Introduction The COVID-19 vaccine is a key intervention toward containing the pandemic. Vaccines are thought to be a form of defense. One of the major challenges to managing the COVID-19 pandemic is the uncertainty or willingness to accept vaccinations. Our study aimed willingness to get the COVID-19 vaccine and the factors that influence it in Mettu Woreda, Ilu Ababor Zone, Ethiopia. Methodology Cross-sectional study design was conducted from August 1, 2021, to September 1, 2021, among rural residents of Mettu woreda’s of Ilu Ababor Zone, Oromia, Ethiopia. The semi-structured data collection format was prepared to assess the magnitude of the communities’ acceptance of the COVID-19 vaccine. A multivariable logistic regression analysis was used to determine the predictors of communities’ acceptance of the COVID-19 vaccine at 95% CI. Results Of 350 participants from the study area, 59% of them were males and 41% females. Less than one-third (29.8%) of participants were willing to accept the COVID-19 vaccine. The results multivariable logistic regression revealed that the age group of ≥50 years (OR=0.29; 95% CI: −3.1–0.34) as compare with the 18–29 years, low monthly income (OR=0.85; 95% CI: −0.74–2.33), low perception level (OR=0.35; 95% CI: −2.03–0.24), government unemployed (OR=0.86; 95% CI: −0.72–0.1), low Level of acceptance (OR=0.72; 95% CI: −0.67, 0.08) and unwillingness to test COVID-19 (OR=0.13; 95% CI: −4.47, 0.58) were predictors of willingness to receive COVID-19 vaccine. Conclusion Less than one-third of the study, participants were willing to accept the COVID-19 vaccine. The likelihood of Willingness to accept the COVID-19 vaccine was low in the study area. Overall; low education, low vaccination perception, low income, jobless occupation, older age, and unwillingness to test for COVID-19 were associated with greater willingness to take the COVID-19 vaccine and are significantly associated with willingness to get the COVID-19 immunization.

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Maternal mortality is one of the socio-economic problems and widely considered a serious indicator of the quality of a health. Ethiopia is considered to be one of the top six sub-Saharan countries with severe maternal mortality. The objective of this study was to investigate the effects of the Demographic and Socio-economic determinant factors of maternal mortality in Ethiopia. Data from the 2016 Ethiopia Demographic and Health Survey indicated that the sample of women (15–49) was (n = 10,103). The Bayesian multilevel we were used to explore the major risk factors and regional variations in maternal mortality in Ethiopia. Markov chain Monte Carlo methods with non-informative priors have been applied. The Deviance Information Criterion model selection criteria were used to select the appropriate model. The analysis result, 145 (1.43%) mothers were died due to pregnancy. Using model selection criteria Bayesian multilevel random coefficient was found to be appropriate. With this model, Age of mother, marital status, number of living children, wealth index and Education are found to be the significant determinants of maternal mortality in Ethiopia. The study indicated that there was within and between regional variations in maternal mortality. Inference is the fully Bayesian multilevel model based on recent Markov chain Monte Carlo techniques. The socioeconomic, demographic and environmental determinants included in the study were found to be statistically significant. The result of the Bayesian multilevel model in this study has shown that educational attainment, wealth index, an age of mother, status and number of living children was a significant factor of maternal mortality.

Diarrhea is commonly a sign of an infection in the intestinal tract that is caused by different bacteria, virus and parasitic entities. It is one of the leading causes of child mortality worldwide, especially in sub-Saharan Africa countries including Ethiopia. The main objective of this study was to identify spatial disparities and associated factors of under- five diarrhea disease in Ilubabor zone, Oromia regional state, Ethiopia. The study has been conducted in Ilu Aba Bor zone of entire districts and the data is basically both primary and secondary which were obtained from each woreda health office of Ilu Aba Bor zone and corresponding mother or care givers of sampled child. Spatial disparities of under-five diarrhea were identified using global and local measures of spatial autocorrelation. Geo-additive regression model was used to identify the spatial disparities and associated factors of under-five diarrheal disease. The value of global and local measures of spatial autocorrelation shows that under-five diarrheal disease varies according to geographical location and shows significant positive spatial autocorrelation. The results of Geo-additive regression model showed that statistically significant relationship between under-five diarrhea disease and independent variables .There is evidence of significant under-five diarrheal disease clustering in Ilu Aba Bor zone, southwest Ethiopia. Model based data analysis showed that there is significant relationship between Under-five diarrhea and covariates (mother’s age, mother’s education, source of drinking water, quality of toilet facility, DPT 3 vaccination, Polio 3 vaccination and household wealth index.).