International Journal of Social Economics
Trends and causes of socioeconomic inequalities in maternal healthcare in Ghana,
2003–2014
Ama Pokuaa Fenny, Derek Asuman, Aba Obrumah Crentsil, Doreen Nyarko Anyamesem Odame,
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IJSE
46,2 Trends and causes of
socioeconomic inequalities in
maternal healthcare in Ghana,
288 2003–2014
Received 2 April 2018 Ama Pokuaa Fenny, Derek Asuman, Aba Obrumah Crentsil and
Revised 6 July 2018
Accepted 14 August 2018 Doreen Nyarko Anyamesem Odame
Institute of Statistical Social and Economic Research,
University of Ghana, Legon, Ghana
Abstract
Purpose – 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.
Design/methodology/approach – 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.
Findings – 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.
Originality/value – 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.
Keywords Ghana, Delivery, Decomposition, Maternal health, Antenatal care, Socioeconomic inequalities
Paper type Research paper
1. Introduction
A target of the Millennium Development Goals (2001–2015) adopted by the United Nations
General Assembly in 2000 aimed to improve maternal health, committing to reduce the
global maternal mortality rate by three-quarters between 1990 and 2015. Achieving equity
and universal coverage of maternal healthcare delivery remains crucial to efforts toward
poverty reduction, gender equality and women’s empowerment. To this end, the successor
to the MDGs, the sustainable development goals, re-emphasizes the commitment to reducing
maternal mortality through universal coverage of maternal healthcare delivery. Access and
utilization of healthcare services during pregnancy and childbirth are crucial to safe
motherhood. The use of prenatal health services enables the early detection and
management of pregnancy-related complications and provides opportunities for health
promotion ( Joshi et al., 2014). The provision of timely and quality maternal healthcare
services has been found to be essential to the health of mothers and new-borns
International Journal of Social
Economics (Noonan et al., 2013).
Vol. 46 No. 2, 2019
pp. 288-308
© Emerald Publishing Limited The authors of this paper have not made their research data set openly available. Any enquiries
0306-8293
DOI 10.1108/IJSE-03-2018-0148 regarding the data set can be directed to the corresponding author.
Downloaded by University of Ghana At 03:01 03 June 2019 (PT)
In spite of the central role of accessible, affordable and quality maternal healthcare Maternal
services to achieving the global target of reducing maternal and neonatal mortality, and healthcare in
improve early childhood health outcomes, there exist barriers to achieving universal Ghana
maternal healthcare coverage especially in low-income countries. For example, studies by
Silal et al. (2012) and Pulok et al. (2016) report socioeconomic-related inequalities in maternal
healthcare utilization in South Africa and Bangladesh. The presence of such inequalities
implies inequities of the maternal healthcare utilization. Addressing these challenges 289
requires that health agencies pay attention to equity and fairness in the design and
implementation of maternal healthcare interventions so as to achieve universal coverage.
Therefore, eliminating the barriers to universal maternal healthcare utilization requires the
identification and understanding of the contributing factors to the non-utilization of
maternal healthcare among disadvantaged groups. One of such interventions is the
introduction of health insurance schemes and fee-exemptions for maternal healthcare
services (Dixon et al., 2014).
In 2004, Ghana introduced the National Health Insurance Scheme (NHIS). The scheme
aims to improve access to healthcare through the removal of financial barriers and the
provision of financial protection to poor and vulnerable persons. Membership of the scheme
is contingent on the payment of annual premiums. The scheme was designed to be pro-poor,
as the amount of premiums was graduated based on the socioeconomic status of the
individual. In particular, the scheme covered maternal healthcare services in public
healthcare facilities. Maternal health services at private facilities were available at a charge.
The payment of premiums, however, constituted a barrier for pregnant and expectant
women from low-income households to access services covered under the health insurance
scheme ( Jehu-Appiah et al., 2011). To achieve equity and fairness in access to maternal
healthcare under the NHIS, a fee exemption policy for maternal health services was
introduced in 2008. The policy exempted pregnant women and expectant women from
premium payment and extended the provision of free maternal healthcare services to
private healthcare facilities. The introduction of the exemption policy for maternal and child
healthcare services was in fulfillment of the country’s commitment toward achieving the
maternal and child health targets under the Millennium Development Goals. This context
provides a setting to assess the evolution of socioeconomic inequalities in maternal
healthcare utilization in Ghana and assess the effects of fee exemption policies for maternal
healthcare on equity, fairness and universal coverage of maternal healthcare.
Using data from the Ghana Demographic and Health Survey (GDHS), the objectives of
this paper are twofold. First, we estimate the degree and assess the trends of inequalities in
maternal healthcare utilization by employing extensions to the concentration index (CI). The
second objective assesses the causes of inequalities in maternal healthcare by applying a
generalized decomposition technique. This paper contributes significantly to literature.
First, we apply two measures of socioeconomic inequalities in health, the Wagstaff and
Erreygers index. The application of both indices enables us to examine differences in the
degree and trends of inequalities in health based on the measurement approach adopted.
Second, we employ a recently developed generalized decomposition technique to assess the
causes of socioeconomic inequalities in maternal healthcare. At last, by estimating
the degree and causes of socioeconomic inequalities in health over time, the paper examines
the evolution of the factors that causes inequities in maternal healthcare utilization in light
of the introduction fee exemption for maternal health services in Ghana.
The rest of the paper is organized as follows: Section 2 presents a brief review of the
theoretical and empirical literature. Section 3 focuses on the estimation of the degree of
socioeconomic inequalities in maternal healthcare utilization and the decomposition
approach adopted to assess the contributions to the inequalities. Section 4 discusses the
data, variables and summary statistics of the sample. Section 5 discusses the results from
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IJSE the estimations of the degree and causes of socioeconomic inequalities in maternal
46,2 healthcare utilization. The paper concludes with a summary of the main findings and policy
recommendations based on the findings.
2. Literature review
The measurement of socioeconomic inequalities in health has received substantial interest
290 in literature, with improvements in the methodologies for estimating inequalities for
different health indicators. Health economists as well as epidemiologists have applied the CI
extensively to assess the nature and degree of socioeconomic-related inequalities in health
outcomes. The CI is derived from the Gini coefficient from income inequality measurement.
However, unlike the Gini coefficient, the CI is a bivariate rank dependent index; in that the
index summarizes the relationship between cumulative health and socioeconomic rank
(Heckley et al., 2016). For bounded variables such as the binary health outcomes, the bounds
of the CI have received extensive review and debate in literature (see Wagstaff, 2005, 2009,
2011a, b; Erreygers, 2009a, b; Erreygers and Van Ourti, 2011a, b; Kjellsson and Gerdtham,
2013). Particularly, Wagstaff (2005) and Erreygers (2009a) have shown that the minimum
and maximum values of the CI depend on the mean health.
The dependence of the value of the index for a binary variable on the mean health implies
that comparisons between groups with different mean health will be misleading. In addition, the
degree of inequality measured by the CI is sensitive to the choice of indicator – the presence of
health or ill-health. Erreygers (2009a) demonstrates that a desirable property of an index for
socioeconomic inequality in a binary health outcome is the mirror property; such that the
indices of health and ill-health are mirror images of each other. The mirror property implies that
inequality index for health is equal to the index for inequality in ill-health, but has the opposite
sign. To this end, Wagstaff (2005) and Erreygers (2009a) have extend the CI to account for
binary health indicators. However, there exist debates on the choice of estimation technique due
to differences in the assumption underlying each measurement procedure (Wagstaff, 2009,
2011a, b; Erreygers, 2009a, b; Erreygers and Van Ourti, 2011a, b). As a result, Kjellsson and
Gerdtham (2013) urge that health policy analysts compare among the various health inequality
indices when assessing the degree of health inequalities for different populations.
