National health insurance subscription and maternal healthcare utilisation across mothers' wealth status in Ghana

Ameyaw, Edward Kwabena; Kofinti, Raymond Elikplim; Appiah, Francis Article National health insurance subscription and maternal healthcare utilisation across mothers' wealth status in Ghana Health Economics Review Provided in Cooperation with: Springer Nature Suggested Citation: Ameyaw, Edward Kwabena; Kofinti, Raymond Elikplim; Appiah, Francis (2017) : National health insurance subscription and maternal healthcare utilisation across mothers' wealth status in Ghana, Health Economics Review, ISSN 2191-1991, Springer, Heidelberg, Vol. 7, Iss. 16, pp. 1-15, https://doi.org/10.1186/s13561-017-0152-8 This Version is available at: https://hdl.handle.net/10419/175638 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH Open Access National health insurance subscription and maternal healthcare utilisation across mothers’wealth status in Ghana Edward Kwabena Ameyaw 1* , Raymond Elikplim Kofinti 2 and Francis Appiah 1 Abstract Introduction: This study is against the backdrop that despite the forty-nine percent decline in Maternal Mortality Rate in Ghana, the situation still remains high averaging 319 per 100,000 live births between 2011 and 2015. Objective: To examine the relationship between National Health Insurance and maternal healthcare utilisation across three main wealth quintiles (Poor, Middle and Rich). Methods: The study employed data from the 2014 Ghana Demographic and Health Survey. Both descriptive analysis and binary logistic regression were conducted. Results: Descriptively, rich women had high antenatal attendance and health facility deliveries represented by 96. 5% and 95.6% respectively. However, the binary logistic regression results revealed that poor women owning NHIS are 7% (CI = 1.76–2.87) more likely to make at least four antenatal care visits compared to women in the middle wealth quintile (5%, CI = 2.12–4.76) and rich women (2%, CI = 1.14–4.14). Similarly, poor women who owned the NHIS are 14% (CI = 1.42–2.13) likely to deliver in health facility than women in the middle and rich wealth quintile. Conclusion: The study has vindicated the claim that NHIS Scheme is pro-poor in Ghana. The Ministry of Health should target women in the rural area to be enrolled on the NHIS to improve maternal healthcare utilisation since poverty is principally a rural phenomenon in Ghana. Keywords: Antenatal care, Maternal healthcare utilisation, Wealth status, Women, Health insurance Background Maternal related complications constitute the major source of disabilities and mortality among women within reproductive age globally [1]. Despite the tremendous progress made by the global community in combating maternal related complications and mortality, 289,000 women still die yearly owing to pregnancy with low and middle income countries bearing the highest brunt [1]. The disparity between these countries and the developed countries presupposes that income disparities have consequences on maternal health status. The crucial nature of maternal and child health instigated the global community to devote the third Sustainable Development Goal (SDG) to reduction in maternal mortality, neonatal, infant and under five mortality rates [2]. Whilst studies indicate decline in maternal mortality rates since 1990s, the decline is not universal and still remains high in southern Asia and Africa [1, 3, 4]. For instance, it has been realised that risk of maternal mortality for a woman in Sub-Saharan Africa is forty-seven times higher as compared to someone in the United States, meanwhile most of these deaths are avoidable [5]. In the case of Ghana, despite the forty-nine percent decline in Maternal Mortality Rate (MMR), the situation still remains high because as noted by the World Bank, it averaged 319 per 100,000 live births between 2011 and 2015 [6]. The high rate of maternal complications emerge from the numerous threats confronting