examples of obesity in america

Statistics Regarding Childhood Obesity For example, the guidelines' Daily Nutritional Goals for children ages 1 to 3 are based on 1,000. The Dietary Guidelines for Americans is the framework for healthy eating, but you can chose what you like within the food groups! Focus on nutrient-dense foods. Judging from the ongoing obesity epidemic, many Americans are gaining those pounds for example, surveys show that the average American eats only three.

Examples of obesity in america -

Obesity

If you're obese, speak to your GP for advice about losing weight safely.

Your GP can advise you about losing weight safely by eating a healthy, balanced diet and regular physical activity.

They can also let you know about other useful services, such as:

  • local weight loss groups – these could be provided by your local authority, the NHS, or commercial services you may have to pay for
  • exercise on prescription – where you're referred to a local active health team for a number of sessions under the supervision of a qualified trainer

If you have underlying problems associated with obesity, such as polycystic ovary syndrome (PCOS), high blood pressure, diabetes or obstructive sleep apnoea, your GP may recommend further tests or specific treatment. In some cases, they may refer you to a specialist.

Read more about how your GP can help you lose weight

Diet

There's no single rule that applies to everyone, but to lose weight at a safe and sustainable rate of 0.5 to 1kg (1lb to 2lbs) a week, most people are advised to reduce their energy intake by 600 calories a day.

For most men, this will mean consuming no more than 1,900 calories a day, and for most women, no more than 1,400 calories a day.

The best way to achieve this is to swap unhealthy and high-energy food choices – such as fast food, processed food and sugary drinks (including alcohol) – for healthier choices.

A healthy diet should consist of:

  • plenty of fruit and vegetables
  • plenty of potatoes, bread, rice, pasta and other starchy foods (ideally you should choose wholegrain varieties)
  • some milk and dairy foods
  • some meat, fish, aggs, beans and other non-dairy sources of protein
  • just small amounts of food and drinks that are high in fat and suagr

Try to avoid foods containing high levels of salt because they can raise your blood pressure, which can be dangerous for people who are already obese.

You'll also need to check calorie information for each type of food and drink you consume to make sure you don't go over your daily limit.

Some restaurants, cafés and fast food outlets provide calorie information per portion, although providing this information isn't compulsory. Be careful when eating out because some foods can quickly take you over the limit, such as burgers, fried chicken, and some curries or Chinese dishes.

Diet programmes and fad diets

Avoid fad diets that recommend unsafe practices, such as fasting (going without food for long periods of time) or cutting out entire food groups. These types of diets don't work, can make you feel ill, and aren't sustainable because they don’t teach you long-term healthy eating habits.

This isn't to say that all commercial diet programmes are unsafe. Many are based on sound medical and scientific principles and can work well for some people.

A responsible diet programme should:

  • educate you about issues such as portion size, making behavioural changes and healthy eating 
  • not be overly restrictive in terms of the type of foods you can eat 
  • be based on achieving gradual, sustainable weight loss rather than short-term rapid weight loss, which is unlikely to last

Very low calorie diets

A very low calorie diet (VLCD) is where you consume less than 800 calories a day.

These diets can lead to rapid weight loss, but they aren't a suitable or safe method for everyone, and they aren't routinely recommended for managing obesity.

VLCDs are usually only recommended if you have an obesity-related complication that would benefit from rapid weight loss.

VLCDs shouldn't usually be followed for longer than 12 weeks at a time, and they should only be used under the supervision of a suitably qualified healthcare professional.

Speak to your GP first if you're considering this type of diet.

Exercise

Reducing the amount of calories in your diet will help you lose weight, but maintaining a healthy weight requires physical activity to burn energy.

As well as helping you maintain a healthy weight, physical activity also has wider health benefits. For example, it can help prevent and manage more than 20 conditions, such as reducing the risk of type 2 diabetes by 40%.

The Chief Medical Officers recommend that adults should do at least 150 minutes (two-and-a-half hours) of at least moderate-intensity activity a week – for example, five 30-minute bouts a week. Something is better than nothing, and doing just 10 minutes of exercise at a time is beneficial.

Moderate-intensity activity is any activity that increases your heart and breathing rate, such as:

Alternatively, you could do 75 minutes (one hour, fifteen minutes) of vigorous-intensity activity a week, or a combination of moderate and vigorous activity.

During vigorous activity, breathing is very hard, your heart beats rapidly and you may be unable to hold a conversation. Examples include:

  • running
  • most competitive sports
  • circuit training

You should also do strength and balance training two days a week. This could be in the form of a gym workout, carrying shopping bags, or doing an activity such as tai chi. It's also critical that you break up sitting (sedentary) time by getting up and moving around.

Read more about strength and balance exercises.

Your GP, weight loss adviser or staff at your local sports centre can help you create a plan suited to your own personal needs and circumstances, with achievable and motivating goals. Start small and build up gradually.

It's also important to find activities you enjoy and want to keep doing. Activities with a social element or exercising with friends or family can help keep you motivated. Make a start today – it’s never too late.

Read more about the physical activity guidelines for adults and the physical activity guidelines for older adults.

Other useful strategies

Evidence has shown that weight loss can be more successful if it involves other strategies, alongside diet and lifestyle changes. This could include things like:

  • setting realistic weight loss goals – if you're obese, losing just 3% of your original body weight can significantly reduce your risk of developing obesity-related complications
  • eating more slowly and being mindful of what and when you're eating – for example, not being distracted by watching TV
  • avoiding situations where you know you may be tempted to overeat
  • involving your family and friends with your weight loss efforts –they can help to motivate you
  • monitoring your progress – for example, weigh yourself regularly and make a note of your weight in a diary

Getting psychological support from a trained healthcare professional may also help you change the way you think about food and eating. Techniques such as cognitive behavioural therapy (CBT) can be useful.

Avoiding weight regain

It's important to remember that as you lose weight your body needs less food (calories), so after a few months, weight loss slows and levels off, even if you continue to follow a diet.

If you go back to your previous calorie intake once you've lost weight, it's very likely you'll put the weight back on. Increasing physical activity to up to 60 minutes a day and continuing to watch what you eat may help you keep the weight off.

Medication

Many different types of anti-obesity medicines have been tested in clinical trials, but only one has proved to be safe and effective: orlistat.

You can only use orlistat if a doctor or pharmacist thinks it's the right medicine for you. In most cases, orlistat is only available on prescription. Only one product (Alli) is available over the counter directly from pharmacies, under the supervision of a pharmacist.

Orlistat works by preventing around a third of the fat from the food you eat being absorbed. The undigested fat isn't absorbed into your body and is passed out with your faeces (stools). This will help you avoid gaining weight, but won't necessarily cause you to lose weight.

A balanced diet and exercise programme should be started before beginning treatment with orlistat, and you should continue this programme during treatment and after you stop taking orlistat.

When orlistat should be used

Orlistat will usually only be recommended if you've made a significant effort to lose weight through diet, exercise or changing your lifestyle.

Even then, orlistat is only prescribed if you have a:

  • body mass index (BMI) of 28 or more, and other weight-related conditions, such as high blood pressure or type 2 diabetes
  • BMI of 30 or more

Before prescribing orlistat, your doctor will discuss the benefits and potential limitations with you, including any potential side effects (see below). 

Treatment with orlistat must be combined with a balanced low-fat diet and other weight loss strategies, such as doing more exercise. It's important that the diet is nutritionally balanced over three main meals.

If you're prescribed orlistat, you'll also be offered advice and support about diet, exercise and making lifestyle changes.

Orlistat isn't usually recommended for pregnant or breastfeeding women.

Dosage and duration of treatment

One orlistat capsule is taken with water immediately before, during or up to one hour after, each main meal (up to a maximum of three capsules a day).

If you miss a meal, or the meal doesn't contain any fat, you shouldn't need to take the orlistat capsule. Your doctor should explain this to you, or you can check the patient information leaflet that comes with your medicine.

Treatment with orlistat should only continue beyond three months if you've lost 5% of your body weight. It usually starts to affect how you digest fat within one to two days.

If you haven't lost weight after taking orlistat for three months, it's unlikely to be an effective treatment for you. Consult your doctor or pharmacist, as it may be necessary to stop your treatment.

Taking orlistat with other health conditions

See your GP before starting treatment with orlistat if you have another serious health condition, such as type 2 diabetes, high blood pressure, or kidney disease, which you're taking medication for. It may be necessary to change the dose of your medicine.

If you have type 2 diabetes, it may take you longer to lose weight using orlistat, so your target weight loss after three months may therefore be slightly lower.

If orlistat has helped you lose weight after three months, your prescription may be continued for up to a year. After that, your GP will carry out a review and decide whether you should continue taking it.

Side effects

Common side effects of orlistat include:

  • fatty or oily stools
  • needing the toilet urgently
  • passing stools more frequently
  • an oily discharge from your rectum (you may have oily spots on your underwear)
  • flatulence (wind)
  • stomach pain
  • headaches
  • upper respiratory tract infections, such as a cold

These side effects are much less likely to occur if you stick to a low-fat diet.

Women taking the oral contraceptive pill should use an additional method of contraception, such as a condom, if they experience severe diarrhoea while taking orlistat. This is because the contraceptive pill may not be absorbed by your body if you have diarrhoea, so it may not be effective.

Surgery

Weight loss surgery, also called bariatric surgery, is sometimes used to treat people who are severely obese.

Bariatric surgery is usually only available on the NHS to treat people with severe obesity who fulfil all of the following criteria:

  • they have a BMI of 40 or more, or between 35 and 40 and another serious health condition that could be improved with weight loss, such as type 2 diabetes or high blood pressure
  • all appropriate non-surgical measures have been tried, but the person hasn't achieved or maintained adequate, clinically beneficial weight loss
  • the person is fit enough to have anaesthesia and surgery
  • the person has been receiving, or will receive, intensive management as part of their treatment
  • the person commits to the need for long-term follow-up

Bariatric surgery may also be considered as a possible treatment option for people with a BMI of 30 to 35 who have recently (in the last 10 years) been diagnosed with type 2 diabetes.

In rare cases, surgery may be recommended as the first treatment (instead of lifestyle treatments and medication) if a person's BMI is 50 or above.

Treating obesity in children

Treating obesity in children usually involves improvements to diet and increasing physical activity using behaviour change strategies.

The amount of calories your child should eat each day will depend on their age and height. Your GP should be able to advise you about a recommended daily limit, and they may also be able to refer you to your local family healthy lifestyle programme.

Children over the age of five should ideally get at least one hour (60 minutes) of vigorous-intensity exercise a day, such as running or playing football or netball. Sedentary activities, such as watching television and playing computer games, should be restricted.

Read more about the physical activity guidelines for children and young people

Referral to a specialist in treating childhood obesity may be recommended if your child develops an obesity-related complication, or there's thought to be an underlying medical condition causing obesity.

The use of orlistat in children is only recommended in exceptional circumstances, such as if a child is severely obese and has an obesity-related complication.

Bariatric surgery isn't generally recommended for children, but may be considered for young people in exceptional circumstances, and if they've achieved, or nearly achieved, physiological maturity.

Источник: https://www.nhsinform.scot/illnesses-and-conditions/nutritional/obesity

Encouraging Progress on the State of Obesity in the United States

Yet over the past several years, we’ve started to see a change. Obesity rates among adults and kids have started to level off. And, a growing number of states and cities have actually started reporting declines in their obesity rates among some subgroups of children. Slowly but surely, a new story began to emerge: that we’re starting to turn the corner.

That didn’t happen by accident, or by coincidence, or by the efforts of only one person or organization. In many places across the country, we’re seeing a team approach not only to reversing the obesity epidemic, but to building a Culture of Health where all of us—no matter who we are or where we live—have the opportunity to be healthy. And we’re beginning to witness the exciting results of parents, policymakers, community leaders, health officials, educators, business owners, and industry executives coming together.

Schools are setting a great example. Updated nutrition standards for school foods are working, and several districts are implementing minimum time requirements for physical education.

Communities are innovating. Grocery stores and other healthy food retailers are receiving incentives to locate and expand in underserved neighborhoods, and 33 states have implemented policies to encourage walking and biking.

Rates have steadied nationally for both adults and kids, and childhood obesity declines are popping up all over the map. Our 2016 State of Obesity report revealed even more good news: For the first time in the past decade, adult obesity rates actually declined in four states between 2014 and 2015.

And today, we’re sharing another encouraging sign of progress: New data from the Centers for Disease Control and Prevention show obesity rates among 2- to 4-year-olds from low-income families who are enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) declined in 31 states between 2010 and 2014. This is important because kids from lower-income families are especially vulnerable and often face higher risk for obesity.

Let’s be clear—while we’re hopeful about the future, our work is far from over. We still have a long way to go. In four states, obesity rates among 2- to 4-year-olds enrolled in WIC actually increased. And rates remain above 15 percent in 18 states. Nearly one in three kids remains overweight or obese nationally, and in every state, more than one in three adults is obese. Racial, ethnic, and income disparities persist or are actually growing for some segments of our young population. Too many families still lack access to healthy food in their neighborhoods or safe places to play.

RWJF’s $500 million commitment to helping all kids grow up at a healthy weight underscores the belief that achieving a healthy weight is much more than just hitting a number on a scale; it’s in many ways central to kids’ academic, social, and emotional development as well. So let’s redouble our efforts to ensure we’re giving all kids a healthy start from their very first days.

Learn about what your state is doing to promote nutrition and physical activity for children in early child care settings.