The measurement of socioeconomic-related inequalities provides an important overview
of the degree and nature of variations in the access and use of healthcare across
socioeconomic groups. For the design of policies aimed at achieving equity and fairness in
healthcare delivery, it is equally important to understand and explain the underlying causes
of socioeconomic inequalities in health. The dominant procedure for assessing the causes of
socioeconomic inequalities in health is the decomposition technique by Wagstaff et al.
(2003). The decomposition method has been applied extensively to explore the determinants
of the socioeconomic gradient in health based on bivariate dependent rank indices. However,
the Wagstaff et al. (2003) decomposition method has been criticized for a number of
shortcomings (Erreygers and Kessels, 2013; Kessels and Erreygers, 2016; Heckley et al.,
2016). Erreygers and Kessels (2013) and Kessels and Erreygers (2016) criticize the Wagstaff
et al. (2003) technique as unidimensional; as the method examines only variations in health
rather than the covariance between health and socioeconomic rank. In addition, the
interpretation of the parameters and contributions of covariates to health inequalities based
on the Wagstaff et al. (2003) decomposition is unclear.
There is a growing body of literature that have examined the determinants of maternal
healthcare utilization in developing countries (Moyer and Mustafa, 2013; Finlayson and
Downe, 2013). These studies have found significant socioeconomic, demographic and
community-level factors have been identified to influence the access and frequency of use of
maternal healthcare. For example, at the individual-level, characteristics such as educational
attainment (Tarekegn et al., 2014; Ameyaw et al., 2017), place of residence (Arthur, 2012)
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and parity and birth experience have been found to be significant predictors of maternal Maternal
healthcare utilization. In addition, women’s autonomy and decision making power have healthcare in
been shown to increase the prevalence of the maternal healthcare use (Woldemicael and Ghana
Tenkorang, 2010). Financial and physical access to healthcare facilities constitute
significant barriers to maternal health utilization in developing countries (Pfeiffer and
Mwaipopo, 2013; Atuoye et al., 2015). These studies have emphasized women empowerment
and the elimination of financial barriers to maternal healthcare as potential pathways to 291
achieve equitable maternal healthcare utilization.
Another strand of papers has examined the degree and nature of inequities in the
distribution and utilization of maternal healthcare (Zere et al., 2010; Silal et al., 2012; Pulok et al.,
2016; Nababan et al., 2018). These studies have shown the presence of socioeconomic inequalities
in the use of maternal healthcare, as the use of maternal healthcare is lower among women with
low education, residing in poor households and rural areas (Silal et al., 2012; Pulok et al., 2016).
In Ghana, Asamoah et al. (2014) and Asamoah and Agardh (2017) examine the trends and
magnitude of socioeconomic inequalities in antenatal care and delivery under skilled care
between 1988 and 2008 and 2003 and 2014, respectively. Asamoah et al. (2014) find that income
and parity-related inequalities rose between 1988 and 2003 whilst urban-rural and educational-
related inequalities declined over the same period. On the other hand, Asamoah and Agardh
(2017) find declines in urban, education and wealth related inequalities in skilled birth
attendance between 2003 and 2008; increasing, however, between 2008 and 2014. Consistent
declines were, however, observed in inequalities in attending four antenatal care visits in relation
to urbanicity and education, whilst wealth inequalities in antenatal care visits increased over the
period. Our paper differs from Asamoah et al. (2014) and Asamoah and Agardh (2017) in the
methodology for measuring socioeconomic inequalities in maternal healthcare. In addition, this
paper examines the causes of the inequalities in maternal healthcare in Ghana through a
decomposition analysis, focusing on the evolution of the causes of socioeconomic inequalities in
relation to the introduction of fee exempt maternal healthcare services in Ghana.
3. Methodology
3.1 Measuring socioeconomic inequalities in maternal healthcare
The standard CI is expressed as:
C ¼ 1 2 covðH ;Y
m i i
Þ; (1)
H
where Yi is the socioeconomic rank of an individual; mH, the mean health of the population;
and Hi, the health situation of the individual. The CI lies between −1 and 1 (−1⩽C⩽1). A
positive or negative value of the index reflects a negative or positive socioeconomic gradient
in health. Wagstaff (2005) and Erreygers (2009a) have shown that for binary health
indicators, the value of CI lies between mH−1 and 1−mH. Thus, the CI is limited in measuring
socioeconomic-related inequalities in health where the health outcomes are binary in nature.
Wagstaff (2005) proposes a correction to the CI to account for the feasible bounds of the
CI for binary variables. The Wagstaff Index (W) normalizes CI by dividing the value by the
feasible maximum bound (1−m). Thus, the Wagstaff Index for a binary health indicator may
be expressed as:
¼ 2W covðHi;Y Þ: (2)m 1 m iH H
Erreygers (2009a) criticizes the Wagstaff (2005) normalization of the CI as arbitrary.
In addition, Erreygers and Van Ourti (2011a, b) argue that the Wagstaff Index neither
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IJSE measures absolute or relative inequality. The index is not invariant with respect to equal
46,2 increments in health, as the index could rise or fall if the health of population increases by
the same amount. Erreygers (2009a) therefore proposes an alternative normalization
procedure to the CI that measures absolute inequality in health. The Erreygers Index (E) for
a binary variable is reduces to:
8
292 E ¼ covðHi;YiÞ; (3)bH aH
where aH and bH are lower and upper bounds of the health variable, respectively.
Kjellsson and Gerdtham (2013) posit that difference between the normalisations proposed
by Wagstaff (2005) and Erreygers (2009a) are normative, arising from differences in the
underpinning value judgments. Further, Kjellsson and Gerdtham (2013) demonstrate that the
choice of index may affect comparisons on the degree of inequalities between populations with
different mean health. As such, we estimate the degree of socioeconomic-related inequalities in
maternal healthcare using both the Wagstaff and Erreygers Indices.
3.2 Decomposing socioeconomic inequalities in maternal healthcare
In light of the shortcomings of theWagstaff et al. (2003) decomposition technique, Heckley et al.
(2016) propose a generalized method for decomposing the causes of socioeconomic-related
inequalities in health. The Heckley et al. (2016) approach extends the recentered influence
function (RIF) regression-based decomposition developed by Firpo et al. (2009) to bivariate
rank dependent indices. The procedure of decomposing a bivariate rank index of health
inequality based on the RIF is twofold. The first step computes the RIF of the rank dependent
index. The second step then regress the RIF on a set of covariates to yield the marginal effects
of the covariate on the index.
Heckley et al. (2016) denote the general form of a bivariate rank dependent index as:
I ¼ vI F ¼ voI ðF ÞvGC H ;FY H FH ;FY : (4)
where F denotes the probability of the health indicator (H). vωIH (FH) is a weighting function
for a specific form of inequality index. The socioeconomic status of the individual is
represented as Y and FY is the cumulative density function of Y and indicates the
socioeconomic rank of the individual. FH,FY denotes the joint distribution of H and FY.
vGCðFH ;FY Þ is the generalized CI which is equal to twice the covariance between health and
socioeconomic rank.
The standard decomposition technique byWagstaff et al. (2003) is based on a linear health
regression model. Let health, h, be modeled as linear function of covariates, X of the form:
h ¼ aþbKXKþe; (5)
where βK is the regression coefficient corresponding to the kth regressor and e is an error term.
