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EXPLAINING THE GAP IN ANTENATAL CARE SERVICEUTILIZATION BETWEEN YOUNGER AND OLDERMOTHERS IN GHANA

Sheila A. Boamah, Jonathan Amoyaw and Isaac Luginaah

Journal of Biosocial Science / FirstView Article / July 2015, pp 1 - 16DOI: 10.1017/S0021932015000218, Published online: 10 July 2015

Link to this article: http://journals.cambridge.org/abstract_S0021932015000218

How to cite this article:Sheila A. Boamah, Jonathan Amoyaw and Isaac Luginaah EXPLAINING THE GAP INANTENATAL CARE SERVICE UTILIZATION BETWEEN YOUNGER AND OLDER MOTHERS INGHANA. Journal of Biosocial Science, Available on CJO 2015 doi:10.1017/S0021932015000218

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J. Biosoc. Sci., page 1 of 16 © Cambridge University Press, 2015doi:10.1017/S0021932015000218

EXPLAINING THE GAP IN ANTENATAL CARESERVICE UTILIZATION BETWEEN YOUNGER

AND OLDER MOTHERS IN GHANA

SHEILA A. BOAMAH*1, JONATHAN AMOYAW† AND ISAAC LUGINAAH‡

*Arthur Labatt Family School of Nursing, Health Sciences Addition,University of Western Ontario, London, ON, Canada, †Department of Sociology,

University of Western Ontario, London, ON, Canada and ‡Department of Geography,University of Western Ontario, London, ON, Canada

Summary. Over two-thirds of pregnant women (69%) have at least one antena-tal care (ANC) coverage contact in sub-Saharan Africa. However, to achievethe full life-saving potential that ANC promises for women and babies, anuanced understanding of age-specific gaps in utilization of ANC services isrequired. Using the 2008 Ghana Demographic and Health Survey of 1456 indi-viduals, this study examined the disparities in the use of ANC services betweenyounger and older mothers by applying four counterfactual decomposition tech-niques. The results show that cross-group differences in the explanatory vari-ables largely account for the differentials in ANC service utilization betweenyounger and older mothers. Birth order (parity) accounts for the largest share ofthe contribution to the overall explained gap in ANC utilization between theyounger and older mothers, suggesting that ANC differentials between the twogroups are probably due to biosocial factors. To a lesser extent, wealth status ofthe two groups also contributes to the overall explained gap in ANC service uti-lization. The policy implications of these findings are that in order to bridge theANC service utilization gap between the two groups, policymakers must sys-tematically address gaps in cross-group differences in the explanatory variablesin order to increase the utilization of ANC to attain the minimum recommen-dation of four visits as per World Health Organization guidelines.

Introduction

Maternal and newborn mortality is problematic in Ghana and other sub-Saharan Africacountries (Magadi et al., 2007). In an attempt to achieve Millennium Development Goals4 and 5, which are aimed at reducing maternal and child mortality by three-quarters by2015, the government of Ghana has implemented relevant initiatives to improve access to,and utilization of, reproductive/maternal health services (Doku et al., 2012). The National

1Corresponding author. Email: sboamah@uwo.ca

1

Health Insurance Scheme (NHIS), introduced in 2003, is a notable example of such aninitiative. The NHIS was designed to provide free access to maternal health care servicespurposely to encourage timely and regular antenatal care, delivery at medical facilitiesand early postnatal care (Witter & Garshong, 2009; Dixon et al., 2011; Dzakpasu et al.,2012). Despite this and other initiatives, child survival remains an urgent concern sincethere is an estimated rate of 102 infant deaths per 1000 live births in Ghana (UNICEF,2013). This figure is about nine times higher than that in the WHO European region(11 per 1000 live births).

Antenatal care (ANC), in particular, has been recognized as an important part of thematernal care continuum (Dixon et al., 2014). The importance of ANC for maternalhealth lies in its capacity to detect health complications in expectant mothers and theopportunity for acceptable remedial interventions (Carroli et al., 2001; WHO, 2003).Generally, the number of ANC visits depends on the specific medical needs of women,but the World Health Organization recommends at least four ANC visits duringpregnancy, with the first visit made by the end of the first trimester (Owoo & Lambon-Quayefio, 2013; Dixon et al., 2014). Early timing and regular ANC provides time andspace for health education on diet and other safety practices during and after pregnancy(WHO, 2003). Although about 90% of Ghanaian women report seeing a healthprofessional at least once during pregnancy, most of them make their visit after the firsttrimester (Owoo & Lambon-Quayefio, 2013). It is, however, apparent from the literaturethat the number and timing of ANC visits varies by education, parity and maternal age.

Research suggests that extremes of maternal age and parity (i.e. young and older ages,or higher parity) are associated with higher maternal morbidity and mortality and thusmay be related to use of biomedical care. These factors may also reflect experience withpregnancy-related matters and, as such, older and multiparous women may be lessinclined to seek care. The relationship between maternal age and maternal health careservice utilization has been widely explored. Although the findings are inconsistent acrossstudies, it is frequently suggested that age is positively associated with the utilization ofmaternal health care services (Doku et al., 2012; Owoo & Lambon-Quayefio, 2013). Thesestudies generally conclude that older women may have accumulated knowledge of healthcare services, and hence may demonstrate a higher rate of utility. It is important to note,however, that most of the studies treat age as continuous, and hence fail to capture howmaternal health care utilization varies across different age groups. The work of Magadiet al. (2007) and Doku et al. (2012) are some of the few exceptions. Magadi et al. (2007)compared the use of maternal health care services between teenage mothers, youngmothers (20–34 years) and older mothers (35 years and above) from 21 African countriesand found that teenage mothers generally started ANC late and made fewer antenatalvisits relative to older mothers, especially those aged 20–30 years. Doku et al. (2012) alsofound that Ghanaian women aged 25–34 years attended early ANC and were more likelyto have trained delivery attendants during birth compared with those aged 15–24 years,but no significant difference was found between older mothers (35–49) and those aged15–24 years. These exceptional studies show that the relationship between maternal healthcare service utilization and maternal age is rather complex and there are significantvariations in use by maternal age. According to the 2008 DHS, older mothers aged 35–49years, on average, make fewer ANC visits, and attend their first ANC check-up later thanyounger women aged 20–34 years.

