Reasons for underutilization of bariatric surgery: The role of insurance benefit design.

Obesity: Scope, Lifestyle Interventions, and Medical Management

OBJECTIVE To evaluate obesity-related costs and body mass index (BMI) as a cost predictor among privately insured employees by industry. METHODS Individuals with/without obesity were identified using the Optum Health Reporting and Insights employer claims database (January, 2010 to March, 2017). Direct/indirect costs were reported per-patient-per-year (PPPY). Multivariate models were used to estimate the association between obesity and high costs (more than or equal to 80th percentile) by industry. RESULTS Overall (N = 86,221), direct and absenteeism/disability cost differences between class I obesity (BMI 30.0 to 34.9) and reference were $1,775 and $617 PPPY, respectively (P < 0.05). Among employees with obesity (BMI more than or equal to 30), highest total costs were observed in the government/education/religious services, food/entertainment services, and technology industries. Class I obesity increased the odds of high costs (more than or equal to 80th percentile) within each industry (odds ratios vs reference = 1.09-5.17). CONCLUSIONS Obesity (BMI more than or equal to 30) was associated with high costs among employees of major US industries.

Direct and Indirect Cost of Obesity Among the Privately Insured in the United States: A Focus on the Impact by Type of Industry.

Importance Personalized interventions that leverage workplace data and environments could improve effectiveness, sustainability, and scalability of employee wellness programs. Objective To test an automated behavioral intervention to prevent weight gain and improve diet using employee cafeteria purchasing data. Design, setting, and participants This individual-level randomized clinical trial of a 12-month intervention with 12 months of follow-up was conducted among employees of a hospital in Boston, Massachusetts, who purchased food at on-site cafeterias that used traffic-light labels (ie, green indicates healthy; yellow, less healthy; red, unhealthy). Participants were enrolled September 2016 to February 2018. Data were analyzed from May to September 2020. Interventions For 12 months, participants in the intervention group received 2 emails per week with feedback on previous cafeteria purchases and personalized health and lifestyle tips and 1 letter per month with peer comparisons and financial incentives for healthier purchases. Emails and letters were automatically generated using survey, health, and cafeteria data. Control group participants received 1 letter per month with general healthy lifestyle information. Main outcomes and measures The main outcome was change in weight from baseline to 12 months and 24 months of follow-up. Secondary outcomes included changes in cafeteria purchases, including proportion of green- and red-labeled purchases and calories purchased per day, from baseline (12 months preintervention) to the intervention (months 1-12) and follow-up (months 13-24) periods. Baseline Healthy Eating Index-15 (HEI-15) scores were compared to HEI-15 scores at 6, 12, and 24 months. Results Among 602 employees enrolled (mean [SD] age, 43.6 [12.2] years; 478 [79.4%] women), 299 were randomized to the intervention group and 303 were randomized to the control group. Baseline mean (SD) body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was 28.3 (6.6) and HEI-15 score was 60.4 (12.4). There were no between-group differences in weight change at 12 (0.2 [95% CI, -0.6 to 1.0] kg) or 24 (0.6 [95% CI, -0.3 to 1.4] kg) months. Compared with baseline, the intervention group increased green-labeled purchases by 7.3% (95% CI, 5.4% to 9.3%) and decreased red-labeled purchases by 3.9% (95% CI, -5.0% to -2.7%) and calories purchased per day by 49.5 (95% CI, -75.2 to -23.9) kcal more than the control group during the intervention period. In the intervention group, differences in changes in green (4.8% [95% CI, 2.9% to 6.8%]) and red purchases (-3.1% [95% CI, -4.3% to -2.0%]) were sustained at the 24-month follow-up. Differences in changes in HEI-15 scores were not significantly different in the intervention compared with the control group at 6 (2.2 [95% CI, 0 to 4.4]), 12 (1.8 [95% CI, -0.6 to 4.1]), and 24 (1.6, 95% CI, -0.7 to 3.8]) months. Conclusions and relevance The findings of this randomized clinical trial suggest that an automated behavioral intervention using workplace cafeteria data improved employees' food choices but did not prevent weight gain over 2 years. Trial registration ClinicalTrials.gov Identifier: NCT02660086.

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Automated Behavioral Workplace Intervention to Prevent Weight Gain and Improve Diet: The ChooseWell 365 Randomized Clinical Trial.

Obesity has reached epidemic proportions globally, with at least 2.8 million people dying each year as a result of being overweight or obese. Once associated with high-income countries, obesity is now also prevalent in low- and middle-income countries. Not only an individual suffers from ill health, but also family members and organizations bear its consequences. The purpose of this study is to identify the prevalence of obesity and overweight and its association with five risk factors, namely unhealthy diet, physical inactivity, stress, alcohol consumption and smoking, in corporate organizations. High expenses born by corporations because of high BMIs, which lead to overweight and obesity, are a problem. Descriptive research based on a cross-sectional study is used to target office workers in three different groups: group A is people aged 20–30 years old, group B is people 30–40 years old, and group C is people aged > 40 years. This study was conducted in West Asia, using a questionnaire as the instrument for the survey. The data were analyzed using regression and the Simple Lifestyle Index (SLSI). The higher the index, the healthier the person's lifestyle is. The professional’s working hours in the office were observed. The results indicated that the more working hours in a day, the greater the chances of becoming obese or overweight. It is found that of the five risk factors, only two, unhealthy diet and physical inactivity, were found to have significant relationships (p < 5%) with overweight and obesity. The other three factors, stress, alcohol consumption and smoking, did not play a significant role in increasing an individual's weight. As a person’s BMI increases, the number of sick days, medical claims and health care costs increase. To reduce the burden of high medical costs on individuals and businesses, it is essential to have a health care awareness program.

Obesity and overweight—their impact on individual and corporate health

Objective:To conduct a comprehensive literature review to develop recommendations for managing obesity among workers to improve health outcomes and to explore the impact of obesity on health costs to determine whether a case can be made for surgical interventions and insurance coverage.Methods:We se

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Jamie T. Stark

Papers

Obesity in the Workplace: Impact, Outcomes, and Recommendations.