Descriptive statistics on the socioeconomic characteristics of the two samples and primary results of the two-steps cluster analyses can be found in the appendices (supplementary files). Below, we present the key findings of the cluster analyses as well as the results of the regression analyses.
Cluster analysis
In the two-step cluster analyses for Ghana and Nigeria (Table 1) regarding family planning services, three distinct clusters are identified for access to these services in each country. The clusters are labeled as high, medium, and poor access to family planning services based on the services used by women in each cluster. The cluster with high-access to family planning services captures 19.1% and 21.4% of women in the Nigeria sample and Ghana sample respectively. The other extreme is the third cluster that consists of women whose access can be described as poor; 71.5% of women in Nigeria belong to this cluster and 64.2% in Ghana.
Both two-step cluster analyses for access to maternal health services result in five clusters of women of reproductive ages in Nigeria and Ghana categorized as higher, high, medium, low and poor access to maternal health services based on the type of services used by the women. The higher-access cluster captures 29.6% of women in the Nigeria sample and 26.3% of the women in the Ghana sample. Relative to the other four clusters, a larger proportion of members of this cluster report that they accessed government hospitals for antenatal care and used institutional maternal care more. The high-access cluster consists of 21.6% of women in Nigeria and 23.0 % in Ghana. For both countries, this cluster has a lower proportion of women who accessed government health centers for antenatal care or got assistance from physicians during childbirth. Members of the medium-access cluster in both countries accessed private facilities for antenatal care as well as for childbirth. This cluster of women makes up 25.9% of the Nigeria sample and 18.2% of the Ghana sample. Members of the low-access cluster in both countries are women who report that they accessed government health posts/dispensaries for antenatal care but did not have skilled assistance during childbirth. In the Nigeria sample, 4.4% of women fall into this cluster and in the Ghana sample, this share is 7.1%. Lastly, 18.5% and 25.4% of women from the Nigeria and Ghana sample respectively are members of the poor-access cluster who did not receive institutionalized maternal care. For both countries, the poor-access cluster has a high proportion of members who had home childbirth and used traditional birth attendants during childbirth.
Regression analysis
The dependent variables in the four multinomial logistic regressions were the four cluster membership variables generated in the cluster analyses. Tables 2 and 3 present the odds ratios for the four regressions, information about the independent variables used and the full results of the regression analyses can be found in Appendix B of the supplementary file.
For family planning services, the results in Table 2 show that in both countries, women with no education, compared to women with secondary or higher education, are more likely to have poor-access to family planning in Nigeria (OR=2.544, 95% CI:1.907- 3.395, p≤ 0.01) and in Ghana (OR=1.527, 95% CI: 1.173- 1.988, p≤ 0.01). Increased odds of having poor-access to family planning services also apply to women in Ghana who do not belong to white -collar workers, who belong to the bottom two wealth quintiles, but not women who live in rural areas. The same higher odds of poor-access apply to women in Nigeria belong to the services occupational category (OR=1.283, 95% CI: 1.002- 1.642, p≤0.05), compared with white-collar workers; as much as 3 times among the poorest quintile than the richest quintile (95% CI: 1.825- 6.396, p≤ 0.01); and who have no insurance (OR=1.374, 95% CI: 1.011- 1.867, p≤0.05) compared to those with insurance.
Table 3 shows the regression results of access to maternal health services in Nigeria and Ghana. In Nigeria, women with primary or no education are more likely to have poor- (OR= 1.387, 95% CI: 1.140- 1.687, p≤ 0.01) or low-access to maternal health services (OR= 1.786, 95% CI: 1.247- 2.557, p≤ 0.01); women who are not working have only poor-access maternal health services in Nigeria (OR= 1.579, 95% CI 1.081- 2.307, p≤ 0.01); women in all household wealth quintile are more likely to have high- or poor-access to maternal health services; and women without insurance are more likely to have high or poor-access to maternal health services. Ghana data show that women with primary (OR= 1.38, 95% CI: 1.036- 1.838, p≤0.05) or no education (OR= 1.542, 95% CI: 1.115- 2.132, p≤0.01) have higher odds of poor-access to maternal health services; only women in agriculture occupational group have high-access to maternal health services compared to women in white-collar sector (OR= 1.781, 95% CI: 1.022- 3.104, p≤0.05 ); and women without health insurance have higher odds of access to maternal health care service.