essay on obesity in school children's

134 Childhood Obesity Essay Topics & Examples

If you’re writing an academic paper or speech on kids’ nutrition or weight loss, you will benefit greatly from our childhood obesity essay examples. Besides, our experts have prepared a list of original topics for your work.

Obesity Essay | Essay on Obesity for Students and Children in English

February 12, 2024 by Prasanna

Obesity Essay: Obesity is a condition that occurs when a person puts on excess body fat. It is a sudden and unusual increase in body fat. It can lead to heart-related diseases, blood pressure, hypertension, cholesterol, and various other health issues. The main cause of obesity is over-eating. Consuming junk food and staying away for physical activities can lead to an increase in the cases of obesity. Every 1 out of 5 children is facing obesity around the globe.

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Long and Short Essays on Obesity for Students and Kids in English

In this article, we have provided a long essay and a short essay, along with ten lines on the topic, to help students write this essay in examinations. Given below is a long essay composed of about 500 words and a short essay comprising 100-150 words on Obesity in English.

Long Essay on Obesity 500 words in English

Obesity essay is usually given to classes 7, 8, 9, and 10.

The world today is facing various complex diseases. Out of them, obesity is one. Obesity is a condition wherein a person starts to gain unnecessary body fat. This is an excessive and abnormal increase in body fat which can lead to various other related health issues like heart problems, blood pressure, hypertension, cholesterol, and many more. Some people think of obesity as only a cosmetic and physical concern but that’s not true.

The lifestyle of people has changed a lot. Instead of focusing more on physical activities, there has been a paradigm shift to adapting non-physical activities. Children used to play in parks and playgrounds with friends whereas now the preference has been shifted to mobile and computer games. Not only children but also elders have changed their lifestyle a lot. Previously, people preferred to do everything by themselves. Right from doing household chores to getting things from the market, everything was done manually. But time has changed a lot. Now, everything gets delivered at the doorstep. This type of lifestyle has lead to various diseases including obesity.

Additionally, obesity is even caused due to genetics as well. Some people have heredity or have genes that force them to gain weight faster as compared to others. Also, there are some medications like those consumed by bodybuilders (steroids), antidepressants, and medicines for diabetes that make changes in the body metabolism in such a way that the appetite increases resulting in gaining weight. Some people are couch potatoes and foodaholic which means they can’t stay away from food. Under such a situation the appetite increase and the chances to fill oneself with junk food enhances. This kind of habit positively increases the chances of becoming obese.

Ever-increasing cases of obesity are surely a cause of concern, but there are various cures available to treat it. Also, not every treatment is related to medication or surgery. Some of the treatments are such that are related to changes in diet and adapting to physical activities. Eating a healthy, fibrous, and nutritious diet can help reduce that excess weight. Also not munching in between and following a diet routine can help to cure obesity.

Secondly, by doing some physical activities like walking, jogging, running, or exercising one can also burn unwanted fat and calories, thereby reducing obesity. There are various drug therapies as well as surgeries like bariatric surgery that can help reduce the weight. The drug therapies can be long term as well as short term depending on the weight to be reduced. But usually, these are clubbed with natural therapies like exercising and yoga.

Obesity is now concerning more and more people. It is thus necessary to make people aware of the symptoms, causes, and cures of the disease as well. This will help to take the necessary steps and combat obesity. Everybody should adapt to the health-enhancing lifestyle and should try to reduce unhealthy habits as much as possible. It is quite true that junk food is attractive and a healthy plate looks dull but to stay healthy and fit, one needs to choose the healthy plate over junk. This is the best way to keep the self and family away from obesity.

Short Essay on Obesity 150 words in English

Obesity essay is usually provided to classes 1, 2, 3, 4, 5, and 6.

Obesity is a cause of serious concern today. Although, many may not think of obesity as the disease still the effects of obesity can lead to various health issues. Obesity is a situation where a person faces a continuous increase in body mass. This increase is usually not normal and hence is a cause of concern. The diseases linked to obesity range from blood pressure, heart issues, hypertension, and diabetes as well. There are many causes of obesity. The most common cause of obesity is unhealthy food habits. An increase in the consumption of junk foods and munching in between leads to obesity.

The second cause of obesity is a decrease in physical activities. People have turned to couch potatoes. They prefer to sit and watch television rather than going out and doing physical exercises like running, walking, jogging, or yoga. The third reason is related to heredity or genetics. Apart there are other reasons related to medication that result in weight gain. There are various natural as well as medical treatments available for obesity. Adapting to healthy food habits and daily exercising can reduce weight. It can lead to a reduction in obesity. Apart there are drug-related therapies as well as surgeries like bariatric surgery available to reduce that excess weight.

It is important to adapt to a healthy lifestyle which includes intake of nutritious food and exercise to reduce obesity. Also making people aware of cause and cures of obesity can be of great use. The best way to keep the self and family away from obesity is to have a healthy lifestyle.

10 Lines on Obesity Essay in English

• Obesity is very common today. It is a situation where a person gains excessive and abnormal weight.
• It has affected 1 out of every 5 individuals in the world.
• It can lead to various diseases such as heart-related, hypertension, blood pressure, and many more.
• There are various causes of obesity right from genetic to habit related.
• Increased intake of junk food, decreased physical activities, increased medication, and unhealthy lifestyle is some major causes of obesity.
• The cases of obesity are more prevalent in children as they tend to be couch potatoes.
• Obesity can be cured by natural as well as medical ways.
• Natural ways to cure obesity include healthy food habits, a healthy lifestyle, and exercising.
• Medical treatments for obesity include drug treatment and surgeries like bariatric surgery.
• Making people aware of how to adapt to a better lifestyle can reduce the chances of obesity.

FAQ’s on Obesity Essay

Question 1. What is Obesity?

Answer: Obesity refers to a situation where the person gains abnormal and excessive weight. Such an increase in weight can lead to health issues.

Question 2. What are the causes of Obesity?

Answer: There are various causes of obesity. The main causes of obesity are unhealthy food habits, reduced physical exercises, increased medication, couch potato nature, and heredity.

Question 3. How can we cure obesity?

Answer: There are various natural and medical cures available for obesity. These include healthy food habits, exercising, drug treatment, and surgeries like bariatric surgery.

Question 4. What steps can be taken to reduce cases of obesity?

Answer: The steps that can be taken to reduce the chances of obesity are as follow:

• Intake of healthy food
• Adapting to a better lifestyle
• Say no to munching and junk food.
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Prevalence of overweight and obesity among school children in Mysuru, Karnataka

Thomas, Usha M 1, ; D, Narayanappa 2 ; MS, Sujatha 3

1 Associate Professor, JSS College of Nursing, Mysuru, Karnataka, India

2 Professor of Pediatrics, Department of Obstetrics, JSS Medical College, Mysuru, Karnataka, India

3 Professor and HOD, Department of Obstetrics, JSS Medical College, Mysuru, Karnataka, India

Address for correspondence: Dr. Usha M Thomas, JSS College of Nursing, Ramanuja Road, Mysore - 570 004, Karnataka, India. E-mail: [email protected]

Received November 30, 2020

Received in revised form February 16, 2021

Accepted April 10, 2021

Childhood obesity has become a major public health challenge in developing countries including India due to the changes in the lifestyle and food habits of children owing to the influence of urban culture and technological growth. The present study is a cross-sectional, school-based study conducted to assess the prevalence of obesity and to determine the demographic variables influencing the obesity among school children.

Methods: 

The study included 440 students (Boys: 240, Girls: 200) from two randomly selected schools of Mysuru city, Karnataka. WHO Standard Age and Sex specific Growth Reference charts were used for defining overweight and obesity. Modified Kuppuswamy's socioeconomic scale (2019) was adopted to assess the socioeconomic status of the family.

Results: 

Obesity prevalence among the study subjects was 3.86% and overweight was 12.27%. The mean body mass index (BMI) among boys was 18.13 and girls was 18.80. The difference in the distribution of BMI between male and female groups was statistically significant ( P = 0.023). Age and obesity status of the children was found to have a significant association ( P = 0.022). Prevalence of overweight and obesity was more among children from higher socioeconomic class ( P = 0.01).

Conclusion: 

Prevalence of obesity and overweight among school children is comparatively higher. The higher familial income, dietary patterns, parental history of obesity and diabetes and having urban residence were identified as the major factors which influenced the obesity status of the school children.

Introduction

Childhood obesity is emerging as a major public health issue of the twenty-first century with an alarming rise in its prevalence in several developing countries.[ 1 ] As per the WHO statistics, the prevalence of obesity among children in the age group of 5-19 years has increased from 4% in 1975 to 18% in 2016, which is much higher than a three-time increase.[ 2 ]

India is witnessing a rapid economic development and nutritional transition which is linked with a change in the eating habits and physical activity of people, especially among children.[ 3 4 5 6 ] Many children today are living in an obesogenic environment. Recent globalization and urbanization has forced the children from all socioeconomic strata to depend heavily on ultra-processed, calorie rich, cheap and readily available foods which are poor in nutrients.[ 2 4 5 7 ]

Introduction of online food apps, increased pressure on academics with less or no time spent for outdoor activities, increased screen time, increased ‘pocket money’ and busy working pattern of parents add to the magnitude of the problem.[ 4 5 ]

A growing prevalence of obesity from 5.5 % to 17% has been reported by many Indian studies.[ 6 8 9 10 11 ] A higher prevalence of obesity was reported among children from urban areas compared to rural areas. Changes in the lifestyle of people in urban areas especially among children can be a major contributing factor for this disparity in the prevalence rate of obesity among children from urban areas.[ 5 9 10 ]

Most of the research studies in India have focussed on the children in metropolitan cities and very few studies are conducted in other cities which are also under the influence of lifestyle changes related to the economic and nutritional transition. Earlier studies conducted in Mysuru reported a prevalence of 4% of obesity and 8% overweight among school children.[ 8 11 ] The assessment of the magnitude of this emerging problem is crucial for implementing effective preventive strategies to ensure a healthy transition of children into adults.[ 3 ] The present study was an attempt to analyse the prevalence of obesity and its contributing factors among school children from urban areas of Mysuru.

To assess the prevalence of obesity and to determine the demographic variables influencing the obesity among school children.

Subjects and Methods

A cross-sectional study was conducted among school children in Mysuru city, Karnataka, India between December 2019 and February 2020. The present study was a pilot project carried out in 440 school children aged between 11 and 15 years. Formal administrative approval from the BEO of Mysuru city was obtained prior to the study. The list of schools in the North and South zones of the city was collected. Using simple random sampling technique, two schools from the South zone were selected for the study. All children studying in the 6 th , 7 th , 8 th , 9 th and 10 th standards from the selected schools participated in the study with the prior permission from school authorities. The study was initiated after obtaining ethical committee approval from the Institutional Ethical Clearance Committee. (Date of Ethical committee approval: 02-11-2018). Parents of each participant were briefed about the study purpose through telephone. Informed written consent from the parents and assent from the participating children were procured.

All children were interviewed personally by the investigator. Information regarding the sociodemographic variables influencing the weight status of children was collected using a pretested structured interview schedule. Standardised instruments and techniques were used for anthropometric measurements such as height and weight of the children. A standardised, calibrated digital weighing scale was used to measure the weight. The weight of the students was obtained while the students stood upright barefooted on the weighing machine. The height was measured by standardised, calibrated digital stadiometer. The height was recorded in centimetres while the students stood straight with horizontal gaze and barefooted. WHO Standard Growth Reference for BMI for specific age and gender was used as reference standards. BMI was computed using the formula: BMI = bodyweight in kilograms divided by height in meters squared.

According to WHO Standard Age and Sex specific Growth Reference charts for children within the age of 5-19 years (2007), weight of the children was categorized as: (i) Normal weight: Weight corresponding to the WHO Growth Standard median, (ii) Overweight: BMI for age greater than 1 standard deviation above the WHO Growth Standard median, (iii) Obesity: BMI for age greater than 2 standard deviations above the WHO Growth Standard median and (iv) Underweight: BMI for age less than 2 standard deviations below the WHO Growth Standard median.

Modified Kuppuswamy's method of socioeconomic scale (2019) was used to assess the socioeconomic status of the family. The scale was based on the following three characteristics of the family:

• Educational qualification of the head of the family (maximum score - 7)
• Type of occupation of the head of the family (maximum score - 10)
• Monthly income of the family (maximum score -12)

Based on the total score, the socioeconomic status of the families of the children were classified as “Upper, Upper-Middle, Lower-Middle, Upper-Lower and Lower classes”. Data was compiled using Microsoft Excel software. The results were analysed statistically using descriptive and inferential statistical tests. P value < 0.05 was considered as significant.

The study included a total of 440 students (240 boys 54.5% and 200 girls 45.5%). The distribution of maximum number of boys (22.5%) and girls (17.5%) was in the age of 14 years and 12 years Table 1 . Majority of children (66.5%) were from nuclear families and 51.3% of study subjects had a family history of obesity. Family history of diabetes was reported from 48.6% of children. Most of the children (80%) were residing in the urban areas of Mysuru, while 12.7% were from semi-urban areas and only 7.3% were from rural areas. Majority of children (57.4%) represented the upper-middle class of the society, 22.2% belonged to the lower-middle, 16% belonged to the upper-middle and 4.4% children belonged to upper-class, respectively. There were no subjects from the lower class. The prevalence of obesity, overweight and underweight is shown in Table 2 . Age wise and gender wise prevalence of obesity and overweight are presented in Table 1 and the Figures 1 - 3 respectively.

