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In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

The purpose of this paper is to assess the trends of socioeconomic-related inequalities in maternal healthcare utilization in Ghana between 2003 and 2014 and examine the causes of inequalities in maternal healthcare utilization in Ghana.

Data are drawn from three rounds of the Ghana Demographic and Health Survey collected in 2003, 2008 and 2014, respectively. The authors employ two alternative measures of socioeconomic inequalities in health – the Wagstaff and Erreygers indices – to examine the trends of socioeconomic inequalities in maternal healthcare utilization. The authors proceed to decompose the causes of inequalities in maternal healthcare by applying a recently developed generalized decomposition technique based on recentered influence function regressions.

The study finds substantial pro-rich inequalities in maternal healthcare utilization in Ghana. The degree of inequalities has been decreasing since 2003. The elimination of user fees for maternal healthcare has contributed to achieving equity and inclusion in utilization. The decomposition analysis reveals significant contributions of individual, household and locational characteristics to inequalities in maternal healthcare. The authors find that educational attainment, urban residence and challenges with physical access to healthcare facilities increase the socioeconomic gap in maternal healthcare utilization.

There is a need to target vulnerable women who are unlikely to utilize maternal healthcare services. In addition to the elimination of user fees, there is a need to reduce inequalities in the distribution and quality of maternal health services to achieve universal coverage in Ghana.

This study aims to estimate the degree and nature of socioeconomic-related inequalities in sexual and reproductive health in Ghana and further assesses causes of these inequalities using decomposition technique. The authors assess the contribution of personal characteristics of the woman including access to health information and health seeking behaviours, household and locational characteristics to inequalities in sexual and reproductive health in Ghana. The study uses data from the three rounds of the Ghana Demographic and Health Survey conducted in 2003, 2008 and 2014.

Two indicators – use of modern contraceptives and intention to use modern contraceptives – are used to measure sexual and reproductive health of sexually active women. A wealth index, based on household ownership of assets, consumer goods and living conditions, is used as a measure of socioeconomic status. The paper estimates a concentration index to the relationship between cumulative health and socioeconomic rank. Paper procedures to apply decomposition techniques to determine the causes of socioeconomic inequalities in health based on a linear health regression model.

The study finds evidence of varying degrees of socioeconomic-related inequalities in sexual and reproductive health indicators. Specifically, the study finds that whilst use of modern contraceptives was concentrated among women in households with high socioeconomic status in 2003 and 2008, modern contraceptive use was prevalent among women in low socioeconomic status households in 2014. Equally, the study finds significant pro-poor inequalities in the intentions to use modern contraceptives in 2003 and 2014. The degree of socioeconomic inequalities in the intentions to use modern contraceptives increased between 2003 and 2014.

There is the lack of evidence on the degree, nature and causes of socioeconomic-related inequalities, which in tend impedes the design and implementation of sexual and reproductive health policies targeted at vulnerable and under-served populations. In addition, there is the need to study inequalities in health over time to monitor progress of health delivery systems towards equitable and universal coverage and understand the evolution of the determinants.

This work aims to assess the effects of information and communication technology (ICT) on inequalities in access to professional training (PT) in Cameroon.

This study used data from the fourth Cameroonian Household Survey (ECAM 4), the concentration index (CI) calculations and the Wagstaff et al. (2003) decomposition.

The preliminary results show that the CI calculations by groups of individuals reveal the existence of significant inequalities in favour of the poor. This is the case for all groups of individuals who use ICT tools, namely radio, internet, telephone and television. The results of the Wagstaff et al. (2003) decomposition reveal that an equitable distribution of income between those who use and those who do not use the telephone, radio and internet reduces inequalities in access to FP in favour of the poor.

Despite the wealth of literature devoted to the study of inequalities in access to education, the consideration of PT is still very marginal. In Cameroon, the literature devoted to the study of inequalities in access to PT is still almost non-existent, probably because of a low level of interest in the scientific community. However, as just seen, PT is a tool for combating unemployment, particularly in economies where the informal sector is important, insofar as the proportion of unemployed and inactive people is very low amongst the ones that have taken a PT course. Moreover, studies on the effects of ICT on inequalities in access to PT are still rare in the literature.

One of the targets of sustainable development goal (SDG) 2030 is to reduce maternal mortality ratio to 70 per 100,000 live births and ensure pregnant women attend at least four antenatal visits. In urban Pakistan, it is expected that more women utilize antenatal care (ANC) because urban areas have more resources, higher education and wealthier people. Despite these facilities, the lack of utilization of antenatal care among pregnant women is abysmal—the latest estimate by Pakistan Demographic and Health Survey (PDHS) places the figure at 63%. Therefore, the paper attempts to identify the factors that affect the utilization of ANC in urban areas by using the PDHS 2017–2018.