the health sector of most African countries and cost associated with healthcare * Correspondence: [email protected] 1 Department of Population and Health, Faculty of Social Sciences, University of Cape Coast, Cape Coast, Ghana Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Ameyaw et al. Health Economics Review (2017) 7:16 DOI 10.1186/s13561-017-0152-8 utilisation. Arguably, economic standing of women is critical to the extent to which maternal healthcare can be utilised and as indicated by Marmot [7], income relates to health in three principal ways: countries’gross national product; individual’s income; and variation in income. In lightofthis,thepoorarethemostvulnerableintermsof maternal healthcare access [8, 9]. Since investment in maternal health constitutes not only social and political imperatives but also cost effective investment, a number of initiatives have been instituted manifesting in interventions such as health insurance scheme. Health insurance exists as an essential pro-poor initiative and despite its enormous benefits, evidence suggests that maternal healthcare utilisation especially Antenatal Care (ANC) attendance and supervised delivery are still induced by maternal wealth status in some countries [10]. Through rise in health insurance subscription, a growing body of studies have investigated the essence of health insurance to utilisation of healthcare [11–14], meanwhile, the impact of health insurance on maternal healthcare utilisation across wealth status in Ghana has not gained recognition in literature. The National Health Insurance Scheme (NHIS) commenced in 2005 as a demand side initiative to overcome financial obstacles to healthcare utilisation. As far as the link between NHIS ownership and maternal healthcare utilisation is concerned, some questions remain unanswered in the literature: (1) is NHIS a prerogative of the rich, the poor or both?; (2) which of these groups should be the primary target of the NHIS?; and (3) which of these groups are currently benefitting from NHIS ownership via maternal health utilisation? Whilst some evidence point out that women with higher socio-economic standing least utilise NHIS due to their access to multiple options to enhance their health status in terms of accessing quality and or private healthcare and good nutrition during pregnancy [15, 16], counter evidence have also been reported [17]. Considering the fact that divergent results have been reported in Ghana about NHIS and maternal healthcare utilisation [18, 19], this study intends to unearth the current direction as far as the relationship between health insurance and maternal healthcare utilisation across wealth status is concerned. There is therefore the need for this investigation with the 2014 Ghana Demographic Health Surveys (GHDS) to know the current status of how well the NHIS has impacted maternal healthcare utilisation (antenatal visits and place of delivery) across wealth status in Ghana. Theoretical framework Several and complex drivers influencing healthcare utilisation revolve around social, cultural, economic and religious factors [20, 21]. In order for the concept of healthcare utilisation and its drivers which are at the core of this paper to be well-understood and for conceptual clarity, it demands theoretical guidance. Anderson’s Behavioural Model (BM) of healthcare utilisation [22, 23] shall guide this paper since the primary focus of the paper is to investigate the relationship between insurance subscription and maternal healthcare utilisation (measured by ANC attendance and place of delivery) across wealth status of women (poor, middle, rich). The BM postulates that healthcare utilisation rest on predisposing, enabling and need factors operating at both individual and contextual domains [23, 24]. Within the predisposing factors, individual predisposing factors constitute the aggregate of demographic, biological and social while the contextual predisposing factors also encompass