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Household wealth and maternal Householdwealth and
health: evidence from Ghana maternal
health
Christian Kwaku Osei, Edward Nketiah-Amponsah and
Monica Puoma Lambon-Quayefio 63
Department of Economics, University of Ghana, Legon, Ghana
Received 16 March 2020
Abstract Revised 26 September 2020
Accepted 29 October 2020
Purpose – In 2016, the World Health Organization (WHO) revised upwards the recommended contacts for
antenatal care (ANC) by expectant mothers with a health provider from a minimum of four to eight over the
pregnancy period. Although Ghana is yet to adopt the new recommendation, some women choose to adhere to
the new protocol because of its enormous health benefits to the expectingmother and the unborn child. As part
of ANC, family planning services are also provided to ensure child spacing and birth control. To reduce health
costs, government introduced the free maternal health policy, Community-based Health Planning Services,
Livelihood Empowerment Against Poverty and established the Northern Development Authority to increase
access to healthcare and also create wealth. Given these interventions, the study hypothesizes that household
wealth would not have a significant influence on antenatal visits andmodern contraceptive use. Therefore, this
paper aims to examine whether household wealth would play any significant role on the new minimum
contacts proxied by antenatal visits and also on the use of modern contraceptives as a family planning
counselling tool during ANC visits. The study further examines a possible heterogeneity effect of paternal
characteristic on maternal health service utilization.
Design/methodology/approach – The study used data from the most recent Ghana Demographic and
Health Survey (GDHS, 2014). Both bivariate and multivariate analyses were used to investigate the effects of
household wealth on the number of antenatal visits and modern contraceptive use. The bivariate analysis
employed the use of chi-square test whiles, the multivariate analysis involved estimations using logistic
regressions.
Findings – The findings show that household wealth would play a critical role given the revised WHO
minimum ANC contacts by expectant mothers. Household wealth exerts a positive and significant effect on
ANC for all wealth quintiles for women who attended at least eight ANC visits, but was insignificant for the
poorer and middle quintiles of those who attended four to seven visits. Wealth, however, had an insignificant
relationship with modern contraceptive use. Generally, education, age, birth order, media exposure as well as
geographical locations had a significant influence on bothANCvisits andmodern contraceptive use. The study
further revealed a heterogeneous effect onANC attendance. In particular, despite the relatively poor conditions,
women in rural areas whose partners/husbands have attained a minimum of secondary education are about
twice more likely to attend 4–7 antenatal visits compared to their counterparts whose husbands/partners are
without education. Hence, a holistic health education, which includes husbands/partners in the rural areas as
well as strengthening interventions that improve livelihoods, is crucial.
Originality/value – Health guidelines are constantly reviewed, and government policies must adapt
accordingly. This paper looks at the significant role household wealth still plays on modern contraceptive use
and ANC visits, given the revised WHO minimum ANC contacts and uniquely underscores the influence of
paternal characteristics on the utilization of these maternal health services.
Keywords Household wealth, Antenatal care, Modern contraceptive, Heterogeneity, Ghana
Paper type Research paper
Funding: No funding
Authors’ contributions: Conceptualization of the study and data analysis were done by CKO and
ENA. CKO, ENA and ML contributed to the methodology and the writeup. The final manuscript was
read and approved by all the authors.
Availability of data and materials: The full dataset supporting the research findings are available International Journal of Social
upon request. Economics
Vol. 48 No. 1, 2021
Ethics approval and consent to participate: Not applicable. pp. 63-83
Consent for publication: Not applicable. © Emerald Publishing Limited
0306-8293
Competing interests: The authors declare no competing interests. DOI 10.1108/IJSE-03-2020-0153
IJSE Introduction
48,1 Prior to the adoption of the Sustainable Development Goals (SDGs), Ghanawas one of the few
countries in sub-Saharan Africa (SSA) to have halved extreme poverty (UNDP, 2015).
However, progress towards improvement in maternal mortality has been slow primarily due
to inaccessibility and unaffordability (UNDP, 2015) of health care. The Government of Ghana
has subsequently introduced a number of health access and poverty alleviation programmes
to improve household wealth and maternal health. For instance, the National Health
64 Insurance Scheme (NHIS) law was promulgated in 2003 to abolish user fees and ensure more
equitable access to health care, and this includes the introduction of the free maternal health
care policy between 2003 and 2005. Also, the Community-based Health Planning and Services
(CHPS) model was adopted in 1999 to provide doorstep health care in all regions in Ghana to
reduce the indirect costs such as distance associated with health-care access. The Livelihood
Empowerment Against Poverty (LEAP) programme was introduced in 2012 to act as a social
cash transfer programme that provides cash and health insurance to the extremely poor
households across the country to alleviate poverty and encourage long-term human capital
development. In addition, the establishment of the NorthernDevelopment Authority (NDA) in
2017 (erstwhile Savannah Accelerated Development Authority (SADA) in 2010) was to
further provide avenues to bridge thewealth and health gap between the north and the south.
In general, whereas the NHIS and CHPS were to improve health access, the LEAP and the
NDA were to address issues related to poverty alleviation. In effect, these policies were
enacted to minimize financial barriers and to also stimulate the utilization of maternal and
reproductive health services with the overarching aim of helping to achieve the health-
related SDGs.
Despite this, inadequate access and under-utilization of modern health-care services are
considered major reasons for poor maternal health outcomes in Ghana (Nketiah-Amponsah
et al., 2013). To improve maternal health, one of the key indicators of socioeconomic
development recognized by the Government of Ghana is the use of antenatal care (ANC)
services by expectant women. ANC includes antenatal visits, counselling on nutrition, alcohol
and tobacco abuse, tetanus toxoid injections (TTI), malaria treatment and management, iron
folate supplements, voluntary counselling and testing of Sexually Transmitted Infections
(STI), among others (Goodchild and Zheng, 2018; Okutu, 2013).
