EpiCast Report: Pulmonary Arterial Hypertension – Epidemiology Forecast to 2026

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PAH is a rare and severe condition characterized by vascular proliferation and remodeling of the small pulmonary arteries. This results in progressively increasing pulmonary vascular resistance, leading to an increase in pulmonary arterial pressure. The disease typically results in right-sided heart failure and premature death

To forecast the diagnosed incident and diagnosed prevalent cases of PAH in the 7MM, GlobalData epidemiologists selected nationally representative, registry-based studies and Ministry of Health statistics studies that provided the diagnosed incidence and/or diagnosed prevalence of PAH in the 7MM. For the US and 5EU (France, Germany, Italy, Spain, and UK), GlobalData epidemiologists provide two different forecasts for the diagnosed incident and diagnosed prevalent cases of PAH—a forecast based on registry data and a forecast adjusted for the underestimation of registry-based data.

GlobalData epidemiologists forecast an increase in the adjusted diagnosed incident cases of PAH in the 7MM, from 11,271 adjusted diagnosed incident cases in 2016 to 11,659 adjusted diagnosed incident cases in 2026, at an annual growth rate (AGR) of 0.34% during the forecast period.

Scope

The Pulmonary arterial hypertension (PAH) EpiCast Report and EpiCast Model provide an overview of the risk factors and global trends of PAH in the seven major markets (7MM: US, France, Germany, Italy, Spain, UK, and Japan).

It includes a 10-year epidemiological forecast for the diagnosed incident cases of PAH and diagnosed prevalent cases of PAH, segmented by age (at 10-year intervals, for all ages) and sex. The diagnosed prevalent cases are further segmented according to the New York Heart Association (NYHA) functional classes I–IV, and comorbidities (connective tissue disease, systemic sclerosis, diabetes, chronic obstructive pulmonary disease, portal hypertension, and congenital heart disease).

The PAH epidemiology report and model were written and developed by Masters- and PhD-level epidemiologists.

The EpiCast Report is in-depth, high quality, transparent and market-driven, providing expert analysis of disease trends in the 7MM.

The EpiCast Model is easy to navigate, interactive with dashboards, and epidemiology-based with transparent and consistent methodologies. Moreover, the model supports data presented in the report and showcases disease trends over a 10-year forecast period using reputable sources.

Reasons to Buy

The PAH EpiCast series will allow you to:

Develop business strategies by understanding the trends shaping and driving the global PAH market.

Quantify patient populations in the global PAH market to improve product design, pricing, and launch plans.

Organize sales and marketing efforts by identifying the age groups and sex that present the best opportunities for PAH therapeutics in each of the markets covered.

Understand magnitude of PAH population by disease comorbidity.

Table of Contents

Table of Contents

1 Table of Contents

1.1 List of Tables

1.2 List of Figures

2 Pulmonary Arterial Hypertension: Executive Summary

2.1 Related Reports

2.2 Upcoming Reports

3 Epidemiology

3.1 Disease Background

3.2 Risk Factors and Comorbidities

3.3 Global and Historical Trends

3.4 Forecast Methodology

3.4.1 Sources

3.4.2 Forecast Assumptions and Methods

3.4.3 Diagnosed Incident Cases and Diagnosed Prevalent Cases of PAH (Forecast Based on Registry Data)

3.4.4 Diagnosed Prevalent Cases of PAH by NYHA Functional Class I–IV, 7MM (Forecast Based on Registry Data)

3.4.5 Diagnosed Prevalent Cases with Comorbidities (Forecast Based on Registry Data)

3.4.6 Diagnosed Incident Cases and Diagnosed Prevalent Cases — US, France, Germany, Italy, Spain, and UK (Forecast Adjusted for Underestimation of Registry Data)

3.4.7 Diagnosed Prevalent Cases by NYHA Functional Class I–IV, US, France, Germany, Italy, Spain, and UK (Forecast Adjusted for Underestimation of Registry Data)

3.4.8 Diagnosed Prevalent Cases by Comorbidities (Adjusted for Underestimation)

3.5 Epidemiological Forecast for PAH (2016–2026) – Based on Registry Data

3.5.1 Diagnosed Incident Cases of PAH (Forecast Based on Registry Data)

3.5.2 Diagnosed Prevalent Cases of PAH (Forecast Based on Registry Data)

3.5.3 Diagnosed Prevalent Cases of PAH by NYHA Functional Classes (Forecast Based on Registry Data)

3.5.4 Comorbidities Among the Diagnosed Prevalent Cases of PAH (Forecast Based on Registry Data)

3.6 Epidemiological Forecast for PAH (2016–2026) – Adjusted for Underestimation of Registry Data

3.6.1 Diagnosed Incident Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.6.2 Age-Specific Diagnosed Incident Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.6.3 Sex-Specific Diagnosed Incident Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.6.4 Diagnosed Prevalent Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.6.5 Age-Specific Diagnosed Prevalent Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.6.6 Sex-Specific Diagnosed Prevalent Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.6.7 Diagnosed Prevalent Cases of PAH by NYHA Functional Class I–IV (Forecast Adjusted for Underestimation of Registry Data)

