Comparison of Two Types of Epidemiological Surveys Aimed at Collecting Daily Clinical Symptoms in Community-Based Longitudinal Studies

Background: Investigators use prospective community-based studies to collect longitudinal information on childhood diarrhea. The interval in which data are collected may affect the accuracy and interpretation of results. Our objective was to compare data of reported daily clinical symptoms from surveys conducted daily versus twice-weekly surveys. Methods: We conducted our study in Lima, Peru, between October and December 2007. We asked 134 mothers to report daily symptoms by using a twice-weekly survey. We conducted daily surveys for the same data on 25% of participants randomly selected each day. We analyzed intersurvey variability by using Cohen's kappa and Signal Detection Theory (SDT). Results: We collected 6157 and 1181 child-days of data through the twice-weekly and daily surveys, respectively. The prevalence of diarrhea, fever, vomiting, and cough were 6.4%, 1.6%, 2.1%, and 22.7% from the twice-weekly survey and, 6.4%, 2.0%, 2.4%, and 26% from the daily survey, respectively. Despite similar prevalence, 20% of days with reported diarrhea were discrepant between the two surveys, and agreement in the report of diarrhea decreased as time between the interviews increased (p = .03). Conclusions: Although twice-weekly surveys provide an adequate estimate of diarrheal prevalence compared with daily surveys, the prevalence of other symptoms based on dichotomous questions was lower under the former. Additionally, the agreement between the two surveys in the report of diarrhea decreased as the recall period increased, suggesting that data from daily interviews were of greater quality. Our analysis is a novel application of SDT to measure respondent certainty and bias, from which better inference about the quality of collected data may be drawn.