scholarly journals Health inequalities among middle-aged and elderly people in China: analyses of cross-sectional surveys from the China Health and Retirement Longitudinal Study 2011–2016

2020 ◽  
Author(s):  
Qinxiao Qiu ◽  
Jinfeng Zeng ◽  
Liyuan Han ◽  
Zhuo Chen ◽  
Hongpeng Sun
Keyword(s):  
Longitudinal Study ◽  
Chronic Diseases ◽  
Health Equity ◽  
Health Inequalities ◽  
Health Inequality ◽  
Gdp Per Capita ◽  
Calculated Concentration ◽  
Kendall’S Τ ◽  
Per Capita ◽  
Concentration Indices

Abstract Objectives: China has a history of striving to achieve health equity, including efforts to prevent and control infectious diseases. However, to date, there is no comprehensive assessment of inequalities in chronic diseases in China. Methods: Data for this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS) conducted from 2011 to 2016. A total of 50,244 Chinese adults aged 45 years and older were included (16,128 in 2011, 16,646 in 2013, and 17,470 in 2015). Principal component analysis was used to construct the socioeconomic status indicator. We calculated concentration indices and corresponding CIs for 14 chronic diseases and comorbidities. We then estimated the Kendall rank correlation coefficient for inequalities and GDP per capita among provinces. Results: For 10 of the 14 chronic diseases, prevalence rates were higher for the poorest tertiles than for the richest tertiles. The concentration indices of dyslipidaemia, diabetes or high blood sugar, and cancer or malignant tumour were, respectively, 0.1256 (95% confidence interval, 0.1052–0.151), 0.098 (0.0704–0.1244), and 0.1305 (0.0528–0.215) in 2015–2016, which indicated pro-rich inequality. Health inequality for chronic lung diseases and eight other diseases grew markedly from 2011 to 2016. Overall, health inequality was lower for urban residents (−0.035 in 2011–2012, −0.036 in 2013–2014, and −0.05 in 2015–2016) than rural residents (−0.053, −0.064, and −0.08, respectively), and inequality was twice as high among women (−0.051, −0.05, and −0.072, respectively) than among men (−0.023, −0.02, and −0.032, respectively). Provinces that were ranked higher for GDP per capita were also ranked higher in the degree to which disease prevalence was higher in people with lower income (Kendall’s τ=−0.2328, p=0.015; Kendall’s τ=−0.3545, p=0.0077; Kendall’s τ=−0.2646, p=0.0079, respectively). Conclusions: Pro-poor health inequalities for many diseases in China are large and widening. Policies associated with health equity, including free public health services and community health programmes, are needed to achieve the Sustainable Development Goals.

Health Difference, Disparity, Inequality, or Inequity—What Difference Does It Make What We Call It?

2016 ◽  
Author(s):  
Paula Braveman
Keyword(s):  
Human Rights ◽  
Empirical Evidence ◽  
Health Equity ◽  
Health Inequalities ◽  
Health Inequality ◽  
Social Disadvantage ◽  
The Past ◽  
Health Differences ◽  
Technical Issues

Over the past two and a half decades, distinct approaches have been taken to defining and measuring health inequalities or disparities and health equity. Some efforts have focused on technical issues in measurement, often without addressing the implications for the concepts themselves and how that might influence action. Others have focused on the concepts, often without addressing the implications for measurement. This chapter contrasts approaches that have been proposed, examining their conceptual bases and implications for measurement and policy. It argues for an approach to defining health inequalities and health equity that centers on notions of justice and has its basis in ethical and human rights principles as well as empirical evidence. According to this approach, health inequality or disparity is used to refer to a subset of health differences that are closely linked with—but not necessarily proven caused by—social disadvantage. The term “inequity,” which means injustice, could also be used, but arguments are presented for using it somewhat more sparingly, for those inequalities or disparities in health or its determinants that we know are caused by social disadvantage.

