obesity deaths in america 2019

More than two thirds of deaths related to high BMI were due to cardiovascular disease. The disease burden related to high BMI has increased. When the American Medical Association declared obesity a disease, that correlations between “obesity” and morbidity and mortality rates. America's Health Rankings finds that nationally, 31.2% of children aged 10 to 17 years are overweight or obese, which is often associated with.

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Unhealthy eating and physical inactivity are leading causes of death in the U.S. 

Unhealthy diet contributes to approximately 678,000 deaths each year in the U.S., due to nutrition- and obesity-related diseases, such as heart disease, cancer, and type 2 diabetes.1 In the last 30 years, obesity rates have doubled in adults, tripled in children, and quadrupled in adolescents.2, 3, 4

Risk Factors and the Number of Deaths in the US, 2016​1

Risk FactorNo. of Deaths in 2016% of Total Deaths
Dietary risks (other than BMI) 529,999 19.1%
Tobacco 492,437 17.8%
High Blood Pressure 481,501 17.4%
High BMI 385,965 13.9%
High FPG 376,498 13.6%
High Total Cholesterol 233,233 8.41%
Impaired Kidney Function 174,559 6.30%
Alcohol & Drug Use 155,575 5.61%
Air Pollution 105,084 3.79%
Low Physical Activity 91,670 3.31%
Occupational Risks 89,684 3.23%
Low Bone Mineral Density 25,994 0.94%
Other Environmental Risks 24,356 0.88%
Unsafe Sex 13,465 0.49%
Malnutrition 11,019 0.40%
Sexual Abuse & Violence 2,458 0.09%
WaSH 2,121 0.08%

The typical American diet is too high in calories, saturated fat, sodium, and added sugars, and does not have enough fruits, vegetables, whole grains, calcium, and fiber. Such a diet contributes to some of the leading causes of death and increases the risk of numerous diseases5, including:

  • heart disease;
  • diabetes;
  • obesity;
  • high blood pressure;
  • stroke;
  • osteoporosis;6 and
  • cancers, including cervical, colon, gallbladder, kidney, obesity deaths in america 2019, ovarian, uterine, and postmenopausal breast cancers; leukemia; and esophageal cancer (after researchers took smoking into account).7

Leading Causes of Death (2012)7

1. Heart Disease599,711
2. Cancer582,623
3. Chronic lower respiratory disease143,489
4. Cerebrovascular disease (stroke and related conditions)128,546
5. Unintentional injuries (accidents)127,792
6. Alzheimer’s disease83,637
7. Diabetes mellitus73,932
8. Influenza and pneumonia50,636
9. Nephritis, nephrotic syndrome and nephrosis (kidney disease)45,622
10. Intentional self-harm (suicide)40,600

*Diseases to which poor diet contributes are in bold


Unhealthy eating habits and inactivity affect quality of life and cause disabilities

Few recognize that unhealthy diet is a leading cause of disability. Yet unhealthy eating habits and physical inactivity are leading causes of loss of independence:

  • Diabetes is a leading obesity deaths in america 2019 of blindness and amputations. Roughly capital one personal loan pre qualify people have lower-limb amputations each year due to diabetes.8
  • Bone injuries due to osteoporosis are most likely to occur in the hips, spine, and wrist. Even just a slight fracture in these areas can result in loss of independence. Twenty percent of seniors who break their hip die within just one year. Those who survive often require long-term (nursing home) care.8, 9
  • Heart attack or stroke can result in difficulty with everyday activities—such as walking, bathing, or getting into or out of bed—or cognitive impairment.10

Number of Americans Living with Diseases Related to Diet and Inactivity

Obesity1178,100,000
High Blood Pressure1266,900,000
Diabetes1329,100,000
Heart disease1426,600,000
Cancer1420,073,000
Osteoporosis159,900,000
Stroke146,400,000

Obesity rates are skyrocketing

Over two-thirds (67.5%) of American adults are overweight or obese.2

Obesity rates in children have tripled over the last three decades, and one in three children and adolescents 2-19 years old is overweight or obese.4, 16

Read more about how public policies could help reduce obesity.


It's expensive to ignore prevention

Costs of Diseases Associated with Diet and Inactivity*

Diabetes8$245 billion
Cancer18$216.6 billion
Coronary heart disease9$204.4 Billion
Obesity20$190 Billion
High blood pressure19$46.4 Billion
Stroke19$36.5 Billion
Osteoporosis9$19 Billion

* Estimates of annual direct + indirect costs for diseases overall (including portions caused by factors other than diet and physical inactivity), except for the figure for obesity, which is an estimate of direct (medical) costs only.

Health care costs $8,900 per person per year.21 According to the Centers for Disease Control and Prevention (CDC), a 1% reduction in dietary health risks such as weight, blood pressure, glucose, and cholesterol risk factors would save $83 to $103 per person per year in medical costs.22

According to the Trust for America’s Health, if obesity trends were lowered by reducing the average adult body mass index (BMI) by only 5 percent, millions of Americans could be spared serious health problems, and our country could save $158 billion over the next 10 years.23


Current investments to promote healthy eating and physical activity are insufficient