The decomposition is obtained by substituting Equation (5) into Equation (4) to obtain:
¼ I
XK
I v F ¼ voIH ;FY ðFH Þ bk2covðXK ;FY ÞþvoI ðFH Þ2covðe;FY Þ; (6)
k¼1
where 2cov(XK, FY) is the generalized CI for the kth covariate XK and 2cov(e, FY) is the
generalized CI of the error term, e. The Wagstaff et al. (2003) decomposition consists of two
parts – an explained part which shows the contribution of each covariate (X ) to the index
and an unexplained part that measures the component of socioeconomic inequality in health
not explained by the variations in the contributing covariates across groups.
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The RIF is derived from a directional statistics, the influence function (IF). The IF is an Maternal
analytical technique which measures the influence of a particular observation on the healthcare in
distributional statistics. Extending the IF to a bivariate rank index, Heckley et al. (2016) Ghana
obtain the IF of a bivariate rank index as:
I Ið Þ I ¼ v ð1eÞFH ;FY þedH ;F v FH ;FIF h;FY y ; v lim Y ðY Þ Y 0pep1; (7)
e-0 e 293
where edH ;FY ðY Þ denotes the joint cumulative distribution of the joint probability measure
that puts a mass at the joint values of H and FY(Y ).
The RIF of the bivariate rank index, I, is obtained by adding the value statistic vI FH ;FY
to the IF. The RIF is therefore presented as:
RIF h;FY ðyÞ; vI ¼ vI FH ;FY þ IF h;FY ðyÞ; vI : (8)
Recentering the IF to yield the RIF implies that the value statistic vI FH ;FY is expressed as
the expected value of the RIF such that:
vI FH ;FY ¼
IE RIF H ;FY ; v : (9)
The RIF decomposition comes down to estimating the conditional expectation of the RIF by
ordinary least squares (OLS) regressions. The RIF-I-OLS equation can thus be expressed as
function of covariates, X, and an error term ε. The covariates include a vector of personal
characteristics of the woman, access to health information, health seeking behavior and
household characteristics including location:
I E RIF H ;FY ; v 9X ¼ X 0jþE (10)
Under the assumptions of additive linearity and zero conditional mean, the RIF-I-OLS
identifies the parameters of interest – the marginal effect of a covariate and unconditional
bivariate rank index partial effect, φ. The parameters of the RIF-I-OLS are easy to interpret,
as they are analogous to the coefficients of an OLS regression. We apply the RIF-I-OLS
approach to decompose both the Wagstaff and Erreygers indices for indictors of maternal
healthcare examined in this paper.
4. Data, variables and summary statistics
4.1 Data
The data used in this paper come from the GDHS, implemented by the Ghana Statistical
Service, the Ghana Health Service (GHS) and the National Public Health Reference
Laboratory of the GHS. ICF International provided technical assistance through the DHS
Program, a USAID-funded project offering support and technical assistance in the
implementation of population and health surveys. The primary objective of the GDHS is to
generate reliable information on fertility, family planning, infant and child mortality,
maternal and child health, and nutrition. The data set contains information on the
characteristics of the respondents and the household. To date, six rounds of the GDHS have
been collected 1988, 1993, 1998, 2003, 2008 and 2014. Each round of data collection uses
similar procedures. The surveys follow a two-stage sample design. The first stage involves
selecting sample points or clusters, consisting of enumeration areas. The second stage
involves a systematic listing of households. A pre-determined number of households are
randomly selected from each cluster to constitute the total sample size of households. All
women aged 15–49 years who are either permanent residents of the household or visitors
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IJSE who stayed in the household the night before the survey are interviewed. The birth history
46,2 of all eligible women is recorded; including information pertaining to maternal healthcare.
A key objective of this is to examine the evolution of socioeconomic-related inequalities
in maternal healthcare in Ghana in light of the reforms implemented in maternal healthcare
delivery between 2003 and 2014. As such the empirical estimations of this paper employ
data from the three recent rounds of the GDHS. The 2003 round of the survey interviewed
294 6,251 households and 5,691 women, respectively. In 2008, 11,778 households and 4,916
women in the eligible age category were interviewed. The 2014 round of the survey
interviewed 11,835 households and 9,396 eligible women. The working sample for the
estimation is restricted to women who had given birth in the last five years preceding the
survey. For women with multiple births within the five-year period, we include the last birth
for the analysis. The sample sizes for the analyses are as follows; 2,612 in 2003; 2,052 in 2008
and 4,170 in 2014.
4.2 Variables
Socioeconomic inequalities in maternal healthcare are assessed using three binary indictors.
These indicators are timing of first antenatal visits, number of antenatal visits and the place
of delivery. The World Health Organisation (WHO) recommends that first antenatal visit
occurs as soon as possible; and preferably within the first trimester of pregnancy, in order to
detect and effectively treat underlying problem. We assigned a value of one to a woman
whose first antenatal visit occurred in the first trimester and zero if otherwise. Equally, the
WHO recommends a minimum threshold of four antenatal care visits as adequate for
antenatal care coverage. As such, the indictor for number of antenatal visits is equal to one if
a woman attends a minimum of four antenatal visits and zero if the woman attended less
than four visits. Delivery in a medically controlled facility and by skilled attendants is
deemed as integral for the reduction of maternal and neonatal mortality. The indicator of
place of delivery is therefore defined to be equal to one if a woman delivered in a health
facility (public or private) and zero if delivery occurs outside a health facility.
A major limitation of the GDHS data is the absence of information on consumption and
household income. Filmer and Pritchett (2001) and Sahn and Stifel (2003) demonstrate that
an index based on assets as a valid indicator of socioeconomic status. We therefore measure
socioeconomic status using a composite index of household wealth. The index is constructed
using a principal component analysis (PCA) of information of household ownership of
assets. The index is based on household ownership of consumer goods, characteristics of
place of dwelling, source of drinking water and sanitation facilities. The score generated by
the PCA is used for the analysis.
To decompose the causes of socioeconomic inequalities in maternal healthcare, we
employ a set of demographic and socioeconomic characteristics of the woman as well as
household-level characteristics as explanatory variables. The demographic characteristics
of the woman include the age at pregnancy, marital status, religious affiliation and previous
birth. The age at pregnancy of woman is a key predictor of access and use of maternal
health services. In traditional societies, pregnant young woman face a number of social and
cultural stigmas that serve as barriers to access and use of healthcare services. We include
the religious affiliation of woman to assess differences in the access and use of maternal
healthcare. We include a dummy variable that identifies whether a woman has previous
birth experience. Women who had previously given birth are expected to gain some
knowledge and experience that influences their use of maternal healthcare services.
Other socioeconomic characteristics included are education, labor market status and the
economic status of the woman in the household. Education has been found to be a key
determinant of health seeking behavior and the demand for health services. Education is
measured by the years of completed schooling, to examine the effect of an additional year of
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completed schooling on inequalities in maternal healthcare in Ghana. The labor market Maternal
status of a woman serves as a proxy for financial access to healthcare as well as captures the healthcare in
opportunity cost of time use. Women who are employed may have access to more financial Ghana
resources which may encourage them to seek better healthcare during pregnancy and
childbirth. On the other hand, engagement in an economic activity increases the opportunity
cost of time for women; which may reduce the demand for perinatal healthcare if access to
these services involve long waiting times. The economic status of a woman within the 295
household is defined by a dummy variable that captures whether the woman is the
economic head of the household.
