1 Chapter One: Definition of Obesity: Current Trends and Statistics

Authored by Karen Ensle, EdD, RDN, FAND, CFCS

Edited by Sherri M. Cirignano, MS, RDN, LDN

 

Obesity Prevalence in the United States

State of Obesity 2022: Better Policies for a Healthier America

The U.S. obesity epidemic in 2022 is growing and 19 states now have adult obesity rates above 35 percent which has increased from 16 states last year in 2021. The 19th annual State of Obesity: Better Policies for a Healthier America report, released by Trust for America’s Health (TFAH), finds that obesity rates continue to climb nationwide and within population groups.¹ These persistent increases underscore that obesity is caused by a combination of factors including societal, biological, genetic, and environmental, which are beyond personal choice. Addressing the obesity crisis will require attending to the economic and structural factors of where and how people live.

The report amplifies the importance of the White House Conference on Hunger, Nutrition and Health.² The Conference and the TFAH report are intended to spotlight the links between hunger, nutrition, health, and diet-related diseases, including obesity, and recommend needed policy action.

Key Findings in the TFAH Report:¹

• Nineteen states have adult obesity rates over 35 percent. West Virginia, Kentucky, and Alabama have the highest rate of adult obesity at 40.6 percent, 40.3 percent, and 39.9, respectively. The District of Columbia, Hawaii, and Colorado had the lowest adult obesity rates at 24.7 percent, 25 percent, and 25.1 percent, respectively. A decade ago, no state had an adult obesity rate at or above 35 percent.
• Obesity rates are highest in communities of color where barriers to healthy food choices and being physically active are often most prevalent.
• Obesity rates are also increasing among children and adolescents with nearly 20 percent of U.S. children ages 2 to 19 having obesity. These rates more than tripled since the mid-1970s, and Black and Latino youth have substantially higher rates of obesity than do their White peers.
• Structural and social determinants are significantly influencing the rates of obesity among adults and youth. Factors such as structural racism, discrimination, poverty, food insecurity, housing instability, and lack of access to quality healthcare are key drivers of the differences in obesity rates across racial and ethnic groups.

Key Recommendations:¹

• Increase funding for the CDC’s National Center for Chronic Disease Prevention and Health Promotion to prevent obesity and related chronic diseases. Funding increases need to be sufficient to put proven obesity prevention programs to work in every state and should prioritize those communities where the need is greatest to address health inequities.
• Make healthy school meals for all students a permanent policy, extend COVID-19 flexibilities that expand nutrition access for students and their families, strengthen school nutrition standards, and increase students’ opportunities for physical activity during the school day.
• Expand the CDC’s social determinants of health program to address the upstream, structural drivers of chronic diseases.
• Decrease food insecurity and improve the nutritional quality of available food by increasing funding for and participation in nutrition assistance programs, such as the Supplemental Nutrition Assistance Program (SNAP), the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), and the Child and Adult Care Food Program.
• Fund active transportation projects like pedestrian and biking paths in all communities and make local spaces more conducive to physical activity, such as opening school recreational facilities to community groups outside of school hours.
• Expand access to healthcare and require insurance coverage with no cost sharing for U.S. Preventive Task Force recommended obesity prevention programs.

Defining Obesity

Obesity often results from taking in more calories than are burned by exercise and normal daily activities. Obesity occurs when a person’s body mass index is 30 or greater. The main symptom is excessive body fat, which increases the risk of serious health problems. The mainstay of treatment is lifestyle changes such as diet and exercise.

The World Health Organization (WHO) defines obesity as “a condition in which a percentage of body fat (PBF) is increased to an extent in which health and well-being are impaired, and, due to the alarming increase in prevalence has declared obesity as a “global epidemic”.³

The Centers for Disease Control and Prevention (CDC) defines obesity as a body weight that is higher than what is considered healthy for a person’s given height which is described as overweight or obese. Body Mass Index (BMI) is a screening tool used to measure overweight and obesity. See Defining Adult Overweight and Obesity and Defining Childhood Weight Status for more information on these topics.

Body Mass Index (BMI)

Body mass index (BMI) is commonly used to determine childhood weight status. BMI is calculated by dividing a person’s weight in kilograms by the square of their height in meters. For children and teens, BMI is age-and-sex-specific and is often referred to as BMI-for-age. A child’s weight status is calculated differently from adult BMI categories. Children’s body composition varies as they age and varies between boys and girls. Therefore, BMI levels among children and teens need to be expressed relative to other children of the same age and sex.