demographic, social composition of communities and cultural norms that interact to influence healthcare utilisation [23, 24]. The enabling factors include but not limited to one’s income and wealth status that enables individuals to pay for healthcare services and the effective price of healthcare influenced by one’s health insurance status. However, due to inherent weaknesses, the model was revised and as such healthcare systems, service availability, population-based factors and consumer satisfaction in the initial model are considered as drivers of healthcare utilisation. Methods Data from the 2014 GDHS was used for this study. Specifically, the women and child files were used for the study. GDHS is carried out by the Ghana Statistical Service and Macro International under the auspices of DHS programs. The survey captures data on various aspects of maternal health conditions within the country and as such was deemed suitable for this study. The dataset was requested online from Measure DHS website on the 16th October, 2015. In all, 9,396 women (aged 15–49) from 11,835 households nationwide were interviewed [25]. However, 4,294 women had birth history within the last 5 years preceding the survey and as such they constituted the sample size for this study. The 2014 GDHS was conducted with an updated frame from the 2010 Population and Housing Census (PHC) prepared by the Ghana Statistical Service (GSS). The frame exempted institutional and nomadic groups including hotel occupants and prisoners. The survey constituted a two-stage sample design for the purpose of allowing estimates of core indicators at the national level. The initial phase constituted selection of sample points (clusters) involving enumeration areas (EAs) outlined for the 2010 PHC in which 427 clusters were designated in all constituting 216 from urban and 211 from rural areas. The next stage utilised systematic sampling of households in which household inventory operation was carried out in all the identified EAs Ameyaw et al. Health Economics Review (2017) 7:16 Page 2 of 15 between January and March 2014. Afterwards, the households to be considered for the survey were selected from the list randomly [25]. Econometric model To investigate the effect of National Health Insurance Subscription (NHIS) on maternal healthcare utilisation across the three main wealth quintiles (Poor, Middle and Rich) in Ghana we relied on theorising maternal healthcare utilisation specified by Anderson [22, 23]. The study assumes that the mother derives utility from (1) making at least four antenatal care visits during pregnancy and, (2) delivering in a health facility/hospital, and that there is disutility to the mother and the husband in the form of complications during pregnancy and time of delivery when the mother fails to either make at least four antenatal care (ANC) visits or delivers at a health facility. It can be elaborated that the mother makes a conscious effort to improve her own survival and that of the child during pregnancy through investment in health care which can take the form of either curative or preventive health care. Therefore, it can be argued that the decision of the mother to utilise maternal health is the responsibility of the mother as Anderson [22] later considered individuals as the unit of analysis which goes beyond health care utilisation only. The probability that the mother utilises maternal health care is a function of a key enabling factor of National Health Insurance Subscription NHIS (N)and the level of education of the mother (E m ), the level of education of the partner/husband (E f ). Some of the predisposing factors considered for the study are religious affiliation (R A ), household purchasing decision (HP)and household health care decision making (HC). The maternal health care probability production function of the mother is thus specified as: MHi¼πN;Em;Ef;RA;HP;HC;X  ð1Þ Where MH i is the probability that the mother utilises the