Welcome!

News Feed Item

EpiCast Report: Overweight and Obesity - Epidemiology Forecast to 2022

LONDON, March 20, 2014 /PRNewswire/ -- Reportbuyer.com just published a new market research report:

EpiCast Report: Overweight and Obesity - Epidemiology Forecast to 2022


EpiCast Report: Overweight and Obesity - Epidemiology Forecast to 2022

Summary

Overweight and obesity are characterized by abnormal or excessive fat accumulation in the body, which can increase the likelihood of developing type 2 diabetes and cardiovascular diseases. Obesity is an escalating public health problem with an increasing worldwide prevalence and has become a well-known threat to public health. As obesity is a complex condition associated with a wide range of lifestyle, genetic, social, cultural, and environmental factors, its prevalence may vary across countries, depending on the variations in these contributing factors.

GlobalData's epidemiological analysis forecasts that prevalent cases of overweight in the 9MM (US, France, Germany, Italy, Spain, UK, Japan, Brazil, and Canada) will increase from 250.05 million in 2012 to 271.86 million cases in 2022 at an annual growth rate (AGR) of 0.86%. Approximately 57% of the 250.05 million prevalent cases of overweight adults in the 9MM in 2012 were in men. GlobalData epidemiologists forecast that there were 167.39 million prevalent cases of obesity in the 9MM in 2012, which will increase to 213.34 million cases in 2022, increasing at an AGR of 2.75%. Approximately 52% of the 167.39 million prevalent cases of obese adults in the 9MM in 2012 were in women.

According to GlobalData's forecast, the prevalent cases of both overweight and obesity will increase dramatically over the next decade, with the number of obese adults increasing by an alarming 27.50%. Overweight and obesity have reached pandemic proportions; however, GlobalData epidemiologists believe that with the alarming increase in the prevalence of both overweight and obesity, the morbidity and mortality associated with the disease will continue to increase dramatically as well.

Scope

- The Overweight and Obesity EpiCast Report provides an overview of the risk factors, comorbidities, and global trends of overweight and obesity in the 9MM (US, France, Germany, Italy, Spain, UK, Japan, Brazil, and Canada). It includes a 10-year epidemiology forecast of the prevalent cases of overweight, obesity, obesity class I, obesity class II, and obesity class III segmented by sex and age. In addition, the report includes a 10-year forecast of the prevalent cases of obesity-associated comorbidities, including diagnosed diabetes, diagnosed hypertension, and dyslipidemia, among adults with overweight/obesity in some of these markets.
- The overweight and obesity epidemiology report is written and developed by Masters- and PhD-level epidemiologists.
- The EpiCast Report is in-depth, high quality, transparent and market-driven, providing expert analysis of disease trends in the 9MM.

Reasons to buy

- Develop business strategies by understanding the trends shaping and driving the global overweight and obesity market.
- Quantify patient populations in the global overweight and obesity market to improve product design, pricing, and launch plans.
- Organize sales and marketing efforts by identifying the age groups and sex that present the best opportunities for overweight and obesity therapeutics in each of the markets covered.
- Identify the number of obesity class I, obesity class II, and obesity class III prevalent cases.
1 Table of Contents
1 Table of Contents 4
1.1 List of Tables 7
1.2 List of Figures 8
2 Introduction 10
2.1 Catalyst 10
2.2 Related Reports 11
3 Epidemiology 12