In 2016, the World Health Organization (WHO) released a new comprehensive ANC
guideline, which includes an upward review of the minimum number of contacts a pregnant
woman should have with health-care providers throughout her pregnancy from four to eight
(WHO, 2016c; 2016a). Eight or more contacts for ANC can reduce perinatal deaths by up to 8
per 1,000 births when compared to four visits (WHO, 2016a). According to the WHO, a
woman’s “contact” with her health provider should go beyond a simple “visit” to include
provision of care and support during the pregnancy. Pregnant women are recommended to
have their first contact in the first 12 weeks’ gestation, with subsequent contacts taking place
at 20, 26, 30, 34, 36, 38 and 40 weeks’ gestation (WHO, 2016a). The Family Health Division of
the Ghana Health Service (FHD/GHS) has argued that interventions to reduce negative
pregnancy outcomes are most effective if contact with mothers is frequent (GHS, 2016).
Although Ghana is yet to formally adopt the revised ANC schedule (Ghana Health Service,
2016), somewomen choose to behave in linewith the new recommendation to achieve positive
pregnancy outcomes. As a result, this study seeks to provide foundational insights into the
effects of household wealth status on at least eight ANC contacts compared to the previous
four as Ghana prepares to adopt the new model (GHS, 2016). Evidently, literature on the
comparative effect on the variations in ANC attendance for the twomodules in Ghana is non-
existent.
The paper further seeks to examine the effect of household wealth status on
contraceptive usage. According to the GHS (2014), during ANC, family planning services
are also provided to ensure child spacing and birth control. The increased use of modern Household
contraceptives through family planning counselling has been cited as one of the significant wealth and
tools that is embarked upon during ANC attendance in Ghana (Haruna et al., 2019). Indeed, maternal
target 3.7 of SDG 3 is “to ensure universal access to sexual and reproductive health-care
services, including for family planning, information and education, and the integration of health
reproductive health into national strategies and programmes by 2030” (UNDP, 2016, p. 8).
Also, it is argued that one of the major challenges of developing countries is the surge in
population growth, which is unmatched with the corresponding economic growth 65
(IGC, 2015).
Socioeconomic differences, especially the uneven distribution of wealth, are widely known
to have long and adverse effects on maternal health services utilization (Arthur, 2012; Fenny
et al., 2019). Hence, given government interventions to reduce cost and improve access, this
study seeks to fill the research gap to assess if household wealth would still play a significant
role in maternal health utilization using antenatal visits and modern contraceptive use as
proxies, in addition to other covariates. Based on the literature, the study further envisages
that paternal characteristics such as education could also influence women’s reproductive
healthcare service utilization and antenatal care attendance (Adjiwanou et al., 2018; Mboane
and Bhatta, 2015). Hence, this study is distinct, as it estimates a two-way factorial interaction
of paternal education on the chances of maternal use of health services in the rural settings of
Ghana where it is generally observed that inadequate household wealth and access to health
services is a challenge compared to those in urban localities (Afful-Mensah et al., 2014; Kumi-
Kyereme and Amo-Adjei, 2013).
Related literature on household wealth and antenatal care and modern contraceptive use
in Ghana
The index of economic status of households, called the wealth index, is based on household
asset holdings and housing characteristics. The index is constructed from household asset
data, which consist of a television, bicycle or car, as well as characteristics, such as source of
drinking water, sanitation facilities and type of flooring material (GDHS, 2014). Wealth is
used in Demographic and Health Surveys (DHS) primarily because of the absence of
information on household incomes and expenditures. It is also widely used across the world
as a statistic, which is consistent with the expenditure levels of households (Rutstein and
Johnson, 2004). In this regard, the differentials in household wealth has spill-over effects on
health-care service utilization.
Wealth and antenatal care use
The utilization of ANC services in Ghana remains inconsistent (Nketiah-Amponsah et al.,
2013). For instance, despite the free maternal care policy, the average ANC coverage in the
country has been falling consistently from 93.8% in 2012 to 79.4% in 2018 (Ghana Health
Service, 2018; ISSER, 2019). Consequentially, Ghana’s institutional maternal mortality ratio
(iMMR) is still high, averaging around 142 deaths per 100,000 live births between 2012 and
2018 (ISSER, 2019). According to Ediau et al. (2013) and WHO (2016b), maternal morbidity
and mortality continue to remain an issue of global concern due to inadequate ANC.
Across the literature, the utilization of ANC services reduces maternal deaths within and
amongst countries (Arthur, 2012; Dahiru and Oche, 2015; Nketiah-Amponsah et al., 2013).
Using the 2008 GDHS, Nketiah-Amponsah et al. (2013) argue that, among other contributing
factors that influenced the utilization of ANC in Ghana, wealth status, in particular, has a
significant and positive effect on the utilization rate of ANC services. However, their study
failed to recognize the relative influence of paternal education and media exposure on
variations in ANC attendance. Dixon et al. (2014) realized that although more than half of
IJSE Ghanaian women received ANC within their first three months of pregnancy, it was those
48,1 with higher socioeconomic characteristics, including the wealthy and the employed, who
benefited. However, the study is limited as the aggregation of the timing of visits considered
only the first trimester, which could partially address the probability of maternal health
outcome in terms of safe delivery. The study by Arthur (2012) using the 2008 GDHS also
shows that despite the introduction of the freematernal health care policy in 2008, wealth still
had significant effect on ANC usage. Further, Fenny et al. (2019) argue that there are
66 significant pro-rich inequalities in the use of maternal health care in Ghana. Applying more
recent data, this study differs from the existing literature by looking at the comparative
effects of wealth distribution on ANC, as well as possible heterogeneity in ANC attendance in
the face of the revised WHO minimum contacts.
Wealth and modern contraceptive use
Modern contraceptives in this study consist of female and male sterilization, intrauterine
devices (IUDs), injectables, implants, the pill, male and female condoms, lactational
amenorrhoea method (LAM) and emergency contraception (GDHS, 2014). The three most
popular modern methods used by married women in Ghana consists of the injectables,
implants and the pill (GDHS, 2014).
According to the National Population Council (NPC) of Ghana, 17% of the pregnancies in
Ghana are unwanted (NPC, 2018). The use of modern contraceptives as a tool for family
planning is essential to sustainable economic development. According to Kwankye and Cofie
(2015), over the years, the modern methods of contraceptive have not steadily increased.
The existing literature showsmixed results on the effects of household wealth distribution on
contraceptive use. The study by Nketiah-Amponsah et al. (2012) shows that the proportion of
women in Ghana using modern contraceptives increases consistently as household wealth
status improves. However, their study could not tell whether the differences in the use of
modern contraceptives is linked to access to care, contraceptive awareness and myths or
misconceptions. Moreover, Nyarko (2015) found no significant relationship between the
wealth of households and contraceptive use in Ghana. Similarly, Ejembi et al. (2015) found
that poverty status and rural residence had no significant effect on the use of modern
contraceptives in Nigeria. Okezie et al. (2010) also found a negative association between
income and contraceptive use in Nigeria, albeit insignificant.
