Atrial Fibrillation Epidemiology Analysis and Forecast to 2032
Summary
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.
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.
- 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
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- List of Tables Table 1: Summary of newly added data typesTable 2: Summary of updated data typesTable 3: Risk factors and comorbidities for AF
- List of Figures Figure 1: 8MM, total prevalent cases of AF, both sexes, N, ages ≥40 years, 2022 and 2032Figure 2: 8MM, diagnosed prevalent cases of AF, both sexes, N, ages ≥40 years, 2022 and 2032Figure 3: 8MM, diagnosed prevalence of AF (%), men and women, ages ≥40 years, 2022Figure 4: 8MM, sources used and not used to forecast the diagnosed prevalent cases of AFFigure 5: 8MM, sources used to forecast the diagnosed prevalent cases of AF by temporal pattern of arrhythmiaFigure 6: 8MM, sources used to forecast the diagnosed prevalent cases of AF by CHADS2 stroke risk scoreFigure 7: 8MM, sources used to forecast the diagnosed prevalent cases of AF by CHA2DS2 - VASc score in menFigure 8: 8MM, sources used to forecast the diagnosed prevalent cases of AF by CHA2DS2 - VASc score in womenFigure 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 valveFigure 10: 8MM, sources used to forecast the diagnosed prevalent cases of AF with CKDFigure 11: 8MM, sources used to forecast the diagnosed prevalent cases of AF with major bleeding risk by HAS‐BLED scoreFigure 12: 8MM, sources used to forecast the diagnosed prevalent cases of AF admitted to the EDFigure 13: 8MM, sources used to forecast the diagnosis rate of AFFigure 14: 8MM, total prevalent cases of AF, N, both sexes, ages ≥40 years, 2022Figure 15: 8MM, diagnosed prevalent cases of AF, N, both sexes, ages ≥40 years, 2022Figure 16: 8MM, diagnosed prevalent cases of AF by age, N, both sexes, 2022Figure 17: 8MM, diagnosed prevalent cases of AF by sex, N, ages ≥40 years, 2022Figure 18: 8MM, diagnosed prevalent cases of AF by temporal pattern of arrhythmia, N, both sexes, ages ≥40 years, 2022Figure 19: 8MM, diagnosed prevalent cases of AF by CHADS2 stroke risk score, N, both sexes, ages ≥40 years, 2022Figure 20: 8MM, diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in men, N, men, ages ≥40 years, 2022Figure 21: 8MM, diagnosed prevalent cases of AF by CHA2DS2 - VASc stroke risk score in women, N, women, ages ≥40 years, 2022Figure 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, 2022Figure 23: 8MM, diagnosed prevalent cases of AF with CKD by stage, N, both sexes, ages ≥40 years, 2022Figure 24: 8MM, diagnosed prevalent cases of AF by HAS‐BLED score, N, both sexes, ages ≥40 years, 2022Figure 25: 8MM, diagnosed prevalent cases of AF admitted to ED, N, both sexes, ages ≥40 years, 2022
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