Economic value of nonsurgical weight loss in adults with obesity

Weight Loss Using an mHealth App Among Individuals With Obesity in Different Economic Regions of China: Cohort Study

Background

With the increasing prevalence of obesity, weight loss has become a critical issue in China. Self-managed weight loss through a mobile health (mHealth) app may be a prospective method. However, its practicability in different economic regions of China is unknown.

Objective

This study aims to evaluate the effectiveness of self-managed weight loss through an mHealth app among individuals with obesity in different economic regions of China and to demonstrate the feasibility of online self-management for weight loss.

Methods

A total of 165,635 Chinese adults who signed up for the mHealth app were included to analyze the body composition characteristics of individuals from different economic regions by χ2 analyses. Furthermore, 2 types of participants with obesity using mHealth monitoring, including 74,611 participants with a BMI ≥24.0 kg/m2 and 22,903 participants with a normal BMI but an excessive percentage of body fat (PBF), were followed for 6 months to explore the weight loss and fat loss effects in different economic regions of China and to find independent predictors associated with weight loss success by 2-tailed Student t test and multivariable logistic regression analysis.

Results

There were 32,129 users from low-income regions and 133,506 users from high-income regions. The proportion of users with obesity in low-income regions was higher than in high-income regions, both based on BMI (15,378/32,129, 47.9% vs 59,233/133,506, 44.4%; P<.001) and PBF classification (19,146/32,129, 59.6% vs 72,033/133,506, 54%; P<.001). Follow-up analyses showed that the weight loss effect among participants with overweight or obesity in low-income regions was greater than in high-income regions (mean -4.93, SD 6.41 vs mean -4.71, SD 6.14 kg; P<.001), while there was no significant difference in fat loss (mean -2.06%, SD 3.14% vs mean -2.04%, SD 3.19%; P=.54). In the population with normal-weight obesity, the weight loss (mean -2.42, SD 4.07 vs mean -2.23, SD 4.21 kg; P=.004) and fat loss effects (mean -1.43%, SD 2.73% vs mean -1.27%, SD 2.63%; P<.001) were stronger in high-income regions than in low-income regions. In addition, multivariable logistic regression analyses showed that age, baseline PBF, skeletal muscle rate, and measurement frequency were related to weight loss, whereas gender and baseline body metabolic rate only showed a correlation with weight loss in the population in high-income regions.

Conclusions

This study found a high proportion of mHealth app users with obesity in low-income regions. Individuals with overweight and obesity in different economic regions of China experienced significant weight loss and fat loss using an mHealth app. Moreover, individuals in high-income regions paid more attention to body fat and had better fat reduction effects. Therefore, promoting self-monitoring of weight and PBF through an mHealth app could be an important intervention that could be implemented across all regions of China.

Association between weight loss and health care resource utilization in adults living with obesity: Evidence from a UK primary care database

AIMS

We investigated the impact of intentional weight loss on health care resource utilization (HCRU) and costs among people with obesity.

MATERIALS AND METHODS

This retrospective, observational cohort study used data from the Clinical Practice Research Datalink (CPRD) GOLD database. Adults >18 years at index date [first recorded body mass index (BMI) of 30-50 kg/m2 between 2006 and 2015 with a further BMI record 4 years later] were assigned to an intentional weight loss cohort (-25% to -10% BMI change) or a stable weight cohort (-3% to +3%), based on their BMI change during a 4-year baseline period from index date. Evidence of intention to lose weight during the baseline period was required. Linked Hospital Episode Statistics datasets captured HCRU and costs over an 8-year follow-up period. Mixed effects models adjusted for demographics, total costs during baseline and baseline comorbidities were used.

RESULTS

Baseline characteristics were similar between cohorts with weight loss (n = 8676) and stable weight (n = 44 519). Over follow-up, the weight loss cohort experienced a significantly lower mean annual increase in total costs [2.1% (95% confidence interval: 1.3-2.8)] than the stable weight cohort [4.3% (95% confidence interval: 4.0-4.6); p < .0001]. Weight loss was associated with a lower mean annual increase in multiple HCRU and cost components compared with maintaining a stable high weight.