Another explanatory variable is access to information. Health authorities have promoted
mass communication as a means to educate women on maternal healthcare. In addition,
informal channels of information sharing may exist within the household. We include two
variables to capture access to maternal healthcare information. Media exposure is defined as
a dummy to indicate whether a woman accesses information through radio, newspaper or
television. The number of co-resident women in their reproductive years is also included as a
proxy for informal communication channels in the household. Physical access and
proximity to a healthcare facility is captured by a dummy variable that indicates whether
distance to a health facility is a problem for the woman. The last set of explanatory variables
related to location of the household. There exist significant regional and locational
differences in the access to and quality of healthcare services. The presence of such regional
differences induces differences in health seeking behaviors. To account for such regional
fixed effects with the inclusion of regional dummies. A variable that captures whether a
household is located in a rural or urban area is included.
4.3 Summary statistics
Table I presents a summary statistics of the variables employed for the estimations. The
prevalence of maternal healthcare utilization measured by the three indicators adopted
Variables 2003 2008 2014
Timing of first antennal visit 0.4644 0.5570 0.6403
Number of antenatal visits (4+) 0.7179 0.8033 0.8767
Delivery at health facility 0.4810 0.6029 0.7558
Age at pregnancy (years) 28.77 28.53 29.08
Years of schooling (years) 4.70 5.30 6.11
Number of co-resident woman 0.4324 0.3873 0.3138
Head of household 0.1797 0.2120 0.1939
Media exposure 0.8638 0.8532 0.9062
Urban 0.3578 0.4027 0.4621
Distance is a problem 0.3791 0.2885 0.2643
First birth 0.2155 0.2222 0.2252
Labor market status
Unemployed 0.1159 0.1008 0.1758
Employed – family/other 0.1484 0.1958 0.2078
Employed – self 0.7356 0.7034 0.6164
Religion
No/other religion 0.0616 0.1003 0.0676
Christian 0.8992 0.7186 0.7649
Moslem 0.0392 0.1812 0.1675 Table I.
Observations 2,619 2,052 4,170 Summary statistics of
Note: 1Estimates are adjusted for survey settings sample (mean)1
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IJSE increased between 2003 and 2014. In 2003, the prevalence of first antenatal visit during the
46,2 first trimester of pregnancy was 46.4 percent; increasing to 55.7 percent in 2008 and further
to 64.0 percent in 2014. We find similar increases in the prevalence of the women attending
the minimum required antenatal visits of four between 2003 and 2014. The prevalence rate
of women who attended a minimum of four antenatal visits increased from 71.8 percent in
2003 to 80.3 percent in 2008 and further to 87.7 percent in 2014. In addition, 48.1 percent of
296 women who delivered in the five-year period prior to the survey in 2003 delivered in a health
facility. Facility-based deliveries increased to 60.3 percent in 2008 and 75.6 percent in 2014.
The average regnancy age for the women included in the sample was 29 years across the
three survey rounds. On the other hand, we witness improvements in the average education of
the sample between 2003 and 2014. The average years of completed schooling increased from
4.7 years in 2003 to 5.3 years in 2008. Average years of completed schooling
increased further to 6.11 years in 2014, corresponding to primary school completion of the
Ghanaian educational system. We also observe changes in the composition of households
during the three rounds of the survey as depicted by the average number of co-resident
women in their reproductive years in the household. The number of co-resident women in the
household declined from 0.43 in 2003 to 0.39 in 2008 and decreased further to 0.31 in 2014.
Exposure to mass media communications is high among the sample of women across the
three survey rounds. Equally, the results show an increasing proportion of the sample
residing in urban areas. From 35.8 percent of the sample as urban residents in 2003, the
share of the sample residing in urban areas increased to 40.3 percent in 2008. The proportion
of the sample residing in urban areas increased to 46.2 percent in 2014. The increase in the
share of urban residents in the sample is consistent in the rate of urbanization in Ghana.
Over the period, a major intervention in the maternal healthcare delivery system was the
expansion of primary healthcare facilities aimed at improving physical access to healthcare
delivery. The proportion of women reporting distance to a health facility decreased over the
time, an indication of improved physical access to healthcare facilities. The share of women
who indicated distance to the healthcare facility as a problem declined from 37.9 percent in
2003 to 28.9 percent in 2008. In 2014, 26.4 percent of the sample indicated distance to a
healthcare facility as a problem. The share of the sample that were first-time pregnant
women or expectant mother averaged 22.2 percent across the three rounds of the survey
employed for the estimations.
Predominantly, the women included in the sample are self-employed, though the share of
self-employment has been decreasing over time. The share of self-employed women declined
from 73.6 percent in 2003 to 70.3 percent in 2008. The proportion of the sample self-employed
declined further to 61.6 percent in 2014. On the other hand, the proportion of the sample
engaged in family or other employment increased over the period. About 15 percent of the
sample was employed by family or others in 2003, increasing to about one-fifth of the sample
in 2008 and 2014; 19.6 percent and 20.8 percent, respectively. The proportion of sample that
was unemployed in 2003 was 11.6 percent, dropping marginally to 10.1 percent in 2008.
However, we observe a steep increase in the rate of unemployed among the sample in 2014,
reaching 17.6 percent. The increase in unemployment is partly attributable to the
macroeconomic challenges that occurred from 2012 due to the shortfalls in electricity supply.
5. Results and discussions
5.1 Trends in socioeconomic inequalities in maternal healthcare in Ghana
The degree of socioeconomic inequalities is measured by the Wagstaff and Erreygers
indices, respectively, are presented in Table II. We observe that the degree and evolution of
inequalities in maternal healthcare differ between the indices. Overall, the results indicate
pervasive pro-rich inequalities in maternal healthcare in Ghana, although the degree of
inequalities has decreasing since 2003. Thus access and utilization of maternal healthcare
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Maternal
healthcare in
Ghana
297
Table II.
Trends of
socioeconomic
inequalities in
maternal healthcare in
Ghana, 2003/2014
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Wagstaff Index Erreygers Index
Variables 2003 2008 2014 2003 2008 2014
Timing of first visit 0.2066*** (0.0216) 0.2142*** (0.0243) 0.1876*** (0.0176) 0.2049*** (0.0214) 0.2116*** (0.0240) 0.1740*** (0.0163)
Number of visits 0.3294*** (0.0215) 0.3817*** (0.0259) 0.3357*** (0.0211) 0.2738*** (0.0185) 0.2556*** (0.0191) 0.1569*** (0.0111)
Place of delivery 0.5977*** (0.0173) 0.5922*** (0.0191) 0.4901*** (0.0152) 0.5889*** (0.0172) 0.5801*** (0.0191) 0.3919*** (0.0136)
Notes: Standard errors in parentheses. *po0.1; **po0.05; ***po0.01
IJSE are concentrated among women residing in households with higher wealth index.
46,2 Socioeconomic inequalities over the period were highest in delivery at a health facility.
Specifically, the findings show that the degree of inequalities in first antenatal visit in the
first trimester of pregnancy increased marginally in 2008 from 2003; and decreased in 2014.
The Erreygers index suggests a faster rate of decline of inequality in timing of first
antenatal visit between 2008 and 2014 compared to the Wagstaff Index. TheWagstaff index
298 estimates the degree of inequality in timing of first antenatal at 0.207 in 2003; increasing to
0.214 in 2008. In 2014, the level of inequality had decreased to 0.188. The Erreygers index on
the other hand estimates the degree of inequality at 0.205 in 2003, 0.212 in 2008 and 0.174 in
2014. The presence of a pro-rich gap in the timing of first antenatal visits is consistent with
the findings of Doku et al. (2012) and Dixon et al. (2014) who report that women residing in
households with high socioeconomic status are likely to seek antenatal care in the early
stages of pregnancy.
The results show a divergence in the trends of socioeconomic inequalities in attending
the required number of antenatal visits measured by the Wagstaff and Erreygers indices.
