Assignment Questions
The price that an obese nation has to pay is manifold, and this fact is explained and illustrated by the people who are interviewed in the episode “Consequences” created by Chaykin and Johnson (2012). First of all, as Thomas Frieden insists, the costs of healthcare are bound to rise for a nation where many people suffer from obesity and related diseases. Secondly, as stated by Susan Combs and America Bracho, a diseased workforce harms the economic development of a country to the point of a “productivity crisis.” However, America Bracho proceeds to explain that obesity does not only “cripple” the workforce: it also cripples the lives of communities, families, and individuals. As a result, the ultimate price of this disease is the happiness and eventually the lives of individual people.
From the interactive tool of CDC (n.d.), it can be concluded that the latest statistics on obesity in the US appeared in 2014, but the most recent data is only available for the adult population of the country. CDC (2014; 2015a) states that almost 35% of the US adult population is currently obese; 69% is overweight. The rates vary certain groups: 47.8% of Non-Hispanic, 42.5% of Hispanic, 32.6% of white non-Hispanic, and 10.8% for non-Hispanic Asian Americans are now obese. For people under 39, the obesity rate amounts to 30.3%, for those older than 60 it reaches 35.4%. The greatest rate of obesity is found among middle-aged people (40-59): it equals 39.5%. With childhood obesity, the rates are lower: 17% of people aged 2-19 (CDC, 2015b). As of 2014, in 17 states the percentage of overweight adults (including obese ones) is 66-67% (CDC, n.d.). Also, while the percentage of overweight men has always been higher than that of overweight women, the obesity rates have always been lower for males than females. At the same time, the difference is slight: according to NCHS (2015), in 2012, the rate was 35.9% for women and 34.6% for men (216).
According to Dr. Katzmarzyk, Arkansas, Mississippi, and Louisiana are the regions where the obesity rates in the US are the highest (Chaykin & Johnson, 2012). CDC (n.d.) shows that in 2014, the three states are still among the most obese ones with Arkansas taking the lead (35.9%); West Virginia follows it with the obesity rate of 35.7%. In general, the obesity rate of over 30% is characteristic of 22 states. In the episode, it is stated that nine out of the ten of the states that are known for the highest obesity rates are also the poorest, and poverty can be used as an explanation for the issue.
The Bogalusa Heart Study (BHS) was started in 1979 in Bogalusa, Louisiana and was aimed at investigating the development of heart diseases (HD). In 2009, the project had accumulated the 40-year-long health history of its participants: 16,000 black and white American people who had been monitored since their childhood. These characteristics of the sample allow making conclusions that are of consequence for the entire nation if not the population of the world.
According to the episode, the discoveries of the nature of HD achieved by BHS are groundbreaking and concerning. In particular, it was proven that HD can begin in childhood. As the first project to carry out the autopsy of children regardless of the cause of death to search for HD traces, BHS was able to prove that plaques can be formed in a child’s arteries. Apart from that, BHS has managed to demonstrate the correlation between obesity and high blood pressure as well as child obesity and adult obesity: according to the study, 77% of children who were obese in childhood remained obese; only 7% of children who were not obese grew into obese adults (Chaykin & Johnson, 2012).
One of the recent BHS-related articles is that by Nguyen, Xu, Chen, Srinivasan, and Berenson (2012). It is devoted to type 2 diabetes and researches its age onset in black and white young people aged 18-50. Since the topic is relatively underresearched, the study was aimed at examining and did not pose a hypothesis. Being a part of BHS, the study followed its participants for 16 years on average. The number of participants amounted to 2,603 with 2,459 of them staying normoglycemic and 144 developing diabetes during the study.
57% of the people were female, 34% of them were black. The data that was being gathered and monitored included the personal health history, blood pressure, BMI, plasma glucose measurements, and other relevant parameters. The results were analyzed statistically and indicated a number of conclusions. In general, the correlations between type 2 diabetes and parental history, BMI (obesity) as well as glucose, cholesterol and triglyceride levels were found (Nguyen et al., 2012, pp. 1344-1345). The early onset of type 2 diabetes had a significant correlation with parental history of the disease. The incidence of diabetes was higher among black and female participants. The authors recommend using the new information about risk groups to adjust school-age health examinations.
In his interview in the episode, Jack Shonkoff confesses that he is frustrated and almost angry with the fact that obesity is possible to avoid but is not being avoided even though it is a condition that makes people’s lives harder in many ways (Chaykin & Johnson, 2012). Mr. Shonkoff is concerned with the fact that people willingly subject themselves to this kind of misfortune while they could work to prevent the condition or alleviate it. He points out that the life of the obese is difficult both in the terms of social acceptance and health, and he is especially unhappy about the fact that obese people shorten their own lives. He does not mention the social implications of obesity but instead points out the way it cripples the lives of people and the way people cripple their own lives.