3.1 Disease Overview 12
3.2 Risk Factors and Comorbidities 13
3.2.1 Family history is a strong predictor of obesity 13
3.2.2 Physical inactivity is an independent predictor of obesity 14
3.2.3 Excessive caloric intake doubles the risk for obesity 15
3.2.4 Hypertension is as high as 42% in obese adults 16
3.2.5 Dyslipidemia and type 2 diabetes are common comorbidities in obese patients 17
3.3 Global Trends 19
3.3.1 US 20
3.3.2 5EU 22
3.3.3 Japan 28
3.3.4 Brazil 29
3.3.5 Canada 31
3.4 Forecast Methodology 32
3.4.1 Sources Used 39
3.4.2 Forecast Assumptions and Methods: Prevalent Cases of Overweight, Obesity, Obesity by Class, and Comorbidities 46
3.4.3 Sources Not Used 63
3.5 Epidemiology Forecast of Overweight (2012–2022) 63
3.5.1 Prevalent Cases of Overweight 63
3.5.2 Age-Specific Prevalent Cases of Overweight 65
3.5.3 Sex-Specific Prevalent Cases of Overweight 67
3.5.4 Age-Standardized Prevalence of Overweight 70
3.6 Epidemiology Forecast of Obesity (2012–2022) 71
3.6.1 Prevalent Cases of Obesity 71
3.6.2 Age-Specific Prevalent Cases of Obesity 73

3.6.3 Sex-Specific Prevalent Cases of Obesity 75
3.6.4 Age-Standardized Prevalence of Obesity 76
3.7 Epidemiology Forecast of Obesity Class I (2012–2022) 77
3.7.1 Prevalent Cases of Obesity Class I 77
3.7.2 Age-Specific Prevalent Cases of Obesity Class I 79
3.7.3 Sex-Specific Prevalent Cases of Obesity Class I 81
3.7.4 Age-Standardized Prevalence of Obesity Class I 83
3.8 Epidemiology Forecast of Obesity Class II (2012–2022) 85
3.8.1 Prevalent Cases of Obesity Class II 85
3.8.2 Age-Specific Prevalent Cases of Obesity Class II 87
3.8.3 Sex-Specific Prevalent Cases of Obesity Class II 89
3.8.4 Age-Standardized Prevalence of Obesity Class II 91
3.9 Epidemiology Forecast of Obesity Class III (2012–2022) 93
3.9.1 Prevalent Cases of Obesity Class III 93
3.9.2 Age-Specific Prevalent Cases of Obesity Class III 95
3.9.3 Sex-Specific Prevalent Cases of Obesity Class III 97
3.9.4 Age-Standardized Prevalence of Obesity Class III 99
3.10 Epidemiology Forecast of Comorbidities among Adults with Overweight/Obesity (2012 and 2022) 101
3.10.1 Prevalent Cases of Diagnosed Diabetes among Adults with Overweight/Obesity 101
3.10.2 Prevalent Cases of Diagnosed Hypertension in Adults with Overweight/Obesity 104
3.10.3 Prevalent Cases of Dyslipidemia among Adults with Overweight/Obesity 106
3.11 Discussion 109
3.11.1 Conclusions on Epidemiological Trends 109
3.11.2 Limitations of the Analysis 110

3.11.3 Strengths of the Analysis 111
4 Appendix 112
4.1 Bibliography 112
4.2 About the Authors 118
4.2.1 Epidemiologists 118
4.2.2 Reviewers 118
4.2.3 Global Director of Epidemiology and Health Policy 119
4.2.4 Global Head of Healthcare 120
4.3 About GlobalData 121
4.4 About EpiCast 121
4.5 Disclaimer 121

1.1 List of Tables

Table 1: The WHO Classification System of Adult Overweight and Obesity According to BMI 12
Table 2: Risk Factors and Comorbidities of Obesity 13
Table 3: 9MM, Age-Adjusted and Crude Prevalence (%) of Obesity, by Sex, Ages ?20 Years, 2008 20
Table 4: JASSO and WHO Classifications of Obesity 28
Table 5: Sources of Epidemiological Data Used for Forecasting the Prevalent Cases of Overweight 33
Table 6: Sources of Epidemiological Data Used for Forecasting the Prevalent Cases of Obesity 34
Table 7: Sources of Epidemiological Data Used for Forecasting the Prevalent Cases of Obesity Class I, Class II, and Class III 36
Table 8: Sources of Epidemiological Data Used for Forecasting the Prevalent Cases of Comorbidities in Overweight/Obese 38
Table 9: 9MM, Prevalent Cases of Overweight, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 64
Table 10: 9MM, Prevalent Cases of Overweight, By Age, Both Sexes, N (Millions), Row (%), 2012 66
Table 11: 9MM, Prevalent Cases of Overweight, Ages ?18 Years, By Sex, N (Millions), Row (%), 2012 68