CONCLUSIONS

Our findings suggest that intentional weight loss of 10-25% is associated with lower HCRU and costs in the long term among individuals living with obesity, relative to stable weight.

Characterization of an obese population: a retrospective longitudinal study from real-world data in northern Portugal

BACKGROUND

Obesity is a serious and largely preventable global health problem. Obesity-related electronic health records can be a useful resource to identify and address obesity. The analysis of real-world data from T82-coded (International Classification of Primary Care coding, for obesity) primary care individuals can be an excellent national source of data on obesity's prevalence, characteristics, and impact on the National Health Service.

METHODS

Retrospective longitudinal study, based on a database of electronic medical records, from the Regional Health Administration of northern Portugal. The study objectives were to determine the prevalence of obesity and to characterize an adult obese population in northern Portugal from a bio-demographic point of view along with profiles of comorbidities and the use of health resources. This study used a database of 266,872 patients in December 2019 and screened for diagnostic code T82 from the International Classification of Primary Care.

RESULTS

The prevalence of obesity was 10.2% and the highest prevalence of obesity was in the 65-74 age group (16.1%). The most prevalent morbidities in patients with obesity as coded through ICPC-2 were K86 (uncomplicated hypertension), T90 (non-insulin-dependent diabetes), and K87 (complicated hypertension). Descriptive information showed that T82 subjects used more consultations, medications, and diagnostic tests than non-T82 subjects.

CONCLUSIONS

Routine recording of weight and height deserves special attention to allow obesity recognition at an early stage and move on to the appropriate intervention. Future work is necessary to automate the codification of obesity for subjects under 18 years of age, to raise awareness and anticipate the prevention of problems associated with obesity. Practical strategies need to be implemented, such as the creation of a specific program consultation with truly targeted approaches to obesity.

The Relative Value of Anti-Obesity Medications Compared to Similar Therapies

Purpose

To demonstrate a need for improved health insurance coverage for anti-obesity medications (AOMs) by comparing clinical and economic benefits of obesity treatments to covered medications for selected therapeutic areas.

Methods

Using a grey literature search, we identified and prioritized therapeutic areas and treatment analogues for comparison to obesity. A targeted literature review identified clinical and economic outcomes research across the therapeutic area analogues. Associated comorbidities, clinical evidence, indirect costs (ie, absenteeism and productivity loss), and direct medical costs were evaluated to determine the relative value of treating obesity.

Results

Four therapeutic areas/treatment analogues were selected for comparison to obesity: smoking cessation (varenicline), daytime sleepiness (modafinil), migraines (erenumab), and fibromyalgia (pregabalin). Obesity was associated with 17 comorbidities, more than migraine (9), smoking (8), daytime sleepiness (5), and fibromyalgia (2). Economic burden was greatest for obesity, followed by smoking, with yearly indirect and direct medical costs totaling $676 and $345 billion, respectively. AOMs resulted in cost savings of $2586 in direct medical costs per patient per year (PPPY), greater than that for varenicline at $930 PPPY, modafinil at $1045 PPPY, and erenumab at $468 PPPY; pregabalin utilization increased costs by $924 PPPY. AOMs were covered by 10-16% of United States health insurance plans, compared to 45-59% for the four comparators.

Conclusion

Compared to four therapeutic analogues, obesity represented the highest economic burden and was associated with more comorbidities. AOMs provide greater cost savings compared to selected analogues. However, AOMs have limited formulary coverage. Improved coverage of AOMs may increase access to these treatments and may help address the clinical and economic burden associated with obesity and its comorbidities.

