Spatial distribution and associated factors of community based health insurance coverage in Ethiopia: further analysis of Ethiopian demography and health survey, 2019.

Community-Based Health Insurance is an emerging concept for providing financial protection against the cost of illness and improving access to quality health services for low-income households excluded from formal insurance and taken as a soft option by many countries. Therefore, exploring the spatial distribution of health insurance is crucial to prioritizing and designing targeted intervention policies in the country. A total of 8,663 households aged 15-95 years old were included in this study. The Bernoulli model was used by applying Kulldorff methods using the SaTScan software to analyze the purely spatial clusters of community based health insurance. ArcGIS version 10.3 was used to visualize the distribution of community-based health insurance coverage across the country. Mixed-effect logistic regression analysis was also used to identify predictors of community-based health insurance coverage. Community based health insurance coverage among households had spatial variations across the country by regions (Moran's I: 0.252, p < 0.0001). Community based health insurance in Amhara (p < 0.0001) and Tigray (p < 0.0001) regions clustered spatially. Age from 15-29 and 30-39 years (Adjusted Odds Ratio 0.46(AOR = 0.46, CI: 0.36,0.60) and 0.77(AOR = 0.77, CI: 0.63,0.96), primary education level 1.57(AOR = 1.57, CI: 1.15,2.15), wealth index of middle and richer (1.71(AOR = 1.71, CI: 1.30,2.24) and 1.79(AOR = 1.79, CI: 1.34,2.41), family size > 5, 0.82(AOR = 0.82, CI: 0.69,0.96),respectively and regions 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 likely utilize community based health insurance than the Amhara region respectively in Ethiopia. Community based health insurance coverage among households in Ethiopia was found very low still. The government needs to develop consistent financial and technical support and create awareness for regions with lower health insurance coverage.

Terefe B ，Alemu TG ，Techane MA ，Wubneh CA ，Assimamaw NT ，Belay GM ，Tamir TT ，Muhye AB ，Kassie DG ，Wondim A ，Tarekegn BT ，Ali MS ，Fentie B ，Gonete AT ，Tekeba B ，Kassa SF ，Desta BK ，Ayele AD ，Dessie MT ，Atalell KA ... -  《BMC PUBLIC HEALTH》

Spatial Pattern and Associated Factors of ANC Visits in Ethiopia: Spatial and Multilevel Modeling of Ethiopian Demographic Health Survey Data.

Although there is an increase in having antenatal care (ANC), still many women lack recommended ANC contacts in Ethiopia. Therefore, this study was aimed at determining spatial patterns and associated factors of not having ANC visits using the Ethiopian Demographic and Health Survey (EDHS) 2016 data. A two-stage stratified cluster sampling technique was employed based on EDHS data from January 18 to June 27, 2016. A total of 7,462 women were included in the study. ArcGIS version 10.7 software was used to visualize the spatial distribution. The Bernoulli model was applied using Kilduff SaTScan version 9.6 software to identify significant purely spatial clusters for not having ANC visits in Ethiopia. A multivariable multilevel logistic regression model was used to identify individual- and community-level determinants of not having antenatal care. Model comparison was checked using the likelihood test and goodness of fit was assessed by the deviance test. The primary clusters' spatial window was located in Somalia, Oromia, Afar, Dire Dawa, and Harari regions with the log-likelihood ratio (LLR) of 133.02, at p < 0.001 level of significance. In this study, Islam religion (adjusted odds ratio (AOR) = 0.7 with 95% confidence interval (CI) (0.52,0.96)), mother education being primary (AOR = 0.59, 95% CI (0.49,0.71)), distance from health facility being a big problem (AOR = 0.76, CI (0.65,0.89)), second birth order (AOR = 1.35, CI (1.03, 1.76)), richer wealth index (AOR = 0.65, CI (0.51,0.82)), rural residence (AOR = 2.38, CI (1.54,3.66)), and high community media exposure (AOR = 0.68, CI (0.52,0.89)) were determinants of not having antenatal care in Ethiopia. The spatial distribution of ANC in Ethiopia is non-random. A higher proportion of not having ANC is found in northeast Amhara, west Benishangul Gumuz, Somali, Afar, north, and northeast SNNPR. On the other hand, a low proportion of not having ANC was found in Tigray, Addis Ababa, and Dire Dawa. In Ethiopia, not having antenatal care is affected by both individual- and community-level factors. Prompt attention by the Federal Ministry of Health is compulsory to improve ANC especially in rural residents, uneducated women, poor households, and regions like Oromia, Gambella, and Somalia.

