多発性骨髄腫の疫学分析と2032年までの予測

多発性骨髄腫（MM）は血液がんです。形質細胞と呼ばれる白血球。正常な形質細胞は、細菌を認識して攻撃する抗体を生成することで感染症と闘うのに役立ちますが、MMはがん細胞を骨髄に蓄積させ、そこで健康な血球を締め出し、感染症と闘う能力を損ないます。がん細胞は、有用な抗体を産生するのではなく、モノクローナル免疫グロブリン、モノクローナルタンパク質（Mタンパク質）、Mスパイク、またはパラタンパク質と呼ばれる異常タンパク質を産生します。初期段階では、何の症状も引き起こさない場合があります。最終的に、通常は背中、肋骨、腰などの持続的な骨の痛み、疲労、脱力感、息切れ、体重減少、繰り返しの感染症、打撲傷や異常な出血、脆弱な骨、喉の渇きなど、幅広い問題を引き起こします。

主要8ヶ国市場では、MMと診断された症例数は2022年の80,305例から2032年には95,349例まで、年間増加率（AGR）1.87%で増加すると予想されています。2032年には、主要8ヶ国市場でMMと診断された発症症例数が最も多いのは米国で35,307件と診断されますが、スペインでは診断された発症症例数が3,517件で最も少ないと予想されます。主要8ヶ国市場では、MMと診断された蔓延症例は2022年の272,948例から2032年には305,020例に増加し、AGRは1.18%になると予想されています。

当レポートでは、主要8ヶ国市場における多発性骨髄腫（MM）の危険因子、併存疾患、世界および過去の疫学動向について概説し、多発性骨髄腫（MM）の診断済み発症例と診断済み有病率に関する10年間の疫学予測などをまとめています。

目次

第1章 多発性骨髄腫（MM）：エグゼクティブサマリー

第2章 疫学

• 病気の背景
• 危険因子と併存疾患
• 世界的および歴史的動向
• 主要8ヶ国市場予測調査手法
• 多発性骨髄腫（MM）の疫学予測（2022年～2032年）
  • 多発性骨髄腫（MM）の診断済み症例件数
  • 多発性骨髄腫（MM）の診断済み発症例、年齢別
  • 多発性骨髄腫（MM）の診断済み発症例、性別
  • 多発性骨髄腫（MM）の診断済み発症例、タイプ別
  • 多発性骨髄腫（MM）の診断済み発症例、診断時病期別
  • 多発性骨髄腫（MM）の診断済み発症例、SCT適格性別
  • 多発性骨髄腫（MM）の診断済み発症例、遺伝子・表面マーカー別
  • 多発性骨髄腫（MM）の診断済み罹患件数
  • 多発性骨髄腫（MM）の診断済み罹患症例、性別
  • 多発性骨髄腫（MM）の診断済み罹患症例、診断時病期別
  • 多発性骨髄腫（MM）の罹患症例、遺伝子・表面マーカー別
• 議論
  • 疫学予測の洞察
  • COVID-19の影響
  • 分析の限界
  • 分析の強み

第3章 付録

List of Tables

List of Tables

• Table 1: Summary of newly added data types
• Table 2: Summary of updated data types
• Table 3: Risk factors and comorbidities for MM