As shown in Table 2 , prevalence of obesity and overweight among the study subjects was 3.86% and 12.27%, respectively. Prevalence of underweight was 16.3%. Mean BMI among the boys was 18.13% and BMI of girls was 18.80%. The difference in the distribution of BMI between male and female groups was statistically significant ( P = 0.023).

The present study has shown an increase in the prevalence of obesity with the age of school children; with the highest prevalence of obesity in the age group of 13-15 years and least prevalence in the age group of 11-12 years [Figures 1 - 3 ]. Results revealed statistically significant association between the age and obesity status of study subjects ( P = 0.22).

Increased prevalence of obesity was observed among children from nuclear families and among those from urban areas, but this difference was not statistically significant ( P > 0.05). Prevalence of overweight and obesity was more among children from higher socioeconomic class ( P = 0.01). Overweight and obesity were observed more among children who reported a family history of diabetes mellitus and obesity. This difference was not statistically significant ( P > 0.05).

Obesity among children has emerged as an important public health hazard and is reaching epidemic proportions in many Asian countries including India. It is associated with a high risk of morbidity and mortality from cardiovascular diseases and Type 2 diabetes mellitus. Hence, it is important to assess the prevalence of obesity and overweight among children to identify the magnitude of the problem and to implement appropriate action strategies to stop its progression into adulthood.

The present cross-sectional study was carried out among the school children between the age group of 11-15 years from two urban schools of Mysuru city, Karnataka. Findings of the study revealed 3.86% obese and 12.27% overweight children. Earlier studies conducted in Mysuru by Premanath et al .[ 11 ] and Kavita et al .[ 8 ] reported a prevalence of 4% obesity and 8% overweight among children of various age groups. Prevalence of overweight reported among the present-study subjects was much higher than the earlier reports, which strongly suggest the influence of progressive urbanization on the food habits and weight status of people, especially, among children in major cities.

Previous studies conducted in different cities of India reported an increased prevalence of obesity among school children. A school-based obesity prevalence study carried out in Bengaluru by Sunil Kumar et al . documented that prevalence of overweight and obesity among school children was 7.09% and 4.08%.[ 12 ] A study conducted by Ramesh among high school students of Trivandrum city, Kerala, reported that the prevalence of overweight was 12% and obesity was 6.3% among the study subjects.[ 13 ] Shashidhar et al . reported 9.9% overweight and 4.8% obesity among school children in Mangalore, South Karnataka.[ 14 ]

Additionally, research studies conducted during the recent COVID pandemic reported an increasing prevalence of obesity among children due to the restrictions of the lockdown. A literature search conducted by Stavidrou et al . documented that changes in the dietary habits of children with consumption of more fried foods, snacking and sedentary behaviour due to the forced home stay have resulted in the increased prevalence of obesity among them.[ 15 ] Another study by Dunton et al . also reported that COVID restrictions have impacted physical activity and sedentary behaviour among children.[ 16 ] Peng et al . reported a significant increase in BMI (21.8-22.1 kg/m 2 , P < 0.001) and increased prevalence of obesity from 10.5 to 12.6% among children of various age groups during the COVID lockdown.[ 17 ]

The study has also revealed that undernutrition was prevalent at a higher magnitude among the study subjects (16.3%). A study by Saraswathi et al . documented an increased prevalence of undernutrition among school children from urban and rural areas of Mysuru (31.8 and 45.3%).[ 18 ]

Mean BMI among boys was 18.13 and girls was 18.80. The difference in distribution of BMI between boys and girls was statistically significant ( P = 0.023). Contrary to these findings, a study conducted by Sunil Pathak et al . reported that the difference in the distribution of BMI between boys and girls was statistically not significant ( P = 0.129).[ 10 ]

Study findings revealed a significant socioeconomic gradient in the prevalence of overweight and obesity among children, which was consistent with the findings of the other studies. Prevalence of overweight and obesity was observed more among children from higher socioeconomic class. A study conducted by Supreet Kaur et al . among school children in Delhi reported an increased prevalence of overweight (15.3%) and obesity (6.8%) among the children from high-income groups.[ 19 ] Similar findings are reported in the studies by Kavita et al .[ 8 ] and Shashidhar et al .[ 14 ] Anoop et al . also documented that prevalence of obesity was more among children from higher socioeconomic classes of the metropolitan cities.[ 9 ] Lifestyle patterns among urban children leading to unhealthy dietary habits, easy affordability of junk foods and decreased physical activity can be attributed to the higher prevalence of obesity among them.

An increase in the prevalence of obesity was seen with the age of children; with highest prevalence of obesity in the age group of 13-15 years and least prevalence in the age group of 11-12 years. Cross-sectional study conducted by Kavita et al . in Mysuru also reported increasing prevalence of obesity with the age of children.[ 8 ] Similar findings are observed in the study by Ramesh.[ 13 ]

The current study revealed no significant differences between the dietary patterns of children and their risk of being overweight/obese. Similar findings are reported by Sujan Gautam et al . among the school children in Udupi, Karnataka.[ 20 ] Kavita et al . in Mysuru reported a higher prevalence of obesity among children who consumed vegetarian foods.[ 8 ]

Overweight and obesity were more prevalent among children belonging to nuclear families. Sunil Pathak et al . reported a statistically significant positive correlation between type of residence and BMI categories among school children in Vadodara.[ 10 ] A study conducted by Ramesh reported that children from nuclear families were more obese than those from joint families.[ 13 ] This difference could be attributed to the extra pampering of children and busy working schedule of the parents in nuclear families.

The present study observed an increased rate of obesity and overweight in the children who reported a family history of diabetes mellitus and obesity. However, this difference was not statistically significant ( P > 0.05). Contrary to these findings, significant difference was observed in the study by Ramesh, which reported a statistically significant association between prevalence of obesity and family history of diabetes and obesity.[ 13 ]

The present study is an overview of an emerging health issue of obesity among school children in a major city of Karnataka. Higher prevalence of overweight was observed among the children. Higher familial income, dietary patterns, parental history of obesity and diabetes and having urban residence were identified as the major factors which influenced the obesity status of school children in the current study. Urbanization has resulted in a change in the lifestyle and eating patterns of people, especially among children. Tight school schedule and academic competitiveness in the present school curriculum has hindered the participation of children in outdoor activities and sports in urban areas which also add to the magnitude of the problem. Additionally, the restrictions imposed by the COVID-19 pandemic have much impact on the physical activity and sedentary behaviour among children. Taking into consideration of the alarming increase in the prevalence of obesity among children, preventive measures should be initiated by all primary health care practitioners to curb this problem at the earliest. Early identification of the problem by screening the BMI and assessment of the sedentary behaviour and eating practices of school children should be implemented at the schools. Parents and teachers need to be educated about the obesity preventive strategies with appropriate lifestyle modification practices to enable the children to go through a healthy transition into adulthood.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgements

Authors gratefully acknowledge the cooperation of the BEO of Mysuru and the concerned school authorities for giving permission and also the students for participating in the study.

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• Second Opinion

Obesity in Teens

What is obesity in teens?

Obesity is when a teen has too much body fat. Obesity is a serious, long-term disease.

What causes obesity in a teen?

In many ways, childhood obesity is a puzzling disease. Doctors do not fully understand how the body controls weight and body fat. On one hand, the cause seems simple. If a person takes in more calories than he or she uses for energy, then he or she will gain weight.

But a teen's obesity can be caused by a combination of things. It can be linked to:

• Socioeconomic issues
• How the body turns food into energy (metabolism)
• Not getting enough sleep
• Lifestyle choices

Some endocrine disorders, diseases, and medicines may also have a strong effect on a child’s weight.

Which teens are at risk for obesity?

Things that may put your teen at risk for obesity are:

• Genes. Obesity may be passed down through families. Having even one obese parent may raise a child’s risk for it. Experts are looking at the link between genes, the ever-changing environment, and obesity.
• Metabolism. Each person’s body uses energy differently. Metabolism and hormones don’t affect everyone the same way. They may play a role in weight gain in children and teens.
• Socioeconomic factors. There is a strong tie between economic status and obesity. Obesity is more common among low-income people. In some places, people may have limited access to affordable healthy foods. Or they may not have a safe place to exercise.
• Lifestyle choices. Overeating and an inactive lifestyle both contribute to obesity. A diet full of sugary, high-fat, and refined foods can lead to weight gain. So can a lack of regular exercise. In children, watching TV and sitting at a computer can play a part.

What are the symptoms of obesity in a teen?

Too much body fat is the main symptom of obesity. But it’s hard to directly measure body fat. A guideline called the body mass index (BMI) is used to estimate it. The BMI uses a teen’s weight and height to come up with a result. The result is then compared with standards for children of the same gender between the ages of 2 and 20.

A teen who is overweight has a BMI between the 85th and 95th percentile for age and gender. He or she is obese if the BMI is greater than the 95th percentile for age and gender.

How is obesity diagnosed in a teen?

Obesity is diagnosed by a healthcare provider. BMI is often used to define obesity in teens. It has 2 categories:

• BMIs at the 95th percentile or more for age and gender, or BMIs of more than 30, whichever is smaller. BMI findings in this category mean the child should have a full health checkup.
• Family history of cardiovascular disease, high cholesterol, diabetes, and obesity
• High blood pressure
• Total cholesterol level
• Large gains in BMI from year to year
• Concerns about weight, including the child’s own concerns about being overweight

How is obesity treated in a teen?

Treatment depends on your teen’s symptoms, age, and health. It also depends on how severe the condition is.

Treatment for obesity may include:

• Diet counseling
• Changes to diet and amount of calories eaten
• More physical activity or an exercise program
• Behavior changes
• Individual or group therapy that focuses on changing behaviors and facing feelings linked to weight and normal developmental issues
• Support and encouragement for making changes and following recommended treatments

Treatment often involves the help of a nutritionist, mental health professionals, and an exercise specialist. Your teen’s treatment goals should be realistic. They should focus on a modest cutting back of calories, changing eating habits, and adding more physical activity.

What are possible complications of obesity in a teen?

Obesity can affect your teen’s health in a number of ways. These include:

• High blood pressure and high cholesterol. These are risk factors for heart disease.
• Diabetes. Obesity is the major cause of type 2 diabetes. It can cause resistance to insulin, the hormone that controls blood sugar. When obesity causes insulin resistance, blood sugar becomes higher than normal.
• Joint problems, such as osteoarthritis. Obesity can affect the knees and hips because of the stress placed on the joints by extra weight.
• Sleep apnea and breathing problems. Sleep apnea causes people to stop breathing for brief periods. It interrupts sleep throughout the night and causes sleepiness during the day. It also causes heavy snoring. The risk for other breathing problems such as asthma is higher in an obese child.
• Psychosocial effects. Modern culture often sees overly thin people as the ideal in body size. Because of this, people who are overweight or obese often suffer disadvantages. They may be blamed for their condition. They may be seen as lazy or weak-willed. Obese children can have low self-esteem that affects their social life and emotional health.

What can I do to help prevent obesity in a teen?

Young people often become overweight or obese because they have poor eating habits and aren’t active enough. Genes also play a role.

Here are some tips to help your teen stay at a healthy weight:

• Focus on the whole family.  Slowly work to change your family’s eating habits and activity levels. Don’t focus on a child’s weight.
• Be a role model. Parents who eat healthy foods and are physically active set an example. Their child is more likely to do the same.
• Encourage physical activity. Children should get at least 60 minutes of physical activity each day.
• Limit screen time. Cut your teen’s screen time to less than 2 hours a day in front of the TV and computer.
• Have healthy snacks on hand. Keep the refrigerator stocked with fat-free or low-fat milk instead of soft drinks. Offer fresh fruit and vegetables instead of snacks high in sugar and fat.
• Aim for 5 or more. Serve at least 5 servings of fruits and vegetables each day.
• Drink more water. Encourage teens to have water instead of drinks with added sugar. Limit your child’s intake of soft drinks, sports drinks, and fruit juice drinks.
• Get enough sleep. Encourage teens to get more sleep every night. Earlier bedtimes have been found to decrease rates of obesity.

Key points about obesity in teens

• Obesity is a long-term disease. It’s when a teen has too much body fat.
• Many things can lead to childhood obesity. These include genes and lifestyle choices.
• Body mass index (BMI) is used to diagnose obesity. It’s based on a child’s weight and height.
• Treatment may include diet counseling, exercise, therapy, and support.
• Obesity can lead to many other health problems. Some of these are heart disease, type 2 diabetes, and joint problems.
• Obesity can be prevented with healthy lifestyle choices like being more physically active and eating more fruits and vegetables.

