Heart Failure Epidemiology Analysis and Forecast to 2032: Focus on 7 Major Markets - US, France, Germany, Italy, Spain, UK, and Japan


Dublin, Oct. 09, 2023 (GLOBE NEWSWIRE) -- The "Heart Failure Epidemiology Analysis and Forecast to 2032" report has been added to ResearchAndMarkets.com's offering.

This report provides an overview of the risk factors, comorbidities, and the global and historical epidemiological trends for HF in the seven major markets (7MM: US, France, Germany, Italy, Spain, UK, and Japan).

The report includes a 10-year epidemiology forecast for the diagnosed incident cases and diagnosed prevalent cases of HF. The diagnosed incident cases and the diagnosed prevalent cases of HF are segmented by age (0-18 years, 19-44 years, 45-49 years, 50-59 years, 60-69 years, 70-79 years, and 80 years and above) and sex.

The report also includes the diagnosed incident cases and diagnosed prevalent cases of HF by ejection fraction (HF-PEF = LVEF =50%; HF-mrEF = LVEF = 40-49%; HF-REF = LVEF < 40%). Diagnosed incident cases of HF are further segmented based on acute HF hospitalizations, acute HF hospitalizations based on presentation, hospital length of stay, and re-admissions within 30 days post-discharge.

Additionally, diagnosed prevalent cases of HF are segmented based on NYHA classes (class I-IV), diagnosed prevalent cases of HF-PEF, HF-mrEF, and HF-REF segmented based on NYHA classes, and diagnosed prevalent cases of HF by ACCF/AHA stages (stage B, C, and D). Although not covered in this report, the diagnosed prevalent cases of HF segmented by comorbidities such as coronary artery disease, hypertension, previous myocardial infarction, renal dysfunction or failure, anemia, diabetes mellitus, and atrial fibrillation can be found in the model.

This epidemiology forecast for HF is supported by data obtained from peer-reviewed articles and population-based studies. The forecast methodology was kept consistent across the 7MM to allow for a meaningful comparison of the forecast diagnosed incident cases and diagnosed prevalent cases of HF across these markets.

Reasons to Buy

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

Key Topics Covered:

1 Heart Failure: 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 7MM forecast methodology
2.4.1 Sources
2.4.2 Forecast assumptions and methods
2.4.3 Forecast assumptions and methods: diagnosed incident cases of HF - 7MM
2.4.4 Forecast assumptions and methods: diagnosed incident cases of HF by EF
2.4.5 Forecast assumptions and methods: hospitalizations for acute HF
2.4.6 Forecast assumptions and methods: acute HF hospitalizations based on presentation
2.4.7 Forecast assumptions and methods: hospital LoS days for acute HF hospitalizations
2.4.8 Forecast assumptions and methods: re-admissions within 30 days post-discharge after acute HF hospitalization
2.4.9 Forecast assumptions and methods: diagnosed prevalent cases of HF - 7MM
2.4.10 Forecast assumptions and methods: diagnosed prevalent cases of HF by EF
2.4.11 Forecast assumptions and methods: diagnosed prevalent cases of HF by NYHA classes
2.4.12 Forecast assumptions and methods: diagnosed prevalent cases of HF-PEF (LVEF =50%) by NYHA class
2.4.13 Forecast assumptions and methods: diagnosed prevalent cases of HF-mrEF (LVEF = 40-49%) by NYHA class
2.4.14 Forecast assumptions and methods: diagnosed prevalent cases of HF-REF (LVEF < 40%) by NYHA class
2.4.15 Forecast assumptions and methods: diagnosed prevalent cases of HF by ACCF/AHA stages
2.5 Epidemiological forecast for heart failure (2022-32)
2.5.1 Diagnosed incident cases of HF
2.5.2 Age-specific diagnosed incident cases of HF
2.5.3 Sex-specific diagnosed incident cases of HF
2.5.4 Diagnosed incident cases of HF by EF
2.5.5 Hospitalizations for acute HF
2.5.6 Acute HF hospitalizations based on presentation
2.5.7 Hospital LoS for acute HF
2.5.8 Re-admissions within 30 days post-discharge after acute HF
2.5.9 Diagnosed prevalent cases of HF
2.5.10 Age-specific diagnosed prevalent cases of HF
2.5.11 Sex-specific diagnosed prevalent cases of HF
2.5.12 Diagnosed prevalent cases of HF by EF
2.5.13 Diagnosed prevalent cases of HF by NYHA classes
2.5.14 Diagnosed prevalent cases of HF-PEF (LVEF =50%) by NYHA class
2.5.15 Diagnosed prevalent cases of HF-mrEF (LVEF = 40-49%) by NYHA class
2.5.16 Diagnosed prevalent cases of HF-REF (LVEF < 40%) by NYHA class
2.5.17 Diagnosed prevalent cases of HF by ACCF/AHA stages
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

For more information about this report visit https://www.researchandmarkets.com/r/u84ifd

Source: GlobalData

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