Social effect of obesity

The High Cost of Excess Weight

Apart from tobacco, there is perhaps no greater harm to the collective health in the U.S. than obesity. Worldwide, too, obesity’s health effects are deep and vast-and they have a real and lasting impact on communities, on nations, and most importantly, on individuals, today and across future generations

In the U.S., among adults under the age of 70, obesity is second only to tobacco in the number of deaths it causes each year. (1) As tobacco use continues to decline, and obesity rates continue to rise, the number of deaths due to obesity may soon exceed that of tobacco.

Like tobacco, obesity causes or is closely linked with a large number of health conditions, including heart disease, stroke, diabetes, high blood pressure, unhealthy cholesterol, asthma, sleep apnea, gallstones, kidney stones, infertility, and as many as 11 types of cancers, including leukemia, breast, and colon cancer. No less real are the social and emotional effects of obesity, including discrimination, lower wages, lower quality of life and a likely susceptibility to depression.

Read more: health risks and why being overweight does not decrease mortality.

It is a broad swath of harms that has a huge societal effect—on the economy, national productivity, and even national defense. The health care costs of obesity in the U.S. were estimated to be as high as $190 billion in 2005, (2) a number that is double earlier estimates, and that is expected to rise, along with obesity rates, over the coming decades. This includes money spent directly on medical care and prescription drugs related to obesity. But obesity has other costs associated with it, too, among them, the cost of lost days of work, higher employer insurance premiums, and lower wages and incomes linked to obesity-related illnesses. Countries with lower obesity rates than the U.S. spend a smaller share of their healthcare dollars on obesity, but the burden is still sizable. Perhaps one of the most surprising consequences of the current obesity epidemic in the U.S. is its impact on recruitment for the armed services, with data showing that close to 30 percent of young people in the U.S. are now too heavy to qualify for military service. (3)

Read more: economic costs

Taken together, it’s clear that obesity is a global crisis that already touches everyone in one manner or another. And this realization should be a call to action, because there is good news amidst the bad: Obesity is preventable. We can reverse the trends that led to the current epidemic by making changes in public policies and practices, so that healthy food and activity choices are easy choices, for all.

1. Danaei G, Ding EL, Mozaffarian D, et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009; 6:e1000058.

2. Cawley J, Meyerhoefer C. The medical care costs of obesity: an instrumental variables approach. J Health Econ. 2012; 31:219-30.

3. Mission: Readiness. Too Fat to Fight. Washington, DC: Mission: Readiness; 2010.

Nutrition, Physical Activity, and Obesity

Nutrition, Physical Activity, and Obesity Across the Life Stages

Good nutrition, regular physical activity, and achieving and maintaining a healthy body weight are cornerstones of health at every stage of life:

Children

  • Children and adolescents who eat a healthful diet are more likely to reach and maintain a healthy weight, achieve normal growth and development, and have strong immune systems.
  • Children and adolescents who get regular physical activity have improved muscle development, bone health, and heart health.
  • Children and adolescents who are overweight or obese are at increased risk for developing diabetes and heart disease; they are also likely to stay overweight or obese into adulthood, placing them at increased risk for serious chronic diseases.

Adults

  • Adults who eat a healthful diet and stay physically active can decrease their risk of a number of adult-onset health conditions and diseases, including heart disease and diabetes.
  • Regular physical activity can lower an adult’s risk of depression.
  • Adults who maintain a healthy weight are less likely to die prematurely.

Pregnant Women

  • Good nutrition helps pregnant women support the healthy development of their infants.
  • Regular physical activity throughout pregnancy can help women control their weight, make labor more comfortable, and reduce their risk of postpartum depression.3
  • Staying at a healthy body weight can help women reduce their risk of complications during pregnancy.

Determinants of Nutrition, Physical Activity, and Obesity

A number of factors affect a person’s ability to eat a healthful diet, stay physically active, and achieve or maintain a healthy weight. The built environment has a critical impact on behaviors that influence health. For example, in many communities, there is nowhere to buy fresh fruit and vegetables, and no safe or appealing place to play or be active. These environmental factors are compounded by social and individual factors—gender, age, race and ethnicity, education level, socioeconomic status, and disability status—that influence nutrition, physical activity, and obesity. Addressing these factors is critically important to improving the nutrition and activity levels of all Americans; only then will progress be made against the Nation’s obesity epidemic and its cascading impact on health.