Tips to help you get the most from a visit to your child’s healthcare provider:

• Know the reason for the visit and what you want to happen.
• Before your visit, write down questions you want answered.
• At the visit, write down the name of a new diagnosis, and any new medicines, treatments, or tests. Also write down any new instructions your provider gives you for your child.
• Know why a new medicine or treatment is prescribed and how it will help your child. Also know what the side effects are.
• Ask if your child’s condition can be treated in other ways.
• Know why a test or procedure is recommended and what the results could mean.
• Know what to expect if your child does not take the medicine or have the test or procedure.
• If your child has a follow-up appointment, write down the date, time, and purpose for that visit.
• Know how you can contact your child’s provider after office hours. This is important if your child becomes ill and you have questions or need advice.

Related Links

• The Center for Healthy Weight
• Adolescent Bariatric Surgery
• Adolescent Medicine
• BMI Calculator for Children and Teens
• Weight Management
• Determining Body Mass Index for Teens

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A Chubby Baby Is Not a Sign of Future Obesity

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Evaluating the benefits of and barriers to pediatric obesity programs.

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Obesity now affects more than one in five children in the United States, and while there are effective, recommended interventions, availability is limited for most children. In two new studies, Yale researchers assessed the cost-effectiveness of one intervention and factors that have hindered and facilitated implementation of another to uncover strategies for improving access to effective pediatric obesity treatment.

The publications are timely as Yale experts, working as members of national medical organizations, have supported a proposal under consideration by the Centers for Medicare and Medicaid Services for a new billing code that could allow facilities to be reimbursed by health insurance for intensive health behavior and lifestyle treatment interventions for childhood obesity. Such a change would thereby encourage implementation of these programs and improve access to them, the researchers say.

The studies were published Aug. 28 in the journal Obesity.

Previous research has shown that interventions that provide comprehensive, family-centered nutrition and behavioral education, and at least 26 contact hours with families over 3 to 12 months, are effective at treating childhood obesity. These types of programs have been recommended by both the U.S. Preventative Service Task Force and the American Academy of Pediatrics.

“ We have treatment options that work,” said Mona Sharifi , an author of both studies and an associate professor of pediatrics at Yale School of Medicine. “But we have these systematic barriers to access that we need to address rapidly.”

Cost is a perennial concern affecting health care programs, obesity treatments included. In the first new study, Sharifi and her colleagues evaluated the costs — from both a health care and a societal perspective — associated with implementing the Healthy Weight Clinic intervention in federally qualified health centers.

The Healthy Weight Clinic is a program that delivers intensive health behavior and lifestyle treatment for children and adolescents with obesity or overweight that is consistent with guidelines from the American Academy of Pediatrics. The treatment model brings together teams of pediatricians, dieticians, and community health workers within primary care settings where families are already likely to be engaged.

For the first new study , the researchers looked at federally qualified health centers specifically, as they provide services in underserved communities.

“ This was purposeful to access communities that are disproportionately affected by obesity disparities,” said Sharifi.

In their analysis, the researchers broke down the intervention to its smallest pieces — personnel, materials, etc. — and determined their costs. They also estimated costs incurred by families in the form of time, transportation, and childcare expenses associated with participating in a Healthy Weight Clinic. They then entered those costs into a model that simulated a sample of patients over a 10-year period, some of whom entered a Healthy Weight Clinic intervention.

“ We were able to extrapolate those calculations out and ask, if we were able to spread this intervention to all eligible federally qualified health centers in the U.S., what would the scene look like in 10 years?” said Sharifi. “How many cases of obesity would we prevent? How much would it cost and how much might we save by improving the health of children reached by the intervention?”

They found that if Healthy Weight Clinics were made available in all federally qualified health centers over 10 years, the intervention would reach 888,000 children with obesity or overweight and prevent 12,100 cases of obesity and 7,080 cases of severe obesity.

Costs were estimated at $667 per child reached — with $456 paid by the health care sector and $211 incurred by families. Over the same time, however, the reduction in obesity cases would save approximately $14.6 million dollars in health care costs.

“ It’s a relatively low-cost intervention that our study team previously found to be effective,” said Sharifi. “And given the populations federally qualified health centers serve, our findings also project that scaling up this intervention could mitigate health inequities affecting underserved populations.”

In the second study , the researchers evaluated another intervention, by studying the dissemination of a curriculum called Smart Moves that came out of a Yale-developed program named Bright Bodies. Previous research from Sharifi, Mary Savoye (the founder of Smart Moves), and their colleagues has shown Bright Bodies to be both effective at improving health outcomes in children with obesity and overweight and, compared with usual clinical care, cost-saving .

From 2003 to 2018, the SmartMoves curriculum was disseminated to over 30 U.S.-based sites. The new study collected experiences from staff that worked at those sites to identify what factors facilitated the program’s implementation and what barriers exist to its success.

Two of the strongest facilitators of SmartMoves implementation were local partnerships with schools and exercise facilities that helped provide resources and demand for programming from families.

The biggest barrier to sustainability was funding insecurity; more often than not, this barrier resulted in failed efforts to implement or sustain new programs.

“ When a child breaks their arm, the family seeks care, and the clinic or hospital bills their insurance company to cover the cost of treatment. This model of funding doesn’t work as well for health behavior and lifestyle treatment programs,” said Sharifi. “For example, Bright Bodies involves group visits with families and is run by a dietician, an exercise physiologist, and a social worker. So you typically can’t get reimbursement from insurance companies even though Bright Bodies appears to be more effective and cost saving compared with usual clinical care. These programs often rely on grants, but grants run out and programs disappear, leaving communities lacking access to standard of care treatment.”

To pave the way for effective programs like Bright Bodies and Healthy Weight Clinic to receive reimbursement, several organizations including the American Academy of Pediatrics, the American Academy of Family Physicians, and the U.S. Centers for Disease Control and Prevention, submitted an application that would establish a new billing code. The proposal will be deliberated over the next few months by the Centers for Medicare and Medicaid Services.

“ If approved, I think it would open the door to funding the most efficient and appropriate way to deliver this treatment and give families more options for interventions,” said Sharifi. “This kind of thing — treatment that is standard of care not being reimbursed — would never happen in a field like surgery. But it happens in pediatrics because children often get neglected in U.S. health care policy and pediatricians often get shortchanged in billing.”

Policy change, she said, is needed to ensure this first-line treatment is accessible to families throughout the country.

“ Expanding access is an urgent need,” said Sharifi. “And not providing equitable access to effective, low-cost treatment for children is unethical.”

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The effectiveness of physical activity intervention at school on bmi and body composition in overweight children: a pilot study.

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1. introduction, 2. materials and methods, 2.1. participants, 2.2. procedure, 2.4. body composition analysis, 2.5. statistical analysis, 3.1. characteristics of the group, 3.2. anthropometric indicators, 3.3. fat tissue, 3.4. fat-free mass, 3.5. skeletal muscle mass, 3.6. total body water, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Kolanowski, W.; Ługowska, K. The Effectiveness of Physical Activity Intervention at School on BMI and Body Composition in Overweight Children: A Pilot Study. Appl. Sci. 2024 , 14 , 7705. https://doi.org/10.3390/app14177705

Kolanowski W, Ługowska K. The Effectiveness of Physical Activity Intervention at School on BMI and Body Composition in Overweight Children: A Pilot Study. Applied Sciences . 2024; 14(17):7705. https://doi.org/10.3390/app14177705

Kolanowski, Wojciech, and Katarzyna Ługowska. 2024. "The Effectiveness of Physical Activity Intervention at School on BMI and Body Composition in Overweight Children: A Pilot Study" Applied Sciences 14, no. 17: 7705. https://doi.org/10.3390/app14177705

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Obesity and Chronic Diseases in Children

Childhood obesity has become a pervasive global health concern with far-reaching implications beyond cosmetic considerations.

Dr. Abhigya Sharma

Medically reviewed by

Dr. Veerabhadrudu Kuncham

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Introduction

In recent years, the rapid increase in childhood obesity has emerged as a notable global public health issue. This escalating epidemic is not just a cosmetic issue; it poses severe risks to the health and well-being of the younger generation. The link between obesity and chronic diseases in children has become increasingly evident, highlighting the urgent need for comprehensive strategies to address this pressing issue.

Why Is Childhood Obesity Increasing?

Childhood obesity has emerged as a global health crisis, affecting millions of children and reaching unprecedented levels. This escalating epidemic is driven by several interrelated factors that have become integral to the modern lifestyle, significantly impacting the health and well-being of the younger generation.

Sedentary Lifestyles: One of the primary contributors to the surge in childhood obesity is the pervasive sedentary lifestyle prevalent today. The rise of technology has resulted in heightened usage of screens and reduced levels of physical activity. Children spend more time indoors engaged in activities like video games, watching television, or using smartphones, reducing overall physical activity levels.

Increased Consumption of Processed Foods: Modern dietary habits, characterized by a high intake of processed and convenience foods, play a crucial role in the obesity epidemic. These foods are often energy-dense, high in unhealthy fats and sugars, and low in essential nutrients. Fast food, sugary snacks, and sweetened beverages have become staples in many children's diets, contributing significantly to excess calorie intake.

Decline in Physical Activity: Changes in the built environment, urbanization, and transportation patterns have resulted in declining physical activity among children. Factors such as reduced opportunities for outdoor play, limited access to safe recreational spaces, and an increase in passive modes of transportation contribute to a sedentary lifestyle.

What Are the Health Implications of Childhood Obesity?

The health implications are mentioned below:

Type 2 Diabetes: The association between childhood obesity and a higher likelihood of developing type 2 diabetes is a worrying health consequence that warrants closer scrutiny. Obesity, defined by an excessive buildup of body fat, initiates a series of metabolic alterations, including insulin resistance. Insulin, a hormone crucial for regulating blood sugar levels, becomes less effective in obese individuals. This compromised insulin functionality can result in heightened blood glucose levels, raising the risk of developing type 2 diabetes. The consequences of early-onset diabetes in childhood are profound, posing long-term health challenges and emphasizing the urgency of addressing the root causes of obesity.

Cardiovascular Diseases: Childhood obesity sets the stage for a cascade of cardiovascular risks that manifest in adulthood. Excess fat accumulation contributes to hypertension (high blood pressure) and elevated cholesterol levels. Over time, these factors can lead to atherosclerosis, a condition where arteries become narrowed and hardened, restricting blood flow. The long-term implications are significant, as atherosclerosis is a precursor to potentially life-threatening cardiovascular diseases such as heart attacks and strokes. Interventions targeting childhood obesity are critical for immediate health and mitigating cardiovascular risks that may otherwise manifest in later years.

Respiratory Problems: The striking connection between childhood obesity and respiratory problems sheds light on the intricate interplay between body weight and lung function. Excess weight places a strain on the respiratory system, contributing to the prevalence of conditions like asthma and sleep apnea in obese children. The compromised lung function and increased respiratory difficulties have implications for the overall health and quality of life of affected children. Tackling childhood obesity becomes imperative not only for preventing respiratory issues but also for improving respiratory health in those already affected.

Psychosocial Impact: Beyond the physical health implications, the psychosocial impact of childhood obesity adds another layer of complexity to this health challenge. Obese children often face social stigma, discrimination, and a heightened risk of low self-esteem and depression. The emotional impact of these occurrences can have enduring consequences on a child's psychological welfare, impacting their social engagements, academic achievements, and overall life satisfaction. Recognizing and addressing the psychosocial aspects of childhood obesity is crucial for promoting holistic well-being and resilience in affected children.

What Steps Should Be Taken to Address the Issue?

Education and Awareness: Promoting awareness about the multifaceted risks of childhood obesity is a cornerstone in the battle against this epidemic. Educational programs must be comprehensive, targeting children, parents, schools, and healthcare providers. By fostering a collective understanding of the long-term health implications, we can empower communities to take proactive measures in preventing and managing childhood obesity. Public health campaigns, workshops, and informative materials can serve as powerful tools to disseminate essential knowledge and drive informed decision-making.

Encouraging Healthy Lifestyles: To address childhood obesity effectively, it is imperative to instill and promote healthy lifestyles from an early age. Initiatives should focus on encouraging regular physical activity, providing accessible spaces for exercise, and promoting sports and recreational activities. Equally important is the emphasis on nutrition—guaranteeing that children have access to nourishing meals and snacks while discouraging the intake of sugary and high-calorie foods. Collaboration with local communities, sports clubs, and nutritionists can create a supportive ecosystem that reinforces the significance of maintaining a harmonious lifestyle for holistic wellness.

School-Based Interventions: Recognizing the influential role of schools in shaping children's habits, implementing interventions within the educational system is crucial. Nutrition education programs should be integrated into the curriculum, offering students practical knowledge about making healthy food choices. Additionally, physical activity should be woven into the daily routine, with designated times for exercise, sports, and play. Furthermore, schools can take the lead in transforming their cafeterias by offering healthier food options and promoting nutritious eating habits. A holistic approach within the school environment creates an atmosphere that fosters healthy habits, shaping children's behaviors for the long term.

Family-Centered Approaches: Engaging families is fundamental in the fight against childhood obesity. Family-centered approaches recognize parents and caregivers' vital role in shaping a child's lifestyle. Encouraging family meals, involving parents in school-based programs, and fostering a supportive home environment are key components of this strategy. By providing resources and guidance to parents, we empower them to model and reinforce healthy behaviors at home. Family involvement also creates a consistent message, ensuring that the lessons learned at school about nutrition and physical activity are reinforced within the child's daily life, ultimately contributing to sustained positive changes.

