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. 2011 Oct 4;89(12):881–890. doi: 10.2471/BLT.11.087536

Towards universal health coverage: the role of within-country wealth-related inequality in 28 countries in sub-Saharan Africa

نحو تغطية صحية شاملة: تأثير تباين الثروة داخل البلد في 28 بلداً جنوب الصحراء الأفريقية

Hacia la cobertura sanitaria universal: el papel de la desigualdad nacional en cuanto a riqueza en 28 países del África subsahariana

Vers une couverture de santé universelle: le rôle de l'inégalité intra-nationale liée à la richesse, dans 28 pays d'Afrique sub-saharienne

На пути к универсальному охвату услугами здравоохранения: роль связанного с материальным благосостоянием внутристранового неравенства в 28 странах Африки к югу от Сахары

实现全民医疗覆盖：撒哈拉以南非洲地区28个国家内部与财富相关的不平等所扮演的角色

Ahmad Reza Hosseinpoor a,✉, Cesar G Victora b, Nicole Bergen c, Aluisio JD Barros b, Ties Boerma a
PMCID: PMC3260894  PMID: 22271945

Abstract

Objective

To measure within-country wealth-related inequality in the health service coverage gap of maternal and child health indicators in sub-Saharan Africa and quantify its contribution to the national health service coverage gap.

Methods

Coverage data for child and maternal health services in 28 sub-Saharan African countries were obtained from the 2000–2008 Demographic Health Survey. For each country, the national coverage gap was determined for an overall health service coverage index and select individual health service indicators. The data were then additively broken down into the coverage gap in the wealthiest quintile (i.e. the proportion of the quintile lacking a required health service) and the population attributable risk (an absolute measure of within-country wealth-related inequality).

Findings

In 26 countries, within-country wealth-related inequality accounted for more than one quarter of the national overall coverage gap. Reducing such inequality could lower this gap by 16% to 56%, depending on the country. Regarding select individual health service indicators, wealth-related inequality was more common in services such as skilled birth attendance and antenatal care, and less so in family planning, measles immunization, receipt of a third dose of vaccine against diphtheria, pertussis and tetanus and treatment of acute respiratory infections in children under 5 years of age.

Conclusion

The contribution of wealth-related inequality to the child and maternal health service coverage gap differs by country and type of health service, warranting case-specific interventions. Targeted policies are most appropriate where high within-country wealth-related inequality exists, and whole-population approaches, where the health-service coverage gap is high in all quintiles.

Introduction

Established in 2000, the eight Millennium Development Goals (MDGs) represent a global commitment to eliminating poverty. MDG 4 and MDG 5 are devoted to child and maternal health, with 2015 targets of a two-thirds reduction in the 1990 mortality rate for children under 5 years of age, a three-quarters reduction in the 1990 maternal mortality rate and universal access to reproductive health services.1,2

Although some promising gains have been made worldwide, in 2008, about 358 000 mothers3 and 8.8 million children under 5 years of age4 lost their lives, many from preventable or treatable causes.3–5

The African Region of the World Health Organization (WHO) is falling behind on MDG child and maternal health targets. In many countries these are advancing too slowly, stagnating or deteriorating.1,3–9 Between 1990 and 2008, the worldwide mortality rate for children under 5 years of age dropped by 27%;8 however, in 2008 more than half of these deaths occurred in sub-Saharan Africa.5,8 The maternal mortality ratio in the African Region is 900 maternal deaths per 100 000 live births – at least double that of any other WHO region.8 Access to services such as antenatal care and skilled birth attendance in the African Region are among the lowest in the world.1,3–6,8

Improving child and maternal health requires health systems to be strengthened through both long-range investments (e.g. development of health facility infrastructure and programmes to train health workers) and initiatives that can be rapidly deployed (e.g. community immunization days, vitamin A campaigns and distribution of insecticide-treated bednets).4,10,11 In 2010, the Countdown to 2015 decade report made a special appeal for improving the child and maternal health situation in sub-Saharan Africa, calling for renewed and accelerated political and financial commitment to MDG 4 and MDG 5 in this region.4