According to the NPC, Ghana’s current population growth rate of 2.5% is alarming
because it is not matched by the corresponding increase in the economic growth (NPC, 2018).
As a result, the NPC has initiated strong campaigns to encourage the use of modern
contraceptives. It is also championing a national agenda of proposing a maximum of three
children per couple. It is against this backdrop that the study further examines the effect of
household wealth status on contraceptive usage.
Methodology and data source
Theoretical framework
This study is guided by the Anderson–Newman behavioural model, a conceptual framework
widely used to analyze the determinants of health-care utilization. This theory postulates that
an individual’s access to and use of health-care services is considered to be a function of three
characteristics: predisposing, enabling and need factors (Andersen, 1995; Andersen and
Newman, 1973). Using the model, Li et al. (2016) showed that age, gender, marital status,
ethnicity and family size are significant predisposing factors affecting health service
utilization. In addition, Tesfaye et al. (2018) further identified education as a predisposing
factor to ANC use. The study by Tesfaye et al. (2018) and Zhang et al. (2019) also identified Household
wealth index, income, travel cost and husband’s attitude towards ANC as significant wealth and
enabling factors, whereas need factors were generated from functional and health problems maternal
that create the need for health-care services (Kim and Lee, 2016; Neupane et al., 2020).
This study focused on the predisposing and enabling factors because these variables are health
easily captured in the GDHS. The enabling factors include household wealth, distance to
health facility (as a proxy for travel cost) and partner’s educational status on maternal health
service utilization. Moreover, the predisposing factors consists of maternal education, 67
employment, age, location, number of children, media exposure, religion and ethnicity.
Empirical estimation
As the GDHS does not contain information on “ANC contacts”, this paper uses the number of
ANC visits to a health-care provider by an expectant mother as a proxy. To justify “contacts”
beyond the normal “visits”, it is expected that health-care centres, especially under the CHPS
policy, ensure there is immediate provision of adequate care and support by a health provider.
To investigate the effect of household wealth status on the selected maternal health
indicators, the study starts with a health model specified in a cross-sectional form:
yi ¼ αi þ Xiβi þ ui i ¼ 1; 2; . . . n (1)
Where yi is a vector of the dependent variables;
αi is the intercept that represents the household/individual specific effect;
Xi is the vector of exogenous independent variables;
βi is the vector coefficients of the independent variables; and
ui is the error term, which is assumed to be normally distributed.
Given that the main aim of the paper is to assess the effect of household wealth on maternal
healthcare utilization in Ghana, the paper includes a set of other control variables that have
been identified in the literature as determinants of ANC and modern contraceptive use.
Transforming equation (1), we have:
ANCi ¼ αi þ β1Wei þ β2Rgi þ β3Rei þ β4Edi þ β5Emi þ β6Agi þ β7Pei þ β8Mei þ β9Boi
þ β10Dhi þ β11Rli þ β12Ethi þ ui (2)
CUi ¼ αi þ β1Wei þ β2Rgi þ β3Rei þ β4Edi þ β5Emi þ β6Agi þ β7Pei þ β8Mei þ β9Chi
þ β10Dhi þ β11Rli þ β12Ethi þ ui (3)
Unique to this paper, we envisage a heterogeneity effect by analyzing the influence of
paternal characteristics beyondwealth on the health decision of rural womenwhere the use of
health services is relatively low in addition to low household wealth. We, therefore, employed
a two-way factorial to explore the interaction of paternal educational level with the residential
location of the woman. Equations (2) and (3), therefore, become:
ANCi ¼ αi þ β1Wei þ β2Rgi þ β3Rei þ β4Edi þ β5Emi þ β6Agi þ β7Pei þ β8Mei þ β9Boi
þ β10Dhi þ β11Rli þ β12Ethi þ β13Res*Pei þ ui (2a)
IJSE CUi ¼ αi þ β1Wei þ β2Rgi þ β3Rei þ β4Edi þ β5Emi þ β6Agi þ β7Pei þ β8Mei þ β9Chi
48,1 þ β10Dhi þ β11Rli þ β12Ethi þ β13Res*Pei þ ui (3a)
ANC represents the number of ANC visits, which is captured in this study as 4–7 visits
(to capture those who meet the current requirement but below the new recommendation) and
eight visits or more (as proxy for the new WHO minimum contact schedule), whereas CU is
68 modern contraceptive use. We is the household wealth quintile index, Rg represents the ten
administrative regions, Re is the residential locality, Ed is the female educational level and
Em is the occupational status of the female. In addition, Ag is the age group of the woman,
Pe is the partner/husband’s education level,Me capturesmedia exposure,Bo is the birth order
of the child from first to last born,Ch is the number of living children born to themother,Dh is
the distance to health facility (captured as whether it was a problem or not), while Rl and
Eth are the religious affiliation and the ethnicity of the woman, respectively. Re*Pe is the
interactive effect to explore possible heterogeneity in health-care service utilization for ANC
and modern contraceptive use.
Estimation technique
Given the dichotomous outcome for our dependent variables (ANC visits and modern
contraceptive usage), logistic regression is employed. The logistic regression predicts the
probability of an event occurring. For our estimation, we specify the logit function (Li) as
follows: 
 
¼ PiLi ln ¼ β 1 þ β Xi þ ui (1)
1 P 2i
where ui is the stochastic error term.
Pi =1− Pi is simply the odds ratio in favour of whether a mother attended 4–7 or at least
eight ANC visits, as well as whether she uses modern contraceptives. The logit (natural logs)
regression expresses the odds of the unknown binomial variable as linearly dependent on the
explanatory variable, and this linear relationship is derived from the logistic cumulative
density function (CDF). To sXuit our esXtimationXequation, we have:
Pi ¼ ðβ1þ β2i X2iþ β3i X3iþ βe 4i X4i Þ ¼ eβ1 eβ2 X2 . . . e
βi Xi (2)
1 Pi
P
P X2i 5 vector of the effect of household wealth;
X3i 5 vector of other socioeconomic factors (such as mother’s education and
Pemployment status); and
X4i 5 vector of demographic and other co-variates.
