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Background: Increasing the use of healthcare is a significant step in improving health outcomes in both the short and long term. However, the degree of the relationship between utilization of health services and health outcomes is affected by the quality of the services rendered, the timeliness of treatment and follow-up care. In this study, we investigated whether the National Health Insurance Scheme (NHIS) is helping pregnant women in accessing health services in Ghana. Methods: Data for the study were obtained from the women’s file of the 2014 Ghana Demographic and Health Survey. All women with birth history and aged 15–49 constituted our sample (n = 4271). We employed binary logistic regression analysis in investigating whether the NHIS was helping pregnant women in accessing health service. Statistical significance was set at <0.05. Results: Most women had subscribed to the NHIS [67.0%]. Of the subscribed women, 78.2% indicated that the NHIS is helping pregnant women in accessing healthcare. Women who had subscribed to the NHIS were more likely to report that it is helping pregnant women in accessing health service [aOR = 1.70, CI = 1.38–2.10]. We further noted that women who had at least four antenatal visits were more likely to indicate that NHIS is helping pregnant women in accessing health services [aOR = 3.01, CI = 2.20–4.14]. Women with secondary level of education [aOR= 1.42; CI: 1.04–1.92] and those in the richest wealth quintile [aOR = 3.51; CI = 1.94–6.34] had higher odds of indicating that NHIS is helping pregnant women in accessing healthcare. However, women aged 45–49 [aOR = 0.49; CI = 0.26–0.94], women in the Greater Accra [aOR = 0.29; CI = 0.16–0.53], Eastern [aOR = 0.12; CI = 0.07–0.21], Northern [aOR = 0.29; CI = 0.12–0.66] and Upper East [aOR = 0.17; CI = 0.09–0.31] regions had lower odds of reporting that NHIS is helping pregnant women in accessing health services. Conclusion: To enhance positive perception towards the use of health services among pregnant women, non-subscribers need to be encouraged to enrol on the NHIS. Together with non-governmental organizations dedicated to maternal and child health issues, the Ghana Health Service’s Maternal and Child Health Unit could strengthen efforts to educate pregnant women on the importance of NHIS in maternity care.
We used data from the women recode file of the 2014 GDHS. This is the sixth version since the survey started in Ghana in 1988. It forms part of the Measure DHS Program which seeks to monitor core health indicators in LMICs. Two stage sample design was carried out. The initial stage involved the selection of 427 clusters constituting the enumeration areas (EAs). The enumeration areas emerged from urban (216) and rural (211) locations across all the ten regions at the time. The second phase involved the selection of 11,835 households from the EAs and this resulted in a total sample of 9396 women aged 15–49. The survey had 97.3% response rate [31]. For the purpose of our study, 4271 women with complete data were included. The dependent variable was whether the NHIS is helping pregnant women for health services or not. The question was posed to women aged 15–49 during the 2014 GDHS. It was accompanied by two responses: “Yes” and “No”. This variable was chosen on the premise that one of the priorities of the pro-poor NHIS in Ghana is to ease the financial burden in accessing maternity services [32]. As a result, investigating the perception of women on whether this mandate is being achieved is essential for future health financing policy directions. Eight independent variables were included in this study. Of these, the main independent variable was health insurance subscription. The other included variables were Age (15–19,20-24,25-29,30–34-35-39,40-44,45–49), education (No education, primary, secondary, tertiary), residence (rural,urban), antenatal care (ANC) visits (Below 4 Visits, At least 4 Visits), current pregnancy status (pregnant, not pregnant) and region (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, Upper West) and wealth quintile (poorest, poorer, middle, richer, richest). Wealth, in the DHS, is a composite measure computed by combining data on a household’s ownership of carefully identified assets including television, bicycle, materials used for house construction, sanitation facilities and type of water access. Principal component analysis was used to transform these variables into wealth index by placing individual households on a continuous measure of relative wealth. The DHS segregates households into five wealth quintiles; poorest, poorer, middle, richer and richest. These variables have been reported as essential for investigating NHIS [33, 34]. Stata version 13 was used to analyse the data using both descriptive and inferential statistics. In our descriptive analysis, we computed the proportion of women in each of the aforementioned independent variables. The proportion of women who indicated either “Yes” or “No” on whether the NHIS is helping pregnant women in health services was also calculated (see Table 1). Chi-square tests were conducted in order to ascertain the independent variables that had significant association with the dependent variable. With the exception of “current pregnancy status”, all the independent variables were significant and were included in our inferential analysis, where three Binary Logistic Regression models were fitted in all. This analytical approach was the most suitable option premised on the fact that our dependent variable had two outcomes. The first model (Model I) accounted for NHIS subscription and whether it helps pregnant women in accessing health services. In model two, we adjusted for the effect of ANC visit-as a woman needs to first access healthcare during pregnancy in order to know whether the NHIS helps in healthcare during pregnancy or otherwise. All the seven significant independent variables were fitted in the final model (Model III) after which post-estimation test (Linktest) was conducted to determine whether the model is devoid of model specification error and also to ensure that relevant variables have not been omitted. Multicollinearity was also checked and we found no evidence of multicollinearity. Results for Model I was reported as odds ratio (OR) whilst that of Model II and III were reported as adjusted odds ratios (aOR) with their respective confidence intervals which were considered statically significant at 95%. Samples were weighted to adjust for the sample design. Socio-demographic characteristics of women (N = 4271) Source: 2014 GDHS
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