Sociodemographic characteristics of the population studied
A total of 8,663 household members were interviewed. All 5,999 (69.25%) of respondents were rural residents, and all 6,751 and 2,189 (77.93% and 25.27%) of study participants’ age were male and classified respectively. between 30 and 39 years old. Regarding the level of education, 4,120 (47.56%) of them had no formal school. The household wealth of 24.44% of the study subjects was in the richest wealth quintiles, 17.30% were in the poorest wealth quintiles, respectively (Table 1).
AMC coverage by regions in Ethiopia
In Ethiopia (in 2019), the weighted overall health insurance coverage showed higher coverages in Amhara, SNNPR, while lower in Somali, Afar and Gambela regions with overall coverage of around 28 %. (Fig. 1).
Spatial distribution of AMC coverage
This study showed that the spatial distribution of AMC coverage was spatially clustered in Ethiopia with I 0.252 from Global Moran (p
Spatial SaTScan analysis of CBHI coverage (model based on Bernoulli)
The most likely clusters (primary clusters) and secondary health insurance coverage have been identified. In the 2019 EDHS, spatial analysis statistics identified a total of high-performing and low-performing health insurance coverage spatial groups. Of these, spatial scan analysis demonstrated that a total of 81 significant clusters, including primary, secondary, tertiary, quaternary, quinary and senary clusters consisted of sixty-five clusters, ten clusters, two clusters, two clusters, one cluster, and one cluster respectively. The primary satellite scan window was detected in Amhara, Tigray, a small part of areas two and four in Afar, and part of Metekel and Agew Awi in Benishangul regions at (12.322718 N, 37.959425 E with a radius value of 265.25 km This cluster window includes 1859 residents and 874 cases with a relative risk of 3.67 and a log odds ratio (LLR) of 463.18 at a p-value of 0.0001 Health insurance coverage was 2.46 times higher for using health insurance services than other parties outside the window in this scan window Bright blue colors (rings) indicate the most spatial windows statistically significant health insurance coverage There was higher insurance coverage within the cluster than outside the cluster.
The secondary cluster was also identified by SaT Scan as the most likely cluster and did not spatially overlap with the most likely cluster. The secondary SaTScan circular window is mainly observed in the SNNPR region of Ethiopia (Dawro, Gamo Gofa and Wolayita areas) at (6.272978 N, 36.862733 E) with a distance of 124.08 km. This cluster also has 292 inhabitants and 119 cases with a relative risk of 2.10 and 33.52 are the probability with a p-value of 0.0001. This group was statistically interpreted as health insurance coverage inside the circular window had a 2.10 times higher probability of accessing it than households outside the window. Similarly, analysis of the SaTscan window demonstrated that the third group was located mainly in the central Oromia of the southwestern zones of Shewa and Arsi at (7.531183 N, 38.662596 E) with a radius of 34.42 km. Moreover, it consists of s57 and 60 cases with a relative risk of 3.54, and the Log-likelihood ratio is 33.45 with the p-value of 0.0001. The study population inside the circular window was 3.54 times more likely to be a user of the health insurance service, unlike the household population outside the spatial window.
The Quaternary cluster is geostatistically located (7.648661 N, 39.688764 E) / and with a radius of 61.78 km and does not spatially overlap with any other significant cluster. 57 and 36 populations and cases existed respectively in this cluster with a relative risk of 3.18 and a log-likelihood of 22.14 with a P-value of 0.0001. Compared to households outside the circular window, the household inside the circular window had 3.18 times the probability of becoming a health insurance user. The Quinary and Senary clusters have also been geostatistically located (9.227458 N, 42.199756 E)/0 km and (9.227458 N, 42.199756 E)/0 km and do not spatially overlap with other clusters significant, respectively. Each of them has 28 and 29 populations; 22 and 18 cases existed respectively in these clusters with a relative risk of 3.94, 3.10 and a log-likelihood of 22.14, 12.12 with a P-value of 0.0001, 0.0024. Compared to households outside the circular window, households inside the circular window had a 3.94 and 3.10 times probability of becoming health insurance users, respectively (Table 2 and Fig. 4 ).
The use of Kriging interpolation technique generates a forecast of high health insurance coverage, which has been identified in parts of Ethiopia including almost most of Amhara and Tigray regions. The following regions of central Oromia and northeastern SNNPR also showed 13-37% coverage. On the contrary, low coverage forecast was observed in southern and northeastern regions of Somali, South Afar, Gambella and central Benishangul Gumuz regions (Fig. 5).
Individual and community factors associated with health insurance in Ethiopia
The result of the random effects analysis
In the null model, the ICC value was 39.3%, implying that the total variability in health insurance resulted from the difference between the clusters, while the remaining 60.7% was attributable to the differences. individual. The combined individual and community level model (Model III) was the best fitting model as it had the lowest deviance value unlike the other models.
The result of the fixed effects analysis
There are various variables run in multivariate logistic regression analysis at multiple levels including age, education level, wealth index, gender, radio and television, family size, place of residence, region. Among these variables, age, education level, wealth index, family size and region were significantly associated with health insurance coverage.
The probability of the age groups 15-29 and 30-39 enrolling in a health insurance plan was 0.46 (AOR=0.46, CI: 0.36, 0.60) and 0.77 (AOR = 0.77, CI: 0.63, 0.96) times less than among age groups >= 60 years.
With respect to education level, the probability of primary school teacher household members enrolling in a community health insurance scheme was 1.57 (AOR = 1.57, CI: 1.15, 2, 15) times higher than those with higher education, respectively.
According to their wealth index, the probability of the middle and affluent classes to join a community health insurance scheme was 1.71 (AOR = 1.71, CI: 1.30, 2.24) and 1.79 ( AOR = 1.79, CI: 1.34, 2.41) times higher than the poorest class, respectively.
Households with a member of more than five people have a probability of being a member of the community health insurance scheme was 0.82 (AOR = 0.82, CI: 0.69, 0.96) times lower than households with less than or equal to five children.
The probability of having community health insurance by region in Afar, Oromia, Somali, Benishangul Gumuz, SNNPR, Gambella, Harari, Addis Ababa and Dire Dawa was 0.002 (AOR=0.002, CI: 0.006, 0.04) , 0.11 (AOR=0.11, CI: 0.06, 0.21) 0.02 (AOR=0.02, CI: 0.007, 0.04), 0.04 (AOR=0.04, CI: 0.02, 0.08), 0.09 (AOR=0.09, CI: 0.05, 0.18), 0.004 (AOR=0.004, CI: 0.02, 0.08), 0 .06 (AOR = 0.06, CI: 0.03, 0.14), 0.07 (AOR = 0.07, CI: 0.03, 0.16) and 0.03 (AOR = 0.03 , CI: 0.02, 0.07) times less used than their counterparts from the Amhara region respectively (Table 3).