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- Title
Investigating Bias from Missing Data in an Electronic Health Records-Based Study of Weight Loss After Bariatric Surgery.
- Authors
Koffman, Lily; Levis, Alexander W.; Arterburn, David; Coleman, Karen J.; Herrinton, Lisa J.; Cooper, Julie; Ewing, John; Fischer, Heidi; Fraser, James R.; Johnson, Eric; Taylor, Brianna; Theis, Mary Kay; Liu, Liyan; Courcoulas, Anita; Li, Robert; Fisher, David P.; Amsden, Laura; Haneuse, Sebastien
- Abstract
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.
- Subjects
BARIATRIC surgery; WEIGHT loss; ELECTRONIC health records; GASTRIC banding; GASTRIC bypass; LOGISTIC regression analysis
- Publication
Obesity Surgery, 2021, Vol 31, Issue 5, p2125
- ISSN
0960-8923
- Publication type
Article
- DOI
10.1007/s11695-021-05226-y