two maternal health care services: (1) probability that the mother makes at least four antenatal care visits; and (2) the probability that the woman delivers in a health facility, and Xis a vector of other exogenous variables such as ecological zone and the urban dummy. MHi¼φiβþδiwith MHi¼1if MHi>0 0otherwise nð2Þ Where MH i is the probability of maternal health care utilisation by the mother, which is broken up into two in this study: (1) the probability that the mother makes at least four antenatal care visits; and (2) the probability that the mother delivers in a health facility. Equation two is therefore estimated for the three wealth quintiles, viz., poor, middle and rich. φ i is a vector of exogenous factors influencing maternal health care utilisation; βis a vector of unknown parameters; and δ i is an error term with zero mean and a constant variance, which also captures the unobserved factors in the model. In order to estimate equation (2), the maximum likely estimation (MLE) technique in logistic regression is employed. This is against the background that the logistic regression satisfies the main assumption underlying MLE, viz., the dependent variable, maternal health care utilisation, is dichotomous. Dependent variables Utilisation of maternal healthcare services, comprising ANC attendance and place of delivery, was the outcome variable. The first dependent variable, ANC visits was recoded into a binary outcome variable with zero ‘0’ denoting less than four and one ‘1’at least four. Similarly, the second dependent variable, place of delivery, was recoded into a binary outcome variable with zero ‘0’denoting delivery at home and one ‘1’denoting delivery in a health facility. Independent variables of interest The main independent variable of the study was health insurance ownership which is a dummy variable where one ‘1’represents women who have subscribed to NHIS, and zero ‘0’otherwise. The effect of NHIS ownership on maternal healthcare utilisation was analysed across the wealth status of women. Wealth status is a categorical variable with zero ‘0’denoting women in the poor wealth quintile, one ‘1’denoting women in the middle wealth quintile and two ‘2’denoting women in the rich wealth quintile. As with any good model specification and taking into cognizance the theoretical literature review, other socio-demographic variables were controlled for in the estimation. These are residential status, religion, marital status, frequency of watching television and listening to radio, frequency of reading newspaper, ecological zone (made up of the ten administrative regions), occupation, partner’s occupation and education, contraceptive usage and birth order. Results Descriptive statistics for the independent variables Table 1 presents the descriptive statistics of the independent variables. The analysis indicated that across wealth status, the highest NHIS subscription occurred among the poor (42.9%) whilst the least subscription occurred among those in the middle wealth status (19.4%). Specifically, 67.1% of the poor had subscribed to the scheme, whereas 68.7% of those in the middle wealth category had subscribed. However, among the rich women, NHIS subscription stood at 72.9%. It was Ameyaw et al. Health Economics Review (2017) 7:16 Page 3 of 15 Table 1 Descriptive Statistics for the Independent Variables Variable Poor Middle Rich N= 4,294 Row (Col.) Row (Col.) Row (Col.) NHIS Owned 42.9 (67.1) 19.4 (68.7) 37.7 (72.9) 100 45.0 (32.9) 21.8 (31.3) 33.2 (27.1) 100 Not Owned (100) (100) (100) Residence Rural 77.1 (86.3) 16.7 (51.9) 6.2 (12.6) 100 Urban 17.2 (13.7) 21.8 (48.1) 61.0 (87.4) 100 (100) (100) (100) Religion Others 65.6 (35.6) 16.0 (24.0) 18.4 (18.0) 100 Christianity 46.9 (64.4) 20.0 (76.0) 33.1 (82.0) 100 (100) (100) (100) Marital Status Not Married 48.6 (32.1) 26.2 (48) 25.2 (30.1) 100 Married 54.1 (67.9) 15.0 (52) 30.9 (69.9) 100 (100) (100) (100) Occupation Not working 45.4 (15.0) 24.1 (22.2) 30.5 (18.2) 100 