ArticlePreventive medicine reports2025
Reliability of self-reported medical history and selected health characteristics in the environmental health study for Western New York cohort.
Article in Preventive medicine reports, 2025. 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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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.
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5 authors.
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Abstract
Objective: We examined the reliability of self-administered questions used to assess medical and family history and other characteristics in the Environmental Health Study for Western New York (EHS4WNY), a prospective cohort study designed to investigate industrial pollution and health. Methods: Between 2018 and 2022, 13,306 adults completed a self-administered, either mailed or identical online questionnaire version. Two hundred and ninety-five participants completed duplicate baseline questionnaires. Reliability was assessed using Cohen's kappa (K), weighted kappa (Kw), percent agreement, and intraclass correlation coefficient (ICC). Results: Common medical conditions, including hypertension (K = 0.80; 95 % CI: 0.71, 0.89) and high cholesterol (K = 0.76; 95 % CI: 0.66,0.86) yielded excellent reliability. A personal medical history of colorectal cancer was moderately reliable (K = 0.67; 95 % CI: 0.05, 1.00). Family history of breast cancer had the highest reliability (K = 0.81; 95 % CI: 0.72, 0.91) and non-melanoma skin cancers had the lowest (K = 0.47; 95 % CI: 0.28, 0.65). Recalled height (ICC = 0.98; 95 % CI: 0.97, 0.98) and weight at age 18 (ICC = 0.96; 95 % CI: 0.94, 0.97) showed excellent reliability. Conclusions: This questionnaire demonstrated fair-to-excellent test-retest reliability of self-reported medical and family history and selected factors. Although we did not assess validity, self-reported medical history collected via questionnaire can be used to generate reliable data in general population studies.
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