References

  1. https://vizhub.healthdata.org/gbd-compare/
  2. Centers for Disease Control and Prevention (CDC), NCHS Health E-Stat: Prevalence of Overweight, Obesity, and Extreme Obesity among Adults, United States, 1960-1962 through 2011-2012. Accessed here on November 3, 2014.
  3. Ogden C, Carrol M. Prevalence of Obesity among Children and Adolescents: United States, Trends 1963-1965 through 2007-2008, 2010. Accessed here on October 2, 2015.
  4. CDC. Childhood Obesity Facts. Accessed here on December 17, 2014.
  5. U.S. Department of Agriculture (USDA), U.S. Department of Health and Human Services. Dietary Guidelines for Americans 2010. U.S. Government Printing Office, December 2010.
  6. Bhaskaran K, Douglas I, Forbes H, dos-Santos-Silvia I, Leon DA, Smeeth L. "Body-Mass Index and Risk of 22 Specific Cancers: A Population-Based Cohort Study of 5.34 Million UK Adults." The Lancet 2014, vol. 384, pp. 755-765.
  7. Xu J, Kochanek KD, Murphy SL, Arias E. NCHS Data Brief: Mortality in the United States, 2012. Accessed here on November 3, 2014.
  8. CDC. National Diabetes Statistics Report, 2014. Accessed here on April 30, 2015.
  9. National Osteoporosis Foundation. What Is Osteoporosis? Accessed at on September 11, 2014.
  10. Levine DA, Davydow DS, Hough CL, et al. "Functional Disability and Cognitive Impairment after Hospitalization for Myocardial Infarction and Stroke." Circulation: Cardiovascular Quality and Outcomes 2014, vol. 7, pp. 863-871.
  11. Ogden CL, Carroll MD, Kit BK, Flegal KM. "Prevalence of Obesity in the United States, 2009–2010." National Center for Health Statistics 2012, NCHS data brief, no 82. Accessed here on October 2, 2015.
  12. CDC. Vital Signs: Awareness and Treatment of Uncontrolled Hypertension among Adults-United States, 2003-2010. MMWR 2012, vol. 61, no. 35, pp. 703-9.
  13. CDC. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014. Atlanta, GA: U.S. Department of Health and Human Services.
  14. Blackwell DL, Lucas JW, Clarke TC. "Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2012." National Center for Health Statistics. Vital Health Stat 2014, vol. 10, no. 260.
  15. National Osteoporosis Foundation. 2014 Clinician's Guide to Prevention and Treatment of Osteoporosis. Accessed here on April 30, 2015.
  16. Ogden CL, Carroll MD, Kit BK, Flegal KM. "Prevalence of Childhood and Adult Obesity in the United States, 2011-2012." Journal of the American Medical Association 2014, vol. 311, pp. 806-814.
  17. Child Trends Data Bank. Percentage of Children Who Are Overweight, by Selected Groups. Accessed here on November 3, 2014.
  18. CDC. United States Cancer Statistics: Technical Notes. Accessed on April 30, 2015.
  19. American Heart Association (AHA). Heart Disease and Stroke Statistics-At a Glance. Accessed here on October 2, 2015.
  20. Cawley J, et al. "The Medical Care Costs of Obesity: An Instrumental Variables Approach." Journal of Health Economics 2012, vol. 31, pp. 219‐230.
  21. Centers for Medicare and Medicaid Services. National Health Care Expenditures: Aggregate and Per Capita Amounts. Accessed here on October 31, 2014.
  22. Centers for Disease Control and Prevention. Investing in Prevention Improves Productivity and Reduces Employer Costs. Accessed here on October 31, 2014.
  23. Trust for America's Health. The State of Obesity: Better Policies for a Healthier America 2014. Accessed here on November 3, 2014.
  24. The Hershey Company. Form 10-K, 2013. Accessed here.
  25. U.S. Department of Agriculture (USDA). Food Dollar Series: Documentation.Washington, DC: USDA, March 2013. Accessed here.
  26. U.S. Department of Agriculture (USDA). Food and Alcoholic Beverages: Total Expenditures. Washington, DC: USDA, November 2013. Accessed here.
  27. Federal Trade Commission (FTC). A Review of Marketing Food to Children and Adolescents: Follow-Up Report. Washington, D.C.: Federal Trade Commission, 2012. Accessed here.
Источник: https://www.cspinet.org/eating-healthy/why-good-nutrition-important

Nutrition, Physical Activity, and Obesity

Good nutrition, physical activity, and a healthy body weight are essential parts of a person’s overall health and well-being. Together, these can help decrease a person’s risk of developing serious health conditions, such as high blood pressure, high cholesterol, diabetes, heart disease, stroke, and cancer. A healthful diet, regular physical activity, and achieving and maintaining a healthy weight also are paramount to managing health conditions so they do not worsen over time.

Most Americans, however, do not eat a healthful diet and are not physically active at levels needed to maintain proper health. Adults in the U.S. consume fruit about 1.1 times per day and vegetables about 1.6 times per day; adolescents showed even lower intake.1 Across age and gender, Americans' average daily fruit and vegetable consumption does not meet intake recommendations.2 Compounding this is the fact that a majority of adults (81.6%) and adolescents (81.8%) do not get the recommended amount of physical activity.3

As a result of these behaviors, the Nation has experienced a dramatic increase in obesity. Today, approximately 1 in 3 adults (34.0%) and 1 in 6 children and adolescents (16.2%) are obese. Obesity-related conditions include heart disease, stroke, and type 2 diabetes, which are among the leading causes of death. In addition to grave health consequences, overweight and obesity significantly increase medical costs and pose a staggering burden on the U.S. medical care delivery system.

Ensuring that all Americans eat a healthful diet, participate in regular physical activity, and achieve and maintain a healthy body weight is critical to improving the health of Americans at every age.

Источник: https://www.healthypeople.gov/2020/leading-health-indicators/2020-lhi-topics/Nutrition-Physical-Activity-and-Obesity

US Obesity Mortality Trends and Associated Noncommunicable Diseases Contributing Conditions Among White, Black, and Hispanic Individuals by Age from 1999 to 2017

Abstract

This study aims to assess the effect of obesity as an underlying cause of death in association with four main noncommunicable diseases (NCDs) as contributing causes of mortality on the age of death in White, Black, and Hispanic individuals in the USA. To estimate mortality hazard ratios, we ran a Cox regression on the US National Center for Health Statistics mortality integrated datasets from 1999 to 2017, which included almost 48 million cases. The variable in the model was the age of death in years as a proxy for time to death. The cause-of-death variable allowed for the derivation of predictor variables of obesity and the four main NCDs. The overall highest obesity mortality HR when associated with NCD contributing conditions for the year 1999–2017 was diabetes (2.15; 95% CI: 2.11–2.18), while Whites had the highest HR (2.46; 95% CI: 2.41–2.51) when compared with Black (1.32; 95% CI: 1.27–1.38) and Hispanics (1.25; 95% CI: 1.18–1.33). Hispanics had lower mortality HR for CVD (1.21; 95% CI: 1.15–1.27) and diabetes (1.25; 95% CI: 1.18–1.33) of the three studied groups. The obesity death mean was 57.3 years for all groups. People who die from obesity are, on average, 15.4 years younger than those without obesity. Although Hispanics in the USA have a higher prevalence of diabetes and cardiovascular disease (CVD), they also have the lowest mortality HR for obesity as an underlying cause of death when associated with CVD and cancer. While there is no obvious solution for fifththird com login and its complications, continued efforts to address obesity are needed.

Introduction

The prevalence of overweight and obesity has nearly tripled since 1975 worldwide, with obesity emerging as one of the most important chronic diseases and a major burden for society [1]. It is estimated that obesity was responsible obesity deaths in america 2019 4.7 million deaths and 148 million disability-adjusted life-years in 2017 [2]. The prevalence of obesity in the United States of America (USA) from 2015 to 2016 was 39.8%, affecting 93.3 million US adults and making it the highest obesity prevalence in OECD countries [3, 4]. There is a higher prevalence of obesity among women than men, as well as a higher prevalence among non-Hispanic Black individuals (46.8%) and Hispanic individuals (47.0%) than non-Hispanic White individuals (38.0%) [4]. It is well-documented that obesity is associated with an increased risk of disease, although it is identified as an underlying cause of death in less than 20% of deaths in the USA [5].

