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Obesity increases the risk of developing type 2 diabetes, hypertension, and hyperlipidemia. We sought to determine the impact of obesity maintenance, weight regain, weight loss maintenance, and magnitudes of weight loss on future risk and time to developing these cardiometabolic conditions. This was a retrospective cohort study of adults receiving primary care at Geisinger Health System between 2001 and 2017. Using electronic health records, patients with ≥3-weight measurements over a 2-year index period were identified and categorized. Obesity maintainers (OM) had obesity (body mass index ≥30 kg/m²) and maintained their weight within ±3% from baseline (reference group). Both weight loss rebounders (WLR) and weight loss maintainers (WLM) had obesity at baseline and lost5% body weight in year 1; WLR regained ≥20% of weight loss by end of year 2 and WLM maintained ≥80% of weight loss. Incident type 2 diabetes, hypertension, and hyperlipidemia, and time-to-outcome were determined for each study group and by weight loss category for WLM. Of the 63,567 patients included, 67% were OM, 19% were WLR, and 14% were WLM. The mean duration of follow-up was 6.6 years (SD, 3.9). Time until the development of electronic health record-documented type 2 diabetes, hypertension, and hyperlipidemia was longest for WLM and shortest for OM (log-rank test p0.0001). WLM had the lowest incident type 2 diabetes (adjusted hazard ratio [HR] 0.676 [95% confidence interval [CI] 0.617 to 0.740]; p0.0001), hypertension (adjusted HR 0.723 [95% CI 0.655 to 0.799]; p0.0001), and hyperlipidemia (adjusted HR 0.864 [95% CI 0.803 to 0.929]; p0.0001). WLM with the greatest weight loss (15%) had a longer time to develop any of the outcomes compared with those with the least amount of weight loss (7%) (p0.0001). In an integrated delivery network population, sustained weight loss was associated with a delayed onset of cardiometabolic diseases, particularly with a greater magnitude of weight loss.

BACKGROUND: After a dramatic increase in prevalence over several decades, obesity has become a major public health crisis in the United States. Research to date has consistently demonstrated a correlation between obesity and higher medical costs for a variety of U.S. subpopulations and specific categories of care. However, by examining associations rather than causal effects, previous studies likely underestimated the effect of obesity on medical expenditures. OBJECTIVE: To estimate the causal effect of obesity on direct medical care costs at the national and state levels. METHODS: This study is a pooled cross-sectional analysis of retrospective data from the 2001-2016 Medical Expenditure Panel Surveys. Adults aged 20-65 years with a biological child living in the household were included in the study sample. Primary outcomes were individual-level medical expenditures due to obesity, overall, as well as separately by type of payer and category of medical care. Results were reported at the national level and separately for the 20 most populous states. The expenditure estimates were obtained from 2-part models of instrumental variables in which the respondent's body mass index (BMI) was instrumented using the BMI of their biological child. RESULTS: Adults with obesity in the United States compared with those with normal weight experienced higher annual medical care costs by $2,505 or 100%, with costs increasing significantly with class of obesity, from 68.4% for class 1 to 233.6% for class 3. The effects of obesity raised costs in every category of care: inpatient, outpatient, and prescription drugs. Increases in medical expenditures due to obesity were higher for adults covered by public health insurance programs ($2,868) than for those having private health insurance ($2,058). In 2016, the aggregate medical cost due to obesity among adults in the United States was $260.6 billion. The increase in individual-level expenditures due to obesity varied considerably by state (e.g., 24.0% in Florida, 66.4% in New York, and 104.9% in Texas). CONCLUSIONS: The 2-part models of instrumental variables, which estimate the causal effects of obesity on direct medical costs, showed that the effect of obesity is greater than suggested by previous studies, which estimated only correlations. Much of the aggregate national cost of obesity-$260.6 billion-represents external costs, providing a rationale for interventions to prevent and reduce obesity. DISCLOSURES: Novo Nordisk financed the development of the study design, analysis, and interpretation of data, as well as writing support of the manuscript. Cawley, Biener, and Meyerhoefer received financial support from Novo Nordisk to conduct the research study on which this manuscript is based. Smolarz and Ramasamy are employees of Novo Nordisk. Ding and Zvenyach have no conflicts to declare. Our research has been presented as a poster at the 2020 Academy Health Annual Research Meeting (Virtual), July 28-August 6, 2020.

