with serology, but the same is not true for HBV. The prevalence of liver disease, kidney disease, depression, and post-traumatic stress disorder as reported by participants may differ from clinical estimates. Our findings highlight the complexity of aligning self-report data with clinical estimates and suggest that both types of data should be used for a comprehensive assessment of prevalence in research.
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