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Acute Myeloid Leukemia - Epidemiology Forecast to 2029

Published by GlobalData Product code 943319
Published Content info 37 Pages
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Acute Myeloid Leukemia - Epidemiology Forecast to 2029
Published: June 29, 2020 Content info: 37 Pages
Description

Acute myeloid leukemia (AML) is a cancer of the blood and bone marrow in which the bone marrow produces abnormal red blood cells, white blood cells, or platelets, and generally progresses at a rapid pace. AML is more common in the elderly, is associated with severe complications and high mortality, and accounts for a disproportionately high number of cancer-related deaths. AML is often called acute myelogenous leukemia, acute myeloblastic leukemia, acute granulocytic leukemia, and acute nonlymphocytic leukemia.

In 2019, there were 72,164 diagnosed incident cases of AML in the 8MM in men and women combined for ages 18 years and older. GlobalData epidemiologists estimate that globally, cases will rise to 90,264 at an Annual Growth Rate (AGR) of 2.51% by 2029. Urban China is expected to contribute the highest proportion of growth over this forecast period with diagnosed incident cases growing from 29,535 in 2019 to 39,991 in 2029 at an AGR of 3.54%. Similarly, the five-year diagnosed prevalent cases of AML in the 8MM are expected to grow from 99,624 cases to 124,065 cases by 2029.

Scope

  • This report provides an overview of the risk factors, comorbidities, and global and historical trends for AML in the eight major markets (8MM) (US, France, Germany, Italy, Spain, UK, Japan, and urban China). It includes a 10-year epidemiological forecast for the diagnosed incident cases of AML segmented by sex, and age by 10-year age groups beginning at age 18 years. The diagnosed incident cases of AML are also segmented by prognostic risk group (favorable-risk, intermediate-risk, and high-risk), and by type (secondary AML and acute promyelocytic leukemia [APL]).
  • Additionally, this forecast provides the five-year diagnosed prevalent cases of AML. The accompanying epidemiology forecast model includes additional analysis on the diagnosed incident cases of AML, with associated mutations and biomarkers FLT3, IDH1, IDH2, BCR-ABL1/t(9;22)(q34.1;q11.2) translocation, CD-33, and CBF-AML with KIT mutation.
  • The AML 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 AML Epidemiology series will allow you to -

  • Develop business strategies by understanding the trends shaping and driving the global AML market.
  • Quantify patient populations in the global AML market to improve product design, pricing, and launch plans.
  • Organize sales and marketing efforts by identifying the age groups that present the best opportunities for AML therapeutics in each of the markets covered.
  • Understand magnitude of secondary AML and APL.
Table of Contents
Product Code: GDHCER242-20

Table of Contents

  • 1.1 List of Tables
  • 1.2 List of Figures

2 AML: Executive Summary

  • 2.1 Catalyst
  • 2.2 Related Reports
  • 2.3 Upcoming Reports

3 Epidemiology

  • 3.1 Disease Background
  • 3.2 Risk Factors and Comorbidities
  • 3.3 Global and Historical Trends
  • 3.4 Forecast Methodology
    • 3.4.1 Sources Used and Not Used
    • 3.4.2 Forecast Assumptions and Methods
  • 3.5 Epidemiological Forecast for AML (2019-2029)
    • 3.5.1 Diagnosed Incident Cases of AML
    • 3.5.2 Age-Specific Diagnosed Incident Cases of AML
    • 3.5.3 Sex-Specific Diagnosed Incident Cases of AML
    • 3.5.4 Diagnosed Incident Cases of AML by Risk Group
    • 3.5.5 Diagnosed Incident Cases of Secondary AML
    • 3.5.6 Diagnosed Incident Cases of APL
    • 3.5.7 Five-Year Diagnosed Prevalent Cases of AML
  • 3.6 Discussion
    • 3.6.1 Epidemiological Forecast Insight
    • 3.6.2 Coronavirus Disease 2019 (COVID-19) Impact
    • 3.6.3 Limitations of Analysis
    • 3.6.4 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

  • Table 1: Summary of Newly Added Data Types and Countries
  • Table 2: Summary of Updated Data Types
  • Table 3: Risk Factors for AML

List of Figures

  • Figure 1: 8MM, Diagnosed Incident Cases of AML, Men and Women, Ages 18 Years and Older, 2019 and 2029
  • Figure 2: 8MM, Five-Year Diagnosed Prevalent Cases of AML, Men and Women, Ages 18 Years and Older, 2019 and 2029
  • Figure 3: 8MM, Diagnosed Incidence of AML, Men and Women, Ages 18 Years and Older, 2009-2019
  • Figure 4: Sources Used to Forecast the Diagnosed Incident Cases of AML
  • Figure 5: Sources Used to Forecast the Diagnosed Incident Cases of AML by Risk Group
  • Figure 6: Sources Used to Forecast the Diagnosed Incident Cases of Secondary AML
  • Figure 7: Sources Used to Forecast the Diagnosed Incident Cases of APL
  • Figure 8: Sources Used to Forecast the Five-Year Diagnosed Prevalent Cases of AML
  • Figure 9: 8MM, Diagnosed Incident Cases of AML, Men and Women, Ages ≥18 Years, 2019
  • Figure 10: 8MM, Diagnosed Incident Cases of AML by Age Group, Men and Women, 2019
  • Figure 11: 8MM, Diagnosed Incident Cases of AML by Sex, Ages ≥18 Years, 2019
  • Figure 12: 8MM, Diagnosed Incident Cases of AML by Risk Group, Men and Women, Ages ≥18 Years, 2019
  • Figure 13: 8MM, Diagnosed Incident Cases of Secondary AML, Men and Women, Ages ≥18 Years, 2019
  • Figure 14: Diagnosed Incident Cases of APL, 8MM, Men and Women, Ages ≥18 Years, 2019 and 2029
  • Figure 15: 8MM, Five-Year Diagnosed Prevalent Cases of AML, Men and Women, Ages ≥18 Years, 2019
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