Assessing the impoverishment effects of out-of-pocket healthcare payments prior to the uptake of the national health insurance scheme in Ghana

Background: There is a global concern regarding how households could be protected from relatively large healthcare payments which are a major limitation to accessing healthcare. Such payments also endanger the welfare of households with the potential of moving households into extreme impoverishment. This paper examines the impoverishing effects of out-of-pocket (OOP) healthcare payments in Ghana prior to the introduction of Ghana’s national health insurance scheme. Methods: Data come from the Ghana Living Standard Survey 5 (2005/2006). Two poverty lines ($1.25 and$2.50 per capita per day at the 2005 purchasing power parity) are used in assessing the impoverishing effects of OOP healthcare payments. We computed the poverty headcount, poverty gap, normalized poverty gap and normalized mean poverty gap indices using both poverty lines. We examine these indicators at a national level and disaggregated by urban/rural locations, across the three geographical zones, and across the ten administrative regions in Ghana. Also the Pen’s parade of “dwarfs and a few giants” is used to illustrate the decreasing welfare effects of OOP healthcare payments in Ghana. Results: There was a high incidence and intensity of impoverishment due to OOP healthcare payments in Ghana. These payments contributed to a relative increase in poverty headcount by 9.4 and 3.8% using the $1.25/day and$2.5/day poverty lines, respectively. The relative poverty gap index was estimated at 42.7 and 10.5% respectively for the lower and upper poverty lines. Relative normalized mean poverty gap was estimated at 30.5 and 6.4%, respectively, for the lower and upper poverty lines. The percentage increase in poverty associated with OOP healthcare payments in Ghana is highest among households in the middle zone with an absolute increase estimated at 2.3% compared to the coastal and northern zones. Conclusion: It is clear from the findings that without financial risk protection, households can be pushed into poverty due to OOP healthcare payments. Even relatively richer households are impoverished by OOP healthcare payments. This paper presents baseline indicators for evaluating the impact of Ghana’s national health insurance scheme on impoverishment due to OOP healthcare payments

Explaining changes in wealth inequalities in child health: The case of stunting and wasting in Nigeria.

BackgroundMalnutrition is a major cause of child death, and many children suffer from acute and chronic malnutrition. Nigeria has the second-highest burden of stunting globally and a higher-than-average child wasting prevalence. Moreover, there is substantial spatial variation in the prevalence of stunting and wasting in Nigeria. This paper assessed the socioeconomic inequalities and determinants of the change in socioeconomic inequalities in child stunting and wasting in Nigeria between 2013 and 2018.MethodsData came from the 2013 and 2018 Nigeria Demographic and Health Survey. Socioeconomic inequalities in stunting and wasting were measured using the concentration curve and Erreygers' corrected concentration index. A pro-poor concentration index is negative, meaning that the poor bear a disproportionately higher burden of stunting or wasting than the wealthy. A positive or pro-rich index is the opposite. Standard methodologies were applied to decompose the concentration index (C) while the Oaxaca-Blinder approach was used to decompose changes in the concentration indices (ΔC).FindingsThe socioeconomic inequalities in child stunting and wasting were pro-poor in 2013 and 2018. The concentration indices for stunting reduced from -0.298 (2013) to -0.330 (2018) (ΔC = -0.032). However, the concentration indices for wasting increased from -0.066 to -0.048 (ΔC = 0.018). The changes in the socioeconomic inequalities in stunting and wasting varied by geopolitical zones. Significant determinants of these changes for both stunting and wasting were changes in inequalities in wealth, maternal education and religion. Under-five dependency, access to improved toilet facilities and geopolitical zone significantly explained changes in only stunting inequality, while access to improved water facilities only significantly determined the change in inequality in wasting.ConclusionAddressing the socio-economic, spatial and demographic determinants of the changes in the socioeconomic inequalities in child stunting and wasting, especially wealth, maternal education and access to sanitation is critical for improving child stunting and wasting in Nigeria

Explaining the role of the social determinants of health on health inequality in South Africa

Background: Action on the social determinants of health (SDH) is relevant for reducing health inequalities. This is particularly the case for South Africa (SA) with its very high level of income inequality and inequalities in health and health outcomes. This paper provides evidence on the key SDH for reducing health inequalities in the country using a framework initially developed by the World Health Organization. Objective: This paper assesses health inequalities in SA and explains the factors (i.e. SDH and other individual level factors) that account for large disparities in health. The relative contribution of different SDH to health inequality is also assessed. Design: A cross-sectional design is used. Data come from the third wave of the nationally representative National Income Dynamics Study. A subsample of adults (18 years and older) is used. The main variable of interest is dichotomised good versus bad self-assessed health (SAH). Income-related health inequality is assessed using the standard concentration index (CI). A positive CI means that the rich report better health than the poor. A negative value signifies the opposite. The paper also decomposes the CI to assess its contributing factors. Results: Good SAH is significantly concentrated among the rich rather than the poor (CI=0.008; p<0.01). Decomposition of this result shows that social protection and employment (contribution=0.012; p<0.01), knowledge and education (0.005; p<0.01), and housing and infrastructure (−0.003; p<0.01) contribute significantly to the disparities in good SAH in SA. After accounting for these other variables, the contribution of income and poverty is negligible. Conclusions: Addressing health inequalities inter alia requires an increased government commitment in terms of budgetary allocations to key sectors (i.e. employment, social protection, education, housing, and other appropriate infrastructure). Attention should also be paid to equity in benefits from government expenditure. In addition, the health sector needs to play its role in providing a broad range of health services to reduce the burden of disease

Income inequality and pandemics: insights from HIV/AIDS and COVID-19—a multicountry observational study

Objectives Assess the relationship between income inequality and HIV incidence, AIDS mortality and COVID-19 mortality.Design Multicountry observational study.Setting 217 countries for HIV/AIDS analysis, 151 countries for COVID-19 analysis.Participants Used three samples of national-level data: a sample of all countries with available data (global sample), a subsample of African countries (African sample) and a subsample excluding African countries (excluding African sample).Main outcome measures HIV incidence rate per 1000 people, AIDS mortality rate per 100 000 people and COVID-19 excess mortality rate per 100 000 people. The Gini index of income inequality was the primary explanatory variable.Results A positive and significant relationship exists between the Gini index of income inequality and HIV incidence across all three samples (p&lt;0.01), with the effect of income inequality on HIV incidence being higher in the African sample than in the rest of the world. Also, a statistically positive association exists for all samples between income inequality and the AIDS mortality rate, as higher income inequality increases AIDS mortality (p&lt;0.01). For COVID-19 excess mortality rate, a positive and statistically significant relationship exists with the Gini index for the entire sample and the excluding African sample (p&lt;0.05), but the African sample alone did not deliver significant results (p&lt;0.1).Conclusion COVID-19 excess deaths, HIV incidence and AIDS mortality are significantly associated with income inequality globally—more unequal countries have a higher HIV incidence, AIDS mortality and COVID-19 excess deaths than their more equal counterparts. Income inequality undercuts effective pandemic response. There is an urgent need for concerted efforts to tackle income inequality and to build pandemic preparedness and responses that are adapted and responsive to highly unequal societies, prioritising income inequality among other social determinants of health