2 S. Boamah et al.

Clearly, there is a need to explore the relevant factors that account for this disparity.For instance, different mechanisms underlie the association of younger and oldermaternal age with adverse perinatal outcomes, which are likely to be explained bysocioeconomic position. Disaggregating this observed gap between the younger womenand older women is important in order to develop age-specific initiatives to encouragematernal health care service utilization. To the best of the authors’ knowledge, no studyhas previously explored this, and this research lacuna is an opportunity to explore theage disparities in the timing and number of antenatal visits between younger and oldermothers in Ghana.

Based on the relevant literature, the time and number of ANC visits is more or lessinfluenced by some compositional variables, such as education, employment and wealth.Generally, education and employment status have been found to be positively associatedwith access to, and utilization of, maternal health care services (Arthur, 2012). A numberof studies have also found that women from poor households are less likely to utilizematernal health care services (WHO, 2007; Babalola & Fatusi, 2009; Doku et al., 2012;Atunah-Jay et al., 2013; Khanal et al., 2014). As expected, rural–urban disparity inutilization of maternal health care is also evident in the literature. Studies suggest thatrural residents are less likely to utilize these services because there are fewer healthfacilities in these areas (Peters et al., 2008).

Besides these compositional factors, important contextual considerations such asregion, accessibility of and conditions within health care facilities, fertility behaviour andspecific national policy initiatives have been found to influence the timing and number ofANC visits. For instance, Dixon et al.’s (2014) study shows that women who experiencebarriers to accessing health facilities make fewer ANC visits. Likewise, Koblinsky et al.(2006) noted that women’s reluctance to use maternal care services can be attributed topoor service delivery at some medical facilities. Magadi et al. (2007) have alsodocumented that women with more children are more likely to attend ANC check-upslater than those with fewer children. What is more, Dixon et al. (2014) acknowledged thepositive effect of Ghana’s NHIS on maternal health outcomes. In particular, womenenrolled under the insurance scheme made more ANC visits (Arthur, 2012; Dixon et al.,2014). Interestingly, however, Dixon et al. (2014) found that the insurance scheme didnot have a significant effect on the timing of ANC visits. Against this backdrop, thesefactors are considered theoretically relevant in explaining the disparities in the numberand timing of first ANC check-up between younger and older women in Ghana.

Methods

Data

Data for this study came from the 2008 Ghana Demographic and Health Survey(GDHS), a nationally representative data set collected by the Ghana Statistical Service(GSS) with technical input from ICF Macro International as part of the GlobalDemographic and Health Surveys (GDHS) programme. The GDHS employed a two-stage, stratified sample frame, where systematic sampling with probability proportionalto size was applied. The survey covered all ten regions of the country and was designedto collect, analyse and disseminate information on the compositional characteristics of

Antenatal care service utilization in Ghana 3

the population, as well as issues related to maternal health, media access and fertilitybehaviour, which makes it suitable for this study. About 5096 eligible women aged15–49 years from 11,778 households were identified during the survey, but only 4916were interviewed. This study, however, restricted the data to only those who had theirlast birth three years before the survey was administered in order to minimizemisreporting due to recall bias.

Measures

The outcome variables for the study were ‘number of ANC visits during pregnancy’and ‘timing of first ANC check’. With regards to the number of ANC visits, respondentswere asked: ‘How many times did you receive ANC during this pregnancy?’ Those whoreported at least one ANC visit were asked to indicate the timing of their first visit.As already indicated, previous studies have established the link between ANC and somecompositional factors, such as health insurance coverage, fertility behaviour and age. Age,a binary variable coded as ‘0’ for young women aged 20–34 years and ‘1’ for oldermothers aged 35–39 years, was used to stratify the data. It was hypothesized thatcharacteristics that distinguish childbearing teenagers from others – such as familialdisadvantage, parental absence, low aspirations, abuse and certain partner characteristics– will also distinguish young childbearing women who had intended to become pregnantfrom those who had not. This study was concerned with women at least 20 years old andtheir decision to utilize ANC services, and for this reason teenagers were excluded.

With regards to fertility behaviour, respondents were asked the birth order of their lastbirth. This was coded as first child= 0, second or third child= 1, fourth or fifth child= 2,and sixth child or more= 3. Respondents were also asked the type of health insurance theyhad. Respondents who answered they had health insurance and then specified the type as‘National/District Health Insurance’ were considered enrolled in the NHIS. The mediavariable included in this study was ‘listening to radio’ (coded as: yes= 1 and no= 0).

The compositional (biosocial and socio-cultural) variables included in the study were asfollows: employment status of respondents (not employed = 0; employed= 1), educationalbackground of respondents (no education= 0; primary education= 1; secondary/highereducation= 2) and wealth status, a composite index based on the household’s ownership ofa number of consumer items including television and a car, flooring material, drinkingwater, toilet facilities, etc. (poorest = 0; poorer= 1; middle= 2; richer= 3; richest= 4),marital status (not currently married= 0; currently married= 1) and the religiousdenomination of respondents (Christians= 0; Muslims= 1; Traditional= 2;No religion= 3). Contextual variables included place of residence (urban= 0; rural = 1)and region of origin (Greater Accra= 0; Central= 1; Western= 2; Volta= 3; Eastern= 4;Ashanti= 5; Brong Ahafo= 6; Northern= 7; Upper East= 8; Upper West= 9).

