Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE (www.oxfordhandbooks.com). © Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

Subscriber: null; date: 17 September 2019

Body Mass Index Through Time: Explanations, Evidence, and Future Directions

Abstract and Keywords

Measuring the health of a population during the process of economic development is a principle objective in health economics and economic history, and the body mass index (BMI) plays an important role in such studies. Using data on convicts, the author finds that African American BMIs were historically greater than that of whites by 5%. In addition, the differences between average BMIs and obesity narrowed between the two ethnic groups in the late 20th and early 21st centuries, and both are now much more likely to be obese than they were earlier. About 1% of males in the 19th century were obese, whereas between 35% and 40% of their modern counterparts are obese. Whereas greater BMIs were once more common among physically active workers, obesity is now more common for workers in sedentary occupations. Explanations are considered for the documented increases in BMIs and obesity.

Keywords: body mass index, U.S. obesity epidemic, long-term health, obesity by race, BMIs race, obesity by gender, BMIs by gender

6.1 Introduction

Obesity is a well-established risk factor for various health conditions. There are multiple explanations that shed light on the modern obesity epidemic, and long-term studies are instrumental in understanding its development over time. The body mass index (BMI)—weight in kilograms divided by height in meters squared—is the primary means of classifying obesity.1 However, interpreting BMI variation is more problematic than interpreting stature variation—a measure for cumulative net nutrition—because BMIs increase when weight in the numerator increases or when height in the denominator is low. This implies that BMI must be interpreted with caution because it is the ratio of net current to net cumulative nutrition until about age 20 and does not change thereafter. BMI is also more difficult to interpret than stature because its variation depends on when privation occurs. For example, if an individual receives poor nutrition as a child, she is less likely to reach her genetically predetermined stature. If this short stature persists, and a short person receives abundant calories as an adult, that individual is more likely to be obese because more weight is distributed over smaller physical dimensions (Sorkin, Muller, & Andres, 1999, p. 257). On the other hand, a well-fed child is more likely to reach her genetically determined stature and have lower BMIs in later life because her weight is distributed over larger physical dimensions, ceteris paribus.

Although tautological, the traditional explanation for obesity as calories-in versus calories-out treats the source of calories consumed from carbohydrates, proteins, and fats equally, as if the source of calories is irrelevant. However, this explanation was not always the only accepted one for BMI variation, and, prior to the 1980s, a widely held view for changes in obesity was that it varied with the types of calories consumed. For (p. 134) example, it was once believed that obesity resulted from consuming proportionally more calories from sugars and simple carbohydrates relative to proteins and fats (Ebbeling et al., 2012; Taubes, 2010, 2012), and recent obesity studies are re-evaluating this interpretation (Riera-Crichton & Tefft, 2014; Seidell, 1998; van Dam & Seidell, 2007). Part of the modern obesity increase may be related to an increase in the consumption of beverages high in sugar (Lieberman, 2000, p. 1066; Nielsen & Popkin, 2004, pp. 451–452; van Dam & Seidell, 2007, pp. s75, s78–s88). The omitted factor in the calories-in versus calories-out interpretation may be insulin and insulin resistance, which is associated with diets high in sugars and simple carbohydrates. Insulin resistance develops when cells become insensitive to insulin, meaning they cannot use glucose for energy. Insulin resistance is also associated with high triglyceride levels in the bloodstream and develops when high amounts of lipids are stored as fatty acids in adipose tissue, and it is the simultaneous increase in insulin resistance, type 2 diabetes, high blood pressure, and obesity that is associated with metabolic syndrome and poor health (Eckel, Grundy, & Zimmet, 2005).

Another alternative to the calories-in, calories-out explanation is the thrifty-gene hypothesis, which holds that certain populations are genetically predisposed to obesity (Neel, 1962, p. 354). The effects of these genes act through hyperinsulinemia, which promotes fat accumulation when calories are abundant to be used for survival later during periods of nutritional and dietary stress. Later life obesity associated with the thrifty gene propagates through gestational diabetes if a genotype modifies metabolic adaptations for survival when malnourished fetuses adapt to dietary stress by storing fat in utero for survival after birth.2 The hypothesis was first used to explain an increase in modern type 2 diabetes because individuals with this genetic expression were at greater risk of diabetes after nutrition became more abundant and reliable (Joffe & Zimmet, 1998, p. 139; Neel, 1962; Prentice, Hennig, & Fulford, 2008; Prentice, Rayco-Solon, & Moore, 2005). However, as an explanation for adult-onset diabetes, the thrifty-gene hypothesis has been challenged (Speakman, 2006, 2008) and remains controversial (Prentice et al., 2008, p. 160).

Related to the thrifty-gene hypothesis is the Barker hypothesis, which postulates that many adult chronic conditions are not always the result of bad genes and unhealthy lifestyles but instead the result of poor intrauterine conditions and early postnatal nutrition (Wells, 2003). For example, a fetus that receives an insufficient amount of iodine during the second trimester of pregnancy is more likely to develop diseases of the nervous system—such as multiple sclerosis—later in life. The Barker hypothesis also suggests that in-utero conditions are related to later-life risks of heart disease and stroke (Barker, 1992, 1997). Obesity, BMI, cardiovascular disease, stroke, and even cognitive function vary with a mother’s net nutritional conditions that affect her child both before and shortly after birth (Barker, 1992, 1997, 1998; Ellias, Ellias, Sullivan, Wolf, & D’Agostro, 2003, 2005; Osmond & Barker, 2000; Ost et al., 2014).

Fat accumulation (for sake of simplicity) may also be related to stress and hormones. Stress is related to the proteins associated with the hormones adiponectin, cortisol, ghrelin, and leptin. The adiponectin protein regulates glucose levels and how fatty acids are (p. 135) digested, and adiponectin levels are inversely related to percent body fat. The hormone cortisol is a steroid released under stressful conditions, and women may respond differently than men to increased amounts of cortisol because they consume more fatty acids and simple sugars under stressful conditions (Epel, Lapidus, McEwen, & Brownell, 2001; Newman, O’Connor, & Conner, 2007). Ghrelin is an amino acid that lines the stomach and increases before meals are consumed and decreases after consumption. Leptin is a cell signaling protein released from adipose tissue that regulates hunger, appetite, energy intake, and metabolism (Sills & Crawley, 1996; Teff et al., 2004). Leptin’s absence is also associated with obesity and excess food consumption. Adiponectin, ghrelin, and leptin function in the brain where they stimulate glucocorticoids, and elevated glucocorticoids are associated with obesity-related stress (Björntorp, 2001; Offer, Pechey, & Ulijaszek, 2012). Market economies are based on occupational and economic hierarchies, and social stress accrues from occupational subordination; therefore, stress-related hormones may be associated with economic development (Offer et al., 2012).3 In sum, whereas the modern explanation of an excess of calories consumed above calories expended for work is undeniably true, alternative explanations are important in evaluating how BMI and obesity varied over the long run.