Achieving the child and maternal health MDGs will require policy and programme planners to identify and reach those who are most in need of health services.2,5,9 To maximize and improve progress towards the MDG targets in Africa, it is important to have strong national and regional monitoring systems12 that can identify which populations are benefiting from programmes and initiatives, and which are not.13 Progress on MDG 4 and MDG 5 has been variable across sub-Saharan African countries; also, national indicators may mask inequalities between subgroups of the population, 4,6,8 and improvements at a country level may occur alongside a widening inequality gap.14 Addressing inequalities and their root causes is an important step towards improving health outcomes.5

Measurements of service coverage capture both provision and use of services and interventions, since they express the percentage of people receiving a specified service or intervention among those requiring that service.13 The health service coverage gap represents an estimate of the increase in coverage needed to achieve universal coverage for a given service.15 The ability of a programme or initiative to reduce the health service coverage gap is an important indicator of success; comparing the gap across populations can help to target action to reduce disparities.13,15

Previous monitoring of health service coverage and the health service coverage gap for several child and maternal health services revealed between-country inequality and varying patterns of within-country wealth-related inequality.15,16 Further delineation of the coverage gap within countries is needed to more accurately define the current reach of child and maternal health services and to inform programme and policy direction.17–19 Thus, our objective was to measure the magnitude of within-country wealth-related inequality in the health service coverage gap of maternal and child health indicators and to quantify the contribution of this inequality to the national coverage gap within sub-Saharan African countries.

Methods

Coverage data for child and maternal health services were obtained from the 28 sub-Saharan African countries that participated in the Demographic Health Survey (DHS) between 2000 and 2008.4 This sample included 13 of the 15 African countries with the highest number of neonatal deaths.9 The DHS is a large-scale, nationally representative survey that conducts standardized face-to-face interviews with women aged 15–49 years.20 Where countries had multiple DHS data sets for the 2000–2008 period, we selected the most recent set for analysis.

We used an index of several health services to display an overview of the child and maternal health service coverage gap within each study country. The index – referred to as the overall coverage gap – captured the coverage gap in four areas of intervention with different delivery strategies: maternal and neonatal care, immunization, treatment of sick children and family planning. Each of the four interventional areas comprised a small number of indicators for which reliable long-term and comparable data were available. The validity of the index has been discussed previously; it performed well in comparison to several alternative measures.13 To further illustrate select components of each of the four areas of intervention included in the index, we calculated coverage gaps separately for the following health service indicators: skilled birth attendance; one or more antenatal care visits; measles immunization; receipt of a third dose of vaccine against diphtheria, pertussis and tetanus (DPT3); treatment of acute respiratory infection in children under 5 years of age; and family planning. These interventions represent diverse types of child and maternal health services and interventions, and are associated with a range of aspects of health system delivery.13

For each country, the national coverage gaps were calculated and additively broken down into two parts: the coverage gap in the wealthiest quintile (i.e. the proportion of this quintile that did not receive a required health service) and the population attributable risk (an absolute measure of within-country wealth-related inequality that summarizes the differences between the richest quintile and each of the four other wealth quintiles). Thus, population attributable risk, PAR, shows the improvement possible if the total population had the same health service coverage as the wealthiest quintile. PAR can be expressed as follows:

PAR = CGpop − CGref

where CGpop is the average coverage gap across all wealth quintiles (the population), representing the national coverage gap, and CGref is the coverage gap in the wealthiest quintile (the reference group).18,21,22 The relative version of population attributable risk – population attributable risk percentage – is calculated by dividing population attributable risk by the national coverage gap. It indicates the proportional reduction in national coverage gap that would be achieved if the total population had the same health service coverage gap as the wealthiest quintile.

