Spatial clusters distribution and modelling of health care autonomy among reproductive‐age women in Ethiopia: spatial and mixed‐effect logistic regression analysis

BACKGROUND: While millions of women in many African countries have little autonomy in health care decision-making, in most low and middle-income countries, including Ethiopia, it has been poorly studied. Hence, it is important to have evidence on the factors associated with women’s health care decision making autonomy and the spatial distribution across the country. Therefore, this study aimed to investigate the spatial clusters distribution and modelling of health care autonomy among reproductive-age women in Ethiopia. METHODS: We used the 2016 Ethiopian Demographic and Health Survey (EDHS) data for this study. The data were weighted for design and representativeness using strata, weighting variable, and primary sampling unit to get a reliable estimate. A total weighted sample of 10,223 married reproductive-age women were included in this study. For the spatial analysis, Arc-GIS version 10.6 was used to explore the spatial distribution of women health care decision making and spatial scan statistical analysis to identify hotspot areas. Considering the hierarchical nature of EDHS data, a generalized linear mixed-effect model (mixed-effect logistic regression) was fitted to identify significant determinants of women’s health care decision making autonomy. The Intra-Class Correlation (ICC) were estimated in the null model to estimate the clustering effect. For model comparison, deviance (-2LLR), Akakie Information Criteria (AIC), and Bayesian Information Criteria (BIC) parameters were used to choose the best-fitted model. Variables with a p-value < 0.2 in the bivariable analysis were considered for the multivariable analysis. In the multivariable mixed-effect logistic regression analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were reported to declare the strength and significance of the association between women’s decision-making autonomy and independent variables. RESULTS: In this study, about 81.6% (95% CI: 80.6%, 82.2%) of women have autonomy in making health care decisions. The spatial distribution of women’s autonomy in making health decisions in Ethiopia was non-random (global Moran’s I = 0.0675, p < 0.001). The significant hotspot areas of poor women’s autonomy in making health care decisions were found in north Somali, Afar, south Oromia, southwest Somali, Harari, and east Southern Nations Nationalities and Peoples (SNNP) regions. In the mixed-effect logistic regression analysis; being urban (AOR = 1.59, 95% CI: 1.04, 2.45), having secondary education (AOR = 1.60, 95% CI: 1.06, 2.41), having an occupation (AOR = 1.19, 95% CI: 1.01, 1.40) and being from the richest household (AOR = 2.14, 95% CI: 1.45, 3.14) were significantly associated with women autonomy in deciding for health care. CONCLUSIONS: The spatial distribution of women’s autonomy in making the decision for health care was non-random in Ethiopia. Maternal education, residence, household wealth status, region, and maternal occupation were found to influence women’s autonomy. Public health interventions targeting the hotspot areas of poor women autonomy through enhancing maternal occupation and employment is needed to improve women empowerment in making decisions for health care.