The study used cross-tabs to determine the socioeconomic characteristics of women, and used the marginal effects from the probit model to evaluate the significance and relationship between socioeconomic determinants and antenatal visits. Finally, the study used Adam Wagstaff's decomposition analysis to identify the magnitude and main determinants of inequality.

The marginal effects show that socioeconomic variables such as education, province of residence, birth of a first child, age, education and consulting a doctor predicted the probability of 4+ antenatal visits. The decomposition analysis shows that women who consulted a doctor, belonged to non-poor class, were more educated and older contributed significantly to the inequality of antenatal care utilization in urban areas.

The study calls for increasing the number of doctors, promoting education, increasing awareness related to pregnancy complications and reducing wealth inequality. Moreover, the study also calls for increasing global intervention by implementing programs similar to ending preventable maternal mortality (EPPM) to increase antenatal coverage.

The distinctiveness of the study can be found in the fact that no study has been conducted that analyses the inequality related to the usage of ANC in urban areas of Pakistan.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2022-0390

The purpose of this paper is threefold: first, to measure the health inequalities among malnourished children; second, to decompose the health inequalities to identify key socioeconomic predictors for child malnutrition; and third, to assess the change in the proportional contribution of key predictors over time.

The study has used data of National Family Health Survey (NFHS) conducted in 1992-1993, 1998-1999 and 2005-2006. The information on anthropometric indicators for children below three years of age is provided; however the study is restricted to “weight-for-age,” as it is considered to be a comprehensive indicator of child nutritional status. In the first stage of analysis, health inequalities are measured among malnourished children using concentration indices (CI) for each round of NFHS. In second stage, the inequalities are decomposed to estimate the proportional contribution of socioeconomic predictors. In the third stage, change in the relative contribution of socioeconomic predictors over three rounds is assessed to suggest target-specific policies and programs.

The results highlight a slow decline of only seven percentage points in the proportion of malnourished children in India during 1992-2006. The increasing values of CIs from −0.13 (1992) to −0.18 (2006) demonstrates the concomitant rise in economic inequalities among malnourished children. The results of decomposition analysis point toward household poor economic status and mother’s illiteracy as the major contributor of inequalities during 1992-2006. During the study period, the economic status explained 50, 65 and 59 percent of inequalities, whereas mother’s illiteracy explained 40, 30 and 29 percent of inequalities, respectively. Overall, the contributors to health inequalities remained the same over time with a change in their relative contribution.

The present study is focussed on three rounds of NFHS data conducted at different time period and so it has certain limitations which should be kept in mind while interpreting the results. The study has revealed mother’s education and economic status of the household as the major contributory factors toward child health inequalities. However, one should not forget that the level and quality of education has undergone tremendous change from 1992 to 2006 which the authors could not capture considering the availability of data in the form of years of schooling. Second, since the NFHS-1 has collected the information about the caste groups in only three categories, i.e. schedule caste, schedule tribe and others; the authors have to pool the other backward caste groups with the general caste groups. Third, the authors have used the broad classification of place of residence, i.e. rural and urban area to analyze the inequalities assuming the uniform level of development across the urban regions; however there exists huge disparities within urban areas which leave scope for further research. Fourth, though, the authors have estimated the wealth based inequalities, but NFHS does not provide the absolute level of wealth and so the authors have computed the proxy measure for wealth based on the household assets which has been extensively used in the field of research. Despite these limitations, the authors however believe that the present research work has appropriately decomposed the inequalities among malnourished children and have revealed the changes in the proportional contribution of socioeconomic factors over time.

The decomposition analysis brought into light that average health indicators are insufficient for determining the right approach to health intervention programs. Health policy interventions have to focus ideally on both health averages and within and between group inequalities based on varying contributions of socioeconomic determinants.

Concentrated efforts along with the inter-sectoral concurrence, good nutrition governance, effective investment and unequal distribution of resources are pre-requisites to ameliorate the level and existing inequalities in child malnutrition in India.

The distinctiveness of this study can be primarily found in the use of all three rounds of NFHS data to estimate health inequalities among underweight children. The study has also decomposed the health inequalities to estimate and analyze the change in relative contribution of socioeconomic predictors for each round to facilitate the formulation of target-specific policies and programs.

The purpose of this paper is to quantify inequalities in utilization of maternal health care services and measure the relative contribution of different factors affecting it in the context of Nepal.

The paper uses data from the latest round of the Nepal Demographic and Health Survey. Two stages of stratified cluster samplings were used. A total of 13,200 women aged 15-49 were interviewed.