Specialized Medical Weight Management Intervention for High-Risk Obesity

Background: Bundled payments are services rendered at pre-determined costs with the goal of providing high value care. Our institution's Episodes of Care team partnered with its tertiary care obesity center to design a novel medical weight management bundle for employers that would collectively deliver high value obesity services. Objective: As a first step, we sought to evaluate short-term medical weight loss outcomes over 6 months at the obesity center. Methods: We retrospectively analyzed weight loss outcomes on 157 patients with commercial insurance coverage over a period of 6 months. Results: Patients ranged in age from 18-72 years, and 77.7% were female. Patients ranged in weight from 160-443 pounds, with a mean body mass index (BMI) of 42.7 kg/m2 (Class 3a severe obesity; BMI range 28.4-74.5). The prevalence of any obesity-related medical condition was 54.1%; at least a quarter of the patients had either prediabetes or Type 2 diabetes mellitus, approximately a third had hypertension, and over 8% had hyperlipidemia. Mean weight loss from the initial program start date was 6.28% (+/-0.48% standard error of mean [SEM]; 95% confidence interval [CI] 5.34-7.23%). Completers (defined as having at least 6 visits with a medical provider) achieved a higher percentage of weight loss (7.06%) from the initial program start compared to non-completers (4.68%; at least 4-5 visits with a medical provider; P<0.0158). Approximately 50% of patients were able to achieve >7% weight loss, with over 55% of patients achieving at least 3% weight loss or higher irrespective of BMI classification. Conclusions: Specialized medical weight intervention is effective in treating high-risk obesity with complications. This has implications for enhanced long-term cost savings related to employer coverage of such programs for their employees with obesity.

Effectiveness of Combining Antiobesity Medication With an Employer-Based Weight Management Program for Treatment of Obesity: A Randomized Clinical Trial

IMPORTANCE

The clinical efficacy of antiobesity medications (AOMs) as adjuncts to lifestyle intervention is well characterized, but data regarding their use in conjunction with workplace wellness plans are lacking, and coverage of AOMs by US private employers is limited.

OBJECTIVE

To determine the effect of combining AOMs with a comprehensive, interdisciplinary, employer-based weight management program (WMP) compared with the WMP alone on weight loss, treatment adherence, and work productivity and limitations.

DESIGN, SETTING, AND PARTICIPANTS

This 1-year, single-center, open-label, parallel-group, real-world, randomized clinical trial was conducted at the Cleveland Clinic's Endocrinology and Metabolism Institute in Cleveland, Ohio, from January 7, 2019, to May 22, 2020. Participants were adults with obesity (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared] ≥30) enrolled in the Cleveland Clinic Employee Health Plan.

INTERVENTIONS

In total, 200 participants were randomized 1:1, 100 participants to WMP combined with an AOM (WMP+Rx), and 100 participants to WMP alone. The WMP was the Cleveland Clinic Endocrinology and Metabolism Institute's employer-based integrated medical WMP implemented through monthly multidisciplinary shared medical appointments. Participants in the WMP+Rx group initiated treatment with 1 of 5 US Food and Drug Administration-approved medications for chronic weight management (orlistat, lorcaserin, phentermine/topiramate, naltrexone/bupropion, and liraglutide, 3.0 mg) according to standard clinical practice.

MAIN OUTCOMES AND MEASURES

The primary end point was the percentage change in body weight from baseline to month 12.

RESULTS

The 200 participants were predominately (177 of 200 [88.5%]) women, had a mean (SD) age of 50.0 (10.3) years, and a mean (SD) baseline weight of 105.0 (19.0) kg. For the primary intention-to-treat estimand, the estimated mean (SE) weight loss was -7.7% (0.7%) for the WMP+Rx group vs -4.2% (0.7%) for the WMP group, with an estimated treatment difference of -3.5% (95% CI, -5.5% to -1.5%) (P < .001). The estimated percentage of participants achieving at least 5% weight loss was 62.5% for WMP+Rx vs 44.8% for WMP (P = .02). The rate of attendance at shared medical appointments was higher for the WMP+Rx group than for the WMP group. No meaningful differences in patient-reported work productivity or limitation measures were observed.

CONCLUSIONS AND RELEVANCE

Clinically meaningful superior mean weight loss was achieved when access to AOMs was provided in the real-world setting of an employer-based WMP, compared with the WMP alone. Such results may inform employer decisions regarding AOM coverage and guide best practices for comprehensive, interdisciplinary employer-based WMPs.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03799198.