Results

The national overall coverage gap ranged from 24% (Namibia) to 77% (Chad), with a median of 43% (Fig. 1). In 26 of the 28 countries, within-country wealth-related inequality constituted at least one quarter of the national overall coverage gap (Table 1). In four countries – Burkina Faso, Madagascar, Mozambique and Nigeria – within-country wealth-related inequality accounted for about 50% of the national overall coverage gap. The lowering of wealth-related inequality had the potential to decrease the national overall coverage gap by levels of between 16% (Swaziland) and 56% (Madagascar).

Fig. 1.

National overall health service coverage gap versus within-country relative inequality in 28 sub-Saharan African countries, 2000–2008

a Relative inequality, calculated by dividing population attributable risk by the national health service coverage gap.

Fig. 1

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Table 1. Overall health service coverage gap – national average versus within-country inequality in 28 sub-Saharan African countries, 2000–2008.

Country Year Coverage gap (%)
 PARa (percentage points) PAR%b
National In richest quintile
Benin 2006 43 28 15 35
Burkina Faso 2003 52 27 25 48
Cameroon 2004 39 22 17 43
Chad 2004 77 55 22 29
Congo 2005 31 20 11 36
Democratic Republic of the Congo 2007 44 28 16 36
Ethiopia 2005 74 52 21 29
Gabon 2000 34 25 9 27
Ghana 2008 36 21 15 42
Guinea 2005 53 35 17 33
Kenya 2003 39 26 12 32
Lesotho 2004 32 20 12 37
Liberia 2007 49 30 20 40
Madagascar 2003–2004 43 19 24 56
Malawi 2004 35 24 11 30
Mali 2006 60 37 22 38
Mozambique 2003 37 19 17 47
Namibia 2006–2007 24 14 10 42
Niger 2006 60 38 22 37
Nigeria 2008 58 28 31 53
Rwanda 2005 48 37 11 24
Senegal 2005 44 30 14 31
Sierra Leone 2008 48 35 13 27
Swaziland 2006–2007 25 21 4 16
United Republic of Tanzania 2004–2005 39 26 14 35
Uganda 2006 48 34 14 29
Zambia 2007 39 26 12 32
Zimbabwe 2005–2006 33 22 12 35
Median 2000–2008 43 27 14 35
95% CI of the median 2000–2008 37–48 23–30 12–17 32–37
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CI, confidence interval; PAR, population attributable risk.

a Absolute inequality.

b Relative inequality, calculated by dividing population attributable risk by the national health service coverage gap.

Note: Figures may be affected by rounding.

Fig. 1 demonstrates the national overall coverage gap against the wealth-related relative inequality in overall coverage gap observed within each of the 28 study countries. There was no relation between the two parameters (ρ: 0.03; P-value: 0.88).

The relationship between wealth and coverage gaps for specific indicators varied, depending on the type of health service. Breakdown of the national health service-specific coverage gaps revealed that within-country wealth-related inequality was particularly important for some components of the overall coverage gap (e.g. skilled birth attendance and one or more antenatal care visits) (Table 2). The coverage gap in skilled birth attendance generally showed a high proportion of within-country wealth-related inequality. Depending on the country, the coverage gap in skilled birth attendance could be reduced by 22% to 93% if no wealth-related inequality existed. For 25 of the 28 countries in the study, eliminating the wealth-related inequality would at least halve the coverage gap for skilled birth attendance. Similarly, within-country wealth-related inequality in one or more antenatal care visits accounted for at least 50% of the coverage gap in 21 countries.

Table 2. Health service coverage gap of skilled birth attendance and one or more antenatal care visits – national average versus within-country inequality in 28 sub-Saharan African countries, 2000–2008.