The Wagstaff index shows that the level of inequality increased from 0.329 in 2003 to 0.382
in 2008. In 2014, the Wagstaff index estimates socioeconomic inequality in attending the
required number of antenatal visits at 0.336. The Erreygers index on the other hand
indicates a consistent downward trend in the level of inequalities in number of antenatal
visits. The degree of inequalities decreased from 0.274 in 2003 to 0.256 in 2008 and further to
0.157 in 2014.
Delivery at a health facility shows a declining trend in inequalities measured by the
Wagstaff and Erreygers indices. However, the level of socioeconomic inequality in delivery
at a health facility remains very high. Between 2003 and 2008, inequalities in delivery at a
health facility decreased marginally from 0.598 to 0.592 by the Wagstaff index and 0.589 to
0.580 by the Erreygers index. Our findings show rapid decline in inequalities in deliveries at
a health facility between 2008 and 2014. Abor et al. (2011) and Ameyaw et al. (2017) find that
delivery at a heath facility was higher among women from high socioeconomic households,
a situation that resulted in the concentration of health facility deliveries among women in
high socioeconomic status households.
Available evidence have found out-of-pocket expenditures as a major barrier to access
and utilization of maternal healthcare services (Lagarde and Palmer, 2011; Finlayson and
Downe, 2013). In addition, a number of studies have established a positive relationship
between wealth status and the use of maternal health services (Abor et al., 2011; Arthur,
2012; Tarekegn et al., 2014; Ameyaw et al., 2017). The reduction of out-of-pocket
expenditures through enrollment on a health insurance scheme has been found to increase
the utilization of general healthcare services (Giedion et al., 2013) and maternal health
services in particular (Were et al., 2017). Dixon et al. (2014) report that physical access to
healthcare facilities is a barrier to the use of maternal healthcare services; whilst
Arthur (2012), and Atuoye et al. (2015) find that transportation cost constitutes another
barrier to the use of maternal healthcare services. The findings of these studies
therefore suggest the presence of significant direct and indirect financial barriers to
maternal healthcare access, leading to significant pro-rich inequalities in maternal
healthcare utilization.
We discuss the trends in socioeconomic inequalities in the indicators of maternal
healthcare within the context of the reforms in maternal healthcare during the period
covered by the paper. The decreasing levels of socioeconomic-related inequalities in the
utilization of maternal healthcare service between 2003 and 2014 provide an indication of
the effect of the reforms in the delivery of maternal healthcare in Ghana toward achieving
equity and universal coverage. Our findings support the hypothesis that the removal of
financial barriers to maternal healthcare is important in achieving universal maternal
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healthcare coverage (Dixon et al., 2014). The introduction of the NHIS in 2004 and free Maternal
maternal healthcare policy in 2008 increased access to these services for women in healthcare in
low-socioeconomic groups. Particularly, the introduction of the free maternal health policy Ghana
in 2008 has served as a pro-poor equalizing measure, by providing women in low-income
households with financial protection to access maternal healthcare services. Studies such as
Brugiavini and Pace (2016) and Ameyaw et al. (2017) report that women enrolled on the
NHIS are more likely to deliver at a healthcare facility in Ghana. The decline in the degree of 299
inequality in delivery at a health facility between 2008 and 2014 could therefore be
attributed to the free maternal healthcare policy that exempted pregnant women from
paying premiums and extended free deliveries to private health facilities.
5.2 Decomposing the causes of socioeconomic inequalities in maternal healthcare
Our results show that an increase in the age of the woman at pregnancy increases the degree
of socioeconomic inequality in the timing of first antenatal visit in 2014. Similarly, we find a
positive effect of the age of the woman at pregnancy on the inequalities in number of
antenatal visits in 2003 and 2014; and delivery at health facilities in 2008 and 2014. A
number of studies have found positive relationships between the age of a woman and the
use of maternal healthcare services in Ghana (Abor et al., 2011; Doku et al., 2012; Owoo and
Lambon-Quayefio, 2013). Abor et al. (2011) and Owoo and Lambon-Quayefio (2013) argue
that the positive relationship between age and the use of maternal healthcare services is a
reflection of the knowledge accumulated by older women on maternal health services from
previous births. Equally, older women face higher biological risk during pregnancy (Guliani
et al., 2013), thus inducing high use of maternal healthcare services to avert complications.
On the other hand, women’s autonomy has been found to exert a positive effect on maternal
health seeking behavior (Woldemicael and Tenkorang, 2010; Tarekegn et al., 2014; Pulok
et al., 2016). However, younger women tend to have less autonomy in decisions concerning
their health (Acharya et al., 2010). Thus, the positive effect of age at pregnancy on
inequalities on maternal healthcare could be reflective of the differences in autonomy
between older and young women in making decisions concerning their healthcare needs.
The level of educational attainment of a woman is an important determinant of maternal
health seeking behavior. Ensor and Cooper (2004) find that educational attainment improves
the ability of women to evaluate their healthcare demands whilst Titaley et al. (2010)
demonstrate that women with higher educational attainment are more aware of health services
in Indonesia. Another pathway by which educational attainment influences the utilization of
maternal health services is through increased autonomy and decision making power in the
household (Pulok et al., 2016). Our findings indicate that years of completed schooling
contribute to increasing the inequalities in the indicators of maternal healthcare in Ghana. The
relationship between educational attainment and the use of maternal healthcare services are
consistent with earlier studies from Ghana. Arthur (2012), Dixon et al. (2014) and Ameyaw et al.
(2017) find positive relationships between educational attainment and the use of maternal
health services in Ghana. Asamoah et al. (2014) and Asamoah and Agardh (2017) report
significant education-related inequalities in antenatal care utilization and skilled attendants at
birth in Ghana between 1998 and 2008 and 2003 and 2014, respectively (Table III).
Women’s access to maternal health information through mass media and informal
interactions influence their health seeking attitudes and the use of maternal health services
(Pulok et al., 2016; Tarekegn et al., 2014; Ameyaw et al., 2017). We find that the number of
co-resident women positively affects inequalities in the timing of first antenatal visit in 2003.
Equally, we observe a positive effect of the number of co-resident on inequalities in the
antenatal visits and delivery at a health facility in 2014. These findings suggest that
co-resident women in the household share knowledge and experiences of pregnancy and
childbirth. As a result, women residing in households with a larger number of women in
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Table III.
