Data Completeness and Quality in a Community-Based and Participatory Epidemiologic Study

Background: The principles of community-based participatory (CBPR) research challenge traditional scientific standards of objectivity and neutrality. Little work has been done to evaluate the quality of data obtained from CBPR studies.

Objectives: We examined factors associated with the completeness and quality of data that participants collected for the Community Health Effects of Industrial Hog Operations (CHEIHO) study, a community-based, participatory, longitudinal, epidemiologic investigation.

Methods: Twice daily for 2 weeks, 101 eastern North Carolina residents collected data on odor from industrialized hog operations, physical health, and mood. Data collected at a single point in time constitute a record. For each record, participant responses were classified as error free or not and missing or not. We used mixed models to quantify associations between errors or missing values and time of day, odor rating, week-in-participation, and presence of a person to assist with data collection.

Results: Participants collected data out of order in 2% of 2,949 total records. On average, individual variables were incomplete in 2% of records. Errors and missing data were most common for lung function measurements. Missing data for lung function and blood pressure were less common after the first week of participation (odds ratio [OR], 0.41; 95% confidence interval [CI], 0.20–0.84). Saliva samples were more frequently missing when participants reported odor than when they did not (OR, 1.59; 95% CI, 0.97–2.59). For women, the odds that yes/no variables were missing in week 2 records were higher relative to week 1 (OR, 1.46; 95% CI, 1.01–2.12).

Conclusions: Community members collected relatively complete and consistent data. Better training in use of mechanical devices and more frequent input from researchers could help to improve data quality in CBPR studies.