Women’s health service access and associated factors in Ethiopia: application of geographical information system and multilevel analysis

Introduction

Globally, around 800 women died daily from preventable causes, pregnancy and childbirth.1 Worldwide, nearly 300 000 women were predicted to be died in 2010.2 Maternal and child mortality remains a major public health challenge in the developing world, and the discrepancy is high according to developed countries. The maternal mortality ratio in low-income and middle-income countries is 15 times higher than in developed countries.3 Sub-Saharan Africa had the highest maternal mortality ratio which is 64 deaths per 10 000 live births, and 900 maternal deaths occurred in Ethiopia.3 According to the 2011 Ethiopian Demographic and Health Survey (EDHS), maternal mortality rates were 6.76 per 1000 live births.4

Maternal and child mortality occur due to countries’ poor utilisation of healthcare services.4 An average of 52% of women received at least four antenatal care (ANC) services in developing regions. It was 36% in Asia, 49% in sub-Saharan Africa5 and 32% in Ethiopia.6 In addition, 77.69%, 73.95% and 67.61% of women delayed their first ANC visit in 2005, 2011 and 2016 EDHS, respectively.7 Pregnancy-induced high blood pressure, stillbirth and unsafe abortions are extremely high. Though institutional delivery is used to reduce pregnancy and birth risks, home birth in low-income countries is high and institutional delivery is 26%–32.5% in Ethiopia.8

Women’s postnatal care (PNC) service utilisation is insufficient, which is 6.9% with marked spatial heterogeneity in Ethiopia.9 Maternal and child health problems occur due to the inaccessibility of health institutions, poor women’s health-seeking behaviours, maternal and child health services inaccessibility, low media exposure, poor attitude and knowledge, and low-quality service provision.8 10 11 The geographical accessibility of healthcare services, rural women lack reliable transportation, and women are late in starting ANC service.12

In resource-limited settings, women’s access to health services is mainly affected by four principal factors such as not want to go alone, distance to health facilities, getting the money needed for treatment, and getting permission for medical care. Previous studies have proven that distance to a health facility (long distance) and the geographical position of health facilities,13 poverty, low monthly income and not having an occupation that makes women not have enough money for medical care,12 inadequate awareness and low-risk perception of women, laziness and disease severity that make women not want to go alone for medical care,14 and husbands’ and relatives’ complete decision-making culture for women’s health service access15 16 are challenges for maternal and child health service access among women.

Maternal and child healthcare services are the most effective and potential health interventions to overcome maternal and child mortality. Of these maternal and child healthcare services, the provision of ANC and PNC services,17 institutional delivery services,18 skilled birth assistance, and nutritional and breastfeeding counselling services are the main strategies of the Millennium Development Goal to reduce mortality and morbidity. Moreover, health facilities provide preventative, curative health services to prevent maternal and child deaths.9

Maternal health services would be accessible and fairly distributed,19 the quality of health service provision should be ensured20 and sufficient health professionals would available in health facilities. Plus, policy-makers understand women’s health services access problem and geographical variations of health service access to formulate strategies and interventions to solve women’s health services problems and to provide equity and quality of services provision.17 21 Therefore, this study would be an input for policy-makers to alleviate women’s health service access problems. Studies regarding women’s health service access are not adequate, and the findings of previous studies were insufficient and limited in spatial variation analysis of women’s health service access in Ethiopia. Moreover, the findings of this study would be important for women’s decision-making regarding maternal health service access, and the finding could support policy-makers, and programmers to design interventions for achieving women’s health service access. Therefore, this study aimed to assess women’s health services access, locate women’s health service access spatially and identify factors associated with women’s health service access.

Methods

Results

Spatial distribution of women’s health service access

The women were assessed whether they had problems regarding health service access or not. Accordingly, women had a problem with not wanting to go alone (42%), distance to health facilities (51%), getting the money needed for treatment (55%) and getting permission to go for medical care (32.3%). Overall, 70.2% of women had at least one of the mentioned problems for health service access in Ethiopia (figure 1).

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Figure 1

Health service access problem in Ethiopia, 2016 EDHS. EDHS, Ethiopian Demographic and Health Survey.