CDC Growth Charts are commonly used to measure the size and growth patterns of children and teens in the United States. BMI-for-age weight status categories and the corresponding percentiles, are based on expert CDC committee recommendations, and are listed in the Table 1 for an eight-year-old child as an example:⁴

Table 1. BMI-for-Age Eight Status Categories and the Corresponding Percentiles

Weight Status Category	Percentile Range
Underweight	Less than the 5th percentile
Healthy Weight	5th percentile to less than the 85th percentile
Overweight	85th to less than the 95th percentile
Obesity	95th percentile or greater

Adapted from the Centers for Disease Control and Prevention. Defining Childhood Weight Status. 

https://www.cdc.gov/obesity/basics/childhood-defining.html

In children, BMI percentile cutoffs for obesity are intended to reliably define a level above which a child is more likely to have or be at risk of developing obesity-associated adverse health outcomes or diseases. For more information, see the National Collaborative on Childhood Obesity’s page on Measures for Children at High Risk for Obesity.⁵

What is the Difference Between Overweight, Obesity and Morbid Obesity?

Obesity, having too much body fat, is defined as having a body mass index (BMI) of greater than 30. Morbid obesity is when a person has excessive weight with a body mass index of 35 to 40 or more. Table 2 defines the four categories of BMI.

Table 2. Four categories of obesity

Overweight (not obese), if BMI is:	25.0 to 29.9
Class 1 (low risk) obesity if BMI is:	30.0 to 34.9
Class 2 (moderate risk) obesity if BMI is:	35.0 to 39.9
Class 3 (high-risk) obesity if BMI is equal to or greater than:	40.0

Morbid obesity, which is also termed “clinically severe obesity,” is typically defined as being more than 100 pounds overweight or having a BMI of 40 or higher. A clear definition of morbid obesity is very important because this definition is used to guide doctors in the selection of treatment options for people who are overweight. Individuals are usually considered morbidly obese if their weight is more than 80 to 100 pounds above their ideal body weight.

According to four phenotypes of obesity, based on body fat composition and distribution: (1) normal weight obese; (2) metabolically obese normal weight; (3) metabolically healthy obese; and (4) metabolically unhealthy obese. Sarcopenic obesity has been characterized, related to all the described phenotypes.

How Obesity Data are Collected

An Overview of Obesity Prevalence in the United States and Data Collection

According to the Trust for America’s Health (TFAH) website, the rate of U.S. adult obesity now stands at 42.4 percent.¹ This is the first time the national rate has passed the 40 percent mark and is further evidence of our country’s obesity crisis. According to the CDC the national adult obesity rate has increased by 26 percent since 2008. For an excellent overview of obesity, see The State of Obesity: Better Policies for a Healthier America 2022 with a Special Feature on Food Insecurity and its Connection to Obesity. This current report will set the stage for understanding the breadth of the epidemic called obesity.

Obesity in the United States is a major health issue resulting in numerous diseases, specifically increasing the risk of certain types of cancer, coronary artery disease, type 2 diabetes, stroke, cardiovascular disease, as well as significant increases in early mortality and economic costs. Based in part on newly released 2019 data from the Centers for Disease Control and Prevention’s Behavioral Risk Factors Surveillance System⁶ (BRFSS) and analysis by TFAH, this report provides an annual snapshot of rates of overweight and obesity nationwide including age, gender, race, and state of residence. Obesity rates vary considerably between states and regions of the country. Mississippi has the highest adult obesity rate in the country at 40.8 percent and Colorado has the lowest at 23.8 percent. Twelve states have adult rates above 35 percent, they are: Alabama, Arkansas, Indiana, Kansas, Kentucky, Louisiana, Michigan, Mississippi, Oklahoma, South Carolina, Tennessee, and West Virginia. As recently as 2012, no state had an adult obesity rate above 35 percent; in 2000 no state had an adult obesity rate above 25 percent.

The Behavioral Risk Factor Surveillance System (BRFSS) is the nation’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

Rates of childhood obesity are also increasing with the latest data showing that 19.3 percent of U.S. young people, ages 2 to 19, have obesity. In the mid-1970s, 5.5 percent of young people had obesity. Being overweight or having obesity as a young person puts them at higher risk for having obesity and its related health risks as an adult. Furthermore, children are exhibiting earlier onset of what used to be considered adult conditions, including hypertension and high cholesterol.