Table 12: 9MM, Prevalent Cases of Obesity, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 72
Table 13: 9MM, Prevalent Cases of Obesity, By Age, Both Sexes, N (Millions), Row (%), 2012 74
Table 14: 9MM, Prevalent Cases of Obesity, Ages ?18 Years, by Sex, N (Millions), Row (%), 2012 75
Table 15: 9MM, Prevalent Cases of Obesity Class I, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 78
Table 16: 9MM, Prevalent Cases of Obesity Class I, by Age, Both Sexes, N (Millions), Row (%), 2012 80
Table 17: 9MM, Prevalent Cases of Obesity Class I, Ages ?18 Years, By Sex, N (Millions), Row (%), 2012 82
Table 18: 9MM, Prevalent Cases of Obesity Class II, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 86

Table 19: 9MM, Prevalent Cases of Obesity Class II, By Age, Both Sexes, N (Millions), Row (%), 2012 88
Table 20: 9MM, Prevalent Cases of Obesity Class II, Ages ?18 Years, By Sex, N (Millions), Row (%), 2012 90
Table 21: 9MM, Prevalent Cases of Obesity Class III, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 94
Table 22: 9MM, Prevalent Cases of Obesity Class III, By Age, Both Sexes, N (Millions), Row (%), 2012 96
Table 23: 9MM, Prevalent Cases of Obesity Class III, Ages ?18 Years, By Sex, N (Millions), Row (%), 2012 98
Table 24: 5MM*, Prevalent Cases of Diagnosed Diabetes among Adults with Overweight/Obesity, Both Sexes, N (Millions), 2012 and 2022 102
Table 25: 6MM*, Prevalent Cases of Diagnosed Hypertension among Adults with Overweight/Obesity, Both Sexes, N (Millions), 2012 and 2022 105
Table 26: 4MM, Prevalent Cases of Dyslipidemia* among Adults with Overweight/Obesity, Both Sexes, N (Millions), 2012 and 2022 107

1.2 List of Figures

Figure 1: US, Overweight and Obesity Age-Adjusted Prevalence (%), Ages 20–74 Years, Men, 1960–2010 21
Figure 2: US, Overweight and Obesity Age-Adjusted Prevalence (%), Ages 20–74 Years, Women, 1960–2010 21
Figure 3: France, Overweight and Obesity Prevalence (%), Ages ?15 Years, Men, 1997–2006 23
Figure 4: France, Overweight and Obesity Prevalence (%), Ages ?15 Years, Women, 1997–2006 23
Figure 5: Italy, Overweight and Obesity Prevalence (%), Ages ?18, Men, 1983–2010 25
Figure 6: Italy, Overweight and Obesity Prevalence (%), Ages ?18, Men, 1983–2010 25
Figure 7: UK, Overweight and Obesity Prevalence (%), Ages ?16 Years, Men, 1993–2011 27
Figure 8: UK, Overweight and Obesity Prevalence (%), Ages ?16 Years, Women, 1993–2011 27
Figure 9: Japan, Obesity Prevalence (%), Ages ?20 Years, By Sex, 1976–2006 29
Figure 10: Brazil, Overweight and Obesity Prevalence (%), Ages ?20 Years, Men, 1975–2003 30
Figure 11: Brazil, Overweight and Obesity Prevalence (%), Ages ?20 Years, Women, 1975–2003 30
Figure 12: Canada, Obesity Prevalence (%), Ages ?18 Years, Both Sexes, 1978–2008 32
Figure 13: 9MM, Prevalent Cases of Overweight, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 65
Figure 14: 9MM, Prevalent Cases of Overweight, by Age, Both Sexes, N (Millions), 2012 67
Figure 15: 9MM, Prevalent Cases of Overweight, Ages ?18 Years, by Sex, N (Millions), 2012 69
Figure 16: 9MM, Overweight Age-Standardized Prevalence (%), Ages ?18 Years, By Sex, 2012 71
Figure 17: 9MM, Prevalent Cases of Obesity, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 73