Working 53.7 (85.0) 17.7 (77.8) 28.6 (81.8) 100 (100) (100) (100) Partner’s occupation Primary 87.5 (9.6) 9.6 (20.9) 2.9 (45.9) 100 Secondary 23.9 (76.3) 30.7 (25.0) 45.4 (4.6) 100 Tertiary 23.1 (14.1) 16.9 (54.1) 60.0 (49.5) 100 (100) (100) (100) Education No education 80.6 (51.4) 12.0 (21.2) 7.4 (8.5) 100 Primary 61.6 (23.9) 20.3 (21.8) 18.1 (12.7) 100 At least secondary 27.8 (24.7) 23.1 (56.9) 49.1 (78.9) 100 (100) (100) (100) Partner’s education No education 85.9 (48.4) 8.5 (14.2) 5.5 (5.6) 100 Primary 38.8 (50.3) 21.7 (83.0) 39.5 (93.1) 100 At least secondary 44.1 (1.3) 32.2 (2.8) 23.7 (1.3) 100 (100) (100) (100) Ecological zone Coastal 27.1 (14.7) 23.7 (35.6) 49.2 (48.1) 100 Savannah 81.2 (33.6) 8.3 (49.9) 10.5 (39.8) 100 Forest 45.6 (51.7) 24.4 (14.5) 30.0 (12.1) 100 (100) (100) (100) Frequency of listening to radio Not at all 69.8 (25.7) 14.9 (15.2) 15.3 (10.1) 100 Ameyaw et al. Health Economics Review (2017) 7:16 Page 4 of 15 found that most poor women reside in rural settings (77.1%) as compared to women in other wealth categories. Within the poor, rural residents accounted for 86.3%. This observation implies that most poor women in Ghana reside in rural settings. This residential status might have potential implications on their access to maternal health services. Poor women affiliated to non- Christian religious bodies (65.6%) exceeded their non- Christian counterparts in middle (16.0%) and rich (18.4%) wealth categories. The proportion of poor women who were Christians (46.9%) was the highest. It was found that across wealth status, marriage was high among poor women (54.1%). Similarly, marriage stood at 69.9% among the rich women. With regard to occupation across wealth status, it was realised that the greatest proportion of working women were poor (53.7%) with the least being women in the middle wealth status (17.7%). As depicted in Table 1, most women whose partners were engaging in primary occupation were poor (87.5%) when considered across wealth status. On education, most uneducated women were poor (80.6%) whereas the highest proportion of those with at least secondary education was recorded among rich women (49.1%). Specifically, 51.4% of the poor were uneducated with 24.7% having at least secondary education. Also, majority of the rich women had at least secondary education (78.9%) whilst only 8.5% had no formal education. This finding imply that at least two out of ten Ghanaian women in the reproductive age group have had some formal education. Upon analyzing partners’education, it was noted that partners of most poor women were uneducated (85.9%). Investigation among the poor indicated that half of their partners had attained primary education (50.3%) as indicated in Table 1. Similarly, majority of the rich women’s partners had attained primary education (93.1%). Although educational attainment is generally high among the women, it is obvious that women from Table 1 Descriptive Statistics for the Independent Variables (Continued) Less than once a Week 50.1 (29.4) 21.8 (35.5) 28.1 (29.8) 100 At least once a week 46.7 (44.9) 18.6 (49.4) 34.7 (60.1) 100 (100) (100) (100) Water source Pipe 5.3 (1.0) 13.3 (5.1) 81.4 (20.2) 100 Others 48.0 (99.0) 21.0 (94.9) 31.0 (79.8) 100 (100) (100) (100) Contraceptive usage No modern contraceptive 43.3 (75.5) 19.8 (71.7) 36.9 (73.9) 100 Uses modern 44.6 (24.5) 22.1 (28.3) 33.3 (26.1) 100 Contraceptive (100) (100) (100) Healthcare decision making Alone 46.3 (19.0) 20.3 (25.3) 33.4 (24.4) 100 Not alone 54.7 (81.0) 16.7 (74.7) 28.6 (75.6) 100 (100) (100) (100) Household purchase decision making Alone 50.7 (17.9) 19.6 (20.9) 29.7 (18.7) 100 Not alone 53.4 (82.1) 16.9 (79.1) 29.7 (81.3) 100 (100) (100) (100) Decision making on visits Alone 48.9 (20.2) 18.9 (23.7) 32.0 (23.5) 100 Not alone 53.9 (79.8) 17.0 (76.3) 29.0 (76.5) 100 (100) (100) (100) Shared toilet facility No 1.7 (0.6) 8.4 (5.9) 90.0 (58.3) 100 With other household only 53.2 (99.4) 22.9 (94.1) 23.9 (41.7) 100 (100) (100) (100) Computed from GHDS 2014 Data Ameyaw et al. Health Economics Review (2017) 7:16 Page 5 of 15 wealthier homes have dominated. It was evident from the study that a significant share of the rich women were within the Coastal zone (49.2%) with major of the poor residing in the Savanna zone (81.2%). Further analysis indicated half of the poor women were within the Forest zone (51.7%). Most poor women were found not to listen to radio at all (69.8%) as compared with women in other wealth categories. Among the poor, 44.6% were listening to radio at least once a week with 25.7% not listening at all. With respect to the rich, 60.1% were listening to radio at least once a week whilst 10.1% were not listening to radio. Upon exploring among women in these wealth categories, it was noted that almost all poor women obtained water from sources other than pipe (99.0%). Among those in the middle wealth status, 94.9% were obtaining water from other sources. Hence, a greater proportion of Ghanaian women obtain water from sources other than pipe. As such, it is more probable that sources such as bole holes, wells and streams are much utilised, meanwhile, the health implications of these sources are sometimes adverse. Investigation into contraceptive use revealed that as compared to women in other wealth categories, non-use was high among poor women (43.3%). Specifically, 75.5% of the poor were not using contraceptives, meanwhile the proportion of the rich who were not using contraceptives stood at 73.9%. Analysis of decision making on healthcare unraveled that women who were not taking the decision alone were predominantly poor (54.7%) as compared with middle and rich women. Among the poor, 81.0% were not taking the decision alone, whereas 74.7% of those in the middle wealth status were also not taking the decision alone. Almost all poor women were sharing (99.4%) and this was not so different from the observation made among those in the middle wealth status as 94.1% were sharing toilet facility with other households. Maternal healthcare utilisation by wealth status Assessment of delivery in health facility across wealth status revealed that generally the rich tend to deliver in health facilities more than their poor counterparts. This is because 95.6% of rich women delivered in health facilities compared to 57.2% health facility deliveries among poor women as illustrated in Fig. 1. Similarly, attendance of antenatal care was relatively high among rich women (96.5%) than their poor counterparts (80.6%). The Figure has indicated a trend whereby the rich appears to make more use of maternal healthcare services (place of delivery and antenatal visits). The low utilisation among the poor do not necessarily indicate that they are not interested in accessing the services but might be disadvantaged by their low economic status. This is because the rich are more likely to have multiple avenues of accessing these services which the poor might not be able to utilise. From the Figure, more than half of poor mothers deliver at home (43%) compared to the mothers in the middle wealth quintile (22%) and rich mothers (4%). Similarly, majority of mothers who fail to make the recommended WHO healthy antenatal visits of four are poor, thus 19.9% compared to mothers in the middle and rich wealth quintile of 12.5 and 3.5% respectively. 57.2 42.8 78.1 21.9 95.6 4.4 020 40 60 80 100 Poor Middle Rich Place of Delivery Health Facility Home n=4,294 80.6 19.5 87.5 12.5 96.5 3.5 020 40 60 80 100 Poor Middle Rich Number of Antenatal Care visits More than four Less than four n=4,294 Fig. 1 Maternal healthcare utilisation by wealth status. Source: GDHS 2014 Ameyaw et al. Health Economics Review (2017) 7:16 Page 6 of 15 Maternal healthcare utilisation by wealth and zonal distribution It was observed that home deliveries dominated among poor women in all the three zones of the country namely Coastal Poor (43.6%), Forest Poor (38.9%) and Savannah Poor (45.1%). At the