Источник: https://www.rwjf.org/en/blog/2016/11/encouraging_progress.html

Childhood obesity, prevalence and prevention

Nutrition Journalvolume 4, Article number: 24 (2005) Cite this article

  • 751k Accesses

  • 395 Citations

  • 23 Altmetric

  • Metrics details

Abstract

Childhood obesity has reached epidemic levels in developed countries. Twenty five percent of children in the US are overweight and 11% are obese. Overweight and obesity in childhood are known to have significant impact on both physical and psychological health. The mechanism of obesity development is not fully understood and it is believed to be a disorder with multiple causes. Environmental factors, lifestyle preferences, and cultural environment play pivotal roles in the rising prevalence of obesity worldwide. In general, overweight and obesity are assumed to be the results of an increase in caloric and fat intake. On the other hand, there are supporting evidence that excessive sugar intake by soft drink, increased portion size, and steady decline in physical activity have been playing major roles in the rising rates of obesity all around the world. Consequently, both over-consumption of calories and reduced physical activity are involved in childhood obesity.

Almost all researchers agree that prevention could be the key strategy for controlling the current epidemic of obesity. Prevention may include primary prevention of overweight or obesity, secondary prevention or prevention of weight regains following weight loss, and avoidance of more weight increase in obese persons unable to lose weight. Until now, most approaches have focused on changing the behaviour of individuals in diet and exercise. It seems, however, that these strategies have had little impact on the growing increase of the obesity epidemic. While about 50% of the adults are overweight and obese in many countries, it is difficult to reduce excessive weight once it becomes established. Children should therefore be considered the priority population for intervention strategies. Prevention may be achieved through a variety of interventions targeting built environment, physical activity, and diet. Some of these potential strategies for intervention in children can be implemented by targeting preschool institutions, schools or after-school care services as natural setting for influencing the diet and physical activity. All in all, there is an urgent need to initiate prevention and treatment of obesity in children.

Peer Review reports

Introduction

Childhood obesity has reached epidemic levels in developed countries. Twenty five percent of children in the US are overweight and 11% are obese. About 70% of obese adolescents grow up to become obese adults [1–3]. The prevalence of childhood obesity is in increasing since 1971 in developed countries (Table 1). In some European countries such as the Scandinavian countries the prevalence of childhood obesity is lower as compared with Mediterranean countries, nonetheless, the proportion of obese children is rising in both cases [4]. The highest prevalence rates of childhood obesity have been observed in developed countries, however, its prevalence is increasing in developing countries as well. The prevalence of childhood obesity is high in the Middle East, Central and Eastern Europe [5]. For instance, in 1998, The World Health Organization project monitoring of cardiovascular diseases (MONICA) reported Iran as one of the seven countries with the highest prevalence of childhood obesity. The prevalence of BMI (in percentage) between 85th and 95th percentile in girls was significantly higher than that in boys (10.7, SD = 1.1 vs. 7.4, SD = 0.9). The same pattern was seen for the prevalence of BMI > 95th percentile (2.9, SD = 0.1 vs. 1.9, SD = 0.1) [6]. In Saudi Arabia, one in every six children aged 6 to 18 years old is obese [7]. Furthermore, in both developed and developing countries there are proportionately more girls overweight than boys, particularly among adolescent [6, 8, 9].

Full size table

Overweight and obesity in childhood have significant impact on both physical and psychological health; for example, overweight and obesity are associated with Hyperlipidaemia, hypertension, abnormal glucose tolerance, and infertility. In addition, psychological disorders such as depression occur with increased frequency in obese children [10]. Overweight children followed up for 40 [11] and 55 years [12] were more likely to have cardiovascular and digestive diseases, and die from any cause as compared with those who were lean.

Definition of childhood obesity

Although definition of obesity and overweight has changed over time [13, 14], it can be defined as an excess of Body Fat (BF). There is no consensus on a cutoff point for excess fatness of overweight or obesity in children and adolescents. Williams et al. [15] measured skin fold thickness of 3320 children aged 5–18 years and classified children as fat if their percentage of body fat was at least 25% and 30%, respectively, for males and females. The Center for Disease Control and Prevention defined overweight as at or above the 95th percentile of BMI for age and "at risk for overweight" as between 85th to 95th percentile of BMI for age [16, 17]. European researchers classified overweight as at or above 85th percentile and obesity as at or above 95th percentile of BMI [18].

There are also several methods to measure the percentage of body fat. In research, techniques include underwater weighing (densitometry), multi-frequency bioelectrical impedance analysis (BIA) and magnetic resonance imaging (MRI). In the clinical environment, techniques such as body mass index (BMI), waist circumference, and skin fold thickness have been used extensively. Although, these methods are less accurate than research methods, they are satisfactory to identify risk. While BMI seems appropriate for differentiating adults, it may not be as useful in children because of their changing body shape as they progress through normal growth. In addition, BMI fails to distinguish between fat and fat-free mass (muscle and bone) and may exaggerate obesity in large muscular children. Furthermore, maturation pattern differs between genders and different ethnic groups. Studies that used BMI to identify overweight and obese children based on percentage of body fat have found high specificity (95–100%), but low sensitivity (36–66%) for this system of classification [19]. While health consequences of obesity are related to excess fatness, the ideal method of classification should be based on direct measurement of fatness. Although methods such as densitometry can be used in research practice, they are not feasible for clinical settings. For large population-based studies and clinical situations, bioelectrical impedance analysis (BIA) is widely used. Cross-sectional studies have shown that BIA predicts total body water (TBW), fat-free mass (FFM), and fat mass or percentage of body fat (%BF) among children [20–23]. Also, it has been shown that BIA provides accurate estimation of changes on %BF and FFM over time [24]. Waist circumference, as a surrogate marker of visceral obesity, has been added to refine the measure of obesity related risks [25]. Waist circumference seems to be more accurate for children because it targets central obesity, which is a risk factor for type II diabetes and coronary heart disease. To the best of our knowledge there is no publication on specific cut off points for waist circumference, but there are some ongoing studies.

Causes of obesity

Although the mechanism of obesity development is not fully understood, it is confirmed that obesity occurs when energy intake exceeds energy expenditure. There are multiple etiologies for this imbalance, hence, and the rising prevalence of obesity cannot be addressed by a single etiology. Genetic factors influence the susceptibility of a given child to an obesity-conducive environment. However, environmental factors, lifestyle preferences, and cultural environment seem to play major roles in the rising prevalence of obesity worldwide [26–29]. In a small number of cases, childhood obesity is due to genes such as leptin deficiency or medical causes such as hypothyroidism and growth hormone deficiency or side effects due to drugs (e.g. – steroids) [30]. Most of the time, however, personal lifestyle choices and cultural environment significantly influence obesity.

Behavioral and social factors

I. Diet

Over the last decades, food has become more affordable to larger numbers of people as the price of food has decreased substantially relative to income and the concept of 'food' has changed from a means of nourishment to a marker of lifestyle and a source of pleasure. Clearly, increases in physical activity are not likely to offset an energy rich, poor nutritive diet. It takes between 1–2 hours of extremely vigorous activity to counteract a single large-sized (i.e., >=785 kcal) children's meal at a fast food restaurant. Frequent consumption of such a diet can hardly be counteracted by the average child or adult [31].

Calorie intake

although overweight and obesity are mostly assumed to be results of increase in caloric intake, there is not enough supporting evidence for such phenomenon. Food frequency methods measure usual diet, but estimate caloric intake poorly [32]. Other methods such as 24-hour recall or food diaries evaluate caloric intakes more accurately, however, estimate short-term not long-term intake [32]. Total energy intake is difficult to measure accurately at a population level. However, a small caloric imbalance (within the margin of error of estimation methods) is sufficient over a long period of time to lead to obesity. With concurrent rise in childhood obesity prevalence in the USA, the National Health and Nutrition Examination Survey (NHANES) noted only subtle change in calorie intake among US children from the 1970s to 1988–1994. For this period, NHANES III found an increase calorie intake only among white and black adolescent females. The same pattern was observed by the latest NHANES (1999–2000). The Bogalusa study which has been following the health and nutrition of children since 1973 in Bogalusa (Louisiana), reported that total calorie intake of 10-year old children remained unchanged during 1973–1988 and a slight but significant decrease was observed when energy intake was expressed per kilogram body weight [33]. The result of a survey carried out during the past few decades in the UK suggested that average energy intakes, for all age groups, are lower than they used to be [34]. Some small studies also found similar energy intake among obese children and their lean counterparts [6, 35–37].

Fat intake

while for many years it has been claimed that the increase in pediatric obesity has happened because of an increase in high fat intake, contradictory results have been obtained by cross-sectional and longitudinal studies. Result of NHANES has shown that fat consumption of American children has fallen over the last three decades. For instance; mean dietary fat consumption in males aged 12–19 years fell from 37.0% (SD = 0.29%) of total caloric intake in 1971–1974 to 32.0% (SD = 0.42%) in 1999–2000. The pattern was the same for females, whose fat consumption fell from 36.7% (SD = 0.27%) to 32.1% (SD = 0.61%) [38, 39]. Gregory et al. [40] reported that the average fat intake of children aged 4–18 years in the UK is close to the government recommendation of 35% energy. On the other hand, some cross-sectional studies have found a positive relationship between fat intake and adiposity in children even after controlling for confounding factors [41, 42]. The main objection to the notion that dietary fat is responsible for the accelerated pediatric obesity epidemic is the fact that at the same time the prevalence of childhood obesity was increasing, the consumption of dietary fat in different populations was decreasing. Although fat eaten in excess leads to obesity, there is not strong enough evidence that fat intake is the chief reason for the ascending trend of childhood obesity.

Other dietary factors

there is a growing body of evidence suggesting that increasing dairy intake by about two servings per day could reduce the risk of overweight by up to 70% [43]. In addition, calcium intake was associated with 21% reduced risk of development of insulin resistance among overweight younger adults and may reduce diabetes risk [44]. Higher calcium intake and more dairy servings per day were associated with reduced adiposity in children studied longitudinally [45, 46]. There are few data reporting the relation between calcium or dairy intake and obesity among children.

Between 1970 and 1997, the United State Department of Agriculture (USDA) surveys indicated an increase of 118% of per capita consumption of carbonated drinks, and a decline of 23% for beverage milk [47]. Soft drink intake has been associated with the epidemic of obesity [48] and type II diabetes [49] among children. While it is possible that drinking soda instead of milk would result in higher intake of total energy, it cannot be concluded definitively that sugar containing soft drinks promote weight gain because they displace dairy products.

II. Physical Activity

It has been hypothesized that a steady decline in physical activity among all age groups has heavily contributed to rising rates of obesity all around the world. Physical activity strongly influenced weight gain in a study of monozygotic twins [50]. Numerous studies have shown that sedentary behaviors like watching television and playing computer games are associated with increased prevalence of obesity [51, 52]. Furthermore, parents report that they prefer having their children watch television at home rather than play outside unattended because parents are then able to complete their chores while keeping an eye on their children [53]. In addition, increased proportions of children who are being driven to school and low participation rates in sports and physical education, particularly among adolescent girls [51], are also associated with increased obesity prevalence. Since both parental and children's choices fashion these behaviors, it is not surprising that overweight children tend to have overweight parents and are themselves more likely to grow into overweight adults than normal weight children [54]. In response to the significant impact that the cultural environment of a child has on his/her daily choices, promoting a more active lifestyle has wide ranging health benefits and minimal risk, making it a promising public health recommendation.

Prevention

Almost all public health researchers and clinicians agree that prevention could be the key strategy for controlling the current epidemic of obesity [55]. Prevention may include primary prevention of overweight or obesity itself, secondary prevention or avoidance of weight regains following weight loss, and prevention of further weight increases in obese individuals unable to lose weight. Until now, most approaches have focused on changing the behavior of individuals on diet and exercise and it seems that these strategies have had little impact on the growing increase of the obesity epidemic.

What age group is the priority for starting prevention?

Children are often considered the priority population for intervention strategies because, firstly, weight loss in adulthood is difficult and there are a greater number of potential interventions for children than for adults. Schools are a natural setting for influencing the food and physical activity environments of children. Other settings such as preschool institutions and after-school care services will have similar opportunities for action. Secondly, it is difficult to reduce excessive weight in adults once it becomes established. Therefore it would be more sensible to initiate prevention and treatment of obesity during childhood. Prevention may be achieved through a variety of interventions targeting built environment, physical activity and diet.

Built Environment

The challenge ahead is to identify obesogenic environments and influence them so that healthier choices are more available, easier to access, and widely promoted to a large proportion of the community (Table 2). The neighborhood is a key setting that can be used for intervention. It encompasses the walking network (footpaths and trails, etc.), the cycling network (roads and cycle paths), public open spaces (parks) and recreation facilities (recreation centers, etc.). While increasing the amount of public open space might be difficult within an existing built environment, protecting the loss of such spaces requires strong support within the community. Although the local environment, both school and the wider community, plays an important role in shaping children's physical activity, the smaller scale of the home environment is also very important in relation to shaping children's eating behaviors and physical activity patterns. Surprisingly, we know very little about specific home influences and as a setting, it is difficult to influence because of the total numbers and heterogeneity of homes and the limited options for access [56]. Of all aspects of behavior in the home environment, however, television viewing has been researched in greatest detail [57–59].

Full size table

Physical activity

Stone et al. [60] reviewed the impact of 14 school-based interventions on physical activity knowledge and behavior. Most of the outcome variables showed significant improvements for the intervention. One interdisciplinary intervention program in the USA featured a curriculum-based approach to influence eating patterns, reduce sedentary behaviors (with a strong emphasis on television viewing), and promote higher activity levels among children of school grades 6 to 8. Evaluation at two years showed a reduction in obesity prevalence in girls (OR = 0.47; 95%CI: 0.24 – 0.93), but not in boys (OR = 0.85; 95%CI: 0.52 – 1.39) compared to controls. The reduction in television viewing (by approximately 30 min/day) was highly significant for both boys and girls. Increases in sports participation and/or physical education time would need policy-based changes at both school and education sector levels [61]. Similarly, increases in active modes of transport to and from school (walking, cycling, and public transport) would require policy changes at the school and local government levels, as well as support from parents and the community. In some communities a variety of such programs have been implemented e.g. road crossings, 'walking bus', and designated safe walking and cycling routes [51].