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Social Factors in Childhood and Adulthood Associated with Adult Obesity in African American and White Women

Abstract

Background. Few studies have examined how individual and neighborhood poverty in childhood and adulthood influence the likelihood of adult obesity. We used a longitudinal cohort to examine these associations. Methods. Our cohort consisted of children born in Baltimore, MD, USA with followup as adults from ages 27 to 33. We used logistic regression to examine the multivariate association between individual and neighborhood poverty in childhood and adulthood and adult obesity, (body mass index ≥ 3 0 ), based on self-reported height and weight. Results. Of the 986 female respondents, 82% were African American and 18% were White. Both groups had similar rates of adulthood obesity (African American 25% versus Whites 26% , 𝑃 = 0 . 9 1 ), and similar rates of poverty as children and adults. There was no statistically significant association between individual or neighborhood poverty during childhood and the likelihood of adult obesity. Adults at risk for overweight or overweight as children had significantly greater odds of adult obesity (OR 2.8 and 12.1, resp.). Conclusion. In this sample of women with high rates of childhood and adulthood poverty, obesity rates were high. Childhood risk for overweight and overweight were strongly associated with adult obesity. Individual and neighborhood poverty in childhood were not independently associated with adulthood obesity.

1. Background

Over two-thirds (67%) of US adults are obese or overweight. The immediate cause of obesity is sustained positive energy balance, where energy intake exceeds energy expenditure. However, a variety of individual and social factors also contribute to obesity. At the individual level, gender, race, and socioeconomic status are important predictors of obesity. For example, women are more susceptible to weight gain . African American, native American, and Hispanic women have a higher prevalence of obesity than their White counterparts . In addition, White women show a consistent inverse relationship between weight and socioeconomic status, while for African American women, this relationship is inconsistent or absent .

Risks of obesity also vary at the neighborhood level, and poor neighborhoods bear the disproportionate burden. Due to ongoing residential segregation, poor and minority individuals may be clustered in “obesegenic” neighborhoods that promote and sustain obesity. Neighborhood can influence the likelihood of obesity by influencing behavioral norms, access to food, and opportunities for physical activity . Women may get a larger “dose” of neighborhood since women spend more time at home and within the neighborhood due to gender differences in workforce participation and leisure activities . Children growing up in poor families or poor neighborhoods may also be at an increased risk for obesity.

There is a growing body of literature emphasizing the adult health risks of childhood overweight. Children who are overweight are at an increased risk for obesity and its associated conditions such as hypertension, hyperlipidemia, and diabetes . Research has not focused on how childhood overweight leads to adult obesity. In poor neighborhoods, children are exposed to more fast food, calorie-dense food options, and fresh fruits and vegetables that are difficult to obtain or afford . Safety concerns and lack of green space limit opportunities for outdoor play. Childhood experiences influence adult behaviors. As a result, children in neighborhoods that do not encourage physical activity and healthy eating may be at increased risk for overweight in both childhood and adulthood. To examine how childhood social context affects adult obesity, one must disentangle both the effects of childhood and adulthood poverty, and individual versus neighborhood poverty. To date, only a few studies have examined how the neighborhood or family poverty in childhood may be associated with adulthood obesity .

We used a unique longitudinal cohort of inner city women and their children in order to determine how socioeconomic factors across the lifespan contribute to adult obesity. Our three aims were (1) to quantify the association between individual and neighborhood poverty and obesity in women; (2) to examine whether individual and neighborhood poverty in childhood influenced the likelihood of adult obesity; (3) to characterize whether these relationships varied between African American and White women.

2. Methods

2.1. Study Population

We used a subset of a longitudinal study, the Johns Hopkins Perinatal Collaborative Study (PCS), and the Pathways to Adulthood (PTA) followup. These studies followed three generations of families initially living in inner city Baltimore. The Perinatal Collaborative Study enrolled 2307 inner-city women (referred to throughout as first-generation mothers ) who were selected at the time of their first prenatal visit to a public obstetric clinic at Johns Hopkins Hospital between 1959 and 1965 . The women lived within a 10-block radius of the hospital. The study later collected data on the 2694 children (referred to throughout as second generation ) born between 1960 and 1965 to the first-generation mothers. These children were initially studied prospectively with data gathered between birth and age 8 regarding their development, health, and socioeconomic characteristics .