It is increasingly apparent that obesity and premature mortality obesity deaths in america 2019 directly related. It is well-known that overweight and obesity are associated with increased risk of mortality from (CVD), type 2 diabetes, and certain cancers, among other noncommunicable diseases (NCDs) [6], although this association varies between populations and causes of death [7, 8]. The risk of mortality increases directly in relation to the number of years lived with obesity [8], up to 64% compared to 19% in persons with normal weight, with no difference by race or sex [9, 10].

Obesity was first included in the International Classification of Diseases Sixth Revision (ICD-6) in 1948 [11] with the code E66, which was initially used either as a cause or as a contributing cause of death rather than a disease in its own right. In 2008, a panel of experts convened by The Obesity Society concluded that considering obesity as a disease had positive consequences [12], and in 2013, the American Medical Association voted to declare obesity a disease [13, 14].

Despite the fact that the link between obesity and mortality has been well-established since the 1980s [15], the recognition of obesity’s contribution to death and of obesity-associated conditions is limited on death certificates [16]. Currently, there are very few studies using mortality vital statistics databases to analyze obesity-related mortality [5], including its associated conditions with the underlying cause of death. A review of the mortality databases of the Pan American Health Organization (PAHO) for the region of the Americas [17] found that the use of the E66 code for certifying obesity as the underlying cause of death increased from 4,050 in 1999 to 12,087 in 2015, with the USA accounting for 64.4% and 61.1% of these deaths, respectively. The underreporting of obesity as a cause or contributing factor to death can lead to underestimations in regard to the effect of obesity on mortality [18,20,20].

Although different studies have found that excess weight is a risk factor for diseases and mortality, other studies have documented that individuals with excess weight and NCDs live longer than individuals with a normal weight, giving rise to the “obesity paradox.” Overweight or grade/class 1 obesity is associated with significantly lower all-cause mortality [21, 22], but grades/classes 2 and 3 obesity are associated with significantly higher all-cause mortality [23, 24]. Moreover, other studies report that the relationship between body mass index (BMI) and mortality exhibits a J-shaped [3] or a U-shaped curve [21].

The reporting of race and ethnicity on death certificates accuracy is high for White and Black populations, and for Hispanics “is almost as good as that of white and black populations” [25]. In the USA, the positive predictive value of ICD 10 codes for obesity administrative diagnosis is above 92%, with a higher likelihood of using the E66 code with grade/class 1 or 3 [26].

The objective of this study was to assess the effects of obesity as an underlying cause of death in association with four main NCDs—cardiovascular disease, diabetes, cancer, and chronic respiratory disease—as contributing causes in relation to the age of death in White, Black, and Hispanic individuals in the USA.

Methods

We integrated all the mortality datasets from 1999 to 2017 from the US National Center for Health Statistics, obtaining data from almost 48 million individuals registered [27], including people of all ages. Integration included matching all equivalent variables in the death certificate records according to the corresponding data dictionaries.

During processing, we generated indicator variables to identify the cases with any of the selected health conditions as the cause of death originally coded according to the International Classification of Diseases, Tenth Revision (ICD-10). We adopted the codes defined by the World Health Organization [28]: obesity E66; circulatory diseases I00–I99; cancer C00–C97; diabetes E10–E14; and chronic lower respiratory diseases J40–J47. For this analysis, the data on the underlying cause of mortality were disaggregated into deaths that had the code E66 (obesity) to be compared with all other codes that contributed to the main cause of death. Estimations were made for the annual and total percentage of deaths with obesity as an underlying cause of death and its contributing NCDs as cause of death in the selected studied period. All race/ethnic groups include all deaths.

We used population data by age, sex, and race (White and Black) and ethnicity (Hispanic origin), as defined by the US census as they represent more than 95% of all obesity deaths) from the Centers for Disease Control and Prevention (CDC) WONDER [29] as denominators for descriptive background analysis based on mortality rates. We included in the analyses sex, race, and ethnicity (Hispanic origin group) as covariates that could also have effects on the results. We also defined age strata of 0–14, 15–29, 30–49, 50–69, 70–84, and older than 84 years.

A Cox regression was used to estimate mortality hazard ratios (HRs) with 95% confidence intervals (95% CIs). For the Cox regression, age was used as the time to death in simple years for all cases. Obesity, according to the described coding, was the factor variable. Members 1st federal credit union routing number were no censored cases, because of the type of data. To examine the single and simultaneous effect of factors besides obesity, we included as regressors (predictor variables) each of the four NCDs listed above, age, race/ethnicity, and sex, available in the dataset.

Descriptive analyses framed our understanding of obesity as an underlying cause of death. Trends of death rates presented an evolution and magnitude, while frequencies according to sex, age groups, and ethnicity showed differential distribution.

By examining death rates, we addressed the risk of death associated with obesity and NCDs in the population. The average age of death and hazard ratios from Cox regressions allowed us to evaluate the relative effect of obesity on the length of life. To run all of these analyses, we used the SPSS program version 26.

The results are presented in tables as frequencies, mortality rates, and mortality hazard ratios. Figures are also included to identify tendencies in the studied period.

Results

Between 1999 and 2017, there were 47,812,945 deaths, of which 99,388 were related to obesity as the underlying cause of death (49.9% male and 50.1% female) in all race/ethnic groups. For the purpose of this study, only 97,689 deaths met the race/ethnicity inclusion criteria (White, Black, and Hispanic); of these, 77,846 (50.2% male and 49.8% female) had at least one of the selected four NCDs associated with multiple causes of death.

During the study period, there was a 276% increase in the reporting of obesity in the death certificates, from 2061 in 1999 to 7752 in 2017 (189.6% in White, 179.9% in Black, and 361% in Hispanic individuals). Of the total cases with obesity as an underlying cause of death, 72,321 (72.8%) were accounted for by White individuals, 18,377 (18.5%) by Black individuals, and 6991 (7.1%) by Hispanic individuals (Table 1a).

Full size table

Overall, the mortality rate trend in the period of study increased by 221.6% (0.74 to 2.38/1,000,000). The White population had an increased mortality rate of 183.7%, followed by Hispanics (165.0%) and Blacks (124.7%) respectively (Fig. 1). When mortality in the study period was separated by sex, males’ mortality rates were higher than females’ (Figs. 2 and 3), with the exception of Black females. Hispanics had the lowest mortality rates for both females and males.

