Atrial Fibrillation Epidemiology Analysis and Forecast to 2032

1.1 Catalyst
1.2 Related Reports
1.3 Upcoming Reports

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

3.1 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 the Publisher

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