Results of concentration index estimates in three selected indicators suggest considerable inequalities in maternal health care utilization. The decomposition analyses indicate that the critical factors contributing to inequalities in <3 antenatal care visits are poor economic status of households (32 percent) and women (23 percent) and their partners’ illiteracy (23 percent). However, in case of no institutional delivery, apart from the poor economic status of household (51 percent) and women's illiteracy (16 percent), the rural place of residence (21 percent) has emerged as critical factors contributing to inequalities. In case of no postnatal care within a day, birth order (21 percent) becomes a significant factor, next to the poor economic status of the household (41 percent) in terms of the relative contribution to total inequalities.

Policies and program targeting maternal health interventions need to consider equity with efficiency in utilization of maternal health care services, and further to achieve the targets of millennium development goal 5 in Nepal.

This study is an innovative effort to estimate inequalities in maternal health care services in the context of Nepal by using inequality decomposition model. For the first time, this study estimates the relative contribution of different socioeconomic factors contributing to inequalities in maternal health care services in Nepal.

Poor health-related quality of life (HRQoL) is one of the important issues in the health sector. The purpose of this paper is to investigate the prevalence and socio-economic inequality in poor HRQoL in Tehran city, Iran.

In total, 562 adults were included in this cross-sectional study. The cluster sampling method was used for data collection from May to June, 2016 in Tehran city, Iran. Data on HRQoL, using EuroQol 5-dimensions questionnaire, and data on socio-economic and demographic variables were gathered. Convenience regression method was performed to measure the concentration index (CI). Decomposition analysis was performed to determine the contribution of variables on socio-economic inequality in poor HRQoL. All analyses were performed by Stata v.14.

The prevalence of poor HRQoL was 28.3 percent. The value of CI for “poor HRQoL” was −0.299 (95% confidence interval: −0.402 to −0.195). Socio-economic status (SES) was the largest contributor to socio-economic inequality in poor HRQoL (69.44 percent of inequality was explained by SES). Age, obesity and race had a positive contribution to socio-economic inequality in poor HRQoL among the participants. Nonetheless, sex and smoking intensity had a negative contribution to inequality in poor HRQoL.

There is little evidence about the prevalence of poor HRQoL in insured people. This study provided new evidence in this area through the investigation of socio-economic inequality in poor HRQoL and its determinants among people with health insurance in Iran using decomposition analysis.

This paper uses a unique nationwide survey data derived from the 2003 Utilisation of Health Services Survey (UHSS) in Iran (n=16,935) to analyse inequities of health care utilisation.

Concentration indices are used to measure socioeconomic inequality in actual use of the five types of health services, and in unmet need for two of those types of service (any ambulatory care and hospital admissions). Horizontal inequity indices are employed to examine inequity in ambulatory and hospital care. Generalised linear model (GLM) was employed to investigate factors contributing to the phenomena of “unmet need” and “met unneed”. Moreover, a decomposition analysis of inequality is performed to determine the contributions of each factor to the inequality of “unmet need”.

Results suggest that self‐reported need for ambulatory and inpatient care is concentrated among the poor, whereas the utilisation of ambulatory and inpatient care were generally distributed proportionally. Results of horizontal inequity indices show that the distributions of any ambulatory care and hospital admissions are pro‐rich. The probability of “unmet need” for ambulatory care was higher among wealthier individuals. The decomposition analysis demonstrates that the wealth index, health insurance, and region of residence are the most important factors contributing to the concentration of “unmet need” for ambulatory health care among the poor. Results also illustrate that higher wealth quintiles used more unneeded ambulatory care than their poorer counterparts.

A special characteristic of the UHSS is that it contains questions about the need for medical services use and about actual services use. This characteristic provides an opportunity to measure the inequality of health care consumption against self‐assessed treatment needs, as well as an analysis of which observables are associated with “unmet need”. Moreover, the incidence of health care use when it is reported as not needed can be analysed with this dataset. The analysis of this phenomenon – which we refer to as “met unneed” – is another novel aspect of this work.

Longitudinal data are required to characterise and measure the dynamics of income-related health inequalities (IRHI). This chapter develops a framework to evaluate the impact of population changes on the level of cross-sectional IRHI over time and thereby provides further insight into how health inequalities develop or perpetuate themselves in a society. The approach is illustrated by an empirical analysis of the increase in IRHI in Great Britain between 1999 and 2004 using the British Household Panel Survey. The results imply that levels of IRHI would have been even higher in 2004 but for the entry of youths into the adult population and deaths, with these natural processes of population turnover serving to partially mask the increase in IRHI among the resident adult population over the five-year period. We conclude that a failure to take demographic changes into account may lead to erroneous conclusions on the effectiveness of policies designed to tackle health inequalities.