The rate of the increase in the numbers of obese people rocketed since 1988. According to Marlene Schwartz, morbid obesity experienced a particularly sharp growth, the most drastic and alarming one (Chaykin & Johnson, 2012). WHO (2015) points out that the modern environment has changed with respect to two obesity-relevant aspects: new nutrition and physical activity (PA) patterns. Nowadays, the characteristics of new foods (affordable, easier to cook, more energy-dense, better marketed) lead to obesity and overconsumption. Overconsumption is promoted by food and media industries and spurred by the growing quality of life all over the world (Swinburn et al., 2011, p. 805).
The urbanization and the urban style of life also promote the modern nutritional patterns (Popkin, 2015, p. 2). At the same time, PA is less necessary due to mechanization and automation. As a result, the energy balance is more difficult to maintain: people consume more calories than they can spend. Therefore, as stated by CDC (2013a), the modern environment promotes obesity. As suggested by Herrera and Lindgren (2010), it can be termed “obesogenic” (p. 503).
Of the two changes, the nutritional transition has been especially blamed for the sharp increase in the obesity rates (Popkin, 2015). Swinburn et al. (2011) point out that the process of automation started about a century ago, but the food supply was scantier, and its characteristics were different at the time (p. 804). Therefore, it is the change in nutritional patterns that is truly responsible for the epidemic of obesity. Still, the eventual effect is the result of the combination of the two aspects.
In the episode, Marlene Schwartz insists that obesity has always been very common among poor people. She explains that the link between obesity and poverty was obvious before 1994, but after that year, all the income groups started to exhibit alarming rates of obesity. For example, before 1994, obesity was reaching 30% for the people living beyond poverty line, but the wealthiest groups had the rate of about 17%.
In the episode, Dr. Farley and Debora Lomax demonstrate a map of the New York City where it is clear that the poorest regions have greater rates of obesity and diseases related to it (here, diabetes). Also, as stated in the episode, 9 out of the 10 of the states that have the highest obesity rates also have significant rates of poverty (Chaykin & Johnson, 2012). Żukiewicz-Sobczak et al. (2014) explain the key reasons for such an effect: the combination of lower income (possibly, due to unemployment) and lower education level leads to incorrect nutritional choices that can also be forced by the environment. The cheapest foods are low in nutritional value but contain many calories; apart from that, poor people are more likely to have irregular meals.
With the change of nutritional patterns, wealth is less likely to ensure the “protection” against obesity as it secures access to food resources (Swinburn et al., 2011, p. 805). Ĺ»ukiewicz-Sobczak et al. (2014) also mention that the developing countries tend to exhibit obesity rates that increase with economic growth (p. 590). Still, CDC (2015a) informs that even nowadays higher income people (especially females) develop obesity less often (par. 6-7). It can be concluded that the problems that connect obesity to low income did not disappear, but the nutritional changes of the modern lifestyle have endangered even higher-income groups that do have the opportunity of making proper nutrition choices.
The interactive tool of CDC (n.d.) illustrates the idea of the “degrees of terrible” as phrased by Dr. Brownell (Chaykin & Johnson, 2012). Dr. Brownell insists that the rates of obesity and the burden they place on healthcare and people are extremely high in any region (in the US and the world), which means that all the states need to take the situation into account and actively work to improve it. The fact that there are no states where adult obesity rates would be below 20% while the absolute majority of states (45) exhibits the rates that are above 25% testifies to it (CDC, n.d.).
According to CDC (n.d.), Florida is among the states with the highest numbers of overweight adults (36%); their percentage reaches 41.9% among the people in the age group of 55-64. The obesity rate among adults is naturally lower, 26.2%; higher among females (26.6%) than males (25.8%). The Non-Hispanic black population demonstrates the highest rates of adult obesity in the region (36.3%), followed by Hispanic (27.2%) and non-Hispanic white (24.4%) people; the Asian population is the least obese (12.8%). Therefore, the racial and ethnicity-related tendencies in Florida are similar to those that the US demonstrates as a whole. With respect to adolescent obesity, Florida performs relatively well: 11.6% of youngsters in grades 9-12 are obese in the region. Similarly, the young child obesity is 13.7% in Florida, which means that the region outperforms 32 other states.