Country Skilled birth attendance
 One or more antenatal care visits
Coverage gap (%)
 PARa (percentage points) PAR%b Coverage gap (%)
 PARa (percentage points) PAR%b
National In richest quintile National In richest quintile
Benin 22 2 20 90 12 1 11 92
Burkina Faso 62 16 47 75 27 4 23 85
Cameroon 38 5 33 86 17 3 14 82
Chad 84 49 35 42 58 23 35 60
Congo 16 2 14 88 13 2 11 85
Democratic Republic of the Congo 25 2 23 93 14 4 10 72
Ethiopia 94 73 21 22 72 42 30 42
Gabon 13 3 10 80 4 2 2 55
Ghana 41 5 36 87 4 0 4 100
Guinea 62 12 50 80 19 2 17 89
Kenya 58 25 34 58 12 6 6 49
Lesotho 44 16 28 64 10 4 6 58
Liberia 53 18 35 67 20 4 16 80
Madagascar 54 8 46 85 20 3 17 85
Malawi 43 15 27 64 7 3 4 56
Mali 73 24 49 67 63 20 43 68
Mozambique 52 11 41 79 15 1 14 94
Namibia 18 2 16 88 5 3 2 40
Niger 82 41 42 51 54 17 37 68
Nigeria 61 14 47 77 45 6 39 87
Rwanda 71 40 31 43 6 5 1 12
Senegal 48 10 37 78 12 2 10 84
Sierra Leone 58 29 29 50 13 3 10 77
Swaziland 26 8 18 70 3 1 2 65
United Republic of Tanzania 54 13 41 76 6 3 3 47
Uganda 57 23 35 60 6 3 3 49
Zambia 53 8 45 84 6 1 5 84
Zimbabwe 31 5 27 85 6 3 3 47
Median 53 13 34 76 13 3 10 70
95% CI of the median 42–58 8–17 28–40 65–83 6–18 2–4 5–16 57–84
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CI, confidence interval; PAR, population attributable risk.

a Absolute inequality.

b Relative inequality, calculated by dividing population attributable risk by the national health service coverage gap.

Note: Figures may be affected by rounding.

For some health services, the role of within-country wealth-related inequality was less pronounced. For example, such inequality accounted for a smaller proportion of the national coverage gap in measles immunization, DPT3, care seeking for suspected pneumonia, and family planning (Table 3), with some notable variations. The national DPT3 coverage gap was 64% in both Nigeria and the United Republic of Tanzania; however, within-country inequality accounted for only 3% of the coverage gap in the United Republic of Tanzania but 63% of the gap in Nigeria. Both Cameroon and Zimbabwe had a 34% national coverage gap in measles immunization but differed widely in terms of within-country inequality contribution to the national coverage gap (53% in Cameroon and 24% in Zimbabwe).

Table 3. Health service coverage gap for measles immunization, DPT3 immunization, treatment of acute respiratory infection in children under 5 years of age and family planning services, national average versus within-country inequality in 28 sub-Saharan African countries, 2000–2008.

