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EpiCast Report:慢性リンパ性白血病の疫学的予測

Chronic Lymphocytic Leukemia: Epidemiology Forecast to 2027

発行 GlobalData 商品コード 301138
出版日 ページ情報 英文 43 Pages
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EpiCast Report:慢性リンパ性白血病の疫学的予測 Chronic Lymphocytic Leukemia: Epidemiology Forecast to 2027
出版日: 2018年11月20日 ページ情報: 英文 43 Pages
概要

慢性リンパ性白血病(CLL、慢性リンパ様白血病)とは、白血球(リンパ球)のがんの一種で、特定のリンパ球(B細胞)−−主に骨髄・血液内に集積し、感染症と闘う細胞です−−に悪影響を及ぼします。CLLは西欧諸国では一般的な白血病の形態で、年間平均発症率は人口10万人当たり5.0件(年齢問わず)となっています。基本的には高齢者が発症する疾患であり、40歳以下の患者の割合は10%以下となっています。世界の主要6ヶ国における発症件数は、2013年の37,795件から、2023年には45,683にまで増加する見通しで、その年平均成長率(CAGR)は2.09%と推計されています。また、治療済みの有病者数も、2013年の150,800人から、2023年には183,165にまで増加すると見られています(年平均成長率(CAGR)は2.15%)。

当レポートでは、世界の主要6ヶ国(米国・フランス・ドイツ・イタリア・スペイン・英国)における慢性リンパ性白血病(CLL)の発症状況や今後の見通しについて分析し、疾患の特徴(リスク要因や主な共存症など)や世界各国での動向、現在の有病者の発症状況、今後10年間の発症・有病件数の予測値などを調査・推計して、その結果を概略以下の内容でお届けいたします。

第1章 目次

第2章 イントロダクション

  • 概要
  • 近日刊行の関連調査

第3章 疫学

  • 疾患の背景
  • リスク要因と共存症
  • 世界的な傾向
    • 発症件数
    • 生存率:米国・欧州5ヶ国の場合
  • 予測手法
    • 利用した情報源
    • 利用しなかった情報源
    • 予測の前提条件と手法:診断された発症件数
    • 予測の前提条件と手法:診断された有病者数(今後5年間分)
    • 予測の前提条件と手法:診断時のRai分類
  • 慢性リンパ性白血病(CLL)の疫学予測(今後11年間分)
    • 診断された発症件数
    • 診断された発症件数(年齢別)
    • 診断された発症件数(性別)
    • 診断された発症件数(年齢調整済み)
    • 診断された発症件数(診断時のRai分類別)
    • 診断された有病者数:今後5年間の予測
  • 議論
    • 疫学的な予測に関する結論
    • 分析の限界
    • 分析の強み

第4章 付録

図表一覧

図表

1.1 List of Tables

  • Table 1: Risk Factors and Comorbid Conditions Associated with CLL 8
  • Table 2: 7MM, Diagnosed Incident Cases of CLL by High-Risk Cytogenetics, Both Sexes, Ages ≥18 Years, N, 2017 34

1.2 List of Figures

  • Figure 1: 7MM, Diagnosed Incident Cases of CLL, Both Sexes, Ages ≥18 Years, N, 2017 and 2027 5
  • Figure 2: 7MM, Diagnosed Prevalent Cases of CLL, Both Sexes, Ages ≥18 Years, N, 2017 and 2027 6
  • Figure 3: 7MM, Age-Standardized Diagnosed Incidence of CLL (Cases per 100,000 Population), Men, Ages ≥18 Years, 2017-2027 9
  • Figure 4: 7MM, Age-Standardized Diagnosed Incidence of CLL (Cases per 100,000 Population), Women, Ages ≥18 Years, 2017-2027 10
  • Figure 5: 7MM, Sources Used, Diagnosed Incident Cases of CLL 11
  • Figure 6: 7MM, Sources Used, Diagnosed Incident Cases of CLL by Stage at Diagnosis (Rai Staging and Modified Rai Staging) 12
  • Figure 7: 7MM, Sources Used, Diagnosed Incident Cases of CLL by Stage at Diagnosis (Binet Staging) 13
  • Figure 8: 7MM, Sources Used, 11q and 13q Deletion Among the Diagnosed Incident Cases of CLL 14
  • Figure 9: 7MM, Sources Used, 17p Deletion Among the Diagnosed Incident Cases of CLL 15
  • Figure 10: 7MM, Sources Used, TP53 Among the Diagnosed Incident Cases of CLL 16
  • Figure 11: 7MM, Sources Used, CD38 Among the Diagnosed Incident Cases of CLL 17
  • Figure 12: 7MM, Sources Used, ZAP70 Among the Diagnosed Incident Cases of CLL 18
  • Figure 13: 7MM, Sources Used, IGHV (Unmutated) Among the Diagnosed Incident Cases of CLL 19
  • Figure 14: 7MM, Sources Used, Diagnosed Prevalent Cases of CLL 20
  • Figure 15: 7MM, Diagnosed Incident Cases of CLL, Both Sexes, Ages ≥18 Years, N, 2017 28
  • Figure 16: 7MM, Age-Specific Diagnosed Incident Cases of CLL, Both Sexes, N, 2017 29
  • Figure 17: 7MM, Sex-Specific Diagnosed Incident Cases of CLL, Ages ≥18 Years, N, 2017 30
  • Figure 18: 7MM, Diagnosed Incident Cases of CLL by Rai Stage at Diagnosis, Both Sexes, Ages ≥18 Years, N, 2017 31
  • Figure 19: 7MM, Diagnosed Incident Cases of CLL by Modified Rai Stage at Diagnosis, Both Sexes, Ages ≥18 Years, N, 2017 32
  • Figure 20: 7MM, Diagnosed Incident Cases of CLL by Binet Stage at Diagnosis, Both Sexes, Ages ≥18 Years, N, 2017 33
  • Figure 21: 7MM, Diagnosed Prevalent Cases of CLL, Both Sexes, Ages ≥18 Years, N, 2017 35
目次
Product Code: GDHCER193-18