From 1992 to 1994, the Pathways to Adulthood Study (PTA) collected additional information from 1758 G-2s (then aged 27 to 33) about their lives from age 9 to present. Followup data included information on education, employment, family composition, health, health care usage, and income . In addition, census data was used for information regarding the neighborhood characteristics of each G-2 at birth, at 11-12 years of age, at 16-17 years of age, and at age of followup (ages 27–33). For the Pathways to Adulthood Study, 75% of G-1s and 67% of G-2s responded between 1992 and 1994. For a full description of sample population and methods, see Hardy et al. .

Our final sample included the 986 female G-2s (75%) who provided information (in person or via telephone) for the Pathways to Adulthood Study, had information on self-reported height and weight, and could be linked to the Perinatal Collaborative Study for data on their childhood clinical and sociodemographic characteristics. We excluded women who were pregnant at the time of the interview ( 𝑛 = 3 4 ), and women whose race by self-report was neither African American nor White ( 𝑛 = 1 ). We obtained the data from the Inter-University Consortium for Political and Social Research. This study was approved by the University of Chicago Medical Center Institutional Review Board (IRB). The study was exempt from full IRB review because the data was preexisting and deidentified.

2.2. Outcome Variable

Obesity was our outcome variable which we defined as a body mass index (BMI) greater than or equal to 30. Information on height (feet and inches) and weight (lbs) was obtained by self-report. First, we calculated BMI as a continuous linear outcome variable according to the formula: (weight (lbs)/height (inches)2) × 703. Then we defined our categorical outcome variable—nonobese versus obese—using cutoffs consistent with those used by the Centers for Disease Control, the World Health Organization, and the National Institutes of Health .

2.3. Covariates
2.3.1. Measures in Adulthood

Demographic and Health-Related Characterisitics
We adjusted for individual characteristics including demographic (age, race, number of children, age at birth of first child, and marital status) and health related (current smoking status and self-reported health) characteristics.

Socioeconomic Status
As an indicator of socioeconomic status (SES), we used years of education and homeownership. Since little consensus exists in the literature regarding how best to measure SES, we also examined education, income, and assets as potential measures of SES. We treated years of education as a continuous variable and also coded as a binary variable for college graduate or not. Although we had two measures for income, self-reported total household income and personal income, the high rate of missing values precluded their use. We had six measures of assets. We treated assets as a continuous variable ranging from 0 to 6, with one point given for a “yes” response when asked about each of six assets (current personal checking account; current IRA or pension; own house or condo; car, truck, or motorcycle ownership; credit or charge account; current savings account). Homeownership was the most robust asset measure with the largest effect in both magnitude and statistical significance. Therefore, we used homeownership as our additional SES measure.

Neighborhood Characteristics
We derived G-2s adult neighborhood characteristics from the 1990 census data. The PTA data link each respondent’s address at the time of the interview to the appropriate census tract. For measures of neighborhood SES, we examined median household income for census tract as a continuous variable and also categorized neighborhoods based on percent of respondents below federal poverty level (nonpoor and poor ). We based our categories of percent poverty on previous literature and the Census Bureau definition of poverty areas as those in which at least 20% of the population lives below the federal poverty line . Neighborhood racial composition was based on percent of African American residents. We created a binary variable for a predominately African American neighborhood or not . There is no convention for categorizing neighborhoods based on racial composition; the proportion of residents who are African Americans across neighborhoods is highly skewed .

2.3.2. Measures in Childhood

Demographic and Health-Related Characteristics
We used childhood BMI at age 7 as a covariate. BMI was categorized as underweight, normal weight, at-risk for overweight and overweight according to CDC BMI-for-age percentile formulas . We also evaluated whether the G-1 mother was married at G-2s birth, and whether G-2s parents were married when the G-2 was 8.

Socioeconomic Status
Individual childhood poverty status was characterized by a binary variable to indicate whether the individual was ever poor in childhood. In addition, we used parental homeownership as a proxy for assets.

Neighborhood Characteristics
Childhood neighborhood characteristics were based on G-2 report at age 8 and were derived from 1970 census data by the PTA investigators. Categories for neighborhood racial composition and neighborhood poverty were consistent with those mentioned above. We based neighborhood racial composition on percentage of African American residents. There was a binary variable for African American neighborhood or not . We also categorized neighborhoods based on percent of respondents below federal poverty level (nonpoor and poor ).

