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Multiple Myeloma: Epidemiology Forecast to 2027
[Published by Global Data]

Published by Global Data: 20 Mar 2019 | 230880 | In Stock
Related Topics: Multiple Myeloma , Myeloma

Introduction

Multiple Myeloma: Epidemiology Forecast to 2027

Summary

Multiple myeloma (MM) (International Statistical Classification of Diseases and Related Health Problems, 10th Revision [ICD-10] code = C90.0) is a hematologic cancer that forms in a type of white blood cell called a plasma cell.

In 2017, the 8MM had 81,380 diagnosed incident cases of MM. This is expected to increase to 103,272 diagnosed incident cases by 2027, at an Annual Growth Rate (AGR) of 2.69%. The increase is driven by the aging population in the 8MM. In 2017, the 8MM had 353,890 diagnosed prevalent cases of MM. This is expected to increase to 555,243 diagnosed prevalent cases by 2027, at an AGR of 5.69%. The US had the highest number of diagnosed incident and diagnosed prevalent cases of MM. The development of more effective therapies, particularly for elderly patients, would improve survival and increase disease prevalence.

GlobalData’s epidemiological forecast for the diagnosed incident cases of MM in the 8MM is supported by country-specific data for the age- and sex-specific diagnosed incidence of MM. Additionally, GlobalData adjusted the forecast to account for coding changes in MM diagnosis after 2014. The use of a consistent methodology across the 8MM to forecast the diagnosed incident cases and the diagnosed prevalent cases of MM allows for a meaningful comparison of the forecast incident cases and the forecast prevalent cases of MM in these markets.

Scope

The Multiple Myeloma Epidemiology Report provides an overview of the risk factors and global trends of multiple myeloma in the eight major markets (8MM: US, France, Germany, Italy, Spain, UK, Japan, and China (Urban)). This report also includes a 10-year epidemiological forecast for the following segmentations -

- Diagnosed incident cases of MM

- Diagnosed prevalent cases of MM

- Diagnosed incident cases of MM by symptom status

- Diagnosed incident cases by stem cell transplantation (SCT) eligibility

- Diagnosed incident cases by high-risk cytogenetics (chromosomal abnormalities): t(4;14), t(14;16), t(14;20), and del(17/17p)

- Diagnosed incident cases by revised-international staging system (R-ISS) staging

- The multiple myeloma epidemiology report is written and developed by Masters- and PhD-level epidemiologists.

- The Epidemiology Report is in-depth, high quality, transparent and market-driven, providing expert analysis of disease trends in the 8MM.

Reasons to buy

The Multiple Myeloma Epidemiology report will allow you to -

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

- Quantify patient populations in the global multiple myeloma 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 multiple myeloma therapeutics in each of the markets covered.

- Understand magnitude of multiple myeloma patient population by risk, SCT eligibility, and R-ISS stages.

Table of Contents
for Multiple Myeloma: Epidemiology Forecast to 2027 [Published by Global Data]