The results of the estimates are interpreted using the odds ratio computed as eβi .
e raised to the power βi is the factor by which the odds ratio changes when the ith
independent variable increases by one unit.
Data
This study uses the 2014 Ghana Demographic and Health Survey (GDHS) – the most recent
survey – for its analysis. This is a nationally representative survey of 9,396 women aged
15–49 and 4,388men aged 15–59 from 11,835 interviewed households (GDHS, 2014). The 2014
GDHS is the sixth in a series of population and health surveys conducted in Ghana as part of
the global DHS programme. The earlier rounds of the surveys were conducted in 1988, 1993, Household
1998, 2003 and 2008. The surveywas implemented by the Ghana Statistical Service (GSS), the wealth and
GHS and the National Public Health Reference Laboratory (NPHRL) of the GHS using three maternal
types of questionnaires, namely, household questionnaire, women’s questionnaire and men’s
questionnaire. The survey provides information of women aged 15–49 with the most recent health
live birth in the five years prior to the survey. The 2014 GDHS provides data onmaternal and
child health, family planning methods, fertility, HIV/AIDS, among others at the national level
as well as for the urban and rural areas in each of Ghana’s ten administrative regions to help 69
assess the regional and spatial disparities in health and wealth, especially between the
relatively poor north and the relatively rich south.
Model diagnostics
The goodness of fit of the model was determined by the likelihood ratio (LR) chi-square.
The importance of this statistic was to show whether the model fits significantly, especially
with the inclusion of the interaction effect (IE) of paternal education with maternal residential
location. The LR chi-square of 5.46 (p-value 5 0.0652) for the IE was significant at 10%.
Also, the variance inflation factor (VIF) was conducted to detect multicollinearity between the
explanatory variables. The mean VIF score of 4.46 confirms the non-existence of serious
multicollinearity.
Results and discussion
Descriptive analysis
The GDHS provides information on antenatal visits for the most recent birth in the five years
prior to the survey. The chi-square test is used to determine whether there exists some
association between the dependent and independent variables. From Table 1, the results of
the chi-square test show that there is some level of association, except for age and
employment. It is observed that those in the higher wealth quintile groups attend more ANC
visits than the lower quintile groups. However, whereas 95.9% of the women in households in
the richest quintile attended 4–7 visits, 52.2% of these women attended eight or more ANC
visits. For both categories, utilization of ANC is seen to be higher for women in urban areas
than for those in rural areas, those who have had at least primary education, working,
exposed to the media, as well as those who do not consider distance to the health facility as a
problem.
Table 2 further shows that there is some association between modern contraceptive use
and the various independent variables, except for maternal education. In terms of wealth,
the table indicates that the highest wealth quintile is associated with the least number of
women who use modern contraceptives (16%) with the poorer registering the highest
utilization (21.3%). Less than 10% of women in the Northern region and at least 20% of
women who are working as well as women aged between 20 and 44 years use modern
contraceptives.
Empirical results
Antenatal care visits
Given the newWHO guideline that increases the number of contacts a pregnant woman has
with health providers throughout her pregnancy from four to eight, multivariate estimations
were carried out to compare and assess the effects in the variation of wealth and other
socioeconomic factors on ANC attendance in Ghana. Specifically, to properly understand the
relative impacts, the paper assessed the comparative effects of women who attended four to
sevenANC visits (i.e. those who at least met the current requirement in Ghana butwere below
IJSE
4–7 ANC visits 8 or more ANC visitsþ (ANC 8þ)
48,1 Less than 4 4 to 7 visits Less than 8 8 visits and Pearson’s
Variables visits (%) (%) visits (%) more (%) chi-square
Wealth 56.49***
Poorest 24.29 75.71 86.41 13.59
Poorer 22.67 77.33 76.09 23.91
70 Middle 18.26 81.74 68.53 31.47
Richer 8.16 91.84 57.39 42.61
Richest 4.14 95.86 47.84 52.16
Region 90.13***
Western 17.49 82.51 51.74 48.26
Central 16.18 83.82 62.67 37.33
Greater Accra 14.63 85.37 58.07 41.93
Volta 31.05 68.95 71.88 28.12
Eastern 27.86 72.14 81.77 18.23
Ashanti 8.37 91.63 60.63 39.37
Brong Ahafo 13.16 86.84 70.23 29.77
Northern 30.77 69.23 90.31 9.69
Upper East 8.52 91.48 70.93 29.07
Upper West 10.58 89.42 85.71 14.29
Residence 22.51***
Urban 12.45 87.55 61.63 38.37
Rural 22.64 77.36 77.92 22.08
Education 78.69***
No education 23.78 76.22 83.03 16.97
Primary 22.46 77.54 71.64 28.36
Secondary and 12.78 87.22 62.6 37.4
above
Partner/husband 188.14***
education
No education 24.07 75.93 84.58 15.42
Primary 20.52 79.48 75.05 24.95
Secondary 14.01 85.99 62.62 37.38
Employment status 0.04
Not working 23.66 76.34 73.91 26.09
Working 17.97 82.03 70.59 29.41
Age (years) 12.51
15–19 26.57 73.43 78.14 21.86
20–24 20.42 79.58 77.17 22.83
25–29 17.35 82.65 69.65 30.35
30–34 18.56 81.44 66.42 33.58
35–39 16.91 83.09 69.12 30.88
40–44 19.80 80.20 73.95 26.05
45–49 22.81 77.19 77.55 22.45
Distance to health 19.17***
facility
A big problem 24.49 75.51 78.43 21.57
Not a big problem 16.17 83.83 67.93 32.07
Birth order 24.69***
1 child 16.74 83.26 68.42 31.58
2–4 children 16.81 83.19 67.95 32.05
Table 1. 5 or more children 23.93 76.07 79.25 20.75
Descriptive statistics
for ANC visits (continued )
Household
4–7 ANC visits 8 or more ANC visitsþ (ANC 8þ)
Less than 4 4 to 7 visits Less than 8 8 visits and Pearson’s wealth and
Variables visits (%) (%) visits (%) more (%) chi-square maternal
health
Media exposure 86.32***
Not exposed 34.15 65.85 86.65 13.35
Exposed 16.36 83.64 69.03 30.97
Religion 87.49*** 71
Orthodox Christian 17.98 82.02 69.87 30.13
Pentecost/ 19.18 80.82 66.6 33.4
Charismatic
Islam 12.57 87.43 76.64 23.36
Traditional/ 37.06 62.94 94.7 5.3
Spiritualist
Others 34.27 65.73 84.62 15.38
Ethnicity 71.63***
Akan 15 85 62.81 37.19
Ga/Dangbe 26.81 73.19 70.05 29.95
Ewe 25.47 74.53 67.09 32.91
Mole/Dagbani 12.08 87.92 78.08 21.92
Others 29.76 70.24 80.73 19.27
Note(s): ANC8þ (new WHO minimum requirement)
Source(s): Author’s computation from the 2014 GDHS Table 1.
the newWHO minimum contacts) with those who attended at least eight visits (as proxy for
the newWHO recommendation). The new directive was initiated to increase the survival rate
of both mother and child (WHO, 2016b).