Effects of dietary pattern and TV watching

It appears that gains can be made in obesity prevention through restricting television viewing. Although, it seems that reduced eating in front of the television is at least as important as increasing activity [58]. Fast foods are one of the most advertised products on television and children are often the targeted market. Reducing the huge volume of marketing of energy-dense foods and drinks and fast-food restaurants to young children, particularly through the powerful media of television, is a potential strategy that has been advocated. Television advertising to children under 12 years of age has not been permitted in Sweden since commercial television began over a decade ago, although children's television programs from other countries, and through satellite television, probably dilute the impact of the ban in Sweden. Norway, Denmark, Austria, Ireland, Australia, and Greece also have some restrictions on television advertising to young children [51]. The fact that children would still be seeing some television advertisements during adult programs or other types of marketing, such as billboards, does not contradict the rationale for the control on the television watching of young children.

Food Sector

Food prices have a marked influence on food-buying behaviour and, consequently, on nutrient intake [62]. A small tax (but large enough to affect sales) on high-volume foods of low nutritional value, such as soft drinks, confectionery, and snack foods, may discourage their use. Such taxes currently applied in some parts of the USA and Canada [63]. In addition, food labeling and nutrition 'signposts' such as logos that indicate that a food meets certain nutrition standards might help consumers make choices of healthy foods. An example is the 'Pick the Tick' symbol program run by the National Heart Foundations in Australia and New Zealand [64]. The 'Pick the Tick' symbols made it easier for consumers to identify healthier food choices and are frequently used by shoppers. In addition, the nutrition criteria for the products serve as 'de facto' standards for product formulation, and many manufacturers will formulate or reformulate products to meet those standards.

Effectiveness of the prevention methods

It has been shown that focusing on reducing sedentary behaviour and encouraging free play has been more effective than focusing on forced exercise or reducing food intake in preventing already obese children from gaining more weight [65]. Recent efforts in preventing obesity include the initiative of using school report cards to make the parents aware of their children's weight problem. Health report cards are believed to aid prevention of obesity. In a study in the Boston area, parents who received health and fitness report cards were almost twice as likely to know or acknowledge that their child was actually overweight than those parents who did not get a report card [66]. They also were over twice as likely to plan weight-control activities for their overweight children.

A summary of prevention and intervention strategies is presented in Table 2.

Conclusion

Obesity is a chronic disorder that has multiple causes. Overweight and obesity in childhood have significant impact on both physical and psychological health. In addition, psychological disorders such as depression occur with increased frequency in obese children. Overweight children are more likely to have cardiovascular and digestive diseases in adulthood as compared with those who are lean. It is believed that both over-consumption of calories and reduced physical activity are mainly involved in childhood obesity.

Apparently, primary or secondary prevention could be the key plan for controlling the current epidemic of obesity and these strategies seem to be more effective in children than in adults. A number of potential effective plans can be implemented to target built environment, physical activity, and diet. These strategies can be initiated at home and in preschool institutions, schools or after-school care services as natural setting for influencing the diet and physical activity and at home and work for adults. Both groups can benefit from an appropriate built environment. However, further research needs to examine the most effective strategies of intervention, prevention, and treatment of obesity. These strategies should be culture specific, ethnical, and consider the socio-economical aspects of the targeting population.

Abbreviations

National Health and Nutrition Examination Survey

Multinational Monitoring of trends and determinants in cardiovascular disease

Body Fat

Body Mass Index

Bioelectrical Impedance Analysis

Magnetic Resonance Imaging

Total Body Water

Fat-Free Mass

United State Department of Agriculture

References

  1. 1.

    Nicklas TA, T. B, K.W. C, G. B: Eating Patterns, Dietary Quality and Obesity. Journal of the American College of Nutrition. 2001, 20: 599-608.

    CASArticlePubMed Google Scholar

  2. 2.

    Parsons TJ, Power C, Logan S, Summerbell CD: Childhood predictors of adult obesity: a systematic review. International Journal of Obesity. 1999, 23: S1-S107.

    PubMed Google Scholar

  3. 3.

    Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH: Predicting obesity in young adulthood from childhood and parental obesity. New England Journal of Medicine. 1997, 337: 869-873. 10.1056/NEJM199709253371301.

    CASArticlePubMed Google Scholar

  4. 4.

    Livingstone MB: Childhood obesity in Europe: a growing concern. Public Health Nutr. 2001, 4: 109-116.

    CASPubMed Google Scholar

  5. 5.

    James PT: Obesity: The worldwide epidemic. Clinics in Dermatology. 2004, 22: 276-280. 10.1016/j.clindermatol.2004.01.010.

    ArticlePubMed Google Scholar

  6. 6.

    Kelishadi R, Pour MH, Sarraf-Zadegan N, Sadry GH, Ansari R, Alikhassy H, Bashardoust N: Obesity and associated modifiable environmental factors in Iranian adolescents: Isfahan Healthy Heart Program - Heart Health Promotion from Childhood. Pediatr Int. 2003, 45: 435-442. 10.1046/j.1442-200X.2003.01738.x.

    ArticlePubMed Google Scholar

  7. 7.

    AlNuaim AR, Bamgboye EA, AlHerbish A: The pattern of growth and obesity in Saudi Arabian male school children. International Journal of Obesity. 1996, 20: 1000-1005.

    CAS Google Scholar

  8. 8.

    McCarthy HD, Ellis SM, Cole TJ: Central overweight and obesity in British youth aged 11-16 years: cross sectional surveys of waist circumference. BMJ. 2003, 326: 624-10.1136/bmj.326.7390.624.

    ArticlePubMedPubMed Central Google Scholar

  9. 9.

    Ruxton CH, Reilly JJ, Kirk TR: Body composition of healthy 7-and 8-year-old children and a comparison with the 'reference child'. International Journal of Obesity. 1999, 23: 1276-1281. 10.1038/sj.ijo.0801067.

    CASArticlePubMed Google Scholar

  10. 10.

    Daniels SR, Arnett DK, Eckel RH, Gidding SS, Hayman LL, Kumanyika S, Robinson TN, Scott BJ, St Jeor S, Williams CL: Overweight in children and adolescents: pathophysiology, consequences, prevention, and treatment. Circulation. 2005, 111: 1999-2012. 10.1161/01.CIR.0000161369.71722.10.

    ArticlePubMed Google Scholar

  11. 11.

    Mossberg HO: 40-Year Follow-Up of Overweight Children. Lancet. 1989, 2: 491-493. 10.1016/S0140-6736(89)92098-9.

    CASArticlePubMed Google Scholar

  12. 12.

    Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH: Long-Term Morbidity and Mortality of Overweight Adolescents - A Follow-Up of the Harvard Growth Study of 1922 to 1935. New England Journal of Medicine. 1992, 327: 1350-1355.

    CASArticlePubMed Google Scholar

  13. 13.

    Flegal KM, Carroll MD, Ogden CL, Johnson CL: Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002, 288: 1723-1727. 10.1001/jama.288.14.1723.

    ArticlePubMed Google Scholar

  14. 14.

    Kuczmarski RJ, Flegal KM: Criteria for definition of overweight in transition: background and recommendations for the United States. Am J Clin Nutr. 2000, 72: 1074-1081.

    CASPubMed Google Scholar

  15. 15.

    Williams DP, Going SB, Lohman TG, Harsha DW, Srinivasan SR, Webber LS, Berenson GS: Body Fatness and Risk for Elevated Blood-Pressure, Total Cholesterol, and Serum-Lipoprotein Ratios in Children and Adolescents. American Journal of Public Health. 1992, 82: 358-363.

    CASArticlePubMedPubMed Central Google Scholar

  16. 16.

    Flegal KM, Wei R, Ogden C: Weight-for-stature compared with body mass index-for-age growth charts for the United States from the Centers for Disease Control and Prevention. American Journal of Clinical Nutrition. 2002, 75: 761-766.

    CASPubMed Google Scholar

  17. 17.

    Himes JH, Dietz WH: Guidelines for Overweight in Adolescent Preventive Services - Recommendations from An Expert Committee. American Journal of Clinical Nutrition. 1994, 59: 307-316.

    CASPubMed Google Scholar

  18. 18.

    Flodmark CE, Lissau I, Moreno LA, Pietrobelli A, Widhalm K: New insights into the field of children and adolescents' obesity: the European perspective (vol 28, pg 1189, 2004). International Journal of Obesity. 2004, 28:

    Google Scholar

  19. 19.

    Lazarus R, Baur L, Webb K, Blyth F: Body mass index in screening for adiposity in children and adolescents: systematic evaluation using receiver operating characteristic curves. Am J Clin Nutr. 1996, 63: 500-506.

    CASPubMed Google Scholar

  20. 20.

    Danford LC, Schoeller DA, Kushner RF: Comparison of two bioelectrical impedance analysis models for total body water measurement in children. Ann Hum Biol. 1992, 19: 603-607.

    CASArticlePubMed Google Scholar

  21. 21.

    Deurenberg P, van der KK, Paling A, Withagen P: Assessment of body composition in 8-11 year old children by bioelectrical impedance. Eur J Clin Nutr. 1989, 43: 623-629.

    CASPubMed Google Scholar

  22. 22.

    Deurenberg P, Kusters CS, Smit HE: Assessment of body composition by bioelectrical impedance in children and young adults is strongly age-dependent. Eur J Clin Nutr. 1990, 44: 261-268.

    CASPubMed Google Scholar

  23. 23.

    Deurenberg P, Pieters JJ, Hautvast JG: The assessment of the body fat percentage by skinfold thickness measurements in childhood and young adolescence. Br J Nutr. 1990, 63: 293-303. 10.1079/BJN19900116.

    CASArticlePubMed Google Scholar

  24. 24.

    Phillips SM, Bandini LG, Compton DV, Naumova EN, Must A: A longitudinal comparison of body composition by total body water and bioelectrical impedance in adolescent girls. Journal of Nutrition. 2003, 133: 1419-1425.

    CASPubMed Google Scholar

  25. 25.

    Stevens J: Obesity, fat patterning and cardiovascular risk. Adv Exp Med Biol. 1995, 369: 21-27.

    CASArticlePubMed Google Scholar

  26. 26.

    Hill JO, Peters JC: Environmental contributions to the obesity epidemic. Science. 1998, 280: 1371-1374. 10.1126/science.280.5368.1371.

    CASArticlePubMed Google Scholar

  27. 27.

    Goodrick GK, Poston WS, Foreyt JP: Methods for voluntary weight loss and control: update 1996. Nutrition. 1996, 12: 672-676. 10.1016/S0899-9007(96)00243-2.

    CASArticlePubMed Google Scholar

  28. 28.

    Eckel RH, Krauss RM: American Heart Association call to action: obesity as a major risk factor for coronary heart disease. AHA Nutrition Committee. Circulation. 1998, 97: 2099-2100.

    CASArticlePubMed Google Scholar

  29. 29.

    Grundy SM: Multifactorial causation of obesity: implications for prevention. Am J Clin Nutr. 1998, 67: 563S-572S.

    CASPubMed Google Scholar

  30. 30.

    Link K, Moell C, Garwicz S, Cavallin-Stahl E, Bjork J, Thilen U, Ahren B, Erfurth EM: Growth hormone deficiency predicts cardiovascular risk in young adults treated for acute lymphoblastic leukemia in childhood. J Clin Endocrinol Metab. 2004, 89: 5003-5012. 10.1210/jc.2004-0126.

    CASArticlePubMed Google Scholar

  31. 31.

    Styne DM: Obesity in childhood: what's activity got to do with it?. American Journal of Clinical Nutrition. 2005, 81: 337-338.

    CASPubMed Google Scholar

  32. 32.

    Willett W: Food Frequency Methods. Nutritional Epidemiology. 1998, Oxford University Press, 5: 74-2

    Chapter Google Scholar

  33. 33.

    Nicklas TA: Dietary Studies of Children - the Bogalusa Heart-Study Experience. Journal of the American Dietetic Association. 1995, 95: 1127-1133. 10.1016/S0002-8223(95)00305-3.

    CASArticlePubMed Google Scholar

  34. 34.

    Prentice AM, Jebb SA: Obesity in Britain - Gluttony Or Sloth. British Medical Journal. 1995, 311: 437-439.

    CASArticlePubMedPubMed Central Google Scholar

  35. 35.

    Bellisle F, Rolland-Cachera MF, Deheeger M, Guilloud-Bataille M: Obesity and food intake in children: evidence for a role of metabolic and/or behavioral daily rhythms. Appetite. 1988, 11: 111-118.

    CASArticlePubMed Google Scholar

  36. 36.

    Griffiths M, Payne PR: Energy expenditure in small children of obese and non-obese parents. Nature. 1976, 260: 698-700. 10.1038/260698a0.

    CASArticlePubMed Google Scholar

  37. 37.

    Maffeis C, Zaffanello M, Pinelli L, Schutz Y: Total energy expenditure and patterns of activity in 8-10-year-old obese and nonobese children. J Pediatr Gastroenterol Nutr. 1996, 23: 256-261. 10.1097/00005176-199610000-00009.

    CASArticlePubMed Google Scholar

  38. 38.

    Troiano RP, Briefel RR, Carroll MD, Bialostosky K: Energy and fat intakes of children and adolescents in the united states: data from the national health and nutrition examination surveys. Am J Clin Nutr. 2000, 72: 1343S-1353S.