Statistical analysis

Inferential and multivariate techniques were applied to examine associations betweenmaternal health outcomes (frequency and timing of ANC visits) and theoretically relevantsocio-cultural and biosocial variables using STATA 13 SE software (StataCorp, 2013).Univariate analysis of the predictors on older and younger mothers was operationalizedvia Pearson’s chi-squared statistics.

4 S. Boamah et al.

The counterfactual decomposition method used in this study, known as the Blinder–Oaxaca decomposition (Oaxaca 1973; Jann, 2008), explains the gap in the means ofmaternal health outcomes (frequency and timing of ANC visits) between two groups (inthis instance, between the older and younger mothers) in Ghana. O’Donnell et al. (2008)gives a comprehensive account on the technique. The gap is decomposed into that partthat is due to group differences in the magnitudes of the determinants of maternal healthoutcome scores, on the one hand, and group differences in the effects of thesedeterminants, on the other hand. For example, younger mothers in rural Ghana may beless healthy not only because they have less access to health facilities but also becausethey are less knowledgeable about how to obtain the maximum health benefits fromhealth posts in the respective districts.

Although two maternal health outcome variables (frequency and timing of ANCvisits) were analysed in this study, only frequency of antenatal visits is used here toillustration the technique. Let frequency of ANC visits be yi, the outcome variable ofinterest. There are two groups in this study, referred to as the ‘older mothers’ and‘younger mothers’. Frequency of ANC visits is assumed to be explained by a vector ofdeterminants, x, according to a regression model:

yi ¼βyoungermothersxi + εyoungermothers

i if youngermother

βoldermothersxi + εoldermothersi if oldermother

(

(1)

where the vectors of β parameters include intercepts. Older mothers are assumed to havea more advantageous regression line (lower scores on maternal health) than the youngermothers. Also, older mothers are assumed to have a higher mean of x. It is assumed thatexogeneity, and thus the conditional expectations of the error terms in equation (1), arezero. The gap in mean maternal health scores between the younger (yyounger) and oldermothers (yolder), is given by:

yyounger"yolder ¼ ðβyounger xyoungerÞ " ðβolder xolderÞ (2)

where xyounger and xolder are vectors of the independent variables evaluated at the meansfor the younger and older mothers, respectively. For the set of independent variables, theformulations are expressed as follows:

yyounger"yolder ¼ðβyounger0 "βolder0 Þ + ðβyounger1 "βolder1 Þ

+ ðβyounger2 "βolder2 Þ % % % + % % % ðβyoungern "βoldern Þ

¼ G0 +G1 +G2 % % % + % % %Gn ð3Þ

so that the gap in maternal health outcomes between the younger and older mothers canbe thought of as being due in part to (i) differences in the intercepts (G0), (ii) differencesin x1 and β1 (G1), and (iii) differences in x2 and β2 (G2). For example, G1 might measurethe part of the gap in mean score of frequency of ANC visits (y) due to differences ineducational attainment (x1) and the effects of educational attainment (β1), and G2 might

Antenatal care service utilization in Ghana 5

measure the part of the gap due to the gap in wealth of respondents (x2) and differencesin the effects of wealth of respondents (β2). Estimates of the difference in the gap in meanantenatal visits can be obtained by substituting sample means of the x values andestimates of the parameters β values into equation (2).

How much of the overall gap or the gap specific to any one of the x values(e.g. G1 or G2) is attributable to (i) differences in the x values (sometimes called theexplained component) rather than (ii) differences in the β values (sometimes calledthe unexplained component) was estimated. In doing so, two options were considered.In the first, the differences in the x values were weighted by the coefficients of theyounger mother group and the differences in the coefficients were weighted by the xvalues of the older mother group, whereas in the second the differences in the x valueswere weighted by the coefficients of the older mother group and the differences in thecoefficients were weighted by the x values of the younger mother group. Either way,there was a way of partitioning the gap in outcomes between the younger and oldermothers into a part attributable to the fact that the younger mothers have worse x valuesthan the older mothers, and a part attributable to the fact that ex hypothesi they haveworse β values than the older mothers. These formulations are expressed as follows:

yyounger"yolder¼Δxβpoor +Δβxpoor +ΔxΔβ ¼ E +C +CE (4)

From equation (4), the gap in mean frequency of ANC visits can be thought of asderiving from a gap in x values or endowments (E), a gap in β values or coefficients (C)and a gap arising from the interaction of endowments and coefficients (CE). Theendowments term represents the contribution of differences in explanatory variablesacross groups, and the coefficients term is the part that is due to group differences in theestimated coefficients. So, in effect, equation (5) places the interaction in the unexplainedpart, whereas equation (6) places it in the explained part:

yyounger"yolder ¼ Δxβyounger +Δβxolder ¼ E + ðCE +CÞ (5)

yyounger"yolder ¼ Δxβolder +Δβxyounger ¼ ðE +CEÞ +C (6)

Oaxaca’s decomposition is also written as a unique case of equation:

yyounger"yolder ¼ Δx ½Dβyounger + ðI"DÞβolder' +Δβ½xyoungerðI"DÞ + xolderD' (7)

where I is the identity matrix and D a matrix of weights. In the simple case, where x is ascalar rather than a vector, I is equal to one and D is a weight. In this case, D = 0 inequation (5), and D = 1 in equation (6).

