Atrial Fibrillation Epidemiology Analysis and Forecast to 2032

• 1. About GlobalData
• 2. List of Contents
• 3. List of Tables
• 4. List of Figures
• 5. Atrial Fibrillation: Executive Summary

  • 5.1. Catalyst
  • 5.2. Related Reports
  • 5.3. Upcoming Reports

• 6. Epidemiology

  • 6.1. Disease background
  • 6.2. Risk factors and comorbidities
  • 6.3. Global and historical trends
  • 6.4. 8MM forecast methodology

    • 6.4.1. Sources
    • 6.4.2. Forecast assumptions and methods
    • 6.4.3. Forecast assumptions and methods: total prevalent cases of AF – 8MM
    • 6.4.4. Forecast assumptions and methods: diagnosed prevalent cases of AF
    • 6.4.5. Forecast assumptions and methods: diagnosed prevalent cases of AF by temporal pattern of arrhythmia
    • 6.4.6. Forecast assumptions and methods: diagnosed prevalent cases of AF by CHADS2 stroke risk score
    • 6.4.7. Forecast assumptions and methods: diagnosed prevalent cases of AF by CHA2DS2 – VASc stroke risk score in men
    • 6.4.8. Forecast assumptions and methods: diagnosed prevalent cases of AF by CHA2DS2 – VASc stroke risk score in women
    • 6.4.9. Forecast assumptions and methods: diagnosed prevalent cases of AF with/without moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve
    • 6.4.10. Forecast assumptions and methods: diagnosed prevalent cases of AF with CKD by stage
    • 6.4.11. Forecast assumptions and methods: diagnosed prevalent cases of AF with major bleeding risk by HAS‐BLED score
    • 6.4.12. Forecast assumptions and methods: diagnosed prevalent cases of AF admitted to ED

  • 6.5. Epidemiological forecast for atrial fibrillation (2022–32)

    • 6.5.1. Total prevalent cases of AF
    • 6.5.2. Diagnosed prevalent cases of AF
    • 6.5.3. Age-specific diagnosed prevalent cases of AF
    • 6.5.4. Sex-specific diagnosed prevalent cases of AF
    • 6.5.5. Diagnosed prevalent cases of AF by temporal pattern of arrhythmia
    • 6.5.6. Diagnosed prevalent cases of AF by CHADS2 stroke risk score
    • 6.5.7. Diagnosed prevalent cases of AF by CHA2DS2 – VASc stroke risk score in men
    • 6.5.8. Diagnosed prevalent cases of AF by CHA2DS2 – VASc stroke risk score in women
    • 6.5.9. Diagnosed prevalent cases of AF with or without moderate-to-severe mitral stenosis and/or an artificial (mechanical) heart valve
    • 6.5.10. Diagnosed prevalent cases of AF with CKD by stage
    • 6.5.11. Diagnosed prevalent cases of AF with major bleeding risk by HAS‐BLED score
    • 6.5.12. Diagnosed prevalent cases of AF admitted to ED

  • 6.6. Discussion

    • 6.6.1. Epidemiological forecast insight
    • 6.6.2. COVID-19 impact
    • 6.6.3. Limitations of the analysis
    • 6.6.4. Strengths of the analysis

• 7. Appendix

  • 7.1. Bibliography
  • 7.2. About the Authors

    • 7.2.1. Epidemiologist
    • 7.2.2. Reviewers
    • 7.2.3. Global Director of Therapy Analysis and Epidemiology
    • 7.2.4. Global Head and EVP of Healthcare Operations and Strategy

• 8. Contact Us

Table 1: Summary of newly added data types

Table 2: Summary of updated data types

Table 3: Risk factors and comorbidities for AF

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