scholarly journals Socioeconomic Status and Morbidity Rate Inequality in China: Based on NHSS and CHARLS Data

2019 ◽  
Vol 16 (2) ◽  
pp. 215 ◽  
Author(s):  
Yunyun Jiang ◽  
Haitao Zheng ◽  
Tianhao Zhao
Keyword(s):  
Socioeconomic Status ◽  
Longitudinal Study ◽  
Incidence Rate ◽  
Chronic Diseases ◽  
Education Level ◽  
Morbidity Rate ◽  
Age Cohorts ◽  
Per Capita ◽  
New Evidence ◽  
The Relationship

Previous studies have shown there are no consistent and robust associations between socioeconomic status and morbidity rates. This study focuses on the relationship between the socioeconomic status and the morbidity rates in China, which helps to add new evidence for the fragmentary relationship between socioeconomic status and morbidity rates. The National Health Services Survey (NHSS) and China Health and Retirement Longitudinal Study (CHARLS) data are used to examine whether the association holds in both all-age cohorts and in older only cohorts. Three morbidity outcomes (two-week incidence rate, the prevalence of chronic diseases, and the number of sick days per thousand people) and two socioeconomic status indicators (income and education) are mainly examined. The results indicate that there are quadratic relationships between income per capita and morbidities. This non-linear correlation is similar to the patterns in European countries. Meanwhile, there is no association between education years and the morbidity in China, i.e., either two-week incidence rate or prevalence rate of chronic diseases has no statistically significant relationship with the education level in China.

scholarly journals Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Chijioke O. Nwosu ◽  
Adeola Oyenubi
Keyword(s):  
South Africa ◽  
Health Inequalities ◽  
Health Inequality ◽  
National Income ◽  
Decomposition Analysis ◽  
Good Health ◽  
Poor Health ◽  
Income Loss ◽  
Per Capita ◽  
Self Assessed Health

Abstract Background The coronavirus disease 2019 (COVID-19) has resulted in an enormous dislocation of society especially in South Africa. The South African government has imposed a number of measures aimed at controlling the pandemic, chief being a nationwide lockdown. This has resulted in income loss for individuals and firms, with vulnerable populations (low earners, those in informal and precarious employment, etc.) more likely to be adversely affected through job losses and the resulting income loss. Income loss will likely result in reduced ability to access healthcare and a nutritious diet, thus adversely affecting health outcomes. Given the foregoing, we hypothesize that the economic dislocation caused by the coronavirus will disproportionately affect the health of the poor. Methods Using the fifth wave of the National Income Dynamics Study (NIDS) dataset conducted in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) dataset conducted in May/June 2020, this paper estimated income-related health inequalities in South Africa before and during the COVID-19 pandemic. Health was a dichotomized self-assessed health measure, with fair and poor health categorized as “poor” health, while excellent, very good and good health were categorized as “better” health. Household per capita income was used as the ranking variable. Concentration curves and indices were used to depict the income-related health inequalities. Furthermore, we decomposed the COVID-19 era income-related health inequality in order to ascertain the significant predictors of such inequality. Results The results indicate that poor health was pro-poor in the pre-COVID-19 and COVID-19 periods, with the latter six times the value of the former. Being African (relative to white), per capita household income and household experience of hunger significantly predicted income-related health inequalities in the COVID-19 era (contributing 130%, 46% and 9% respectively to the inequalities), while being in paid employment had a nontrivial but statistically insignificant contribution (13%) to health inequality. Conclusions Given the significance and magnitude of race, hunger, income and employment in determining socioeconomic inequalities in poor health, addressing racial disparities and hunger, income inequality and unemployment will likely mitigate income-related health inequalities in South Africa during the COVID-19 pandemic.

scholarly journals Income-related health inequalities associated with the coronavirus epidemic in South Africa: A decomposition analysis