2.4. Analyses

We conducted univariate and multivariate analysis for our outcome variables and our covariates of interest. For univariate analyses, we compared baseline characteristics for African American and White women in childhood and adulthood. Then we conducted a bivariate analysis to compare characteristics of African American and White women by obesity status. We used cross tabulations to compare all categorical variables by race and obesity status. We used chi-square statistics as the corresponding measure of heterogeneity. For continuous variables, we determined summary measures (mean and standard deviation) for each subgroup. We used analysis of variance (ANOVA) to compare mean values across subgroups.

We examined the multivariate associations between obesity status and the covariates of interest using logistic regression. During this process, we considered those in which we had substantive a priori interest based on prior literature. In the final model, we included the covariates that were at least of borderline statistical significance during forward stepwise selection ( 𝑃 < 0 . 1 5 ). In a stepwise fashion, we conducted multiple analyses to determine the relative effects. To examine childhood factors related to adult obesity, we estimated childhood individual effect versus neighborhood effect on adulthood overweight. To examine adult factors, we estimated childhood neighborhood effect versus adult neighborhood effect. Finally, we examine adulthood neighborhood effect versus individual effect on adult obesity. For these analyses, we controlled for marital status, age at first child, and number of children. To account for the multilevel nature of the data, respondents within families within census tracts, we clustered on census tract. All analyses were done using Intercooled Stata (version 11.0; Stata Corporation, College Station, TX).

3. Results

3.1. Baseline Characteristics

A summary of baseline sociodemographic, health, and neighborhood characteristics of the cohort is presented in Table 1. The G-2 female sample was 82% African American and 18% White. As adults, the women had similarly high rates of obesity (26% and 25% resp.). There was no statistically significant difference between the African Americans and Whites in the proportion overweight (36% versus 31%, 𝑃 = 0 . 2 8 ) or obese (25% versus 25%, 𝑃 = 0 . 9 0 8 ) or mean BMI (26.6 versus 26.1, 𝑃 = 0 . 4 2 2 ).

Table 1 Comparison of participant characteristics by race.

Although household income was not significantly different between African American and White women ($31,825 versus $35,164, 𝑃 = 0 . 1 9 ), they differed on many other socioeconomic indicators. Compared to their White counterparts, African American women had more years of education (12.8 versus 10.9 for Whites), were more likely to have completed college (15% versus 3%), and had higher personal income (all 𝑃 < 0 . 0 0 1 ). African American women had a lower mean number of assets than their White counterparts (2.5 versus 3.2, 𝑃 < 0 . 0 0 1 ). African American women were also significantly less likely to be married (27% versus 59%), which may, in part, account for the differences in both assets and household income. African American and White women did not differ on the number of children or age at first child.

African American and White women also substantially differed by current neighborhood characteristics (Table 1). African American women lived in neighborhoods with an average household income that was significantly lower than Whites ($25,323 versus $33,352). The neighborhoods in which the African American study participants lived had a significantly higher proportion of African Americans than their White counterparts ( 𝑃 < 0 . 0 0 1 ).

Both African American and White women experienced similarly high rates of childhood poverty (32% and 33% resp., 𝑃 = 0 . 8 5 ). African American women were more likely to have been born to a single mother, to have parents separate in early childhood, and to have lived in a poor neighborhood as a child (all 𝑃 < 0 . 0 0 1 ).

3.2. Bivariate Analysis for Obesity

Respondents who were obese were similar to their nonobese counterparts in racial makeup, neighborhood characteristics, and family composition (Table 2). There was no significant difference in age at first child, marital status, or number of children. They differed on two SES measures, personal income and assets. Overall, obese women were more likely to be overweight as a child and to live in neighborhoods with lower median household income. Obese White women were significantly more likely than their normal weight counterparts to live in a poor neighborhood ( 𝑃 = 0 . 0 0 2 ), while that relationship was not found for African American women.

Table 2 Comparison of participant characteristics by race and obesity status.

3.3. Multivariate Models
3.3.1. Childhood Factors Related to Obesity in Adulthood

Table 3 examines childhood individual and neighborhood characteristics associated with adult obesity. In adjusted analysis, childhood overweight and at risk for overweight were associated with an increased likelihood of obesity as an adult. Children who were at risk for overweight at age 7 were significantly more likely to be obese as adults (OR 2.8, 95% CI 1.5–5.2). Similarly, children who were overweight at age 7 have significantly greater odds of being obese as adults (OR 12.1, 95% CI 4.9–30.0). Growing up in a poor or predominately African American neighborhood was not associated with an increased risk for adult obesity. Maternal characteristics such as homeownership and marital status were not associated with adult weight.

