株式会社グローバルインフォメーション
TEL: 044-952-0102
表紙
市場調査レポート

EpiCast Report:急性骨髄性白血病 (AML) - 2026年までの疫学予測

EpiCast Report: Acute Myeloid Leukemia - Epidemiology Forecast to 2026

発行 GlobalData 商品コード 274254
出版日 ページ情報 英文 47 Pages
即納可能
価格
本日の銀行送金レート: 1USD=113.99円で換算しております。
Back to Top
EpiCast Report:急性骨髄性白血病 (AML) - 2026年までの疫学予測 EpiCast Report: Acute Myeloid Leukemia - Epidemiology Forecast to 2026
出版日: 2017年07月01日 ページ情報: 英文 47 Pages
概要

急性骨髄性白血病 (AML) は、骨髄性白血病 (ML) または急性非リンパ性白血病 (ANLL) としても知られています。世界の主要7ヶ国 (米国、フランス、ドイツ、イタリア、スペイン、英国、日本)で急性骨髄性白血病 (AML) と診断された患者数は、2014年の40,661人から、今後10年間で48,918人まで、1年に2.03%ずつ増加すると予測されています。

当レポートでは、世界の主要7ヶ国における急性骨髄性白血病 (AML) について調査分析し、疾患の背景、危険因子と併存疾患、世界の動向、疫学予測などについて、体系的な情報を提供しています。

第1章 目次

第2章 イントロダクション

第3章 疫学

  • 疾患の背景
  • 危険因子と併存疾患
    • 高齢化
    • 喫煙
    • スペイン系・イタリア系
    • 化学療法・放射線治療
    • ベンゼン
  • 世界の動向
    • 発症率
    • 生存率・有病者数
    • 部分型と突然変異
    • リスク群
  • 予測手法
    • 利用した情報源
    • 予測の前提条件と手法
    • 利用しなかった情報源
  • AMLの疫学予測
    • AMLと診断された患者数
    • AMLと診断された患者数 (年齢別)
    • AMLと診断された患者数 (性別)
    • AMLと診断された患者数 (年齢標準化) 、など
  • 議論
    • 疫学予測に関する考察
    • 分析の限界
    • 分析の強み

第4章 付録

図表

このページに掲載されている内容は最新版と異なる場合があります。詳細はお問い合わせください。

目次
Product Code: GDHCER153-17

Acute Myeloid Leukemia (AML), also known as myelogenous leukemia, acute myelocytic leukemia, or acute nonlymphocytic leukemia, is a rare cancer that accounts for a disproportionally high number of cancer-related deaths. The disease is more common in the elderly, and is relatively more common in men than in women (ACS, 2013; O'Donnell et al., 2012). AML starts in the bone marrow, where the developing white blood cells-including granulocytes and monocytes-mature abnormally, grow uncontrollably, and quickly overcrowd the blood, spreading into other parts of the body, including the lymph nodes, liver, and brain (ACS, 2013; ASCO, 2012). The symptoms are nonspecific and may include weight loss, fatigue, fever, problems with bleeding and clotting, and swelling of the liver and spleen (ACS, 2013).

In 2016, the 7MM had 43,592 diagnosed incident cases of AML. This is expected to increase to 52,526 diagnosed incident cases of AML by 2026, at an Annual Growth Rate (AGR) of 2.05%. The increase is driven by the aging population in the 7MM.

In 2016, the 7MM had 57,581 five-year diagnosed prevalent cases of AML. This is expected to increase to 66,743 by 2026, at an AGR of 1.54%. The US had the highest number of diagnosed incident and five-year prevalent cases of AML. The development of more effective therapies, particularly for elderly patients, would improve survival and increase disease prevalence.

Our epidemiologists provide a detailed segmentation of the diagnosed incident and five-year diagnosed prevalent cases of AML by subtypes and risk group classification, which are both important factors for predicting the prognosis in patients as well as specific treatment modalities for AML.

The report "EpiCast Report: Acute Myeloid Leukemia - Epidemiology Forecast to 2026", provides an overview of the risk factors and the global and historical epidemiological trends for AML in the seven major markets (7MM): US, France, Germany, Italy, Spain, UK, and Japan.

In addition, this report also includes a 10-year epidemiological forecast for the following segmentations in adults ages 18 years and older across the 7MM -

  • Diagnosed incident cases of AML (adjusted for coding differences between cancer registries), segmented by ages 18-59 years and ages 60 years and older
  • Five-year diagnosed prevalent cases of AML, segmented by ages 18-59 years and ages 60 years and older
  • Diagnosed incident cases of APL and secondary AML, segmented by ages 18-59 years and ages 60 years and older
  • Diagnosed incident cases of AML with mutations (FLT3-ITD [internal tandem duplications], FLT3-TKD [tyrosine kinase domain], IDH [isocitrate dehydrogenase] 1 and IDH2), core binding factor (CBF) AML with KIT mutation, and biomarker CD33+
  • Diagnosed incident cases of AML classified into favorable-, intermediate-, and adverse-risk groups.

Scope

  • The Acute Myeloid Leukemia (AML) EpiCast Report provides an overview of the risk factors, comorbidities, and global trends of AML in the 7MM (US, France, Germany, Italy, Spain, UK, and Japan). It includes a 10-year epidemiological forecast for the following segmentations in ages 18 years and older across the 7MM: diagnosed incident cases of AML, segmented by ages 18-59 years and ages 60 years and older; five-year diagnosed prevalent cases of AML, segmented by ages 18-59 years and ages 60 years and older; diagnosed incident cases of (acute promyelocytic leukemia) APL and secondary AML, segmented by ages 18-59 years and ages 60 years and older; diagnosed incident cases of AML with mutations (FLT3-ITD [internal tandem duplications], FLT3-TKD [tyrosine kinase domain], IDH [isocitrate dehydrogenase] 1 and IDH2), core binding factor (CBF) AML with KIT mutation, and biomarker CD33+; and diagnosed incident cases of AML classified into favorable-, intermediate-, and adverse-risk groups.
  • The AML 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 7MM.