2020 ◽  
Author(s):  
Chijioke O. Nwosu ◽  
Adeola Oyenubi
Keyword(s):  
South Africa ◽  
Health Inequalities ◽  
Health Inequality ◽  
National Income ◽  
Decomposition Analysis ◽  
Good Health ◽  
Poor Health ◽  
Income Loss ◽  
Per Capita ◽  
Self Assessed Health

Abstract Background: The coronavirus pandemic (covid-19) has resulted in an enormous dislocation of society especially in South Africa. The South African government has imposed a number of measures aimed at controlling the epidemic, chief being a nationwide lockdown. This has resulted in income loss for individuals and firms, with vulnerable populations (low earners, those in informal and precarious employment, etc.) more likely to be adversely affected through job losses and the resulting income loss. Income loss will likely result in reduced ability to access healthcare and a nutritious diet, thus adversely affecting health outcomes. Given the foregoing, we hypothesize that the economic dislocation caused by the coronavirus will disproportionately affect the health of the poor.Methods: Using the fifth wave of the National Income Dynamics Study (NIDS) dataset conducted in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) dataset conducted in May/June 2020, this paper estimated income-related health inequalities in South Africa before and during the covid-19 epidemic. Health was a dichotomized self-assessed health measure, with fair and poor health categorized as “poor” health, while excellent, very good and good health were categorized as “better” health. Household per capita income was used as the ranking variable. Concentration curves and indices were used to depict the income-related health inequalities. Furthermore, we decomposed the covid-19 era income-related health inequality in order to ascertain the significant predictors of such inequality.Results: The results indicate that poor health was pro-poor in the pre-covid-19 and covid-19 periods, with the latter six times the value of the former. Being African (relative to white), per capita household income and household experience of hunger significantly predicted income-related health inequalities in the covid-19 era, while being in paid employment had a nontrivial but statistically insignificant contribution to health inequality.Conclusion: Addressing racial disparities, tackling hunger, income inequality and unemployment will likely mitigate income-related health inequalities in South Africa during the covid-19 epidemic.

scholarly journals Joint Action Health Equity Europe - turning knowledge on health inequality monitoring into actions

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
G Olsson ◽  
G Henriksson
Keyword(s):  
Health Equity ◽  
Health Inequalities ◽  
Health Inequality ◽  
Joint Action ◽  
Social Inequalities ◽  
National Level ◽  
Health Agency ◽  
Monitoring Systems ◽  
Specific Objective ◽  
The Status

Abstract Growing social inequalities in health challenge a sustainable development. To reduce health inequalities, it will be necessary to provide relevant data to policymakers on how health inequalities and the social determinants of health are distributed within populations over time, i.e. it will be necessary to form appropriate health inequality monitoring systems (HIMS). One of the aims with Joint Action Health Equity Europe (JAHEE) and the specific objective of work package five (WP5) is to advance partner countries ability to monitor national health inequalities. Country assessments has been conducted to assess the status of the HIMS in the participating countries, and a joint framework has been developed, describing the core components of an “ideal” HIMS. All partners have committed to the overall objective of the WP5 through the identification and implementation of concrete actions, which is now ongoing. The countries in JAHEE WP5 are Cyprus, Finland, Germany, Italy, Lithuania, Netherlands, Poland, Romania, Serbia, Slovenia, Spain and Sweden, where The Public Health Agency Sweden is the lead. The aim of this presentation is to present the experiences from JAHEE WP 5, and what they imply is needed to build coherent health inequality monitoring systems at a national level. More specifically, the JAHEE project in general, the WP5 project in particular will be presented, the structured work process described, and some general results and conclusions discussed

Searching for new Sources of growth. Are the Developing Countries Catching up with the Developed ones?

