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Investigating Bias from Missing Data in an Electronic Health Records-Based Study of Weight Loss After Bariatric
Lily Koffman1, Alexander W Levis2, David Arterburn3
1Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave Building 2, Boston, MA, 02115, USA. lkoffman@hsph.harvard.edu.
Purpose:
Missing data is common in electronic health records (EHR)-based obesity research. To avoid bias, it is critical to understand mechanisms that underpin missingness. We conducted a survey among bariatric surgery patients in three integrated health systems to (i) investigate predictors of disenrollment and (ii) examine differences in weight between disenrollees and enrollees at 5 years.
Materials And Methods:
We identified 2883 patients who had bariatric surgery between 11/2013 and 08/2014. Patients who disenrolled before their 5-year anniversary were invited to participate in a survey to ascertain reasons for disenrollment and current weight. Logistic regression was used to investigate predictors of disenrollment. Five-year percent weight change distributions were estimated using inverse-probability weighting to adjust for (un)availability of EHR weight data at 5 years among enrollees and survey (non-)response among disenrollees.
Results:
Among 536 disenrolled patients, 104 (19%) completed the survey. Among 2347 patients who maintained enrollment, 384 (16%) had no weight measurement in the EHR near 5 years. Insurance, age, Hispanic ethnicity, and site predicted disenrollment. Disenrollees had slightly greater weight loss than enrollees.
Conclusion:
We found little evidence of weight loss differences by enrollment status. Collecting information through surveys can be an effective tool to investigate and adjust for missingness in EHR-based studies.
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