same time, poor women in rural settings were noted to have the highest prevalence of home deliveries (45.2%) as depicted in Table 2. It is not surprising that rural poor women have high prevalence of home deliveries considering the poor road networks linking these rural areas to health facilities coupled with refusal of healthcare providers to accept postings to rural settings. When ANC visit was viewed across Rural–urban dimension, it was clear that having at least four visits was prevalent among both Rural Rich (97.4%) and Urban Rich (96.4%) as projected in Table 2. Results of econometric models (logistic regression) Logistic regression results on ANC visit In all, six logistic regression models were constructed in explaining the effect of health insurance ownership on maternal healthcare utilisation across wealth status. Table 3 presents the results of antenatal care visit whilst Table 4 presents the results of place of delivery. With regard to antenatal care (ANC) visits, the logistic regression analysis indicated that poor women who were subscribed to the NHIS were about 7% (CI = 1.76–2.87) likely to have more ANC visits than poor women who have not subscribed to the scheme. Whilst urban poor residents were about 3% (CI = 1.66–3.21) more probable to attend ANC than rural residents, poor Christians were about 5% less likely to attend ANC (−4.5%, CI = 0.67–1.34) as compared to poor women affiliated to other religions. Married women in the poor category were noted to have about 7% (CI = 1.41–2.44) likelihood of accessing ANC than the unmarried. Meanwhile, poor women who were working were 10% (CI = 1.11–2.26) more likely to access ANC than their non-working counterparts (reference category). Common knowledge would argue that nonworking women might have had enough time to attend ANC as compared to working women but that is not the case in the Ghanaian context. This, however, points to the notion that attendance or non-attendance of ANCisnotonlyafunctionofavailabilityoftimebut perception about the need to access such service. Poor women with primary education (−1.0%, CI = 0.86–1.69) and those with at least secondary education were all less probable to utilise ANC as compared to those without formal education, meanwhile, contrary observation was made among those whose partners had at least secondary education (2.3%, CI = 0.98–1.96). It was also evident that ANC visits among poor women listening to radio at least once a week was 10% (CI = 1.83–3.28) higher than those who did not listen to radio at all. Less possibility of ANC attendance was associated with poor women who could not decide on their healthcare alone (−5%, CI = 1.21–3.43) as compared to those taking such decision on their own. However, poor women who were unable to decide household purchases (8%, CI = 2.32–4.51) and visits (2%, CI = 1.32–4.12) alone were more probable to utilise ANC as compared to those taking such decisions alone. Among those in the middle wealth status, higher likelihood of ANC visits was observed among those subscribed to the NHIS as compared to those who had not subscribed (5%, CI = 2.12–4.76). This is expected, considering the fact that maternal health services are absorbed by the NHIS. Consequently, women who are subscribed to the scheme will be highly exposedto access the service as compared to their counterparts who are not subscribed to the scheme. Unlike the observation made among the poor, urban residents were less probable to utilise ANC as compared to rural women (−3%, CI = 0.98–1.64), however, married women in this category were more likely to utilise ANC than the unmarried (8%, CI = 0.74–2.86). Those residing in the Savannah zone were more probable to utilise ANC (2%, CI = 0.49–1.52) unlike their Forest zone counterparts (−3%, CI = 0.64–1.73) when compared with those in the Coastal zone (reference category). Women in the middle wealth quintile who were obtaining water from sources other than pipe were relatively less probable to