  • 1 Table of Contents

    1.1 List of Tables

    1.2 List of Figures

    2 Multiple Myeloma: Executive Summary

    2.1 Related Reports

    2.2 Upcoming Reports

    3 Epidemiology

    3.1 Disease Background

    3.2 Risk Factors and Comorbidities

    3.3 Global and Historical Trends

    3.3.1 Diagnosed Incidence of MM

    3.4 Forecast Methodology

    3.4.1 Sources

    3.4.2 Forecast Assumptions and Methods

    3.4.3 Diagnosed Incident Cases of MM

    3.4.4 Diagnosed Incident Cases of MM by Symptom Status

    3.4.5 Diagnosed Incident Cases of MM by SCT Eligibility

    3.4.6 Diagnosed Incident Cases of MM by Chromosomal Abnormalities

    3.4.7 Diagnosed Incident Cases of MM by R-ISS Staging

    3.4.8 Diagnosed Prevalent Cases of MM

    3.5 Epidemiological Forecast for Multiple Myeloma (2017-2027)

    3.5.1 Diagnosed Incident Cases of MM

    3.5.2 Age-Specific Diagnosed Incident Cases of MM

    3.5.3 Sex-Specific Diagnosed Incident Cases of MM

    3.5.4 Diagnosed Incident Cases of MM by Symptom Status

    3.5.5 Diagnosed Incident Cases of MM by SCT Eligibility

    3.5.6 Diagnosed Incident Cases of MM by Chromosomal Abnormalities

    3.5.7 Diagnosed Incident Cases of MM by R-ISS Staging

    3.5.8 Diagnosed Prevalent Cases of MM

    3.6 Discussion

    3.6.1 Epidemiological Forecast Insight

    3.6.2 Limitations of Analysis

    3.6.3 Strengths of Analysis

    4 Appendix

    4.1 Bibliography

    4.2 About the Authors

    4.2.1 Epidemiologist

    4.2.2 Reviewers

    4.2.3 Global Director of Therapy Analysis and Epidemiology

    4.2.4 Global Head and EVP of Healthcare Operations and Strategy

    4.3 About GlobalData

    4.4 Contact Us

    4.5 Disclaimer

List Of Tables
in Multiple Myeloma: Epidemiology Forecast to 2027 [Published by Global Data]

1.1 List of Tables

Table 1: Risk Factors for MM

List Of Figures, Charts and Diagrams
in Multiple Myeloma: Epidemiology Forecast to 2027 [Published by Global Data]

1.2 List of Figures

Figure 1: 8MM, Diagnosed Incident Cases of MM, Both Sexes, Ages ≥40 Years, 2017 and 2027

Figure 2: 8MM, Diagnosed Prevalent Cases of MM, Both Sexes, Ages ≥40 Years, 2017 and 2027

Figure 3: 8MM, Age-Standardized Diagnosed Incidence of MM (Cases per 100,000 Population), Men, Ages ≥40 Years, 2007 to 2027

Figure 4: 8MM, Age-Standardized Diagnosed Incidence of MM (Cases per 100,000 Population), Women, Ages ≥40 Years, 2007 to 2027

Figure 5: 8MM, Sources Used to Forecast the Diagnosed Incident Cases of MM

Figure 6: 8MM, Sources Used to Forecast the Diagnosed Prevalent Cases of MM

Figure 7: 8MM, Sources Used to Forecast the Diagnosed Incident Cases of MM by Symptom Status

Figure 8: 8MM, Sources Used to Forecast the Diagnosed Incident Cases of MM by SCT Eligibility

Figure 9: 8MM, Sources Used to Forecast the Diagnosed Incident Cases of MM by Chromosomal Abnormalities

Figure 10: 8MM, Sources Used to Forecast the Diagnosed Incident Cases of MM by R-ISS Staging

Figure 11: 8MM, Diagnosed Incident Cases of MM, Both Sexes, Ages ≥40 Years, N, 2017

Figure 12: 8MM, Age-Specific Diagnosed Incident Cases of MM, Both Sexes, Ages ≥40 Years, N, 2017

Figure 13: 8MM, Sex-Specific Diagnosed Incident Cases of MM, Ages ≥40 Years, N, 2017

Figure 14: 8MM, Diagnosed Incident Cases of MM by Symptom Status, Both Sexes, Ages ≥40 Years, N, 2017

Figure 15: 8MM, Diagnosed Incident Cases of MM by SCT Eligibility, Both Sexes, Ages ≥40 Years, N, 2017

Figure 16: 8MM, Diagnosed Incident Cases of MM by Chromosomal Abnormalities, Both Sexes, Ages ≥40 Years, N, 2017

Figure 17: 8MM, Diagnosed Incident Cases of MM by R-ISS Staging, Both Sexes, Ages ≥40 Years, N, 2017

Figure 18: 8MM, Diagnosed Prevalent Cases of MM, Both Sexes, Ages ≥40 Years, N, 2017

Additional Details

Publisher

Global Data

Publisher Information

Reference

230880 | GDHCER200-19

Number of Pages

42

Report Format

PDF

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