    CASPubMed Google Scholar

  39. 39.

    Wright JD, Kennedy-Stephenson J, Wang CY, McDowell MA, Johnson CL: Trends in intake of energy and macronutrients - United States, 1971-2000 (Reprinted from MMWR, vol 53, pg 80-82, 2004). Journal of the American Medical Association. 2004, 291: 1193-1194. 10.1001/jama.291.18.2194.

    CASArticle Google Scholar

  40. 40.

    Gregory JW, Lowe S: National Diet and Nutrition Survery: Young People Aged 4 to 18 Years : Report of the Diet and Nutrition Survey. 2000, London, The Stationery Office.

    Google Scholar

  41. 41.

    Maffeis C, Pinelli L, Schutz Y: Fat intake and adiposity in 8 to 11 year-old obese children. International Journal of Obesity. 1996, 20: 170-174.

    CASPubMed Google Scholar

  42. 42.

    Tucker LA, Seljaas GT, Hager RL: Body fat percentage of children varies according to their diet composition. Journal of the American Dietetic Association. 1997, 97: 981-986. 10.1016/S0002-8223(97)00237-X.

    CASArticlePubMed Google Scholar

  43. 43.

    Heaney RP, Davies KM, Barger-Lux MJ: Calcium and weight: clinical studies. J Am Coll Nutr. 2002, 21: 152S-155S.

    CASArticlePubMed Google Scholar

  44. 44.

    Pereira MA, Jacobs DRJ, Van Horn L, Slattery ML, Kartashov AI, Ludwig DS: Dairy consumption, obesity, and the insulin resistance syndrome in young adults: the CARDIA Study. JAMA. 2002, 287: 2081-2089. 10.1001/jama.287.16.2081.

    ArticlePubMed Google Scholar

  45. 45.

    Carruth BR, Skinner JD: The role of dietary calcium and other nutrients in moderating body fat in preschool children. Int J Obes Relat Metab Disord. 2001, 25: 559-566. 10.1038/sj.ijo.0801562.

    CASArticlePubMed Google Scholar

  46. 46.

    Skinner JD, Bounds W, Carruth BR, Ziegler P: Longitudinal calcium intake is negatively related to children's body fat indexes. J Am Diet Assoc. 2003, 103: 1626-1631. 10.1016/j.jada.2003.09.018.

    ArticlePubMed Google Scholar

  47. 47.

    Putnam JJ, Allshouse JE: Food consumption, prices, and expenditures, 1970-97. 1999, Washington,D.C., Food and Consumers Economics Division, Economic Research Service, US Department of Agriculture

    Google Scholar

  48. 48.

    Ludwig DS, Peterson KE, Gortmaker SL: Relation between consumption of sugar-sweetened drinks and childhood obesity: a prospective, observational analysis. Lancet. 2001, 357: 505-508. 10.1016/S0140-6736(00)04041-1.

    CASArticlePubMed Google Scholar

  49. 49.
Источник: https://nutritionj.biomedcentral.com/articles/10.1186/1475-2891-4-24

Overweight and Obesity

Preventing kids from becoming overweight means making choices in the way your family eats and exercises, and how you spend time together. Helping kids lead healthy lifestyles begins with parents who lead by example.

What Health Problems Can Obesity Cause?

Obesity puts kids at risk for medical problems that can affect their health now and in the future. These include serious conditions like type 2 diabetes, high blood pressure, and high cholesterol — all once considered adult diseases.

Overweight and obese kids are also at risk for:

  • bone and joint problems
  • shortness of breath that makes exercise, sports, or any physical activity more difficult. This also can make asthma symptoms worse or lead kids to develop asthma.
  • restless sleep or breathing problems at night, such as obstructive sleep apnea
  • a tendency to mature earlier. Overweight kids may be taller and more sexually mature than their peers, raising expectations that they should act as old as they look, not as old as they are. Overweight girls may have irregular menstrual cycles and fertility problems in adulthood.
  • liver and gallbladder disease

Cardiovascular risk factors (including high blood pressure, high cholesterol, and diabetes) that develop in childhood can lead to heart disease, heart failure, and stroke in adulthood. Preventing or treating overweight and obesity in kids may help protect them from these problems as they get older.

Obese kids also might have emotional issues to deal with (such as low self-esteem), and may be teased, bullied, or rejected by peers. Kids who are unhappy with their weight can be at risk for:

How Are Overweight and Obesity Defined?

Body mass index (BMI) uses height and weight measurements to estimate a person's body fat. But calculating BMI on your own can be complicated. An easier way is to use a BMI calculator.

On a standard BMI chart, kids ages 2 to 19 fall into one of four categories:

  1. underweight: BMI below the 5th percentile
  2. normal weight: BMI at the 5th and less than the 85th percentile
  3. overweight: BMI at the 85th and below 95th percentiles
  4. obese: BMI at or above 95th percentile

For kids younger than 2 years old, doctors use weight-for-length charts instead of BMI to determine how a baby's weight compares with his or her length. Any child under 2 who falls at or above the 95th percentile may be considered overweight.

BMI is not a perfect measure of body fat and can be misleading in some cases. For example, a muscular person may have a high BMI without being overweight (extra muscle adds to body weight — but not fatness). Also, BMI might be hard to interpret during puberty when kids have periods of fast growth. Remember, BMI is usually a good indicator of body fat, but it's not a direct measurement.

If you're worried, take your child or teen to see the doctor. The doctor will ask about eating and activity habits and make suggestions on how to make positive changes. The doctor also may order blood tests to look for some of the medical problems associated with obesity.

Depending on your child's BMI (or weight-for-length measurement) and health, the doctor may refer you to a registered dietitian or a weight management program.

page 2

Why Do Kids Become Overweight or Obese?

A number of things contribute to a person becoming overweight. Diet habits, lack of exercise, genetics, or a combination of these can be involved. In some instances, too much weight gain may be due to an endocrine problem, genetic , or some medicines.

Diet and Lifestyle

Much of what we eat is quick and easy — from fat-filled fast food to processed and prepackaged meals. Daily schedules are so busy that there's little time to make healthier meals or to squeeze in some exercise. Portion sizes, in the home and out, are too large.

Plus, modern life is sedentary. Kids spend more time playing with electronic devices than actively playing outside. Kids who watch TV more than 4 hours a day are more likely to be overweight compared with kids who watch 2 hours or less. And kids who have a TV in the bedroom also are more likely to be overweight.

Exercise and Physical Activity

Many kids don't get enough physical activity. Older kids and teens should get 1 hour or more of moderate to vigorous exercise every day, including and muscle- and bone-strengthening activities. Kids ages 2 to 5 years should play actively several times each day.

Genetics

Genetics can play a role in what kids weigh. Our genes help determine body type and how the body stores and burns fat. But genes alone can't explain the current obesity crisis. Because both genes and habits are passed down from one generation to the next, multiple members of a family may struggle with weight.

People in the same family tend to have similar eating patterns, levels of physical activity, and attitudes toward being overweight. A child's chances of being overweight increase if one or both parent is overweight or obese.

page 3

How Can We Prevent Overweight and Obesity?

The key to keeping kids of all ages at a healthy weight is taking a whole-family approach. Make healthy eating and exercise a family affair. Get your kids involved by letting them help you plan and prepare healthy meals. Take them along when you go grocery shopping. Teach them how to make good food choices.

Try to avoid these common traps:

  • Don't reward kids for good behavior or try to stop bad behavior with sweets or treats. Find other ways to change behavior.
  • Don't have a clean-plate policy. Even babies turn away from the bottle or breast to send signals that they're full. If kids are satisfied, don't force them to keep eating. Reinforce the idea that they should only eat when they're hungry.
  • Don't talk about "bad foods" or completely ban all sweets and favorite snacks. Kids may rebel and overeat forbidden foods outside the home or sneak them in on their own. Serve healthy foods most of the time and offer treats once in a while.

Recommendations by Age

Additional recommendations for kids of all ages:

  • Birth to age 1: Besides its many health benefits, breastfeeding may help prevent excessive weight gain.
  • Ages 1 to 5: Start good habits early. Help shape food preferences by offering a variety of healthy foods. Encourage kids' natural tendency to be active and help them build on developing skills.
  • Ages 6 to 12: Encourage kids to be physically active every day, whether through an organized sports team or a pick-up game of soccer during recess. Keep your kids active at home with everyday activities like playing outside or going for a family walk. Let them be more involved in making good food choices, such as packing lunch.
  • Ages 13 to 18: Teach teens how to prepare healthy meals and snacks at home. Encourage them to make healthy choices when outside the home and to be active every day.
  • All ages: Cut down on TV, phone, computer, and video game time and discourage eating in front of a screen (TV or otherwise). Serve a variety of healthy foods and eat family meals together as often as possible. Encourage kids to eat breakfast every day, have at least five servings of fruits and vegetables daily, and limit sugar-sweetened beverages.

Talk to kids about the importance of eating well and being active. Be a role model by eating well, exercising regularly, and building healthy habits into your own daily life. Make it a family affair that will become second nature for everyone.

Источник: https://kidshealth.org/en/parents/overweight-obesity.html

Overweight and obesity statistics 2021

What is obesity?

examples of obesity in america

Rural Health Information Hub

Obesity and overweight examples of obesity in america ongoing health concerns nationwide. They are risk factors for a range of chronic diseases, including heart disease, stroke, some cancers, and type 2 diabetes.

Rural areas experience higher rates of obesity and overweight than the nation as a whole, yet many rural communities do not have the resources to address this critical health concern. Rural healthcare facilities are less likely to have nutritionists, dietitians, or weight management experts available. Rural areas may lack exercise facilities and infrastructure to encourage physical activity. Access to healthy and affordable food is also limited in many rural communities. Further, the distance that many rural residents must travel to access healthcare facilities, exercise facilities, and healthy food is an ongoing barrier.

To address these challenges, rural communities can develop programs and services that help rural residents learn about the health risks of overweight and obesity, and adopt healthy lifestyle behaviors to control their weight. Communities may also choose to invest in facilities and infrastructure that support fitness and health.

Frequently Asked Questions


How do rural areas compare to urban areas regarding obesity rates?

Obesity Prevalence Among Adults Living in Metropolitan and Nonmetropolitan Counties — United States, 2016 reports that, based on 2016 Behavioral Risk Factor Surveillance System data, self-reported obesity is more prevalent among adults residing in nonmetropolitan areas (34.2%) than among adults residing in metropolitan areas (28.7%). The largest nonmetropolitan and metropolitan differences in obesity prevalence occurred in the South (5.6%) and Northeast (5.4%) Census regions.

Obesity Prevalence by Census Region

This report also found obesity to be more prevalent among nonmetropolitan Hispanics (36.0%), non-Hispanic blacks (44.2%), and non-Hispanic whites (33.2%) compared to metropolitan Hispanics (32.9%), non-Hispanic blacks (37.7%), and non-Hispanic whites (27.5%).

Obesity Prevalence by Race/Ethnicity

A 2018 JAMA article, Differences in christmas tree in the park san jose Obesity Prevalence by Demographic Characteristics and Urbanization Level Among Adults in the United States, 2013-2016, examined rural obesity based on measured rather than self-reported height and weight. This study reported that the prevalence of obesity among nonmetropolitan adults was 43.1%, compared to 42.4% for adults from small metropolitan areas and 35.1% for adults from large metropolitan areas.


Are rural children at greater risk of obesity and overweight?

Differences in Obesity Prevalence by Demographics and Urbanization in US Children and Adolescents, 2013-2016 reports a higher prevalence of obesity among youth aged 2 to 19 years old in nonmetropolitan statistical areas (21.7%) compared to youth in large metro areas (17.1%) and medium or small metro areas (17.2%). This report also found that severe obesity was more prevalent among youth in nonmetro areas (9.4%) than among youth in large metro areas (5.1%) and medium or small MSAs (5.3%).


What are some factors contributing to rural obesity?

Obesity and overweight have long been considered causes for concern in rural areas. The National Advisory Committee on Rural Health and Human Services (NACRHHS), in its 2005 Report to the Secretary, dedicated a chapter to obesity. In the 2011 NACRHHS Report to the Secretary, the committee focused on rural childhood obesity. The State of Obesity 2020: Better Policies for a Healthier America also reports that rural counties have a higher prevalence of obesity for adults and children than their counterparts in urban counties. Some contributors to rural obesity identified in these reports include:

  • Influence of poverty
  • Limited access to healthy and affordable food
  • Higher calorie consumption
  • Lack of nutrition education and services
  • Limited access to obesity prevention programs and weight management services
  • Fewer opportunities for children to be physically active in afterschool sports or events
  • Scarcity of parks, recreational areas, sidewalks, bike trails, and exercise facilities that promote physical activity
  • Reliance on automobiles to meet transportation needs, fidelity online test questions for experienced than walking or biking

This map, from a 2015 Preventing Chronic Disease article, shows county-level access to parks and recreation facilities, based on prefab shipping container homes for sale in north carolina collected for the 2014 County Health Rankings and Roadmaps. Darker counties indicate areas where fewer residents have adequate access to exercise opportunities:

Percent of Population with Adequate Access to Exercise Opportunities

A childhood obesogenic environment index (COEI), created by the Rural & Minority Health Research Center, combines 10 factors related to obesity, including exercise opportunities and access to healthy foods, into an overall score. Based on this index, Development of a National Childhood Obesogenic Environment Index in the United States: Differences by Region and Rurality reports a higher average score for rural counties (52.9 points) compared to metropolitan counties (46.5 points), with greater values assigned to environments with an increased risk of childhood obesity. This map shows county-level data on average COEI scores in the United States.

Childhood Obesogenic Environment Index by County

How does obesity impact health?