Mortality rate* associated with obesity as an underlying cause of death in all three races/ethnic groups (White, Black, and Hispanic) in the USA from 1999 to 2017. *Rate per 100,000. Source: Mortality trends constructed from the US Mortality Database 1999–2017. Population obtained from the CDC WONDER database platform

Full size image

Male mortality rate* associated with obesity as an underlying cause of death in all three races/ethnic groups (White, Black, and Hispanic) in the USA from 1999 to 2017. *Rate per 100,000. Source: Mortality trends constructed from the US Mortality Database 1999–2017. Population obtained from the CDC WONDER database platform

Full size image

Female mortality rate* associated with obesity as an underlying cause of death in all three races/ethnic groups (White, Black, and Hispanic) in the USA from 1999 to 2017. *Rate per 100,000. Source: Mortality trends constructed from the US Mortality Database 1999–2017. Population obtained from the CDC WONDER database platform

Full size image

In the USA, the mean age of death from any cause was 72.7, but if the underlying cause was obesity, this mean decreased to 56.5 years, resulting in a reduction of 16.2 years of life (Table 1b). It appears that when NCDs are associated with obesity as an underlying cause of death, the mean age of death from all NCDs drops from 74.9 to 57.3 years.

When comparing the mean age of death by race/ethnicity, we found that Hispanic individuals died 11 years earlier than White individuals and 0.9 years earlier than Black individuals, when obesity was an underlying cause (Table 1b).

When analyzing the age of death from all underlying causes by age group and race/ethnicity, we found that more than half of Black and Hispanic individuals died for any causes of death at younger ages (before age 70), while almost 70% of deaths among White individuals occurred after 70 years of age (Table 1c). When obesity was the underlying cause of death, more than 85% of Black and Hispanic individuals died before 70 years of age compared with 78% of White individuals. If obesity was the underlying cause of death associated to one or more NCDs, more than 40% of Hispanic and Black individuals died at ages younger than 70, compared to 28% of White individuals (Table 1c).

The highest mortality rates for obesity and associated conditions (per 1,000,000 inhabitants) for the 1999–2017 period were for cardiovascular disease, followed by diabetes, chronic respiratory disease, and cancer. Black individuals had the highest mortality rates for all four NCDs except cancer, while Hispanic individuals had the lowest rates for the four selected causes of death (Table 1d).

Mortality Hazard Ratios for Obesity and Selected Associated Noncommunicable Chronic Diseases

The highest HRs for obesity and associated conditions for the 1999–2017 period were for diabetes (2.15), followed by cardiovascular disease (1.60), chronic respiratory disease (1.22), and cancer (0.05). Whites had the highest HR for diabetes, while Black individuals had the highest mortality HR for cardiovascular disease and chronic respiratory disease, and Hispanic individuals had the lowest HR for the selected causes of death except chronic respiratory disease. In all ethnic groups, the cancer mortality HR was less than one (Table 2a).

Full size table

Mortality Hazard Ratio for Obesity and Associated Cardiovascular Disease

The overall obesity mortality HRs by sex, age group, and race/ethnic origin for the associated cardiovascular disease condition were higher among males than obesity deaths in america 2019, with the exception of Hispanic females in the over 85 years of age group, where the mortality HR was obesity deaths in america 2019 than that of males in the same age group. The 0–14 and 15–29 age group had the highest HRs for males in all race/ethnic groups. In this study, obesity and cardiovascular disease mortality HRs decreased with age (Table 2b).

Mortality Hazard Ratio for Obesity and Associated Diabetes

For all three races/ethnic groups, mortality HRs for obesity and the associated diabetes were higher among males than female in all ages group, except for the 30–49 and 50–69 age groups. HRs were higher among males between the ages of 15 and 29 and over 85 years of age. Hispanic and Black males had almost double the mortality HRs of Hispanic and Black women in the 15–29 age group, while White and Hispanic females had higher HRs in the 30–49 age group and 50–69 age group. In general, the mortality HR increased with age (Table 2c).

Mortality Hazard Ratio for Obesity and Associated Cancer

In all age groups, HRs were below one for obesity mortality and cancer (Table 2d). HR increases with age after age 50.

Mortality Hazard Ratio for Obesity and Associated Chronic Respiratory Disease

The mortality HRs for obesity and associated chronic respiratory disease in all races and Hispanic origin decreases between the 15–29 and 50–69 age groups, and then start to increase from the age of 70 and above. The HRs were higher in females than in males in the 0–14 age group, except for Black females; in the 15–29 age group, the mortality HRs were higher among males than females in all races and Hispanic origin ethnic groups. Male Hispanics had higher mortality HRs than White and Black individuals in all age groups, except for males in the 15–29 to 30–49 age groups (Table 2e); Female Hispanics had higher mortality HRs than White and Black individuals between the ages of 30 and 84.

Discussion

In this study, although the relative percentage seems to be small, 0.2% accounts for 99.388 cases, which was large enough for inferential analyses by Cox regression. With this type of analysis, rather than focusing on pooled proportions, accounts for comparisons of hazards, and instantaneous risk rates of time to death, we found that people who have obesity as an underlying cause of death die, on average, 16.2 years younger than those without the condition [30]. We found that obese Hispanic individuals die on average 6.8 years earlier than White individuals and 0.9 years earlier than Black individuals. Several studies have documented that mild and severe obesity are associated with the loss of one in ten and one in four potential disease-free years during adulthood, respectively [12, 30, 31]. Our 19 years of analyzed data showed a steady increase in the obesity mortality trend among the three main races/ethnic groups, while other studies document that overall mortality among obese persons is declining over time [31, 32]. More research is needed on obesity mortality when it is part of multiple conditions in death certificates.

We found that even though Hispanic individuals in the USA live in disadvantaged conditions, with a higher prevalence of diabetes and cardiovascular diseases, they have the lowest mortality HRs for obesity as the underlying cause of death with cardiovascular diseases and diabetes [32,34,35,36,37,38,38]. Many studies about the Hispanic paradox have tried to explain why this group has lower mortality, but none has fully explained the reasons for these ambivalences. Although White individuals had a higher prevalence of cancer and chronic respiratory disease [35], in this study, we found that Hispanic individuals between the ages of 30 and 84 had higher mortality HRs for chronic respiratory disease. We found that the HRs for obesity and cancer, in most cases, were below one in the three race/ethnic groups, suggesting that it is possible that the role of overall obesity is overlooked in cancer mortality, possibly because of the weight loss resulting from cancer [5]. More research is needed to understand the reason for the low HR, as it is known that there are some forms of cancer that have an increased risk if obesity is observed (breast, endometrial, and pancreatic cancer) [39].

As mentioned before, although the use of ICD codes has a positive prediction value of more than 90%, the present study has the limitation that we only used mortality datasets without further information about BMI, obesity class, and other risk factors such as physical activity or smoking, as the existing data in the mortality datasets were either incomplete or unavailable; therefore, their effect and relationship with NCDs cannot be evaluated [40]. While we could not establish how these risk factors impact obesity mortality, it has been well-documented that smoking, unhealthy eating, and physical inactivity play an important role in higher BMI levels, and as such are contributing factors for adverse health outcome [41]. In this study, deaths from cardiovascular conditions and cancer were not disaggregated into specific types, and mortality hazard ratios were estimated only for obesity and the selected major chronic conditions.

