心房顫動的流行病學分析與2032年前的預測

主要8個國家，心房顫動的整體患病人數預計將從 2022 年的 14,457,906 例增加到 2032 年的 17,515,229 例，年增長率 (AGR) 為 2.11%。2032年，美國的病例數將在八個主要國家中最高，為7,038,607例，而加拿大的病例數最低，為712,577例。

本報告提供主要8市場(美國，法國，德國，義大利，西班牙，英國，日本，與加拿大)的心房顫動的危險因素，彙整合併症，世界及過去的流行病學趨勢的相關概述，心房顫動的確診發病病例確診盛行率相關10年的流行病學預測等資料。

目錄

第1章 心房顫動:摘要整理

第2章 流行病學

• 病的背景
• 危險因素和合併症
• 世界的及歷史的趨勢
• 的預測調查手法主要8個國家
• 心房顫動的流行病學預測(2022年～2032年)
• 議論
  • 流行病學預測的洞察
  • COVID-19影響
  • 分析的限制
  • 分析的優勢

第3章 附錄

• 參考文獻
• 關於作者
• 諮詢方式

Abstract

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It occurs due to abnormal electrical activity within the atria of the heart, causing them to fibrillate, and is characterized as a tachyarrhythmia (Wakai and O'Neill, 2003; Burdett and Lip, 2022). Due to its rhythm irregularity, blood flow through the heart becomes turbulent and has a high chance of forming a thrombus or blood clot, which can ultimately dislodge and cause a stroke. AF is the leading cardiac cause of stroke (Centers for Disease Control and Prevention, 2022).

Both men and women can have the disease. Major risk factors for AF are advancing age, hypertension, obesity, chronic diseases such as diabetes, heart failure, ischemic heart disease, hyperthyroidism, chronic kidney disease (CKD), alcohol intake, smoking, and enlargement of the chambers on the left side of the heart (Mayo Clinic, 2021b; Centers for Disease Control and Prevention, 2022; American Heart Association, 2023d). There is no cure for AF, however treatment and lifestyle changes can reduce symptoms, abnormal heart rhythms and prevent complications.

In the 8MM, total prevalent cases of AF are expected to increase from 14,457,906 cases in 2022 to 17,515,229 cases in 2032, at an annual growth rate (AGR) of 2.11%. In 2032, the US will have the highest number of total prevalent cases of AF in the 8MM, with 7,038,607 cases, whereas Canada will have the fewest total prevalent cases of AF with 712,577 cases. In the 8MM, diagnosed prevalent cases of AF are expected to increase from 12,862,824 cases in 2022 to 15,640,567 cases in 2032, at an annual growth rate (AGR) of 2.16%. In 2032, the US will have the highest number of diagnosed prevalent cases of AF in the 8MM, with 6,411,373 cases, whereas Canada will have the fewest diagnosed prevalent cases of AF with 649,076 cases. GlobalData epidemiologists attribute the increase in the total and diagnosed prevalent cases of AF to changes in population dynamics and the diagnosis rate in each market.

Scope

• This report provides an overview of the risk factors, comorbidities, and the global and historical epidemiological trends for AF in the eight major markets (8MM: US, France, Germany, Italy, Spain, UK, Japan, and Canada). The report includes a 10-year epidemiology forecast for the total prevalent cases and diagnosed prevalent cases of AF. The total prevalent cases and the diagnosed prevalent cases of AF are segmented by age (40-49 years, 50-59 years, 60-69 years, 70-79 years, and 80 years and above) and sex. The report also includes the diagnosed prevalent cases of AF by temporal pattern of arrhythmia (paroxysmal, persistent, and permanent) and by stroke risk score based on CHADS2 score, and CHA2DS2-VASc score by sex. Diagnosed prevalent cases of AF are further segmented based on presence or absence of moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve, and stages of CKD. Additionally, diagnosed prevalent cases of AF are segmented based on major bleeding risk by HAS-BLED score (low risk = 0, moderate risk = 1-2, and high risk = ≥3) and diagnosed prevalent cases of AF admitted to ED. This epidemiology forecast for AF 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 total prevalent cases and diagnosed prevalent cases of AF across these markets.