Reasons to buy

The AML EpiCast report 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 and sex that present the best opportunities for AML therapeutics in each of the markets covered.
  • Identify the percentage of AML diagnosed incident and five-year diagnosed prevalent cases by various clinical segmentations.

Table of Contents

1 Table of Contents

1 Table of Contents 2

  • 1.1 List of Tables 3
  • 1.2 List of Figures 3

2 Acute Myeloid Leukemia: Executive Summary 4

  • 2.1 Related Reports 6
  • 2.2 Upcoming Reports 7

3 Epidemiology 8

  • 3.1 Disease Background 8
  • 3.2 Risk Factors and Comorbidities 9
  • 3.3 Global and Historical Trends 10
    • 3.3.1 Incidence 10
    • 3.3.2 Relative Survival 12
    • 3.3.3 Subtypes 13
  • 3.4 Forecast Methodology 13
    • 3.4.1 Sources 15
    • 3.4.2 Forecast Assumptions and Methods - Population 19
    • 3.4.3 Forecast Assumptions and Methods - Incidence 19
    • 3.4.4 Forecast Assumptions and Methods - Relative Survival 23
    • 3.4.5 Forecast Assumptions and Methods - Subtypes of AML 24
    • 3.4.6 Forecast Assumptions and Methods - Mutations and Biomarkers 25
    • 3.4.7 Forecast Assumptions and Methods - Risk Groups 27
  • 3.5 Epidemiological Forecast for Acute Myeloid Leukemia (2016-2026) 29
    • 3.5.1 Adjusted Diagnosed Incident Cases of AML 29
    • 3.5.2 Age-Specific Diagnosed Incident Cases of AML 30
    • 3.5.3 Diagnosed Incident Cases of APL 31
    • 3.5.4 Diagnosed Incident Cases of Secondary AML 32
    • 3.5.5 Diagnosed Incident Cases of AML by Mutations and Biomarkers 33
    • 3.5.6 Diagnosed Incident Cases of AML by Risk Groups 34
    • 3.5.7 Five-Year Diagnosed Prevalent Cases of AML 35
  • 3.6 Discussion 37
    • 3.6.1 Epidemiological Forecast Insight 37
    • 3.6.2 Limitations of Analysis 37
    • 3.6.3 Strengths of Analysis 39

4 Appendix 40

  • 4.1 Bibliography 40
  • 4.2 About the Authors 45
    • 4.2.1 Epidemiologist 45
    • 4.2.2 Reviewers 45
    • 4.2.3 Global Director of Therapy Analysis and Epidemiology 45
    • 4.2.4 Global Head and EVP of Healthcare Operations and Strategy 46
  • 4.3 About GlobalData 47
  • 4.4 Contact Us 47
  • 4.5 Disclaimer 47

List of Tables

1.1 List of Tables

  • Table 1: Risk Factors for AML in Adults 10
  • Table 2: AML Coding System by Country 11
  • Table 3: Five-Year Relative Survival of AML by Age, 2016 12
  • Table 4: Risk Group Classification Guidelines 28
  • Table 5: 7MM, Adjusted Diagnosed Incident Cases of AML, Ages ≥18 Years, Both Sexes, Select Years 2016-2026 30
  • Table 6: 7MM, Age-Specific Adjusted Diagnosed Incident Cases of AML, Both Sexes, 2016 31
  • Table 7: 7MM, Mutations and Biomarkers in Diagnosed Incident Cases of AML, Ages ≥18 Years, Both Sexes, 2016 34
  • Table 8: 7MM, Five-Year Diagnosed Prevalent Cases of AML, Ages ≥18 Years, Both Sexes, Select Years 2016-2026 36

List of Figures

1.2 List of Figures

  • Figure 1: 7MM, Adjusted Diagnosed Incident Cases of AML, Both Sexes, Ages ≥18 Years, 2016 and 2026 5
  • Figure 2: 7MM, Five-Year Diagnosed Prevalent Cases of AML, Both Sexes, Ages ≥18 Years, 2016 and 2026 6
  • Figure 3: 7MM, Age-Standardized Adjusted Diagnosed Incidence of AML, Ages ≥18 Years, 2016 12
  • Figure 4: 7MM, Sources Used and Not Used, Diagnosed Incident Cases of AML 15
  • Figure 5: 7MM Sources Used, Relative Survival of AML 16
  • Figure 6: 7MM, Sources Used, Diagnosed Incident Cases of APL 17
  • Figure 7: 7MM, Sources Used, Diagnosed Incident Cases of Secondary AML 18
  • Figure 8: 7MM, Diagnosed Incident Cases of APL, Both Sexes, Ages ≥18 Years, 2016 32
  • Figure 9: 7MM, Diagnosed Incident Cases of Secondary AML, Both Sexes, Ages ≥18 Years, 2016 33
  • Figure 10: 7MM, Diagnosed Incident Cases of AML by Risk Group, Both Sexes, Ages ≥18 Years, 2016 35
  • Figure 11: 7MM, Five-Year Diagnosed Prevalent Cases of AML by Age, Both Sexes, 2016 36
Back to Top