The situation varies across Florida. For example, childhood obesity in Miami-Dade County amounts to 9% (CDC, 2013b, par, 1). The relatively high level of life can contribute to it, but, according to CDC (2013b), many of South Florida schools (at least, those in Miami-Dade County) emphasize the importance of nutritional education, physical activity promotion, and improvement of the school environment (for example, rejection of vending machines with soda and similar products). It can be of consequence for the current rates of childhood obesity that can be expected to transfer to adult health as was demonstrated by BHS. It can be concluded that the prospects of South Florida are quite positive due to its emphasis on combating obesity since childhood.
Dr. Collins from the National Institutes of Health points out that obesity is a complex phenomenon, and several aspects contribute to it, including genetic and environmental factors. According to him, 60-70% of the obesity risks are caused by genetics (Chaykin & Johnson, 2012). The mechanisms of the heritability of the disease are still being studied. For example, Herrera and Lindgren (2010) dwell on genome-wide association studies that allowed singling out obesity-related regions (loci) but they admit that this research is capable of explaining only part of the genetic causes of obesity.
Dr. Altshuler and Dr. Leibel add that there are numerous genes (possibly hundreds of them) that tend to contribute to the susceptibility to obesity, and they are often the ones that also influence food intake (Chaykin & Johnson, 2012). This aspect is proved by the recent article by Qi et al. (2012), which demonstrates that the genetic predisposition to obesity can influence the lifestyle of a person by, for example, making him or her more prone to drinking sugar-sweetened beverages.
Still, the genes only create the susceptibility, and specific environmental conditions are needed for obesity to take place (Herrera & Lindgren, 2010). The examples of the latter include, for example, poverty, low-nutrients foods, a decreased necessity of PA and so on: as it has been pointed out, modern environment promotes obesity (Swinburn et al., 2011; Popkin, 2015). Swinburn et al. (2011) cite George Bray saying: “the genetic background loads the gun, but the environment pulls the trigger” (p. 810). As a result, neither the genetic predisposition nor the lack of it can define the BMI of a person: the interaction of the two groups of the factors is needed for that.
I weigh 140 pounds, and my height is 5 feet 6 inches, which means that my BMI is normal and healthy equaling 22.6 (CDC, 2012). My genetics appears to be largely positive: only one of my relatives seems to be somewhat overweight (my grandmother on my father’s side). It is noteworthy, though, that we are very similar in appearance. This fact may indicate that I do have a genetic risk of gaining excessive weight, but it is not evident so far. The rest of my relatives have normal weight; none of them is notably over- or underweight. I look like them, and it has never been difficult for me to maintain a healthy weight; therefore, I can assume that genetics played an important part in my BMI.
My lifestyle also contributes to my weight as I tend to eat regularly and choose good-quality food. I prefer low-fat meat like chicken, and I like salads: these are my natural nutritional preferences, not a fashion-induced diet. Naturally, my menu is not limited to chicken, and I tend to regard it as a balanced one. I think I consume all the necessary nutrients, but I do not monitor them. Also, I have to admit that occasionally I eat “junk food,” especially pizzas. Such is the influence of my environment: this kind of food is extremely popular, and when we meet with friends we often have something unhealthy. On the other hand, such improper nutritional choices are rare for me, and I do not feel them affecting my BMI.
My PA is also influenced by the environment: automation and mechanization do discourage it. Also, I am not a fan of sports. Still, I lead quite an active life, so I believe that I manage to spend the consumed energy, even though I do not usually work out for that.
To sum up, I suppose that my lifestyle is affected by the environment in a mostly negative way, and my genetics and lifestyle exert a positive influence on my BMI. At the same time, my environment also allows me to make healthy lifestyle choices (for example, the availability of quality food and my income). As a result of the interaction of several positive influences that counteract the negative ones, my BMI is healthy.
Cardiovascular diseases (CVD) are a number of disorders that are related to the health of the heart and blood vessels (NHLBI, 2014). Obesity is correlated with the development of various problems of these organs (Chaykin & Johnson, 2012). The walls of the heart of an obese person are covered in fat. As a result, it is more difficult for such a heart to operate (to pump), so it grows muscle mass, and its walls become very thick while the cavity gets smaller. At first, the muscle mass makes the heart stronger, but at a certain point, it begins to weaken instead, which leads to heart failure. As for blood vessels, plaques and fat tend to accumulate in them, making them stiff and literally “stuffing” them, restricting the blood flow. In the end, a stiff and plaque-filled artery can cut off the heart’s access to oxygen (which is CVD process). To sum up, obesity makes the functioning of the heart and blood vessels more difficult.
