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Epiomic Epidemiology Series: Chronic heart failure forecast in 12 major markets 2018–2028

November 2017 | 58 pages | ID: ED83973E06CEN
Black Swan Analysis limited

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Black Swan Analysis Epiomic Epidemiology Series Forecast Report on Chronic Heart Failure in 12 Major Markets

Chronic heart failure (CHF) is a chronic disease during which the heart is not able to supply sufficient blood flow to the body. Exacerbations of CHF are characterised by dyspnoea at rest or on exertion and worsening fluid retention, apparent as lung congestion and/or swollen ankles and legs. The disease is associated with toxic, inflammatory and infective heart damage, structural, metabolic and genetic heart abnormalities, as well as cardiovascular risk factors, such as smoking, sedentary lifestyle, unhealthy diet and alcohol consumption.

This report provides the current prevalent population for chronic heart failure across 12 Major Markets (USA, Canada, France, Germany, Italy, Spain, UK, Russia, Japan, China, Brazil & India) split by gender and 5-year age cohort. Along with the current prevalence, the report also contains a disease overview of the risk factors, disease diagnosis and prognosis along with specific variations by geography and ethnicity.

Providing a value-added level of insight from the analysis team at Black Swan, patient populations with the two types of chronic heart failure, as well as several of the main symptoms and co-morbidities of chronic heart failure have been quantified and presented alongside the overall prevalence figures. These sub-populations within the main disease are also included at a country level across the 10-year forecast snapshot.

Main symptoms and co-morbidities for chronic heart failure include:
  • Arterial hypertension
  • Coronary artery disease
  • Atrial fibrillation
  • Diabetes mellitus
  • Metabolic syndrome
  • Obesity
  • Renal dysfunction
  • Chronic obstructive pulmonary disease (COPD)
  • Pulmonary hypertension
  • Sleep apnoea
  • Anaemia
This report is built using data and information sourced from the proprietary Epiomic patient segmentation database. To generate accurate patient population estimates, the Epiomic database utilises a combination of several world class sources that deliver the most up to date information form patient registries, clinical trials and epidemiology studies. All of the sources used to generate the data and analysis have been identified in the report.

Reason to buy
  • Able to quantify patient populations in global chronic heart failure market to target the development of future products, pricing strategies and launch plans.
  • Gain further insight into the prevalence of the subdivided types of chronic heart failure and identify patient segments with high potential.
  • Delivery of more accurate information for clinical trials in study sizing and realistic patient recruitment for various countries.
  • Provide a level of understanding on the impact from specific co-morbid conditions on chronic heart failure’s prevalent population.
  • Identify sub-populations within chronic heart failure which require treatment.
  • Gain an understanding of the specific markets that have the largest number of chronic heart failure patients.
INTRODUCTION