2015 ◽  
pp. 30-53
Author(s):  
V. Popov
Keyword(s):  
Developing Countries ◽  
Western Europe ◽  
Gdp Per Capita ◽  
The West ◽  
Catching Up ◽  
The Us ◽  
The World ◽  
Economic Divergence ◽  
Per Capita ◽  
Relative Standing

This paper examines the trajectory of growth in the Global South. Before the 1500s all countries were roughly at the same level of development, but from the 1500s Western countries started to grow faster than the rest of the world and PPP GDP per capita by 1950 in the US, the richest Western nation, was nearly 5 times higher than the world average and 2 times higher than in Western Europe. Since 1950 this ratio stabilized - not only Western Europe and Japan improved their relative standing in per capita income versus the US, but also East Asia, South Asia and some developing countries in other regions started to bridge the gap with the West. After nearly half of the millennium of growing economic divergence, the world seems to have entered the era of convergence. The factors behind these trends are analyzed; implications for the future and possible scenarios are considered.

Atlas of economic complexity, Russian regional pages

2018 ◽  
pp. 71-91 ◽  
Author(s):  
I. L. Lyubimov ◽  
M. V. Lysyuk ◽  
M. A. Gvozdeva
Keyword(s):  
Value Added ◽  
Growth Strategy ◽  
Gdp Per Capita ◽  
Export Diversification ◽  
Russian Regions ◽  
Economic Complexity ◽  
Diversification Strategies ◽  
Central Regions ◽  
Per Capita ◽  
Regions Of Russia

Well-established results indicate that export diversification might be a better growth strategy for an emerging economy as long as its GDP per capita level is smaller than an empirically defined threshold. As average incomes in Russian regions are likely to be far below the threshold, it might be important to estimate their diversification potential. The paper discusses the Atlas of economic complexity for Russian regions created to visualize regional export baskets, to estimate their complexity and evaluate regional export potential. The paper’s results are consistent with previous findings: the complexity of export is substantially higher and diversification potential is larger in western and central regions of Russia. Their export potential might become larger if western and central regions, first, try to join global value added chains and second, cooperate and develop joint diversification strategies. Northern and eastern regions are by contrast much less complex and their diversification potential is small.

The Reproduction Vector of the Russian Economy: 1999-2007

2008 ◽  
pp. 94-109 ◽  
Author(s):  
D. Sorokin
Keyword(s):  
Economic Policy ◽  
Human Capital ◽  
Growth Rates ◽  
Developed Countries ◽  
Gdp Per Capita ◽  
Real Sector ◽  
Russian Economy ◽  
Per Capita ◽  
The Developed Countries

The problem of the Russian economy’s growth rates is considered in the article in the context of Russia’s backwardness regarding GDP per capita in comparison with the developed countries. The author stresses the urgency of modernization of the real sector of the economy and the recovery of the country’s human capital. For reaching these goals short- or mid-term programs are not sufficient. Economic policy needs a long-term (15-20 years) strategy, otherwise Russia will be condemned to economic inertia and multiplying structural disproportions.

The monetary value of disability-adjusted-life-years lost in Kenya in 2017.pdf

2019 ◽  
Author(s):  
Joses Kirigia ◽  
Rose Nabi Deborah Karimi Muthuri
Keyword(s):  
New York ◽  
International Monetary Fund ◽  
Health Expenditure ◽  
Analytical Framework ◽  
World Health ◽  
Gdp Per Capita ◽  
Current Health ◽  
Monetary Value ◽  
Life Years ◽  
Per Capita

<div>A variant of human capital (or net output) analytical framework was applied to monetarily value DALYs lost from 166 diseases and injuries. The monetary value of each of the 166 diseases (or injuries) was obtained through multiplication of the net 2019 GDP per capita for Kenya by the number of DALYs lost from each specific cause. Where net GDP per capita was calculated by subtracting current health expenditure from the GDP per capita. </div><div> </div><p>The DALYs data for the 166 causes were from IHME (Global Burden of Disease Collaborative Network, 2018), GDP per capita data from the International Monetary Fund world economic outlook database (International Monetary Fund, 2019), and the current health expenditure per person data from the WHO Global Health Expenditure Database (World Health Organization, 2019b). A model consisting of fourteen equations was calculated with Excel Software developed by Microsoft (New York).</p><p> </p>

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