Reasons to Buy

The atrial fibrillation epidemiology series will allow you to -

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

Table of Contents

Table of Contents

• About GlobalData

1 Atrial Fibrillation: 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: total prevalent cases of AF - 8MM
  • 2.4.4 Forecast assumptions and methods: diagnosed prevalent cases of AF
  • 2.4.5 Forecast assumptions and methods: diagnosed prevalent cases of AF by temporal pattern of arrhythmia
  • 2.4.6 Forecast assumptions and methods: diagnosed prevalent cases of AF by CHADS2 stroke risk score
  • 2.4.7 Forecast assumptions and methods: diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in men
  • 2.4.8 Forecast assumptions and methods: diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in women
  • 2.4.9 Forecast assumptions and methods: diagnosed prevalent cases of AF with/without moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve
  • 2.4.10 Forecast assumptions and methods: diagnosed prevalent cases of AF with CKD by stage
  • 2.4.11 Forecast assumptions and methods: diagnosed prevalent cases of AF with major bleeding risk by HAS-BLED score
  • 2.4.12 Forecast assumptions and methods: diagnosed prevalent cases of AF admitted to ED
• 2.5 Epidemiological forecast for atrial fibrillation (2022-32)
  • 2.5.1 Total prevalent cases of AF
  • 2.5.2 Diagnosed prevalent cases of AF
  • 2.5.3 Age-specific diagnosed prevalent cases of AF
  • 2.5.4 Sex-specific diagnosed prevalent cases of AF
  • 2.5.5 Diagnosed prevalent cases of AF by temporal pattern of arrhythmia
  • 2.5.6 Diagnosed prevalent cases of AF by CHADS2 stroke risk score
  • 2.5.7 Diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in men
  • 2.5.8 Diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in women
  • 2.5.9 Diagnosed prevalent cases of AF with or without moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve
  • 2.5.10 Diagnosed prevalent cases of AF with CKD by stage
  • 2.5.11 Diagnosed prevalent cases of AF with major bleeding risk by HAS-BLED score
  • 2.5.12 Diagnosed prevalent cases of AF admitted to ED
• 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 Global Director of Therapy Analysis and Epidemiology
  • 3.2.4 Global Head and EVP of Healthcare Operations and Strategy
• Contact Us

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 AF

List of Figures

List of Figures

• Figure 1: 8MM, total prevalent cases of AF, both sexes, N, ages ≥40 years, 2022 and 2032
• Figure 2: 8MM, diagnosed prevalent cases of AF, both sexes, N, ages ≥40 years, 2022 and 2032
• Figure 3: 8MM, diagnosed prevalence of AF (%), men and women, ages ≥40 years, 2022
• Figure 4: 8MM, sources used and not used to forecast the diagnosed prevalent cases of AF
• Figure 5: 8MM, sources used to forecast the diagnosed prevalent cases of AF by temporal pattern of arrhythmia
• Figure 6: 8MM, sources used to forecast the diagnosed prevalent cases of AF by CHADS2 stroke risk score
• Figure 7: 8MM, sources used to forecast the diagnosed prevalent cases of AF by CHA2DS2 - VASc score in men
• Figure 8: 8MM, sources used to forecast the diagnosed prevalent cases of AF by CHA2DS2 - VASc score in women
• Figure 9: 8MM, sources used to forecast the diagnosed prevalent cases of AF with/without moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve
• Figure 10: 8MM, sources used to forecast the diagnosed prevalent cases of AF with CKD
• Figure 11: 8MM, sources used to forecast the diagnosed prevalent cases of AF with major bleeding risk by HAS-BLED score
• Figure 12: 8MM, sources used to forecast the diagnosed prevalent cases of AF admitted to the ED
• Figure 13: 8MM, sources used to forecast the diagnosis rate of AF
• Figure 14: 8MM, total prevalent cases of AF, N, both sexes, ages ≥40 years, 2022
• Figure 15: 8MM, diagnosed prevalent cases of AF, N, both sexes, ages ≥40 years, 2022
• Figure 16: 8MM, diagnosed prevalent cases of AF by age, N, both sexes, 2022
• Figure 17: 8MM, diagnosed prevalent cases of AF by sex, N, ages ≥40 years, 2022
• Figure 18: 8MM, diagnosed prevalent cases of AF by temporal pattern of arrhythmia, N, both sexes, ages ≥40 years, 2022
• Figure 19: 8MM, diagnosed prevalent cases of AF by CHADS2 stroke risk score, N, both sexes, ages ≥40 years, 2022
• Figure 20: 8MM, diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in men, N, men, ages ≥40 years, 2022
• Figure 21: 8MM, diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in women, N, women, ages ≥40 years, 2022
• Figure 22: 8MM, diagnosed prevalent cases of AF with and without moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve, N, both sexes, ≥40 years, 2022
• Figure 23: 8MM, diagnosed prevalent cases of AF with CKD by stage, N, both sexes, ages ≥40 years, 2022
• Figure 24: 8MM, diagnosed prevalent cases of AF by HAS-BLED score, N, both sexes, ages ≥40 years, 2022
• Figure 25: 8MM, diagnosed prevalent cases of AF admitted to ED, N, both sexes, ages ≥40 years, 2022