The empirical results in Table 3 show that household wealth status has a positive and
highly significant effect on ANC8þ. Evidently, despite the government policies on reducing
financial barriers, the results show that household wealth will play a crucial role in the
determination of the required contact schedules by expectant mothers as Ghana prepares to
adopt the new guideline.
Unlike ANC8þ, for women who attended 4–7 ANC visits, there were no statistically
significant differences between the poorer and the middle wealth quintile groups with
reference to the poorest (base category). This may be due to the additional costs associated
with increases in the number of ANC attendance. For the richer and richest category, the odds
of attending 4–7 ANC visits during pregnancy is about three and six times higher,
respectively, compared to the poorest category, and this is statistically significant at 1%.
Comparing this to ANC8þ, it is observed that the odds of women attending ANC increases
with each higher level of the wealth quintile. For ANC8þ, the estimates for wealth were
statistically significant at 1% for all quintiles, except the poorer quintile, which is statistically
significant at 5%. For instance, for the rich and the richest wealth quintile, the odds of women
attending ANC8þ are about three and four times higher, respectively, compared to women in
the poorest category. The results, therefore, suggest that government should address
householdwealth differentials, which could be an important catalyst for antenatal attendance
in Ghana. This will help in achieving the SDGs on reducing maternal mortality rates to 25
deaths per 100,000 live births by 2030. In effect, even thoughmaternal health-care services are
rendered free in Ghana in addition to other health and social reform programmes such as the
NHIS and LEAP, the estimates show that household wealth is still crucial in explaining ANC
uptake in Ghana.
IJSE
Modern contraceptive use Pearson’s chi-square
48,1 Individual variables No method (%) Modern method (%)
Wealth 17.26***
Poorest 82.37 17.63
Poorer 78.70 21.3
Middle 79.10 20.9
72 Richer 80.71 19.29
Richest 83.11 16.89
Region 108.04***
Western 77.87 22.13
Central 78.48 21.52
Greater Accra 82.99 17.01
Volta 77.13 22.87
Eastern 81.17 18.83
Ashanti 83.98 16.02
Brong Ahafo 74.90 25.1
Northern 90.27 9.73
Upper East 80.57 19.43
Upper West 78.80 21.2
Residence 11.83***
Urban 82.30 17.7
Rural 79.45 20.55
Education 5.96
No education 82.45 17.55
Primary 79.53 20.47
Secondary and above 80.54 19.46
Employment status 83.66***
Not working 87.36 12.64
Working 78.66 21.34
Age(years) 262.36***
15–19 93.38 6.62
20–24 77.05 22.95
25–29 74.75 25.25
30–34 75.87 24.13
35–39 78.65 21.35
40–44 78.97 21.03
45–49 85.41 14.59
Distance to health facility 9.52***
A big problem 82.83 17.17
Not a big problem 80.02 19.98
Birth order 17.07***
1 child 80.69 19.31
2–4 children 74.82 25.18
5 or more children 76.54 23.46
Husband/partner education 33.05***
No education 81.71 18.29
Primary 72.37 27.63
Secondary and above 75.94 24.06
Religion 34.49***
Orthodox Christian 79.88 20.12
Pentecost/Charismatic 79.46 20.54
Islam 84.55 15.45
Traditional/spiritualist 90.18 9.82
Table 2. Others 80.74 19.26
Descriptive statistics Ethnicity 16.02***
for modern
contraceptive use (continued )
Household
Modern contraceptive use Pearson’s chi-square
Individual variables No method (%) Modern method (%) wealth and
maternal
Akan 79.12 20.88 health
Ga/Dangbe 82.69 17.31
Ewe 79.83 20.17
Mole/Dagbani 82.84 17.16
Others 82.02 17.98 73
Heard of family planning on media 27.99***
No 83.56 16.44
Yes 79.08 20.92
Source(s): Author’s computation from the 2014 GDHS Table 2.
Dixon et al. (2014) argue that while there may be multiple factors affecting the use of ANC in
most developing countries, direct financial burdens are found to be a primary reason. There
is, therefore, the need to go beyond free delivery to providing additional means of health
support. Policies on alleviating poverty in households that fall within the lower wealth
quintiles should be intensified. For instance, to increase household wealth, the government
may exploit the chances of reviewing the LEAP cash transfer benefit from every two months
to monthly and of increasing the minimum beneficiary amount including finding ways of
relaxing the inclusion criteria, especially for the relatively poor households in Northern
Ghana (Cooke et al., 2016). According to Jewell (2009), as no direct cost of ANC exists in the
DHS, availability of maternal services varies over geographic location with the less deprived
areas accessing less medical care services. The results for ANC8þ show that women in the
Northern and Eastern regions were significantly less likely to attend ANC compared to
women in the Greater Accra region where most of the health facilities and personnel are
located. Hence, government’s construction of the CHIPS zones should also be accompanied by
adequate and qualified health professionals to reduce other indirect costs such as distance
associated with health care.
With education, the odds of womenwho have attained aminimum of secondary education
were about twice more likely to attend 4–7 visits compared to their counterparts with no
education, and this was significant at 1%. According to Novignon et al. (2019), women’s
education levels are important contributors to the reduction of the inequality in maternal
health-care utilization. In Ghana, the positive relationship between the frequency of antenatal
visits and educationmay be accounted for by the employment opportunities largely available
for the educated more than for the uneducated. Better educated women are more likely to be
engaged in a relatively high-income generating sector, hence the ability to afford or overcome
other indirect costs associated with the free maternal health care in Ghana. From the results,
for womenwhowereworking, the odds ratio shows that they are 76%and 23%more likely to
attend 4–7 and at least 8 visits, respectively, compared to those not working.
