Welcome!

News Feed Item

EpiCast Report: Non-Small Cell Lung Cancer (NSCLC) - Epidemiology Forecast to 2022

NEW YORK, April 22, 2013 /PRNewswire/ -- Reportlinker.com announces that a new market research report is available in its catalogue:

EpiCast Report: Non-Small Cell Lung Cancer (NSCLC) - Epidemiology Forecast to 2022

http://www.reportlinker.com/p01163175/EpiCast-Report-Non-Small-Cell-Lung...

EpiCast Report: Non-Small Cell Lung Cancer (NSCLC) - Epidemiology Forecast to 2022

Summary

Lung cancer is a disease of uncontrolled cell growth in the lung tissues. It is one of the most commonly occurring cancers in the world, representing almost 13% of all cancers (Ferlay et al., 2010). NSCLC is the most common type of lung cancer, comprising about 85% of all lung cancers. An estimated 1.37 million new cases of NSCLC occurred worldwide in 2008 (Ferlay et al., 2010).

This forecast was built on an in-depth analysis of historical trends for lung cancer incidence, mortality, and survival; smoking prevalence, disease stage at diagnosis, and the status of lung cancer screening programs in each market.

GlobalData epidemiologists forecast that the number of incident cases of NSCLC in the 9MM will grow from 1.03 million cases in 2012 to 1.26 million by 2022 at a rate of 2.2% per year. The increase in the number of NSCLS incident cases is driven by changes in the incidence of lung cancer, an aging population, population growth, smoking habits in men and women, and exposure to environmental risk factors. Of the nine markets, China will have the highest number of incident cases of NSCLC, growing from 0.55 million cases in 2012 to 0.69 million by 2022 at a rate of 2.5% per year. In addition to China, India and Japan are also important markets for NSCLC in the next 10 years because these countries have a growing older population, a high smoking prevalence, and few smoking bans compared with western countries.

Scope

- The NSCLC EpiCast Report provides overview of the risk factors and the global and historical trends for NSCLC in the nine major markets (9MM): US, France, Germany, Italy, Spain, UK, Japan, China, and India.

- It also includes a 10-year epidemiology forecast of the diagnosed incident cases of NSCLC in these markets from 2012–2022.

- The incident cases are further segmented by sex and age (40–49 years, 50–59 years, 60–69 years, 70–79 years, and at ages 80 years and older), cancer stage at diagnosis (AJCC Stage I-IV), cancer histological subtype (adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and others), and the proportion of smokers among NSCLC cases.

Reasons to buy

- Develop business strategies by understanding the trends shaping and driving the global NSCLC market.

- Quantify patient populations in the global NSCLC market to improve product development, pricing, and launch plans.

- Organize sales and marketing efforts by identifying sex, age groups, and cancer histological subtypes that present the best opportunities for NSCLC therapeutics in each of the markets covered.

Table of Contents

1 Table of Contents 3

1.1 List of Tables 5

1.2 List of Figures 5

2 Introduction 7

2.1 Catalyst 7

2.2 Upcoming Reports 7

3 Epidemiology 8

3.1 Risk Factors and Comorbidities 9

3.1.1 Smokers are more likely to have lung cancer than non-smokers 10

3.1.2 Long-term exposure to radon increases the risk of lung cancer 11

3.1.3 Family history of any cancer increases lung cancer risk 11

3.1.4 TB increases the risk of lung cancer and increases mortality risk 12

3.1.5 COPD is a comorbidity that reduces lung cancer survival 12

3.1.6 Lung cancer screening programs may not be beneficial 13

3.1.7 Stage at diagnosis significantly impacts the survival of lung cancer cases 14

3.2 Global Trends 14

3.2.1 US 18

3.2.2 France 22

3.2.3 Germany 24

3.2.4 Italy 25

3.2.5 Spain 27

3.2.6 UK 29

3.2.7 Japan 30

3.2.8 China 32

3.2.9 India 34

3.3 Forecast Methodology 35

3.3.1 Sources Used 37

3.3.2 Sources Not Used 41

3.3.3 Forecast Methods and Assumptions 42

3.4 Epidemiology Forecast (2012–2022) 44

3.4.1 Incident Cases of NSCLC 44

3.4.2 Age-Specific Incident Cases of NSCLC 46

3.4.3 Sex-Specific Incident Cases of NSCLC 47

3.4.4 Age-Standardized Incidence Rates of NSCLC 48

3.4.5 Segmentation of NSCLC Incident Cases 50

3.5 Discussion 58

3.5.1 Limitations of the Analysis 59

3.5.2 Strengths of the Analysis 59

4 Appendix 61

4.1 Bibliography 61

4.2 About the Authors 69

4.2.1 Epidemiologists 69

4.2.2 Reviewers 70

4.2.3 Global Director of Epidemiology and Clinical Trials Analysis 71

4.2.4 Global Head of Healthcare 71

4.3 About GlobalData 72

4.4 About EpiCast 72

4.5 Contact Us 72

4.6 Disclaimer 73

List of Tables

Table 1: Risk Factors and Comorbidities for Lung Cancer 9

Table 2: 9MM, Age-Standardized (World) Smoking Prevalence, Men and Women, 2006 and 2009 17

