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EpiCast Report: Acute Myeloid Leukemia - Epidemiology Forecast to 2026

Published By :

GlobalData

Published Date : Jul 2017

Category :

Pharmaceutical

No. of Pages : 47 Pages

EpiCast Report: Acute Myeloid Leukemia - Epidemiology Forecast to 2026

Summary

Acute myeloid leukemia (AML) 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. AML progresses quickly, has low survival rates, and has high rates of relapse, even with treatment. The cause of AML is unknown, but an increased risk of the disease is associated with factors such as age, male sex, a history of smoking, and exposure to high doses of radiation and cytotoxic chemicals. Due to the rarity of the disease, the relationships between most risk factors and the disease have not been strongly established through epidemiological studies.

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.

The use of a consistent methodology across the 7MM to forecast the diagnosed incident cases and the diagnosed five-year prevalent cases of AML allows for a meaningful comparison of the forecast incident cases and the forecast five-year prevalent cases of AML in these markets. Additionally, GlobalData 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. Finally, this report reflects numerous updates to the AML literature, including more up-to-date, country-specific AML incidence rates, the impact of coding differences on incidence, and more up-to-date relative survival rates.

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.
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

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

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

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