List of Figures

List of Figures

• Figure 1: 8MM, diagnosed incident cases of MM, both sexes, N, ages ≥18 years, 2022 and 2032
• Figure 2: 8MM, diagnosed prevalent cases of MM, both sexes, N, ages ≥18 years, 2022 and 2032
• Figure 3: 8MM, diagnosed incidence of MM, men, cases per 100,000 population, ages ≥18 years, 2012-32
• Figure 4: 8MM, diagnosed incidence of MM, women, cases per 100,000 population, ages ≥18 years, 2012-32
• Figure 5: 8MM, sources used to forecast the diagnosed incident cases of MM
• Figure 6: 8MM, sources used to forecast the diagnosed prevalent cases of MM
• Figure 7: 8MM, sources used and not used to forecast the diagnosed incident cases of MM by type
• Figure 8: 8MM, sources used to forecast the diagnosed incident cases of MM by stage at diagnosis (R-ISS stage)
• Figure 9: 8MM, sources used to forecast the diagnosed incident cases of MM by SCT eligibility
• Figure 10: 8MM, sources used to forecast the diagnosed incident cases and diagnosed prevalent cases of MM by genetic and surface markers
• Figure 11: 8MM, sources used to forecast the diagnosed prevalent cases of MM by stage at diagnosis (R-ISS stage)
• Figure 12: 8MM, diagnosed incident cases of MM, N, both sexes, ages ≥18 years, 2022
• Figure 13: 8MM, diagnosed incident cases of MM by age, N, both sexes, 2022
• Figure 14: 8MM, diagnosed incident cases of MM by sex, N, ages ≥18 years, 2022
• Figure 15: 8MM, diagnosed incident cases of MM by type, N, both sexes, ages ≥18 years, 2022
• Figure 16: 8MM, diagnosed incident cases of MM by stage at diagnosis (R-ISS stage), N, both sexes, ages ≥18 years, 2022
• Figure 17: 8MM, diagnosed incident cases of MM by SCT eligibility, N, both sexes, ages ≥18 years, 2022
• Figure 18: 8MM, diagnosed incident cases of MM by genetic and surface markers, N, both sexes, ages ≥18 years, 2022
• Figure 19: 8MM, diagnosed prevalent cases of MM, N, both sexes, ages ≥18 years, 2022
• Figure 20: 8MM, diagnosed prevalent cases of MM by sex, N, ≥18 years, 2022
• Figure 21: 8MM, diagnosed prevalent cases of MM by stage at diagnosis (R-ISS stage), N, both sexes, ages ≥18 years, 2022
• Figure 22: 8MM, diagnosed prevalent cases of MM by genetic and surface markers, N, both sexes, ages ≥18 years, 2022

Abstract

Multiple myeloma (MM) (International Statistical Classification of Diseases and Related Health Problems, 10th Revision [ICD-10] code = C90.0 or ICD-O-3 code: 9732/3) is a hematologic cancer that forms in a type of white blood cells called plasma cells. Normal plasma cells help fight infections by making antibodies that recognize and attack germs, but MM causes cancer cells to accumulate in the bone marrow where they crowd out healthy blood cells, impairing their ability to fight infections. Rather than producing helpful antibodies, the cancer cells produce abnormal proteins called monoclonal immunoglobulin or monoclonal protein (M-protein) or M-spike or paraprotein (American Cancer Society, 2018; Mayo Clinic, 2023). In the early stages, MM may not cause any symptoms. Eventually, MM causes a wide range of problems, including a persistent bone pain, usually in the back, ribs, or hips, tiredness, weakness, shortness of breath, weight loss, repeated infections, easy bruising and unusual bleeding, fragile bones, thirst, frequent urination, and kidney problems (American Cancer Society, 2018; National Health Service, 2021; Mayo Clinic, 2023).

In the 8MM, the diagnosed incident cases of MM are expected to increase from 80,305 cases in 2022 to 95,349 cases in 2032, at an Annual Growth Rate (AGR) of 1.87%. In 2032, the US will have the highest number of diagnosed incident cases of MM in the 8MM, with 35,307 diagnosed incident cases, whereas Spain will have the fewest diagnosed incident cases with 3,517 cases. In the 8MM, the diagnosed prevalent cases of MM are expected to increase from 272,948 cases in 2022 to 305,020 cases in 2032, at an AGR of 1.18%. GlobalData epidemiologists attribute the increase in the diagnosed incident cases and diagnosed prevalent cases of MM to a certain extent with the moderately rising trend in the incidence of MM in the 8MM, combined with underlying demographic changes in the respective markets.