CAUSE OF THE DISEASE

RISK FACTORS & PREVENTION

DIAGNOSIS OF THE DISEASE

VARIATION BY GEOGRAPHY/ETHNICITY

DISEASE PROGNOSIS & CLINICAL COURSE

KEY COMORBID CONDITIONS / FEATURES ASSOCIATED WITH THE DISEASE

METHODOLOGY FOR QUANTIFICATION OF PATIENT NUMBERS

TOP-LINE PREVALENCE FOR CHRONIC HEART FAILURE

CHRONIC HEART FAILURE WITH REDUCED EJECTION FRACTION

FEATURES OF CHF-REF PATIENTS

COMORBID CONDITIONS OF CHF-REF PATIENTS

CHRONIC HEART FAILURE WITH PRESERVED EJECTION FRACTION

FEATURES OF CHF-PEF PATIENTS

COMORBID CONDITIONS OF CHF-PEF PATIENTS

ABBREVIATIONS USED IN THE REPORT

OTHER BLACK SWAN ANALYSIS PUBLICATIONS

BLACK SWAN ANALYSIS ONLINE PATIENT-BASED DATABASES

PATIENT-BASED OFFERING

ONLINE PRICING DATA & PLATFORMS

REFERENCES

APPENDIX

LIST OF TABLES AND FIGURES

Table 1. Aetiologies of Heart Failure
Table 2. Symptoms and Signs Typical of Heart Failure
Table 3. Classification and Common Clinical Characteristics of Patients with Acute Heart Failure
Table 4. NYHA Classification of Heart Failure
Table 5. Clinical-hemodynamic Profiles of Heart Failure (following Stevenson et al)
Table 6. Markers of Worse Prognosis in Patients with Heart Failure
Table 7. Prevalence of Chronic Heart Failure, total (000s)
Table 8. Prevalence of Chronic Heart Failure, males (000s)
Table 9. Prevalence of Chronic Heart Failure, females (000s)
Table 10. Prevalence of CHF-REF, total (000s)
Table 11. Prevalence of CHF-REF, males (000s)
Table 12. Prevalence of CHF-REF, females (000s)
Table 13. CHF-REF Patients by NYHA Class, total (000s)
Table 14. CHF-REF Patients with Diabetes, total (000s)
Table 15. CHF-REF Patients with COPD, total (000s)
Table 16. CHF-REF Patients with Anaemia, total (000s)
Table 17. CHF-REF Patients with Atrial Fibrillation, total (000s)
Table 18. CHF-REF Patients with Coronary Artery Disease, total (000s)
Table 19. CHF-REF Patients with previous Myocardial Infarction, total (000s)
Table 20. CHF-REF Patients with Muscle Wasting, total (000s)
Table 21. Prevalence of CHF-PEF, total (000s)
Table 22. Prevalence of CHF-PEF, males (000s)
Table 23. Prevalence of CHF-PEF, females (000s)
Table 24. CHF-PEF Patients by NYHA Class, total (000s)
Table 25. CHF-PEF Patients with Diabetes, total (000s)
Table 26. CHF-PEF Patients with COPD, total (000s)
Table 27. CHF-PEF Patients with Anaemia, total (000s)
Table 28. CHF-PEF Patients with Atrial Fibrillation, total (000s)
Table 29. CHF-PEF Patients with Coronary Artery Disease, total (000s)
Table 30. CHF-PEF Patients with previous Myocardial Infarction, total (000s)
Table 31. CHF-PEF Patients with Muscle Wasting, total (000s)
Table 32. Abbreviations and Acronyms used in the report
Table 33. USA Prevalence of CHF by 5-yr age cohort, males (000s)
Table 34. USA Prevalence of CHF by 5-yr age cohort, females (000s)
Table 35. Canada Prevalence of CHF by 5-yr age cohort, males (000s)
Table 36. Canada Prevalence of CHF by 5-yr age cohort, females (000s)
Table 37. France Prevalence of CHF by 5-yr age cohort, males (000s)
Table 38. France Prevalence of CHF by 5-yr age cohort, females (000s)
Table 39. Germany Prevalence of CHF by 5-yr age cohort, males (000s)
Table 40. Germany Prevalence of CHF by 5-yr age cohort, females (000s)
Table 41. Italy Prevalence of CHF by 5-yr age cohort, males (000s)
Table 42. Italy Prevalence of CHF by 5-yr age cohort, females (000s)
Table 43. Spain Prevalence of CHF by 5-yr age cohort, males (000s)
Table 44. Spain Prevalence of CHF by 5-yr age cohort, females (000s)
Table 45. UK Prevalence of CHF by 5-yr age cohort, males (000s)
Table 46. UK Prevalence of CHF by 5-yr age cohort, females (000s)
Table 47. Japan Prevalence of CHF by 5-yr age cohort, males (000s)
Table 48. Japan Prevalence of CHF by 5-yr age cohort, females (000s)
Table 49. China Prevalence of CHF by 5-yr age cohort, males (000s)
Table 50. China Prevalence of CHF by 5-yr age cohort, females (000s)
Table 51. Russia Prevalence of CHF by 5-yr age cohort, males (000s)
Table 52. Russia Prevalence of CHF by 5-yr age cohort, females (000s)
Table 53. Brazil Prevalence of CHF by 5-yr age cohort, males (000s)
Table 54. Brazil Prevalence of CHF by 5-yr age cohort, females (000s)
Table 55. India Prevalence of CHF by 5-yr age cohort, males (000s)
Table 56. India Prevalence of CHF by 5-yr age cohort, females (000s)


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