According to the National Institute of Diabetes and Digestive and Kidney Diseases, overweight and obesity increase the risk of several other credit one platinum visa extra problems. These include:

  • High blood pressure
  • Type 2 diabetes
  • Heart disease
  • Stroke
  • Liver disease
  • Osteoarthritis
  • Sleep apnea and respiratory problems
  • Certain types of cancer
  • Pregnancy complications

The higher prevalence of obesity and overweight among rural residents may be a contributing factor for higher rates of chronic diseases in rural communities. The Centers for Disease Control and Prevention’s Health, United States, 2018Table 13 reports higher rates of heart disease, cancer, and stroke within non-metropolitan areas. The following rates are for 2016-2017:

 Metropolitan AreaOutside Metropolitan Area
Heart disease10.1%12.6%
Cancer6.3%6.6%
Stroke2.7%3.0%
Source: Health, United States, 2018. Table 13

For more information about chronic disease prevalence and treatment in rural areas, see RHIhub’s Chronic Disease in Rural America topic guide.


What can rural healthcare providers do to address obesity and overweight?

www cvnb com To address obesity and overweight, rural clinics and hospitals can offer wellness classes and activities that encourage healthy diet and exercise, such as sessions on nutrition, preventing heart disease, and controlling diabetes. Hospitals that have exercise equipment for rehabilitation may want to make their workout areas first financial bank texas customer service number available to the entire community. RHIhub's Rural Obesity Prevention Toolkit identifies a range of evidence-based programs for healthcare providers to address obesity.

Primary care providers can serve as an information source to their patients on healthy diet and physical activity. The Agency for Healthcare Research and Quality offers a toolkit, Integrating Primary Care Practices and Community-based Resources to Manage Obesity: A Bridge-building Toolkit for Rural Primary Care Practices, to help rural primary care practices connect their patients to obesity management resources. In addition to offering a step-by-step process, the toolkit includes sample forms, worksheets, and other materials that can be adapted.


How can local public health agencies help prevent obesity?

Local public health agencies may find that by developing community partnerships with schools, healthcare providers, local businesses, and community groups, they can strengthen their mission to create opportunities for healthy living and reduce obesity and overweight in their communities. No one intervention or activity alone may solve the problem of obesity. However, when a variety of activities and programs are offered collaboratively, they can encourage and reinforce lifestyle changes that support healthy behaviors and reduce obesity. Projects or programs that can be conducted by public health agencies and their partners may include:

  • Creating a community food policy council focused on healthy food choices as well as their availability and distribution
  • Providing nutrition education and wellness classes for the community and schools to increase the consumption of fruits and vegetables, and increase physical activity
  • Working with local grocery stores and restaurants to help consumers make healthier choices by offering affordable and healthier foods
  • Developing or expanding farm-to-institution programs in schools, hospitals, and workplaces
  • Developing or expanding a farmers' market that is easily accessible within the community
  • Partnering with schools to establish policies offering healthier food choices in the school cafeteria and in vending machines
  • Promoting the maintenance of parks and recreational areas for walking, biking, and other physical activities for the entire community
  • Collaborating with healthcare services, wellness centers, food vendors, and local businesses to support wellness events or health fairs
  • Supporting community projects that make neighborhoods safer for outside activities including bicycle riding 1st financial federal credit union routing number and walking to school

To guide public health practitioners and program managers in developing obesity prevention programs, the Centers for Disease Control and Prevention's (CDC) Overweight & Obesity Prevention Strategies and Guidelines provides a variety of resources and describes strategies to increase physical activity and the consumption of healthy food.

CDC also publishes Healthier Food Retail: An Action Guide for Public Health Practitioners. This publication discusses ways public health agencies can partner and coordinate with food retailers to support healthier eating, and includes examples of obesity prevention initiatives that can be implemented at the local or regional level.


What role can rural schools play in encouraging healthy weight?

Schools can play a key role in encouraging healthy weight of children and adolescents by developing programs and policies supporting healthy lifestyle behaviors, such as good eating habits and regular physical activities. Schools can begin by offering healthy choices in school lunches and nutritious snacks in vending machines, and by providing learning opportunities that promote healthy eating and an understanding of good nutrition. Schools can also design physical education programs to encourage children to develop an active lifestyle.

From the Harvard T.H. Chan School of Public Health, the School examples of obesity in america Obesity Prevention Recommendations: Complete List suggests several strategies for obesity prevention that support healthy lifestyle behaviors among children and adolescents. Suggested strategies include:

  • Incorporating nutrition and physical education as part of the curriculum
  • Offering healthier food choices in cafeterias that meet national nutritional standards
  • Giving adequate time for students to eat their lunch
  • Supplying adequate access to drinking water
  • Encouraging physical activity during recess
  • Developing walking and bicycle routes that are safe
  • Establishing and supporting school gardens
  • Providing wellness programs for faculty and staff
  • Training food service staff on ways to provide healthy food
  • Continuing health education training for teachers
  • Inspiring school employees to model healthier lifestyle behaviors

CDC's School Health Guidelines to Promote Healthy Eating and Physical Activity provides guidelines for developing, implementing, and evaluating school-based healthy eating and physical activity programs and policies for students in grades K-12. Also, CDC's School-Based Obesity Prevention Strategies for State Policymakers identifies strategies, as well as policies, that have been shown to help address childhood obesity in schools.

The most effective school programs are comprehensive ones that address food service, physical education, classroom education in the importance of healthy lifestyles and decision-making, and include community/parent involvement. The CATCH Program (Coordinated Approach to Child Health) is an example of a comprehensive obesity prevention program. For other examples of evidence-based programs schools can implement, see How Can Rural Schools Address Obesity? in RHIhub's Rural Obesity Prevention Toolkit.

For additional information about the role rural schools can play in children's wellness, including nutrition and prefab shipping container homes for sale in north carolina physical activity, see RHIhub's Rural Schools and Health topic guide.


What can rural communities do to help reduce obesity?

Walking clubs, support groups for weight management, and healthy cooking and exercise classes are a few possibilities for supporting healthy weight throughout the community. Rural communities may want to develop a wellness center, bike trails, or walking paths to encourage healthy lifestyles, and/or work with neighboring communities to expand opportunities. Facilities may already exist in some communities that could serve the public as a community resource. For example, rural communities could enter into shared user-agreements with a local high school or community college opening their pool to the community for swimming, or gymnasium for early morning or after-hour community activities.

Several resources are available to help rural communities identify a suitable program to meet their needs:


Where can I find examples of obesity prevention or weight control programs that work in rural areas?

The Rural Health Information Hub's Rural Health Models and Innovations features examples of programs and interventions that have shown to be successful in preventing and reducing obesity and improving participation in healthier lifestyles. Examples include:

  • Healthy Early Learning Project (HELP) – An integrated program addressing childhood obesity developed for public school preschool sites and Head Start sites in the rural Kansas counties of Marshall and Nemaha. Each site implemented a research and evidence-based program to increase physical activity and healthy food consumption of preschoolers ages 0 to 5.
  • Win with Wellness – A partnership of county health departments, local healthcare providers, a regional nonprofit, and a medical school organized to develop weight-loss support groups and health education classes addressing obesity and chronic disease in rural Stephenson and Carroll counties of Illinois. A large portion of the adult population in this rural area is overweight or obese, and the rates of diabetes, heart disease, and smoking are higher than in other parts of the state.

Examples of successful programs for rural obesity prevention are also available in the Rural Obesity Prevention Toolkit.

In addition, NACCHO's Model Practice Database identifies and describes many promising approaches public health agencies have used to address obesity, including examples from rural communities.

Источник: https://www.ruralhealthinfo.org/topics/obesity-and-weight-control
Obesity statistics by age The cost of obesity

Recent origin and evolution of obesity-income correlation across the United States

Abstract

From a gene-culture evolutionary perspective, the recent rise in obesity rates around the Developed world is unprecedented; perhaps the most rapid population-scale shift in human phenotype ever to occur. Focusing on the recent rise of obesity and diabetes in the United States, we consider the predictions of human behavioral ecology (HBE) versus the predictions of social learning (SL) of obesity through cultural traditions and/or peer–to–peer influence. To isolate differences that might discriminate these different models, we first explore temporal and geographic trends in the inverse correlation between household income and obesity and diabetes rates in the U.S. Whereas by 2015 these inverse correlations were strong, these correlations were non-existent as recently as 1990. The inverse correlations have evolved steadily over recent decades, and we present equations for their time evolution since 1990. We then explore evidence for a “social multiplier” effect at county scale over a ten-year period, as well as a examples of obesity in america diffusion pattern at state scale over a 26–year period. We conclude that these patterns support HBE and SL as factors driving obesity, with HBE explaining ultimate causation. As a specific “ecological” driver for this human behavior, we speculate that refined sugar in processed foods may be a prime driver of increasing obesity and diabetes.

Introduction

In the United States, where adult obesity prevalence rates have been rising since the 1970s (Kranjac and Wagmiller, 2016), about two-thirds of adults are now overweight and over 100,000 U.S. deaths per year are attributed to obesity (Ogden et al., 2014). With obesity rates having tripled in many U.S. states over the past 25 years, this rise in obesity prevalence has accelerated. In 1990, about 11% of a typical U.S. state population was obese and no state had more than 15% obesity in its adult population. By 2015, U.S. obesity rates had more than doubled, with several states above 35% adult obesity and no state below 20% obesity in the population (Centers for Disease Control and Prevention, 2017a). In one generation, the change has been so dramatic that the obesity rate in any U.S. state in 2015 would have been an extreme outlier in the U.S. in 1990.

From a gene-culture evolutionary perspective, the recent rise in obesity rates, occurring across the Developed world (Goryakin et al., 2017), is unprecedented. In the past, human niche construction evolved over a time scale of centuries or millennia (Creanza and Feldman, 2016; Milot et al., 2011). For example, the evolution of lactase persistence among Neolithic populations of central Europe was rapid in evolutionary terms but nevertheless took place over thousands of years, in coevolution with the intensification of dairying economies (Brock et al., 2015; Gerbault et al., 2013). In contrast, industrially–processed foods have transformed Western human diets in less than a century. Not only has this made calories and junk food abundant and inexpensive in high-income countries, but there appear to be other effects such as reduction of gut microbiome diversity (Smits et al., 2017; Muscogiuri et al., 2018).

In the simplest view, obesity in Developed economies is a result of over-abundance of inexpensive food calories combined with decreases in daily physical activity in the industrialized world and its built environment (Mattson et al., 2014; Mullan et al., 2017). Negative energy balance is not the only factor, however, and with heterogeneity across socioeconomic groups, the specific causes for the rapid and recent increase in U.S. obesity remain unclear (Cook et al., 2017; Dwyer–Lindgren et al., 2013; Flegal et al., 2016).

One thing that is clear in high-income countries is that, despite decades of economic growth, obesity disproportionately affects the poor—the “poverty–obesity paradox” (Hruschka and Han, 2017). The proportion of obese individuals in industrialized nations now correlates inversely with median household income. This phenomenon is called the “reverse gradient” because it is the reverse of the pattern in developing countries, where higher income correlates with higher body mass. In the United States and other developed countries, lower income households tend to have higher rates of obesity (Hruschka, 2012; Subramanian et al., 2011). In 2015, over 35% of the population was obese in U.S. states where median household incomes were below $45,000 per year, whereas obesity was less than 25% of state populations where median incomes were above $65,000 (Centers for Disease Control and Prevention, 2017c). Similarly in Europe today, poor individuals are 10% to 20% more likely to be obese (Salmasi and Celidon, 2017). This pattern is unique to Developed economies; within China, for example, an inverse correlation between income and obesity/diabetes is observed only in the most economically developed regions (Tafreschi, 2015).

Cultural evolution potentially offers a less proximate, more ultimate explanation for the recent rise in obesity. Evolutionary approaches to behavioral change include human behavioral ecology and cultural evolutionary theory; the former tends to prioritize optimality of adaptive behavior while the latter spirit airlines phone number usa to prioritize social learning. Generally speaking, human behavioral ecology (HBE) emphasizes the plasticity of human physiology and behavior, by which individuals minimize risk to survival and optimize their long-term reproductive payoffs (Higginson et al., 2017). As wealth mitigates survival risk, HBE predicts a positive correlation between BMI and wealth, as humans have evolved to store calories as insurance against future famine or food shortage (Shrewsbury and Wardle, 2012; Higginson et al., 2017; Tapper, 2017). In the poorest 80% of the world’s societies, body mass index (BMI) generally increases with household wealth (Subramanian et al., 2011)—except below about 400 USD per capita, when poverty is such that BMI is uniformly low (Hruschka et al., 2014). In high-income countries, HBE predicts greater obesity among the poor, partly because humans have evolved behavioral “rules” that lead to overeating in rich environments and partly because poorer people have more immediate risks and concerns than outweigh long-term mortality risk of being obese (Dittmann and Maner, 2017; Dohle and Hafmann, 2017; Higginson et al., 2017; Mani et al., 2013; Smith, 2017).

The HBE hypothesis predicts that obesity has recently evolved in strong correlation with both the food environment and with income/wealth. The “Insurance Hypothesis” (Nettle et al., 2017) uses HBE to explain why the reverse is true in Developed countries where extreme BMI (obesity) is more frequent among the poor. Under the Insurance Hypothesis (IH), “individuals should store more fat when they receive cues that access to food is uncertain” (Nettle et al., 2017). Poor people in high-income countries receive such cues, as they experience more stress and greater existential risk for multiple reasons. Prominent among these risks is malnutrition, yet empty calories are still inexpensively available as processed foods and sugar-sweetened beverages (Bray et al., 2004; Johnson et al., 2007; Jürgens et al., 2005; Bocarsly et al., 2010). The IH is consistent with observations of women in high-income countries, who are more likely to be obese when confronted by food insecurity (Nettle et al., 2017). An alternative explanation, however, occurs at the societal level in high-income countries, where heavier women tend to marry into poorer households due to through “anti-fat discrimination” in marriage (Hruschka, 2012; Hruschka and Han, 2017).

