The Value of Patient Self-report for Disease Surveillance

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Abstract

Objective: To determine the accuracy of self-reported information from patients and families for use in a disease surveillance system. Design: Patients and their parents presenting to the emergency department (ED) waiting room of an urban, tertiary care children's hospital were asked to use a Self-Report Tool, which consisted of a questionnaire asking questions related to the subjects' current illness. Measurements: The sensitivity and specificity of three data sources for assigning patients to disease categories was measured: the ED chief complaint, physician diagnostic coding, and the completed Self-Report Tool. The gold standard metric for comparison was a medical record abstraction. Results: A total of 936 subjects were enrolled. Compared to ED chief complaints, the Self-Report Tool was more than twice as sensitive in identifying respiratory illnesses (Rate ratio [RR]: 2.10, 95% confidence interval [CI] 1.81-2.44), and dermatological problems (RR: 2.23, 95% CI 1.56-3.17), as well as significantly more sensitive in detecting fever (RR: 1.90, 95% CI 1.67-2.17), gastrointestinal problems (RR: 1.10, 95% CI 1.00-1.20), and injuries (RR: 1.16, 95% CI 1.08-1.24). Sensitivities were also significantly higher when the Self-Report Tool performance was compared to diagnostic codes, with a sensitivity rate ratio of 4.42 (95% CI 3.45-5.68) for fever, 1.70 (95% CI 1.49-1.93) for respiratory problems, 1.15 (95% CI 1.04-1.27) for gastrointestinal problems, 2.02 (95% CI 1.42-2.87) for dermatologic problems, and 1.06 (95% CI 1.01-1.11) for injuries. Conclusions: Disease category assignment based on patient-reported information was significantly more sensitive in correctly identifying a disease category than data currently used by national and regional disease surveillance systems. © 2007 J Am Med Inform Assoc.

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APA

Bourgeois, F. T., Porter, S. C., Valim, C., Jackson, T., Cook, E. F., & Mandl, K. D. (2007). The Value of Patient Self-report for Disease Surveillance. Journal of the American Medical Informatics Association, 14(6), 765–771. https://doi.org/10.1197/jamia.M2134

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