Decomposition of
socioeconomic
inequalities in timing
of first antenatal visit
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Wagstaff Index Erreygers Index
Variables 2003 2008 2014 2003 2008 2014
Age at pregnancy 0.0017 (0.0038) −0.0016 (0.0041) 0.0140*** (0.0031) 0.0015 (0.0037) −0.0016 (0.0040) 0.0129*** (0.0028)
Years of schooling 0.0178*** (0.0061) 0.0228*** (0.0067) 0.0240*** (0.0048) 0.0184*** (0.0061) 0.0219*** (0.0067) 0.0200*** (0.0044)
Head of household 0.0705 (0.0648) −0.0293 (0.0662) 0.0094 (0.0517) 0.0694 (0.0643) −0.0314 (0.0653) 0.0052 (0.0480)
Co-resident women 0.0549* (0.0294) −0.0091 (0.0338) −0.0356 (0.0273) 0.0554* (0.0292) −0.0073 (0.0334) −0.0287 (0.0253)
Exposed to media 0.0409 (0.0654) −0.0259 (0.0713) −0.1102* (0.0571) 0.0431 (0.0649) −0.0334 (0.0703) −0.1147** (0.0531)
Religious affiliation
Christian −0.2357*** (0.0886) −0.1243 (0.0827) −0.1359* (0.0705) −0.2249** (0.0879) −0.1299 (0.0815) −0.1399** (0.0655)
Moslem 0.0281 (0.1262) −0.1731* (0.0952) −0.1139 (0.0769) 0.0273 (0.1253) −0.1765* (0.0939) −0.1158 (0.0715)
Labor market status
Unemployed
Employed – family and others −0.0519 (0.0878) 0.0361 (0.0955) 0.0606 (0.0568) −0.0455 (0.0872) 0.0307 (0.0942) 0.0425 (0.0528)
Employed – self −0.0702 (0.0751) 0.1388 (0.0864) 0.0041 (0.0500) −0.0661 (0.0746) 0.1337 (0.0851) −0.0051 (0.0465)
Region
Western
Central −0.0797 (0.1171) −0.0875 (0.1229) −0.2163*** (0.0779) −0.0760 (0.1162) −0.0902 (0.1212) −0.1696** (0.0724)
Greater Accra −0.2367** (0.1071) −0.0363 (0.1171) −0.2765*** (0.0841) −0.2374** (0.1063) −0.0350 (0.1154) −0.2263*** (0.0782)
Volta −0.1318 (0.1122) −0.1683 (0.1175) −0.2264*** (0.0832) −0.1244 (0.1113) −0.1614 (0.1159) −0.1840** (0.0774)
Eastern −0.0956 (0.1075) −0.2908** (0.1161) −0.3332*** (0.0804) −0.0991 (0.1067) −0.2795** (0.1144) −0.2768*** (0.0747)
Ashanti −0.0230 (0.0943) −0.0078 (0.1041) −0.1771** (0.0796) −0.0211 (0.0935) −0.0071 (0.1026) −0.1427* (0.0740)
Brong-Ahafo −0.1229 (0.0985) −0.0814 (0.1139) −0.2389*** (0.0762) −0.1165 (0.0977) −0.0762 (0.1123) −0.2010*** (0.0708)
Northern 0.1433 (0.1004) 0.4001*** (0.1162) 0.0777 (0.0792) 0.1340 (0.0996) 0.4158*** (0.1145) 0.1193 (0.0736)
Upper East −0.4744*** (0.1113) −0.3700*** (0.1223) −0.5788*** (0.0798) −0.4649*** (0.1104) −0.3687*** (0.1205) −0.5342*** (0.0742)
Upper West −0.2205* (0.1142) −0.3599*** (0.1197) −0.4289*** (0.0857) −0.2126* (0.1133) −0.3666*** (0.1180) −0.3896*** (0.0797)
Urban 0.1858*** (0.0575) 0.0504 (0.0602) 0.0434 (0.0405) 0.1909*** (0.0570) 0.0429 (0.0594) 0.0340 (0.0376)
Marital status
Never married
Currently married 0.1740 (0.1353) 0.1196 (0.1174) 0.1136 (0.0728) 0.1855 (0.1343) 0.1119 (0.1158) 0.0696 (0.0676)
Previously married 0.1311 (0.1576) 0.0448 (0.1534) 0.0507 (0.0965) 0.1394 (0.1565) 0.0452 (0.1513) 0.0224 (0.0897)
Distance is a problem 0.1284*** (0.0490) 0.0167 (0.0420) 0.0488 (0.0407) 0.1250** (0.0486) 0.0143 (0.0414) 0.0522 (0.0378)
First birth 0.0350 (0.0671) −0.0696 (0.0577) −0.1851*** (0.0526) 0.0366 (0.0666) −0.0665 (0.0569) −0.1624*** (0.0489)
Constant 0.0811 (0.1945) 0.1864 (0.1962) 0.1026 (0.1355) 0.0556 (0.1930) 0.2067 (0.1934) 0.1432 (0.1259)
Observations 2,612 2,041 4,168 2,612 2,041 4,168
Notes: Standard errors in parentheses. *po0.1; **po0.05; ***po0.01
their reproductive years may have easy access to maternal health information which Maternal
influences their health seeking behavior during pregnancy and childbirth (Owoo and healthcare in
Lambon-Quayefio, 2013). Ghana
On the other hand, exposure to mass media channels exerts a negative effect on the
measured pro-rich inequalities in maternal healthcare utilization in Ghana. Specifically, we
observe women’s exposure to mass media is significant in reducing inequalities in the
timing of first antenatal visits in 2014. Exposure to mass media reduces inequalities in 301
attending at least four antenatal visits during the study period, 2003–2014. Significant
effects of exposure to mass media on reductions in inequalities in delivery at healthcare
facilities are observed in 2008 and 2014. The observed negative effects of mass media
exposure on inequalities indicate improved access to health information especially for
women in households with low-socioeconomic status, resulting in higher use of maternal
healthcare services among these deprived groups (Ameyaw et al., 2017).
Maternal healthcare seeking behaviors are influence by social and religious norms and
beliefs (Tarekegn et al., 2014). Indeed, previous studies such as Abor et al. (2011), Dixon et al.
(2014) Ameyaw et al. (2017) have found significant religious differences in the use of
maternal healthcare services in Ghana. Amu and Dickson (2016) on the other hand report
religious differences in the probability of women in their reproductive years to enroll onto a
health insurance scheme in Ghana. Our findings confirm the presence of religious
differences in maternal healthcare seeking behavior and socioeconomic inequalities in
maternal health utilization in Ghana. Being affiliated with Christian or Islamic beliefs
significantly reduces inequalities in maternal healthcare utilization compared to women who
are affiliated to other or no religious beliefs. Religious organizations have been crucial in the
healthcare delivery system in Ghana; through the provision of healthcare facilities and
health education for members. Religious organizations also promote interactions amongst
members, leading to the sharing of knowledge and experiences on maternal healthcare
utilization (Table IV ).
The nature of the labor market activity of a woman serves as a proxy for access to
financial resources through the wages earned. The labor market engagement of a woman
provides an indication of the potential opportunity cost of time use of the woman through
foregone wages. We observe that being self-employed in 2014 increases the inequalities in
attending the required minimum of four antenatal visits compared to being unemployed.
Self-employed women have the flexibility of time use, enabling them to visit antenatal clinics
for the minimum required visits. Again, self-employed women tend to have better access
and control of financial resources compared to their unemployed counterparts. As such,
they are better able to overcome the indirect costs of antenatal visits such as transportation
to attending antenatal clinics.
Proximity to a health facility reduces the indirect costs associated with maternal
healthcare utilization. Women who report that distance constitutes a problem are less likely
to use maternal healthcare services (Singh et al., 2015). We find that the challenges posed by
the lack of proximity to health facilities increases socioeconomic inequalities in maternal
healthcare utilization. In 2003, the lack of proximity to health facilities contributed to
increased socioeconomic inequalities in attending first antenatal visits in the first trimester
of pregnancy. For inequalities in attending at least four antenatal visits, we find that
distance to health facilities exerted a positive relationship in 2003 and 2014, respectively.
Similarly, we observe a positive relationship between the lack of physical access to health
facilities proxied by proximity to a health facility and inequalities in delivery at a health
facility. These findings suggest that the elimination of financial barriers to maternal
healthcare utilization through the introduction of fee-exempt services may not be sufficient
to achieving equity and universal coverage of maternal healthcare utilization if challenges of
physical access to health facilities persist.
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Table IV.