In addition to the above formulations, three more formulations were considered.Cotton (1988) suggested weighting the differences in the x values by the mean of thecoefficient vectors, which yields:

diag ðDÞ ¼ 0:5 ðCottonÞ (8)

6 S. Boamah et al.

where diag (D) is the diagonal of D. Reimers (1983) also suggested weighting thecoefficient vectors by the proportions in the two groups, so that if fNP is the samplefraction in the older mother group, the following is obtained:

diag ðDÞ ¼ fNPðReimersÞ (9)

Finally, the decomposition proposed by Neumark (1988) was included, which makes useof the coefficients obtained from the pooled data regression, βP:

yolder"yolder ¼ ΔxβP + ½xyoungerðβyounger"βpÞ + xolderðβp"βolderÞ' ðNeumarkÞ (10)

Results

This study explored the gap in the frequency and timing of ANC visits between youngerand older mothers in Ghana. The Blinder–Oaxaca decomposition technique was used toanalyse the gap in ANC utilization between younger and older mothers into a part thatis ‘explained’ by group differences in the magnitudes of the determinants, and a residualpart that cannot be accounted for by such differences in determinants. This technique isexceptionally useful to identify and quantify the separate contributions of groupdifferences in measurable characteristics, such as education, marital status, geographicallocation and age (Fairlie, 2006; Jann, 2008), to maternal health care utilization.In addition, three other counterfactual decomposition techniques (Cotton, Reimer andNeumark) were used to assess the disparities in ANC visits between the two groups.Broadly, these findings indicate that differences in the mean values of the determinant(explained component) account for the vast majority of the difference in frequency andtiming of ANC visits between younger and older mothers in Ghana.

Background characteristics of respondents

To find whether there is some association between age of mothers and otherdemographic factors chi-squared and Cramer’s V statistics were performed. The nullhypothesis (H0) is that there is no relationship. To reject this, a Pr< 0.05 (at 95%confidence) must be obtained. The results show that there are notable associationsbetween mother’s age and most of the demographic factors, as shown in Table 1.Mother’s age was associated with each of the following: wealth status, region ofresidence, marital status, religion, employment status and educational attainment. In allthese cases, the strength of the association was weak, as evidenced by Cramer’s V< 0.3.The strength of association between birth order (parity) and mother’s age was quitestrong (Cramer’s V≈0.6). Interestingly, health insurance coverage, listening to radio(access to information) and rural–urban residence were independent of mother’s age.

Decomposition results

Table 2 shows the mean values of maternal health care utilization (frequency ofantenatal visits and timing of antenatal visits) for younger and older mothers, and thedifference between them. It also shows the contribution attributable to the gaps inendowments (E) and coefficients (C) and their interaction (CE). From the results, the

Antenatal care service utilization in Ghana 7

Table 1. Distribution of explanatory variables of sample women by age (n = 1456),GDHS 2008

Variable Younger mothers (%)Older

mothers (%) Pearson’s χ² (df)

Health insurance coverage χ² (1)= 0.0073; Pr= 0.932No 73.95 26.05 Cramer’s V=−0.0022Yes 74.15 25.85

Wealth index χ² (4)= 13.1847; Pr= 0.010Poorest 67.97 32.03 Cramer’s V=−0.0952Poorer 74.92 25.08Middle 76.71 23.29Richer 76.38 23.62Richest 79.58 20.42

Birth order χ² (3)= 499.5974; Pr= 0.0001 97.77 2.23 Cramer’s V= 0.58582–3 90.39 9.614–5 62.86 37.14≥6 24.18 75.82

Listen to radio χ² (1)= 0.5409; Pr= 0.462No 72.35 27.65 Cramer’s V=−0.0193Yes 74.46 25.54

Region χ² (9)= 19.0816; Pr= 0.025Greater Accra 79.56 20.44 Cramer’s V= 0.1145Western 72.39 27.61Central 67.59 32.41Volta 75.65 24.35Eastern 77.34 22.66Ashanti 78.48 21.52Brong-Ahafo 81.4 18.6Northern 66.97 33.03Upper East 73.15 26.85Upper West 69.23 30.77

Marital status χ² (1) = 7.3395; Pr = 0.007Unmarried 84.82 15.18 Cramer’s V = 0.0710Married 73.14 26.86

Religion χ² (3) = 12.7735; Pr = 0.005Christian 76.85 23.15 Cramer’s V = 0.0937Muslim 68.75 31.25Traditional 66.33 33.67No religion 68.12 31.88

Employment status χ² (1) = 18.9515; Pr = 0.000Unemployed 86.91 13.09 Cramer’s V = 0.1141Employed 72.09 27.91

Educational attainment χ² (2) = 33.4612; Pr = 0.000No education 65.46 34.54 Cramer’s V = 0.1516Primary 76.11 23.89Secondary or higher 80.44 19.56

Residential locality χ² (1) = 0.5985; Pr = 0.439Rural 75.24 24.76 Cramer’s V = 0.0203Urban 73.38 26.62

8 S. Boamah et al.

gap in endowments accounts for the bulk of the gap in the two maternal health careoutcomes (frequency of antenatal visits and timing of antenatal visits).

The fractions of mean frequency of antenatal visits and timing of antenatal visitsbased on each of the four decompositions (Oaxaca (0, 1); Cotton (0.5), Reimer (0.749)and Neumark) are shown in Table 3. Irrespective of the decomposition used, it isapparently the difference in the mean values of the endowments (explained component)that accounts for the vast majority of the difference in frequency of antenatal and timingof antenatal visits between younger and older mothers in Ghana.

Table 4 demonstrates how far gaps in individual x values contribute to the overallexplained gap. For example, focusing on the penultimate column corresponding toReimer’s decomposition, it is recognized that the gaps in the three demographic variables(i.e. employment status, marital status and region) actually favour the younger mothers,whereas the gaps in all other remaining variables do not favour younger mothers.

Table 2. Summary of decomposition resultsa for maternal health care utilization ofsample women, GDHA 2008

Frequency of visits Timing of visits

Mean prediction high (H) 6.009 3.624Mean prediction low (L) 5.784 3.380Raw differentials (R) {H−L}: 0.225 0.244due to endowments (E) 0.664 0.212due to coefficients (C) −0.297 −0.091due to interaction (CE) −0.143 0.122

aHigh: age = 0.0000; low: age = 1.0000.