BACKGROUND: Obesity imposes a substantial economic burden on the United States. The short-term value of nonsurgical weight loss (WL) and nonsurgical sustained WL (i.e., WL not resulting from bariatric surgery) is poorly understood. OBJECTIVES: To assess short-term (1 year) effect of nonsurgical WL and sustained nonsurgical WL (i.e., approximately 2 years) on per-patient-per-month (PPPM) total all-cause health care costs among adults with obesity in the United States. METHODS: In this retrospective cohort study, we analyzed data from the IBM MarketScan Explorys Claims-EMR Data Set from January 1, 2012, through June 30, 2018. Adults aged 18-64 years with a body mass index (BMI) measurement ≥ 30 kg/m2 on the index date and BMI measurements at 12, 24, and 36 months were classified into weight-gain (≥ 3%), no-weight-change (within ± 3%), and WL (≥ 3%-≤ 5%, > 5%-≤ 10%, and > 10%-≤ 20%) cohorts based on the change from first to second BMI measurements (baseline), and sustained nonsurgical WL based on WL during baseline and 5%- ≤ 10% WL cohort (7.7%), and > 10%- ≤ 20% WL cohort (2.8%). Compared with the no-weight-change cohort, adjusted mean ΔPPPM all-cause health care cost from baseline to first year of follow-up was lower in all WL cohorts (≥ 3%- ≤ 5% WL: -$57.36, > 5%- ≤ 10% WL: -$135.35 [P 10%- ≤ 20% WL: -$193.54 [P 10%- ≤ 20% sustained WL (1.8%). Adjusted mean ΔPPPM all-cause health care cost was lower in all sustained WL groups (-$26.38, -$157.41 [P 10%- ≤ 20% WL, respectively). Greater nonsurgical WL and sustained nonsurgical WL were generally associated with larger reduction in short-term all-cause health care costs. Results stratified by index obesity class were mixed, due to small samples. CONCLUSIONS: Substantial all-cause health care cost savings were observed 1 year after nonsurgical WL and after sustained (on average for 2 years) nonsurgical WL in adults with obesity, with greater nonsurgical WL and sustained nonsurgical WL associated with greater cost savings. Comprehensive solutions to chronic weight management, including improved access to antiobesity medications in combination with lifestyle interventions, could be valuable to patients, employers, and payers. DISCLOSURES: This study was sponsored by Novo Nordisk, which also purchased the data. Blanchette is an employee of Novo Nordisk. Smolarz and Ramasamy are employees of Novo Nordisk and hold equity in Novo Nordisk. Ding, Fan, and Weng were employees of Novo Nordisk at the time this study was conducted. The findings from this study were previously presented at Obesity Week 2019; November 3-7, 2019; Las Vegas, NV.

Objective Obesity and its complications place an enormous burden on society. Yet antiobesity medications (AOM) are prescribed to only 2% of the eligible population, even though few individuals can sustain weight loss using other strategies alone. This study estimated the societal value of greater access to AOM. Methods By using a well-established simulation model (The Health Economics Medical Innovation Simulation), the societal value of AOM for the cohort of Americans aged ≥ 25 years in 2019 was quantified. Four scenarios with differential uptake among the eligible population (15% and 30%) were modeled, with efficacy from current and next-generation AOM. Societal value was measured as monetized quality of life, productivity gains, and savings in medical spending, subtracting the costs of AOM. Results For the 217 million Americans aged ≥ 25 years, AOM generated $1.2 trillion in lifetime societal value under a conservative scenario (15% annual uptake using currently available AOM). The introduction of next-generation AOM increased societal value to $1.9 to $2.5 trillion, depending on uptake. Finally, societal value was higher for younger individuals and Black and Hispanic individuals compared with White individuals. Conclusions This study suggests that AOM provide substantial gains to patients and society. Policies promoting broader clinical access to and use of AOM warrant consideration to reach national goals to reduce obesity.

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

Although several obesity clinical practice guidelines are available and relevant for primary care, a practical and effective medical model for treating obesity is necessary. The aim of this study was to develop and implement a holistic population health-based framework with components to support primary care-based obesity management in US health care organizations. The Obesity Care Model Collaborative (OCMC) was conducted with guidance and expertise of an advisory committee, which selected participating health care organizations based on prespecified criteria. A committee comprising obesity and quality improvement specialists and representatives from each organization developed and refined the obesity care framework for testing and implementing guideline-based practical interventions targeting obesity. These interventions were tracked over time, from an established baseline to 18 months post implementation. Ten geographically diverse organizations, treating patients with diverse demographics, insurance coverage, and health status, participated in the collaborative. The key interventions identified for managing obesity in primary care were applicable across the 4 OCMC framework domains: community, health care organization, care team, and patient/family. Care model components were developed within each domain to guide the primary care of obesity based on each organization's structure, resources, and culture. Key interventions included development of quality monitoring systems, training of leadership and staff, identifying clinical champions, patient education, electronic health record best practice alerts, and establishment of community partnerships, including the identification of external resources. This article describes the interventions developed based on the framework, with a focus on implementation of the model and lessons learned.

