Job Absenteeism Costs of Obesity in the United States: National and State-Level Estimates

Multilevel Interventions Targeting Obesity: State of the Science and Future Directions

A seminal report, released in 2001 by the Institute of Medicine, spurred research on the design, implementation, and evaluation of multilevel interventions targeting obesity and related behaviors. By addressing social and environmental factors that support positive health behavior change, interventions that include multiple levels of influence (e.g., individual, social, structural) aim to bolster effectiveness and, ultimately, public health impact. With more than 20 years of multilevel obesity intervention research to draw from, this review was informed by published reviews (n = 51) and identified intervention trials (n = 103), inclusive of all ages and countries, to elucidate key learnings about the state of the science. This review provides a critical appraisal of the scientific literature related to multilevel obesity interventions and includes a description of their effectiveness on adiposity outcomes and prominent characteristics (e.g., population, setting, levels). Key objectives for future research are recommended to advance innovations to improve population health and reduce obesity.

Non-classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity

Estimates of the impact of body mass index and obesity on health and labor market outcomes often use instrumental variables estimation (IV) to mitigate bias due to endogeneity. When these studies rely on survey data that include self- or proxy-reported height and weight, there is non-classical measurement error due to the tendency of individuals to under-report their own weight. Mean reverting errors in weight do not cause IV to be asymptotically biased per se, but may result in bias if instruments are correlated with additive error in weight. We demonstrate the conditions under which IV is biased when there is non-classical measurement error and derive bounds for this bias conditional on instrument strength and the severity of mean-reverting error. We show that improvements in instrument relevance alone cannot eliminate IV bias, but reducing the correlation between weight and reporting error mitigates the bias. A solution we consider is regression calibration (RC) of endogenous variables with external validation data. In simulations, we find IV estimation paired with RC can produce consistent estimates when correctly specified. Even when RC fails to match the covariance structure of reporting error, there is still a reduction in asymptotic bias.

Erectile Dysfunction as an Obesity-Related Condition in Elderly Men with Coronary Artery Disease

Background: This cross-sectional study aimed to investigate the prevalence of erectile dysfunction (ED) in elderly men with overweight or obesity and coronary artery disease. Methods: Patients recruited in cardiac rehabilitation centers post-myocardial infarction provided demographic and anthropomorphic data. ED was assessed using the abbreviated International Index of Erectile Function 5 (IIEF-5) Questionnaire. Results: The study included 661 men with a mean age of 67.3 ± 5.57 years, a mean BMI of 27.9 ± 3.6 m/kg2, and a mean waist circumference of 98.9 ± 10.23 cm. Over 90% of men experienced ED, with similar proportions across BMI categories. The development of ED in men with a waist circumference of ≥100 cm had 3.74 times higher odds (OR 3.74; 95% CI: 1.0-13.7; p = 0.04) than in men with a waist circumference of <100 cm. Men with obesity and moderate-to-severe and severe ED were older compared to those without these disorders (67.1 ± 5.29 vs. 65.3 ± 4.35; p = 0.23). Conclusions: The prevalence of ED in men with coronary artery disease surpasses 90%. An increased body weight raises the risk of ED, with waist circumference proving to be a more reliable predictor of this risk compared to BMI. Physicians are encouraged to screen elderly patients with cardiovascular disease for ED and address obesity to enhance overall health.

Building effective intervention models utilizing big data to prevent the obesity epidemic

INTRODUCTION

The exposome consists of factors an individual is exposed to across the life course. The exposome is dynamic, meaning the factors are constantly changing, affecting each other and individuals in different ways. Our exposome dataset includes social determinants of health as well as policy, climate, environment, and economic factors that could impact obesity development. The objective was to translate spatial exposure to these factors with the presence of obesity into actionable population-based constructs that could be further explored.

METHODS

Our dataset was constructed from a combination of public-use datasets and the Center of Disease Control's Compressed Mortality File. Spatial Statistics using Queens First Order Analysis was performed to identify hot- and cold-spots of obesity prevalence; followed by Graph Analysis, Relational Analysis, and Exploratory Factor Analysis to model the multifactorial spatial connections.

RESULTS

Areas of high and low presence of obesity had different factors associated with obesity. Factors associated with obesity in areas of high obesity propensity were: poverty / unemployment; workload, comorbid conditions (diabetes, CVD) and physical activity. Conversely, factors associated in areas where obesity was rare were: smoking, lower education, poorer mental health, lower elevations, and heat.