A strength of this study, however, is the analysis of a database of almost 48 million death and the three-principal race/ethnic groups in the USA (White, Black, and Hispanic).

Conclusions

Our findings of higher HRs in the younger age groups should be considered to develop interventions to control and prevent obesity and/or delay the development of complications that may lead to premature death, as mentioned by other authors and confirmed in this study [3, 5].

Although there is an increasing trend of obesity mortality in the USA [4], it is difficult to say if the trend is real or if it has occurred because there is more awareness about this problem among health professionals who use the E66 code more frequently. Further research is needed to establish the cause of this increase in obesity mortality.

As shown in Table 2b, 2c, and 2e, HRs were higher in all three races/ethnic groups (males have HRs higher than females) for cardiovascular disease, diabetes, and chronic respiratory disease in the 15–29 year age group than in any other older age group; it is therefore necessary that the public health programs increase their obesity deaths in america 2019 to promote weight management among all obese individuals, with an emphasis on younger groups.

We agree with the conclusion of The Lancet Commission on Obesity, which states that there is not an obvious solution for obesity and its complication [2]; consequently, continued efforts are needed to address the obesity and its corresponding public health and policy concerns in order to prevent the increase of disabilities associated with obesity over time [42].

Finally, in the context of the COVID-19 pandemic, evidence suggests that obesity is an important risk factor not just for noncommunicable diseases but also for communicable ones as well. Obesity has also been reported to play an important role in contributing to higher mortality rates among Hispanic and Black people, where the socioeconomic factor is likely to have some influence on timely access to health services [43]. An effective public health response for addressing obesity is consequently more urgent than ever.

Data Availability

Database is available upon request.

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Affiliations

  1. College of Health Science, University of Texas at El Paso, 1851 Wiggins Rd., El Paso, 79968, Texas, USA

    Federico Gerardo de Cosio

  2. Research and Graduate Studies, Universidad Autonoma de Ciudad Juarez, Ciudad Juárez, Mexico

    Beatriz Diaz-Apodaca

  3. Ministry of Social Development and Family of Chile, Santiago, Chile

    Amanda Baker

  4. Ministerio de Salud y Protección Social, Bogotá, Colombia

    Miriam Patricia Cifuentes

  5. Pan American Health Organization Venezuela, Caracas, Venezuela

    Federico Gerardo de Cosio & Hector Ojeda-Casares

  6. Universidad Autonoma de Ciudad Juarez, Ciudad Juárez, Mexico

    Daniel Constandce

  7. Public Health Developments Organization, Washington, DC, USA

    Francisco Becerra

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de Cosio, F.G., Diaz-Apodaca, B., Baker, A. et al. US Obesity Mortality Trends and Associated Noncommunicable Diseases Contributing Conditions Among White, Black, and Hispanic Individuals by Age from 1999 to 2017. SN Compr. Clin. Med.3, 1334–1343 (2021). https://doi.org/10.1007/s42399-021-00850-2

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Keywords

  • Obesity
  • Cardiovascular disease
  • Diabetes
  • Chronic respiratory disease
  • Hazard ratios (HRs)
Источник: https://link.springer.com/article/10.1007/s42399-021-00850-2

Related Indicators

Definition

Percentage of respondents who have a body mass index (BMI) greater than or equal to 30.0 kg/m2 calculated from self-reported weight and height. BMI is calculated by dividing weight in kilograms by the square of height in meters.

Numerator

Number of respondents who have a body mass index (BMI) greater than or equal to 30.0 kg/m2 calculated from self-reported weight and height.

Denominator

Number of adult respondents for whom BMI can be calculated from their self-reported weight and height (excludes unknowns or refusals for weight and height).

Data Interpretation Issues

Respondents tend to overestimate their height and underestimate their weight leading to underestimation of BMI and the prevalence of obesity. Data from the New Jersey Behavioral Risk Factor Survey are intended to represent non-institutionalized adults in households with telephones. Data are collected using a random sample of all possible telephone numbers. Prior to analysis, data are weighted to represent the population distribution of adults by age, sex, and "race"/ethnicity. As with all surveys, however, some residual bias may result from nonresponse (e.g., refusal to participate in the survey or to answer specific questions) and measurement error (e.g., social desirability or recall). Attempts are made to minimize such error by use of a strict calling protocol (up to 15 calls are made to reach each household), good questionnaire design, standardization of interviewer behavior, interviewer training, and frequent, on-site interviewer monitoring and supervision. Starting in 2011, BRFSS protocol requires that the NJBRFS incorporate a fixed quota of interviews from cell phone respondents along with a new weighting methodology called iterative proportional fitting or "raking". The new weighting methodology incorporates additional demographic information (such as education, race, and marital status) in the weighting process. These methodological changes were implemented to account for the underrepresentation of certain demographic groups in the land line sample (which resulted in part from the increasing number of U.S. households without land line phones). Comparisons between 2011 and prior years should therefore be made with caution. (More details about these changes can be found at [http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6122a3.htm].)

Why Is This Important?

Adults who are obese are at increased risk of morbidity from hypertension, high LDL cholesterol, type 2 diabetes, coronary heart disease, stroke, and osteoarthritis.

Healthy People Objective: Reduce the proportion of adults who are obese

U.S. Target: 30.6 percent (age-adjusted)
State Target: 23.8 percent (age-adjusted)

Other Objectives

'''Healthy New Jersey 2020 Objective NF-1a''': Prevent an increase in the proportion of the adult population aged 20 years and older that is obese. Targets are 23.8% for the total population, 22.4% among Whites, 32.5% among Blacks, 28.0% among Hispanics, and 11.0% among Asians.

How Are We Doing?

The age-adjusted prevalence of obese New Jersey adults increased from 23.8% in 2011 to 27.7% in 2017.

How Do We Compare With the U.S.?

The age-adjusted prevalence of obesity among New Jersey adults is slightly lower than in the U.S. as a whole. In 2017, the obesity prevalence rate among New Jersey adults was 27.7% compared to 30.6% for U.S. adults.

What Is Being Done?

The New Jersey Nutrition, Physical Activity, and Obesity (NPAO) Program within the NJDOH Office of Nutrition and Fitness coordinates efforts to work with communities to develop, implement, and evaluate interventions that address behaviors related to increasing physical activity, breastfeeding initiation and duration, and the consumption of fruits and vegetables, and to decreasing the consumption of sugar-sweetened beverages and high-energy-dense foods, and to decrease television viewing.



Relevant Population Characteristics

Population-based obesity-prevention services may be useful in containing the obesity epidemic. The magnitude of the association appears to be strong among low-income population and among women.^1^ 1. Chen Z, Roy K., Gotway Crawford CA. Obesity Prevention: The Impact of Local Health Departments. Health Services Research. 2013, 48(2):603-627. Note: [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/BMIObese/BMIObeseAA11_.html Custom data views] of the prevalence of obesity among New Jersey adults by selected sociodemographic and other characteristics (including '''local health jurisdiction''') can be generated using the New Jersey Behavioral Risk Factor Survey interactive query module.


