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急性骨髄性白血病 (AML):疫学的予測 (2029年まで)

Acute Myeloid Leukemia - Epidemiology Forecast to 2029

発行 GlobalData 商品コード 943319
出版日 ページ情報 英文 37 Pages
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急性骨髄性白血病 (AML):疫学的予測 (2029年まで) Acute Myeloid Leukemia - Epidemiology Forecast to 2029
出版日: 2020年06月29日 ページ情報: 英文 37 Pages
概要

2019年には、世界の主要8ヶ国の18歳以上の男女で、急性骨髄性白血病 (AML) と診断された事例が72,164件ありました。今後も2.51%のCAGRで拡大し続け、2029年には90,264件に達する、と予測されています。

当レポートでは、世界の主要8ヶ国 (米国、フランス、ドイツ、イタリア、スペイン、英国、日本、中国 (都市部)) における急性骨髄性白血病 (AML) の発症状況と今後の見通しについて分析し、疾患の特徴や現在の有病者の発症状況、今後10年間の発症件数/有病件数の動向見通し、男女別・症状別・年齢別の詳細動向などを調査しております。

目次

第2章 急性骨髄性白血病 (AML):エグゼクティブサマリー

第3章 疫学

  • 疾患の背景
  • 危険因子と併存症
  • 世界の過去の動向
  • 予測手法
    • 今回利用した/しなかった情報源
    • 予測の前提条件と手法
  • 急性骨髄性白血病 (AML):疫学的予測 (2019~2029年)
    • AMLの受診済み発症件数:概況
    • AMLの受診済み発症件数:年齢別
    • AMLの受診済み発症件数:男女別
    • AMLの受診済み発症件数:リスク集団別
    • AMLの受診済み発症件数:二次発症の件数
    • AMLの受診済み発症件数
    • AMLの5年有病者数 (受診済み)
  • 議論
    • 疫学的予測による考察
    • 新型コロナウイルス感染症 (Covid-19) の影響
    • 分析の限界
    • 分析の強み

第4章 付録

図表

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
目次
Product Code: GDHCER242-20

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

  • 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