6.2 Body Mass and Health

Measuring obesity is ideally made by examining an individual’s percent body fat, fat-free mass, or waist-to-hip ratio. Burkhauser and Cawley (2008) indicate that BMI has limitations in measuring obesity because it does not distinguish between fat and fat-free mass, such as bone and muscle (Baum & Ruhm, 2009, pp. 635–648). However, these measurements are expensive to collect, so the BMI, which is easy and inexpensive to calculate, has become the standard measure for obesity. The World Health Organization developed the current BMI classification system. BMIs of less than 18.5 are classified as underweight, BMIs between 18.5 and 24.9 are normal weight, BMIs between 25.0 and 29.9 are overweight, BMIs greater than 30.0 are obese.

Changes in health are frequently associated with changes in weight, so BMI is an important variable that measures health. In modern populations, Waaler (1984) finds a U-shaped relationship between BMIs and relative mortality risk in a Norwegian population, which led to a number of important follow-up studies (Allebeck & Berg, 1992; Andres, Elahis, Tobin, Mueller, & Brant, 1985; Fogel, 1993, 1994; Fogel & Costa, 1997; Koch, 2011; Stevens et al., 1998). Waaler and subsequent studies also find that relative mortality risk is high for populations with BMIs of less than 19, are low and stable for men with BMIs between 19 and 27, and is high for individuals with BMIs above 27. Costa (1993, p. 442), Murray (1997, p. 599), and Henderson (2005, p. 346) show the U-shaped relationship is stable over time, and Jee et al. (2006) show that the relationship is similar across ethnic groups. For BMIs of less than 19, infectious diseases, malnutrition, and respiratory conditions are common (Calle, Thun, Petrelli, Rodriguez, & Meath, 1999, (p. 136) p. 1001; Jee et al., 2006, p. 783), and greater rates of heart disease, stroke, diabetes, high blood pressure, and the likelihood of many cancers are common among people with high BMIs (Atlas, 2011, p. 104; Eckel et al., 2005, pp. 1417–1421; Popkin, 2009, p. 113).

BMIs also vary with diets, and, in the 20th century, a distinct nutritional pattern emerged in which rural diets that were rich in proteins and complex carbohydrates transitioned to urban diets high in saturated fats and simple sugars (Popkin, 1993, pp. 145–148; Popkin, 2009). BMIs and obesity also vary with technological change and physical activity (Lakdawalla & Philipson, 2002, 2009; Laudabaum, Mannalithera, Meyer, & Singh, 2014; Sharma, Zaric, Campbell, & Gilliland, 2014), and changing US labor markets may be associated with BMI variation through the effects associated with less physical activity required at work. (Baum & Ruhm, 2009, p. 638; Church et al., 2011).

6.3 The History of US BMIs

Fogel (1994, p. 373) finds that the bottom 10% of the French labor force in 1790 did not receive a sufficient amount of calories to perform any work. Although more calories were available in England, 3% of the English population also did not receive sufficient nutrition to participate in the labor force. Consequently, 18th-century Europeans and the English were shorter and were mostly underweight (Fogel, 2004, pp. 59–66).

Various studies consider 19th- and early 20th-century US BMI variation over time and by characteristics (Table 6.1). The majority of BMIs by observation period indicate that the BMIs of men decreased throughout the late 19th and early 20th centuries. BMIs began their century-long increase after World War I with a hiatus during the Great Depression and World War II; increase resumed in the 1950s (Komlos & Brabec, 2010, 2011). Moreover, all historical BMI studies show a marked BMI advantage for rural agricultural workers and greater BMIs associated with African Americans. Because rural farmers were in close proximity to food supplies rich in protein and complex carbohydrates, farmers received adequate nutrition, were more physically active, and, as a consequence, probably had greater muscle mass and consequently greater BMI values than did workers in other occupations; this situation persisted until the end of the 19th century. Farmers were also in closer proximity to food supplies, which was associated with lower food prices (Komlos, 1987). Hence, the BMIs of rural unskilled workers as well as farmers were .5% and .7% greater, respectively, than workers with no identified occupations (Carson, 2012a, pp. 383–384; Carson, 2013b).4

Three data sources are used here to consider how BMIs and obesity varied between 1800 and 2010: US prison records, the National Health and Nutrition Examination Survey (NHANES), and the National Health Interview Survey (NHIS). We supplement the NHANES data with NHIS data because the NHANES does not collect valuable economic variables, such as occupations, which are available in the NHIS. This chapter considers obesity patterns using male prison records, NHANES, and NHIS to contrast how obesity varied by demographic and socioeconomic characteristics between 1800 (p. 137) and 2010. There is concern when comparing the NHIS with NHANES data because the NHIS is self-reported, which may misrepresent height and weight measurements; thus, results should be interpreted with caution (Goodman, Hinden, & Khandelwal, 2000; Kuczmarski, Kuczmarski, & Naijar, 2001; Strauss, 1999). The NHIS also omits height below 59 and above 76 inches (150 and 193 cm) and omits weights below 99 and over 285 pounds (45 and 129 kg). Assessing the degree of these potential biases is addressed in part by comparing the percent of the NHANES and NHIS samples in the combined overweight and obese categories.

Table 6.1 Comparison of late 19th and early 20th Century BMIs

Study

Observation Period

Sample

ΔBMI over Time

ΔFarmer

Δ Mulatto Compared to Black

Cuff, 1993, White

1860–1885

West Point Cadets

0.8

Coclanis and Komlos, 1995, White

1860–1930

The Citadel

1.7

Carson, 2009, Black and White

1870–1920

Texas Prisoners

Blacks, −0.4

0.2

−0.3

Whites, 0.2

0.1

Bodenhorn, 2010, White

1795–1844

New York Legislators

−1.7

Carson, 2012, Black and White

1850–1929

US Prisons

Black Youth −1.06

0.4

−0.4

White Youth −1.05

0.5

Carson, 2012, Black and White

1840–1929

US Prisons

Black Adults −2.3

0.2

−0.4

White Adults −2.0

0.3

Carson and Hodges, 2012, Black and White

1870–1910

Philadelphia Prison

Blacks and Whites combined 0.2

0.1

−0.4

Source: Cuff (1993); Coclanis and Komlos (1995); Carson, (2009); Bodenhorn, (2010); Carson (2012a, pp. 383–385 and pp. 389–392); Carson and Hodges (2012).

Notes: ΔBMI is the difference between BMIs at the beginning and end of the series presented in each study’s observation period. ΔFarmer is the unit farmer BMI difference relative to the no occupation category. Δ Mulatto is the unit mulatto BMI difference relative to darker complexioned blacks.