Health Care System Factors

Since September 2018, the USPSTF^1^ has recommended that clinicians offer or refer adults with a body mass index of 30 or higher (calculated as weight in kilograms divided by height in meters squared) to intensive, multicomponent '''behavioral interventions'''. 1. United States Preventive Services Task Force. [https://www.uspreventiveservicestaskforce.org/Page/Document/UpdateSummaryFinal/obesity-in-adults-interventions1 Weight Loss to Prevent Obesity-Related Morbidity and Mortality in Adults: Behavioral Interventions]. [last accessed: 1/29/20]


Risk Factors

'''Genetic''' or '''familial''' factors may increase the risk for being overweight or obese for some people, but anyone whose '''calorie intake''' exceeds the number obesity deaths in america 2019 calories they burn is at risk. '''Physical activity''' and a '''healthy diet''' are both important for obtaining and maintaining a healthy weight. Note: [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/BMIObese/BMIObeseAA11_.html Custom data views] of the prevalence of obesity among New Jersey adults by selected '''sociodemographic and other characteristics''' (including '''physical activity)''' can be generated using the New Jersey Behavioral Risk Factor Survey interactive query module.

Related Risk Factors Indicators:


Health Status Outcomes

According to the CDC^1^, the health consequences of overweight and obesity among adults include '''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'''; '''sleep apnea and breathing problems'''; '''some cancers''' ('''endometrial''', '''breast''', '''colon''', '''kidney''', '''gallbladder''', and '''liver'''); '''low quality of life''', '''mental illness''' (including '''clinical depression''', '''anxiety''', and other mental disorders); '''body pain'''; and difficulty with '''physical functioning'''. Note: Custom data views of the estimated prevalence of [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/DXBPHigh/DXBPHighAA11_.html high blood pressure (hypertension)], [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/DXDiabetes/DXDiabetesAA11_.html diabetes], [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/DXCVD_CHD/DXCVD_CHDAA11_.html coronary heart disease], [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/DXCVDStroke/DXCVDStrokeAA11_.html stroke], [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/DXArthritis/DXArthritisAA11_.html arthritis], [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/DXDepress/DXDepressCrude11_.html clinical depression], and [https://www-doh.state.nj.us/doh-shad/query/builder/njbrfs/PhysHlth14/PhysHlth14AA11_.html difficulty with physical functioning] among New Jersey adults by selected '''sociodemographic and other characteristics''' can be generated using the New Jersey Behavioral Risk Factor Survey interactive query module. 1. Centers for Disease Control and Prevention. [https://www.cdc.gov/healthyweight/effects/index.html The Health Effects of Overweight and Obesity] [last reviewed: 1/27/20]

Related Health Status Outcomes Indicators:




Percentage of Adults Aged 20+ Who Are Obese, New Jersey and U.S., 2011-2017 (HNJ2020)

YearUS/NJEstimated Percent (Age-adjusted)Lower LimitUpper Limit
2011US27.927.728.2
2011NJ23.822.724.9
2012US28.227.928.5
2012NJ24.723.725.8
2013US28.728.529.0
2013NJ26.825.528.0
2014US29.429.129.7
2014NJ26.925.728.2
2015US29.329.029.6
2015NJ25.824.427.2
2016US30.029.830.4
2016NJ27.025.228.8
2017US30.630.230.9
2017NJ27.726.029.4

Data Notes

All prevalence estimates are age-adjusted to the U.S. 2000 standard population (except for estimates by age group).   All prevalence estimates are age-adjusted to the U.S. 2000 standard population. Prevalence estimates for 2011 and forward are consistent with those used to track the corresponding Healthy New Jersey 2020 objective (NF-1a) and are for adults aged 20 and over.

Data Source

Behavioral Risk Factor Survey, Center for Health Statistics, New Jersey Department of Health, [http://www.state.nj.us/health/chs/njbrfs/]



Percentage of Adults Aged 18+ Who Are Obese, New Jersey and U.S., 2001-2010

YearUS/NJEstimated Percent (Age-adjusted)Lower LimitUpper Limit
2001US21.6%21.3%21.9%
2001NJ19.6%18.3%21.1%
2002US21.8%21.4%22.1%
2002NJ19.0%17.0%21.2%
2003US22.7%22.4%23.0%
2003NJ20.1%19.2%21.0%
2004US23.4%23.1%23.7%
2004NJ21.9%21.0%22.9%
2005US24.4%24.1%24.7%
2005NJ22.1%21.1%23.1%
2006US25.0%24.6%25.3%
2006NJ22.6%21.6%23.6%
2007US25.9%25.6%26.2%
2007NJ24.1%22.6%25.7%
2008US25.5%26.2%26.8%
2008NJ23.6%22.4%24.8%
2009US27.1%26.8%27.4%
2009NJ23.9%22.8%25.1%
2010US27.4%27.1%27.7%
2010NJ24.0%22.8%25.3%

Data Notes

All prevalence estimates are age-adjusted to the U.S. 2000 standard population (except for estimates by age group).   Prevalence estimates for 2001-2010 are consistent with those used to track the corresponding Healthy New Jersey 2010 objective (3D-3) and are for adults aged 18 and over.

Data Source

Behavioral Risk Factor Survey, Center for Health Statistics, New Jersey Department of Health, [http://www.state.nj.us/health/chs/njbrfs/]



Percentage of Adults Aged 20+ Who are Obese by Race/Ethnicity, New Jersey, 2011-2017 (HNJ2020)

YearRace/EthnicityEstimated Percent (Age-adjusted)Lower LimitUpper Limit
2011White22.421.123.8
2011Black32.529.435.9
2011Hispanic28.024.931.4
2011Asian11.07.415.9
2011Total23.822.724.9
2012White23.722.325.1
2012Black37.434.140.9
2012Hispanic27.324.330.5
2012Asian7.35.210.3
2012Total24.623.625.7
2013White26.124.527.8
2013Black36.633.040.3
2013Hispanic30.226.833.8
2013Asian9.66.414.1
2013Total26.825.528.1
2014White25.523.827.2
2014Black38.234.641.9
2014Hispanic31.227.734.8
2014Asian7.85.211.7
2014Total26.925.728.2
2015White24.022.325.7
2015Black38.133.842.5
2015Hispanic26.923.230.9
2015Asian11.27.416.8
2015Total25.824.427.2
2016White25.322.927.9
2016Black37.632.543.1
2016Hispanic37.032.641.6
2016Asian9.25.515.0
2016Total27.025.228.8
2017White27.024.829.3
2017Black37.633.142.3
2017Hispanic33.228.638.0
2017Asian10.46.915.4
2017Total27.726.029.4

Data Notes

All prevalence estimates are age-adjusted to the U.S. 2000 standard population (except for estimates by age group).   This is Healthy New Jersey 2020 (HNJ2020) Objective NF-1a.