To estimate the causal effect of obesity on job absenteeism and the associated lost productivity in the United States, both nationwide and by state.We conducted a retrospective pooled cross-sectional analysis using the 2001 to 2016 Medical Expenditure Panel Survey and estimated two-part models of instrumental variables.Obesity, relative to normal weight, raises job absenteeism due to injury or illness by 3.0 days per year (128%). Annual productivity loss due to obesity ranges from $271 to $542 (lower/upper bound) per employee with obesity, with national productivity losses ranging from $13.4 to $26.8 billion in 2016. Trends in state-level estimates mirror those at the national level, varying across states.Obesity significantly raises job absenteeism. Reductions in job absenteeism should be included when calculating the cost-effectiveness of interventions to prevent or reduce obesity among employed adults.

OBJECTIVE To compare obesity-related costs of employees of the healthcare industry versus other major US industries. METHODS Employees with obesity versus without were identified using the Optum Health Reporting and Insights employer claims database (January, 2010 to March, 2017). Employees working in healthcare with obesity were compared with employees of other industries with obesity for absenteeism/disability and direct cost differences. Multivariate models estimated the association between industries and high costs compared with the healthcare industry. RESULTS Obesity-related absenteeism/disability and direct costs were higher in several US industries compared with the healthcare industry (adjusted cost differences of $-1220 to $5630). Employees of the government/education/religious services industry (GERS) with obesity (BMI of 30 or greater) had significantly higher odds of direct costs at the 80th percentile and above (odds ratio vs healthcare industry = 2.20; P

Obesity is a potentially modifiable risk factor for many diseases, and a better understanding of its impact on health care utilization, costs, and medical outcomes is needed. The ability to accurately evaluate obesity outcomes depends on a correct identification of the population with obesity. The primary objective of this study was to determine the prevalence and accuracy of International Classification of Diseases, Ninth Revision (ICD-9) coding for overweight and obesity within a US primary care electronic health record (EHR) database compared against actual body mass index (BMI) values from recorded clinical patient data; characteristics of patients with obesity who did or did not receive ICD-9 codes for overweight/obesity also were evaluated. The study sample included 5,512,285 patients in the database with any BMI value recorded between January 1, 2014, and June 30, 2014. Based on BMI, 74.6% of patients were categorized as being overweight or obese, but only 15.1% of patients had relevant ICD-9 codes. ICD-9 coding prevalence increased with increasing BMI category. Among patients with obesity (BMI ≥30 kg/m2), those coded for obesity were younger, more often female, and had a greater comorbidity burden than those not coded; hypertension, dyslipidemia, type 2 diabetes mellitus, and gastroesophageal reflux disease were the most common comorbidities. Key findings: US outpatients with overweight or obesity are not being reliably coded, making ICD-9 codes undependable sources for determining obesity prevalence and outcomes. BMI data available within EHR databases offer a more accurate and objective means of classifying overweight/obese status.

Objective Determine the impact of long-term non-surgical weight loss maintenance on clinical relevance for osteoarthritis, cancer, opioid use, and depression/anxiety and healthcare resource utilization. Methods A cohort of adults receiving primary care within Geisinger Health System between 2001–2017 was retrospectively studied. Patients with ≥3 weight measurements in the two-year index period and obesity at baseline (BMI ≥30 kg/m2) were categorized: Obesity Maintainers (reference group) maintained weight within +/-3%; Weight Loss Rebounders lost ≥5% body weight in year one, regaining ≥20% of weight loss in year two; Weight Loss Maintainers lost ≥5% body weight in year one, maintaining ≥80% of weight loss. Association with development of osteoarthritis, cancer, opioid use, and depression/anxiety, was assessed; healthcare resource utilization was quantified. Magnitude of weight loss among maintainers was evaluated for impact on health outcomes. Results In total, 63,567 patients were analyzed including 67% Obesity Maintainers, 19% Weight Loss Rebounders, and 14% Weight Loss Maintainers; median follow-up was 9.7 years. Time until osteoarthritis onset was delayed for Weight Loss Maintainers compared to Obesity Maintainers (Logrank test p 15% weight loss was associated with the greatest decrease in incident osteoarthritis. Healthcare resource utilization was significantly higher for Weight Loss Rebounders and Maintainers compared to Obesity Maintainers. Increased weight loss among Weight Loss Maintainers trended with lower overall healthcare resource utilization, except for hospitalizations. Conclusions In people with obesity, sustained weight loss was associated with greater clinical benefits than regained short-term weight loss and obesity maintenance. Higher weight loss magnitudes were associated with delayed onset of osteoarthritis and led to decreased healthcare utilization.

