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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.
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