Pooja Dewan1, Rasmus Rørth2, Pardeep S Jhund1, Joao Pedro Ferreira3, Faiez Zannad3, Li Shen1, Lars Køber4, William T Abraham5, Akshay S Desai6, Kenneth Dickstein7, Milton Packer8, Jean L Rouleau9, Scott D Solomon6, Karl Swedberg10, Michael R Zile11, John J V McMurray12. 1. British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom. 2. British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom; Department of Cardiology, Rigshospitalet Copenhagen University Hospital, Copenhagen, Denmark. 3. Inserm CIC 1433, Université de Lorraine, CHRU de Nancy, Nancy, France. 4. Department of Cardiology, Rigshospitalet Copenhagen University Hospital, Copenhagen, Denmark. 5. Division of Cardiovascular Medicine, Davis Heart and Lung Research Institute, Ohio State University, Columbus, Ohio. 6. Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 7. Department of Cardiology, University of Bergen, Stavanger University Hospital, Stavanger, Norway. 8. Baylor Heart and Vascular Institute, Baylor University Medical Center, Dallas, Texas. 9. Institut de Cardiologie de Montréal, Université de Montréal, Montréal, Quebec, Canada. 10. Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden; National Heart and Lung Institute, Imperial College, London. 11. Division of Cardiology, Medical University of South Carolina, and Ralph H. Johnson Veterans Administration Medical Centre, Charleston, South Carolina. 12. British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom. Electronic address: john.mcmurray@glasgow.ac.uk.
Abstract
OBJECTIVES: This study examined the relationship between income inequality and heart failure outcomes. BACKGROUND: The income inequality hypothesis postulates that population health is influenced by income distribution within a society, with greater inequality associated with worse outcomes. METHODS: This study analyzed heart failure outcomes in 2 large trials conducted in 54 countries. Countries were divided by tertiles of Gini coefficients (where 0% represented absolute income equality and 100% represented absolute income inequality), and heart failure outcomes were adjusted for standard prognostic variables, country per capita income, education index, hospital bed density, and health worker density. RESULTS: Of the 15,126 patients studied, 5,320 patients lived in Gini coefficient tertile 1 countries (coefficient: <33%), 6,124 patients lived in tertile 2 countries (33% to 41%), and 3,772 patients lived in tertile 3 countries (>41%). Patients in tertile 3 were younger than tertile 1 patients, were more often women, and had less comorbidity and several indicators of less severe heart failure, yet the tertile 3-to-1 hazard ratios (HRs) for the primary composite outcome of cardiovascular death or heart failure hospitalization were 1.57 (95% confidence interval [CI]: 1.38 to 1.79) and 1.48 for all-cause death (95% CI: 1.29 to 1.71) after adjustment for recognized prognostic variables. After additional adjustments were made for per capita income, education index, hospital bed density, and health worker density, these HRs were 1.46 (95% CI: 1.25 to 1.70) and 1.30 (95% CI: 1.10 to 1.53), respectively. CONCLUSIONS: Greater income inequality was associated with worse heart failure outcomes, with an impact similar to those of major comorbidities. Better understanding of the societal and personal bases of these findings may suggest approaches to improve heart failure outcomes.
RCT Entities:
OBJECTIVES: This study examined the relationship between income inequality and heart failure outcomes. BACKGROUND: The income inequality hypothesis postulates that population health is influenced by income distribution within a society, with greater inequality associated with worse outcomes. METHODS: This study analyzed heart failure outcomes in 2 large trials conducted in 54 countries. Countries were divided by tertiles of Gini coefficients (where 0% represented absolute income equality and 100% represented absolute income inequality), and heart failure outcomes were adjusted for standard prognostic variables, country per capita income, education index, hospital bed density, and health worker density. RESULTS: Of the 15,126 patients studied, 5,320 patients lived in Gini coefficient tertile 1 countries (coefficient: <33%), 6,124 patients lived in tertile 2 countries (33% to 41%), and 3,772 patients lived in tertile 3 countries (>41%). Patients in tertile 3 were younger than tertile 1 patients, were more often women, and had less comorbidity and several indicators of less severe heart failure, yet the tertile 3-to-1 hazard ratios (HRs) for the primary composite outcome of cardiovascular death or heart failure hospitalization were 1.57 (95% confidence interval [CI]: 1.38 to 1.79) and 1.48 for all-cause death (95% CI: 1.29 to 1.71) after adjustment for recognized prognostic variables. After additional adjustments were made for per capita income, education index, hospital bed density, and health worker density, these HRs were 1.46 (95% CI: 1.25 to 1.70) and 1.30 (95% CI: 1.10 to 1.53), respectively. CONCLUSIONS: Greater income inequality was associated with worse heart failure outcomes, with an impact similar to those of major comorbidities. Better understanding of the societal and personal bases of these findings may suggest approaches to improve heart failure outcomes.
Authors: Muthiah Vaduganathan; Brian L Claggett; Akshay S Desai; Stefan D Anker; Sergio V Perrone; Stefan Janssens; Davor Milicic; Juan L Arango; Milton Packer; Victor C Shi; Martin P Lefkowitz; John J V McMurray; Scott D Solomon Journal: J Am Coll Cardiol Date: 2019-11-11 Impact factor: 24.094
Authors: Khalid F Alhabib; Habib Gamra; Wael Almahmeed; Ayman Hammoudeh; Salim Benkheddah; Mohammad Al Jarallah; Ahmed Al-Motarreb; Mothanna Alquraishi; Mohamed Sobhy; Magdi G Yousif; Fahad Alkindi; Nadia Fellat; Mohammad I Amin; Muhammad Ali; Ayman Al Saleh; Anhar Ullah; Faiez Zannad Journal: PLoS One Date: 2020-07-22 Impact factor: 3.240
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