Unique to this paper, we sought out to explore the role of paternal characteristics on the
utilization of ANC services in Ghana. The paper focused on a two-way factorial interaction
between the residential location of the woman and the educational level of the partner.
In 2015, 99% of all maternal deaths occurred in developing countries, and it was higher in
women living in rural areas and among poorer communities (WHO, 2015). Hence, the paper
sought to find out whether there could be other interacting paternal factors that could
increase the chances of ANC uptake, especially in the rural environment where ANC
attendance is comparatively lower. From the results, it is realized that, though residential
location and paternal educational level independently proved to be statistically insignificant
in the determination of ANC attendance, the results of the factorial interaction provide a
IJSE
8 ANC visits or more (ANC
48,1 4–7 ANC visits 8þ)
Variables Logit coefficient Odds ratio Logit coefficient Odds ratio
Wealth (ref: poorest)
Poorer 0.0638 0.938 0.331** 1.393**
(0.160) (0.150) (0.139) (0.194)
74 Middle 0.0918 1.096 0.562*** 1.754***
(0.202) (0.221) (0.154) (0.271)
Richer 1.024*** 2.784*** 1.005*** 2.732***
(0.299) (0.832) (0.174) (0.475)
Richest 1.758*** 5.799*** 1.330*** 3.780***
(0.444) (2.574) (0.204) (0.772)
Regional location (ref: Greater Accra)
Western 0.551 1.734 0.858*** 2.358***
(0.343) (0.595) (0.180) (0.423)
Central 0.694** 2.002** 0.453** 1.573**
(0.338) (0.678) (0.179) (0.281)
Volta 0.243 1.275 0.0224 0.978
(0.322) (0.410) (0.217) (0.212)
Eastern 0.0756 1.079 0.505*** 0.603***
(0.295) (0.318) (0.192) (0.116)
Ashanti 0.923** 2.517** 0.275 1.317
(0.366) (0.922) (0.174) (0.230)
Brong Ahafo 0.930*** 2.535*** 0.399** 1.491**
(0.338) (0.856) (0.182) (0.271)
Northern 0.372 1.451 0.598*** 0.550***
(0.343) (0.497) (0.230) (0.127)
Upper East 1.722*** 5.594*** 0.587*** 1.799***
(0.399) (2.233) (0.217) (0.390)
Upper West 1.405*** 4.077*** 0.354 0.702
(0.387) (1.577) (0.242) (0.170)
Residential location (ref: urban)
Rural 0.305 0.737 0.313 0.731
(0.240) (0.177) (0.193) (0.141)
Husband/partner education (ref: no education)
Primary 0.301 0.740 0.0241 1.024
(0.380) (0.281) (0.249) (0.255)
Secondary and above 0.0717 0.931 0.0597 1.061
(0.276) (0.257) (0.174) (0.185)
(Interaction of residence and husband/partner education level)
Ref: Rural#No education 0 1 0 1
(0) (0) (0) (0)
Rural#Primary education 0.504 1.655 0.254 1.289
(0.418) (0.692) (0.301) (0.389)
Rural#Secondary education and above 0.514* 1.673* 0.338 1.402
(0.295) (0.493) (0.211) (0.296)
Table 3. Education (ref: no education)
Logistic regression Primary 0.0558 1.057 0.195 1.215
estimates of the effect (0.157) (0.166) (0.122) (0.148)
of household wealth on
ANC use in Ghana (continued )
Household
8 ANC visits or more (ANC
4–7 ANC visits 8þ) wealth and
Variables Logit coefficient Odds ratio Logit coefficient Odds ratio maternal
health
Secondary and above 0.459*** 1.583*** 0.151 1.163
(0.170) (0.269) (0.121) (0.141)
Employment (ref: not working) 75
Working 0.564*** 1.758*** 0.208* 1.231*
(0.142) (0.249) (0.110) (0.136)
Age (ref: 15–19 years)
20–24 years 0.332 1.394 0.170 0.844
(0.327) (0.456) (0.276) (0.233)
25–29 years 0.404 1.498 0.0958 1.101
(0.336) (0.503) (0.274) (0.301)
30–34 years 0.540 1.716 0.322 1.380
(0.349) (0.600) (0.282) (0.389)
35–39 years 0.644* 1.905* 0.317 1.373
(0.364) (0.694) (0.290) (0.398)
40–44 years 0.684* 1.983* 0.310 1.363
(0.387) (0.767) (0.310) (0.422)
45–49 years 0.680 1.973 0.543 1.721
(0.428) (0.844) (0.355) (0.611)
Distance to health facility (ref: big problem) 0.0609 1.063 0.0212 0.979
Not a big problem 0.0609 1.063 0.0212 0.979
(0.117) (0.124) (0.0926) (0.0907)
Birth order (ref: 1 child)
2–4 children 0.231 0.793 0.106 0.899
(0.190) (0.151) (0.115) (0.103)
5 or more children 0.559** 0.572** 0.425*** 0.654***
(0.239) (0.136) (0.160) (0.104)
Media (ref: not exposed)
Exposed 0.518*** 1.679*** 0.141 1.151
(0.135) (0.226) (0.152) (0.175)
Religion (ref: Orthodox Christian)
Pentecost/Charismatic 0.231 1.260 0.0873 1.091
(0.141) (0.178) (0.0964) (0.105)
Islam 0.674*** 1.962*** 0.0106 1.011
(0.191) (0.375) (0.134) (0.135)
Traditional/spiritualist 0.185 0.831 1.068*** 0.344***
(0.233) (0.194) (0.407) (0.140)
Others 0.0127 0.987 0.268 0.765
(0.231) (0.228) (0.253) (0.193)
Ethnicity (ref: Akan)
Ga/Dangbe 0.344 0.709 0.0669 0.935
(0.276) (0.196) (0.200) (0.187)
Ewe 0.215 0.807 0.178 1.195
(0.228) (0.184) (0.165) (0.197)
Mole/Dagbani 0.237 1.268 0.0908 1.095
(0.241) (0.305) (0.152) (0.166)
Others 0.298 0.742 0.0476 1.049
(continued ) Table 3.