In contrast, social learning (SL) explanations emphasize 1st grade math packet behavior in groups: behaviors are inherited from parents and learned socially from contemporaries through the generations of family traditions or community cultures (Bentley et al., 2016; Colleran and Mace, 2015; Colleran, 2016). Dietary habits are often determined as much by cultural traditions as they are by nutritional needs and family economics (Anderson and Spirit airlines phone number usa, 2010; Anderson, 2012; Hughes et al., 2010; Lhila, 2011; Mata et al., 2017; Redsell et al., 2010; Vizireanu and Hruschka, 2018). Cultural factors may therefore underlie local differences in obesity and diabetes rates, which exhibit effects of local neighborhood and its built environment (Alvarado, 2016; Carroll et al., 2016; Mullan et al., 2017; Kowaleski-Jones et al., 2017), family size (Datar, 2017), ethnic group and age group (Cook et al., 2017).

Under SL, obesity may also increase through social influence. A widely-discussed argument, first presented by Christakis and Fowler (2007), is that obesity “spreads” through social influence in networks of family and friends (Christakis and Fowler, 2013). Relatedly, recent modeling and experimental studies show how a minority group can initiate rapid change in social conventions, provided the minority reaches a ‘critical mass’ (Centola et al., 2018; Couzin et al., 2011). Under SL, therefore, a new behavior can become a new social norm relatively quickly, if obesity were indeed a new social norm.

The alternative to the social-learning explanation is homophily, if obesity clusters in social networks merely because those clusters are similar people in the same environments (Shalizi and Thomas, 2011). Homophily could be viewed either as similar behavior derived from shared cultural ancestry or else as similar behavior that reflects adaptations to similar environments. Outside of carefully monitored conditions (Centola et al., 2018; Hobaiter et al., 2014), however, it is difficult if not impossible to distinguish social influence from homophily, even if obesity is observed to cluster in social networks, without a fine–grained temporal dimension to the data (Christakis and Fowler, 2013; Shalizi and Thomas, 2011; Thomas, 2013).

Unlike the small-scale social network study of obesity versus specific friends and kin members (Christakis and Fowler, 2007), this study examines annual, population-scale obesity rates aggregated by U.S. county. If the aggregated data are time–stratified, however, we can still attempt to test the SL hypothesis. We will use multiple measures (obesity, leisure, income) and ten years of county-scale data to assess any “social multiplier” effects. The social multiplier effect is identified when the rate of behavior among a group is greater than what would be predicted based on individual–scale variables alone. Identification of groups is a problem in the empirical literature on social interactions (Blume et al., 2011), but useful proxies have based on Zip codes (Corcoran et al., 1992) and census tracts (Weinberg et al., 2004).

A study of crime rates (Glaeser et al., 2003), for example, used statistics of individuals to predict crime rates and regressed those on crime rates in groups. This is how the social multiplier was defined at the county level, specifically by comparing the coefficient b in the regression,

$$\omega _{ig} = a + bx_i + \epsilon _i{\mathrm{,}}$$

(1)

with the coefficient b' in its group counterpart

$$\overline \omega _g = a^{\prime} + b^{\prime} \overline x _g + \overline \epsilon _g,$$

(2)

where ωig denotes the choice of individual i in county g and xi is a vector of observable individual-specific characteristics; the social multiplier is defined as the coefficient ratio, b'/b (Blume et al., 2011).

In this approach, estimating the social multiplier requires an estimate of individual-level rates, which do not exist in aggregated data. Faced with this problem, Glaeser et al., (2003) used nationwide arrest rates by age that, when combined with demographic data, provided a predicted level of crime in each neighborhood. These predicted rates were then regressed actual crime rates at the county level, yielding a coefficient of 1.7 at the county level, which was their estimate of the social multiplier at that scale of aggregation (Glaeser et al., 2003).

The data we use here are aggregated by U.S. county annually: over three thousand county-level time series of obesity, leisure and income rates over ten-year period (2004 to 2013). This amounts to ten sets of annual data, on several variables, for 3110 U.S. counties. If, controlling for the effects of household income, we find that lack of physical activity, or leisure rate, has a disproportionate effect on obesity rate in 2013 compared with 2004, then there may be support for the social multiplier effect. We have only aggregated statistics but we have the advantage of a time series. In principle there exists an individual-level, effectively physiological, connection between lack of exercise (leisure) and obesity rates, which we assume remains constant through time. The correlation between obesity and leisure rates should reflect this individual relationship as a baseline, plus any social multiplier effects over time.

In other words, change in the leisure–obesity correlation between 2004 and 2013 ought to reflect the social multiplier effect. As there are examples of obesity in america unobservable connections between leisure and obesity, however, we follow the cautious approach of Glaeser et al., (2003), who “take these results warily, as they may well overstate union savings bank com true social multiplier.”

To investigate whether obesity increased in the classic S–shaped pattern consistent with social learning, we carried out regression analysis on the annual data for each state over the 1990–2016 period. A simple linear increase in obesity rate since 1990 serves as our null hypothesis, with the alternative hypothesis being a non-linear time trend. If the null hypothesis of non-linearity could not be rejected, by implication an S–curve would not be present in the data.

We applied two separate but complementary approaches. First, we tested the null of linearity against a general non–linear alternative, using the methodology of local linear regression (Cleveland and Devlin, 1988). We used the “loess” command in R. Local linear regression fits simple linear models to localized subsets of the data to describe the deterministic part of the variation in the data, point by point, without specifying a global functional form. An input parameter in the loess command (“span”) allows the “equivalent number of parameters” (ENP) to be varied. ENP serves as a measure of the non–linearity of the series. The approach enables ANOVA tests to be carried out of the null of linearity against a range of non-linear alternatives. Local linear regression is a powerful approach, but does not yield a specific functional form.

For the second approach, we tested the null of linearity against a specific non–linear functional form, namely that of a classic adoption curve (Bass, 1969; Bentley and Ormerod, 2010; Henrich, 2001). We have:

$$\frac{{dF_{t,i}}}{{dt}} = \left( {\mu _i + q_iF_{t,i}} \right)\left( {1 - F_{t,i}} \right),$$

(3)

where Ft,i is the obesity rate in year t of U.S. state i, μi is the chance in state i that a person becomes obese (i.e., BMI of 30 or above) through individual behavior and qi is the probability within state i, where Ft,i are already obese, that a person becomes obese through social influence. This ODE can be solved for Ft,i,

$$F_{t,i} = F_{0,i} + M\frac{{1 - e^{- \left( {\mu + q} \right)t}}}{{1 +\frac{q}{\mu} e^{ - \left( {\mu + q} \right)t}}},$$

(4)

where M is the magnitude of change and F0,i is set as the obesity rate of U.S. state i in year 1990. This equation can be fitted to the obesity data, with the goodness of fit reported as the adjusted R2 statistic, defined as:

$$1 - \frac{{\left( {1 - R^2} \right)\left( {n - 1} \right)}}{{n - v - 1}},$$

app store gift card 10 (5)

where v is the total number of explanatory variables in the model (not including the constant term), and n is the sample size. The linear model has v = 2 parameters (slope and intercept), whereas the Bass model has v = 3 parameters (p, q, and M).

Here we focus on an under–studied, but revealing question: in the U.S., how has the correlation between household income and obesity changed in the past 25 years? In the U.S., obesity and diabetes rates currently have a strong negative correlation with household income (Hruschka, 2012). These correlations have been demonstrated cross-sectionally but not longitudinally, however, and therefore it is not possible to establish their causality (Boden and McLeod, 2017). In industrialized economies, the increase in obesity prevalence has been fastest among low income levels, as fast as tripling within a generation among certain subpopulations.

Data

Annual, age-adjusted data on obesity rates at the county level, for years 2004 to 2015, were obtained from the publicly accessible archive maintained by the Center for Disease Control and Prevention (www.cdc.gov/diabetes/data). For these county-level data, we also make use of the CDC age-adjusted estimates, (Klein and Schoenborn, 2001), in which rates are age adjusted to the 2000 U.S. standard population using age groups, 20–44, 45–64, and 65 or older (Centers for Disease Control and Prevention, 2017b). Older obesity data at the state level since 1990 were obtained from the annual reports of the Trust for America’s Health and the Robert Wood Johnson Foundation (stateofobesity.org). For analyzing the time-series of state-level obesity rates, we cautiously added data from the years 1991 and 1998 from a different source (Mokdad et al., 1999) to examine more closely any potential non-linear change in the 1990s.

Due to missing data at the county level, we excluded Alaska from all analyses at county level, while including Alaska for state-level analysis. We also used five years of CDC data on age-adjusted annual CDC diabetes rates, 2009–2013, in U.S. counties (Centers for Disease Control and Prevention, 2017d). The estimates of diabetes rates are derived through telephone surveys, normalizes the data using population data from the US Census, and smooths the estimates such that three years of data are averaged in each annual estimate (Centers for Disease Control and Prevention, 2017c).

Importantly, we use age–adjusted rates for both obesity and diabetes in our analysis, and so we do not include the age profile of an area as an explanatory factor. This adjustment has already been carried out by the CDC in the data which we use. By using age–adjusted data, we minimize the effect of demographics in our results. To anticipate, we also note for reference that our results were essentially the same when we used data that were not age–adjusted.

Estimates of leisure–time physical inactivity come from the CDC Behavioral Risk Factor Surveillance System, a system of health-related telephone surveys, which began in 1984 with 15 U.S. states, and now collects data in all 50 states through over 400,000 adult interviews each year (Centers for Disease Control and Prevention, 2017c). The “leisure” statistic indicates the fraction of population who are designated as physically inactive, meaning they answered “no” to the question, “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?”

Food desert data were recently made available through the Food Access Research Atlas (FARA) project (Rhone et al., 2017). The estimates are derived from the 2010 US Census and the 2010–2014 American Community Survey, in which census tracts are categorized by median income, vehicle availability, and Supplemental Nutrition Assistance Program (SNAP) participation (Rhone et al., 2017). To this geographic dataset are added two 2015 lists of supermarkets, supercenters, and large grocery stores to represent sources of affordable and nutritious food (Rhone et al., 2017). The FARA records for each U.S. Census tract the number and share of people more than a certain distance to a supermarket: in urban areas, that specified distance is half a mile or 1 mile, whereas in rural areas the distances are 10 miles or 20 miles (Rhone et al., 2017). Also recorded is whether the population in the census tract has overall low access to vehicles. Census tracts are also designated as rural, urbanized (over 50,000 people) or urban cluster (2500 to 50,000 people); for the purposes of estimating the urban/rural ratio of a county, we counted both urbanized and urban cluster tracts as being urban. Because food deserts are defined quite differently for urban (0.5 mile) versus rural (10 miles) counties in the FARA, we consider just those counties whose populations we calculated as at least seven–eighths (87.5%) urban, totaling n = 250 counties across the U.S.

Results

We find the reverse gradient has only existed examples of obesity in america less than thirty years. In the U.S. in 1990, when population–scale obesity rates were about a third of what they are today, there was no correlation between income and obesity or diabetes. The inverse correlations between income and diabetes/obesity rates have developed only within the past thirty years. By 2015, the correlation was stronger than ever: in states where median household incomes were below $45,000 per year, like Alabama, Mississippi and West Virginia, over 35% of the population was obese, whereas obesity was less than 25% of state populations where median incomes were above $65,000, such as in Colorado, Massachusetts or California.

In the U.S., there is considerable geographic heterogeneity in obesity prevalence. Figure 1 shows maps of obesity rates and diabetes rates by county in 2013 (Table 1). By 2013, the reverse gradient in the U.S. was pronounced; the simple correlation between obesity and ln (income) across n = 3110 U.S. counties was r = −0.486 and between ln (income) and diabetes was r = −0.531. All reverse gradients are better determined against the logged income data than against median income itself. Plots of ln (income) against both diabetes and obesity (Fig. 2), aggregated at county level, reveal mild degrees of non–linearity in each of the relationships.

Prevalence of adult a obesity and b diabetes in 2013, mapped at the scale of U.S. county for CDC age-adjusted figures

Full size image

Reverse gradients across U.S. counties in 2013 (in all states except Alaska) between the natural logarithm of household income and rates of a obesity (regression slope = −9.66) and b diabetes (slope = −4.93). Also shown are correlations between prevalence of physical inactivity (“leisure”) versus c obesity (slope = 0.643) and d diabetes (slope = 0.288). Correlation values within each state are listed in Table 1

Full size image

Full size table

State-level data on annual obesity and diabetes rates, available for years 1990 through present, together with age–adjusted and inflation–adjusted income data from the U.S. Census, show how the reverse gradient in the U.S. changed over twenty–five years (Fig. 3). For the year 1990 (data from n = 43 states for this first year of data), the Pearson correlation between state-level obesity and the natural log of median household income was r = −0.240 [−0.502, 0.061], which is not significant (p = 0.116).

Negative gradient between household income and obesity and diabetes rates. Scatterplots showing a Obesity vs. ln (income) by state, 1990 and 2015; b Diabetes vs. ln (income) by state, 1990 and 2015. For 1990 (blue), the slopes are −2.39 for obesity and −0.75 for diabetes; for 2015, the slopes −16.26 for obesity and −8.18 for diabetes. Panels c, d show the change in these correlations over time, between ln (income) and c Obesity and d Diabetes, at both state (solid) and county (dashed) levels

Full size image

Twenty–five years later, a strong inverse correlation had developed between median household income and rates of obesity and diabetes (Figs. 3a, b). In 2015, the correlation between ln (income) and obesity rate across all 50 states was r = −0.697 [−0.816, −0.522], which is highly significant (p < 0.00001); even limited to the 43 states also recorded in 1990, the correlation in 2015 still yields r = −0.699 [−0.824, −0.508] (p < 0.00001).