Childhood obesity and the consequential chronic diseases present a formidable public health challenge that requires urgent attention. Society, collectively, must unite to deploy comprehensive strategies that target the root causes of this epidemic. Emphasizing education, advocating for healthy lifestyles, and cultivating supportive environments for children are pivotal in navigating this complex issue.

Childhood Obesity: A Complex Disease

https://www.healthychildren.org/English/health-issues/conditions/obesity/Pages/childhood-obesity-a-complex-disease.aspx

Childhood Obesity Is a Chronic Disease Demanding Specific Health Care - a Position Statement from the Childhood Obesity Task Force (COTF) of the European Association for the Study of Obesity (EASO)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5644867/

Chronic diseases in the paediatric population: Comorbidities and use of primary care services

https://pubmed.ncbi.nlm.nih.gov/32178966/

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How ‘white’ bread can make school cafeterias healthier.

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Bread and grains can now be healthier for kids and taste great

Innovative grains that deliver both taste and health can be a bonanza for food companies

After a long summer, back-to-school is suddenly upon us. This is a good time to examine what school-aged children and teens are eating for lunch. While nutrition content is improving, rising obesity rates and food waste persist. Food companies can – and should – step up and deliver better nutrition without compromising on taste. It’s a huge business opportunity for the taking while doing good for our kids.

America’s school aged children are at their unhealthiest levels in history. The Centers for Disease Control (CDC) estimates that 1 in 5 children and adolescents in the U.S. are living with obesity, up from 6.2 percent in 1971-1974. The CDC notes that obesity rates for the poorest children are more than twice that of the children from the wealthiest families.

Distilling state-by-state obesity data from the CDC and ProCare’s State of School Lunch Report reveals that child obesity rates are higher in the 10 states with the least healthiest school lunch programs compared to the top 10 states. More concerning is that the gap in obesity rates between these states increases as kids gets older, from 2.1 percentage points for children 10-17, to 2.7 percentage points for high schoolers, to 4.0 percentage points for adults.

If we don’t start kids off on the right path, they are doomed to fight obesity and overweight problems over their lifetimes.

Government programs have attempted to make school lunches healthier. In 2010, the Obama administration signed into law The Healthy, Hunger-Free Kids Act which set new nutrition standards for schools, a centerpiece of First Lady Michelle Obama’s Let's Move! initiative to combat childhood obesity. The legislation included provisions such as reduced portion sizes in meals, mandating a minimum on fruit, vegetables, and whole grain servings, and establishing a maximum sodium, sugar, and fat content.

Some nutritional improvements have been made since the Act was passed. A Washington state study found an increase in six nutrients: fiber, iron, calcium, vitamin A, vitamin C, and protein. While whole grain density among children 2-19 improved from 0.36 ounce equivalents per 1,000 calories in 2009 - 2010 to 0.47 ounce equivalents in 2017-2018, kids are still eating much more refined grains than the recommended whole grains. Fiber consumption remains woefully short of kids’ dietary needs.

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Best 5% interest savings accounts of 2024, food companies must innovate.

All food and beverage marketers know that the #1 reason consumers select something to eat or drink is taste. This includes school children and high schoolers. Nothing else – healthfulness, convenience, mouthfeel – comes close. School children often complain that whole grain foods just don’t taste good. This is reflected in what they consume in the cafeteria: A 2022 report by Chartwells, a supplier to 4500 school cafeterias, found that flour-based items like bread, tortillas, pizza and pasta continue to be popular options. And let’s not forget the cookies, brownies and cakes that populate many menus.

Developing healthier alternatives to white flour that taste the same to finicky school children is a challenge that only the private sector and their R&D programs can handle. One promising area I’ve come across is substituting the flour used to make breads, pizza crusts, spaghetti, etc. with high-amylose wheat (HAW). This is wheat that contains higher levels of resistant starch than standard wheat which delivers augmented levels of fiber without the whole grain nutty taste or the off-white appearance. HAW has great potential to increase fiber consumption among children.

Resistant starches are now available from companies such as Bay State Milling, ADM and Ingredion. Bay State’s testing of its HealthSense flour found that a trained sensory panel couldn’t tell the difference between pasta made from their HAW flour and traditional pasta. Incorporating this type of product into school foods would go a long way in nutritionally improving the laundry list of grain-based products that are served most often in schools.

A Great Byproduct: Less Food Waste

With improved taste and appearance comes less food waste. According to a study by Penn State, plate waste in United States cafeterias ranges from 27% to 53% of the food served. A huge portion of school lunches end up in the trash, producing 530,000 tons of food waste yearly, according to a study done by K-12 Dive . American school cafeterias waste more food than those in other developed countries like Sweden (23%), Italy (20% - 29%) and Spain (30%).

Our kids need healthier mac & cheese, buns, pizza and spaghetti that taste great. Food companies would be wise to innovate and adopt these high-fiber ingredients into their grain-based offerings. Delivering products that satisfy both the tastes and health needs of Gen Alphas and Gen Zs during their formative school years would create a tremendous opportunity to add them to the fold for food companies, and to reduce food waste. As smart marketers, why would they pass this up?

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• How Project 2025 Would Jeopardize Americans’ Health

W ith fewer than 70 days to the election, Americans are starting to learn about the distinct visions that Vice President Kamala Harris and former President Donald Trump have for our country’s future. Nowhere is the divide between the two candidates wider than in health care . Though health care has frequently played a pivotal role in presidential contests, this November’s election may be among the most consequential for our patients.

While Harris and Governor Tim Walz will work to build on the successes of the Biden-Harris administration over the past four years—continuing efforts to lower prescription drug costs and address medical debt , for instance—Trump and his running mate J.D. Vance have said relatively little about their health policy agenda. But Project 2025 offers a glimpse of what it could look like. 

Sponsored by the right-wing think tank, the Heritage Foundation, the Project 2025 policy agenda was written by more than 400 conservative experts and published in a book titled Mandate for Leadership: The Conservative Promise . While Trump has publicly disavowed the initiative, he has endorsed (and even tried to implement) many of its core proposals, several of which were penned by his former staffers.

Here are a few examples of how the Project 2025 agenda would leave my patients—and those of clinicians across the country—worse off.

Making medications more expensive

I’ve taken care of countless patients who rely on insulin to control their diabetes. When these patients don’t take their insulin, they’re at risk of not just worsened diabetes (which increases the risk of heart attacks and strokes) but also life-threatening medical emergencies from dangerously high blood-sugar levels. In the decades preceding the Biden-Harris administration, the out-of-pocket cost of insulin steadily grew , forcing many patients to cut back elsewhere to afford their insulin and others to forgo it altogether. 

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The Inflation Reduction Act (IRA)—signed into law by President Biden two years ago—capped insulin costs at $35 per month for people on Medicare. The data show that this cap increased the number of insulin prescriptions that were filled, ensuring more patients with diabetes got what they needed to stay healthy. The IRA will also cap annual out-of-pocket spending on prescription drugs (not just insulin) for seniors starting next year. And despite aggressive lobbying and legal challenges from drugmakers, the law empowered Medicare to negotiate prices with Big Pharma for the first time in history, achieving significant discounts and saving billions . These are just a few of the many reasons more than 500 health professionals recently signed an open letter to protect the IRA.

Yet at a time when a third of Americans report not filling a prescription due to cost , Project 2025 calls for these life-saving provisions to be rolled back. IRA repeal, supported by Trump and Vance, would make drugs like insulin more expensive and force patients to shoulder more of the costs of the medications we prescribe them, forcing impossible choices between health and livelihood. 

Decreasing access to Medicaid

Project 2025 doesn’t just try to scale back recent successes in making care more affordable for people on Medicare. It also seeks to weaken Medicaid, which more than 70 million low-income Americans rely on for health care. The authors propose instituting lifetime caps on benefits and adding work requirements as a condition for coverage, creating administrative hurdles that make life harder for people who have the least. Together, these provisions could cause millions of people—including those who are currently working —to lose coverage. These policies punish patients for being poor, and in one of the harshest ways: by denying them health care.

Read More : 7 Metrics Everyone Should Know About Their Own Health

Restricting reproductive health care 

And at a time when reproductive rights are under attack in so many states, Project 2025 would not only further restrict abortion at the national level but also eliminate no-cost coverage for some contraception, erecting more barriers to evidence-based care for patients of reproductive age.

Endangering childrens’ health

Finally, Project 2025 takes particular aim at the well-being of children. The authors seek to prevent public health agencies from requiring vaccination in school children, which could cause more outbreaks of preventable diseases like measles . They also propose invalidating state laws intended to stem gun violence, a leading cause of death for children in the U.S. Project 2025 would even eliminate Head Start, a critical program for early childhood development, especially in low-income and rural communities.

If elected, Trump might not adopt all of these proposals. But if he institutes even some of these plans, it would set back decades of progress in medicine and public health. 

As doctors, we know that progress on these issues did not come easily. The Affordable Care Act was hotly contested and survived multiple partisan efforts to dismantle it—including by Trump during his first term. For more than ten years, Medicaid expansion has been a fierce and ongoing state-by-state battle. The pharmaceutical industry fought tooth-and-nail against drug-price negotiation. At every step of the way, countless doctors have stood up for their patients, advocating for greater access to affordable medical care. The prospect of these hard-fought victories being reversed—and the suffering it would cause our patients—is worth another fight.

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Junk Food in Schools and Childhood Obesity

Ashlesha datar.

RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407, USA, gro.dnar@ratad , Phone: 1-310-393-0411 x7367, Fax: 1-310-260-8161

Nancy Nicosia

RAND Corporation, 20 Park Plaza, 7th Floor, Suite 720, Boston, MA 02116, USA, gro.dnar@aisocin , Phone: 1-617-338-2059 x4227

Despite limited empirical evidence, there is growing concern that junk food availability in schools has contributed to the childhood obesity epidemic. In this paper, we estimate the effects of junk food availability on BMI, obesity, and related outcomes among a national sample of fifth-graders. Unlike previous studies, we address the endogeneity of the school food environment by controlling for children’s BMI at school entry and estimating instrumental variables regressions that leverage variation in the school’s grade span. Our main finding is that junk food availability does not significantly increase BMI or obesity among this fifth grade cohort despite the increased likelihood of in-school junk food purchases. The results are robust to alternate measures of junk food availability including school administrator reports of sales during school hours, school administrator reports of competitive food outlets, and children’s reports of junk food availability. Moreover, the absence of any effects on overall food consumption and physical activity further support the null findings for BMI and obesity.

1. Introduction

The prevalence of childhood obesity in the US is at an all-time high with nearly one-third of all children and adolescents now considered overweight or obese ( Ogden et al 2008 ). Considerable attention has been focused on schools in an attempt to identify policy levers that will help reverse the obesity epidemic. In particular, the availability of “competitive foods”, defined as foods and beverages available or sold in schools outside of the school lunch and breakfast programs, has been a much debated issue. On the one hand, opponents question the nutritional value of competitive foods and consider them the primary source of “junk foods” in schools. Indeed, the available evidence suggests that these foods are higher in fat compared with foods sold as part of the school meal programs ( Gordon et al 2007b , Harnack et al 2000 , Wechsler et al 2000, Story, Hayes & Kalina 1996 ). On the other hand, supporters argue that revenues from these food sales provide much-needed funding for schools, especially in times of budgetary pressures ( Gordon et al 2007a ).

The debate draws from largely cross-sectional research that rarely addresses the potential endogeneity of the school food environment. Our paper advances the literature by attempting to isolate the causal effect of junk food availability on children’s food consumption and BMI. We use longitudinal data on BMI for a national sample of fifth graders from the Early Childhood Longitudinal Study – Kindergarten Class (ECLS-K) and an instrumental variables (IV) approach that leverages the well-documented fact that junk foods are significantly more prevalent in middle and high schools relative to elementary schools ( Finkelstein, Hill and Whitaker 2008 ). Plausibly exogenous variation in junk food availability across a cohort of fifth graders is identified using the grade structure in their schools. We argue that a fifth grader attending a combined (e.g. K-8, K-12) or middle school (e.g. 5–8) is more likely to be exposed to junk foods compared to a fifth grader in an elementary school (e.g. K-5, K-6), but that the school’s grade span has no direct effect on a child’s weight. First-stage regressions confirm that combined school attendance is a strong predictor of junk food availability. Further tests for instrument validity including an examination of sorting and peer effects support our use of the instrument.

We find that junk food availability has small positive associations with BMI and obesity in basic OLS models that only control for a limited set of covariates, but those associations become insignificant when controls for BMI at school entry and state fixed effects are added. Our IV models, which address potential bias in the OLS models, generate somewhat larger, albeit less precise, point estimates that are also not statistically significant. Even if the IV point estimates were statistically significant, they would still represent only minor increases in BMI and obesity, generally one-third of one percent. Moreover, reduced form estimates, which are more precisely estimated than IV estimates, provide further support because combined school attendance has no significant effects on 5 th graders’ BMI and obesity. These results are robust to alternative measures of junk food availability and sample restrictions. The models also produce the expected findings on various falsification tests.