Data Source

Behavioral Risk Factor Survey, Center for Health Statistics, New Jersey Department of Health, [http://www.state.nj.us/health/chs/njbrfs/]



Percentage of Adults Aged 18+ Who are Obese by Gender and Age Group, New Jersey, 2015-2017

Age GroupGenderEstimated PercentLower LimitUpper Limit
18-34Male19.717.222.4
18-34Female19.817.222.6
35-49Male33.030.235.9
35-49Female24.922.727.4
50-64Male34.732.437.1
50-64Female27.625.729.6
65+Male27.425.229.6
65+Female26.824.828.8

Data Notes

All prevalence estimates are age-adjusted to the U.S. 2000 standard population (except for estimates by age group).



Percentage of Adults Aged 25+ Who are Obese by Education Level, New Jersey, 2015-2017

Education LevelEstimated Percent (Age-adjusted)Lower LimitUpper Limit
Less than high school36.832.841.0
High school or G.E.D32.230.134.4
Some college or tech school28.827.030.6
College graduate or higher19.218.120.4

Data Notes

All prevalence estimates are age-adjusted to the U.S. 2000 standard population (except for estimates by age obesity deaths in america 2019 SourceBehavioral Risk Factor Survey, Center for Health Statistics, New Jersey Department of Health, [http://www.state.nj.us/health/chs/njbrfs/]



Percentage of Adults Aged 20+ Who are Obese by County, New Jersey, 2015-2017

CountyEstimated Percent (Age-adjusted)Lower LimitUpper Limit
Atlantic27.323.831.0
Bergen19.316.422.6
Burlington27.724.031.7
Camden34.230.138.5
Cape May26.922.831.3
Cumberland35.031.039.1
Essex28.726.031.5
Gloucester32.828.237.6
Hudson27.624.231.2
Hunterdon18.014.721.8
Mercer27.824.032.1
Middlesex29.125.532.9
Monmouth24.120.727.9
Morris19.015.622.9
Ocean29.826.033.8
Passaic29.325.533.4
Salem37.132.242.3
Somerset21.017.125.4
Sussex27.022.931.6
Union26.022.429.9
Warren25.521.430.0
New Jersey26.825.927.7

Data Notes

All prevalence estimates are age-adjusted to the U.S. 2000 standard population (except for estimates by age group).

Data Source

Behavioral Risk Factor Survey, Center for Health Statistics, New Jersey Department of Health, [http://www.state.nj.us/health/chs/njbrfs/]


References and Community Resources

NJDOH Office of Nutrition and Fitness: [https://nj.gov/health/nutrition/]


Page Content Updated On 08/27/2018, Published on 02/20/2020

Источник: https://www-doh.state.nj.us/doh-shad/indicator/complete_profile/Obese.html?ListCategoryFirst=x

Obesity

About this data

This indicator story presents findings on the prevalence of what are f&o stocks and obesity for adults from the Health Survey for England and for children from the National Child Measurement Programme.

The Health Survey for England (HSE) consists of an interview at which height and weight are measured. This enables the calculation of body mass index (BMI), which is defined as weight in kilograms divided by the height in metres squared (kg/m2), a measurement which is used to define overweight or obesity. Adults were classified into the following BMI groups according to the World Health Organisation (WHO) BMI classification:

  • Underweight – less than 18.5kg/m2
  • Normal – 18.5 to less than 25kg/m2
  • Overweight, not obese – 25 to less than 30kg/m2
  • Obese, including morbidly obese - 30kg/m2 or more
  • Morbidly obese – 40kg/m2 or more

HSE data up to and including 2002 are unweighted, and from 2003 onwards data have been weighted for non-response. For more information, please see the methods report and data quality statement.

The National Child Measurement Programme (NCMP) was introduced in 2006/07 and collects height and weight measurements for children in Reception (aged 4-5 years) and Year 6 (aged 10-11 years) in mainstream state schools in England. The programme now holds 13 years of data and annually measures over one million children. The national participation rate increased from 80% in 2006/07 to 95% in 2018/19. In the 2019/20 school year, data collection finished early when schools closed in March 2020 due to the Covid-19 pandemic. Data quality analysis carried out indicated that figures from 2019/20 are directly comparable to previous years. For more information, see the National Child Measurement Programme – England, 2019/20: Data Quality Statement. The HSE also collects data on childhood obesity, however as it is a sample the estimates are less precise than those for NCMP.

The BMI classification of each child is derived by calculating the child's BMI centile and classifying according to age and sex to take into obesity deaths in america 2019 different growth patterns in boys and girls. The NCMP uses the British 1990 growth reference (UK90) to define BMI classifications. Deprivation is defined by the deprivation decile of the lower super output area of the school the child attends.

It is likely that Year 6 obesity prevalence in the first years of the NCMP (2006/07 to 2008/09) were underestimates due to low participation. This, and the impact of other improvements in data quality, should be considered when making comparisons over time. For further information, see the National Child Measurement Programme - England, 2019/20: Appendices.

Источник: https://www.nuffieldtrust.org.uk/resource/obesity

Editor’s note: This analysis was originally published in The Conversation on Jan. 27, 2020. 

The opioid crisis and deaths related to e-cigarette use among teenagers have dominated news headlines. Recently, the Centers for Disease Control and Prevention reported that 34 people had died as a result of vaping and, in 2017, opioid addiction was responsible for more than 47,000 deaths in the U.S. Opioid addiction has been declared a public health emergency.

Yet these serious public health threats obscure an ever-present and growing calamity of obesity in the United States. Obesity is second only to cigarette smoking as a leading preventable death in the U.S. Nearly one in five deaths of African Americans and Caucasians age 40 to 85 is attributed to obesity, a rate that is increasing across generations.

Clearly society needs better strategies to address this public health emergency. As a health economist who has spent decades studying ways to prevent disease, I believe there are some policy options that could help.

The American Obesity Crisis

Many factors contribute to obesity, including genetics, diet, physical inactivity, medications, lack of education and food marketing.

People who are obese face heightened risk for diabetes, heart disease, stroke, high blood pressure and certain types of cancers, among other conditions. The estimated annual medical cost of obesity in the United States is $147 billion, with most of those costs hitting public programs such as Medicare and Medicaid. Similar trends have been observed internationally among developed countries.

So what can we do about it? The massive public and private efforts to control smoking provide both a template for addressing obesity and a benchmark for social impact. Tactics such as education, cigarette taxes, and smoke-free public spaces resulted in a 66% decline in smoking between 1965 and 2018, when cigarette smoking reached an all-time low of 13.7% among U.S. adults.