Table 3 Simple and multiple logistic regression.

3.3.2. Adult Factors Related to Obesity in Adulthood

Table 3 examines individual versus neighborhood characteristics associated with adult obesity. Homeownership is associated with a 57% decreased likelihood of obesity (OR 0.43, 95% CI 0.21–0.87). Number of children and age at first child were not associated with adult obesity. However, being married was associated with an increased risk for obesity (OR 2.2, 95% CI 1.3–3.6). Current residence in a poor or predominately African American neighborhood was not associated with adult obesity.

3.3.3. African Americans Racial Differences in Factors Related to Obesity

and Whites did not differ in the characteristics associated with weight status. Multiple tests for the interaction terms for race were not significant (analysis not shown).

4. Discussion

In this longitudinal cohort of African American and White women from Baltimore, MD, we sought to quantify the association between adult obesity and individual and neighborhood socioeconomic status in both childhood and adulthood, and to determine whether these relationships varied by race.

We found that rates of obesity were high and similar between the African American and White women. Several things could potentially explain this finding. One possibility is that the number of White respondents was too small to detect a difference if it exists. However, an additional reason is that there is little racial difference in characteristics associated with adulthood obesity due to socioeconomic homogeneity in our cohort. Studies that show differences in obesity rates between African Americans and White women may be unable to fully control for differences in socioeconomic status.

We also found a strong and consistent relationship between childhood overweight at age 7 and adult obesity. Our work supports the importance of identifying and managing overweight in childhood to prevent life-long morbidity. Children who are overweight are at an increased risk for obesity and its associated conditions such as hypertension, hyperlipidemia, and diabetes . The association between childhood overweight and adult obesity increases with age. Adult obesity is strongly associated with adolescent overweight, while overweight infants and toddlers have a lower risk . Our research was consistent with previous literature that showed weight status at early school age (ages 6–9) is predictive of adult weight status .

Married women were more likely to be obese than their unmarried counterparts even after controlling for assets and parity. The effects of marriage on obesity did not significantly vary by race. The literature on the effect in marital status and BMI has been mixed. Some studies show married women have increased BMI, while others show mixed or no relationship . Marital status can affect weight through both direct and indirect pathways. Being married can increase one’s likelihood of being overweight through decreased leisure and increased prompts for eating . However, marriage also increases SES by increasing assets and household income, which in turn decreases odds of overweight.

One strength of the study is the longitudinal, multilevel nature of the data. Many studies that attempt to measure childhood effects on adulthood health outcomes rely on retrospective data which is subject to recall bias. Our measures were obtained prospectively. Another strength of this study is its use of census data for aggregate neighborhood measures in childhood and adulthood. Studies that aggregate respondents’ characteristics to determine community SES may be subject to atomistic fallacy because the study population may not be a representative sample of the population .

The pathways to adulthood data had a limited geographic focus, children born in the Johns Hopkins Hospital catchment area. This group had a higher proportion of African Americans, higher poverty rates, and higher obesity rates than the US as a whole for that time period . Within our study population, African American and White women had similar rates of childhood and adulthood poverty. While this homogeneity limits study generalizability, our findings suggest that racial differences in obesity may be due, in part, to differences in socioeconomic status or neighborhood environment. Even within this group where African Americans and Whites had similar rates of poverty in childhood and adulthood, they still differed on markers of SES. African American women had higher education, but lower household income and assets than their White counterparts. Incomplete accounting for SES may explain some residual differences in health outcomes of African Americans and Whites with the similar education or income. In our study population, African American women, regardless of income, were more likely to live in lower income neighborhoods and neighborhoods that were predominately African American. This finding is supported by social science literature that finds that racial segregation still constrains neighborhood choices among African Americans of all income levels .