While we acknowledge their limitations, ancillary analyses of children’s in-school junk food purchases, total consumption of healthy and unhealthy foods, and physical activity are consistent with our null findings for BMI and obesity. Our estimates suggest that the caloric contributions of in-school junk food purchases are likely to be small. Moreover, we find evidence consistent with substitution between in- and out-of-school consumption. Specifically, the total amount of soda and fast food consumed in- and out-of-school, is not significantly higher among those children with greater exposure to junk food in school (i.e. attending a combined school). And, finally, we find little support for the notion that children substitute calories from healthy foods or increase their physical activity to compensate for increased junk food intake.

The remainder of this paper is organized as follows. We first discuss junk food availability in schools and the findings from the existing literature in Section 2. Section 3 describes our data and relevant analysis variables. In Section 4, we describe our empirical strategy, which leverages longitudinal information on BMI and implements an instrumental variables approach to identify the causal impact of junk food availability. In Section 5, we first discuss our main results for children’s BMI and obesity and then support these findings with robustness checks and falsification tests. We also present supporting evidence from models of in-school purchases of junk food, total consumption of various healthy and unhealthy items, and physical activity. Finally, Section 6 concludes with the policy implications of our findings.

2. Background and Literature

Competitive foods are sold through a la carte lines, vending machines, school canteens/stores, and fundraisers and, in contrast to the federally-reimbursable school meal programs, are not subject to federal nutritional standards. As a result, competitive foods account for much of the variation in the food environment across schools. Competitive foods are available in a large share of schools, although the availability of these foods varies significantly across elementary, middle, and high schools. For example, as many as 97% of high schools and 82% of middle schools have vending machines compared to only 17% of elementary schools ( Gordon et al 2007a ). However, a la carte lines, which are the predominant source of competitive food sales, operate not only in most high (93%) and middle (92%) schools, but also in a large proportion of elementary schools (71%) ( Gordon et al 2007b ).

Sales of competitive foods have the potential to generate significant revenues for schools. During 2005–2006, middle and high schools earned an average of $10,850 and $15,233, respectively, from a la carte sales alone ( Gordon et al 2007a ). In addition, nearly a third of high schools and middle schools earned between $1,000–$9,999 during that same year from vending machines, another ten percent earned between $10,000–$50,000, and a small number earned in excess of $50,000 per year. These revenues may in turn be supplemented by on-site school stores and pouring contracts with beverage companies. While availability and revenues were less common in elementary schools, nearly half of elementary schools had pouring rights contracts, and competitive food sales from fundraising activities were also common.

The U.S. Department of Agriculture’s regulations on competitive foods in schools had been comprehensive, but in 1983, a successful lawsuit by the National Soft Drink Association limited the scope of these regulations to food service areas during meal hours ( Institute of Medicine 2007 ). In recent years, several states, districts, and schools have enacted competitive food policies that are more restrictive than federal regulations. And, between 2003 and 2005, approximately 200 pieces of legislation were introduced in US state legislatures to establish nutritional standards in schools or to address the availability or quality of competitive foods ( Boehmer et al 2007 ). At the federal level, legislation was passed in 2004 requiring local education agencies to develop a “wellness policy” by 2006 that included nutrition guidelines for all of the foods available in schools. More recently, there has been debate in the US Congress over enacting an amendment to the farm bill that would further restrict the sale of unhealthy foods and beverages in schools ( Black 2007 ). At the local level, two of the largest school districts in the nation, New York City Public School District and Los Angeles Unified School District, imposed a ban on soda vending in schools in 2003 and 2004, respectively.

Despite the growing support for competitive food regulation, it is hard to deny opponents’ claims that the evidence against competitive foods is limited. Existing research does show that competitive food availability is associated with a decline in nutritional quality of meals consumed at school ( Cullen et al 2000 , Cullen & Zakeri 2004 ; Templeton, Marlette & Panemangalore 2005 ). 1 However, less is known about the effects on overall diet quality (consumed both in and out of school) and children’s weight. The literature does provide some evidence of substitution of caloric intake across meals and locations among adults ( Anderson and Matsa 2011 ), but the evidence is less clear regarding children for whom parental oversight can also play a role. Only Kubik and colleagues have examined 24 hour dietary recall (2003) and BMI (2005) among children, however these studies are based on small cross-sectional samples and do not address the potential endogeneity of the school food environment. 2 , 3

The only effort to address endogeneity is in Anderson and Butcher (2006) , who use national data on adolescents aged 14–20 years to examine whether various school food policies influence BMI (based on self-reported height and weight data). In the absence of a single data source containing information on school food policies and BMI among adolescents, the authors use a two-sample IV approach that employs county, state, and regional characteristics as instruments to capture budgetary pressures on schools. They find that a 10 percentage point increase in the proportion of schools in the county that offer junk foods leads to a 1 percent increase in BMI. But this effect is primarily driven by adolescents with an overweight parent, which the authors interpret as a measure of family susceptibility. 4 Their IV approach constitutes an innovation over the literature, but the authors acknowledge that their results may be undermined by a weak first stage.

Our paper adds to the existing literature in its sample, methodology and scope. First, to our knowledge, ours is the only study that addresses the endogeneity of the school food environment among younger children. The focus on fifth graders is useful because junk food regulations are increasingly targeting elementary and middle schools. 5 And our national sample of children provides a larger and more representative sample with significant variation in school environments. Second, our data contain actual measurements of children’s height and weight, unlike the self-reports from other national datasets that have been used to examine this question previously. Third, our approach improves on the common cross-sectional designs by controlling for children’s BMI at school entry and state fixed-effects, and leveraging variation in schools’ grade spans to estimate IV models. Finally, unlike previous studies, we also provide evidence on the underlying mechanisms by examining effects on food consumption and physical activity.

The ECLS-K is a panel dataset on a nationally representative cohort of kindergarteners in the U.S. who entered school in fall 1998. In the fall and spring of kindergarten and the spring of the first, third, and fifth grades, the study collected information from the children and their parents, teachers, and schools on children′s cognitive, social, emotional, physical development (including BMI), and their home, classroom, and school environments. One limitation is that the information on the school food environment and children’s food consumption was collected only in the fifth grade. Our analysis sample includes the approximately 9,380 children attending the fifth grade in public and private schools in the 2003–04 school year. 6 In this section, we describe the key variables for our analyses.

3.1. Dependent Variables Measuring BMI, Food Consumption and Physical Activity

Body mass index (bmi).

A distinct advantage of the ECLS-K is that it collected height and weight measurements from children at kindergarten (school) entry and in the spring of kindergarten and first, third, and fifth grades. Measurements are superior to self- or parent-reported height and weight data that may introduce non-random measurement error. These measurements are used to compute BMI, defined as weight in kilograms divided by height in meters squared. The average BMI in our sample during the fifth grade is 20.4 ( Table 1 ). Approximately 20% of the ECLS-K sample is categorized as obese – this is nearly identical to prevalence rates among 6–11 years olds from the 2007–8 National Health and Nutrition Examination Survey ( Ogden et al 2010 ). 7

Descriptive Statistics in the Fifth Grade

Notes: N=9,380. Means are unweighted. Standard deviation in parentheses.

Junk Food Purchase in School

The food consumption questionnaire collected information on in-school junk food purchase during the fifth grade. These questions asked children about their purchases of sweets, salty snack foods, and sweetened beverages (hereafter, referred to as “soda”) during the previous week. 8 A substantial majority of the children did not purchase junk food in school during the reference week: 77% for sweets, 84% for salty snacks, and 88% for soda (see Appendix Table A1 ). But a large share of these children did not have junk food available in their schools (see Section 3.2). Conditional on availability, about half the sample purchased any of these unhealthy foods at least once a week in school. Among those who did purchase, the modal response was 1 to 2 purchases per week: 68 percent for sweets, 72 percent for salty snacks, and 70 percent for soda. 9

In-School and Total Food Consumption in Fifth Grade

Notes: N=9,380. Percentages are unweighted. Figures in the top panel are not conditional on availability in school.

Total Consumption of Selected Foods and Beverages

The child food consumption questionnaire asked about the frequency of overall consumption of specific food items during the past week. Children were asked to include foods they ate at home, at school, at restaurants, or anywhere else. We examine the consumption of two unhealthy items - soda and fast food, and six healthy food items – milk, green salad, potatoes 10 , carrots, other vegetables, and fruits. The percentage of children not consuming any soda or fast food during the previous week was 16 and 29 percent, respectively, with modal responses at 1 to 3 times per week (see Appendix Table A1 ). Among the healthy foods, green salad, carrots and potatoes were consumed most infrequently with nearly half of children reporting no consumption during the past week. The modal responses for the other healthy foods were 1 to 3 times during the past week.

3.2. Junk Food Availability

Detailed information on junk food availability in schools was collected from the school administrators and from children in the fifth grade. School administrators were asked whether students could purchase 17 individual food and beverage items, either from vending machines, school store, canteen, snack bar or a la carte items from the cafeteria during school hours. From these responses, we constructed an indicator variable of junk food availability in school that equals 1 if the administrator reports that students can purchase food and beverage items containing high sodium and/or sugar, including candy, chocolate, baked foods (e.g. cookies), salty snacks (e.g. potato chips), ice cream or frozen yogurt, or sweetened beverages during school hours, and zero otherwise. 11 Based on these school administrator reports, approximately 61 percent of the children had junk food availability in school. For robustness checks, we also considered two alternative measures of availability. The first is based on whether the modal child at each school reports that foods containing sugar, salty snacks, or sweetened beverages can be purchased at school. Based on this measure, about 75 percent of the children had junk foods available. And the second is based on whether the administrator reports any of the following competitive food outlets operate in the school: vending machines, school stores, canteens, snack bars, and a la carte lines. About 60 percent of the sample had at least one competitive food outlet. 12

4. Empirical Approach

4.1. econometric model.

The relationship between junk food availability and children’s BMI in fifth grade can be estimated cross-sectionally using the following linear regression model.

where, BMI iks , denotes fifth grade BMI for child i attending school k located in state s , JF k captures junk food availability in the child’s school, X i and S k are the vectors of individual/family (gender, age, age interacted with gender, race/ethnicity, mother’s education, household income) and school characteristics (private/public, percent minority, enrollment, urbanicity, state/region), respectively, and ε iks is the error term. The child’s baseline BMI (BBMI i ) is included to address potential heterogeneity that can bias OLS estimates such as student demand for junk foods, genetic susceptibility, and sorting. Because junk food availability is collected only in fifth grade, we do not know the length of exposure during prior school years. Therefore, BMI at school entry is the preferred baseline because it is measured prior to any exposure to the school food environment. Finally, since states differ markedly in terms of obesity prevalence in their populations as well as the policy environment geared towards combating obesity, we include state fixed effects (θ s ) to control for state-specific time-invariant unobserved heterogeneity that may be correlated with school food environments and children’s weight.

The parameter of interest in Equation (1) is β 1 . Obtaining an unbiased estimate of β 1 is challenging because the school food environment is not exogenous to the outcomes of interest. Schools that serve high-fat, energy-dense junk foods may differ on many observable and unobservable factors that are correlated with children’s weight and dietary behavior. In particular, the decision to offer junk foods in schools may be influenced by a variety of factors including budgetary pressures, demands of the student population, parental involvement, and state/district policies. These factors could independently influence children’s weight as well. For example, budgetary pressures may induce schools or districts to scale back or eliminate physical education programs, which might increase children’s weight. As a result, coefficient estimates from the ordinary least squares (OLS) estimation of Equation 1 would be biased.

4.2. Addressing Endogeneity of Junk Food Availability in Schools

We address the potential endogeneity of junk food availability using instrumental variables. Specifically, we estimate the model in Equations (2.1) and (2.2) using Two-Stage Least Squares.

Equation 2.1 represents the first-stage regression where junk food availability (JF k ) is regressed on the combined school attendance instrument (CS k ), individual (X i ) and school (S k ) characteristics, baseline BMI (BBMI i ), and state fixed effects (θ s ). Equation 2.2 represents the second stage where children’s BMI (or obesity) is regressed on the predicted availability of junk foods from the first stage (ĴF k ) in addition to the common covariates.

We also report results from the reduced form, which regresses BMI or obesity directly on the instrument ( Equation 3 ). These results have the advantage of being unbiased and providing evidence of whether a causal relationship exists in the regression of interest. 13

4.2.1. Instrument

Our sample consists of a single cohort of 5 th graders attending schools with a variety of grade spans. Given that junk food availability is significantly higher in middle and high schools compared to elementary schools, a potentially useful instrument for junk food availability is whether the 5 th grader attends a combined school (defined as the highest grade is seventh or higher) or whether the 5 th grader is in an elementary school (defined as highest grade is 5 th or 6 th ). Our instrument considers only this dichotomy of school type: elementary versus combined. Over 70 percent of our sample attends elementary schools while the remainder attends combined schools usually with grade spans of K-8, K-12 and 5–8 (see Appendix Table A2 ).

Variation in Grade Span in Fifth Grade

Notes: N=9380. “Combined” schools are defined as schools with highest grade equal to 7 or higher.