Table 3. Proportion of explained and unexplained components for maternal health careutilization of sample women, GDHS 2008

D

0 1 0.5 0.749 Referencea

Frequency of visitsUnexplained (U) {C+ (1−D)CE} −0.439 −0.297 −0.368 −0.334 −0.224Explained (V) {E+D*CE} 0.664 0.521 0.593 0.559 0.449% Unexplained {U/R} −195.6 −132.1 −163.9 −148.9 −99.9% Explained {V/R} 295.6 232.1 263.9 248.9 199.9

Timing of visitsUnexplained (U) {C+ (1−D)CE} 0.032 −0.091 −0.029 −0.000 0.003Explained (V) {E+D*CE} 0.212 0.335 0.273 0.244 0.241% Unexplained {U/R} 13.0 −37.2 −12.1 −0.0 1.1% Explained {V/R} 87.0 137.2 112.1 100.0 98.9

D in the 4th column = relative frequency of high group.aReference = pooled model over both categories.For frequency and timing of visits the total for the explained and unexplained components in eachcolumn adds up to 100%.

Antenatal care service utilization in Ghana 9

Table 5 indicates the coefficient estimates, means and predictions for each x for eachgroup. For frequency of antenatal visits, the ‘high group’ is the younger and the ‘lowgroup’ is the older mothers. For timing of antenatal visits, the ‘high group’ is the olderand the ‘low group’ is the younger mothers. For the first Oaxaca decomposition(equation (5)), columns 2 and 3 of Table 5 allow the identification of how the gap in eachof the β values contributes to the overall unexplained gap.

The results of the decomposition analysis are discussed in two ways: first, thedecomposition results for variables related to frequency of ANC utilization, then resultsof variables pertaining to timing of ANC visits.

In the Oaxaca decomposition, differences in the effects of the determinants(coefficients or unexplained component) play a small part in explaining frequency andtiming of ANC visits between younger and older mothers, as compared with the gap inendowment. Based on Oaxaca’s decomposition D = 0, differences in the mean values of

Table 4. Decomposition results for maternal health care utilization variables for samplewomen, GDHS 2008

Explained D

Variable E (D = 0) C CE 1 0.5 0.749 Referencea

Frequency of visitsWealth 0.063 0.699 0.083 0.146 0.105 0.124 0.127Insurance 0.000 0.323 0.005 0.005 0.003 0.004 0.004Education 0.018 −0.003 −0.001 0.017 0.017 0.017 0.019Employment status −0.114 −0.838 −0.838 0.082 −0.032 −0.053 −0.043Married −0.023 −0.749 0.030 0.007 −0.008 −0.001 0.001Religion −0.013 −0.098 0.026 0.040 0.027 0.033 0.031Residence 0.028 1.320 −0.019 0.009 0.018 0.014 0.014Region −0.022 −0.488 0.027 0.006 −0.008 −0.002 −0.002Radio 0.015 −0.393 −0.011 0.004 0.009 0.007 0.007Birth order 0.677 0.677 −0.363 0.314 0.495 0.410 0.284Constant 0.000 −0.749 0.000 0.000 0.000 0.000 0.000Total 0.664 −0.297 −0.143 0.521 0.593 0.559 0.449

Timing of visitsWealth 0.084 0.864 − 0.088 − 0.004 0.040 0.061 0.066Insurance 0.000 0.157 − 0.001 − 0.001 0.000 0.000 0.000Education −0.011 −0.134 0.035 0.024 0.006 −0.002 −0.004Employment status −0.033 −0.016 −0.016 −0.050 −0.050 −0.041 −0.034Married −0.011 0.149 0.007 −0.004 −0.007 −0.009 −0.011Religion 0.011 −0.002 −0.001 0.011 0.011 0.011 0.011Residence −0.011 1.307 0.018 0.006 −0.003 −0.007 −0.007Region − 0.001 −0.050 −0.003 −0.004 −0.002 −0.002 −0.002Radio 0.001 −0.378 −0.008 0.009 0.005 0.003 0.003Birth order 0.183 0.140 −0.124 0.345 0.264 0.225 0.291Constant 0.000 −1.989 0.000 0.000 0.000 0.000 0.000Total 0.212 −0.091 0.122 0.335 0.273 0.244 0.241

aReference = pooled model over both categories.

10 S. Boamah et al.

x values (gaps in endowment) account for all of the differentials in the frequency of ANCvisits between younger and older mothers. Similarly, for the Cotton decomposition,differences in the mean values of x values (gaps in endowments) explain the entiredisparities in the frequency of ANC visits between younger and older mothers. In termsof the Reimer and Neumark decompositions, the differentials in frequency of ANC visitsbetween younger and older mothers in Ghana are fully explained by the mean values ofx (gaps in endowments).

Using the Cotton decomposition, birth order, or parity, alone account for 83% of theoverall explained gap in frequency of ANC visits between the younger and oldermothers. This is followed by wealth status of the household (16%), while education,religion, residence and exposure to media (radio) jointly account for less than 1% of theoverall explained gap. Similar results were obtained using the Reimer decomposition,where birth order accounts for 73% of the overall explained gap in frequency of ANCvisits between younger and older mothers. This was followed by wealth status (22%),

Table 5. Coefficients, means and predictions for maternal health care utilization forsample women, GDHS 2008

High model Low modelPooled

Variable Coefficient Mean Predicted Coefficient Mean Predicted coefficient

Frequency of visitsWealth 0.504 2.722 1.372 0.217 2.432 0.527 0.439Insurance 0.756 0.447 0.338 0.021 0.440 0.009 0.582Education 0.054 1.138 0.062 0.058 0.832 0.048 0.062Employment 0.346 0.346 0.844 1.241 0.936 1.162 0.464Married −0.193 0.916 −0.177 0.592 0.955 0.565 −0.027Religion −0.247 0.436 −0.107 −0.083 0.597 −0.050 −0.192Residence −0.378 1.643 −0.621 −1.170 1.667 −1.950 −0.596Region −0.020 4.628 −0.095 0.079 4.901 0.388 0.008Radio 0.184 0.808 0.149 0.683 0.787 0.537 0.338Birth order −0.253 1.071 −0.271 −0.546 2.312 −1.261 −0.229Constant 5.056 1.000 5.056 5.805 1.000 5.805 5.163Total 6.009 5.784