The spatial autocorrelation revealed that the spatial distribution of health service access in Ethiopia was clustered (Global Moran’s I=0.102168, p=0.034569). These hot spots of health service access were observed in eastern Benishangul Gumuz, western Amhara, southern Afar, DireDawa, Harari and northern Somali regions (figures 2 and 3).

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Figure 2

Spatial autocorrelation report of health service access in Ethiopia, 2016 EDHS. EDHS=Ethiopian Demographic and Health Survey.

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Figure 3

Hot spot analysis for health service access in Ethiopia, 2016 EDHS. EDHS, Ethiopian Demographic and Health Survey; SNNPR, South Nations Nationalities and People's Region.

Spatial SaTScan analysis

A total of 72 significant clusters were identified. Of these, 9, 62 and 1 cluster were primary, secondary and tertiary clusters, respectively. The primary and secondary clusters were located at 9.614701N, 41.829121E within a 5.69 km radius in Dire Dawa, and at 10.333829 N, 34.842459 E within a 386.61 km radius in Gambela, Benishangul-Gumuz, western Oromia and southwestern Amhara regions, respectively. Women who lived in the primary, and secondary clusters were 96% (RR=1.96, p<0.0001), and 39% (RR=1.39, p<0.0001) more likely to access health service than women who lived outside the window (table 2, figure 4).

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Figure 4

SaTScan analysis of health service access in Ethiopia, 2016 EDHS. EDHS, Ethiopian Demographic and Health Survey.

Table 2

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SaTScan analysis report of significant clusters for women’s health service access in the detected window in Ethiopia, using 2016 EDHS data

Interpolation of women’s health service access

The kriging interpolation of women’s health service access revealed that there would be good health service access among women in Benishangul-Gumuz, western Amhara, Dire Dawa, eastern Oromia and northern Somali regions of Ethiopia. Whereas, women in the remaining parts of Ethiopia would face problems with health service access (figure 5).

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Figure 5

Ordinary kriging interpolation of health service access in Ethiopia, 2016 EDHS. EDHS, Ethiopian Demographic and Health Survey.

Discussion

For this study, 2016 EDHS data were used that was accessed from the DHS website. Through request, permission was obtained to access the data. There are no attributes that uniquely identify individuals’ women or household addresses in the data files. This is because the geographical coordinate files are randomly displaced within a large geographical area, and it is only for EAs as a whole. As a result, specific enumeration areas (ERs), individuals’ women and households cannot be identified uniquely. The shape file of Ethiopia was taken from the open Africa website. A two-stage stratified cluster sampling technique was used, and all women under the age of 15–49 years were the study population. Since the data have hierarchical nature, data dependency might have existed. Therefore, ICC was used to assess data dependency. Based on the result, multilevel mixed-effect logistic regression models were considered to alleviate the data dependency, and different model selection criteria were assumed to select the best-fit model. For spatial analysis, spatial autocorrelation and hot spot analysis were used to assess the distribution of data, and identify the hot or cold spot areas of women’s health service access, respectively. The ordinary Kriging interpolation technique and purely spatial Bernoulli model were used to predict unsampled areas, and to detect local clusters of women’s health service access, respectively.

Women’s health service access was assessed to determine whether they had problems regarding health service access or not. Accordingly, respondents had problems getting the money needed for treatment (55%), distance to health facilities (51%), not wanting to go alone (42%) and getting permission to go for medical care (32.3%). Overall, 70.2% of women had at least 1 of the mentioned problems with health services access, and only 18.9% of women had good health service access in Ethiopia. This evidence was supported by studies done in Nigeria11 and Ethiopia.28 This finding was also supported by women’s suboptimal ANC visits in Ethiopia, which ranged from 10.0% to 32%,29 and low utilisation of PNC service utilisation.9 This might be because women living far from health facilities are less likely to use or access healthcare services, their poor perception of the available healthcare services and lack of transportation services. In addition, mothers might not know about signs of pregnancy complications, women’s low health-seeking behaviours, inaccessibility of health institutions and women’s low ANC and PNC visits.11 Therefore, stakeholders create awareness for women to make them volunteer to go alone for medical care, and husbands and other relatives might prevent women to go to the health facility and access the respective health service. So, awareness is also created for husbands and relatives not to prevent women to access health services. Furthermore, nearby health facility for women is critical to ensure equal health service access and to meet the target of maternal and child healthcare services utilisation. As well as policy-makers should enhance the economic status of women.