For combined school attendance to be a valid instrument, it must be the case that the school’s grade span has no direct effect on children’s weight except through the junk food environment. One potential concern is that there may be unobserved factors that are correlated with both the likelihood of combined school attendance as well as BMI. For example, it is well known that states differ markedly in the prevalence of childhood obesity. But, states are also likely to differ in terms of factors that contribute to school grade span such as: (1) the size of the school-age population, (2) its distribution within the state, (3) differences in the educational systems and policies, as well as (4) education budgets. Similarly, school grade span can vary across urban versus rural areas (even within states), with the latter more likely to have combined schools largely because of a smaller school-age population. The inclusion of state and urbanicity dummies in our regressions controls for unobserved differences across states and across rural/urban areas that may be correlated with combined school attendance (or grade span, more generally) and BMI.

Another potential concern with this identification strategy is that variation in grade span exposes children to older peers who may influence obesogenic behaviors. Peers, defined broadly, have been shown to influence a wide range of adolescent behaviors and outcomes. 14 However, of particular relevance to our identification strategy is the literature examining a specific type of peer effect, namely, the effect of exposure to older peers due to school grade span.

Several studies have examined peer effects on academic, social-behavioral and substance use outcomes by leveraging variation in school grade span ( Clark and Folk 2007 ; Clark and Loheac 2007 ; Eisenberg 2004 ; Bedard and Do 2005 ; Cook et al 2008 ). Most studies compare students in the same grade who attend middle versus combined schools or middle versus elementary schools . 15 These studies generally find that 6 th or 7 th graders who attend middle school fare poorly compared to those who attend elementary or combined schools. 16 However, we are not aware of any studies that compare children in the same grade level who attend elementary versus combined schools . The exception is Rickles (2005) , whose findings suggest inconsistent effects of elementary versus combined schools attendance on achievement.

Furthermore, there is very limited evidence on the influence of older peers on food choices. Cullen and Zakeri (2004) compared changes in food consumption of 4 th graders who transitioned to middle school in 5 th grade and gained access to school snack bars to changes in food consumption of 5 th graders who were already in middle school. Fourth graders who transitioned to middle school consumed fewer healthy foods compared with the previous school year, but it is not clear whether this was due to the presence of older peers or the change in school food environment.

Overall, the literature suggests that the presence of older peers may adversely affect academic and social behavioral outcomes, but there is less evidence to support effects on their eating behaviors. Nevertheless, if such an effect exists, the potential bias in our IV estimates due to peer effects is likely to be upward. That is, 5 th graders might emulate older peers who are more likely to consume junk foods in school and would therefore tend to be overweight, independent of the school food environment. In that case, an insignificant finding is unlikely to be undermined.

4.2.2. Checks for Instrument Validity

Identification in our IV models relies on the assumption that, conditional on state and urbanicity dummies, the school’s grade span does not influence BMI except through differences in the availability of junk foods. Districts typically determine the grade span at the time of the schools’ opening based on a number of factors including transportation costs, length of bus ride, desired number of transitions, population size, site availability, preferred school size, and likelihood of parental involvement ( Paglin and Fager 1997 ) rather than children’s health outcomes. Changes in grade span over time are possible, but infrequent and similarly-motivated. For example, in our ECLS-K sample, less than 4 percent of the children who remained in the same school between kindergarten and fifth grade experienced a grade-span change from combined to elementary school or vice-versa. While unlikely, it is nevertheless possible that schools may change grade span in response to children’s physical size. Therefore, below we report results from several tests that support the validity of our instruments. These analyses are based on our preferred specification, which controls for the full set of covariates, including state and urbanicity dummies and baseline BMI.

First, we report first-stage estimates of the effect of our instrument – combined school attendance – on junk food availability in school. The first-stage estimates show that combined school attendance significantly increases the likelihood of junk food availability with an F-statistic on the instrument that exceeds 22 ( Table 2 ).

First Stage Regression Estimates of Junk Food Availability in Fifth Grade

Notes: Figures in brackets are robust standard errors clustered at the school level. Other covariates in the model include male, age (months), male*age, race/ethnicity, kindergarten BMI, mother’s education, income, private school dummy, categories for percent minority in school and school enrollment, and state and urbanicity dummies.

Second, since our instrument leverages across school variation we might be concerned that selection into different schools (or communities) might undermine the validity of our instrument. To test for differential selection into combined versus elementary schools, we regress BMI, obesity, test scores, social-behavioral outcomes, and parental involvement measured in kindergarten on combined school attendance in 5 th grade ( Table 3 ). 17 Because these outcomes are determined prior to exposure to school, these comparisons allow us to test for selection. The results suggest that, conditional on observed characteristics, combined school attendance is uncorrelated with pre-exposure BMI, obesity, test scores, social-behavioral outcomes and parental involvement.

Effect of Attending a Combined School on Kindergarten Outcomes

Notes: Each estimate represents a separate regression. Other covariates in the models include age, male, age*male, race/ethnicity, kindergarten BMI (not in model in Columns 1 and 2), mother’s education, income, private school dummy, categories for percent minority in school and school enrollment, and state and urbanicity dummies. Robust standard errors clustered at school level are shown in brackets. For reading, math, self control, and interpersonal skills, higher skills indicate better outcomes. For externalizing and internalizing behavior problems, higher scores indicate worse outcomes. Parent involvement is measured as the sum of the number of times/week that the parent engages in 9 activities with the child (e.g. reading books, talk about nature, do science projects, tell stories).

Third, another concern is that combined school attendance might generate peer effects on BMI, obesity, food consumption and physical activity, independent of junk food availability. We test for the presence of peer effects by regressing these outcomes on combined school attendance using only the sample of schools that do not offer junk foods ( Table 4 ). The results do not provide any support for peer effects on BMI, obesity, food consumption or physical activity. 18

Effect of Combined School Attendance on BMI, Obesity and Related Behaviors Without Junk Food Availability in Fifth Grade

Notes: Each estimate represents a separate regression. All models control for the full set of covariates. Robust standard errors clustered at school level are shown in brackets.

Overall, the instrument appears to be strongly predictive of junk food availability and there is no evidence that selection or peer effects threaten its validity.

We now turn to our main results, which examine the effects of junk food availability on BMI and other outcomes. We first estimate basic OLS models of BMI and obesity, then augment with state fixed effects and baseline BMI to address omitted variable bias and selection, and finally estimate the IV and reduced form specifications (Section 5.1). In Section 5.2, we examine the sensitivity of our results to alternate measures of junk food availability and various sample restrictions. We also report findings from falsification tests. And finally, in Section 5.3, we describe results from ancillary regressions that explore the potential mechanisms underlying our BMI findings. In particular, we examine in-school and total consumption of selected foods and beverages and the availability of and participation in physical activity.

5.1. BMI and Obesity

Our main results focus on whether the availability of junk foods increases BMI and obesity among 5 th graders ( Table 5 ). Columns 1 and 4 in Panel A show the results of basic OLS regressions of log BMI and obesity, respectively, on junk food availability controlling for child, household, and school characteristics. 19 These regressions yield a statistically significant increase in both BMI and obesity when junk food is available, although the point estimates are small. The inclusion of state fixed effects and urbanicity dummies (Panel A, columns 2 and 5) and then baseline BMI measured in kindergarten (Panel A, columns 3 and 6) eliminates the significant coefficients. The fully-specified OLS models have very small, precisely estimated, and statistically insignificant point estimates.

Effects of Junk Food Availability on BMI and Obesity in Fifth Grade

Notes: N=9,380. Robust standard errors clustered at school level are shown in brackets. Other covariates in the model include male, age (months), male*age, race/ethnicity, kindergarten BMI, mother’s education, income, private school dummy, categories for percent minority in school and school enrollment, and state and urbanicity dummies. First stage results are shown in Table 2 .

However, the coefficients from these OLS models may be biased if junk food availability is related to unobserved determinants of children’s BMI. For example, districts with a large population of students at risk for obesity may adopt more stringent nutritional policies that reduce the availability of junk foods in school. In such situations, OLS regressions may show no significant relationship or even a negative relationship between junk food availability and BMI. OLS estimates might also suffer from attenuation bias due to the presence of measurement error in the junk food availability measures.

To address these issues, we estimate instrumental variables (IV) and reduced form regressions using grade span as the instrument: whether the 5 th grader attends a combined school with older peers. 20 The IV point estimates are relatively larger than the OLS estimates, but less precisely estimated rendering them statistically insignificant ( Table 5 , Panel B). 21 , 22 IV estimates from models that do not control for state and urbanicity dummies and baseline BMI (columns 1 and 4) are much larger than those in our preferred specification (Columns 3 and 6), although they are not statistically significantly different from each other. Even if the IV point estimates in our preferred specification (columns 3 and 6) were significant, they would represent only small increases in BMI and obesity of less than one-third of one percent. Hausman tests that check for the endogeneity of junk food availability by comparing estimates from the fully-specified OLS regression with the IV cannot reject the null hypothesis that both estimates are consistent. Therefore, we also report the reduced form estimates of BMI and obesity regressed directly on our instrument ( Table 5 , Panel C). The coefficients on the instrument are close to zero and very precisely estimated, which further confirm the null findings. Given concerns about unobserved heterogeneity in the OLS specifications and the larger standard errors in the IV specifications, the reduced form estimates are preferred.

5.2. Sensitivity and Falsification Checks

We conducted a number of sensitivity analyses to test the robustness of our findings. In this section, we report results from a few key analyses and then turn to falsification tests. 23 These analyses control for the full set of covariates, including state and urbanicity dummies and baseline BMI.

For the sensitivity analyses, we first re-estimate our BMI and obesity regressions with the two alternate measures of junk food availability ( Table 6 ). Both the child-reported measure of junk food availability and the school-administrator reported measure of competitive food outlet show no effect of junk food availability on BMI or obesity. Next, we re-estimate the models with the exclusion of three particular groups that might confound our instrument ( Table 7 ). First, because combined schools are much more likely to be private, our instruments may simply capture variation across public versus private schools students, even though the regressions control for private school attendance. We re-estimate the models on a sample that excludes children who attend private schools ( Table 7 , Panel A) and find no effects on BMI and obesity. 24 Second, even though Section 4.2.2 suggests there are no peer effects on BMI and related behaviors, we test the sensitivity of our results to exclusion of the oldest peers (e.g., grade 9 or higher), but still find no evidence of an effect on BMI and obesity ( Table 7 , Panel B). Finally, children who switch schools for unobservable reasons potentially related to junk food availability may bias our estimates, but estimates from models that exclude children who changed schools between kindergarten and fifth grade confirm no effects ( Table 7 , Panel C). The point estimates from the OLS, IV and reduced form regressions for these sensitivity checks are essentially zero, though less precisely estimated in the IV models. 25

Effects of Alternate Measures of Junk Food Availability on BMI and Obesity in Fifth Grade

Notes: N=9,380

Effects of Junk Food Availability on BMI and Obesity in Fifth Grade with Alternate Sample Restrictions

Notes: All models include the full set of covariates. Robust standard errors clustered at school level are shown in brackets. Hausman tests for consistency of OLS estimates could not be rejected in any case. The tests are not reported in the table.

As falsification tests, we examined whether junk food availability in the fifth grade influenced children’s height in the fifth grade and their pre-exposure BMI. Height should clearly be unrelated. And indeed, the coefficients are essentially zero and insignificant ( Table 8 ). Because BMI and obesity in kindergarten is measured prior to exposure to junk foods in school, any effects would suggest unobserved heterogeneity. The OLS, IV and reduced form point estimates are close to zero (though the IV estimates are less precise) and the reduced form specifications also show no relationship ( Table 9 , Panel A). Results for BMI and obesity measured in first and third grade likewise confirm insignificant effects of junk food availability during fifth grade ( Table 9 , Panels B and C). However, because our data do not contain information on junk food availability prior to 5 th grade, these results are also consistent with the absence of junk foods in earlier grades.

Effect of Junk Food Availability in School on Height in Fifth Grade

Note: N=9,380. Robust standard errors clustered at school level are shown in brackets.

Effects of Junk Food Availability on BMI and Obesity in Kindergarten, First, and Third Grade

Notes: Each estimate represents a separate regression. All models include the full set of covariates. Robust standard errors clustered at school level are shown in brackets.

5.3. Effects of Junk Food Availability on Food Consumption and Physical Activity

The consistent lack of significant findings for BMI and obesity raises questions regarding how the energy balance equation is affected by junk food availability. While we cannot measure children’s energy intake and expenditure explicitly with these data, we can examine whether junk food availability influences general food consumption patterns and physical activity. Unlike BMI and obesity, the consumption and physical activity measures are based on parents’ and children’s reports . As a result, they are subject to measurement error and consequently produce noisier estimates particularly for the IV models. Nevertheless, they represent our best opportunity for understanding important mechanisms underlying our null finding. Therefore, for the in-school junk food purchases, total consumption, and physical activity analyses, we focus mainly on the reduced form results (though we provide OLS results for comparison). 26

5.3.1 In-School Purchases and Overall Consumption

One potential explanation for our null findings for BMI and obesity may be that availability does not impact overall food consumption. This may happen for several different reasons. First, young children may not purchase significant amounts of junk food in school either due to limited access to such foods or fewer discretionary resources to purchase them. Second, children may not change their total consumption of junk food because junk food purchased in school simply substitutes for junk food brought from home. Or third, children may not change their overall consumption during the day, but simply substitute between junk food consumed in-school and out-of-school.