The nonalcoholic fatty liver disease now exists in 38% of obese children, but this is an illness that was literally unheard of before; it is one of the results of obesity (Chaykin & Johnson, 2012). Other diseases associated with obesity include diabetes, CVD (according to WHO (2015), chiefly stroke and HD), asthma and other respiratory problems, high blood pressure, cryptogenic cirrhosis (that includes nonalcoholic fatty liver), some kinds of cancer (WHO (2015) mentions breast, colon, and endometrial ones), joint problems (as stated by WHO (2015), along with other musculoskeletal disorders), kidney and bladder diseases (the Obesity Society (2015) points out gallbladder disease), and mental issues (for example, dementia).
The Obesity Society (2015) also lists other health problems that include dyslipidemia and reproductive disorders. Taylor (2014) also focuses on the levels of stress that obese people experience and their health impact. Apart from that, there are complications of the consequences of obesity. For example, diabetes leads to foot infections and blindness. As stated in “Consequences,” there is hardly an organ in the human body that remains unaffected by an excessive BMI (Chaykin & Johnson, 2012).
The seven factors for ideal cardiovascular health include the normal blood pressure, mass index and cholesterol levels, the absence of diabetes and smoking habits, and a healthy amount of PA along with a proper diet. Still, the ideal health proves to be almost unachievable: it is stated that less than 1% of the population of the US corresponds to all these factors (Chaykin & Johnson, 2012).
I do not smoke, I am not diabetic, my MBI is healthy. I have to admit that I do not know my cholesterol level; I do not monitor my blood pressure, although the last time it was checked it was perfect. I would say that some of my nutritional choices are questionable, and my physical activity level can be considered deficient depending on the viewpoint; therefore, I cannot insist that I belong to the 1%.
Deborah Clegg explains that the human body is meant to accumulate fat and that there are two fat accumulation patterns that have different functions intended to facilitate various types of human activities (Chaykin & Johnson, 2012). In general, fat distribution depends on gender. There is 20-27% of fat in a healthy female body while for a male body, the figure is 15-22% (Taylor, 2014, p. 80). The fat that is accumulated in the abdominal region (android pattern used by the male body) is mobilizable and meant for fast energy extraction.
It is also more metabolically active: it releases specific hormones, the effects of which may be detrimental; this kind of fat has an impact on the development of diabetes and CVD (Herrera & Lindgren, 2010, p. 499). The female body accumulates fat according to the gynoid pattern (it is located in hips and thighs). This stock of calories is necessary for pregnancy and breastfeeding. This factor also explains why women obesity is more common: female body is meant to store fat.
Herrera and Lindgren (2010) describe the evidence to the fact that there are sex-specific obesity-related genes and that the greater variance of fat distribution and obesity predisposition in women is also genetically conditioned (p. 500). This gender-differentiated system was created to help the species to survive, but nowadays it does not work to our advantage since there is no shortage of supply of energy in the modern world (Chaykin & Johnson, 2012).
According to CDC (2015a), the costs of the healthcare for the obese were $1,429 higher in 2008 (para. 3). Similar data was mentioned in the episode. Therefore, the introduction of increased insurance premiums for obese employees is understandable. The survey devoted to this issue and described by LaVan and Katz (2009) demonstrated that half of the surveyed HR professionals consider it fair to increase premiums. However, the other half preferred more humane and less discriminatory approaches, for example, health programs, and weight management training. The effectiveness of the methods is not studied in the article, though. My personal opinion is that it may be advisable to try the individual approach. If a person is irresponsible and resists more humane measures, the premium increase may be the only reasonable decision to protect the interests of the employer.
In the episode, it is stated that obese young people who want to take up a military career or, for example, firefighting, cannot achieve their goals. However, in other sectors, where health is not a parameter to be considered during hiring, the obesity of the workforce becomes an issue for employers and enterprises in the form of increased rates of illnesses and absenteeism (LaVan & Katz, 2009). In the episode, Susan Combs speaks about the “productivity crisis” that the US is going to experience because of a sicker workforce.
However, Eric Finkelstein provides another scenario that is evidently taking place nowadays: having noticed the fact that unhealthy employees are not profitable to hire, employers may relocate their production to other regions where the workforce is healthier and also tends to be cheaper (Chaykin & Johnson, 2012). Naturally, the sectors where specific skills are required are less likely to experience substitution, but there are very few indispensable people (LaVan & Katz, 2009). The obesity of the workforce harms not only the obese: it endangers and transforms the economic situation in the country, which will affect all the employees and employers, all the economic sectors, and the entire nation.
The game is not over, as is claimed in the episode. Now that we know the reasons for obesity (in particular, the way lifestyle and environment contribute to it), we can modify the risk factors.