Body Mass Index Through TimeExplanations, Evidence, and Future DirectionsClick to view larger

Figure 6.1 Nineteenth century and modern White male BMI distributions by year of birth.

Note: Historical represents males from the prison records, born between 1800 and 1900 and observed between 1840 and 1920. Modern represents males from the NHANES III.

Source: Historical: Carson (2012a); Modern: NHANES III.

Nineteenth-century BMIs of white men were almost all in the normal category, with few adult men in the obese category (Figure 6.1). The average adult black male BMI in the historical sample is 23.9, with 1.3% in the obese category and .7% in the underweight category. The average adult white male BMI in the historical sample is 22.7, with 1.0% in (p. 138) the obese category and 2.3% in the underweight category. The corresponding values for modern US populations are 27.4 and 27.5 for blacks and whites, respectively, indicating that their BMIs have increased by 15% and 21%, respectively. Greater black compared to white BMIs during the 19th century may reflect greater intrauterine deprivation, thus making these individuals susceptible to obesity if nutrition improved in adulthood (Bodenhorn, 2010 Carson, 2008, 2012c 2015b; Steckel, 1979).

Comparing historical and modern BMI variance is even more striking. The historical adult standard deviation is 2.4, whereas its modern counterpart is between 6.5 and 5.5, indicating that variances increased by more than a factor of two, respectively. In sum, average BMIs and percentages in the obese categories have increased over time, but their variances have increased even more.

6.4 Factors Associated with US BMIs

6.4.1 Over Time

BMIs increase when the percent change in weight is greater than twice the percent change in height5; weight generally contributes more to BMI variation than does height (Carson, 2015a; Dawes, 2014, p. 30). There are two ways to measure BMI variation (p. 139) over time: birth and period effects. Birth effects measure how BMIs varied since birth, whereas period effects measure how BMIs vary by observation period. Measured by birth effects, the increase in obesity may have occurred earlier than currently believed (Coclanis & Komlos, 1995; Komlos & Brabec, 2010, p. 631; 2011, p. 235). Measured by birth cohorts, the epidemic began after World War I and accelerated in the 1950s; whereas, using period effects, US obesity began abruptly in the 1970s and 1980s. Although neither measurement provides a definitive answer, considering birth and period effects together provides a richer explanation for when the increase in obesity began.

Komlos and Brabec (2010, 2011) use NHANES data to show that BMIs began to increase among the birth cohorts of the post-World War I era. Between 1900 and 1965, the average BMI of black females increased by 68%, whereas it increased by 39% for white females. BMI increases were most pronounced after the two world wars.

Using data from 19th-century US prisons and the NHANES III, Figure 6.2 presents average adult US male BMIs. Between 1800 and 1900, black and white adult average BMIs decreased moderately. The average black and white male BMI cohort born in 1910 were 25.4 and 25.7, respectively, whereas birth cohorts in the 1950s were 28.4 and 28.7, indicating that BMIs by birth cohort began to increase much earlier than expected.

Body Mass Index Through TimeExplanations, Evidence, and Future DirectionsClick to view larger

Figure 6.2 Average BMI values of Black and White adult men by birth cohort over time.

Note: Average BMIs are adjusted for age.

Source: Historical: Carson (2012a); Modern: Komlos and Brabec (2010).

On the other hand, since BMI is more responsive to weight than height, BMI by period effects are frequently interpreted as reflecting changes in current net nutrition. Cuff (1993, p. 178) finds that West Point military recruit BMIs in the mid-19th century (p. 140) were sufficiently low that a large proportion of cadets were in the high relative health risk category; 40% of 20- to 21-year-old cadets had BMIs below 19, the threshold at which mortality risk increases (Costa, 1996, pp. 66, 81–86; Costa & Steckel, 1997; Fogel, 1993, p. 15; Murray, 1997, pp. 597–603; Riley, 1994, pp. 486–492; Waaler, 1984, pp. 23–37). Cuff also finds that cadet BMIs increased slightly over time, and BMIs generally decreased in the late 19th century (Carson, 2009, 2012a, 2012b; Carson & Hodges 2012; Costa & Steckel 1997, pp. 51–54; Cuff, 1993, p. 177; Carson, 2014a). Today, the rate of obesity among black and white men is similar.

Body Mass Index Through TimeExplanations, Evidence, and Future DirectionsClick to view larger

Figure 6.3 Percent obese among adult Black and White men by data of measurement.

Source: Historical: Carson (2012a); Modern: Ogden et al. (2006); Flegal et al. (2012).

Using data from 19th-century US state prisons and NHANES measured by observation period between 1840 and 1920 and between 1980 and 2008, Figure 6.3 shows the percentage of black and white adults in the obese category. About 1% of 19th-century blacks and whites were obese, with little change in either average BMIs or the percent of obese men throughout the century. Measured by either birth cohort or period cohorts, these large increases in BMI and obesity indicate that the modern obesity epidemic began earlier than previously believed. Lifestyle changes that began in the 1950s and 1960s, with reduced physical activity associated with transportation technologies and television, deserve additional attention (Komlos & Brabec, 2010, 2011). There is also little evidence that BMIs and obesity have changed since 2000 (Flegal, Carroll, Kit, & Ogden, 2012; Flegal, Carroll, & Ogden, 2010; Flegal, Carroll, Ogden, & Johnson, 2002; Hedley et al., 2004, pp. 2848–2850; Ogden, Carroll, Curtin, Lamb, & Fleagal, 2010; Ogden et al., 2006; Ogden, Carroll, Kit, & Flegal, 2012).

(p. 141) Less is known about 19th-century women’s BMI and obesity variation. Women may have had heavier weights because of evolutionary responses to child-bearing, where more energy is stored in the fat required to fuel the development of large-brain offspring (Dunbar, 2012, p. 55; Pond, 1977). Fat and its distribution may have also played a role in women’s sexual attractiveness and mate selection (Anderson, Crawford, Nadeau, & Lindberg, 1992; Pawlowski & Dunbar, 2005; Tovée et al., 1999). Moreover, women accumulate weight during pregnancy that can be difficult to lose after childbirth (Lieberman, 2000). There are other reasons women may be more likely to be obese than men. For example, BMI is inversely related to height, and women reach shorter terminal statures than do men (Carson, 2011, 2013c; Herbert et al., 1993, p. 1438). There is also a psychosocial relationship between gender and obesity; women may be more likely to be depressed and, subsequently, more likely to be obese (BeLue, Francis, & Colaco, 2009; Needham & Crosnoe, 2005).

Body Mass Index Through TimeExplanations, Evidence, and Future DirectionsClick to view larger

Figure 6.4 Black and White average female BMI over time by birth cohort.

Note: Age-adjusted average BMIs.

Source: Carson data set; Komlos and Brabec (2010).