Decomposition of
socioeconomic
inequalities in number
of antenatal visits
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Wagstaff Index Erreygers Index
Variables 2003 2008 2014 2003 2008 2014
Age at pregnancy 0.0062* (0.0036) 0.0056 (0.0044) 0.0138*** (0.0036) 0.0041 (0.0032) 0.0009 (0.0032) 0.0046** (0.0019)
Years of schooling 0.0306*** (0.0059) 0.0187*** (0.0072) 0.0401*** (0.0056) 0.0167*** (0.0052) 0.0010 (0.0053) 0.0107*** (0.0030)
Head of household 0.0079 (0.0625) −0.1809** (0.0703) 0.0350 (0.0610) −0.0009 (0.0546) −0.1447*** (0.0515) 0.0111 (0.0320)
Co-resident women 0.0025 (0.0286) −0.0141 (0.0362) 0.0634** (0.0322) −0.0028 (0.0250) −0.0299 (0.0265) 0.0467*** (0.0169)
Exposed to media −0.1837*** (0.0633) −0.0882 (0.0769) −0.2738*** (0.0675) −0.2053*** (0.0553) −0.1294** (0.0564) −0.2280*** (0.0354)
Religious affiliation
Christian −0.1446* (0.0855) −0.0866 (0.0892) −0.0952 (0.0833) −0.2107*** (0.0747) −0.2002*** (0.0655) −0.1359*** (0.0438)
Moslem 0.1394 (0.1220) −0.2383** (0.1022) −0.1759* (0.0908) 0.1037 (0.1066) −0.3011*** (0.0750) −0.2204*** (0.0477)
Labor market status
Unemployed
Employed – family and others 0.1189 (0.0854) −0.0698 (0.1016) 0.0910 (0.0670) 0.0895 (0.0746) −0.0572 (0.0745) −0.0160 (0.0352)
Employed – self −0.0425 (0.0732) 0.0432 (0.0916) 0.1955*** (0.0591) −0.0637 (0.0639) 0.0170 (0.0672) 0.0525* (0.0310)
Region
Western
Central −0.0603 (0.1116) 0.0007 (0.1295) −0.1749* (0.0919) −0.1064 (0.0975) −0.0552 (0.0950) −0.0667 (0.0482)
Greater Accra 0.1996* (0.1037) 0.2216* (0.1249) −0.1187 (0.0991) 0.0997 (0.0906) 0.0534 (0.0916) −0.0121 (0.0520)
Volta 0.0844 (0.1073) −0.0417 (0.1246) −0.2910*** (0.0981) 0.0331 (0.0938) −0.0655 (0.0914) −0.0243 (0.0515)
Eastern −0.1166 (0.1025) −0.1715 (0.1231) −0.4810*** (0.0948) −0.1021 (0.0896) −0.1303 (0.0903) −0.0918* (0.0498)
Ashanti 0.0978 (0.0901) 0.0673 (0.1102) −0.0736 (0.0941) −0.0004 (0.0787) −0.0377 (0.0808) −0.0396 (0.0494)
Brong-Ahafo 0.0979 (0.0951) 0.1251 (0.1209) −0.2181** (0.0899) −0.0147 (0.0831) −0.0002 (0.0887) −0.0985** (0.0472)
Northern 0.0080 (0.0973) 0.2399* (0.1235) 0.1299 (0.0934) −0.0573 (0.0850) 0.1165 (0.0906) 0.1886*** (0.0490)
Upper East −0.3784*** (0.1086) −0.2162 (0.1337) −0.7410*** (0.0942) −0.4634*** (0.0948) −0.2927*** (0.0981) −0.3912*** (0.0495)
Upper West −0.0438 (0.1095) −0.2426* (0.1268) −0.4270*** (0.1010) −0.1698* (0.0957) −0.3466*** (0.0931) −0.2284*** (0.0530)
Urban 0.4245*** (0.0558) 0.2543*** (0.0641) 0.1912*** (0.0478) 0.2783*** (0.0488) 0.0718 (0.0470) 0.0486* (0.0251)
Marital status
Never married
Currently married 0.0450 (0.1317) 0.1435 (0.1252) 0.1883** (0.0857) −0.0024 (0.1150) 0.0421 (0.0919) 0.0308 (0.0450)
Previously married 0.0351 (0.1528) 0.2046 (0.1637) 0.1662 (0.1140) 0.0030 (0.1335) 0.1238 (0.1201) 0.0682 (0.0598)
Distance is a problem 0.0418 (0.0474) −0.0162 (0.0445) 0.0887* (0.0480) 0.0698* (0.0414) 0.0046 (0.0326) 0.0623** (0.0252)
First birth −0.0636 (0.0647) −0.1626*** (0.0615) −0.2134*** (0.0620) −0.0330 (0.0565) −0.1013** (0.0451) −0.0738** (0.0326)
Constant 0.1696 (0.1883) 0.2282 (0.2100) 0.0115 (0.1599) 0.4556*** (0.1645) 0.6405*** (0.1541) 0.3164*** (0.0840)
Observations 2,522 1,999 4,148 2,522 1,999 4,148
Notes: Standard errors in parentheses. *po0.1; **po0.05; ***po0.01
In a systematic review of the determinants of facility delivery in sub Saharan Africa, Maternal
Moyer and Mustafa (2013) report significant effects of parity on facility delivery. healthcare in
Tarekegn et al. (2014) find significant relationships between parity and the use of maternal Ghana
healthcare services in Ethiopia, with women of low parity more likely to use maternal
healthcare services. Similar findings have been reported in Bangladesh by Pulok et al.
(2016). In Ghana, Arthur (2012) finds that women with a higher number of previous births
are less likely to utilize maternal healthcare services, whilst Asamoah et al. (2014) report 303
increasing parity-related inequalities in maternal healthcare utilization in Ghana between
1993 and 2008, as the use of maternal healthcare services declined for women with
previous birth experiences and increased uptake for first-time pregnant women and
expectant mothers. Our results confirm the findings of Asamoah et al. (2014) as compared
to women with previous births, being nulliparous reduce inequalities in maternal
healthcare utilization. The relationship of previous births on inequalities in maternal
healthcare utilization reflects changes in healthcare seeking behaviors as women
accumulate experience and knowledge over time, leading to fatigue in the utilization of
such services (Table V ).
There exist spatial disparities in the distribution of healthcare facilities as well as quality
of healthcare services in Ghana. These disparities produce and reinforce differences in
attitudes and behaviors toward maternal healthcare utilization across localities and regions
in Ghana. Pfeiffer and Mwaipopo (2013) argue health facilities are more accessible in urban
areas due to available and reliable transportation networks. Abor et al. (2011) and Ameyaw
et al. (2017) argue that urban residents in Ghana have easier access to healthcare facilities
compared to rural residents. We observe a positive relationship between residence in an
urban area and inequalities in maternal healthcare utilization. Asamoah et al. (2014) report
rural-urban gaps in maternal healthcare utilization in Ghana. Arthur (2012) and Dixon
et al. (2014) report higher utilization of maternal healthcare services among urban resident
women in Ghana. Our findings therefore reflect the urban-rural differences in the
distribution and quality of healthcare facilities and services. Regional variations in the use
of maternal healthcare services have been reported in Ghana (Arthur, 2012). The results
confirm significant regional effects on the degree of socioeconomic-related in maternal
healthcare utilization in Ghana between 2003 and 2014. The presence of regional effects on
the degree of maternal healthcare inequalities requires urgent action toward reducing
regional disparities in the distribution and quality of healthcare facilities and services with a
focusing on achieving accessibility and equity in maternal healthcare delivery.
6. Conclusions and policy recommendations
In this paper, we examined the trends and causes of socioeconomic inequalities in maternal
healthcare utilization in Ghana between 2003 and 2014. The paper uses data from the
GDHSs. Our sample is drawn from women between 15 and 49 years with at least one birth in
the five years preceding the survey. The period under study witnessed substantial reforms
in healthcare delivery in Ghana, with the introduction of a health insurance scheme in 2004
and a free maternal healthcare policy in 2008. First, we estimate the nature and degree of
socioeconomic inequalities in maternal healthcare utilization by applying extension to the
CI. We proceed to apply a recently developed generalized decomposition technique based on
RIF regressions to assess the causes of socioeconomic inequalities in maternal healthcare.