Chronic lymphocytic leukemia (CLL) (International Statistical Classification of Diseases and Related Health Problems, 10th Revision [ICD-10] code = C91.1), also known as chronic lymphoid leukemia, is a type of cancer of the white blood cells (lymphocytes). CLL affects a particular lymphocyte, the B cell, which accumulates mainly in the bone marrow and blood, and normally fights infection.

GlobalData epidemiologists utilized national databases and robust peer-reviewed journal articles to build the CLL diagnosed incident and diagnosed prevalent cases forecast for the 7MM. The disease definition for CLL was consistent with the ICD-10 code C91.1. Whenever available, country-specific sources were utilized. In cases of data scarcity, appropriate proxies were used to fill the data gaps.

The diagnosed incident cases of CLL in the 7MM will increase from 41,974 cases in 2017 to 50,474 cases in 2027, at an Annual Growth Rate (AGR) of 2.03% over the forecast period. For the majority of the forecast period, the US will have the highest number of diagnosed incident cases of CLL. GlobalData epidemiologists attribute the growth in the diagnosed incident cases of CLL in the 7MM to the moderately rising trend in incidence in the 7MM, combined with the changing population demographics in the respective markets.

Scope

  • The Chronic Lymphocytic Leukemia (CLL) Epidemiology Report provides an overview of the risk factors and global trends of CLL in the seven major markets (7MM: US, France, Germany, Italy, Spain, UK, and Canada).
  • The report includes a 10-year epidemiological forecast for the diagnosed incident cases of CLL segmented by age (for ages 18 years and older) and sex. The CLL diagnosed incident cases are segmented by Rai stage, modified Rai stage, and Binet stage at diagnosis, as well as high-risk cytogenetics for the 7MM. Additionally, GlobalData epidemiologists provide a forecast for the diagnosed prevalent cases of CLL in the 7MM. To forecast the diagnosed incident and diagnosed prevalent cases of CLL in the 7MM, GlobalData epidemiologists used data from nationally representative, population-based studies for the 7MM.
  • The CLL 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 7MM.

Reasons to buy

The CLL Epidemiology report will allow you to -

  • Develop business strategies by understanding the trends shaping and driving the global CLL market.
  • Quantify patient populations in the global CLL 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 CLL therapeutics in each of the markets covered.
  • Understand magnitude of CLL patient population by stage.

1 Table of Contents

1 Table of Contents 2

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

2 Chronic Lymphocytic Leukemia: Executive Summary 4

  • 2.1 Related Reports 6
  • 2.2 Upcoming Reports 6

3 Epidemiology 7

  • 3.1 Disease Background 7
  • 3.2 Risk Factors and Comorbidities 7
  • 3.3 Global and Historical Trends 8
  • 3.4 Forecast Methodology 11
    • 3.4.1 Sources Used 11
    • 3.4.2 Forecast Assumptions and Methods - Population 20
    • 3.4.3 Forecast Assumptions and Methods - Diagnosed Incident Cases of CLL 20
    • 3.4.4 Diagnosed Incident Cases by Stage at Diagnosis (Rai Staging and Modified Rai Staging) 23
    • 3.4.5 Diagnosed Incident Cases by Stage at Diagnosis (Binet Staging) 23
    • 3.4.6 High-Risk Cytogenetics (11q Deletion, 13q Deletion, 17p Deletion, p53 mutation, CD38, ZAP70 and IGHV [Unmutated]) Among the Diagnosed Incident Cases of CLL 24
    • 3.4.7 Forecast Assumptions and Methods - Diagnosed Prevalent Cases of CLL 26
  • 3.5 Epidemiological Forecast for CLL (2017-2027) 27
    • 3.5.1 Diagnosed Incident Cases of CLL 27
    • 3.5.2 Age-Specific Diagnosed Incident Cases of CLL 29
    • 3.5.3 Sex-Specific Diagnosed Incident Cases of CLL 30
    • 3.5.4 Diagnosed Incident Cases of CLL by Rai Stage at Diagnosis 31
    • 3.5.5 Diagnosed Incident Cases of CLL by Modified Rai Stage at Diagnosis 32
    • 3.5.6 Diagnosed Incident Cases of CLL by Binet Stage at Diagnosis 33
    • 3.5.7 Diagnosed Incident Cases of CLL by High-Risk Cytogenetics 34
    • 3.5.8 Diagnosed Prevalent Cases of CLL 35
  • 3.6 Discussion 36
    • 3.6.1 Epidemiological Forecast Insight 36
    • 3.6.2 Limitations of the Analysis 36
    • 3.6.3 Strengths of the Analysis 37

4 Appendix 38

  • 4.1 Bibliography 38
  • 4.2 About the Authors 41
    • 4.2.1 Epidemiologist 41
    • 4.2.2 Reviewers 41
    • 4.2.3 Global Director of Therapy Analysis and Epidemiology 42
    • 4.2.4 Global Head and EVP of Healthcare Operations and Strategy 42
  • 4.3 About GlobalData 43
  • 4.4 Contact Us 43
  • 4.5 Disclaimer 43
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