Income Inequality and Outcomes in Heart Failure A Global Between-Country Analysis

被引:84
作者
Dewan, Pooja [1 ]
Rorth, Rasmus [1 ,2 ]
Jhund, Pardeep S. [1 ]
Ferreira, Joao Pedro [3 ]
Zannad, Faiez [3 ]
Shen, Li [1 ]
Kober, Lars [2 ]
Abraham, William T. [4 ]
Desai, Akshay S. [5 ]
Dickstein, Kenneth [6 ]
Packer, Milton [7 ]
Rouleau, Jean L. [8 ]
Solomon, Scott D. [5 ]
Swedberg, Karl [9 ,10 ]
Zile, Michael R. [11 ,12 ]
McMurray, John J., V [1 ]
机构
[1] Univ Glasgow, British Heart Fdn, Cardiovasc Res Ctr, 126 Univ Pl, Glasgow G12 8TA, Lanark, Scotland
[2] Copenhagen Univ Hosp, Dept Cardiol, Rigshosp, Copenhagen, Denmark
[3] Univ Lorraine, CHRU Nancy, Inserm CIC 1433, Nancy, France
[4] Ohio State Univ, Div Cardiovasc Med, Davis Heart & Lung Res Inst, Columbus, OH 43210 USA
[5] Brigham & Womens Hosp, Cardiovasc Med, 75 Francis St, Boston, MA 02115 USA
[6] Univ Bergen, Stavanger Univ Hosp, Dept Cardiol, Stavanger, Norway
[7] Baylor Univ, Med Ctr, Baylor Heart & Vasc Inst, Dallas, TX USA
[8] Univ Montreal, Inst Cardiol Montreal, Montreal, PQ, Canada
[9] Univ Gothenburg, Dept Mol & Clin Med, Gothenburg, Sweden
[10] Imperial Coll, Natl Heart & Lung Inst, London, England
[11] Med Univ South Carolina, Div Cardiol, Charleston, SC 29425 USA
[12] Ralph H Johnson Vet Adm Med Ctr, Charleston, SC USA
关键词
heart failure; income inequality; HEALTH; MORTALITY; ASSOCIATIONS; ENALAPRIL; RISK;
D O I
10.1016/j.jchf.2018.11.005
中图分类号
R5 [内科学];
学科分类号
100201 [内科学];
摘要
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. (C) 2019 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.
引用
收藏
页码:336 / 346
页数:11
相关论文
共 32 条
[1]
[Anonymous], BMJ
[2]
A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications [J].
Austin, Peter C. .
INTERNATIONAL STATISTICAL REVIEW, 2017, 85 (02) :185-203
[3]
Stress, inflammation and cardiovascular disease [J].
Black, PH ;
Garbutt, LD .
JOURNAL OF PSYCHOSOMATIC RESEARCH, 2002, 52 (01) :1-23
[4]
What is the lag time between income inequality and health status? [J].
Blakely, TA ;
Kennedy, BP ;
Glass, R ;
Kawachi, I .
JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH, 2000, 54 (04) :318-319
[5]
Heart Failure Care in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis [J].
Callender, Thomas ;
Woodward, Mark ;
Roth, Gregory ;
Farzadfar, Farshad ;
Lemarie, Jean-Christophe ;
Gicquel, Stephanie ;
Atherton, John ;
Rahimzadeh, Shadi ;
Ghaziani, Mehdi ;
Shaikh, Maaz ;
Bennett, Derrick ;
Patel, Anushka ;
Lam, Carolyn S. P. ;
Sliwa, Karen ;
Barretto, Antonio ;
Siswanto, Bambang Budi ;
Diaz, Alejandro ;
Herpin, Daniel ;
Krum, Henry ;
Eliasz, Thomas ;
Forbes, Anna ;
Kiszely, Alastair ;
Khosla, Rajit ;
Petrinic, Tatjana ;
Praveen, Devarsetty ;
Shrivastava, Roohi ;
Xin, Du ;
MacMahon, Stephen ;
McMurray, John ;
Rahimi, Kazem .
PLOS MEDICINE, 2014, 11 (08)
[6]
Assessing inequalities in preventive care use in Europe [J].
Carrieri, Vincenzo ;
Wuebker, Ansgar .
HEALTH POLICY, 2013, 113 (03) :247-257
[7]
Central Intelligence Agency, 2017, FIELD LIST REL, P1
[8]
Income-related inequalities and inequities in health care services utilisation in 18 selected OECD countries [J].
Devaux, Marion .
EUROPEAN JOURNAL OF HEALTH ECONOMICS, 2015, 16 (01) :21-33
[9]
Dokainish H, 2017, LANCET GLOB HEALTH, V5, pE665, DOI [10.1016/s2214-109x(17)30196-1, 10.1016/S2214-109X(17)30196-1]
[10]
A proportional hazards model for the subdistribution of a competing risk [J].
Fine, JP ;
Gray, RJ .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1999, 94 (446) :496-509