IJSE
8 ANC visits or more (ANC
48,1 4–7 ANC visits 8þ)
Variables Logit coefficient Odds ratio Logit coefficient Odds ratio
(0.215) (0.160) (0.144) (0.151)
Constant 0.651 0.521 2.092*** 0.123***
76 (0.545) (0.284) (0.395) (0.0488)
Observations 2,765 2,765 3,899 3,899
Note(s): Standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1, ANC8þ (new WHO minimum
requirement)
Table 3. Source(s): Author’s computation from the 2014 GDHS
rather intuitive implication. The results show that the odds of women in the rural areaswhose
partners/husbands have attained a minimum of secondary level of education are more likely
to attend ANC, and this was statistically significant for rural women who attended 4–7 ANC
visits who are about twice more likely to attend ANC than rural women whose
partners/husbands have no education. The implication of this further supports the
important role paternal influences play in the health decision-making of the woman
(Danforth et al., 2009; Rempel and Rempel, 2004; Tohotoa et al., 2009) in the rural settings.
The age of the expectant mother, birth order and media exposure are other determining
factors that also influence ANC attendance in Ghana. For age, the odds of women in the age
category of 35–39 and 40–44 years are twice more likely to attend 4–7 visits relative to those
in the 15–19 age bracket (reference category). The results imply that as the woman grows
older, she is more likely to attend more ANC visits. This is consistent with previous studies,
including Nketiah-Amponsah et al. (2013), and also in line with the Grossman theory, which
postulates that the health stock of an individual deteriorates overtime; hence, more medical
services will be demanded as one grows older to minimize birth complications and health
risks at an older age (Grossman, 1972).
Higher birth orders, especially for women who have had at least five children, are
negatively associated with ANC attendance, and this is statistically significant for both 4–7
visits and ANC8þ. This may be because a woman who has given birth to more children may
rely on her past birth experiences and knowledge on pregnancy complications to reduce
physical contacts with the health personnel before child birth. According to Dahiru and Oche
(2015), it could also be that past unpleasant experiences may make the woman perceive ANC
as unnecessary, or it may simply be lack of knowledge.
In terms of media exposure, compared to women who are unexposed, the odds of
attending 4–7 visits were approximately twicemore likely for womenwho are exposed to any
form of media such as television, radio and newspaper. Hence, strengthening public
education through the various media outlets is essential to ANC attendance. Afful-Mensah
et al. (2014) argue that informal education through the media also influences the use of health-
care services.
Modern contraceptive use
Table 4 shows the estimates for modern contraceptive use in Ghana. It can be observed that
household wealth is statistically insignificant in determining the use of modern
contraceptives among women of reproductive age in Ghana. This result is inconsistent
with the study by Nketiah-Amponsah et al. (2012). However, our results are consistent with
the studies by Okezie et al. (2010) and Nyarko (2015). Though the non-robustness of the
Household
Modern contraceptive use
Variables Logit coefficient Odds ratio wealth and
maternal
Wealth (ref: poorest)
Poorer 0.011 1.011 health
(0.109) (0.110)
Middle 0.017 1.017
(0.123) (0.125) 77
Richer 0.084 1.088
(0.145) (0.157)
Richest 0.019 0.981
(0.172) (0.169)
Regional location (ref: Greater Accra)
Western 0.035 1.035
(0.156) (0.162)
Central 0.102 1.107
(0.157) (0.174)
Volta 0.292 1.339
(0.180) (0.241)
Eastern 0.060 1.062
(0.154) (0.164)
Ashanti 0.201 0.818
(0.158) (0.129)
Brong Ahafo 0.269* 1.308*
(0.158) (0.207)
Northern 0.701*** 0.496***
(0.195) (0.097)
Upper East 0.152 1.165
(0.186) (0.216)
Upper West 0.329* 1.389*
(0.193) (0.268)
Residential location (ref: urban)
Rural 0.003 1.003
(0.157) (0.157)
Education (ref: no education)
Primary 0.302*** 1.353***
(0.099) (0.134)
Secondary and above 0.412*** 1.510***
(0.099) (0.149)
Employment (ref: not working)
Working 0.311*** 1.364***
(0.101) (0.138)
Age (ref: 15–19 years)
20–24 years 0.534** 1.705**
(0.255) (0.434)
25–29 years 0.303 1.354
(0.253) (0.342)
30–34 years 0.072 0.931
(0.258) (0.240)
35–39 years 0.404 0.667 Table 4.
(0.264) (0.176) Effect of wealth on
modern contraceptive
(continued ) use in Ghana
IJSE
Modern contraceptive use
48,1 Variables Logit coefficient Odds ratio
40–44 years 0.578** 0.561**
(0.272) (0.152)
45–49 years 1.018*** 0.361***
(0.280) (0.101)
78
Distance to health facility (ref: big problem)
Not a big problem 0.160** 1.174**
(0.075) (0.088)
Number of living children 0.245*** 1.278***
(0.022) (0.028)
Media exposure to family planning (ref: no)
Yes 0.179** 1.196**
(0.072) (0.086)
Husband/partner education (ref: no education)
Primary 0.305 1.356
(0.208) (0.282)
Secondary and above 0.048 1.049
(0.148) (0.155)
(Residence and husband/partner education level)
Ref: Rural#No education 0.000 1.000
(0.000) (0.000)
Rural#Primary education 0.114 0.892
(0.245) (0.219)
Rural#Secondary education and above 0.156 1.169
(0.173) (0.202)
Religion (ref: Orthodox Christian)
Pentecost/Charismatic 0.101 1.106
(0.079) (0.087)
Islam 0.277** 0.758**
(0.112) (0.085)
Traditional/spiritualist 0.798*** 0.450***
(0.250) (0.113)
Others 0.123 0.884
(0.182) (0.161)
(0.098) (0.089)
Ethnicity (ref: Akan)
Ga/Dangbe 0.121 0.886
(0.163) (0.144)
Ewe 0.106 0.899
(0.138) (0.124)
Mole/Dagbani 0.133 1.142
(0.131) (0.149)
Others 0.247** 1.280**
(0.119) (0.152)
Constant 2.789*** 0.061***
(0.343) (0.021)
Observations 6,137 6,137
Note(s): Standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1
Table 4. Source: Author’s computation from the 2014 GDHS
wealth index variable may be unexpected, it is probable that in a bivariate analysis, wealth Household
proves important in explaining contraceptive use, but in multivariate analysis, the wealth and
robustness may be attenuated by covariates such as a woman’s employment status and maternal
educational attainment. We can, therefore, infer that household wealth may not be enough in
determining the use of modern contraceptives among women of reproductive age in Ghana. health
This could be due to other attributable andmore contextual factors such as distance to health
facility, media exposure to family planning education, geographical location and religious
affiliations (Stephenson et al., 2007). 79
For education, the odds of modern contraceptive use by women who have attained
primary and secondary education are 1.4 and 1.5 times higher, respectively, than their
counterparts who have no education. Female education acts as an effective tool for family
planning and gender equity. It is also linked to women’s empowerment, as it gives women the
capacity to negotiate sex in relationships (Crissman et al., 2012). According to Ameyaw et al.