This outcome is associated with major health improvements – reduced cardiovascular disease, stroke, various cancers and mortality from lung cancer. Medicaid alone saves an estimated $2.5 billion a year from smoking-related health improvements.

From a public investment perspective, the potential bang for the buck is even bigger for obesity than it is for tobacco. In my view, a successful anti-obesity campaign must encourage people to be less sedentary; invest in new medical treatments and nutrition science; and create regulatory and health insurance policies that reward behavioral change. It also means broader access to effective therapies.

Good Ideas That Aren’t Working

Our current emphasis on behavioral interventions has been disappointing. Society needs to find a way to talk about obesity and come up with ways to deal with it that do not involve body-shaming. Losing weight means eating less or exercising more, or both, but there are no guarantees with either approach. Getting people to exercise is difficult. Nearly 80 percent of adults are not meeting the key guidelines for both aerobic and muscle-strengthening activity.

Getting people to change their diet is similarly ineffective. According to one study, half of dieters had gained 11 pounds five years after starting their diet; some progress but hardly enough. Similarly, nutritional labels have had little effect on consumers’ food intake and body mass index.

So what should policymakers do? I think it is time to take several new approaches.

Economic Models for Health Intervention

The intellectual property rights of companies that develop novel approaches to weight loss, such as mimicking the effects of exercise, should be protected and rewarded with patent law and other mechanisms. Currently, if a company discovers a way to get people to go for a walk with a new app or program, protection for intellectual property and reimbursement is uncertain.

Given the stakes, the U.S. government should offer greater rewards for behavioral interventions that can demonstrate long-term gains under the same rigorous regulatory standards similar to those required of new drugs. U.S. companies invest billions of dollars to develop pharmaceuticals. By contrast, there is less social investment in other prevention activities.

While not a solution for everyone, gastric bypass and adjustable gastric banding, among other procedures, have proven effective. New incentives could expand access to these surgeries by lowering the BMI threshold for eligibility. Some insurers have put up barriers to this treatment because obesity is not immediately life-threatening or related to our traditional notion of disease.

We need to find better ways to annuitize the cost of surgery and increase access while tying reimbursement to outcomes. Other insurers with an interest in long-term outcomes, including the life insurance industry, can play an important role. They have a vested financial interest in avoiding mortality and disability but have traditionally remained on the sidelines while Americans grow fatter.

Evidence points to a 20 percent reduction in BMI persisting up to 10 years after surgery. In 2017, 228,000 Americans received bariatric surgeries. Of those, only 10 percent of are eligible under current criteria.

Another approach is to consider new medications and utilize the successful approach that has been used to fight high blood pressure. About 50 years ago, hypertension was considered untreatable. Diet and exercise were the predominant means of controlling it. The discovery of multiple agents to combat hypertension, beginning with diuretics and beta blockers, proved transformative. A similar story emerged for elevated cholesterol. About half the decline in U.S. deaths from coronary heart disease can be attributed to medical therapies like these.

Several clinically proven anti-obesity medications are already available for people who do not respond to lifestyle modification. Furthermore, there is a robust clinical pipeline, with approximately 250 compounds under development, including dozens of novel compounds. Drugs such as these can help change the trajectory of the obesity epidemic, if they are made widely available and reimbursed — challenges in today’s health care insurance system.

Another avenue to consider includes levying taxes on sweetened beverages, or the so-called “soda tax.” One study found that implementing a 1 cent per ounce soda tax would reduce sugar-sweetened beverage consumption by 20 percent over 10 years. The result would be a $23.6 billion savings in health care and improved population health.

Finally, the food and restaurant industry deserves some of the blame. Restricting access – like the United States tried with the ban on the consumption and sale of alcohol – won’t work. But responsible steps to regulate portions might.

Smart, bold strategies helped us address public health crises before, including smoking and hypertension. We need to be similarly aggressive with obesity if we want to avert hundreds of thousands of unnecessary deaths. As we did with smoking, it is time to make obesity a number one public health priority.

Источник: https://healthpolicy.usc.edu/article/obesity-second-to-smoking-as-the-most-preventable-cause-of-us-deaths-needs-new-approaches/

Covid-19 has killed more people than obesity in the UK this year

28 September 2020

What was claimed

Coronavirus killed 9 people out of 66.6 million yesterday.

Our verdict

This was the death toll on 9 September, but it has risen since then. At its peak, more than 1,000 people were dying daily in the UK.

What was claimed

Obesity kills 30,000 people a year, or 82 a day.

Our verdict

This was an estimate made by Public Health England in 2017.

1 of 2 claims

A post on Instagram compares the number of deaths on one day from Covid-19 to an estimated number of daily deaths from obesity. 

The post reads: “Coronavirus killed 9 people out of 66.6m yesterday… Obesity kills 30,000 a year or 82 people per day. We’re all running around wearing masks imposed by the same government that was giving us half price McDonalds a fortnight ago.”

The original post was shared last week, when the UK daily death toll had been consistently higher than nine. The last time the death toll was nine was on 9 September. If you look at the deaths by the day they were reported rather than when they actually happened then there were nine deaths reported on 14 September.   

If we look at the number of deaths for which obesity was the direct cause, figures from the Office for National Statistics (ONS) suggest 534 people who died in 2019 in England and Wales had obesity as the underlying cause of death, down from 539 the year before. 

However, the ONS has warned that, because the underlying cause of death is defined as the disease or injury that initiated the train of events directly leading to death, the number of deaths attributed to obesity is likely to be an underestimate, as obesity is usually a background factor that causes another condition.

Obesity is associated with an increased risk of common causes of disease and death, including diabetes, cardiovascular disease and some cancers. 

In March 2017, Public Health England said it was “estimated that obesity is responsible for more than 30,000 deaths each year,” which appears to be the source of this post’s claim and would put the daily death count at around 82. 

These figures shouldn’t be used to compare the total impact of Covid-19 and obesity. 

Assuming that 30,000 is a reasonable estimate for annual deaths caused by obesity, we already know that there have been 42,001 Covid-19 deaths in the UK (and 37,299 deaths in England alone) in the last six months—just half the time. (We also know that the true figure is likely to be higher than this, as government data only includes people who had a positive Covid-19 test before they died, and who died within 28 days of their first positive test).

As for the post’s final comment, it is correct that the government has enforced the wearing of face coverings in certain situations. It is also correct that, under the government’s ‘Eat Out to Help Out’ scheme, diners could receive a discount on meals at McDonalds of up to 50% of their meal—up to £10 per person.

This article is part of our work fact checking potentially false pictures, videos and stories on Facebook. You can read more about this—and find out how to report Facebook content—here. For the purposes of that scheme, we’ve rated this claim as missing context because the selection of statistics makes it appear like obesity kills more people annually than Covid-19.

Источник: https://fullfact.org/online/coronavirus-obesity-mortality/

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