Characterize patterns of weight change among subjects with obesity.A retrospective observational longitudinal study of subjects with obesity was conducted using the General Electric Centricity electronic medical record database. Subjects who were ≥18 years old with BMI ≥30 kg/mA total of 177,743 subjects were included: 85.1% of subjects were in the stable weight, 9.3% in the modest, 2.3% in the moderate, and 3.3% in the high weight loss groups. The proportion of subjects who maintained or continued to lose weight decreased over the 2 year observation period; 11% of those with high weight loss continued to lose weight and 19% maintained their weight loss. This group had the lowest percentage of subjects who regained ≥50% of lost weight and the lowest proportion of subjects with weight cycling (defined as not continuously losing, gaining, or maintaining weight throughout the 2 year observation period relative to its beginning). This trend persisted in subgroups with class II-III obesity, pre-diabetes, and type 2 diabetes.Weight cycling and regain were commonly observed. Subjects losing the most weight during the initial period were more likely to continue losing weight.

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

To evaluate the positive predictive value of claims-based V85 codes for identifying individuals with varying degrees of BMI relative to their measured BMI obtained from medical record abstraction.This was a retrospective validation study utilizing administrative claims and medical chart data from 1 January 2009 to 31 August 2015. Randomly selected samples of patients enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan and with a V85 claim were identified. The claims-based BMI category (underweight, normal weight, overweight, obese class I-III) was determined via corresponding V85 codes and compared to the BMI category derived from chart abstracted height, weight and/or BMI. The positive predictive values (PPVs) of the claims-based BMI categories were calculated with the corresponding 95% confidence intervals (CIs).The overall PPVs (95% CIs) in the MAPD and commercial samples were 90.3% (86.3%-94.4%) and 91.1% (87.3%-94.9%), respectively. In each BMI category, the PPVs (95% CIs) for the MAPD and commercial samples, respectively, were: underweight, 71.0% (55.0%-87.0%) and 75.9% (60.3%-91.4%); normal, 93.8% (85.4%-100%) and 87.8% (77.8%-97.8%); overweight, 97.4% (92.5%-100%) and 93.5% (84.9%-100%); obese class I, 96.9 (90.9%-100%) and 97.2% (91.9%-100%); obese class II, 97.0% (91.1%-100%) and 93.0% (85.4%-100%); and obese class III, 85.0% (73.3%-96.1%) and 97.1% (91.4%-100%).BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research.

Summary Previously in the SCALE Obesity and Prediabetes trial, at 1 year, participants with obesity (or overweight with comorbidities) and prediabetes receiving liraglutide 3.0 mg experienced greater improvements in health-related quality of life (HRQoL) than those receiving placebo. The current study extends these findings by examining 3-year changes in HRQoL. HRQoL was assessed using the obesity-specific Impact of Weight on Quality of Life-Lite (IWQOL-Lite) questionnaire, as well as the Short-Form 36 v2 (SF-36) health survey. At 3 years, mean change (±standard deviation) in IWQOL-Lite total score from baseline for liraglutide (n = 1472) was 11.0 ± 14.2, vs. 8.1 ± 14.7 for placebo (n = 738) (estimated treatment difference [ETD] 3.4 [95% confidence interval (CI): 2.0, 4.7], P

Research suggests that medical student empathy erodes during undergraduate medical education. The authors evaluated the Jefferson Scale of Physician Empathy Medical Student Version (JSPE-MS) scores of two consecutive medical school classes to assess the impact of an educational intervention on the preservation of empathy.The authors conducted a before-and-after study of 209 Robert Wood Johnson Medical School (RWJMS) students enrolled in the classes of 2009 and 2010. Students' clerkships included a mandatory, longitudinal "Humanism and Professionalism" (HP) component, which included blogging about clerkship experiences, debriefing after significant events, and discussing journal articles, fiction, and film. Students completed the JSPE-MS during their first and last clerkships.The results showed that (1) contrary to previous studies' findings, third-year students did not show significant decline in empathy as measured by the JSPE-MS (these students, from two consecutive RWJMS classes, experienced the HP intervention), (2) students selected for the Gold Humanism Honor Society (GHHS) were significantly different from their peers in empathy scores as measured by JSPE-MS, and (3) knowledge of selection for the GHHS seems to positively influence students' JSPE-MS scores.Maintaining empathy during the third year of medical school is possible through educational intervention. A curriculum that includes safe, protected time for third-year students to discuss their reactions to patient care situations during clerkships may have contributed to the preservation of empathy. Programs designed to validate humanism in medicine (such as the GHHS) may reverse the decline in empathy as measured by the JSPE-MS.