Figure 18: 9MM, Prevalent Cases of Obesity, By Age, Both Sexes, N (Millions), 2012 74
Figure 19: 9MM, Prevalent Cases of Obesity, Ages ?18 Years, by Sex, N (Millions), 2012 76
Figure 20: 9MM, Obesity Age-Standardized Prevalence (%), Ages ?18 Years, By Sex, 2012 77
Figure 21: 9MM*, Prevalent Cases of Obesity Class I, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 79
Figure 22: 9MM*, Prevalent Cases of Obesity Class I, By Age, Both Sexes, N (Millions), 2012 81
Figure 23: 9MM*, Prevalent Cases of Obesity Class I, Ages ?18 Years, by Sex, N (Millions), 2012 83
Figure 24: 9MM*, Obesity Class I Age-Standardized Prevalence (%), Ages ?18 Years, by Sex, 2012 84
Figure 25: 9MM*, Prevalent Cases of Obesity Class II, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 87
Figure 26: 9MM*, Prevalent Cases of Obesity Class II, By Age, Both Sexes, N (Millions), 2012 89
Figure 27: 9MM*, Prevalent Cases of Obesity Class II, Ages ?18 Years, by Sex, N (Millions), 2012 91
Figure 28: 9MM*, Age-Standardized Prevalence (%) of Obesity Class II, Ages ?18 Years, By Sex, 2012 92
Figure 29: 9MM*, Prevalent Cases of Obesity Class III, Ages ?18 Years, Both Sexes, N (Millions), 2012–2022 95
Figure 30: 9MM*, Prevalent Cases of Obesity Class III, By Age, Both Sexes, N (Millions), 2012 97
Figure 31: 9MM*, Prevalent Cases of Obesity Class III, Ages ?18 Years, By Sex, N (Millions), 2012 99
Figure 32: 9MM*, Obesity Class III Age-Standardized Prevalence (%), Ages ?18 Years, By Sex, 2012 100
Figure 33: 5MM*, Prevalent Cases of Diagnosed Diabetes among Adults with Overweight/Obesity, Both Sexes, N (Millions), 2012 103
Figure 34: 6MM*, Prevalent Cases of Diagnosed Hypertension among Adults with Overweight/Obesity, Both Sexes, N (Millions), 2012 106
Figure 35: 4MM*, Prevalent Cases of Dyslipidemia** among Adults with Overweight/Obesity, Both Sexes, N (Millions), 2012 108

Read the full report:
EpiCast Report: Overweight and Obesity - Epidemiology Forecast to 2022
http://www.reportbuyer.com/pharma_healthcare/diseases/epicast_report_overweight_obesity_epidemiology_forecast_2022.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=Pathology

For more information:
Sarah Smith
Research Advisor at Reportbuyer.com  
Email: [email protected]  
Tel: +44 208 816 85 48
Website: www.reportbuyer.com

SOURCE ReportBuyer

More Stories By PR Newswire

Copyright © 2007 PR Newswire. All rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