Our study has important limitations. The primary outcome variable, BMI, was calculated through self-reports of height and weight. Several studies have found underreporting of weight, particularly among women and all those of higher weights . However, the distribution of obesity was relatively equal among the sample, which would likely lead to nondifferential misclassification. A larger concern is differential misreport of weight by racial subgroup, although prior work suggests that misreporting is similar across different racial groups . A second limitation is that we were unable to exclude women in the postpartum period. The proportion of postpartum women as well as their pregnancy weight gain and weight loss could have varied between the two groups. A third limitation is the age of the data. All data used to make inferences, for example, federal poverty level and national obesity rates, correspond to rates from that time, so the results are internally consistent. However, the data were collected over 17 years ago which limits its current generalizability. An additional limitation, also faced by other researchers studying neighborhood, was characterizing the neighborhood environment solely though some proxy measures based on administrative data. Each neighborhood was characterized largely by the median household income taken from census data. Despite the wealth of census information, other variables that may be more directly associated with obesity were absent . Asset mapping may allow a fuller characterization of neighborhood and a deeper understanding of what factors confer health risks.

In this sample of women with high rates of childhood and adulthood poverty, obesity rates were high. Although living in a poor neighborhood was not an independent risk factor for obesity, poor neighborhoods in our sample had higher rates of adult obesity. Childhood at risk for overweight and overweight was strongly associated with adult obesity. Being married was also associated with obesity. Efforts to combat obesity should be focused not only on individual patients, but also within at-risk and affected families and communities.

Conflict of Interests

None of the authors of this paper has any conflict of interests to disclose related to employment, consultancies, honoraria, stock, expert testimony, patents, royalties, or any other relationships related to this project.

Acknowledgments

Dr. M. R. Saunders gratefully acknowledges funding support from the NIH Health Disparities Loan Repayment Program. Dr. M. R. Saunders had full access to all of the study data and takes responsibility for the integrity of the data and accuracy of the data analysis. The data was obtained through the Inter-university Consortium for Political and Social Research (ICPSR). No sponsor had any role in the design and conduct of the study: collection, management, analysis, and interpretation of the data: or preparation, review, and approval of the paper. An abstract of this paper was presented at the American Public Health Association Annual Conference in Denver, CO, November 2010.

The Stigma of Obesity: Humiliation is Hard to Shed

The beginning of the new school year is just around the corner. As a child, this was my least favorite time of the year. A summer filled with swimming pools and family outings and friendships would be replaced with homework and teachers and classmates. School is not the most nurturing of places when you’re a fat kid. Worst of all was the weigh-in. Every single September there would be one day when teacher would form two lines: one for boys and one for girls, and we would fall into our places alphabetically by last name. On that day every September I wished that either I had missed school or that my last name began with “Z.” I envied my (fat) best friend Susan, whose last name began with “W.” My last name began with “F” putting me near the front of the line.

One by one we would step onto the scale as the school nurse (in grammar school) or the gym teacher (in middle school and high school) called out our weights to be recorded. Once your weight was recorded you left the room. All of the girls standing in line behind me would hear my weight. That is why I envied Susan on this day. Lucky Susan whose last name began with “W” put her at the very end of the line. In the 6th grade one of those nasty little girls told the boys my weight. I remember the way it happened: We were engrossed in our reading assignments and so the classroom was utterly silent. It was the kind of stillness where one might believe you actually could hear a pin drop. Then with the roar of a thunder clap that jars you from sleep, my classmate Mike’s booming voice breaks the peace and quiet. He said only two words, meant for me, “142 pounds!!”

I hated the 6th grade.

The Consequences of Morbid Obesity

When considering the negative effects of morbid obesity a person might be quick to point out (and accurately so) the physical problems obese people frequently have. Certainly health issues are a concern as the risk for heart disease, diabetes, and high blood pressure are among the illnesses the morbidly obese are at greater risk for. The medical expenditures to address the health issues that generally accompany obesity are equally troublesome and undesirable.

But these are not the only problems that morbidly obese people face.

Obese people are often treated disparagingly by the medical community, a contention not only made by obese individuals but by members of the medical professions themselves.

In addition, there are the psychological effects of ridicule that accompany morbid obesity such as depression, anxiety, and low self-esteem.

Let’s look at each of these issues in brief.

Obesity-Related Illnesses and Death

Morbid obesity adversely effects normal body functions and can result in serious illness. Not only can life span be shorted but the quality of life is often compromised as well.

Among the health risks associated with morbid obesity is heart disease. People who are severely obese are 6 times as likely as normal-weight individuals to develop heart disease. They are 40 times more likely than normal-weight people to have a sudden death. An increased burden on the heart can lead to early development of heart failure.

Obese people have high blood pressure much more often than normal-weight people. High blood pressure can promote heart disease and lead to stroke, kidney damage, and hardening of the arteries. Cholesterol levels are often elevated among the severely obese as well.

