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Background. Inequities in the utilization of maternal health services impede progress towards the MDG 5 target of reducing the maternal mortality ratio by three quarters, between 1990 and 2015. In Namibia, despite increasing investments in the health sector, the maternal mortality ratio has increased from 271 per 100,000 live births in the period 1991-2000 to 449 per 100,000 live births in 1998-2007. Monitoring equity in the use of maternal health services is important to target scarce resources to those with more need and expedite the progress towards the MDG 5 target. The objective of this study is to measure socio-economic inequalities in access to maternal health services and propose recommendations relevant for policy and planning. Methods. Data from the Namibia Demographic and Health Survey 2006-07 are analyzed for inequities in the utilization of maternal health. In measuring the inequities, rate-ratios, concentration curves and concentration indices are used. Results. Regions with relatively high human development index have the highest rates of delivery by skilled health service providers. The rate of caesarean section in women with post secondary education is about seven times that of women with no education. Women in urban areas are delivered by skilled providers 30% more than their rural counterparts. The rich use the public health facilities 30% more than the poor for child delivery. Conclusion. Most of the indicators such as delivery by trained health providers, delivery by caesarean section and postnatal care show inequities favoring the most educated, urban areas, regions with high human development indices and the wealthy. In the presence of inequities, it is difficult to achieve a significant reduction in the maternal mortality ratio needed to realize the MDG 5 targets so long as a large segment of society has inadequate access to essential maternal health services and other basic social services. Addressing inequities in access to maternal health services should not only be seen as a health systems issue. The social determinants of health have to be tackled through multi-sectoral approaches in line with the principles of Primary Health Care and the recommendations of the Commission on Social Determinants of Health. © 2010 Zere et al; licensee BioMed Central Ltd.
Equity may be defined from three perspectives: equity in health; equity in health provision; and equity in health financing. The focus of this study is equity in health provision. Following Whitehead’s seminal definition [16], equity in health is defined as the absence of systematic inequalities in health (or in the major social determinants of health) among people that have different positions in a social hierarchy. Maldistribution of health care is one of the social determinants of health. Equity in health care provision may therefore be defined as the absence of socio-economic inequalities in access to available maternal health services [4,17,18]. Three steps are followed in measuring equity in access to maternal health services: (i) identification of the intervention whose distribution is to be measured (e.g. antenatal care); (ii) classification of the population (women) by an indicator of socio-economic status; and (iii) measuring/quantifying the degree of inequality. The social stratifiers used in the current study are household wealth as derived from asset indices; mother’s education; place of residence (urban/rural); and geographical region as there is a spatial dimension to the distribution of poverty and human development in the country [12]. The study uses information from the Namibia demographic and health survey 2006-2007. The variables measured include antenatal and postnatal checkup, delivery and caesarean section. The health concentration curve and index as well as rate-ratios are used in measuring inequities. The concentration curve plots the cumulative proportion of the pregnant and parturient women ranked by their household wealth against the cumulative proportion of the health care variable (e.g. antenatal care). In other words, concentration curves capture the use of health interventions, cumulatively for each wealth quintile [19]. To demonstrate the use of the concentration curve, the case of use of modern contraception by women is presented in Figure Figure11 using hypothetical data. Concentration curve: use of modern contraception. In the absence of wealth-related inequality in the use of modern contraception, the concentration curve overlaps with the diagonal line (line of equality). This implies that there are no inequities in the use of modern contraception. A concentration curve that lies below the diagonal line signifies the presence of inequities favouring the rich, i.e. there is a disproportionately higher rate of contraceptive use among the wealthiest groups than the poorest. When the concentration curve lies above the line of equality, there is inequity favouring the poor, i.e. more women in the poorest group use modern contraception compared to the wealthiest. The degree of inequity becomes more when the concentration curve is further from the line of equality. Concentration curves are a good graphical illustration to identify whether socioeconomic inequality in some health sector variable exists and whether it is more pronounced at one point in time than another or in one country than another. However, they don’t quantify the magnitude of inequality for convenient comparison across many time periods, countries, regions, or whatever may be chosen for comparison. The concentration index that is computed from the concentration curve quantifies the degree of socio-economic inequality in a health variable and assumes values between -1 and +1. Its value is negative when the concentration curve is above the diagonal and positive when the curve is below the diagonal. In the absence of inequities (the concentration curve coinciding with the diagonal), the value of the concentration index is zero. From grouped data, the concentration index (C) is computed in a spreadsheet programme using the formula [20]: Where p is the cumulative percent of the sample ranked by economic status (in this case the cumulative percentage of pregnant/parturient mothers ranked by wealth); L(p) is the corresponding concentration curve ordinate (e.g. cumulative percentage of caesarean section); and T is the number of socioeconomic groups (in this case T = 5, as there are five wealth quintiles) To test for the statistical significance of the concentration index, standard errors can be computed using the formula given in Kakwani et al [21]. Data is analyzed using STATA 10 statistical software.
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