Data quality from a longitudinal study of adolescent health at schools near industrial livestock facilities.

Longitudinal designs enable examination of temporal relationships between exposures and health outcomes, but extended participation can cause study fatigue. We present an approach for analyzing data quality and study fatigue in a participatory, longitudinal study of adolescents.Participants (n = 340) in the Rural Air Pollutants and Children's Health study completed daily diaries for 3 to 5 weeks in 2009 while we monitored outdoor pollutant concentrations. We used regression models to examine established associations between disease, symptoms, anthropometrics, and lung function as indicators of internal consistency and external validity. We modeled temporal trends in data completeness, lung function, environmental odors, and symptoms to assess study fatigue.Of 5728 records, 94.2% were complete. Asthma and allergy status were associated with asthma-related symptoms at baseline and during follow-up, for example, prevalence ratio = 8.77 (95% confidence interval: 4.33-17.80) for awakening with wheeze among diagnosed asthmatics versus nonasthmatics. Sex, height, and age predicted mean lung function. Plots depicting outcome reporting over time and associated linear trends showed time-dependent declines for most outcomes.We achieved data completeness, internal consistency, and external validity, yet still observed study fatigue, despite efforts to maintain participant engagement. Future investigators should model time trends in reporting to monitor longitudinal data quality.

Duke Scholars

Author Christine L Gray Duke Global Health Institute