Timing of visitsWealth 0.015 2.434 0.037 −0.304 2.711 −0.823 −0.237Insurance 0.231 0.442 0.102 −0.123 0.444 −0.055 −0.034Education −0.081 0.828 −0.067 0.038 1.124 0.043 0.012Employment −0.564 0.934 −0.527 −0.379 0.846 −0.321 −0.388Married −0.083 0.955 −0.246 0.912 0.912 −0.224 −0.259Religion 0.068 0.601 0.071 0.445 0.445 0.032 0.069Residence 0.285 1.664 −0.511 1.642 1.642 −0.840 −0.322Region −0.014 4.918 −0.004 −0.004 4.667 −0.018 −0.009Radio −0.505 0.786 −0.034 −0.034 0.803 −0.028 −0.161Birth order 0.279 2.307 0.148 0.148 1.070 0.158 0.177Constant 3.468 1.000 3.468 5.457 1.000 5.457 5.072Total 3.624 3.380

Antenatal care service utilization in Ghana 11

with education, religion, residence and access to media (radio) together accounting forless than 5% of the overall explained gap. These trends continue when Neumark’sdecomposition is used. Once again, birth order accounts for the largest proportion (63%)of the overall explained gap, followed by wealth status (28%). Education, religion,residence and media (radio) jointly account for less than 10% of the overall explainedgap. Not surprisingly, the magnitudes of contribution of each of variable remain thesame across decomposition techniques (Cotton, Reimer and Neumark).

The pattern of the estimates from the Oaxaca decomposition results for variablesrelated to timing of ANC visits does not differ substantially from those for frequency ofANC visits among younger and older mothers. Based on Oaxaca’s decompositionD = 0, differences in the mean values of x (gaps in endowment) account for about 87%of the differentials in timing of ANC visits between younger and older mothers. UsingOaxaca’s decomposition D = 1, differences in gaps in endowment explain all of thedifferentials in timing of ANC visits. Based on Cotton’s decomposition, differences ingaps in endowment explain the entire differentials in timing of ANC visits. About 100%and 98% of the differentials in timing of ANC visits between younger and older mothersin Ghana are explained by the mean values of x (gaps in endowments) using the Reimerand Neumark decompositions, respectively.

Across decomposition techniques, birth order, once again, accounts for the largestshare of contribution to the overall explained gap in timing of ANC visits betweenyounger and older mothers in Ghana. Using Cotton’s decomposition, birth orderaccounts for a disproportionately large share of the gap in timing of ANC visits betweenthe two groups. Wealth status, educational attainment, religion, residence and access tomedia (radio) jointly account for less than 3% of the overall explained gap. Similarresults were obtained using the Reimer decomposition, where birth order alone accountsfor about 92% of the overall explained gap in timing of ANC visits between younger andolder mothers. These trends continue when Neumark’s decomposition is used. Birthorder accounts for about 90% of the overall explained gap in timing of ANC visitsbetween younger and older mothers. In the Reimer and Neumark decompositions,education, religion, residence and access to media (radio) still cumulatively account forless than 10% of the overall explained gap. This implies that, although the magnitudes ofcontribution of each variable differ across decomposition techniques, the trends andorder of contribution remain almost the same.

Discussion

This study reveals interesting findings that are consistent with previous studies. Althoughthe relationship between parity and ANC has been inconclusive, many studies have foundthat birth order, or parity, is significant in explaining disparities in ANC utilization indeveloping countries. Of the demographic factors, women’s parity displays substantialimpact on the overall explained gap in both frequency and timing of ANC visits betweenyounger and older mothers in Ghana regardless of decomposition technique used,suggesting that experience and knowledge accumulated over time on how to behave duringpregnancy has significant influence on the utilization of ANC services. This is alsosupported in the literature, where it is argued that higher number of previous pregnancies isassociated with decreased attendance at antenatal services (Magadi et al., 2000;

12 S. Boamah et al.

Navaneetham & Dharmalingam, 2002; Rai et al., 2013). It is thus not surprising that olderwomen have decreased ANC attendance due to their experience and familiarity withcommon ailments during the pregnancy period. The findings also reveal that wealth statusof the household significantly influences the explained gap in frequency of ANC visits.

Other personal characteristics, including the mother’s educational attainment,marital status, employment, religion, region of residence and access to media (radio),are also significant in explaining the gap in ANC use between younger and older mothersin Ghana. Although education was significant, the magnitude of the contribution waslower than expected in explaining the gap in number and timing of antenatal visitsbetween the two groups. Evidence from many studies has found that well educatedwomen are more likely than their counterparts with little or no education to use ANC(Buor, 2003; Hug & Tasnim, 2007; Greenaway et al., 2012; Babalola, 2014). Addai(2000) reported that the attitude to ANC in Ghana is influenced by education, withhigher level of education leading to increased and sufficient ANC use. He argued thateducated women tend to be more informed and have better understanding of theadvantages of prenatal care. Bbaale (2011) and Banchani and Tenkorang (2014) alsofound that mothers with secondary or higher education were more likely to make therecommended number of visits (at least four). Likewise, Babalola (2014) found thatbetter educated mothers were more inclined to use less expensive and skilled maternalcare services. These results suggest that maternal education plays an important role ininfluencing the utilization and quality of ANC.