Obese people are 40 times more likely than normal-weight people to develop type II diabetes. Elevated blood sugar leads to damage to tissues throughout the body, and diabetes is the fourth most prevalent cause of death in the United States. Diabetes can cause adult-onset blindness, kidney failure, and is the cause for over half of all amputations.

High Cost of Care for the Morbidly Obese

A study published in the 2005 International Journal of Obesity found that health care costs in 2000 were twice as much for morbidly obese adults than normal-weight adults. Costs resulted from greater number of office visits, outpatient care, in-patient care, and prescription drugs. Total expenditures related with excess body weight exceeded eleven billion dollars in the year 2000.

The Obese Treated Poorly by Health Practitioners

In addition (and unfortunately), the morbidly obese are often viewed harshly by medical professionals. Half of the women who visited doctors because of excess weight issues reported that they felt they had been treated poorly. Both doctors and nurses verify this contention, having reported that they often believe the morbidly obese are lazy, unsuccessful, and non-compliant. Almost one-quarter of nurses stated they were “repulsed” by morbidly obese patients, and doctors and nurses both stated that they viewed and treated the morbidly obese differently.

Poor Emotional Health of Morbidly Obese Persons

Many people who are overweight are the targets for criticism and ridicule by peers. These harsh behaviors can propagate depression, anxiety, and low self-esteem.

A 1991 study showed that obese people believe they are physically unattractive, dislike being seen in public, think other people are making harsh comments about their weight, and feel discriminated against in the workplace.

Another study focused on the stigma of obesity found:

• Being obese carries a social stigma. Nearly all of the participants, 72 of 76, reported they had experienced humiliation and discrimination related to their weight.

• Being obese affects personal identity. Nearly half of the study participants report poor mental and emotional health, including depression, related to their weight.

• Obese persons feel misunderstood by health care providers. More than 25% of the participants report they have gone to great (and unhealthy) lengths to lose weight. They feel they are being judged and victimized for a condition that is out of their control.

As obesity rates soar worldwide reaching near epidemic proportions, so too is discrimination and bias against obese people. It is projected that overweight and obese people will likely total 80% of the adult population by 2020 and more than 1 in 5 children will be obese. The impact of our growing girth as a nation has social, economic, and health consequences that are alarming.

Do you have thoughts on how we might solve obesity in America? I’d like to know your solutions and ideas. Please leave your comments below.

What to read next: Will Obesity be the New Normal?

Gastric Bypass.com – http://www.gastricbypass.com/HEMO.htm – accessed 8/11/12

International Journal of Obesity – http://www.nature.com/ijo/journal/v29/n3/full/0802896a.html – accessed 8/11/12

WebMD – http://www.webmd.com/balance/news/20080619/stigma-of-obesity-not-easy-to-shed – accessed 8/31/12

Obesity, Overweight, Childhood Obesity – http://www.ciofoundation.org/obesity-statistics.html- accessed 8/11/12

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Social network influences and the adoption of obesity-related behaviours in adults: a critical interpretative synthesis review

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Adult Obesity Causes & Consequences

Obesity is a complex health issue to address. Obesity results from a combination of causes and contributing factors, including individual factors such as behavior and genetics. Behaviors can include dietary patterns, physical activity, inactivity, medication use, and other exposures. Additional contributing factors in our society include the food and physical activity environment, education and skills, and food marketing and promotion.

Obesity is a serious concern because it is associated with poorer mental health outcomes, reduced quality of life, and the leading causes of death in the U.S. and worldwide, including diabetes, heart disease, stroke, and some types of cancer.

Behavior

Healthy behaviors include a healthy diet pattern and regular physical activity. Energy balance of the number of calories consumed from foods and beverages with the number of calories the body uses for activity plays a role in preventing excess weight gain.1,2 A healthy diet pattern follows the Dietary Guidelines for Americansexternal icon which emphasizes eating whole grains, fruits, vegetables, lean protein, low-fat and fat-free dairy products and drinking water. The Physical Activity Guidelines for Americansexternal icon recommends adults do at least 150 minutes of moderate intensity activity or 75 minutes of vigorous intensity activity, or a combination of both, along with 2 days of strength training per week.

Having a healthy diet pattern and regular physical activity is also important for long term health benefits and prevention of chronic diseases such as Type 2 diabetes and heart disease.

For more, see Healthy Weight – Finding a Balance.