Tessema ZT ，Akalu TY 《-》

Spatial Distribution and Factors Associated with Khat Chewing among Adult Males 15-59 Years in Ethiopia Using a Secondary Analysis of Ethiopian Demographic and Health Survey 2016: Spatial and Multilevel Analysis.

Khat chewing has become prevalent in the world due to the improvement of road and air transportation. In Ethiopia, khat chewing is more prevalent and widely practiced by men. Khat has a negative effect on social, economic, and mental health. There is variation in khat cultivation, use, and factors that associated with khat chewing in the Ethiopian regions. Therefore, this study is aimed at showing spatial distribution and factors associated with khat chewing among male adults 15-59 years in Ethiopia. A total of 12,594 men were included in this study. ArcGIS version 10.7 software was used to show the spatial distribution of chewing khat among adult men in Ethiopia. The Bernoulli model was applied using Kilduff SaTScan version 9.6 software to identify significant purely spatial clusters for chewing khat in Ethiopia. A multilevel logistic regression model was fitted to identify factors associated with khat chewing. A P value < 0.05 was taken to declare statistically significant predictors. The EDHS 2016 survey showed that the high proportion of chewing khat was found in Dire Dawa, Harari, Southern Oromia, Somali, and Benishangul Gumuz regions. In spatial scan statistics analysis, a total of 126 clusters (LLR = 946.60, P value < 0.001) were identified. Age group 30-44 years old (AOR = 1.60, 95% CI: 1.37, 1.86) and 45-59 years old (AOR = 1.33, 95% CI: 1.09, 1.61), being single (AOR = 1.86, 95% CI: 1.64, 2.12), Muslim religion followers (AOR = 15.03, 95% CI: 11.90, 18.90), media exposed (AOR = 0.77, 95% CI: 0.68, 0.86), had work (AOR = 2.48, 95% CI: 2.08, 2.95), alcohol drinker (AOR = 3.75, 95% CI: 3.10, 4.53), and region (Afar, Amhara, Benishangul Gumuz, Gambela, Harari, Oromia, Somali, Southern Nations, Nationalities, and People's Region (SNNPR), and Tigray) and two cities (Addis Ababa and Dire Dawa) were statistically significant factors affecting chewing khat in Ethiopia. In Ethiopia, the spatial distribution of khat chewing among adult men was nonrandom. A high proportion of khat chewing was observed in Dire Dawa, Harari, Southern Oromia, Somali, and Benishangul Gumuz regions. Older age group, being single marital status, alcohol drinker, media unexposed, had no work, and Muslim religion follower were factors affecting khat chewing. Policymakers should be given spatial attention in reducing the prevalence of chewing khat by teaching the health impact of khat chewing through media in the identified regions.

Tessema ZT ，Zeleke TA 《-》

Spatial distribution and associated factors of health insurance coverage in Ethiopia: further analysis of Ethiopia demographic and health survey, 2016.