Geographic region (or ethnicity) was also significant in explaining the gap in ANCuse. Geographic region has generally been shown to have greater influence on theutilization of maternal health service. Previous empirical studies have found that differentregions are differently endowed with health care infrastructure and personnel, influencingaccess to, and utilization of, maternal health care services. For example, Banchani andTenkorang (2014) reported regional differences in the number and timing of ANC visits,whereby women residing in regions of Ghana other than the Greater Accra region weresignificantly less likely to make the recommended number of visits (at least four). In theliterature, there appears to be a strong link between religion and utilization of ANC. Forinstance, a study by Addai (2000) reported a positive association between being Catholicand the use of ANC in Ghana, while those who practise Traditional religion were lesslikely to make the recommended number of ANC visits. However, the relationshipbetween religion and ANC utilization does not imply that religion contributes much toexplaining the disparity between younger and older mothers in terms of their utilization ofANC services. For instance, this study found that the magnitude of the contribution ofreligion to explaining the gap between the two groups was rather low.

Place of residence (urban or rural) was also significant in the model but had a lesserinfluence in explaining the gap between the two groups. Previous studies have generallyfound residence to be a significant determinant of maternal health service utilization,especially in rural areas in sub-Sahara Africa (Gage, 2007; Gabrysch et al., 2011;Masters et al., 2013). Physical proximity of health care services, especially in thedeveloping country context, plays an important role in utilization of maternal healthservices. Rural dwellers may be more remote from health care facilities than their urbancounterparts, increasing the distance from home to a health facility compared with thoseliving in urban centres. As a result, those residing in rural areas are significantly less

Antenatal care service utilization in Ghana 13

likely to make the recommended number of antenatal visits and/or make their first visitwithin the first trimester of pregnancy (Banchani & Tenkorang, 2014).

This paper makes a number of key contributions to the maternal health literature,and, to the authors’ knowledge, is the first to attempt to decompose the gap in ANCvisits by age in Ghana. Previous studies have largely focused on the relationship betweenthe timing and frequency of ANC visits among Ghanaian women, yet assessing theexplained gap in ANC visits between younger and older mothers is important tounderstand the possible causes of the gap. This study is an important first step inunderstanding the gap in endowments (determinants) and differences in the effects of thedeterminants (coefficients) between younger and older mothers.

There are some limitations to the study that are worth considering. Whiledecomposition techniques are valuable for quantifying the contribution of variousfactors to a difference or change in maternal health outcomes, they do not reveal theunderlying mechanism of the relationship between factors and outcomes. Nonetheless,by indicating which factors are quantitatively important and which are not,decompositions provide useful indications of particular hypotheses or explanations tobe explored in more detail. For example, in this study, the differences in women’s parityaccount for a large fraction of the ANC age gap, and this suggests exploring in moredetail how younger and older mothers time their ANC visits and how often they utilizematernal health care services.

Understanding the gap in endowments (determinants) and differences in the effects ofthe determinants (coefficients) between younger and older mothers will informpolicymakers on how to systematically address these gaps in endowments in order toincrease the utilization of ANC and attain a minimum recommendation of four visits asper WHO guidelines. The present study adds to the evidence underscoring the need forpolicy and programme interventions that cater for the needs of older mothers, which maydiffer from those of younger mothers. Age-specific interventions and the education ofolder mothers about their increased risk and vulnerability to pregnancy-relatedcomplications are required. Previous studies in Ghana have noted that simply providinghealth services, although necessary, is not sufficient to ensure optimal utilization of ANCservices (Arthur, 2012); women have first to perceive that the benefits of the serviceoutweigh the cost. Since the utilization of maternal and child health services is nearlyuniversal in Ghana, with the implementation of the National Health Insurance Scheme in2005 any further gains in infant and maternal health outcomes will come fromimprovement in maternal/ community education to ensure women’s understanding of theimportance of ANC services and their individual health risks to potential complications.

In conclusion, this study disaggregated disparities in ANC services utilizationbetween two mutually exclusive groups (younger and older mothers) in Ghana based oncharacteristic effects (explained variation) and coefficient effects (unexplained variation).Birth order (parity) was found to account for the largest share of the contribution to theoverall explained gap in ANC between younger and older mothers regardless of thedecomposition technique used. This indicates that ANC differentials between the twogroups are probably due to biosocial factors. This paper is unique as it useddecomposition techniques hitherto not extensively considered in the ANC researchdomain. Access to, and utilization of, ANC services is meaningless unless it is situated incontext. Specifically, clarity is required to identify whether it is older unmarried mothers,

14 S. Boamah et al.

younger unmarried mothers, younger married mothers or older married mothers whorequire prompt and significant attention in order to address their deficit in the access andutilization of ANC services. This is imperative considering the significant heterogeneityin the capacities of individuals, even within the same community.

References

Addai, I. (2000) Determinants of use of maternal–child health services in rural Ghana. Journal ofBiosocial Science 32(1), 1–15.

Arthur, E. (2012) Wealth and antenatal care use: implications for maternal health care utilizationin Ghana. Health Economics Review 2, 1–8.

Atunah-Jay, S. J., Pettinggell, S., Ohene, S. A., Micheal Oakes, J. & Borowsky, W. (2013) Therelationship between antenatal provider type and maternal health care utilization in Ghana.Health Economics Review doi: 10.1186/2191-1991-2-14.

Babalola, S. (2014) Women’s education level, antenatal visits and the quality of skilled antenatalcare: a study of three African Countries. Journal of Health Care for the Poor and Underserved25(1), 161–179.

Babalola, S. & Fatusi, A. (2009) Determinants of use of maternal health services in Nigeria –looking beyond individual and household factors. BMC Pregnancy & Childbirth 9(1), 43.

Banchani, E. & Tenkorang, E. Y. (2014) Occupational types and antenatal care attendance amongwomen in Ghana. Health Care for Women International 35, 1040–1064.

Bbaale, E. (2011) Factors influencing timing and frequency of antenatal care in Uganda.Australian Medical Journal 4(8), 431–438.