Country Measles immunization
 DPT3 immunization
 Treatment of acute respiratory infection in children under 5 years of age
 Family planning services
Coverage gap (%)
 PARa (percentage points) PAR%b Coverage gap (%)
 PARa (percentage points) PAR%b Coverage gap (%)
 PARa (percentage points) PAR%b Coverage gap (%)
 PARa (percentage points) PAR%b
National In richest quintile National In richest quintile National In richest quintile National In richest quintile
Benin 38 23 15 40 33 13 20 60 64 52 13 20 64 44 20 31
Burkina Faso 43 29 15 34 43 28 15 35 64 27 37 58 68 41 27 40
Cameroon 34 16 18 53 34 21 13 38 59 48 12 20 44 26 18 40
Chad 77 61 16 21 80 58 22 27 80 61 19 24 88 70 18 20
Congo 33 15 18 55 31 9 22 70 53 43 10 19 27 20 7 27
Democratic Republic of the Congo 36 14 22 62 54 26 28 51 58 46 12 21 54 38 16 30
Ethiopia 63 46 17 27 68 51 16 24 81 67 14 18 69 39 30 44
Gabon 44 27 18 40 64 50 14 22 39 32 7 17 46 35 11 25
Ghana 10 5 5 46 11 7 4 40 51 24 27 54 60 44 17 28
Guinea 48 39 9 19 48 37 12 24 57 39 18 31 70 57 13 18
Kenya 27 12 15 56 27 27 1 3 51 36 15 29 37 24 13 35
Lesotho 15 15 0 0 17 10 7 41 40 27 13 33 45 26 19 42
Liberia 36 13 24 65 49 26 23 47 40 12 28 69 76 61 15 20
Madagascar 41 16 25 61 38 9 29 76 52 34 18 35 47 25 22 47
Malawi 21 12 9 43 18 10 8 45 63 54 9 14 45 34 11 24
Mali 30 20 10 33 31 21 10 32 62 40 22 36 79 64 15 19
Mozambique 23 4 20 84 28 3 25 88 45 38 7 16 42 31 11 26
Namibia 15 5 11 70 16 6 10 64 33 17 16 50 27 13 14 51
Niger 52 26 26 51 60 37 23 38 53 34 19 36 58 50 9 15
Nigeria 58 25 33 58 64 24 40 63 50 31 19 38 58 34 24 41
Rwanda 14 12 2 16 12 12 0 0 72 56 16 22 69 52 17 25
Senegal 26 19 7 28 21 16 6 28 53 39 14 26 73 54 19 26
Sierra Leone 39 32 8 20 39 27 11 29 49 45 4 8 77 57 20 26
Swaziland 8 7 1 14 8 11 0 0 43 41 2 4 32 22 10 33
United Republic of Tanzania 20 9 11 54 64 62 2 3 40 33 7 19 44 25 19 44
Uganda 32 27 5 16 83 81 3 3 26 19 8 29 63 36 28 44
Zambia 15 6 10 63 79 69 10 13 35 39 0 0 39 26 13 34
Zimbabwe 34 26 8 24 38 31 7 18 74 52 22 30 17 10 8 45
Median 33 16 13 42 38 25 11 33 52 39 14 25 56 35 16 30
95% CI of the median 24–39 12–25 9–18 27–55 29–52 12–30 7–19 24–44 46–59 33–45 10–18 19–33 44–67 26–44 13–19 26–40
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CI, confidence interval; DPT3, three doses of vaccine against diphtheria, pertussis and tetanus; PAR, population attributable risk.

a Absolute inequality.

b Relative inequality, calculated by dividing population attributable risk by the national health service coverage gap.

Note: Figures may be affected by rounding.

The 28 countries had different magnitudes and patterns of wealth-related inequality. Ethiopia had one of the highest coverage gaps for every indicator in the study, yet the contribution of within-country inequality tended to be proportionally low. Nigeria had high within-country wealth-related relative inequality for many indicators. In many other countries the situation was mixed. For example, in Mali, within-country wealth-related inequality constituted at least two thirds of the national coverage gap in both skilled birth attendance and one or more antenatal care visits, but only about one third of the national coverage gap in measles immunization.

Discussion

This study of 28 sub-Saharan African countries concurs with other reports in finding that health services in developing countries are not equally accessible to all populations.4,9,12,14,18,23–25 By breaking down the health service coverage gap, we showed that the role of wealth-related inequality differs between countries and types of health service. Even within the same region, countries experience many unique factors that affect health service coverage both directly and indirectly, ranging from health-care financing priorities and political agendas to cultural practices and conflict situations.12,24

Health services require variable amounts of funding, resources and infrastructure, and this may account for some of the differences in the role of wealth-related inequality.4,23 In line with other studies, we found that within-country wealth-related inequality contributed less to the services delivered at the community level (e.g. family planning and immunizations) than to services that require trained health professionals or health facilities (e.g. one or more antenatal care visits and skilled birth attendance).4 An understanding of the complexity and magnitude of wealth-related inequality will improve interventions that aim to increase the coverage of child and maternal health services in developing countries.