The individual level of obesity prevention and treatment is concerned with the modifications of the lifestyle of a person (Taylor, 2014). An example of such treatment is the Cognitive Behavioral Therapy (CBT), which is being actively developed and implemented nowadays as it has proven to provide most satisfactory long- and short-term results (Hagobian & Phelan, 2012, p. 84). It includes creating a strategy for a behavioral change and employs cognitive techniques aimed at identifying and discarding maladaptive (mostly defeatist) thoughts and emotions. Therefore, both the lifestyle and the mindset are affected (hence, cognitive and behavioral elements). Modern technologies advance this method: for example, smartphone applications can be used for self-monitoring (Hagobian & Phelan, 2012).
Still, fighting obesity on the national level is necessary to achieve noticeable results in the situation of the epidemic (Taylor, 2014). This level includes lifestyle-modifying measures as well. For example, WHO (2015) insists that the government that is interested in the health of the nation makes healthy lifestyle choices available and affordable. Similarly, WHO (2015) points out that the food industry is capable of contributing to the health of the nation by providing better food options. However, the national-level obesity-fighting program is also capable of affecting the environment.
For instance, WHO (2015) encourages food industries to take up “responsible” marketing (para. 28). Also, a relatively small environment is easier to control. CDC (2013b) provides the examples of the measures that are aimed at reducing the obesity of school students and that can be implemented in such a restricted environment. They include nutritional education, physical education lessons and clubs, restrictions on junk food selling and advertising, improvement of the school’s menu (with fruits and vegetables) and so on. The combination of changes in the environment and behavioral and cognitive patterns of the entire nation appears to be the key to combating the epidemic.
References
CDC. (2012). Defining Adult Overweight and Obesity. Web.
CDC. (2013a). Public Health Genomics. Web.
CDC. (2013b). The Obesity Epidemic and MiamiÂDade County. Web.
CDC. (2014). Obesity and Overweight. Web.
CDC. (2015a). Adult Obesity Facts. Web.
CDC. (2015b). Childhood Obesity Facts. Web.
CDC. (n.d.). Nutrition, Physical Activity and Obesity: Data, Trends and Maps. Web.
Chaykin, D. (Writer), & Johnson, J. (Director). (2012). The Weight of the Nation: Consequences [Television series episode]. In S. Nevins, & J. Hoffman (Producers), The Weight of the Nation. New York, USA: HBO.
Hagobian, T., & Phelan, S. (2012). Lifestyle Interventions to Reduce Obesity and Diabetes. American Journal of Lifestyle Medicine, 7(2), 84-98. Web.
Herrera, B. M., & Lindgren, C. M. (2010). The Genetics of Obesity. Current Diabetes Reports, 10(6), 498-505. Web.
LaVan, H., & Katz, M. (2009). Managing Obesity: Human Resource Managers’ Perspectives. Compensation & Benefits Review, 41(2), 54-61. Web.
NCHS. (2015). Health, United States, 2014. Web.
Nguyen, Q., Xu, J., Chen, W., Srinivasan, S., & Berenson, G. (2012). Correlates of Age Onset of Type 2 Diabetes Among Relatively Young Black and White Adults in a Community: The Bogalusa Heart Study. Diabetes Care, 35(6), 1341-1346. Web.
NHLBI. (2014). What is Heart Disease? Web.
Popkin, B. (2015). Nutrition Transition and the Global Diabetes Epidemic. Current Diabetes Reports, 15(9), 1-8. Web.
Qi, Q., PhD., Chu, A. Y., PhD., Kang, J. H., ScD., Jensen, M. K., PhD., Curhan, G. C., ScD., Pasquale, L. R., M.D.,… Qi, Lu,M.D., PhD. (2012). Sugar-Sweetened Beverages and Genetic Risk of Obesity. The New England Journal of Medicine,367(15), 1387-1396. Web.
Swinburn, B., Sacks, G., Hall, K., McPherson, K., Finegood, D., Moodie, M., & Gortmaker, S. (2011). The Global Obesity Pandemic: Shaped by Global Drivers and Local Environments. The Lancet, 378(9793), 804-814. Web.
Taylor, S. (2014). Health Psychology (9th ed.). New York, NY: McGraw-Hill Education.
The Obesity Society. (2015). Why Treat Obesity as a Disease? Web.
WHO. (2015). Obesity and overweight. Web.
Żukiewicz-Sobczak, W., Wróblewska, P., Zwoliński, J., Chmielewska-Badora, J., Adamczuk, P., & Krasowska, E. et al. (2014). Obesity and Poverty Paradox in Developed Countries. Annals of Agricultural and Environmental Medicine, 21(3), 590-594. Web.