Buor, D. (2003) Mother’s education and childhood mortality in Ghana.Health Policy 64, 297–309.Carroli, G., Rooney, C. & Villar, J. (2001) WHO programme to map the best reproductive healthpractices: how effective is antenatal care in preventing maternal mortality and seriousmorbidity? Paediatric and Perinatal Epidemiology 15(1), 1–42.

Cotton, J. (1988) On the decomposition of wage differentials. Review of Economics and Statistics70(2), 236–243.

Dixon, J., Tenkorang, E. & Luginaah, I. (2011) Ghana’s national health insurance: helping thepoor or leaving them behind? Environment and Planning C: Government & Policy 29, 1102–1115.

Dixon, J., Tenkorang, E., Luginaah, I. N., Kuuire, V. Z. & Boateng, G. O. (2014) National healthinsurance scheme enrolment and antenatal care among women in Ghana: is there anyrelationship? Tropical Medicine & International Health 19(1), 98–106.

Doku, D., Neupane, S. & Doku, P. N. (2012) Factors associated with reproductive health careutilization among Ghanaian women. BMC International Health & Human Rights 12(1), 29.

Dzakpasu, S., Soremekun, S., Manu, A., Ten Asbroek, G., Tawiah, C., Hurt, L. et al. (2012) Impactof free delivery care on health facility delivery and insurance courage in Ghana’s BrongAhafo region. PLoS One 7, e49430.

Fairlie, R. (2006) An extension of the Blinder–Oaxaca decomposition technique to logit andprobit models. Journal of Economic and Social Measurement 30(4), 305–316.

Gage, A. (2007) Barriers to the utilization of maternal health care in rural Mali. Social Science &Medicine 65(8), 1666–1682.

Gabrysch, S., Cousens, S., Cox, J. & Campbell, O. M. R. (2011) The influence of distance and levelof care on delivery place in rural Zambia: a study of linked national data in a geographicinformation system. PLoS Medicine 8(1), 1–12.

Greenaway, E. S., Leon, J. & Baker, D. P. (2012) Understanding the association between maternaleducation and use of health services in Ghana: exploring the role of health knowledge. Journalof Biosocial Science 44, 733–747.

Antenatal care service utilization in Ghana 15

Hug, M. Z. & Tasnim, T. (2007) Maternal education and child healthcare in Bangladesh.MaternalChild Health Journal 13, 43–51.

Jann, B. (2008) A Stata implementation of the Blinder–Oaxaca decomposition. Stata Journal 8(4),453–479.

Khanal, V., Adhikari, M., Karkee, R. & Gavidia, T. (2014) Factors associated with the utilizationof postnatal care services among the mothers of Nepal: analysis of Nepal Demographic andHealth Survey 2011. BMC Women’s Health 14(1), 19.

Koblinsky, M., Matthews, Z., Hussein, J., Mavalankar, D., Mridha, M. K., Anwar, I. & van Lerberghe,W. (2006) Going to scale with professional skilled care. The Lancet 368(9544), 1377–1386.

Magadi, M. A., Agwanda, A. O. & Obare, F. O. (2007) A comparative analysis of the use ofmaternal health services between teenagers and older mothers in sub-Saharan Africa: evidencefrom Demographic and Health Survey (DHS). Social Science & Medicine 64, 1311–1325.

Magadi, M. A., Madise, N. J. & Rodrigues, R. N. (2000) Frequency and timing of antenatal care inKenya: explaining the variations between women of different communities. Social Science &Medicine 51, 551–561.

Masters, S. H., Burstein, R., Amofah, G., Abaogye, P., Kumar, S. & Hanlon, M. (2013) Travel timeto maternity care and its effect on utilization in rural Ghana: a multilevel analysis. SocialScience & Medicine 9, 147–154.

Navaneetham, K. & Dharmalingam, A. (2002) Utilization of maternal health care services insouthern India. Social Science & Medicine 55, 1849–1869.

Neumark, D. (1988) Employers’ discriminatory behavior and the estimation of wage discrimina-tion. Journal of Human Resources 23(3), 279–295.

Oaxaca, R. (1973) Male–female wage differentials in urban labor markets. International EconomicReview 14(3), 693–709.

O’Donnell, O., Van Doorslaer, E., Wagstaff, A. & Lindelow, M. (2008) Analyzing Health EquityUsing Household Survey Data. A Guide to Techniques and their Implementation. InternationalBank for Reconstruction and Development/The World Bank.

Owoo, N. S. & Lambon-Quayefio, M. P. (2013) National health insurance, social influence andantenatal care use in Ghana. Health Economics Review 3, 19.

Peters, D., Garg, A., Bloom, G., Walker, D., Brieger, W. & Rahman, M. H. (2008) Poverty andaccess to health care in developing countries. Annals of the New York Academy of Sciences 1136,161–171.

Rai, R. K., Singh, P. K., Kumar, C. & Singh, L. (2013) Factors associated with the utilization ofmaternal health care services among adolescent women in Malawi. Home Health Care ServicesQuarterly 32, 106–125.

Reimers, C. W. (1983) Labor market discrimination against Hispanic and black men. Review ofEconomics and Statistics 65(4), 570–579.

StataCorp (2013) Stata Statistical Software: Release 13. StataCorp LP, College Station, TX.UNICEF (2013) Levels and Trends in Child Mortality: Report. United Nations Children’s Fund,New York.

WHO (2003) Antenatal Care in Developing Countries: Promises, Achievements and MissedOpportunities: An Analysis of Trends, Levels, and Differentials: 1990–2001. WHO & UNICEF,Geneva, New York.

WHO (2007) Standards for Maternal and Newborn Health: Provision of Effective Antenatal Care(Section 1.6). World Health Organization, Geneva. URL: http://whqlibdoc.who.int/hq/2007/a91272.pdf.

Witter, S. & Garshong, B. (2009) Something old or something new? Social health insurancein Ghana. BMC International Health & Human Rights doi: 10.1186/1472-698X-9-20.

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