Latest Stories
Apache Hadoop is emerging as a distributed platform for handling large and fast incoming streams of data. Predictive maintenance, supply chain optimization, and Internet-of-Things analysis are examples where Hadoop provides the scalable storage, processing, and analytics platform to gain meaningful insights from granular data that is typically only valuable from a large-scale, aggregate view. One architecture useful for capturing and analyzing streaming data is the Lambda Architecture, represent...
SYS-CON Events announced today that Ocean9will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Ocean9 provides cloud services for Backup, Disaster Recovery (DRaaS) and instant Innovation, and redefines enterprise infrastructure with its cloud native subscription offerings for mission critical SAP workloads.
With billions of sensors deployed worldwide, the amount of machine-generated data will soon exceed what our networks can handle. But consumers and businesses will expect seamless experiences and real-time responsiveness. What does this mean for IoT devices and the infrastructure that supports them? More of the data will need to be handled at - or closer to - the devices themselves.
In his session at DevOps Summit, Tapabrata Pal, Director of Enterprise Architecture at Capital One, will tell a story about how Capital One has embraced Agile and DevOps Security practices across the Enterprise – driven by Enterprise Architecture; bringing in Development, Operations and Information Security organizations together. Capital Ones DevOpsSec practice is based upon three "pillars" – Shift-Left, Automate Everything, Dashboard Everything. Within about three years, from 100% waterfall, C...
Adding public cloud resources to an existing application can be a daunting process. The tools that you currently use to manage the software and hardware outside the cloud aren’t always the best tools to efficiently grow into the cloud. All of the major configuration management tools have cloud orchestration plugins that can be leveraged, but there are also cloud-native tools that can dramatically improve the efficiency of managing your application lifecycle.
SYS-CON Events announced today that Juniper Networks (NYSE: JNPR), an industry leader in automated, scalable and secure networks, will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Juniper Networks challenges the status quo with products, solutions and services that transform the economics of networking. The company co-innovates with customers and partners to deliver automated, scalable and secure network...
DevOps is often described as a combination of technology and culture. Without both, DevOps isn't complete. However, applying the culture to outdated technology is a recipe for disaster; as response times grow and connections between teams are delayed by technology, the culture will die. A Nutanix Enterprise Cloud has many benefits that provide the needed base for a true DevOps paradigm. In his Day 3 Keynote at 20th Cloud Expo, Chris Brown, a Solutions Marketing Manager at Nutanix, will explore t...
SYS-CON Events announced today that SoftLayer, an IBM Company, has been named “Gold Sponsor” of SYS-CON's 18th Cloud Expo, which will take place on June 7-9, 2016, at the Javits Center in New York, New York. SoftLayer, an IBM Company, provides cloud infrastructure as a service from a growing number of data centers and network points of presence around the world. SoftLayer’s customers range from Web startups to global enterprises.
Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development ...
SYS-CON Events announced today that Linux Academy, the foremost online Linux and cloud training platform and community, will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Linux Academy was founded on the belief that providing high-quality, in-depth training should be available at an affordable price. Industry leaders in quality training, provided services, and student certification passes, its goal is to c...
Some people worry that OpenStack is more flash then substance; however, for many customers this could not be farther from the truth. No other technology equalizes the playing field between vendors while giving your internal teams better access than ever to infrastructure when they need it. In his session at 20th Cloud Expo, Chris Brown, a Solutions Marketing Manager at Nutanix, will talk through some real-world OpenStack deployments and look into the ways this can benefit customers of all sizes....
SYS-CON Events announced today that CA Technologies has been named “Platinum Sponsor” of SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY, and the 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. CA Technologies helps customers succeed in a future where every business – from apparel to energy – is being rewritten by software. From ...
Interoute has announced the integration of its Global Cloud Infrastructure platform with Rancher Labs’ container management platform, Rancher. This approach enables enterprises to accelerate their digital transformation and infrastructure investments. Matthew Finnie, Interoute CTO commented “Enterprises developing and building apps in the cloud and those on a path to Digital Transformation need Digital ICT Infrastructure that allows them to build, test and deploy faster than ever before. The int...
In his session at @ThingsExpo, Eric Lachapelle, CEO of the Professional Evaluation and Certification Board (PECB), will provide an overview of various initiatives to certifiy the security of connected devices and future trends in ensuring public trust of IoT. Eric Lachapelle is the Chief Executive Officer of the Professional Evaluation and Certification Board (PECB), an international certification body. His role is to help companies and individuals to achieve professional, accredited and worldw...
SYS-CON Events announced today that Loom Systems will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Founded in 2015, Loom Systems delivers an advanced AI solution to predict and prevent problems in the digital business. Loom stands alone in the industry as an AI analysis platform requiring no prior math knowledge from operators, leveraging the existing staff to succeed in the digital era. With offices in S...