Document Type

Capstone Experience

Graduation Date


Degree Name

Master of Public Health



First Committee Member

Dr Edward S. Peters

Second Committee Member

Dr Kendra Ratnapradipa

Third Committee Member

Dr Fang Yu

Fourth Committee Member

Anthony Fitch


Objective: To estimate and compare the longitudinal trends in the crude prevalence of obesity in the Blue Cross and Blue Shield of Nebraska (BCBSNE) sample of the adult population with the Behavioral Risk Factor Surveillance System (BRFSS) of Nebraska during the pre-pandemic period (2017-2019) and COVID-19 pandemic period (2020-2022) using standardization methods.

Background: Studying the obesity disease burden in a commercial insurance company's administrative claims data can provide critical insights that can inform policy, practice, and interventions aimed at improving health outcomes, managing costs, and reducing the burden of obesity at the population level.

Methods: This study utilized a cohort study design of a random sample of BCBSNE adults starting from the baseline year 2017 to 2022. This study analyzed the longitudinal trends in body mass index and compared the crude prevalence of obesity among BCBSNE with BRFSS sample estimates using age standardization methods This study evaluated the association between several factors, such as common comorbidities of obesity and obesity in order to determine the significant predictors.

Results: This study’s findings revealed statistically significant differences in the longitudinal trends of obesity prevalence among BCBSNE during the pandemic years (2020-2022) compared to pre-pandemic years (2017-2019). Even though similar trends were found for both sample populations, the age-adjusted prevalence was different for BCBSNE insured sample and BRFSS sample estimates (p-value

Conclusion: This study adds to the evidence about the current obesity burden trends among a major insurance provider in Nebraska (BCBSNE) and found a statistically significant increasing trend in obesity burden during the COVID-19 pandemic periods. Researchers can overcome the limitations of using insurance claims data to study obesity by using linked electronic health record (EHR) data to assess the positive predictive value (PPV) and sensitivity of obesity diagnosis codes. Claims data can also be used to identify and characterize high-risk patients as obese, which allows the identification of target populations to assess the use and the effects of interventions for obesity and to investigate the effects of obesity on comorbidities. However, it is important to note that claims data analysis has its limitations, and any analysis must be pursued cautiously using advanced validation and predictive models.