Scope

• This report provides an overview of the risk factors, comorbidities, and the global and historical epidemiological trends for MM in the eight major markets (8MM: US, France, Germany, Italy, Spain, UK, Japan, and Urban China).
• The report includes a 10-year epidemiology forecast for the diagnosed incident and diagnosed prevalent cases of MM. The diagnosed incident cases of MM are segmented by age (18 years and older), sex, by type (asymptomatic and symptomatic), by stage at diagnosis (R-ISS stage I, R-ISS stage II, and R-ISS stage III), by stem cell transplant (SCT) eligibility (eligible and ineligible), and by genetic and surface markers [t(4;14)(p16;q32), t(14;16)(q32;q23), t(14;20) (q32;q12), t(11;14)(q13;q32), and deletion 17p].
• The diagnosed prevalent cases of MM are segmented sex, by stage at diagnosis (R-ISS stage I, R-ISS stage II, and R-ISS stage III), and by genetic and surface markers [t(4;14)(p16;q32), t(14;16)(q32;q23), t(14;20) (q32;q12), t(11;14)(q13;q32), and deletion 17p].
• This epidemiology forecast for MM is supported by data obtained from peer-reviewed articles and population-based studies.
• The forecast methodology was kept consistent across the 8MM to allow for a meaningful comparison of the forecast diagnosed incident and diagnosed prevalent cases of MM across these markets.

Reasons to Buy

The Multiple Myeloma epidemiology series will allow you to -

• Develop business strategies by understanding the trends shaping and driving the global MM market.
• Quantify patient populations in the global MM 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 MM therapeutics in each of the markets covered.

Table of Contents

Table of Contents

1 Multiple Myeloma: Executive Summary

• 1.1 Catalyst
• 1.2 Related Reports
• 1.3 Upcoming Reports

2 Epidemiology

• 2.1 Disease background
• 2.2 Risk factors and comorbidities
• 2.3 Global and historical trends
• 2.4 8MM forecast methodology.
  • 2.4.1 Sources
  • 2.4.2 Forecast assumptions and methods.
  • 2.4.3 Forecast assumptions and methods: diagnosed incident cases of MM.
  • 2.4.4 Forecast assumptions and methods: diagnosed incident cases of MM by type.
  • 2.4.5 Forecast assumptions and methods: diagnosed incident cases of MM by stage at diagnosis (R-ISS stage)
  • 2.4.6 Forecast assumptions and methods: diagnosed incident cases of MM by SCT eligibility.
  • 2.4.7 Forecast assumptions and methods: diagnosed incident cases of MM by genetic and surface markers.
  • 2.4.8 Forecast assumptions and methods: diagnosed prevalent cases of MM
  • 2.4.9 Forecast assumptions and methods: diagnosed prevalent cases of MM by stage at diagnosis (R-ISS stage)
  • 2.4.10 Forecast assumptions and methods: diagnosed prevalent cases of MM by genetic and surface markers.
• 2.5 Epidemiological forecast for multiple myeloma (2022-32)
  • 2.5.1 Diagnosed incident cases of MM.
  • 2.5.2 Age-specific diagnosed incident cases of MM
  • 2.5.3 Sex-specific diagnosed incident cases of MM
  • 2.5.4 Diagnosed incident cases of MM by type.
  • 2.5.5 Diagnosed incident cases of MM by stage at diagnosis (R-ISS stage)
  • 2.5.6 Diagnosed incident cases of MM by SCT eligibility.
  • 2.5.7 Diagnosed incident cases of MM by genetic and surface markers.
  • 2.5.8 Diagnosed prevalent cases of MM.
  • 2.5.9 Sex-specific diagnosed prevalent cases of MM
  • 2.5.10 Diagnosed prevalent cases of MM by stage at diagnosis (R-ISS stage)
  • 2.5.11 Diagnosed prevalent cases of MM by genetic and surface markers.
• 2.6 Discussion
  • 2.6.1 Epidemiological forecast insight
  • 2.6.2 COVID-19 impact.
  • 2.6.3 Limitations of the analysis
  • 2.6.4 Strengths of the analysis

3 Appendix

• 3.1 Bibliography
• 3.2 About the Authors
  • 3.2.1 Epidemiologist
  • 3.2.2 Reviewers
  • 3.2.3 Vice President of Disease Intelligence and Epidemiology
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