Estimating Income-Related Health Inequalities Associated with Tobacco and Alcohol Consumption in Namibia.

Disparities in resources and access to material opportunities are important determinants of income-related health inequality. This paper hypothesises that the gradient of the inequality in health between the poor and the rich is likely to depend on differences in lifestyle practices including tobacco use and alcohol abuse. Using the 2015/16 Namibia Household Income and Expenditure Survey and the Erreygers corrected concentration index, we estimate the effect of tobacco and alcohol use on income-related health inequalities. A decomposition technique was used to estimate the separate and joint contribution of tobacco and alcohol use to income-related health inequalities. The results indicate that tobacco use widens the income-related health inequality gap while alcohol consumption reduces health disparities. The simultaneous consumption of these goods has a stronger multiplicative effect on income-related health inequality. For instance, the simultaneous consumption of both goods contribute up to 1.03% of the inequality in health while tobacco use alone contributed only 0.6%. While policy options for each of these goods could be essential in reducing inequalities in health, there is a need to advocate additional measures that could simultaneously control the consumption of both goods.

When the technical is also normative: a critical assessment of measuring health inequalities using the concentration index-based indices.

BACKGROUND: Concentration index-based measures are one of the most popular tools for estimating socioeconomic-status-related health inequalities. In recent years, several variants of the concentration index have been developed that are designed to correct for deficiencies of the standard concentration index and which are increasingly being used. These variants, which include the Wagstaff index and the Erreygers index, have important technical and normative differences. MAIN BODY: In this study, we provide a non-technical review and critical assessment of these indices. We (i) discuss the difficulties that arise when measurement tools intended for income are applied in a health context, (ii) describe and illustrate the interrelationship between the technical and normative properties of these indices, (iii) discuss challenges that arise when determining whether index estimates are large or of policy significance, and (iv) evaluate the alignment of research practice with the properties of the indices used. Issues discussed in parts (i) and (ii) include the different conceptions of inequality that underpin the indices, the types of changes to a distribution which leave inequality unchanged and the importance of the measurement scale and range of the outcome variable. These concepts are illustrated using hypothetical examples. For parts (iii) and (iv), we reviewed 44 empirical studies published between 2015 and 2017 and find that researchers often fail to provide meaningful interpretations of the index estimates. CONCLUSION: We propose a series of questions to facilitate further sensitivity analyses and provide a better understanding of the index estimates. We also provide a guide for researchers and policy analysts to facilitate the critical assessment of studies using these indices, while helping applied researchers to choose inequality measures that have the normative properties they seek.

Income-related health inequality in urban China (1991-2015): The role of homeownership and housing conditions.

Unprecedented economic growth has been experienced over the several decades worldwide, but such rapid economic growth wasn't accompanied by equally-substantial improvement in health, especially health inequalities between the rich and poor. This study examines the role of housing in income-related health inequalities (income-health gradient) in urban China. We here analyze 1991-2015 China Health and Nutrition Survey data to ask how housing affects income-related health inequalities in urban China. We find pro-poor inequalities in self-reported bad health but pro-rich inequalities in objective bad health (general overweight/obesity, central obesity and high blood pressure). Housing conditions serve to reduce the health gradient, especially for objective health. On the contrary, homeownership exacerbates the health gradient. Improving housing conditions thus appears to be an effective way of reducing the income-health gradient in urban China.

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

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.

Comparing the income-related inequity of tested prevalence and self-reported prevalence of hypertension in China.

BACKGROUND: Hypertension has become a global health challenge given its high prevalence and but low awareness and detection. Whether the actual prevalence of hypertension has been estimated is important, especially for the poor. This study aimed to measure tested prevalence and self-reported prevalence of hypertension and compare the inequity between them in China. METHODS: Data were derived from China Health and Nutrition Survey (CHNS) conducted in 2011. By using the multistage, stratified, random sampling method, 12,168 respondents aged 18 or older were identified for analysis. Both tested prevalence (systolic blood pressure ≥ 140 mmHg or/and diastolic blood pressure ≥ 90 mmHg or /and current use any of antihypertensive medication) and self-reported prevalence (ever diagnosed with hypertension by a doctor) were used to measure the prevalence of hypertension. The concentration index was employed to measure the extent of inequality in tested prevalence and self-reported prevalence. A decomposition method, based on a Probit model, was used to analyze income-related horizontal inequity of tested prevalence and self-reported prevalence. RESULTS: The tested prevalence and self-reported prevalence of total respondents were 28.8% [95% CI (28.0%, 29.6%)] and 15.7% [95% CI (15.0%, 16.3%)], and 26.4% [95% CI (25.1%, 27.6%)] and 19.0% [95% CI (17.9%, 20.1%)] in urban areas, and 30.3% [95% CI (29.3%, 31.4%)] and 13.5% [95% CI (12.7%, 14.3%)] in rural areas. The horizontal inequity indexes of mean tested prevalence and self-reported prevalence were - 0.0494 and 0.1203 of total respondents, - 0.0736 and 0.0748 in urban area, and - 0.0177 and 0.0466 in rural area respectively, indicating pro-poor inequity in tested prevalence and pro-rich inequity in self-reported prevalence of hypertension. Economic status, education attainment and age were key factors of the pro-poor inequity in tested prevalence. Economic status, area and age were key factors to explain the poor-rich inequity in self-reported prevalence. CONCLUSIONS: This study revealed self-reported prevalence of hypertension was much lower than tested prevalence in China, while a larger gap between self-reported and tested prevalence was found in rural areas. Our study suggested social strategies aiming at narrowing economic gap and regional disparities, reducing educational inequity, and facilitating health conditions of the elderly should be implemented. Finally, awareness raising campaigns to test hypertension in rural area need be strengthened by health education programs and improving the access to public health service, especially for those who do not engage with regular health checkups.

Income-related health inequality of migrant workers in China and its decomposition: An analysis based on the 2012 China Labor-force Dynamics Survey data.

BACKGROUND: Although migrant workers are a vulnerable group in China, they demonstrably contribute to the country's economic growth and prosperity. This study aimed to describe and assess the inequality of migrant worker health in China and its association with socioeconomic determinants. METHODS: The data utilized in this study were obtained from the 2012 China Labor-force Dynamics Survey conducted in 29 Chinese provinces. This study converted the self-rated health of these migrant workers into a general cardinal ill-health score. Determinants associated with migrant worker health included but were not limited to age, marital status, income, and education, among other factors. Concentration index, concentration curve, and decomposition of the concentration index were employed to measure socioeconomic inequality in migrant workers' health. RESULTS: Prorich inequality was found in the health of migrant workers. The concentration index was -0.0866, as a score indicator of ill health. Decomposition of the concentration index revealed that the factors most contributing to the observed inequality were income, followed by gender, age, marital status, and smoking history. CONCLUSION: It is generally known that there is an unequal socioeconomic distribution of migrant worker health in China. In order to reduce the health inequality, the government should make a substantial effort to strengthen policy implementation in improving the income distribution for vulnerable groups. After this investigation, it is apparent that the findings we have made warrant further investigation.