Community Environment

People and families may make decisions based on their environment or community. For example, a person may choose not to walk or bike to the store or to work because of a lack of sidewalks or safe bike trails. Community, home, child care, school, health care, and workplace settings can all influence people’s daily behaviors. Therefore, it is important to create environments in these locations that make it easier to engage in physical activity and eat a healthy diet.

Watch The Obesity Epidemicexternal icon to learn about the many community environmental factors that have contributed to the obesity epidemic, as well as several community initiatives taking place to prevent and reduce obesity.

Learn about strategies to improve the environment to make it easier to be physically active.

Strategies to create a healthy environment are listed on the Strategies to Prevent Obesity page. More specifically, strategies to create a healthy school environment are listed on the CDC Adolescent and School Health website.

Genetics

Do Genes Have a Role in Obesity?

Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity.

How Could Genes Influence Obesity?

Genes give the body instructions for responding to changes in its environment. Studies have identified variants in several genes that may contribute to obesity by increasing hunger and food intake.

Rarely, a clear pattern of inherited obesity within a family is caused by a specific variant of a single gene (monogenic obesity). Most obesity, however, probably results from complex interactions among multiple genes and environmental factors that remain poorly understood (multifactorial obesity).3,4

What about Family History?

Health care practitioners routinely collect family health history to help identify people at high risk of obesity-related diseases such as diabetes, cardiovascular diseases, and some forms of cancer. Family health history reflects the effects of shared genetics and environment among close relatives. Families can’t change their genes but they can change the family environment to encourage healthy eating habits and physical activity. Those changes can improve the health of family members—and improve the family health history of the next generation.3,4

Learn more about genetics and obesity here: Obesity and Genomics.

Other Factors: Diseases and Drugs

Some illnesses may lead to obesity or weight gain. These may include Cushing’s disease, and polycystic ovary syndrome. Drugs such as steroids and some antidepressants may also cause weight gain. The science continues to emerge on the role of other factors in energy balance and weight gain such as chemical exposures and the role of the microbiome.

A health care provider can help you learn more about your health habits and history in order to tell you whether behaviors, illnesses, medications, and/or psychological factors are contributing to weight gain or making weight loss hard.

Consequences of Obesity

Health Consequences

People who have obesity, compared to those with a normal or healthy weight, are at increased risk for many serious diseases and health conditions, including the following:5,6,7

  • All-causes of death (mortality)
  • High blood pressure (Hypertension)
  • High LDL cholesterol, low HDL cholesterol, or high levels of triglycerides (Dyslipidemia)
  • Type 2 diabetes
  • Coronary heart disease
  • Stroke
  • Gallbladder disease
  • Osteoarthritis (a breakdown of cartilage and bone within a joint)
  • Sleep apnea and breathing problems
  • Some cancersexternal icon (endometrial, breast, colon, kidney, gallbladder, and liver)
  • Low quality of life
  • Mental illness such as clinical depression, anxiety, and other mental disorders8,9
  • Body pain and difficulty with physical functioning10

For more information about these and other health problems associated with obesity, visit Health Effects of Obesity.

For more information about these and other health problems associated with overweight and obesity, visit Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults pdf iconexternal icon.

Economic and Societal Consequences

Obesity and its associated health problems have a significant economic impact on the U.S. health care system.11 Medical costs associated with overweight and obesity may involve direct and indirect costs.12,13 Direct medical costs may include preventive, diagnostic, and treatment services related to obesity. Indirect costs relate to morbidity and mortality costs including productivity. Productivity measures include ‘absenteeism’ (costs due to employees being absent from work for obesity-related health reasons) and ‘presenteeism’ (decreased productivity of employees while at work) as well as premature mortality and disability. 14

National Estimated Costs of Obesity

The medical care costs of obesity in the United States are high. In 2008 dollars, these costs were estimated to be $147 billion.15

The annual nationwide productive costs of obesity obesity-related absenteeism range between $3.38 billion ($79 per obese individual) and $6.38 billion ($132 per individual with obesity)16.

In addition to these costs, data shows implications of obesity on recruitment by the armed forces. An assessment was performed of the percentage of the US military-age population that exceeds the US Army’s current active duty enlistment standards for weight-for-height and percent body fat, using data from the National Health and Nutrition Examination Surveys. In 2007-2008, 5.7 million men and 16.5 million women who were eligible for military service exceeded the Army’s enlistment standards for weight and body fat.17

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