Planning interventions

Planning interventions that take into account wealth-related inequality may play a significant role in reducing the health service coverage gap. Where the contribution of within-country wealth-related inequality is high, an approach targeted at populations in lower wealth quintiles is justified. This type of approach would be appropriate in countries such as Madagascar and Nigeria, where the relative inequality (PAR%) is high. The use of poverty maps and the prioritization of poor, remote communities in the design of health service delivery have helped countries such as Bangladesh, Brazil and Peru to tackle health service coverage inequality.26,27

In many of the study countries, a targeted approach may be appropriate for interventions in skilled birth attendance or one or more antenatal care visits. Such an approach could include providing free or reduced-fee health services to those in lower wealth quintiles, creating incentives for health workers to practice in underserved communities, offering skill development sessions to build the capacity of health-care providers serving poor communities, or establishing conditional cash-transfer programmes that pay mothers for using services.18 Task-shifting – the deployment of community health workers outside of health facilities – is another low-cost way to increase access to basic health services.14,28

A whole-population approach may work well in situations in which the national coverage gap is high, as is the case for Chad and Ethiopia. Given the widespread coverage gap in all quintiles (including the richest), there is great potential for these countries to benefit from a whole-population approach. In situations where the health system can reach the entire population, this type of approach can provide health-care services with consistent quality and benefit.13 Certain types of health services, such as immunization campaigns and family planning initiatives, may be best delivered using a whole-population approach. The main risk with this approach is that if implementation ends up being partial, inequalities may be exacerbated; that is, the rich may benefit early in the programme and, if the programme is interrupted (e.g. for lack of funds), the poor are yet to be reached.29

In some situations, a combination of targeted and whole-population approaches may help to decrease the health service coverage gap. Mali, for example, may benefit from a targeted approach for skilled birth attendance and one or more antenatal care visits, and from a whole-population approach to reduce the coverage gap in measles immunization. In Nigeria, action to increase coverage of DPT3 may benefit from a strong targeted approach, whereas in the United Republic of Tanzania, a whole-population approach may be more appropriate. Box 1 provides examples of countries adopting different approaches.

Box 1. Country examples of interventions to reduce the health service coverage gap.

A. In Kenya, the Kisumu Medical and Educational Trust programme (KMET), based in the city of Kisumu, aims to increase access to reproductive health services by the poor.30 KMET is strengthening the capacity of health-care providers and facilities in poor, rural areas by providing training sessions, basic equipment and small loans. Taking a targeted approach, the programme has been successful in reaching the poorest populations by enrolling mid-level health-care providers (e.g. nurses and clinical officers) in rural areas.

B. In Ghana, the distribution of insecticide-treated bednets (ITNs) was paired with whole-population measles immunization campaigns.31 Before the campaign, ITN ownership in the Lawra district of Ghana was less than 10% in all wealth quintiles. After the campaign, the coverage rate of ITN ownership increased to over 90% in all wealth quintiles. This community-level intervention was a cost-efficient method of distributing ITNs to a large population.

C. Brazil is on track to achieving MDG 4 and has made good progress towards MDG 5 thanks to a combination of whole-population and targeted approaches to increasing health coverage.32 A unified health system provides comprehensive health care at the whole-population level. Targeted approaches include the Family Health Strategy, which reorganized primary health care by sending teams of health workers to underserved areas. Since its inception, the programme has been scaled up to reach over 50% of the population, and has contributed to declining infant mortality rates.

D. In Egypt, an immunization campaign contained elements of both whole-population and targeted approaches.25 The campaign achieved widespread geographical coverage, with financial and training support from the central government. Health units used disaggregated data to target resources to populations with lower coverage, and non-physician health workers were empowered to assume greater responsibilities.

Limitations and extension

The overall coverage gap was used to obtain a summary measure of coverage gap for a set of maternal and child interventions with different delivery strategies, based on a set of robust indicators. Although there may be correlations between the variables that are used in the index, this does not obviate the need for a cross-cutting coverage measure. By using four intervention areas with different delivery strategies, we obtained a broad index of service delivery. For example, although there was a moderate correlation between the treatment of acute respiratory infection in children under 5 years of age and measles vaccination coverage (ρ: 0.48), no correlation was seen between the former indicator and DPT3 vaccination coverage (ρ: −0.04).