utilise ANC (−9%, CI = 0.65–2.71), however, those using modern contraceptives were more likely to utilise ANC (7%, CI = 0.95–2.63) as compared to those Table 2 Maternal Healthcare Utilisation by Wealth and Zonal Distribution Place of delivery ANC visit Home Health facility Total ˂4≥4 Total Coastal Poor 43.6 56.4 100 17.9 82.1 100 Coastal Middle 29.0 70.9 100 11.9 88.1 100 Coastal Rich 5.7 94.3 100 3.2 96.8 100 Forest Poor 38.9 61.0 100 21.2 78.8 100 Forest Middle 18.4 81.6 100 13.6 86.4 100 Forest Rich 3.1 96.9 100 4.1 95.9 100 Savannah Poor 45.1 54.9 100 18.8 81.2 100 Savannah Middle 16.2 83.8 100 10.3 89.7 100 Savannah Rich 3.3 96.7 100 2.7 97.4 100 Rural Poor 45.2 54.8 100 20.4 79.6 100 Rural Middle 28.3 71.7 100 10.8 89.2 100 Rural Rich 9.1 90.9 100 2.6 97.4 100 Urban Poor 27.9 72.1 100 13.7 86.3 100 Urban Middle 14.8 85.2 100 14.4 85.6 100 Urban Rich 3.7 96.3 100 3.6 96.4 100 Computed from GHDS 2014 Data Ameyaw et al. Health Economics Review (2017) 7:16 Page 7 of 15 Table 3 Logistic regression results on ANC visit ANC Variable Poor 95% CI Middle 95% CI Rich 95% CI Health Insurance Not-subscribed 1,1 1,1 1,1 Subscribed 0.067**(0.028) 1.76–2.87 0.045 (0.031) 2.12–4.76 0.017 (0.013) 1.14–4.14 Residence Rural 1,1 1,1 1,1 Urban 0.026 (0.039) 1.10–2.68 −0.034 (0.026) 0.98–1.64 −0.012 (0.015) 0.28–2.73 Religion Others 1,1 1,1 1,1 Christianity −0.045 (0.028) 0.67–1.34 0.034 (0.034) 0.46–2.51 0.005 (0.022) 0.59–4.21 Marital Status Not Married 1,1 1,1 1,1 Married 0.067*(0.031) 1.41–2.44 0.080**(0.031) 0.74–2.86 0.021 (0.016) 0.75–3.16 Occupation Not working 1,1 1,1 Working 0.104**(0.044) 1.11–2.26 0.095**(0.036) 1.75–4.31 0.049**(0.018) Partner’s occupation Primary 1,1 1,1 1,1 Secondary 0.048 (0.032) 1.15–2.65 −0.002 (0.031) 2.31–3.99 0.010 (0.027) 0.23–2.30 Tertiary 0.129***(0.029) 1.27–3.87 0.027 (0.036) 1.54–2.56 0.019 (0.025) 0.65–5.42 Education No education 1,1 1,1 1,1 Primary −0.009 (0.035) 0.86–1.69 0.012 (0.043) 0.77–1.75 −0.030 (0.025) 0.63–2.71 At least secondary −0.001 (0.037) 0.91–3.01 0.049 (0.040) 0.94–1.88 0.005 (0.017) 0.97–1.72 Partner’s education No education 1,1 1,1 1,1 Primary −0.019 (0.040) 0.80–1.64 −0.029 (0.049) 0.82–1.66 0.040 (0.035) 1.21–3.22 At least secondary 0.023 (0.036) 0.98–1.96 −0.007 (0.039) 0.78–1.84 0.041 (0.033) 2.12–4.21 Ecological zone Coastal 1,1 1,1 1,1 Savannah 0.015 (0.039) 0.58–1.37 0.015 (0.039) 0.49–1.52 0.001 (0.022) 0.82–2.45 Forest −0.033 (0.030) 0.46–1.02 −0.033 (0.030) 0.64–1.73 −0.005 (0.012) 0.94–1.63 Frequency of listening to radio Not at all 1,1 1,1 1,1 Less than once a Week 0.042 (0.041) 1.00–1.84 0.042 (0.041) 2.02–4.89 −0.013 (0.017) 2.42–5.21 At least once a week 0.106***(0.036) 1.83–3.28 0.106**(0.036) 1.86–3.72 0.009 (0.012) 2.01–4.21 Water source Pipe 1,1 1,1 1,1 Others 0.281 (0.154) 0.45–2.14 0.089**(0.033) 0.65–2.71 0.009 (0.015) 0.72–2.71 Contraceptive usage No modern contraceptive 1,1 1,1 1,1 Uses modern Contraceptive 0.064**(0.025) 1.43–2.70 0.065**(0.024) 0.95–2.63 0.000**(0.012) 0.52–2.61 Birth order −0.004*(0.032) 0.33–2.01 0.001 (0.154) 1.22–3.28 0.024 (0.031) 0.74–3.82 Ameyaw et al. Health Economics Review (2017) 7:16 Page 8 of 15 33. Adegoke A, Utz B, Msuya SE, et al. Skilled birth attendants: who is who? A descriptive study of definitions and roles from nine sub-Saharan African countries. PLoS ONE. 2012;7:e40220. 34. Sakeah E, Doctor H, McCloskey L, Bernstein J, Yeboah-Antwi K, Mills S. Using the community-based health planning and services program to promote skilled delivery in rural Ghana: socio-demographic factors that influence women utilization of skilled attendants at birth in Northern Ghana. BMC Public Health. 2014;14:344. 35. Amoakoh-Coleman M, Ansah EK, Agyepong IA, et al. Predictors of skilled attendance at delivery among antenatal clinic attendants in Ghana: a cross-sectional study of population data. BMJ Open. 2015;5: e007810. Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com Ameyaw et al. Health Economics Review (2017) 7:16 Page 15 of 15