DISCUSSION

The spatial methods described within the paper are scalable to large numbers of variables without issues of multiple comparisons lowering resolution. These types of spatial structural methods provide insights into novel variable associations or factor interactions that can then be studied further at the population or policy levels.

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.

Is nutrition labeling associated with decreased obesity? A quantitative approach to nutritional health policy in Ecuador

Few studies assess consumer response to nutrition labeling, especially in less-developed countries. We analyzed the link between nutrition labeling and obesity in Ecuador using a representative cross-sectional sample of 29,770 individuals from the National Health and Nutrition Survey (ENSANUT) in 2018. Nutrition labeling reduced the probability of obesity in adolescent (12-18 years old) and adult (18-59 years old) people by 4% (CI: - 5.7, - 2.2) and 8.4% (CI: - 12.7, - 4.0), respectively. The magnitude of average treatment effect of using nutrition label on obesity ranged from 0.90 (CI: - 1.299, - 0.500) to 1 (CI: - 1.355, - 0.645) BMI points for adolescent, and from 1.16 (CI: - 1.554, - 0.766) to 1.80 (CI: - 2.791, - 0.811) BMI points for adult. The effect of nutrition labeling is greater among the less obese. We recommend that health policy makers and clinicians continue to promote nutrition labeling especially where obesity is not chronic, where nutrition labeling is most successful.

Increasing Pre-pregnancy Body Mass Index and Pregnancy Outcomes in the United States

INTRODUCTION

 As many Americans are becoming overweight or obese, increased body mass index (BMI) is fast becoming normalized. There is a need for more research that highlights the association between pre-pregnancy obesity and adverse pregnancy outcomes.

AIM

To determine the association between increasing pre-pregnancy BMI and adverse pregnancy outcomes.

METHODS

We utilized the United States Vital Statistics records to collate data on all childbirths in the United States between 2015 and 2019. We determined the association between increasing pre-pregnancy BMI and adverse pregnancy outcomes using multivariate analysis. Neonatal outcomes measures include the five-minute Apgar score, neonatal unit admission, neonates receiving assisted ventilation > six hours, neonatal antibiotics use, and neonatal seizures. Maternal outcomes include cesarean section rate, mothers requiring blood transfusion, unplanned hysterectomy, and intensive care unit admission. In addition, we controlled for maternal parameters such as race/ethnicity, age, insurance type, and pre-existing conditions such as chronic hypertension and prediabetes. Other covariates include paternal race, age and education level, gestational diabetes mellitus, induction of labor, weight gain during pregnancy, gestational age at delivery, and delivery weight.

RESULTS

We studied 15,627,572 deliveries in the US Vital Statistics records between 2015 and 2019. Among these women, 3.36% were underweight, 43.19% were with a normal BMI, 26.34% were overweight, 14.73% were in the obese class I, 7.23% were in the obese class II, and 5.14% were in the obese class III. Increasing pre-pregnancy BMI was associated with significant adverse outcomes across all measures of maternal and neonatal outcomes.

CONCLUSION

 A strong association exists between increasing pre-pregnancy BMI and adverse maternal and neonatal outcomes. The higher risk of adverse pregnancy outcomes among overweight and obese women remained even after controlling for other traditional risk factors of adverse maternal and neonatal outcomes.

Occupational exposures and mitigation strategies among homeless shelter workers at risk of COVID-19

Objective

To describe the work environment and COVID-19 mitigation measures for homeless shelter workers and assess occupational risk factors for COVID-19.

Methods

Between June 9-August 10, 2020, we conducted a self-administered survey among homeless shelter workers in Washington, Massachusetts, Utah, Maryland, and Georgia. We calculated frequencies for work environment, personal protective equipment use, and SARS-CoV-2 testing history. We used generalized linear models to produce unadjusted prevalence ratios (PR) to assess risk factors for SARS-CoV-2 infection.

Results

Of the 106 respondents, 43.4% reported frequent close contact with clients; 75% were worried about work-related SARS-CoV-2 infections; 15% reported testing positive. Close contact with clients was associated with testing positive for SARS-CoV-2 (PR 3.97, 95%CI 1.06, 14.93).

Conclusions

Homeless shelter workers may be at risk of being exposed to individuals with COVID-19 during the course of their work. Frequent close contact with clients was associated with SARS-CoV-2 infection. Protecting these critical essential workers by implementing mitigation measures and prioritizing for COVID-19 vaccination is imperative during the pandemic.