The population attributable risk calculation of wealth-related inequality assumed that the wealthiest quintile (the reference population) experienced the lowest coverage gap. In a few instances in our study this was not the case; the health-service-specific coverage gap of the wealthiest quintile was reported to be higher than that of at least one of the other quintiles. For example, the coverage gap of specific health interventions in the richest wealth quintile was slightly higher (0.1–3.6%) than the national coverage gap for the treatment of acute respiratory infections in children under 5 years of age (Zambia), DPT3 immunization (Rwanda and Swaziland) and measles immunization (Lesotho). This was not the case with the overall coverage gap. A possible explanation could be that the sample size of the population at risk in the wealthiest quintile (denominator) is too small to generate a meaningful representation of the coverage gap of a specialized service. For instance, the wealthiest quintile of some countries had only a small number of sick children requiring treatment for acute respiratory infection. Alternatively, data may reflect an unknown and consistent pattern of under- or over-reporting during survey interviews. It is also possible that the data reflect the true situation and that the richest quintile did not experience the lowest health service coverage gap.

Reporting bias tends to attenuate the association between wealth quintile and coverage rates. Although over-reporting of child morbidity in the wealthier quintiles is documented,33 the extent to which it affects the reporting of health service use is less clear.

We defined inequality based on asset-determined wealth quintiles – a common tool for measuring disparity within populations.34 This frame of reference, however, presents certain limitations.13,35,36 The assets chosen to represent wealth must be culturally specific, timely and applicable to all members of a specified population. Wealth quintiles represent only relative wealth differences and may align closely with other forms of disparity (e.g. urban or rural). In certain contexts, other factors (e.g. education, gender or geography) may be more important in determining disparity in health service access.19,35

While breakdown of the coverage gap may be a useful tool to assess the role of within-country inequality, the strength of the measurement relies on the accuracy and availability of data. The lack of high-quality statistical data from developing countries presents challenges for the creation of informed policies,17,18,37 a limitation that is exacerbated when attempting inter-country comparisons.21 By focusing on within-country comparisons, this data analysis reduced the importance of attaining regionally consistent data. As the quality of data and methods of analysis and monitoring improve, African countries will be better able to use this information to improve health service initiatives.6,12 Because coverage gap served as a proxy for health service provision and use, alternative analyses might segregate these components or expand them to include other types of service indicators.

Our methods may easily be used to break down the health service coverage gap by other forms of inequality, such as education, gender or geography. Population attributable risk takes into account both the situation of all social groups (not only the extremes) and the group population size. Hence, it overcomes the limitations of simple range measures of inequality such as differences or ratios. This study focused on wealth-related inequality in sub-Saharan African countries at a recent time point and did not explore trends in health equity situations; the latter may provide more in-depth evidence for equity-focused interventions. In future studies, our methods could be useful for monitoring inequalities over time and assessing the impact of interventions on reducing inequality.

Conclusion

Overall, a comprehensive monitoring programme may help countries to identify relevant forms of inequality and allow for health service initiatives to be targeted accordingly, where appropriate.19,25,38,39 Coverage gap data were presented for select components of the overall coverage gap (one or more antenatal care visits and skilled birth attendance, measles and DPT3 immunization, treatment of acute respiratory infection in children under 5 years of age and family planning); these components correspond to diverse types of child and maternal health indicators. This allowed for within-country comparison, highlighting the variable role of wealth-related inequality within the national coverage gap.

Given the lack of association between the level of national overall coverage gap and the magnitude of relative inequality, policies and programmes that aim to reduce the service coverage gap may not necessarily be effective in tackling within-country inequalities. This finding also reinforces the notion that the determinants of health are not necessarily the same as the determinants of inequalities in health.40

Between 1990 and 2006, patterns of inequality in developing countries remained largely unchanged.13 This trend has been cited as a major contributor to the lack of progress on the child and maternal health MDGs.1,13,35,37,41 Our study demonstrated the contribution of wealth-related inequality to child and maternal health service coverage gap in 28 sub-Saharan African countries and highlighted the implications for health policy approaches. As the deadline for the MDGs approaches, attention is increasingly turning to child and maternal health. Now, more than ever, is the time for strong policies and for interventions that will maximize their impact.

Competing interests:

None declared.

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