Health insurance is one of the instruments to achieve universal health coverage. However, in Ethiopia, the coverage of health insurance is very low and varies from place to place as well. Therefore, exploring the spatial distribution of health insurance is important to prioritize and design targeted intervention programs in the country. A total of 16,583 reproductive age group women (15-49 years) were included in this study. The Bernoulli model was used by applying Kulldorff methods using the SaTScan software to analyse the purely spatial clusters of health insurance coverage. ArcGIS version 10.3 was used to visualize the distribution of health insurance coverage across the country. Mixed-effect logistic regression analysis was also used to identify predictors of health insurance coverage. Health insurance coverage among women aged 15-49 years had spatial variations across the country (Moran's I: 0.115, p < 0.001). Health insurance coverage in Amhara (p < 0.001) and Tigray (p < 0.001) National Regional States clustered spatially. Reading newspapers at least once a week (Adjusted Odds Ratio (AOR) = 1.78, 95% CI: (1.18-2.68))), 40-44 years of age (AOR = 2.14, 95% CI: (1.37-3.35)), clerical working mothers (AOR = 4.33, 95% CI: (2.50-7.49)), mothers' with secondary school education (AOR = 1.77; 95% CI: (1.21-2.58)), mothers' with higher school education (AOR = 2.62; 95% CI: (1.63-4.23)), having more than 5 family members (AOR = 1.25; 95% CI: (1.01-1.55)) and richest wealth quantile (AOR = 3.43, 95% CI: (1.96-6.01)) were predictors of health insurance coverage among reproductive age group women in Ethiopia. Health insurance coverage was very low in Ethiopia and had spatial variations across the country. The hot spot areas with low health insurance coverage need more coherent and harmonized action such as strengthening financial protection through national health packages, sharing experience from regions which have better health insurance coverage and using mass media to increase awareness and confidence of potentials in the systems, which may encourage them to enrol.

Kebede SA ，Liyew AM ，Tesema GA ，Agegnehu CD ，Teshale AB ，Alem AZ ，Yeshaw Y ... -  《-》

Spatial distribution and geographical heterogeneity factors associated with households' enrollment level in community-based health insurance.

Healthcare service utilization is unequal among different subpopulations in low-income countries. For healthcare access and utilization of healthcare services with partial or full support, households are recommended to be enrolled in a community-based health insurance system (CBHIS). However, many households in low-income countries incur catastrophic health expenditure. This study aimed to assess the spatial distribution and factors associated with households' enrollment level in CBHIS in Ethiopia. A cross-sectional study design with two-stage sampling techniques was used. The 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. STATA 15 software and Microsoft Office Excel were used for data management. ArcMap 10.7 and SaTScan 9.5 software were used for geographically weighted regression analysis and mapping the results. A multilevel fixed-effect regression was used to assess the association of variables. A variable with a p < 0.05 was considered significant with a 95% confidence interval. Nearly three out of 10 (28.6%) households were enrolled in a CBHIS. The spatial distribution of households' enrollment in the health insurance system was not random, and households in the Amhara and Tigray regions had good enrollment in community-based health insurance. A total of 126 significant clusters were detected, and households in the primary clusters were more likely to be enrolled in CBHIS. Primary education (AOR: 1.21, 95% CI: 1.05, 1.31), age of the head of the household >35 years (AOR: 2.47, 95% CI: 2.04, 3.02), poor wealth status (AOR: 0.31, 95% CI: 0.21, 1.31), media exposure (AOR: 1.35, 95% CI: 1.02, 2.27), and residing in Afar (AOR: 0.01, 95% CI: 0.003, 0.03), Gambela (AOR: 0.03, 95% CI: 0.01, 0.08), Harari (AOR: 0.06, 95% CI: 0.02, 0.18), and Dire Dawa (AOR: 0.02, 95% CI: 0.01, 0.06) regions were significant factors for households' enrollment in CBHIS. The secondary education status of household heads, poor wealth status, and media exposure had stationary significant positive and negative effects on the enrollment of households in CBHIS across the geographical areas of the country. The majority of households did not enroll in the CBHIS. Effective CBHIS frameworks and packages are required to improve the households' enrollment level. Financial support and subsidizing the premiums are also critical to enhancing households' enrollment in CBHIS.

Demsash AW 《Frontiers in Public Health》