Women were less likely be imprisoned during the 19th century; because prisons recorded weight, this means fewer records of women’s historical weight survive. Like males, average female BMIs by birth cohort were low and declined throughout the 19th century, and average black and white women’s BMIs were similar. By the late 20th century, black women were more likely than white women to have high BMIs and be obese (Figure 6.4).

(p. 142) 6.4.2 Occupations

Whereas workers in all occupations were more obese compared to farmers, in the 19th century, modern sedentary white-collar and skilled workers have become more overweight and obese (Table 6.2; Church et al., 2011; Laudabaum et al., 2014; Sharma et al., 2014, p. 18). Between the late 19th and early 21st centuries, obesity increased for each occupational category and increased by factors of 2.5 and 3.7 for black and white unskilled workers, respectively. In the past, nonagricultural workers required greater physical activity than today because transportation technology and daily activities required workers to be more physically active. In sum, BMIs and obesity have increased for every occupation. (p. 143)

Table 6.2 Nineteenth and 21st Century Black and White Male Weight Distributions (Percent) by Occupation

Blacks

Under

Historical Normal

Over

Obese

Under

Modern Normal

Over

Obese

White-Collar

0.8

73.4

24.4

1.5

0.7

26.0

45.1

28.3

Skilled

0.9

70.4

26.9

1.8

0.4

29.9

41.3

28.4

Agricultural

0.6

68.2

29.9

1.3

0.0

37.3

36.1

26.5

Unskilled

0.8

71.3

26.9

1.1

0.4

29.2

40.9

29.5

No Occupation

0.7

67.7

30.5

1.1

1.4

42.3

36.8

19.5

Whites

Under

Historical Normal

Over

Obese

Under

Modern Normal

Over

Obese

White-Collar

3.6

79.4

15.2

1.9

0.2

29.5

46.8

23.5

Skilled

2.2

82.3

14.6

1.0

0.3

28.7

45.8

25.2

Agricultural

2.0

81.8

15.1

1.1

0.7

27.5

46.2

25.6

Unskilled

2.2

82.8

14.3

0.7

0.4

28.3

44.2

27.2

No Occupation

2.5

83.9

12.9

0.6

1.0

44.1

34.1

20.8

Source: Historical, Carson (2012a); Modern: NHIS.

6.4.3 Residence

BMIs and obesity have also varied by region. Between the late 19th and early 21st centuries, obesity within the Northeast increased by 2,825% for blacks and by 4,350% for white men in the Midwest (Table 6.3; Atlas, 2011, p. 105; Mokdad et al., 1999, 2001, pp. 1196–1198). The percentage in the underweight and normal categories in every region decreased. Modern BMIs and obesity are higher in the southern and central United States and lower in northeastern and western states (Hines, 2011). Southern urban obesity is greater than in other regions (Lieberman, 2000, p. 1065). Regional BMI variation increased because of different socioeconomic status, ethnicities, geographic conditions, access to economic opportunity, education, access to nutrition, and income and inequality differentials (Chang & Lauderdale, 2005; Sobal, 2011, p. 110 Carson, 2013a, 2015d; Carson & Hodges, 2014d). Regional obesity variation may be explained, in part, by diet, and Southern diets were more calorie-dense than elsewhere within the United States (Carson, 2014b; Hilliard, 1972; Ransom & Sutch, 1977). Consequently, over time, by occupations, and across the United States, obesity has increased with the transition to a modern economy. (p. 144)

Table 6.3 Nineteenth and 21st Century Black and White Male Obesity Prevelance (Percent) by Residence

Blacks

Under

Historical Normal

Over

Obese

Under

Modern Normal

Over

Obese

Northeast

0.6

74.2

24.4

0.8

0.4

31.8

44.4

23.4

Midwest

1.3

81.2

16.6

1.0

0.4

29.9

41.5

28.2

South

0.8

68.6

29.4

1.2

0.4

28.6

41.0

30.0

West

0.6

67.9

29.6

2.0

0.8

29.7

41.6

27.9

Whites

Under

Historical Normal

Over

Obese

Under

Modern Normal

Over

Obese

Northeast

1.9

82.7

14.6

0.8

0.3

29.9

45.7

24.1

Midwest

4.2

86.1

9.1

0.6

0.4

28.5

44.4

26.7

South

2.6

82.5

14.0

0.9

0.3

28.9

44.6

26.1

West

1.4

78.4

18.8

1.4

0.2

29.6

47.0

23.2

Source: Historical, Carson (2012a); Modern: NHIS.

6.5 Discussion and Future Directions

Considerable progress has been made in explaining the rise of the modern obesity epidemic, yet there are important gaps in our understanding of its major causes, and progress in interpreting obesity trends will rely on cross-pollination among medicine, the hard sciences, and the social sciences. New areas for obesity research that have developed for modern populations, but with limited application in historical BMI studies, are public policy, obesity and taxation, insurance, pharmaceuticals, and food marketing (Beydoun, Powell, & Wang, 2008; Brownwell et al., 2009; Chou, Rashad, & Grossmand, 2008). Moreover, many other areas in obesity research provide insight into how modern obesity developed. For example, an important question for historical and future studies is the number of calories consumed per day over those required to maintain healthy body weight. This “energy gap” is the number of calories required to reduce current obesity levels to healthy nutritional intake and has policy applications. Wang et al. (Wang, Orleans, & Gortmaker, 2012) consider the Healthy People 2010 goals and show that reducing future obesity levels will require an average reduction in consumption of 120 kilocalories per day among youth. Carson (2014b) and Carson (2015c) use similar calorie extrapolation techniques to show that the average adult calories expended per day during the late 19th and early 20th centuries was around 3,000, and future obesity studies will rely on these relationships to calculate historical and modern calorie intakes.

Health and obesity may also be related to peer effects through selection, in which obese individuals are more likely to associate with individuals of similar weight and physical activity levels. These relationships were first considered by Christakis and Fowler (2007) who demonstrated that the likelihood that a person is obese is 57% higher if the person has an obese friend. These social relationships extend to family members; if one sibling is obese, the other is 40% more likely to be obese as well (Christakis & Fowler, 2007, pp. 370, 375–377; Ozanne, 2015, p. 973). Christakis and Fowler (2007) also find that there are three degrees of separation between individuals before social relationships are no longer significant (Dunbar, 2012, pp. 62–65). Economics also contributes to obesity research through assortative mating studies (Silventoinen, Kaprio, Lahelma, & Viken, 2003). These studies demonstrate that obese individuals are more likely to marry partners with similar attributes.

Farmers and unskilled workers had greater BMIs than workers in other occupations, and men from the Northeast and Middle Atlantic weighed less, whereas Southerners weighed more. The inference is that close proximity to agriculture enabled farmers to consume more calories. Southerners weighed more because their lower population density meant that their disease load was lower (Carson, 2015a). Assessment of these historical international trends awaits the collection of new datasets from historical records.