Unfortunately, we cannot completely separate out these possible explanations because the ECLS-K does not provide us with full information about the daily dietary intake of each child. However, we do have information about in-school purchases of foods with sugar, salty snacks, and sweetened beverages for those children with in-school availability. We also have total (in-school plus out-of-school) consumption of soda, fast food, and a variety of healthy foods for all children in the sample. While not definitive, we can use this information to gain some insight into underlying eating behaviors and lend support for our BMI and obesity findings.

Not surprisingly, our analysis of in-school consumption of junk foods does confirm that children purchase junk food when it is available. 27 The OLS estimates show a significant relationship for purchases of all types of junk food when junk foods are available in schools ( Table 10 , Panel A). And the reduced form estimates show that children in combined schools are between 5 and 9 percentage points more likely to purchase junk foods compared to those in elementary schools Table 10 , Panel B).

Effect of Junk Food Availability on In-School Junk Food Purchases in Fifth Grade

Notes: N=9380. Each estimate represents a separate regression. Dependent variables in columns (1)–(3) are dichotomous and capture whether any purchase of that item was made in school during the last week. All regressions include the full set of covariates. Robust standard errors clustered at school level are shown in brackets.

To provide a sense of the caloric contribution of these purchases, we multiplied the increase in the probability of purchase from attending a combined school by the median number of times that food was purchased among children who purchased at least once, by the number of the calories per unit. 28 Summing across the three junk food groups yields 50 calories per week (7 calories per day) from in-school junk food purchases. The caloric contribution of in-school purchases is much higher (435 calories per week or 62 calories per day for the median child) among children who purchase these foods (as opposed to merely having them available). But even the 62 calories per day represents less than a quarter (23 percent) of the daily discretionary calorie allowance (267 calories) for a moderately active fifth grader. 29

It is possible that children substitute in-school purchases for snacks brought from home or eaten at home either due to satiation or parental monitoring. With our simple dietary recall measures, we cannot explicitly test the nature of potential substitution. We can, however, examine the total intake of soda and fast food consumed in and out of school. Soda is of particular interest because it is the only item for which children were asked about both their in-school and total consumption separately. Fast food, on the other hand, does not correspond exactly to the in-school snack food consumption categories. We find that junk food availability is not associated with significant increases in children’s total consumption of soda or fast foods ( Table 11 , Columns 1 and 2). 30 The OLS regressions show negative, though generally insignificant, estimates. 31 More importantly, the reduced form estimates confirm that there is no relationship between combined school attendance and total consumption of soda and fast food. The fact that children who consume soda and other junk food in schools show no evidence of an increase in total consumption provides support for the substitution hypothesis. This finding is also consistent with the literature, which indicates that only 27 percent of soda and sweetened drinks consumed in elementary schools are bought at school compared to 67 percent brought from home ( Briefel et al 2009 ).

Effect of Junk Food Availability on Total Consumption of Selected Unhealthy and Healthy Foods in Fifth Grade

Notes: N=9380. Each estimate represents a separate regression. Dependent variable captures the number of times the food or beverage item was consumed during the last 7 days. All models include the full set of covariates. Robust standard errors clustered at school level are shown in brackets.

While BMI is a widely-used outcome measure, it does not capture nutritional changes. Just because children are not gaining weight does not mean that their diets are not adversely affected by junk food availability. If children are consuming junk food in lieu of healthy foods, there may still be concerns about their nutrition. Columns 3 through 8 of Table 11 examine whether children with in-school availability of junk foods consume less milk, green salad, carrots, potatoes, other vegetables, and fruit. The OLS results show no significant associations with junk food availability. Moreover, reduced form regressions also show that combined school attendance does not significantly impact total consumption of the healthy foods. 32

Physical Activity

The absence of any effects of junk food availability on BMI despite the in-school purchases of junk food also raises questions regarding potential compensatory changes in the availability of and participation in physical activity. For example, revenues from junk food sales may be used to fund playgrounds or pay for physical education instructors. Or it may be that combined schools simply offer more opportunities for physical activity due to their scale and organization relative to elementary schools. Another possibility is that parents or children may increase children’s physical activity to balance junk food intake. If physical activity is greater, then we may find no change in BMI or obesity despite an increase in caloric intake.

OLS and reduced form estimates for school- and parent-reported physical activity measures are reported in Table 12 . OLS estimates show no relationship between junk food availability and minutes per week of physical education at school, minutes per week of recess at school, and parent-reported participation in physical activity (measured as the number of days per week that the child engaged in exercise that causes rapid heart beat for 20 continuous minutes or more). The reduced form regressions show no significant effects of combined school attendance on minutes per week of physical education instruction. Children attending combined school have fewer minutes of recess ( Table 12 , Column 2), but slightly higher days of parent-reported physical activity ( Table 12 , Column 3) though neither finding is statistically significant at.conventional levels. Overall, the regressions do not provide consistent evidence that increased energy expenditure explains the null finding for BMI and obesity.

Effects of Junk Food Availability on Physical Education, Recess and Physical Activity in Fifth Grade

Notes: Each estimate represents a separate regression. All models include the full set of covariates as well as the baseline (kindergarten) measure of the dependent variable. Robust standard errors clustered at school level are shown in brackets.

6. Conclusion

Junk food availability is a prominent issue for middle and high schools in the U.S. However, there is also widespread legislation and regulation targeting junk foods even in elementary school ( Trust for American’s Health 2009 ). Young children’s access to junk foods in school is an important concern due to the strong correlation between childhood overweight and obesity in adolescence and adulthood ( Institute of Medicine 2005 ). In this paper, we examined whether junk food availability increased BMI and obesity among a national sample of 5th graders. Those 5th graders who attend a combined school are much more likely to have junk food availability relative to those in elementary school. While estimates from naïve models that only control for a limited set of covariates suggest a positive association between junk foods in school and BMI and obesity, fully-specified OLS models that control for BMI at school entry and state fixed-effects demonstrate no statistically or economically significant relationships among these young children. Likewise, the IV and reduced form models, which are not subject to the potential bias undermining OLS models, confirm the null findings for BMI and obesity. These results are not sensitive to various robustness checks including alternate measures of junk food availability and sample restrictions.

Finally, we provide further support for the null findings by examining in-school and overall food consumption patterns as well as physical activity. The null effects on BMI and obesity cannot be explained entirely by limited access or limited discretionary resources among young children because 5 th graders do purchase junk food when it is available in schools. However, our results suggest that the caloric contribution of in-school purchases is likely to be small. Moreover, we find no evidence of significant changes in the overall frequency of consumption of soda and fast food, which is consistent with children substituting in-school purchases of junk food for that taken from or eaten at home. Alternative explanations such as compensatory changes children’s consumption of healthy foods and in their opportunities for and participation in physical activity do not appear to play a significant role in explaining our null findings for BMI and obesity.

Our findings may have implications in the current economic environment. Half of the states are projecting budget shortfalls that threaten staffing, compensation, extracurricular activities, and policy initiatives such as mandated limits on class size. 33 Many schools subsidize their funding with revenue from the sale of junk foods. In total, elementary schools earn approximately $442 million annually from junk food sales ( Institute of Medicine 2007 ). In light of our findings, certain policy measures, such as outright bans on junk food sales (at least among elementary school children), might appear premature given that they remove a key source of discretionary funds.

While our results are robust, we caution that we could not consider the full range of consequences of junk food availability. Not only are the dietary intake measures in the ECLS-K limited, but we are also not able to examine whether related health outcomes such as diet quality or dental caries are influenced by junk food availability. Also, we are unable to examine the generalizability of our findings to older children who may have greater junk food access and intake both in and outside school. And finally, we could not consider whether exclusive contracts between schools and beverage/snack companies influence students’ food choices in the longer run through product or brand recognition. Additional research is necessary to fully understand the potential consequences before costly legislation is implemented. Such research might also consider the consequences of junk food regulations on school finances and the extent to which these financial consequences could be mitigated by the sale of more nutritious alternatives or through alternative financing mechanisms.

Means by Attendance in Elementary Versus Combined School and by Private/Public

Notes: N=9,380.

Effect of Grade-Span on Academic and Social-Behavioral Outcomes Among Schools Without Junk Food Availability in Fifth Grade

Acknowledgments

This research was funded by grants from the Robert Wood Johnson Foundation’s Healthy Eating Research Program, NIH R01 HD057193, the Bing Center for Health Economics at RAND, and the RAND Labor and Population Program. All opinions are those of the authors and do not represent opinions of the funding agencies.

1 Other studies have examined the effects of price reductions, increases in availability, and promotion of low-fat foods in secondary schools on sales and purchases of these foods ( French et al 2004 , 2001 , 1997a , 1997b , Jeffery et al 1994 ) as well as their consumption ( Perry et al 2004 ) within experimental settings and found positive effects.

2 Kubik et al (2003) find that a la carte availability in school is negatively associated with overall intake of fruits and vegetables and positively associated with total and saturated fat intake among 7 th graders attending 16 Minneapolis-St Paul schools. Using the same data, Kubik et al (2005) show that using competitive foods as rewards and incentives is positively associated with BMI.

3 Also, using the ECLS-K, Fernandes (2008) found small positive associations between soda availability in schools and both in-school and overall soda consumption of fifth graders.

4 Their results for the other school policies, pouring rights contracts, and food and beverage advertisements are smaller and less precise.

5 For example, California’s first nutrition policy (SB 677) implemented beverage standards for elementary and middle schools, not high schools.

6 All sample sizes have been rounded to the nearest 10 per the ECLS-K’s restricted-use data agreement.

7 Obesity is defined as BMI greater than the 95 th percentile for age and gender on the Center for Disease Control growth charts.

8 Sweets include candy, ice cream, cookies, brownies or other sweets; salty snack foods include potato chips, corn chips, Cheetos, pretzels, popcorn, crackers or other salty snacks, and sweetened beverages include soda pop, sports drinks or fruit drinks that are not 100 percent juice.

9 To validate the ECLS-K estimates, we examined the Third School Nutrition and Dietary Assessment Study (SNDA-III), which collected 24-hour dietary recall from 2,300 children attending a nationally representative sample of public schools in 2005. Similar to the ECLS-K, eighty percent of elementary school children reported no competitive food purchases. Among children who made a purchase, the median daily caloric intake from these foods was 185 calories. The SNDA estimate is higher than our ECLS-K estimates (62 calories reported in Section 5) because it includes healthy foods purchased from competitive food venues: for example, milk was by far the most popular item purchased from competitive food venues and yogurt also ranked highly.

10 The “potatoes” category excluded French fries, fried potatoes, and potato chips.

11 The questionnaire separately asked about availability of high- and low-fat options for baked foods, salty snacks, and ice cream/frozen yogurt/sherbert. We include both the low- and high-fat options in our measure, however, in sensitivity analyses, we used only the high-fat versions to construct our school-administrator based measure of junk food availability and found results to be similar.

12 We rely mainly on the first measure of junk food availability because it is the most specific with respect to the quality of foods and because school-level policies regarding junk food availability are frequently set by school principals and staff ( Gordon et al 2007a ). We prefer this measure over the simple dichotomy of having any (unregulated) competitive food outlets because the outlet-based measure does not differentiate the type of foods sold (e.g. milk vs. soda). We also prefer it over the child-report because children who do not consume junk foods are less likely to accurately report availability and because children reported only the availability of any sweets, salty snacks, or sweetened beverages, but did not differentiate specific items (e.g. low-fat vs. high-fat).

13 The value of reduced form regressions has been highlighted by Angrist and Krueger (2001) and, more recently, Chernozhukov and Hansen (2008) formally show that the test for instrument irrelevance in the reduced form regression can be viewed as a weak-instrument-robust test of the hypothesis that the coefficient on the endogenous variable in the structural equation is zero.

14 This literature examines peer effects on a wide range of outcomes including substance use ( Lundborg 2006 ; Eisenberg 2004 ; Case and Katz 1991 ; Gaviria and Raphael 2001 ), crime ( Case and Katz 1991 ; Glaeser, Sacerdote, and Scheinkman 1996 ; Regnerus 2002 ), teenage pregnancy ( Crane 1991 ; Evans, Oates and Schwab 1992 ), discipline ( Cook et al 2008 ), academic achievement ( Hanushek et al 2003 ; Cook et al 2008 ), adolescent food choices ( Perry, Kelder, Komro 1993 ; Cullen et al 2001 ; French et al 2004 ) and weight ( Trogdon, Nonnemaker and Pais 2008 ).

15 However, Clark and Loheac (2007) estimate how substance use behavior of students within the same school who are one year older influences adolescent substance use and find a positive relationship.

16 One exception is Eisenberg (2004) who finds that 7 th and 8 th graders who attend schools with older peers are no more likely to use substances relative to those who attend schools with younger peers.