A similar change is evident for the reverse gradient involving diabetes and ln (income); insignificant for 1990 (r = −0.090 [−0.380, 0.216], p = 0.566) and highly significant by 2015 with r = −0.706 [−0.823, −0.532] (p < 0.00001). Again, if we restrict the 2015 data to the 42 states available in 1990 (one state fewer than in the obesity data), the correlation between ln (income) and diabetes rate yields r = −0.684 [−0.816, −0.483] (p < 0.00001).

Figures 3a, b show the actual and fitted values of the regressions of obesity and diabetes, respectively, on the log of income in both 1990 and 2015. In 1990, neither slope was significantly different than zero at the state level. In the regressions using 2015 state data, the slope coefficients for obesity are −0.163 ± 0.024 (R2 = 0.476) and for diabetes −0.080 ± 0.011 (R2 = 0.491). Figure 4 shows how the slope coefficient in these regressions has evolved, using data in 1990, 1995, 2000, 2003, and then annually from 2005 onwards.

a Slopes and b intercepts of the reverse gradients between household income and obesity and diabetes rates, 1990–2015

Full size image

Figure 5 shows how, at 5–year intervals from 1990 to 2015, the rate of growth in obesity or diabetes per year was inversely related to median household income. The temporal evolution of these reverse gradients, for the rates of obesity and diabetes, respectively, can be described by the following equations:

$$\begin{array}{l}{\mathrm{Obesity}}\,{\mathrm{rate}} = \left( { - 0.0341 - 0.0049t} \right)X_t + \left( {0.4812 + 0.0609t} \right),\end{array}$$

(6a)

$${\mathrm{Diabetes}}\,{\mathrm{rate}} = \left( { - 0.0022 - 0.0027t} \right)X_t + \left( {0.0639 + 0.032t} \right).$$

(6b)

where Xt is the natural logarithm of median household income in year t. The colored lines in Fig. 5 show how well Eqs 6a and 6b represent the actual reverse gradients of ln (income) versus obesity and diabetes rates, respectively, across 25 years of evolution of these negative gradients. The evolving slope coefficients imply that above an annual income level of $250,000 for obesity and $150,000 for diabetes, any further increases in income have negligible effects in term of further reducing obesity and diabetes (Fig. 5c).

Evolution of the reverse gradients for a obesity and b diabetes at 5–year intervals from 1990 to 2015. Colored lines show how the time-evolution of these gradients can be described by the equations in Eqs 6a and 6b, which yields c an approximated annual change as a function of household income

Full size image

Available data at the U.S. county level are available only from year 2004, so do not capture the start of this phenomenon, but these data include not only diabetes and obesity rates but also a “leisure” statistic derived from self–reported activity levels. Across all 3110 counties in any given year, the leisure statistic correlates best with both obesity but also with income, reflecting the feedback between income, health habits and obesity (Table 2). For the leisure statistic in 2013, for example, the relationship with both obesity and diabetes is strongly positive and linear (Figs. 2c, d) with r = 0.719 [0.701, 0.735] for leisure versus obesity, and r = 0.686 [0.667, 0.704] for leisure versus diabetes.

Full size table

We find that each year 2004 to 2013 the leisure and income statistics were a good predictor of (r2 > 0.5) obesity rates at the county level (Table 2). Notably, the respective regression coefficient on leisure grew from about 0.40 to 0.63 (Table 2, Fig. 6). This means that if we applied the 2004 regression coefficient, the actual obesity rate would be 1.56 times our prediction based on 2013 leisure rate. Hence following Glaeser et al., (2003), we estimate the social multiplier as at least 1.56, since by 2004 there had already been a decade of sharp increase in obesity rates.

Change in multiple regressions, U.S. counties, 2004–2013. For regressions predicting obesity rates, the open circles (with blue curve) show the coefficient on the leisure statistic; black filled circles show coefficient on ln (income). The dashed line shows the trend through the black filled circles (R2 = 0.36). Data are shown in Table 2

Full size image

This steady increase change in coefficient (Table 2) is not due to change in the leisure rate, which rose and fell: the average county rate increased from 25.3% in 2004 to a peak of 26.9% in 2009 and then fell to 24.7% by 2013. We are not aware of an individual–level reason why the relationship between lack of exercise and obesity will have changed in ten years. We therefore posit 1.56 as a measure of social multiplier effect at county level.

For the time series of state–level obesity rates from 1990 to 2016, regression analyses serve to rule out any strong social–shaped pattern in obesity rise for any of the U.S. states. Figure 7 shows typical examples. Using the local linear regression approach (see Methods), the null hypothesis of linearity could not be rejected for seven of the 46 states examined (the few we excluded lacked data points for the early 1990s): Connecticut; Delaware; Iowa; Louisiana; Maine; Vermont; Wisconsin; Wyoming. For a further seven states, linearity could be rejected at the standard 5 per cent level, but only when the alternative exhibits a mild degree of non-linearity, with the Equivalent Number of Parameters being just 2.33: Idaho, Illinois; Kentucky; North Dakota; Oregon; Virginia; West Virginia. In the adoption-curve analysis, this favors “r–curves” of individual learning (Fig. 7). Adjusted r-squared values (Table 3) are strong where the individual parameter p is the same magnitude as the social parameter q, and fit almost as well with the “social” parameter, q, set to zero (Table 3) to represent pure individual learning.

Rise in state-level obesity rates in four different states, showing the fit of the adoption curve united bank harrisonburg va. 4) with the social parameter, q, set to zero (solid red), as well as a linear fit (dashed black). See Table 3

Full size image

Full size table

Lastly, the effect of food deserts is also evident, among a subset of U.S. counties (Fig. 8, Table 4). As described in our Methods, from the FARA data we consider just those counties whose populations we calculated as at least seven eights (87.5%) urban, totaling n = 250 counties across the U.S. In these urban counties, the regression of obesity rates versus the share of population without access to supermarkets within half a mile yields Pearson’s r = 0.292 [0.175, 0.401], which is significant (p < 0.00001). For diabetes, however, the correlation with this food desert variable in 2013 is not significantly different from zero (p = 0.223). For these same urban counties, food deserts among low income census tracts has stronger correlation with obesity (Fig. 8b), with r = 0.563 [0.472, 0.642], and now a significant correlation with diabetes rate r = 0.462 [0.359, 0.544]; both correlations are highly significant (p < 0.000001).

Food desert index versus obesity rate for 250 urban counties in the U.S. in 2013. In a the food desert index is the share of the urban population living a half mile or more from a supermarket. The blue line shows the regression (Pearson’s r = 0.292 ± 0.11). In b the food desert index is the share of low-income population living a half mile from supermarket. Blue line shows the regression (Pearson’s r = 0.563 ± 0.08)

Full size image

Full size table

Food deserts are, of course, closely related to income. We confirm the validity of the individual correlations between obesity and diabetes and income and leisure in Table 2 by running simple multiple regressions of obesity and diabetes on income and leisure. The results are set in Table 5. Although each overall fit could be slightly improved with mild non–linearities, the simple regressions show that both ln (income) and leisure have significant effects on obesity and diabetes.

Full size table

Our analyses here use data aggregated from both sexes, as the same correlations for each sex were quite similar to the results for both sexes aggregated together. In 2013 for example, among the 43 states with sufficient data points for men and women (excluding AK, CT, DE, HI, NH, and RI), the reverse gradient between income and diabetes is significant for both sexes at p < 0.01 in 30 states. In some states, however, there are small differences. In eight states the reverse gradient in 2013 is significant at p < 0.01 for one sex and p < 0.05 for the other. In Arizona, New Mexico, New Jersey and Utah, the reverse gradient is significant for men but not for women, whereas in Massachusetts, it is significant for women but not for men.

Discussion

Here we have explored the origins and development of the inverse correlation between household income and obesity/diabetes rates in the U.S. We used data on mean household incomes and rates of obesity and diabetes at the level of U.S. state, which date back to 1990, as well as county level statistics that offer larger sample sizes and higher spatial resolution but only extend back the early 2000s.

Using age–adjusted U.S. data on mean household incomes and rates of obesity and diabetes—at state level since 1990 and county level since the mid 2000s—we found that the reverse gradient originated and evolved over a examples of obesity in america of about 25 years. Here, we report that this reverse gradient did not exist in the U.S. in 1990 but has increased markedly since then.

Specifically, across the U.S. states by 2015 there were highly significant correlations between ln (income) and state–level rates of obesity (r = −0.697, p < 0.00001) and diabetes (r = −0.706, p < 0.00001), whereas in 1990 neither correlation was yet evident. By 2013, the age-adjusted prevalence of obesity in the U.S. was 35% among men and 40% among women (Flegal et al., 2016)—across all age adult groups, obesity rates among U.S. women have been 4% to 5% higher than among men (Arroyo–Johnson and Mincey, 2016). Since 1990, this change was continual, such that we determine equations for the linear development of these reverse gradients since 1990.

In the U.S., an inverse correlation between obesity and median household income (logarithm) developed from nonexistent in 1990 into a steep inverse correlation, known as the “reverse gradient”, 25 years later. The reverse gradient involving diabetes prevalence also developed in the U.S. over the same period; this lagged the reverse gradient with obesity in 1990 but had examples of obesity in america caught up with it after 2010 (Fig. 3). Both facets of the reverse gradient developed remarkably quickly in the U.S., in about one generation.

Any hypotheses for the recent rise in obesity must account for the obesity epidemic having emerged only in the last few decades in the U.S. We observed that over ten years, the regression coefficient on leisure in predicting obesity increased from 0.40 to 0.63. We interpret this to be a social multiplier effect, as a physiological effect ought to have had that same coefficient through time. Our social multiplier estimate of 1.56 is close to that of crime and U.S. wages, both about 1.7, estimated by Glaeser et al., (2003) at similar scales of aggregation. While this is some evidence for social influence, these estimates might overstate the true social multiplier, due to correlation between demographics and unobservable elements (Glaeser et al., 2003).

Our simple S-curve approach was more illuminating than we had expected (see Kandler and Powell, 2018), because they were unexpectedly linear or perhaps slightly r-shaped (sensu Henrich 2001), i.e., the adoption patterns do not show much evidence for social (S-shaped) diffusion. A plausible explanation for the steady, why is cinnamon good for you to eat increase in state–level obesity rates is a higher-obesity younger generation progressively entering the adult cohort.

The recent origin of the reverse gradient appears to favor the Insurance Hypothesis of HBE. The time series reveal only weak evidence of social learning in most states, insufficient to falsify the HBE hypothesis that obesity increased due to individual responses to a changing nutritional/economic environment. Additionally, there is a lack of evidence for a deep cultural history to obesity. While economic development may be a prerequisite for the reverse gradient (Tafreschi, 2015), the U.S. and Europe possessed developed economies for a century before the reverse gradient materialized. In Western Europe, there was still no reverse gradient as of 2008 (García Villar and Quintana-Domeque, 2009). The second is the fact that the reverse gradient developed smoothly over time, as described by Eq 6, which indicates the close relationship between income levels and propensity toward examples of obesity in america. These observations are consistent with the Insurance Hypothesis, which is predicated on an evolved tendency for lower-income people to perceive risks in their local environment and over–compensate through excessive calorie intake.

There are alternatives to the Insurance Hypothesis, as an explanation based upon the behavioral responses of individuals. At the scale of economic geography, a significant factor is food deserts, where “easy geographic access to fast-food outlets and convenience stores encourages state parks near myrtle beach south carolina to consume foods that are high in energy and saturated fats” (Mullan et al., 2017). Over 50 million people, almost 18% of the U.S. population, live in low–income areas without convenient access to a supermarket (Rhone et al., 2017). In high–income, highly urbanized countries, diabetes correlates positively with the percentage living in urban areas (Goryakin et al., 2017).

Some research has focused on prefab shipping container homes for sale in north carolina effect of highly processed foods, which typically contain much more added sugar first savings bank odon indiana unprocessed foods (Lhila, 2011; Bocarsly et al., 2010; Jürgens et al., 2005; Martínez Steele et al., 2016; Stanhope et al., 2009). Excessive sugar intake, which may be addictive (Avena et al., 2008) is a causal factor in diabetes (Hu and Malik, 2010; Shang et al., 2012; Cornelsen et al., 2016) and may also be a causal factor in high obesity rates (Basu et al., 2013; Hu and Examples of obesity in america, 2010; Shang et al., 2012).

Americans have consumed refined sugar since the nineteenth century, however, so the question remains why the obesity increase, and the reverse gradient happened only in the past three decades. One possible explanation is the recent introduction of high fructose corn syrup (HFCS) into the food economy. Fructose, which decreases insulin sensitivity in obese people (Stanhope et al., 2009), has been used in commercial sugar–sweetened beverages since about 1970. That said, the trend might be due to unobserved individual–level effects, such as the more leisure, the more HFCS drinks consumed. The timing is suggestive; Fig. 9 shows a timeline of the increase in contribution of refined sugar and HFCS to U.S. diet, together with the increase in U.S. obesity rate. While overall sugar consumption rose gradually in the 20th century, from 12% of U.S. food energy in 1909 to 19% by the year 2000, the use of high fructose corn syrup in union savings bank com U.S. increased from virtually zero per capita in 1970 to over 60 pounds per capita annually in the U.S. in 2000 (Gerrior et al., 2004), about half of total sugar consumption. HFCS became the main sweetener in soft drinks. By 2016 in the U.S., sweetened beverages constituted over 7% of household food expenditures and over 9% of expenditures for low-income households in the SNAP program (Garasky et al., 2016).