Table 3: 9MM, Sources of NSCLC Incidence Data 36

Table 4: 9MM, Incident Cases of NSCLC, Men and Women Ages ?40 Years, N, Selected Years 2012–2022 44

Table 5: 9MM, Incident Cases of NSCLC, by Age, Men and Women, N (Row %), 2012 46

Table 6: 9MM, Incident Cases of NSCLC, by Sex, Ages ?40 Years, N (Row %), 2012 47

Table 7: 9MM, Incident Cases of NSCLC, by Stage at Diagnosis, Men and Women Ages ?40 Years, N (Row %), 2012 51

Table 8: 9MM, Incident Cases of NSCLC, by Histological Subtype, Ages ?40 Years, Men and Women, N (Row %) , 2012 54

Table 9: Comparison of GlobalData Forecast for Lung Cancer Incident Cases with Globocan 2008 60

List of Figures

Figure 1: 9MM, Age-Standardized (World) Lung Cancer Incidence, Men Ages ?40 Years, 1993–2002 15

Figure 2: 9MM, Age-Standardized (World) Lung Cancer Incidence, Women Ages ?40 Years, 1993–2002 16

Figure 3: Age-Standardized (US 2000) Lung Cancer Incidence and Mortality, Men and Women, All Ages, US, 1999–2008 19

Figure 4: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, US, 1993–2008 20

Figure 5: Relative Risk of Death from Lung Cancer in Current Smokers, Men and Women, US 21

Figure 6: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, France, 1993–2008 23

Figure 7: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, Germany, 1993–2008 24

Figure 8: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, Italy, 1993–2008 26

Figure 9: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, Spain, 1991–2007 28

Figure 10: Age-Standardized (World) Lung Cancer Incidence and Mortality, Ages ?40 Years, UK, 1993–2008 29

Figure 11: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, Japan, 1993–2008 31

Figure 12: Age-Standardized (World) Lung Cancer Incidence and Mortality, Men and Women Ages ?40 Years, China, 1993–2008 33

Figure 13: Age-Standardized (World) Lung Cancer Incidence, Men and Women Ages ?40 Years, India, 1993–2002 34

Figure 14: 9MM, Incident Cases of NSCLC, Men and Women Ages ?40 Years, N, Selected Years 2012–2022 45

Figure 15: 9MM, Incident Cases of NSCLC, by Sex, Ages ?40 Years, N, 2012 48

Figure 16: 9MM, Age-Standardized (World) NSCLC Incidence, 2012 49

Figure 17: 9MM, Incident Cases of NSCLC, by Stage at Diagnosis, Men and Women Ages ?40 Years, % and N, 2012 52

Figure 18: 9MM, Incident Cases of NSCLC by Histological Subtype, Men and Women Ages ?40 Years, %, 2012 55

Figure 19: 9MM, Proportion of Smokers Among NSCLC Cases and the General Population, Men and Women, %, 2012 56

Figure 20: Smoking History in NSCLC Cases in the 9MM, Men and Women Ages ?40 Years, N, 2012 57

To order this report:Pathology Industry: EpiCast Report: Non-Small Cell Lung Cancer (NSCLC) - Epidemiology Forecast to 2022

Contact Clare: [email protected]
US:(339) 368 6001
Intl:+1 339 368 6001

 

SOURCE Reportlinker

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
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will d...
While DevOps most critically and famously fosters collaboration, communication, and integration through cultural change, culture is more of an output than an input. In order to actively drive cultural evolution, organizations must make substantial organizational and process changes, and adopt new technologies, to encourage a DevOps culture. Moderated by Andi Mann, panelists discussed how to balance these three pillars of DevOps, where to focus attention (and resources), where organizations might...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Le...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
CI/CD is conceptually straightforward, yet often technically intricate to implement since it requires time and opportunities to develop intimate understanding on not only DevOps processes and operations, but likely product integrations with multiple platforms. This session intends to bridge the gap by offering an intense learning experience while witnessing the processes and operations to build from zero to a simple, yet functional CI/CD pipeline integrated with Jenkins, Github, Docker and Azure...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Dhiraj Sehgal works in Delphix's product and solution organization. His focus has been DevOps, DataOps, private cloud and datacenters customers, technologies and products. He has wealth of experience in cloud focused and virtualized technologies ranging from compute, networking to storage. He has spoken at Cloud Expo for last 3 years now in New York and Santa Clara.
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure ...
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.