17 We also examined unadjusted differences in children’s individual, family and school characteristics during the 5 th grade (see Appendix Table A3 ). There were slight differences for some of the covariates. However, there was no overall pattern in the socioeconomic factors that would threaten the validity of the IV approach: that is, some differences imply better BMI outcomes for one group and others worse. For example, in our sample, elementary school students are more likely to be Hispanic and Asian while combined school students are more likely to be white. There are no differences in the share that are Black. Similarly, there is no consistent pattern in maternal education. Elementary school students are more likely to have poorly and highly educated mothers (less than high school, more than Bachelors).

18 To check whether these null findings are merely due to lack of power instead of absence of peer effects, we estimated the same models using social-behavioral outcomes and test scores as dependent variables because the literature finds evidence of peer effects on these outcomes. We were able to identify statistically significant peer effects on social-behavioral outcomes (but not test scores), which suggests that lack of power is an unlikely explanation for the finding of null peer effects on BMI and related outcomes.

19 In all models, we estimate robust standard errors clustered at the school level.

20 In alternate analyses, we used continuous measures of the highest and lowest grades in the school as instruments. In these over-identified models, both instruments had a strong positive association with junk food availability (i.e. increases in the highest and lowest grades available at the school were strongly predictive of junk food availability). This approach yielded qualitatively similar results as the exactly-identified models (available upon request).

21 The IV regressions were also estimated without baseline BMI. The point estimates, first-stage F-statistics, and Hausman tests yield similar results (available upon request).

22 A concern with our IV specification estimated via two-stage least-squares is that our first stage models do not account for the dichotomous nature of the treatment variable ( Maddala 1983 ). Estimates from binary treatment effect IV models confirm that the effects of junk food availability on BMI are neither substantive nor significant (available upon request).

23 We also conducted additional sensitivity analyses not reported here. First, given that we do not know the exposure to junk food in previous grades and given concerns that genetic susceptibility may not have a constant proportional effect on BMI at every point in the life cycle, we controlled for 1 st or 3 rd grade BMI instead of BMI in Kindergarten and obtained similar results. Second, inclusion of controls for school meal participation did not change our findings. Third, we used BMI z-scores as the dependent variable to accurately control for age and gender influences on BMI and obtained qualitatively similar results. Fourth, we estimated quantile regressions to test whether the effects of junk food availability varied across the BMI distribution, but found no evidence for heterogeneous effects. Finally, we also re-estimated our BMI and obesity models separately for each gender. The results for junk food availability mirrored those for the full sample. The OLS, IV, and RF models show no significant effects of junk food availability for either boys or girls. Still we may be concerned about differential peer effects, for example, if girls are influenced by older peers’ concerns about body image, which would bias our IV estimates downward. Restricting the sample to those boys and girls attending schools without junk food availability, the coefficients from the reduced form were nearly identical to those based on the full sample of boys and girls, which suggests that peer effects are not an issue even when regressions are gender-specific.

24 Estimates based only on the sample of private schools yield small and statistically insignificant effects of competitive food availability on BMI in both OLS and IV specifications, although the F-statistics for the instrument in the first stage were smaller (Results available upon request).

25 Hausman tests cannot reject the consistency of fully-specified OLS estimates in any of our sensitivity checks.

26 Although not shown, the IV (Wald) estimates are easily calculated by dividing the reduced form estimates in Table 10 – Table 12 by 0.2 (first stage estimate from Table 2 ). The IV coefficients are never significant in part due to the larger standard errors in the regressions of reported eating behaviors and physical activity.

27 We dichotomize the in-school purchase variables and estimate linear probability models since much of the variation in junk food purchases at school occurs on the extensive margin.

28 The median number of times an item is purchased in school among children who purchase at least once is 1.5 times (1–2 times per week). We assume that salty snacks add 140 calories (typical calories from a bag of potato chips), sweets add 200 calories (typically calories from a candy bar), and soda adds 150 calories. Given the limitations of the consumption data in the ECLS-K, we caution the reader to treat these caloric intake calculations as approximations.

29 Discretionary calories are the difference between an individual’s total energy requirement and the energy necessary to meet nutrient requirements. According to Dietary Guidelines for Americans, the discretionary allowance for a 2000 calorie diet is 267 calories. See: http://www.health.gov/dietaryguidelines/dga2005/document/html/chapter2.htm#table3 accessed August 22, 2008.

30 The total consumption variables are not dichotomized because there is sufficient variation on the intensive margin.

31 Negative binomial models with a binary treatment variable to account for the count-data distribution of the total consumption variable and the binary nature of junk food availability produced qualitatively similar results. (Results available upon request).

32 Given the limitations of the ECLS-K’s consumption variables, we again examined the SNDA-III data and found no evidence that combined school attendance increases total caloric intake.

33 “Schools expect budget cuts as economy sours: State problems, decline in property values eat away at district funds”. Available at: http://www.msnbc.msn.com/id/23116409/ (Accessed February 10, 2009).

Contributor Information

Ashlesha Datar, RAND Corporation, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407, USA, gro.dnar@ratad , Phone: 1-310-393-0411 x7367, Fax: 1-310-260-8161.

Nancy Nicosia, RAND Corporation, 20 Park Plaza, 7th Floor, Suite 720, Boston, MA 02116, USA, gro.dnar@aisocin , Phone: 1-617-338-2059 x4227.

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• NEWS EXPLAINER
• 28 August 2024

Mpox is spreading rapidly. Here are the questions researchers are racing to answer

• Sara Reardon

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Monkeypox virus particles (shown in this coloured electron micrograph) can spread through close contact with people and animals. Credit: NIAID/Science Photo Library

When the World Health Organization (WHO) declared a public-health emergency over mpox earlier this month , it was because a concerning form of the virus that causes the disease had spread to multiple African countries where it had never been seen before. Since then, two people travelling to Africa — one from Sweden and one from Thailand — have become infected with that type of virus, called clade 1b, and brought it back to their countries.

Monkeypox virus: dangerous strain gains ability to spread through sex, new data suggest

Although researchers have known about the current outbreak since late last year, the need for answers about it is now more pressing than ever. The Democratic Republic of the Congo (DRC) has spent decades grappling with monkeypox clade I virus — a lineage to which Ib belongs. But in the past, clade I infections usually arose when a person came into contact with wild animals, and outbreaks would fizzle.

Clade Ib seems different, and is spreading largely through contact between humans, including through sex . Around 18,000 suspected cases of mpox, many of them among children, and at least 600 deaths potentially attributable to the disease have been reported this year in the DRC alone.

How does this emergency compare with one declared in 2022, when mpox cases spread around the globe? How is this virus behaving compared with the version that triggered that outbreak, a type called clade II? And will Africa be able to rein this one in? Nature talks with researchers about information they are rushing to gather.

Is clade Ib more deadly than the other virus types?

It’s hard to determine, says Jason Kindrachuk, a virologist at the University of Manitoba in Winnipeg, Canada. He says that the DRC is experiencing two outbreaks simultaneously. The clade I virus, which has been endemic in forested regions of the DRC for decades, circulates in rural regions where people get it from animals. That clade was renamed Ia after the discovery of clade Ib. Studies in animals suggest that clade I is deadlier than clade II 1 — but Kindrachuk says that it’s hard to speculate on what that means for humans at this point.

Even when not fatal, mpox can trigger fevers, aches and painful fluid-filled skin lesions.

Growing mpox outbreak prompts WHO to declare global health emergency

Although many reports state that 10% of clade I infections in humans are fatal, infectious-disease researcher Laurens Liesenborghs at the Institute of Tropical Medicine in Antwerp, Belgium, doubts that this figure is accurate. Even the WHO’s latest estimate of a 3.5% fatality rate for people with mpox in the DRC might be high.

There are many reasons that fatality estimates might be unreliable, Liesenborghs says. For one, surveillance data captures only the most severe cases; many people who are less ill might not seek care at hospitals or through physicians, so their infections go unreported.

Another factor that can confound fatality rates is a secondary health condition. For example, people living with HIV — who can represent a large proportion of the population in many African countries — die from mpox at twice the rate of the general population 2 , especially if their HIV is untreated. And the relatively high death rate among children under age 5 could be partly because of malnutrition, which is common among kids in rural parts of the DRC, Liesenborghs says.

Is clade Ib more transmissible than other types?

The clade 1b virus has garnered particular attention because epidemiological data suggest that it transmits more readily between people than previous strains did, including through sexual activity, whereas clade Ia mostly comes from animals. An analysis posted ahead of peer review on the preprint server medRxiv 3 shows that clade Ib’s genome contains genetic mutations that seem to have been induced by the human immune system, suggesting that it has been in humans for some time. Clade Ia genomes have fewer of these mutations.

But Liesenborghs says that the mutations and clades might not be the most important factor in understanding how monkeypox virus spreads. Although distinguishing Ia from Ib is useful in tracking the disease, he says, the severity and transmissibility of the disease could be affected more by the region where the virus is circulating and the people there. Clade Ia, for instance, seems to be more common in sparsely populated rural regions where it is less likely to spread far. Clade Ib is cropping up in densely populated areas and spreading more readily.

Jean Nachega, an infectious-disease physician at the University of Pittsburgh in Pennsylvania, says that scientists don’t understand many aspects of mpox transmission — they haven’t even determined which animal serves as a reservoir for the virus in the wild, although rodents are able to carry it. “We have to be very humble,” Nachega says.

How effective are vaccines against the clade I virus?

Just as was the case during the COVID-19 pandemic, health experts are looking to vaccines to help curb this mpox outbreak. Although there are no vaccines designed specifically against the monkeypox virus, there are two vaccines proven to ward off a related poxvirus — the one that causes smallpox. Jynneos, made by biotechnology company Bavarian Nordic in Hellerup, Denmark, contains a type of poxvirus that can’t replicate but can trigger an immune response. LC16m8, made by pharmaceutical company KM Biologics in Kumamoto, Japan, contains a live — but weakened — version of a different poxvirus strain.

Hopes dashed for drug aimed at monkeypox virus spreading in Africa

Still, it’s unclear how effective these smallpox vaccines are against mpox generally. Dimie Ogoina, an infectious-disease specialist at Niger Delta University in Wilberforce Island, Nigeria, points out that vaccines have been tested only against clade II virus in European and US populations, because these shots were distributed by wealthy nations during the 2022 global outbreak . And those recipients were primarily young, healthy men who have sex with men, a population that was particularly susceptible during that outbreak. One study in the United States found that one dose of Jynneos was 80% effective at preventing the disease in at-risk people, whereas two doses were 82% effective 4 ; the WHO recommends getting both jabs.

People in Africa infected with either the clade Ia or 1b virus — especially children and those with compromised immune systems — might respond differently. However, one study in the DRC found that the Jynneos vaccine generally raised antibodies against mpox in about 1,000 health-care workers who received it 5 .

But researchers are trying to fill in some data gaps. A team in the DRC is about to launch a clinical trial of Jynneos in people who have come into close contact with the monkeypox virus — but have not shown symptoms — to see whether it can prevent future infection, or improve outcomes if an infection arises.

Will the vaccines help to rein in the latest outbreak?

Mpox vaccines have been largely unavailable in Africa, but several wealthy countries have pledged to donate doses to the DRC and other affected African nations. The United States has offered 50,000 Jynneos doses from its national stockpile, and the European Union has ordered 175,000, with individual member countries pledging extra doses. Bavarian Nordic has also added another 40,000. Japan has offered 3.5 million doses of LC16m8 — for which only one jab is recommended instead of two.

Monkeypox in Africa: the science the world ignored

None of them have arrived yet, though, says Espoir Bwenge Malembaka, an epidemiologist at the Catholic University of Bukavu in the DRC. Low- and middle-income nations cannot receive vaccines until the WHO has deemed the jabs safe and effective. And the WHO has not given its thumbs up yet. It is evaluating data from vaccine manufacturers, delaying donors’ ability to send the vaccines.

Even when the vaccines arrive, Bwenge Malembaka says, “it’s really a drop in the bucket”. The African Centres for Disease Control and Prevention in Addis Ababa, Ethiopia, estimates that 10 million doses are needed to rein in the outbreak.

Bwenge Malembaka says that the uncertainty over vaccine arrival has made it difficult for the government to form a distribution plan. “I don’t know how one can go about this kind of challenge,” he says. Bwenge Malembaka suspects that children are likely to receive doses first, because they are highly vulnerable to clade I, but officials haven’t decided which regions to target. It’s also unclear how the government would prioritize other vulnerable populations such as sex workers, who have been affected by clade Ib. Their profession is criminalized in the DRC, so they might not be able to come forward for treatment.

Researchers lament that public-health organizations didn’t provide vaccines and other resources as soon as the clade I outbreak was identified, especially given lessons learnt from the 2022 global mpox outbreak. “The opportunity was there a couple months ago to cut this transmission chain, but resources weren’t available,” Liesenborghs says. “Now, it will be more challenging to tackle this outbreak, and the population at risk is much broader.”

doi: https://doi.org/10.1038/d41586-024-02793-9

Americo, J. L., Earl, P. L. & Moss, B. Proc. Natl Acad. Sci. USA 120 , e2220415120 (2023).

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Yeganeh, N. et al. Vaccine 42 , 125987 (2024).

Priyamvada, L. et al. Vaccine 40 , 7321–7327 (2022).

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