The spatial distribution of health service access in Ethiopia was not random. High health service access was observed in eastern Benishangul Gumuz, southwest Amhara, southern Afar, DireDawa, Harari and northern Somali regions. The primary and secondary clusters were located in Dire Dawa, Gambela, Benishangul-Gumuz, western Oromia and southwest Amhara regions, respectively. Women who lived in the primary, secondary and tertiary clusters were more likely to access health services. The Kriging interpolation of women’s health service access revealed that there would be good women’s health service access in Benishangul-Gumuz, western Amhara, Dire Dawa, eastern Oromia and northern Somali regions. This finding was supported by a similar study done about women’s home delivery that states a high proportion of home delivery is found in Amhara, Afar, Tigray, Oromia, and South Nations Nationalities and People's Region,30 and incomplete maternal continuum care utilisation.13 Therefore, policy-makers should give priority attention to the areas where women had less likely to access health services in Ethiopia.

In the multilevel mixed effect logistic regression analysis, secondary and higher educational status, rich wealth status, and exposure to media were positively associated, and being a rural resident was negatively associated with women’s health service access, respectively.

Women with secondary and higher education were 1.6 and 2 times more likely to access health services. The current evidence was similar to studies done in Ethiopia17 18 and the Republic of Vanuatu.31 This might be education’s power to enhance women’s health-seeking behaviours, educated women actively involved in reading materials and discussions that would enhance their knowledge.17 Moreover, educated women might give priority attention to their health, strive to know the benefits of healthcare services and illiterate women may fail to receive health services during pregnancy.32 In line with this finding, stakeholders should enhance women’s educational status by using different educational delivery mechanisms, for instance, a health professional could provide appropriate consultation service during women’s health facility visits, and educational messages for women could be sent to women through short message services. Rich women were 1.4 times more likely to access health services. This finding was similar to studies done in Ethiopia17 18 and the Republic of Vanuatu.31 This might be women’s better economic status which increases their healthcare-seeking behaviour and autonomy in healthcare decision-making, they may afford to cover medical and transportation costs. Furthermore, wealthy women may cover their drug and transportation costs.18 In addition, poor women could have poor utilisation of preventive, promotive and curative aspects of health services. So, policy-makers should enhance women’s wealth status, and encourage women to have their daily income.

The women who had media exposure were 1.2 times more likely to access health services. This finding was similar to studies done in Ethiopia13 18 and Nepal.33 This could be the power of mass media in disseminating information concerning maternal health that may enhance women’s knowledge and attitude towards health service access and utilisation.33 Furthermore, women who were exposed to the media were more likely to be informed about health services utilisation. Therefore, the availability of media spots is critical to the delivery of health-related information messages that could reach out to women in their homes.

Rural resident women were 82% less likely to access health services. This finding was similar to studies done in Ethiopia.17 18 This might be because health facilities are inadequately accessible and available in rural areas. Rural resident women might be limited in access to education and health information.27 Moreover, in rural areas adequate health professionals might not be available and so appropriate counselling services might not be delivered. In resource-limited settings, health facilities and necessary infrastructure such as roads and clean water are less likely available in the rural side of the country. Therefore, stakeholders better if they close such gaps in the rural areas to ensure women’s health service access and utilisation.

Conclusions

In Ethiopia, inadequate numbers of women had good health service access. Women had faced problems with getting money for treatment, distance to health facilities, not wanting to go alone to health facilities and getting permission to go for medical care. The distribution of women’s health service access was spatially clustered in Ethiopia. Women’s health services access was positively associated with education, wealth and media exposure. However, rural resident women were negatively correlated with health service access. Therefore, stakeholders should pay priority attention to the cold spot areas of women’s health service access. Improving women’s educational status, and providing women with various media access, is critical for health service access. In addition, stakeholders should create job opportunities for poor women, and deliver public health promotion regarding maternal and child health service utilisation. Providing appropriate consolation services and constructing nearby health facilities are also possible interventions to enhance women’s health service access.