3.6.8 Comorbidities Among the Diagnosed Prevalent Cases of PAH (Forecast Adjusted for Underestimation of Registry Data)

3.7 Discussion

3.7.1 Epidemiological Forecast Insight

3.7.2 Limitations of Analysis

3.7.3 Strengths of Analysis

4 Appendix

4.1 Bibliography

4.2 About the Authors

4.2.1 Epidemiologist

4.2.2 Reviewers

4.2.3 Global Director of Therapy Analysis and Epidemiology

4.2.4 Global Head and EVP of Healthcare Operations and Strategy

4.3 About GlobalData

4.4 Contact Us

4.5 Disclaimer

Table

Table 1: WHO Guidelines, Modified NYHA Functional Classes I–IV

Table 2: Risk Factors and Comorbidities for PAH

Table 3: 7MM, Diagnosed Incident Cases of PAH, Both Sexes, All Ages, N, Selected Years 2016–2026 (Based on Registry Data)

Table 4: 7MM, Diagnosed Prevalent Cases of PAH, Both Sexes, All Ages, N, Selected Years 2016–2026 (Forecast Based on Registry Data).

Table 5: 7MM, Diagnosed Incident Cases of PAH, Both Sexes, All Ages, N, Selected Years 2016–2026 (Forecast Adjusted for Underestimation of Registry Data)

Table 6: 7MM, Diagnosed Prevalent Cases of PAH, Both Sexes, All Ages, N, Selected Years 2016–2026 (Forecast Adjusted for Underestimation of Registry Data)

Figures

Figure 1: 7MM, Diagnosed Incident Cases of PAH, Both Sexes, All Ages, N, 2016 and 2026 (Forecast Adjusted for Underestimation of Registry Data)

Figure 2: 7MM, Diagnosed Prevalent Cases of PAH, Both Sexes, All Ages, N, 2016 and 2026 (Forecast Adjusted for Underestimation of Registry Data)

Figure 3: 7MM, Age-Standardized Diagnosed Incidence of PAH (Cases per 100,000 Population), All Ages, by Sex, 2016 (Forecast Based on Registry Data)

Figure 4: 7MM, Age-Standardized Diagnosed Prevalence of PAH (%), All Ages, by Sex, 2016 (Forecast Based on Registry Data)

Figure 5: 7MM, Sources Used and Not Used for the Diagnosed Incident Cases of PAH and Diagnosed Prevalent Cases of PAH (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 6: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH* by NYHA Functional Classes I–IV (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 7: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH with CTD (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 8: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH with SS (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 9: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH with Diabetes (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 10: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH with COPD (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 11: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH with PT (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 12: 7MM, Sources Used for the Diagnosed Prevalent Cases of PAH with CHD (for Forecast Based on Registry Data and Forecast Adjusted for Underestimation of Registry Data)

Figure 13: 7MM, Diagnosed Prevalent Cases of PAH, by NYHA Functional Class I–IV, Both Sexes, All Ages, N, 2016 (Forecast Based on Registry Data)

Figure 14: 7MM, Comorbidities Among the Diagnosed Prevalent Cases of PAH (N), All Ages, 2016 (Forecast Based on Registry Data)

Figure 15: 7MM, Age-Specific Diagnosed Incident Cases of PAH, Both Sexes, All Ages, N, 2016 (Forecast Adjusted for Underestimation of Registry Data)

Figure 16: 7MM, Sex-Specific Diagnosed Incident Cases of PAH, All Ages, N, 2016 (Forecast Adjusted for Underestimation of Registry Data)

Figure 17: 7MM, Age-Specific Diagnosed Prevalent Cases of PAH, Both Sexes, All Ages, N, 2016 (Forecast Adjusted for Underestimation of Registry Data)

Figure 18: 7MM, Sex-Specific Diagnosed Prevalent Cases of PAH, All Ages, 2016 (Forecast Adjusted for Underestimation of Registry Data)

Figure 19: 7MM, Diagnosed Prevalent Cases of PAH Categorized into NYHA Functional Class I–IV (N), Both Sexes, All Ages, 2016 (Forecast Adjusted for Underestimation of Registry Data)

Figure 20: 7MM, Comorbidities Among the Diagnosed Prevalent Cases of PAH (N), Both Sexes, All Ages, 2016 (Forecast Adjusted for Underestimation of Registry Data)

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