Peruse the State of Obesity 2021: Better Policies for a Healthier America Report. Trust for America’s Health’s (TFAH) published this 18th annual report on the nation’s obesity crisis and found that sixteen states have adult obesity rates at 35 percent or higher which is up from 12 states in 2019.⁷ Notice also that social and economic factors linked to obesity were exacerbated by the COVID-19 pandemic. In 2020, 16 states had adult obesity rates at or above 35 percent, up from 12 states the previous year. These and other emerging data show that the COVID-19 pandemic changed eating habits, worsened levels of food insecurity, created obstacles to physical activity, and heightened stress, all exacerbating the decades long pattern of obesity in America. In the report, a call to action addressing the social determinants of obesity, for example, were cited ensuring access to no cost healthy school meals for all students, a program started during the pandemic. Systemic inequities and socioeconomic factors contribute to higher rates of obesity among certain racial and ethnic populations. According to the latest available national data – from the 2017 – 2018 National Health and Nutrition Examination Survey (NHANES), Black adults had the highest level of adult obesity nationally at 49.6 percent; driven mostly by an adult obesity rate among Black women of 56.9 percent. Hispanic adults have an obesity rate of 44.8 percent. White adults have an obesity rate of 42.2 percent. Asian adults have an obesity rate of 17.4 percent.

Rising obesity rates are also a problem among children and adolescents. According to the 2017 – 2018 NHANES data nearly 20 percent (19.3 percent) of U.S. children ages 2 to 19 have obesity. These data more than tripled since the mid-1970s and Black and Latino youth have substantially higher rates of obesity than do their White peers.

The National Center for Health Statistics (NCHS), Division of Health and Nutrition Examination Surveys (DHANES), part of the Centers for Disease Control and Prevention (CDC), has conducted a series of health and nutrition surveys since the early 1960’s. The National Health and Nutrition Examination Surveys⁸ (NHANES) were conducted on a periodic basis from 1971 to 1994. In 1999, NHANES became continuous. Every year, approximately 5,000 individuals of all ages are interviewed in their homes and complete the health examination component of the survey. The health examination is conducted in a mobile examination center (MEC) which provides an ideal setting for the collection of high-quality data in a standardized environment.

Obesity is associated with a range of diseases, including type 2 diabetes, heart disease, stroke, arthritis, sleep apnea, and many types of cancers. Obesity is estimated to increase healthcare spending by $149 billion annually (about half of which is paid for by Medicare and Medicaid). Furthermore, obesity is one of the underlying health conditions associated with the most serious consequences of COVID-19 infection, including higher risk of hospitalization and death.

The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. NHIS data are collected through personal household interviews. As an example, CDC uses data from the Special Supplemental Nutrition Program for Women, Infants, and Children Participants and Program Characteristics (WIC PC) for weight status surveillance among young children in families with lower incomes. Data is available for children aged 2 to 4 years and infants aged 3 to 23 months. The WIC PC data is included in the CDC’s Division of Nutrition, Physical Activity, and Obesity (DNPAO) Data, Trends, and Maps. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined.

The main objective of the NHIS is to monitor the health of the United States population through the collection and analysis of data on a broad range of health topics. A major strength of this survey lies in the ability to categorize these health characteristics by many demographic and socioeconomic characteristics.

NHIS data are used widely throughout the Department of Health and Human Services (HHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs.

While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 15-20 years to incorporate advances in survey methodology and coverage of health topics. In January 2019, NHIS launched a redesigned content and structure that differs from its previous questionnaire design (1997–2018).

The National Center for Health Statistics includes many reports and linkages to several CDC websites such as the new report on life expectancy in the United States with a written report released in August 2021 on Provisional Life Expectancy Estimates for 2021. Another new website hosts the Health, United States program providing national trends in health statistics. Take a look and explore the latest analyses from Health, United States, 2020–2021 in the online topic pages. Links to the Health, United States, 2019 the most recent report and materials are available on the Annual Report page. This report presents trends and current information on selected measures of health in a 20-figure chartbook, with major findings summarized in Highlights.

Starting with the 2020–2021 edition, the Health, United States Annual Perspective will integrate selected analyses from the Health, United States topics released online. The theme for the 44th edition of Health, United States will be on health disparities.

Health, United States provides a wide array of trends in health statistics to policymakers, public health professionals, and the public. It presents statistics in four overarching areas: health status and determinants, health care utilization, health care resources, and health expenditures and payers. In addition to presenting key findings from the major data collection systems of the National Center for Health Statistics and other CDC programs, Health, United States uses data from other federal agencies, as well as private and global sources.

One of 28 topics on the CDC website on Nutrition is located within the Health, United States pages. It includes key findings from data collected up to 2020-2021. Nutrition covers the energy consumed in food which is supplied by three macronutrients: carbohydrates, proteins, and fats. Under-or-overconsumption of individual macronutrients may increase a person’s risk of chronic diseases such as obesity, coronary heart disease, type 2 diabetes, and cancer according to the Institute of Medicine.⁹

See additional information at the National Academies of Sciences, Engineering, and Medicine 2016. Assessing Prevalence and Trends in Obesity: Navigating the Evidence.¹⁰

How Can We Improve Current Surveillance Systems and Data?