A timeline of the increase in contribution of refined sugar and high fructose corn syrup (HFCS) to U.S. diet, together with the increase in U.S. obesity rate. The data for sugar, dairy and HFCS consumption per capita are from USDA Economic Research Service (Johnson et al., 2009) except for sugar consumption before 1967, which are historical estimates (Guyenet et al., 2017). Obesity data (% of U.S. adult population) are from the Robert Wood Johnson Foundation’s Trust for America’s Health (stateofobesity.org). Total U.S. television advertising data are from the World Advertising Research Center (www.warc.com). The y–axis on the left covers all data series except advertising expenditures, which uses the y–axis on the right

Full size image

The metabolic effects of HFCS include complications of glucose metabolism, lipid profile and insulin resistance (Pereira et al., 2017; Johnson et al., 2016; Bocarsly et al., 2010; Bray et al., 2004; Jürgens et al., 2005). HFCS as the driver of obesity and diabetes epidemics would be consistent with HBE in the general sense of human physiology having evolved around a diet containing little sugar and no refined carbohydrates. Td bank credit card cash advance fee generally do not exhibit obesity, diabetes, or cardiovascular disease (Kaplan et al., 2017). The HFCS explanation is also consistent with the Insurance Hypothesis, in that poor families are most subject to food scarcity (Hernandez, 2015) and HFCS-sweetened beverages predominate the food economy of poor regions of the U.S.

Conclusions

In conclusion, we find a steady increase, since 1990, in the “reverse gradient” or negative does td bank have student accounts between median household income and both obesity and diabetes rates. In 1990, there was no correlation across the US between either obesity and income or diabetes and income, yet by 2015 strong negative correlations existed across and within U.S. States. We have determined equations for the continual development of these reverse gradients over the past 25 years.

To explain this change, we find evidence in support for both HBE and “social multiplier” effect, a balance similar to empirical studies of other human behavior (Aral et al., 2009). We ascribe more weight to the HBE explanation, in that evolved mechanisms that increase fat storage in response to resource scarcity should promote obesity in high-income countries, where the poor have greater exposure to junk food and other cheap calories including processed sugars (Hill et al., 2017; How to pay a bill on capital one, 2017).

We speculate that the rapid increase in consumption of high fructose corn syrup (HFCS) may have been a key driver. The obesity and diabetes epidemics could be driven by the commercial oversupply and widespread marketing of inexpensive high-sugar foods, especially HFCS–sweetened beverages (Johnson et al., 2007; Song et al., 2012; Basu et al., 2013).

A fuller explanation of the timing and geography of the obesity epidemic will require the specific history of societal–level factors. Besides the suggestive temporal concurrence between obesity, food deserts and HFCS–sweetened beverages, additional clues lie in the considerable variation in the strength and evolution of the reverse gradient within different states of the U.S. This marked geographic variation in the slope of the reverse gradient indicates that government health policies can mitigate the effect of socioeconomic disparities. To explore the scale of these drivers, future work would review and compare state level health policies versus how the negative gradient evolved in those states.

Data availability

All data we used for this study are publicly–accessible, aggregated data. The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/SMTX3X. These datasets were derived from the following public domain resources: Age–adjusted data on obesity rates at the county and state level for years 2004 to 2015 (Centers for Disease Control and Prevention, 2017d), as well as diabetes rates for 2009-2013 (Centers for Disease Control and Prevention, 2017d), are available from CDC. Available at: www.cdc.gov/diabetes/data/countydata/countydataindic State-level obesity rates since 1990 were obtained from the annual reports of the Trust for America’s Health (Robert Wood Johnson Foundation). Available at: stateofobesity.org/adult-obesity. Estimates of “leisure” (physical inactivity) are available from the CDC Behavioral Risk Factor Surveillance System (Centers for Disease Control and Prevention, 2017a). These CDC data come from health-related telephone surveys, which began in 1984 with 15 U.S. states, and now collects data in all 50 states through over 400,000 adult interviews each year. Available at: www.cdc.gov/diabetes/data/countydata/countydataindic. Food desert designations for the U.S. were recently made available through the First merit loan login Access Research Atlas (FARA) project (Rhone et al., 2017). The estimates are union savings bank com from the 2010 US Census and the 2010-2014 American Community Survey, in which census tracts are categorized by median income, vehicle availability, and SNAP participation. Available at: www.ers.usda.gov/webdocs/publications/82101/eib-165.pdf?v=42752.

References

  1. Alvarado SE (2016) Neighborhood disadvantage and obesity across childhood and adolescence: Evidence from the NLSY children and young adults cohort (1986-2010). Soc Sci Res 57:80–98

    PubMedArticlePubMed Central Google Scholar

  2. Anderson PM (2012) Parental employment, family routines and childhood obesity. Econ Hum Biol 10:340–351

    PubMedArticlePubMed Central Google Scholar

  3. Anderson SE, Whitaker RC (2010) Household routines and obesity in us preschool-aged children. Pediatrics 125:420–428

    PubMedArticlePubMed Central Google Scholar

  4. Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci USA 106(51):21544–21549

    ADSCASPubMedArticlePubMed Central Google Scholar

  5. Arroyo–Johnson C, Mincey KD (2016) Obesity epidemiology trends by race/ethnicity, gender, and education: National Health Interview Survey, 1997–2012. Gastroenterol Clin North Am 45(4):571–579

    PubMedArticlePubMed Central Google Scholar

  6. Avena NM, Rada P, Hoebel BG (2008) Evidence for sugar addiction: behavioral and neurochemical effects of intermittent, excessive sugar intake. Neurosci Biobehav Rev 32:20–39

    CASPubMedArticlePubMed Central Google Scholar

  7. Bass FM (1969) A new product growth model for consumer durables. Manag Sci 15:215–227

    MATHArticle Google Scholar

  8. Basu S, Yoffe P, Hills N, Lustig RH (2013) The relationship of sugar to population-level diabetes prevalence: An econometric analysis of repeated cross-sectional data. PLoS One 8(2):e57873

    ADSCASPubMedArticlePubMed Central Google Scholar

  9. Bentley RA, Ormerod P (2010) A rapid method for assessing social versus independent interest in health issues. Soc Sci Med 71:482–485

    PubMedArticlePubMed Central Google Scholar

  10. Bentley RA, Brock WA, O’Brien MJ (2016) Evaluating reproductive decisions as discrete choices under social influence. Philos Trans R Soc B 371:20150154

    Article Google Scholar

  11. Blume LE, Brock WA, Durlauf SN, Ioannides YM (2011) Identification of social interactions. Handb Social Econ 1B:853–963

    Article Google Scholar

  12. Bocarsly ME, Powell ES, Avena NM, Hoebel BG (2010) High-fructose corn syrup causes characteristics of obesity in rats: Increased body weight, body fat and triglyceride levels. Pharmacol Biochem Behav 97(1):101–106

    CASPubMedArticlePubMed Central Google Scholar

  13. Boden J, McLeod G (2017) Epidemiological foundations for the insurance hypothesis: Methodological considerations. Behav Brain Sci 40:e109

    PubMedArticlePubMed Central Google Scholar

  14. Bray GA, Nielsen SJ, Popkin BM (2004) Consumption of high–fructose corn syrup in beverages may play a role in the epidemic of obesity. Am J Clin Nutr 79(4):537–543

    CASPubMedArticlePubMed Central Google Scholar

  15. Brock WA, O’Brien MJ, Bentley RA (2015) Validating niche–construction theory through path analysis. Archaeol Anthropol Sci 8:819–837

    Article walmart money card number Google Scholar

  16. Carroll SJ, Paquet C, Howard NJ, Coffee NT, Taylor AW, Niyonsenga T, Daniel M (2016) Local descriptive norms for overweight/obesity and physical inactivity, features of the built environment, and 10–year change in glycosylated haemoglobin in an Australian population-based biomedical cohort. Soc Sci Med 166:233–243

    PubMedArticlePubMed Central Google Scholar

  17. Centers for Disease Control and Prevention (2017a) Behavioral risk factor surveillance system. http://www.cdc.gov/brfss. Accessed on 20 Oct 2016

  18. Centers for Disease Control and Prevention (2017b) Behavioral risk factor surveillance system: methods and references for county–level estimates and ranks and state–level modeled estimates. http://www.cdc.gov/brfss. Accessed on 20 Oct 2016

  19. Centers for Disease Control and Prevention (2017c) Data and statistics, diabetes. http://www.cdc.gov/diabetes/data. Accessed on 20 Oct 2016

  20. Centers for Disease Control and Prevention (2017d) Data and statistics, overweight and obesity. http://www.cdc.gov/obesity. Accessed on 20 Oct 2016

  21. Centola D, Becker J, Brackbill D, Baronchelli A (2018) Experimental evidence for tipping points in social convention. Science 360:1116–1119

    ADSCASPubMedArticlePubMed Central Google Scholar

  22. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 357:370–379

    CASPubMedArticlePubMed Central Google Scholar

  23. Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83:596–610

    MATHArticle Google Scholar

  24. Christakis NA, Fowler JH (2013) Social contagion theory: examining dynamic social networks and human behavior. Stat Med 32:556–577

    MathSciNetPubMedArticlePubMed Central Google Scholar

  25. Colleran H (2016) The cultural evolution of fertility decline. Philos Trans R Soc B 371:20150152

    Article Google Scholar

  26. Colleran H, Mace R (2015) Social network-and community-level influences on contraceptive use: evidence from rural Poland. Proc R Soc B 282:20150398

    PubMedArticlePubMed Central Google Scholar

  27. Cook WK, Tseng W, Tam TC, John I, Lui C (2017) Ethnic-group socioeconomic status as an indicator of community–level disadvantage: A study of overweight/obesity in Asian American adolescents. Soc Sci Med 184:15–22

    ADSPubMedArticlePubMed Central Google Scholar

  28. Corcoran M, Gordon R, Laren D, Solon G (1992) The association between men’s economic status and their family and community origins. J Hum Resour 27:575–601

    Article Google Scholar

  29. Cornelsen L, Adams J, Gasparrini A, Iskander D, Knai C, Mytton O, Petticrew M, Scott C, Smith R, Thompson C, White M, Cummins S (2016) Impact of a levy on sales of sugar–sweetened beverages within a national chain of restaurants: Interrupted time-series analysis. Lancet 388:S15

    Article Google Scholar

  30. Couzin ID, Ioannou CC, Demirel G, Gross T, Torney CJ, Hartnett A, Conradt L, Levin SA, Leonard NE (2011) Uninformed individuals promote democratic consensus in animal groups. Science 334(6062):1578–1580

    ADSCASPubMedArticlePubMed Central Google Scholar

  31. Creanza N, Feldman MW (2016) Current opinion in genetics and development. Lancet 41:85–92

    CAS Google Scholar

  32. Datar A (2017) The more the heavier? Family size and childhood obesity in the U.S. Soc Sci Med 180:143–151

    PubMedArticlePubMed Central Google Scholar

  33. Dittmann AG, Maner JK (2017) A life–history theory perspective on obesity. Behav Brain Sci 40:e115

    PubMedArticlePubMed Central Google Scholar

  34. Dohle S, Hafmann W (2017) Toward a mechanistic understanding of the impact of food insecurity on obesity. Behav Brain Sci 40:e116

    PubMedArticlePubMed Central Google Scholar

  35. Dwyer–Lindgren L, Freedman G, Engell RE, Fleming TD, Lim SS, Murray CJL, Mokdad AH (2013) Prevalence of physical activity and obesity in U.S. counties, 2001–2011: A road map for action. Popul Health Metr 11:7

    PubMedArticlePubMed Central Google Scholar

  36. Flegal KM, Kruszon–Moran MS, Caroll MD, Fryar CL, Ogden CL (2016) Trends in obesity among adults in the United States, 2005 to 2014. JAMA 315(21):2284–2291

    CAS

Источник: https://www.nature.com/articles/s41599-018-0201-x

Modern life in America has led many people to eat more unhealthy foods, eat bigger food portions, and be less active. As a result, the number of Americans who are overweight or obese (very overweight) has been rising. More than 1 in 3 American adults is now obese, and another 1 in 3 is overweight.

Being overweight or obese can have far-reaching health consequences. According to the US Centers for Disease Control and Prevention (CDC), excess body weight increases a person’s risk for:

  • Heart disease
  • Type 2 diabetes
  • High blood pressure
  • High cholesterol levels
  • Stroke
  • Gallbladder disease
  • Sleep apnea and breathing problems
  • Arthritis
  • Low quality of life
  • Depression and anxiety
  • Certain cancers

Overweight and obese people, on average, do not live as long as people who stay at a healthy body weight throughout examples of obesity in america lives.

An issue for children and teens as well

Not only are more adults overweight or obese, but more children are, too. Among children and teens, about 20% are now obese. This number is much higher than it was a few decades ago, although it has leveled off in recent years.

Some of the same health problems affecting obese adults can also affect obese children. These include heart disease risk factors such as high cholesterol levels and high blood pressure, as well as asthma, sleep apnea, type 2 diabetes, muscle and joint problems, and liver disease. Obese children and teens are also at higher risk for anxiety, depression, and social and psychological problems, such as being bullied and having poor self-esteem.

Overweight and obese children and teens are more likely to have weight problems as adults, too.

Источник: https://www.cancer.org/cancer/cancer-causes/diet-physical-activity/body-weight-and-cancer-risk/health-issues.html
Obesity epidemic
examples of obesity in america

1 Replies to “Examples of obesity in america”

Leave a Reply

Your email address will not be published. Required fields are marked *