Overall, our results reveal pervasive pro-rich inequalities in maternal healthcare in
Ghana, although the degrees of inequalities have been decreasing since 2003. The maternal
healthcare was concentrated among women in households of high socioeconomic statuses.
Socioeconomic inequalities over the period were highest in delivery at a health facility. We
find that the degree of inequalities declined marginally between 2003 and 2008, whilst the
rate of decline between 2008 and 2014 was steeper. The trends in inequalities in maternal
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46,2
304
Table V.
Decomposition of
socioeconomic
inequalities in delivery
at a health facility
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Wagstaff Index Erreygers Index
Variables 2003 2008 2014 2003 2008 2014
Age at pregnancy −0.0005 (0.0029) 0.0079** (0.0032) 0.0093*** (0.0025) 0.0006 (0.0029) 0.0070** (0.0032) 0.0054** (0.0022)
Years of schooling 0.0199*** (0.0048) 0.0175*** (0.0053) 0.0298*** (0.0039) 0.0257*** (0.0047) 0.0099* (0.0053) 0.0093*** (0.0035)
Head of household −0.0514 (0.0506) −0.0367 (0.0520) −0.0409 (0.0423) −0.0516 (0.0494) −0.0460 (0.0519) −0.0011 (0.0377)
Co-resident women −0.0175 (0.0229) −0.0350 (0.0266) 0.0703*** (0.0223) −0.0121 (0.0224) −0.0409 (0.0265) 0.0685*** (0.0199)
Exposed to media −0.0507 (0.0509) −0.0746 (0.0558) −0.2196*** (0.0467) −0.0525 (0.0497) −0.0993* (0.0558) −0.2660*** (0.0417)
Religious affiliation
Christian −0.1792*** (0.0688) −0.3168*** (0.0646) −0.3868*** (0.0576) −0.1513** (0.0673) −0.3560*** (0.0646) −0.4937*** (0.0514)
Moslem 0.0646 (0.0982) −0.2541*** (0.0745) −0.4344*** (0.0629) 0.0624 (0.0959) −0.3044*** (0.0744) −0.5501*** (0.0561)
Labor market status
Unemployed
Employed – family and others 0.1389** (0.0685) 0.0332 (0.0748) 0.0301 (0.0465) 0.1374** (0.0669) 0.0337 (0.0747) 0.0250 (0.0415)
Employed – self 0.0135 (0.0586) −0.0150 (0.0675) 0.0123 (0.0409) 0.0294 (0.0573) −0.0255 (0.0675) −0.0037 (0.0365)
Region
Western
Central 0.1353 (0.0913) −0.0248 (0.0963) −0.0561 (0.0638) 0.1394 (0.0892) −0.0486 (0.0962) −0.0234 (0.0569)
Greater Accra 0.4077*** (0.0835) 0.2212** (0.0921) 0.1844*** (0.0689) 0.4554*** (0.0816) 0.1783* (0.0920) 0.1112* (0.0614)
Volta 0.1639* (0.0875) 0.0191 (0.0924) −0.1017 (0.0681) 0.1905** (0.0855) −0.0119 (0.0923) −0.0650 (0.0608)
Eastern 0.0141 (0.0838) 0.0347 (0.0913) −0.0420 (0.0658) 0.0341 (0.0819) −0.0097 (0.0912) −0.0058 (0.0587)
Ashanti 0.2294*** (0.0735) 0.1078 (0.0819) 0.0963 (0.0652) 0.2730*** (0.0718) 0.0553 (0.0818) 0.0245 (0.0582)
Brong-Ahafo 0.0938 (0.0768) 0.0379 (0.0894) 0.0235 (0.0624) 0.1309* (0.0750) −0.0085 (0.0893) −0.0286 (0.0557)
Northern 0.3468*** (0.0782) 0.2678*** (0.0913) 0.2690*** (0.0648) 0.3151*** (0.0764) 0.2762*** (0.0912) 0.3697*** (0.0578)
Upper East −0.0584 (0.0866) 0.0877 (0.0957) −0.6414*** (0.0653) −0.0261 (0.0846) 0.0305 (0.0956) −0.6552*** (0.0583)
Upper West 0.2655*** (0.0890) 0.0771 (0.0940) −0.0114 (0.0702) 0.2721*** (0.0870) 0.0340 (0.0939) −0.0637 (0.0626)
Urban 0.3832*** (0.0448) 0.2138*** (0.0472) 0.3743*** (0.0331) 0.4777*** (0.0438) 0.1157** (0.0472) 0.1373*** (0.0296)
Marital status
Never married
Currently married 0.0826 (0.1056) 0.0074 (0.0916) 0.0912 (0.0596) 0.0851 (0.1032) −0.0043 (0.0915) 0.0910* (0.0532)
Previously married −0.0876 (0.1231) −0.0652 (0.1198) 0.0671 (0.0790) −0.0925 (0.1202) −0.0623 (0.1197) 0.0823 (0.0705)
Distance is a problem 0.0308 (0.0382) 0.0884*** (0.0330) 0.0810** (0.0333) 0.0073 (0.0373) 0.1061*** (0.0330) 0.1140*** (0.0297)
First birth −0.0028 (0.0523) −0.0285 (0.0454) −0.0997** (0.0431) −0.0368 (0.0511) −0.0055 (0.0453) −0.0118 (0.0384)
Constant 0.3378** (0.1517) 0.4493*** (0.1536) 0.4267*** (0.1109) 0.2232 (0.1482) 0.6219*** (0.1534) 0.7153*** (0.0989)
Observations 2,619 2,052 4,170 2,619 2,052 4,170
Notes: Standard errors in parentheses. *po0.1; **po0.05; ***po0.01
healthcare are consistent with the objectives of recent health sector reforms implemented in Maternal
Ghana. Particularly, the elimination of user fees for maternal healthcare has contributed to healthcare in
achieving equity and inclusion in utilization. The decomposition analysis of the causes of Ghana
socioeconomic inequalities in maternal healthcare reveals significant contributions
of individual, household and locational characteristics. We find that education, urban
residence and challenges with physical access to healthcare facilities increase the
socioeconomic gap in maternal healthcare utilization. 305
The findings of the paper highlight the need to adopt holistic techniques to measure
socioeconomic-related inequalities in health, given the differences in the assumptions
underlying the various measurement techniques. A number of policy recommendations may
be drawn from the findings of this paper. First, policies aimed at closing the socioeconomic
gaps in maternal healthcare utilization should pay attention to improving women’s
autonomy and decision making power in the household through education. Though this
recommendation has been made in previous studies, the lack of control of resources as well
as limited bargaining power in decision making remains a major impediment to women’s
access to healthcare. In addition, mass media and community-based campaigns on maternal
healthcare should target vulnerable women who are at risk of not utilizing healthcare
services during pregnancy and childbirth. Such campaigns should also target the spouses or
partners of the women, to educate them on the benefits of maternal healthcare to safe
motherhood and neonatal health. Although maternal healthcare services in Ghana are
exempt from the payment of user fees, the unequal distribution in physical access and
quality of maternal healthcare services across regions and localities constitute a significant
barrier to equitable and universal coverage of maternal healthcare. To this end, increasing
investments in the provision as well as improving the quality of healthcare in deprived and
underserved areas must be of critical policy concern.
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Corresponding author
Ama Pokuaa Fenny can be contacted at: amafenny@yahoo.co.uk
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