Patient Retention in Pediatric Weight Management Programs in the United States: Analyses of Data from the Pediatrics Obesity Weight Evaluation Registry.
Asheley Cockrell SkinnerHaolin XuAmy ChristisonCody D NeshterukSuzanne CudaMelissa SantosJennifer K YeeLaine ThomasEileen KingShelley Kirknull nullPublished in: Childhood obesity (Print) (2021)
Objective: Meeting recommended provider contact hours in multicomponent pediatric weight management (PWM) programs is difficult when patient retention is low. Our objective was to examine associations between individual patient characteristics, program characteristics, and patient retention. Methods: Using the Pediatric Obesity Weight Evaluation Registry, a prospective longitudinal study of 32 PWM programs, we included children (≤18 years; n = 6502) enrolled for a full year. We examined associations between retention (any follow-up visit) and patient and program characteristics using multivariable models with site-clustering random effects. Results: Sixty-seven percent of children had at least one follow-up visit, whereas 12% had four or more visits. Compared with non-Hispanic white children, non-Hispanic black children were less likely to have a follow-up visit [adjusted odds ratio (aOR) = 0.79], whereas Hispanic children (any race) were more likely (aOR = 1.22). Children with Medicaid had similar retention to those with private insurance. Retention did not differ by age, gender, weight status, or comorbidities, nor by program characteristics. Conclusions: Few characteristics of PWM programs are clearly associated with retention, indicating that a variety of formats can support continued treatment and likely reflect the influence of unmeasured characteristics. Clearer ways to identify and overcome barriers for individual patients will be needed to improve retention in PWM.
Keyphrases
- weight loss
- young adults
- case report
- weight gain
- body mass index
- physical activity
- public health
- metabolic syndrome
- type diabetes
- quality improvement
- chronic kidney disease
- health insurance
- end stage renal disease
- mental health
- body weight
- prognostic factors
- big data
- ejection fraction
- artificial intelligence
- affordable care act