(p. 145) 6.6 Conclusion

The 20th-century obesity epidemic is a leading health concern in epidemiology, economics, and human biology studies. Although individuals in the 19th century were mostly in the normal BMI category, overweight and obesity are both now much more common than healthy body weights. Whether obesity is measured by birth or period effects, it is clear that obesity increased in the late 20th century, a trend that may have been well under way by mid-century. This indicates that the average BMI for a man 68 inches (163 cm) tall increased by 25% between the mid-19th century and 2010, whereas the percent in the obese category increased by 400%.

There is considerable debate regarding whether BMIs and obesity have increased as a result of increased consumption of calorie-dense foods or if the modern sedentary lifestyle is the main cause. Regardless of the source of obesity, it is clear that obesity has increased across all ethnic groups, socioeconomic status levels, and residence locations, indicating that the plausible explanation for the obesity epidemic is a combination of consuming more calorie-dense foods relative to energy expended during physical activity. The increase has been greatest among black females. Regional variation also indicates that obesity within the United States has been widespread. Recent studies on the obesity epidemic indicate that proximity to fast food establishments and lack of physical activity are factors in obesity’s increase. How BMI and obesity changed over time will also contribute to understanding the policies needed to reduce obesity.

Acknowledgments

I appreciate comments from John Komlos, Gary Taubes, John Cawley, Lee Carson, Doug Henderson, James Eldridge, and Paul Hodges. Shahil Sharma, Chinuedu Akah, Meekam Okeke, Tiffany Grant, Bryce Harper, Greg Davis, Kellye Manning, and Brandon Hayes provided research assistance.

References

Allebeck, P., & Berg, C. (1992). Height, body mass index, and mortality: Do social factors explain the association? Public Health, 106, 375–382.Find this resource:

    Anderson, J. L., Crawford, C. W., Nadeau, J., & Lindberg, T. (1992). Was the Duchess of Windsor right? A cross-cultural study of the socioecology of the ideals of female body shape. Ethnology and Sociobiology, 13, 197–227.Find this resource:

      Andres, R., Elahis, D., Tobin, J., Mueller, M., & Brant, L. (1985). Impact of age of weight goals. Annals of Internal Medicine, 103, 1030–1033.Find this resource:

        Atlas, S. (2011). In excellent health: Setting the record straight on America’s health care. Stanford, CA: Hoover Institution Press.Find this resource:

          Barker, D. (1992). Fetal and infant origins of adult disease. London: British Medical Journal.Find this resource:

            Barker, D. (1997). Maternal nutrition, fetal nutrition, and disease in later life. Nutrition, 13(9), 807–813.Find this resource:

              Barker, D. (1998). In utero programming of chronic disease. Clinical Science, 95, 111–128.Find this resource:

                Baum, C., & Ruhm, C. (2009). Age, socioeconomic status, and obesity growth. Journal of Health Economics, 28, 635–648.Find this resource:

                  BeLue, R., Francis, L. A., & Colaco, B. (2009). Mental health problems and overweight in nationally representative sample of adolescents: Effects of race and ethnicity. Pediatrics, 123, 697–702.Find this resource:

                    Beydoun, M. A., Powell, L. M., & Wang, Y. (2008). The association of fast food, fruit, and vegetable prices with dietary intakes among US adults: Is there modification by family income? Social Science and Medicine, 66, 2218–2229.Find this resource:

                      Björntorp, P. (2001). Do stress reactions cause abdominal obesity and comorbidities? Obesity Reviews, 2, 76–86.Find this resource:

                        Bodenhorn, H. (2010). Height, weight, and body mass index values of 19th century New York legislative officers. Economics and Human Biology, 8, 291–293.Find this resource:

                          Brownwell, K. D., Farley, T., Willett, W. C., Popkin, B. M. Chaloupka, F. J., Thompson, J. W., & Ludwig, D. S. (2009). The public health and economic benefits of taxing sugar-sweetened beverages. New England Journal of Medicine, 361, 1599–1605.Find this resource:

                            Burkhauser, R. V., & Cawley, J. (2008). Beyond BMI: The value of more accurate measures of fatness and obesity in social science research. Journal of Health Economics, 27, 519–529.Find this resource:

                              (p. 147) Calle, E., Thun, M., Petrelli, J., Rodriguez, C., & Meath, C. W. (1999). Body-mass index and mortality in a prospective cohort of U.S. adults. New England Journal of Medicine, 341, 1097–1104.Find this resource:

                                Carson, S. A. (2008). The effect of geography and vitamin D on African-American stature in the 19th century: Evidence from prison records. Journal of Economic History, 68, 812–830.Find this resource:

                                  Carson, S. A. (2009). Racial differences in body-mass indices of men imprisoned in 19th century Texas. Economics and Human Biology, 7, 121–127.Find this resource:

                                    Carson, S. A. (2011). Height of female Americans in the 19th century and the Antebellum Puzzle. Economics and Human Biology, 9, 157–164.Find this resource:

                                      Carson, S. A. (2012a). Nineteenth century race, body mass, and industrialization: Evidence from American prisons. Journal of Interdisciplinary History, 42, 371–391.Find this resource:

                                        Carson, S. A. (2012b). Demographic, residential, and socioeconomic effects on the distribution of 19th century White body mass index values. Mathematical Population Studies, 19(3), 147–157.Find this resource:

                                          Carson, S. A. (2012c). A quantile approach to the demographic, residential, and socioeconomic effects on 19th century African-American body mass index values. Cliometrica, 6(2), 193–209.Find this resource:

                                            Carson, S. A. (2013a). Body mass, wealth, and inequality in 19th century U.S. Joining the debate surrounding equality and health. Economics and Human Biology, 11(1), 90–94.Find this resource:

                                              Carson, S. A. (2013b). The significance and relative contributions of demographic, residence, and socioeconomic status in 19th century US BMI variation. Historical Methods, 46(2), 67–76.Find this resource:

                                                Carson, S. A. (2013c). Socioeconomic effects on the stature of nineteenth century U.S. women. Feminist Economics, 19, 122–143.Find this resource:

                                                  Carson, S. A. (2014a). Institutional change and 19th century southern Black and White BMI variation. Journal of Institutional and Theoretical Economics, 170(2), 296–316.Find this resource:

                                                    Carson, S. A. (2014b). Nineteenth century US Black and White working class physical activity and nutritional trends during economic development. Journal of Economic Issues, 48(3), 765–786.Find this resource:

                                                      Carson, S. A. (2015a). A weighty issue: Diminished 19th century net nutrition among the US working class. Demography, 52, 945–966.Find this resource:

                                                        Carson, S. A. (2015b). Biology, complexion, and socioeconomic status: Accounting for 19th century US BMIs by race. Australian Economic History Review, 55(3), 238–255.Find this resource:

                                                          Carson, S. A. (2015c). The Mexican calorie allocation among the working class in the American West, 1870–1920. Essays in Economic & Business History, 33, 26–50.Find this resource:

                                                            Carson, S. A. (2015d). The relationship between 19th century BMIs and family size: Economies of scale and positive externalities. Journal Homo of Comparative Human Biology, 66(2), 165–175.Find this resource:

                                                              Carson, S. A., & Hodges, P. E. (2012). ‘Black, & white body mass index values in 19th century developing Philadelphia County. Journal of BioSocial Science, 44(3), 273–288.Find this resource:

                                                                Carson, S. A., & Hodges, P. E. (2014d). The relationship between body mass, wealth, and inequality across the BMI distribution: Evidence from nineteenth century prison records. Mathematical Population Studies, 21, 78–94.Find this resource:

                                                                  Chang, V., & Lauderdale, D. S. (2005). Income disparities in body mass index and obesity in the United States, 1971–2002. Archives of Internal Medicine, 165, 2112–2128.Find this resource:

                                                                    Chou, S. Y., Rashad, I., & Grossmand, M. (2008). Fast food restaurant advertising on television and its influence on childhood obesity. Journal of Law and Economics, 51, 599–618.Find this resource:

                                                                      (p. 148) Christakis, N., & Fowler, J. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4), 370–378.Find this resource:

                                                                        Church, T., Thomas, D., Tudor-Locke, C., Katzmarzyk, P. T., Earnest, C. P., Rodarte, R. Q., Martin, C. K., … Bouchard, C. (2011). Trends over five decades in U.S. occupation-related physical activity and their associations with obesity. PlosOne, 6, 5.Find this resource:

                                                                          Coclanis, P., & Komlos, J. (1995). Nutrition, & economic development in post-reconstruction South Carolina. Social Science History, 19, 91–115.Find this resource:

                                                                            Costa, D. (1993). Height, wealth, and disease among the native-born in the rural, antebellum North. Social Science History, 17, 355–383.Find this resource:

                                                                              Costa, D. (1996). Health and labor force participation of older men, 1900–1991. Journal of Economic History, 56(1), 62–89.Find this resource:

                                                                                Costa, D., & Steckel, R. (1997). Long-term in health, welfare, and economic growth in the United States. In: D. Costa & R. Steckel (Eds.), Health and welfare during industrialization (pp. 47–89). Chicago: University of Chicago Press.Find this resource:

                                                                                  Cuff, T. (1993). The body mass index values of mid-19th century West Point cadets. Historical Methods, 26(4), 171–183.Find this resource:

                                                                                    Dawes, L. (2014). Childhood obesity in America. Cambridge, MA: Harvard University Press.Find this resource:

                                                                                      Dunbar, R. I. (2012). Obesity: An evolutionary perspective. In A. Offer, R. Pechey, & S. Ulijaszek (Eds.), Insecurity, inequality, & obesity (pp. 55–68). Oxford: Oxford University Press.Find this resource:

                                                                                        Eckel, R., Grundy, S., & Zimmet, P. (2005). The metabolic syndrome. Lancet, 365(9468), 1415–1428.Find this resource:

                                                                                          Ebbeling, C., Swain, J., Feldman, H., Wong, W., Hachey, D., Garcia-Lago, E., & Ludwig, D. (2012). Effects of dietary composition on energy expenditure during weight loss maintenance. Journal of the American Medical Association, 307(24), 2627–2634.Find this resource:

                                                                                            Ellias, M., Ellias, P., Sullivan, L., Wolf, P., & D’ Agostro, R. (2003). Lower cognitive function in the presence of obesity, & hypertension: The Framingham Heart Study. International Journal of Obesity, 27, 260–268.Find this resource:

                                                                                              Ellias, M., Ellias, P., Sullivan, L., Wolf, P., & D’Agostro, R. (2005). Obesity, diabetes, & cognitive deficit: The Framingham Heart Study. Neurology of Aging, 26, S11–S16.Find this resource:

                                                                                                Epel, E., Lapidus, R., McEwen, B., & Brownell, K. (2001). Stress may add bite to appetite in women: A laboratory study of stress-induced cortisol and eating behavior. Psychoneuroendocrinology, 26, 37–49.Find this resource:

                                                                                                  Flegal, K., Carroll, M., Kit, B., & Ogden, C. (2012). Prevalence of obesity, & trends in the distribution of body mass index among US adults, 1999–2010. Journal of the American Medical Association, 307(5), 491–497.Find this resource:

                                                                                                    Flegal, K., Carroll, M., & Ogden, C. (2010). Prevalence and trends in obesity among US adults. Journal of the American Medical Association, 303(3), 235–241.Find this resource:

                                                                                                      Flegal, K. Carroll, M., Ogden, C., & Johnson, C. (2002). Prevalence, & trends in obesity among US adults, 1999–2000. Journal of the American Medical Association, 288(14), 1723–1727.Find this resource:

                                                                                                        Fogel, R. (1993). New sources and new techniques for the study of secular trends in nutritional status, health, mortality, and the process of aging. Historical Methods, 26(1), 5–38.Find this resource:

                                                                                                          Fogel, R. (1994). Economic growth, population theory, and physiology: The bearing of long-term processes on the making of economic policy. American Economic Review, 84(3), 369–395.Find this resource:

                                                                                                            Fogel, R. (2004). The escape from hunger and premature death, 1700–2000. Cambridge: Cambridge University Press.Find this resource:

                                                                                                              (p. 149) Fogel, R., & Costa, D. (1997). A theory of technophysio evolution, with some implications for forecasting population, health care costs, and pension costs. Demography, 34(1), 49–66.Find this resource:

                                                                                                                Goodman, E. Hinden, B. P., & Khandelwal, S. (2000). Accuracy of teen and parental reports of obesity and body mass index. Pediatrics, 106, 52–58.Find this resource:

                                                                                                                  Hedley, A., Ogden, C., Johnson, C., Carroll, M., Curtin, L., & Flegal, K. (2004). Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. Journal of the American Medical Association, 291(23), 2847–2850.Find this resource:

                                                                                                                    Henderson, R. M. (2005). The bigger the healthier: Are the limits of BMI risk changing over time? Economics & Human Biology, 3, 339–366.Find this resource:

                                                                                                                      Herbert, P., Richards-Edwards, J., Manson, J. A., Ridker, P., Cook, N., O’Conner, G., … Hennekens, C. (1993). Height and incidence of cardiovascular disease in male physicians. Circulation, 88, 1437–1443.Find this resource:

                                                                                                                        Hilliard, S. B. (1972). Hog, meat and hoecake: Food supply in the Old South, 1840–1860. Carbondale: Southern Illinois University Press.Find this resource:

                                                                                                                          Hines, C. (2011). The demography of obesity. In J. Cawley (Ed.), The Oxford handbook of the social science of obesity (pp. 35–47). Oxford: Oxford University Press.Find this resource:

                                                                                                                            Jee, H., Jee, J. W., Sull, P. J., Lee, S. Y., Ohrr, H., Guallar, E., & Samet, J. (2006). Body-mass index, & mortality in Korean men and women. New England Journal of Medicine, 355, 779–787.Find this resource:

                                                                                                                              Joffe, B., & Zimmet, P. (1998). The thrifty genotype in the type 2 diabetes: An unfinished symphony moving to its finale? Endocrine, 9(2), 139–141.Find this resource:

                                                                                                                                Koch, D. (2011). Waaler revisited: The anthropometrics of mortality. Economics and Human Biology, 9(1), 106–117.Find this resource:

                                                                                                                                  Komlos, J. (1987). The Height and Weight of West Point Cadets: Dietary Change in Antebellum America. Journal of Economic History, 47, 897–927.Find this resource:

                                                                                                                                    Komlos, J., & Brabec, M. (2010). The trend of mean BMI values of US adults, birth cohorts 1882–1986 indicates that the obesity epidemic began earlier than hitherto thought. American Journal of Human Biology, 22, 631–638.Find this resource:

                                                                                                                                      Komlos, J., & Brabec, M. (2011). The trend of BMI values of US adult by deciles, birth cohorts 1882–1986 stratified by gender and ethnicity. Economics and Human Biology, 9(3), 234–250.Find this resource:

                                                                                                                                        Kuczmarski, M. F., Kuczmarski, R. S., & Naijar, M. (2001). Effects of age on validity of self-reported height, weight, and body mass index: Findings from the third Health and Nutrition Examination Survey, 1988–1994. Journal of the American Dietetic Association, 101, 28–34.Find this resource:

                                                                                                                                          Lakdawalla, D., & Philipson, T. (2002). The growth of obesity and technological change: A theoretical and empirical examination (NBER Working Paper 8946). Cambridge, MA: National Bureau of Economic Research.Find this resource:

                                                                                                                                            Lakdawalla, D., & Philipson, T. (2009). The growth of obesity. Economics and Human Biology, 7(3), 283–293.Find this resource:

                                                                                                                                              Laudabaum, U., Mannalithera, A., Meyer, P., & Singh, G. (2014). Obesity, abdominal obesity, physical activity, and caloric intake in U.S. adults: 1988–2010. American Journal of Medicine, 127(8), 717–727.Find this resource:

                                                                                                                                                Lieberman, L. S. (2000). Obesity. In K. Kiple & K. Coneè Ornelas (Eds.), The Cambridge world history of food (pp. 1062–1077). Cambridge: Cambridge University Press.Find this resource:

                                                                                                                                                  Mokdad, A., Bowman, B., Ford, E., Vinicor, F., Marks, J., & Koplan, J. (2001). The continuing epidemics of obesity and diabetes in the United States. Journal of the American Medical Association, 186, 1195–1200.Find this resource:

                                                                                                                                                    (p. 150) Mokdad, A., Serdula, M., Dietz, W., Bowman, B., Marks, J., & Koplan, J. (1999). The spread of the obesity in the United States, 1991–1998. Journal of the American Medical Association, 282(16), 1519–1523.Find this resource:

                                                                                                                                                      Murray, J. (1997). Standards of the present for people of the past: Height, weight, and mortality among men of Amherst College, 1834–1949. Journal of Economic History, 57(3), 585–606.Find this resource:

                                                                                                                                                        Needham, B., & Crosnoe, R. (2005). Overweight status and depressive symptoms during adolescence. Journal of Adolescent Health, 36, 48–55.Find this resource:

                                                                                                                                                          Neel, J. (1962). Diabetes mellitus: A “thrifty” genotype rendered detrimental by “progress”? American Journal of American Genetics, 14(4), 353–362.Find this resource:

                                                                                                                                                            Newman, E., O’Connor, D., & Conner, M. (2007). Daily hassles and eating behavior: The role of cortisol reactivity status. Psychoneuroendocrinology, 32, 125–132.Find this resource:

                                                                                                                                                              Nielsen, S. J., & Popkin, B. (2004). Changes in beverage intake between 1977 and 2001. American Journal of Preventative Medicine, 27(3), 205–2010.Find this resource:

                                                                                                                                                                Offer, A., Pechey, R., & Ulijaszek, S. (2012). Insecurity, inequality, & obesity in affluent societies. Oxford: Oxford University Press.Find this resource:

                                                                                                                                                                  Ogden, C., Carroll, M., Curtin, L., Lamb, M., & Flegal K. (2010). Prevalence of high body mass index in US children and adolescents, 2007–2008. Journal of the American Medical Association, 303(3), 242–249.Find this resource:

                                                                                                                                                                    Ogden, C., Carroll, M., Curtin, L., McDowell, M., Tabak, C., & Flegal, K. (2006). Prevalence of overweight and obesity in the United States, 1999–2004. Journal of the American Medical Association, 295(13), 1549–1555.Find this resource:

                                                                                                                                                                      Ogden, C., Carroll, M., Kit, B., & Flegal, K. (2012). Prevalence of obesity and trends in body mass index among US children and adolescents, 1999–2010. Journal of the American Medical Association, 307(5), 483–490.Find this resource:

                                                                                                                                                                        Osmond, C., & Barker, D. (2000). Fetal infant and childhood growth are predictors of coronary heart disease, diabetes, and hypertension in adult men and women. Environmental Health Perspective, 108(3), 545–553.Find this resource:

                                                                                                                                                                          Ost, A., Lempradl, A., Casas, E., Weigert, M., Timko, T., Deniz, M., … Pospisilik, A. (2014). Paternal diet defines offspring chromatin state and intergenerational obesity. Cell, 159(6), 1352–1364.Find this resource:

                                                                                                                                                                            Ozanne, S. (2015). Epigenetic signatures of obesity. New England Journal of Medicine, 372(1), 973–974.Find this resource:

                                                                                                                                                                              Pawlowski, B., & Dunbar, R. I. M. (2005). Waist: Hip ratio vs. BMI as predictors of fitness in women. Human Nature, 16, 50–63.Find this resource:

                                                                                                                                                                                Pond, C. M. (1977). The significance of lactation in the evolution of mammals. Evolution, 31, 177–199.Find this resource:

                                                                                                                                                                                  Popkin, B. (1993). Nutritional patterns and transitions. Population Development and Review, 19, 138–157.Find this resource:

                                                                                                                                                                                    Popkin, B. (2009). The world is fat: The fads, trends, policies, & products that are fattening the human race. New York: Avery Books.Find this resource:

                                                                                                                                                                                      Prentice, A., Hennig, B. J., & Fulford, A. J. (2008). Evolutionary origins of the obesity epidemic: Natural selection of thrifty genes or genetic drift following predation release? International Journal of Obesity, 32, 1607–1610.Find this resource:

                                                                                                                                                                                        Prentice, A., Rayco-Solon, P., & Moore, S. (2005). Insights from the developing world: Thrifty genotypes and thrifty phenotypes. Proceedings of the Nutrition Society, 64, 153–161.Find this resource:

                                                                                                                                                                                          Ransom, R., & Sutch, R. (1977). One kind of freedom: The economic consequences of emancipation. Cambridge: Cambridge University Press.Find this resource:

                                                                                                                                                                                            (p. 151) Riera-Crichton, D., & Tefft, N. (2014). Macronutrients and obesity: Revisiting the calories in, calories out framework. Economics and Human Biology, 14, 33–49.Find this resource:

                                                                                                                                                                                              Riley, J. C. (1994). Height, nutrition, and mortality risk reconsidered. Journal of Interdisciplinary History, 24(3), 465–492.Find this resource:

                                                                                                                                                                                                Seidell, J. C. (1998). Dietary fat and obesity: An epidemiological perspective. American Journal of Clinical Nutrition, 67, 546S–550S.Find this resource:

                                                                                                                                                                                                  Sharma, S., Zaric, G., Campbell, K., & Gilliland, J. (2014). The effect of physical activity on obesity: Evidence from the Canadian NPHS panel. Economics and Human Biology, 14(1), 1–21.Find this resource:

                                                                                                                                                                                                    Sills, T., & Crawley, J. (1996). Individual difference in sugar consumption predict amphetamine-induced dopamine overflow in nucleus accumbens. European Journal of Pharmacology, 303, 177–181.Find this resource:

                                                                                                                                                                                                      Silventoinen, K., Kaprio, J., Lahelma, E., & Viken, R. (2003). Assortative mating in body height, & BMI: Finnish twins, & their spouses. American Journal of Human Biology, 15, 620–627.Find this resource:

                                                                                                                                                                                                        Sobal, J. (2011). The sociology of obesity. In John Cawley (Ed.), The Oxford handbook of the social science of obesity (pp. 105–119). Oxford: Oxford University Press.Find this resource:

                                                                                                                                                                                                          Sorkin, J., Muller, D., & Andres, R. (1999). Longitudinal change in the heights of men and women: Consequential effects on body mass index. Epidemiologic Reviews, 21(2), 247–260.Find this resource:

                                                                                                                                                                                                            Speakman, J. (2006). Thrifty genes for obesity and the metabolic syndrome—time to call off the search? Diabetes & Vascular Disease Research, 3(1), 7–11.Find this resource:

                                                                                                                                                                                                              Speakman, J. (2008). Thrifty genes for obesity, an attractive but flawed idea, and an alternative perspective: The ‘drifty gene’ hypothesis. International Journal of Obesity, 32, 1611–1617.Find this resource:

                                                                                                                                                                                                                Steckel, R. (1979). Slave height profiles from coastwise manifests. Explorations in Economic History, 16, 363–380.Find this resource:

                                                                                                                                                                                                                  Stevens, J., Cai, J., Pamuk, E., Williamson, D., Thun, M., & Wood, J. (1998). The effect of age on the association between body mass index and mortality. New England Journal of Medicine, 338(1), 1–7.Find this resource:

                                                                                                                                                                                                                    Strauss, R. (1999). Comparison of measures and self-reported weight and height in a cross sectional sample of young adolescents. International Journal of Obesity, 23, 904–908.Find this resource:

                                                                                                                                                                                                                      Taubes, G. (2010). Why we get fat: And what to do about it. New York: Anchor Books.Find this resource:

                                                                                                                                                                                                                        Taubes, G. (2012). World view: Treat obesity as physiology, not physics. Nature, 492, 155.Find this resource:

                                                                                                                                                                                                                          Teff, K., Elliott, S., Tschöp, M., Kieffer, T., Rader, D., Heiman, M., … Havel, P. (2004). Dietary fructose reduces circulating insulin and leptin, attenuates postprandial suppression of ghrelin, and increases triglycerides in women. Journal of Clinical Endocrinology & Metabolism, 89, 2963–2972.Find this resource:

                                                                                                                                                                                                                            Tovée M. J., Maisey D. S., Emery, J. L., & Cornelissen, P. L. (1999). Visual cues to female attractiveness. Proceedings of the Royal Society, 266B, 211–218.Find this resource:

                                                                                                                                                                                                                              Van Dam, R. M., & Seidell, J. C. (2007). Carbohydrate intake and obesity. European Journal of Clinical Nutrition, 61, S75–S99.Find this resource:

                                                                                                                                                                                                                                Waaler, H. (1984). Height, weight, and mortality: The Norwegian experience. Acta Medica Scandinavica, 215(679), 1–56.Find this resource:

                                                                                                                                                                                                                                  Wang, C., Orleans, T., & Gortmaker, S. (2012). Reaching the Healthy People goals for reducing childhood obesity. American Journal of Preventive Medicine, 42, 437–444.Find this resource:

                                                                                                                                                                                                                                    Wells, J. (2003). The thrifty phenotype hypothesis: Thrifty offspring or thrifty mother? Journal of Theoretical Biology, 221(1), 143–161.Find this resource:

                                                                                                                                                                                                                                      Notes:

                                                                                                                                                                                                                                      (1.) BMIs are generally high for well-nourished populations relative to work effort expended and low for poorly nourished populations.

                                                                                                                                                                                                                                      (2.) Gestational diabetes occurs when a pregnant women who has never had diabetic symptoms produces high blood sugar levels during pregnancy; this occurs when insulin receptors do not function, likely due to pregnancy-related factors.

                                                                                                                                                                                                                                      (3.) However, the hormone–market stress hypothesis may not be the primary explanation for obesity because, during their economic development, British, French, and US populations faced stress from droughts and disease, but there is little evidence that these populations were obese (Carson, 2009, 2012a).

                                                                                                                                                                                                                                      (4.) Using the 1977 Standard Occupational Classification system. Five broad occupational categories are considered here: white-collar, skilled, farmers, unskilled, and without a listed occupation. Managers, professionals, and sales representatives are classified as white-collar workers. Clerks, craftsmen, and operatives are classified as skilled workers. Farm laborers and farmers are classified as farmers. Household laborers, general laborers, service workers, and transportation workers are classified as unskilled workers. Those with no occupations and not in the labor force are classified as workers with no occupations.

                                                                                                                                                                                                                                      (5.) BMI = w(kg)(h(m))2 = wh2lnBMI = lnw  2lnh.        %ΔBMI%Δw = 1 and %ΔBMI%Δh = 2