(2017), women who are educated are more likely to engage in highly paid jobs, which will
boost their income to enhance their ability to purchase modern contraceptives. From the
results, compared to those not working, our estimations show that the odds ofwomenwho are
working are 36.4% more likely to use modern contraceptives. According to Nyarko (2015),
females who are working are more reluctant to have more babies to keep their jobs.
For age, the odds of women in the age category of 20–24 years were about twice more
likely to use modern contraceptives compared to their adolescent counterparts (15–19 years).
This may be because the young adults who are sexually active may be motivated to use
modern contraceptives as a protective mechanism to avoid unintended pregnancies.
By contrast, older women, especially those in the 45–49 years age group, were less likely to
use modern contraceptives. For older women, Ameyaw et al. (2017) argue that societal
expectations and premium placed on women to give birth may propel women in their late
reproductive age to likely forgo contraceptive use to bear children. Furthermore, contextual
factors such as distance to a health facility (as an indicator of access to health care) andmedia
exposure to family planning methods (contraceptive awareness) are also significant
determinants of modern contraceptive use among women of reproductive age in Ghana.
Albeit insignificant, we can also observe that there could be paternal characteristics that
can also influence the use of modern contraceptives by women of reproductive age in Ghana.
It could be possible that women in rural areas, whose partners/husbands have had at least a
minimum of secondary education, would be more likely to use modern contraceptives.
According to Kumi-Kyereme andAmo-Adjei (2013), this may signify themediating roles men
play in women’s health decision.
The number of living children born to awoman aswell as religious affiliation significantly
influence modern contraceptive use in Ghana. If the number of surviving children was to
increase by one, we expect the odds of modern contraceptive use to increase by 27.8%.
This could be that, with the increase in medical knowledge and the desire for educational
attainment, women tend to use more contraceptives when they attain their ideal family size.
Muslims and traditionalists/spiritualists were less likely to use modern contraceptives
compared to orthodox Christians. For Muslims, this may be due to the fact that they have a
more conservative culture (Gyimah et al., 2008). According to Ejembi et al. (2015),
traditionalists are less susceptible to modern developments, as their religion may be acting
in synergy with other areal factors to negatively influence the use and uptake of modern
contraceptives.
Conclusion
This paper sought to investigate the effect of householdwealth and other contributing factors
on selected indicators of maternal health in terms of antenatal visits and modern
contraceptive use using the most recent GDHS (2014). The paper hypothesized that wealth
IJSE is insignificant on the selected indicators given government’s policies of free maternal care,
48,1 LEAP cash transfer, establishment of CHPS zones and the NDA that seek to improve health
access and household wealth. Moreover, the WHO reviewed upwards the ANC minimum
contacts with health-care providers from four to eight by expectant mothers throughout the
pregnancy period to ensure positive pregnancy outcomes. AlthoughGhana is yet to adopt the
new recommendation, some women already choose to adhere to the new protocol because of
its enormous health benefits to the expecting mother and the unborn child. This study,
80 therefore, has presented a comparative assessment of ANC attendance and to serve as a basis
to inform policy as Ghana prepares to adopt the new WHO recommendation. The empirical
estimations from the logistic regression shows that despite the current government policies,
wealth would still play a very crucial role in the attendance of ANC, especially given the new
WHO’s upward review. However, statistical significance was not observed for the
relationship between wealth and modern contraceptive use in Ghana, although the result
was positive. This, therefore, means that, for the country to achieve maternal health targets
under the SDGs, some form of support should be provided to strengthen the current policies
to further improve household wealth. The NHIS should be effectively resourced and there
should be adequate provision of the CHPS health zones, with enough resources to curtail the
problems associated with “distance cost” to health facilities. Additionally, as has been
suggested above, it is advisable for the government to review the LEAP financial aid from
every two months to monthly payments and to broaden the beneficiary base to partially
increase household wealth. Our results, therefore, lend support to the government’s policy of
bridging the wealth gap by making sure institutions such as the NDA are resourced
effectively to deliver on their mandate.
This paper uniquely highlights a heterogeneity effect by analyzing the critical role
paternal education plays in maternal health-care decisions, especially in the rural areas of
Ghana where household wealth is comparatively low. The IE shows that even in rural
settings where uptake of health services is low, a partner with at least secondary education
will increase the woman’s chances of using health services by making 4–7 ANC visits.
The results further demonstrate the weight of a partner’s influence on a woman’s health
decision-making and lend credence to the relative significance of appealing to partners in
rural areas where male autonomy is high.
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Further reading
Barasa, K.S., Wanjoya, A.K. and Waititu, A.G. (2015), “Analysis of determinants of antenatal care
services utilization in Nairobi county using logistic regression model”, American Journal of
Theoretical and Applied Statistics, Vol. 4 No. 5, pp. 322-328, doi: 10.11648/j.ajtas.20150405.12.
Tuncalp, O€ ., Pena-Rosas, J.P., Lawrie, T., Bucagu, M., Oladapo, O.T., Portela, A. and Metin
Gu€lmezoglu, A. (2017), “WHO recommendations on antenatal care for a positive pregnancy
experience—going beyond survival”, BJOG: An International Journal of Obstetrics and
Gynaecology, Vol. 124 No. 6, pp. 860-862, doi: 10.1111/1471-0528.14599.
Corresponding author
Christian Kwaku Osei can be contacted at: ckosei2000@gmail.com
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