Current surveillance methods and infrastructure need to 1. become more nimble in responding to research findings, 2. provide data that are sufficiently timely and precise to evaluate obesity policies and programs at different geographic levels, and 3. link these data to policy measures. Below are proposed enhancements to existing surveillance systems that can maximize their utility.

Increase the Number and Scope of Environmental Measures and Surveillance Systems

Although much work has been focused on developing and evaluating environmental measures for food and physical activity environments, research and implementation gaps still exist. Recommendations from an expert meeting on environmental and policy research on obesity, physical activity, and diet call for surveillance systems with good measures of the environment, policy surveillance measures, and systems; surveillance to track changes in food industry activities over time; attention to minority and low-income populations; and measures of evaluation.¹¹ Another review identified gaps in research related to macroenvironments (city or larger) and within economic and political microenvironments in home, workplace, and neighborhood settings.¹² To address these issues, greater surveillance infrastructure is needed at the local and state levels, where these policies are often implemented first.

Considerations for socially disadvantaged and culturally diverse populations are often not adequately incorporated into surveillance systems and measures. Low numbers or regional locations of underserved populations, insufficient provisions for differences in language or culture, and lack of infrastructure must be anticipated, and surveillance systems should be monitored to be sure these elements are addressed.¹³

Harmonize Data Across State and National Surveillance Systems

Harmonization of data can be accomplished by coordinating efforts and standardizing protocols across different surveillance and evaluation structures to minimize duplication of effort, leverage resources, and maximize data use.¹³ Coordinating data sources can allow for pooling of data from different sources, leading to increased sample sizes that can facilitate the analyses of obesity determinants and consequences for underrepresented groups. Consistency of obesity-related measures is emphasized in the report Assessing Prevalence and Trends in Obesity: Navigating the Evidence,¹⁰ which proposes 1. use of the new Assessing Prevalence and Trends framework, which integrates end user perspectives with assessment considerations; 2. designation of a national convener to organize stakeholders to standardize data collection methodologies; and 3. research focused on improving obesity assessment methodology.

Improve the Sensitivity and Relevance of Obesity Measures

One of the hallmarks of a robust surveillance system is the ability to measure the same variables over time in a consistent manner. However, recent studies suggest that the addition of new measures or methods can augment current data collection and amplify research and monitoring possibilities.

Additions to BMI Measures 

Measurement of BMI has long been a consistent outcome of obesity surveillance systems, adding to the ease and low cost of directly measured height and weight or even obtaining these data via self-report. BMI misclassifies as nonobese one-quarter of adult males and nearly half of adults whose dual energy x-ray absorptiometry (DXA) measurements classify them as obese.¹⁴ BMI has similarly poor sensitivity as a measure of adiposity in children.¹⁵ In addition, BMI does not detect an accumulation of abdominal fat, which is known to correlate with insulin resistance even in lean individuals.¹⁶ Surveillance science has been slow to take advantage of research that identifies alternative anthropometric measures of obesity.¹⁷ Combining two or more different anthropometric measures, such as waist-to-hip ratio and waist-circumference-to-height ratio, works well and may be more sensitive to the accumulation of abdominal fat,^17–19 although these measurements are more invasive and require additional privacy.

Addition of longitudinal data include methods for longitudinal population-based analyses which are essential to assess the impact of policy-led interventions on the incidence of obesity during crucial developmental time periods, such as childhood or transitions from high school to college.²⁰ Developing cohorts with regularly assessed obesity measures over the lifespan would provide data on incidence of obesity that would coincide with obesity prevention initiatives. Longitudinal BMI measure analyses are uncommon, particularly among low-resource populations, which are at greater risk of obesity. More research on longitudinal BMI studies are needed on low-income individuals of all age groups and backgrounds.

Workforce Development in Surveillance Work

Although few data have assessed the size of the workforce that is engaged in surveillance work, similar studies have documented shortages in related disciplines, such as epidemiologists, public health nurses, and informaticians.²¹ Investigators have estimated that three-quarters or more of the public health workforce do not receive adequate training in public health, and most clinical health professionals are not exposed to population health or public health concepts.^21,22 Initial actions that would help to achieve a competent surveillance workforce include developing learning competencies for surveillance; offering training opportunities for the existing workforce using webinars or online formats; setting up a continuous training process; and planning for future training needs.^21,23

The Future of Obesity Surveillance

Data collection, storage and linkage advances depend on updated technology. Data is becoming more integrated into routine clinical care practices, school settings and obesity surveillance systems. Registries that contain individual data are then augmented or replaced by updated obesity surveillance systems of individual data. As the technology around data collection becomes more integrated, we can envision an aggregation of registries and as with most innovations, these offer both promise and challenges for researchers and practitioners.

Use of Electronic Health Records

EHRs are likely to play a key role in the future of obesity surveillance systems. As of 2014, 80% of physicians reported using EHRs,²⁴ and EHRs have been effective in identifying and flagging obesity.²⁵ As EHRs become more sophisticated, and eventually incorporated into big data analysis systems along with personal data from wearable devices and social media,²⁶ these data will be used for obesity surveillance as well as to inform both individual- and population-level interventions. Statistical analyses with such data are possible and include plotting individual and group-based weight trajectories over the life course, examining demographic subgroups, and examining associations of weight trajectories with behavioral risk factors as well as clinical outcomes.^27,28

Incorporation of Self-Measurements or Individualized Electronic Data Collection into Surveillance Efforts

Recent years have seen the development of a variety of consumer-friendly wearable devices and sensors for self-tracking of health, including activity trackers, smart watches, smart clothing, and smart implants,^23,29 which can provide usable health data. Consumer acceptability of these devices is high, and an intensely competitive market ensures that the cost of these devices stays low. Hence, there is considerable potential for their use in obtaining measures relevant to obesity in a general population. The use of these types of self-measurements has been feasible, even among low-income communities.³⁰ Data from social media postings of individuals offer another source of self-measured data. Social media and text analytics methods make it possible to search through social media postings and extract data relevant to health³¹ and, potentially, to include such data as part of routine data collection systems.²³

Privacy Considerations of Individual Data

The increased use of data registries and surveillance for assessing the effects of obesity initiatives is promising but can be troublesome to participants, especially with current news stories about data leaks and hacks. A recent survey across 27 European Union countries found that participants appreciated the usefulness of EHR data but are concerned about widespread availability of the data to others, especially insurance companies, pharmaceutical companies, and academic researchers.³² To gain the public’s trust, decision makers must emphasize the importance of the information to be gained, as well as put the provisions in place to safeguard the individual’s privacy.³²

Timely Feedback Loops to Data Consumers

Feedback loops to the end user or practitioner are a crucial part of the surveillance system. As previously mentioned, the BRFSS maps have provided an easily digestible visual to represent the rapid onset and widespread reach of the obesity epidemic in the United States.³³ Using GIS, as well as other advanced data visualization techniques, researchers and practitioners can display obesity data and environmental factors such as the density of fast-food restaurants and green space by region; the availability of such data has impacts for policy makers and municipal planners.^34,35 Data dashboards and report cards are also effective means of highlighting surveillance data in easily understandable summaries, especially when compared with standard recommendations.

Summary

Although the United States has robust surveillance of obesity and individual obesity-related behaviors, to fully understand the etiology of obesity and the effects of prevention efforts, we must expand current surveillance systems in terms of settings, measures, periodicity, and populations. Increases in funding and infrastructure for local surveillance would assist in obtaining data on under served populations to better understand health disparities in obesity and prevention efforts. Also critical is the addition of environmental and policy measures to surveillance systems to allow for a better understanding of the effects of obesity prevention initiatives.

With the emerging technological advances in measurements and data management, the ability to obtain more and better surveillance presents unlimited opportunities for obesity prevention efforts, especially when coupled with the presentation of this information using data visualization techniques and easy-to-understand dashboards. To capitalize on this convergence of technology and data collection, researchers will need to nurture the science of surveillance by providing increased funding for new methodologies and outlets for presenting and publishing the resulting findings. Facilitating the development and use of surveillance data for evaluating obesity prevention efforts has the potential to significantly advance action against obesity.

Critical Thinking Questions

1. What is the rate of obesity in your state? What are some explanations why obesity rates vary geographically in different states in the US?
2. As trends indicate, are certain populations more susceptible to moving from overweight to obesity?  Name 3 reasons.
3. Has public health controlled some of the upward trends of obesity with nutrition education, encouragement of physical activity and additional prevention education?

Resources

Preventing Childhood Obesity: 4 Things Families Can Do

Preventing Childhood Obesity – Eating Better, Moving More

Promoting Healthy Behaviors

Whole School, Whole Community, Whole Child (WSCC)

School Health Index

Wellness Policy in Action Tool

Out of School Time

Parent-Perceived Stress and Its Association With Children’s Weight and Obesity-Related Behavior

Obesity Among Young Children Enrolled in WIC

WIC Participant and Program Characteristics 2020 – Charts

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