Background:Complications of pregnancy and childbirth are a leading cause of maternal morbidities and mortalities in developing countries. World Health Organization (WHO) estimates that over 500,000 women and girls die each year from the complications. Despite proven interventions that could prevent death or disability during pregnancy and childbirth, maternal mortality remains a major burden in many developing countries, including Ethiopia. This study aimed to assess the status of antenatal care utilization and modeling Bayesian Count Regression model for the determinants of utilization of antenatal care services visits among pregnant women in Amhara regional state.Methods: It was a community based analytical cross-sectional study, conducted in Amhara region among women in the reproductive age group (age 15-49). The analysis was based on data from women who had at least one birth during the 5 years preceding the survey. The source of data was the 2014 Ethiopia Demographic and Health Survey which was accessed from Central Statistical Agency. Bayesian analytic approach was applied to model the mixture data structure inherent in zero-inflated count data by using the zero-inflated Poisson model. Conclusions: About three-fourth pregnant mothers were not receive adequate number of visits recommended by the World Health Organization. Mother's education, media exposure, residence and wealth index were significant predictors of ANC service utilization. This research suggests that to reduce the inadequate number of ANC visits in Amhara region, attention should be given to women with low educational status and rural women. Open Access© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Workie and Lakew J Big Data (2018) 5:7 RESEARCH

Under-five mortality is defined as the likelihood of a child born alive to die between birth and fifth birthday. Mortality of under the age of five has been the most targets of public health policies and may be a common indicator of mortality levels. Thus, this study aimed to assess the under-five child mortality and modeling Bayesian zero-inflated regression model of the determinants of under-five child mortality. A community-based cross-sectional study was conducted using the 2016 Ethiopia Demographic and Health Survey data. The sample was stratified and selected in a two-stage cluster sampling design. The Bayesian analytic approach was applied to model the mixture arrangement inherent in zero-inflated count data by using the negative Binomial–logit hurdle model. About 71.09% of the mothers had not faced any under-five deaths in their lifetime while 28.91% of the women experienced the death of their under-five children and the data were found to have excess zeros. From Bayesian Negative Binomial—logit hurdle model it was found that twin (OR = 1.56; HPD CrI 1.23, 1.94), Primary and Secondary education (OR = 0.68; HPD CrI 0.59, 0.79), mother’s age at the first birth: 16–25 (OR = 0.83; HPD CrI 0.75, 0.92) and ≥ 26 (OR = 0.71; HPD CrI 0.52, 0.95), using contraceptive method (OR = 0.73; HPD CrI 0.64, 0.84) and antenatal visits during pregnancy (OR = 0.83; HPD CrI 0.75, 0.92) were statistically associated with the number of non-zero under-five deaths in Ethiopia. The finding from the Bayesian Negative Binomial–logit hurdle model is getting popular in data analysis than the Negative Binomial–logit hurdle model because the technique is more robust and precise. Furthermore, Using the Bayesian Negative Binomial–logit hurdle model helps in selecting the most significant factor: mother’s education, Mothers age, Birth order, type of birth, mother’s age at the first birth, using a contraceptive method, and antenatal visits during pregnancy were the most important determinants of under-five child mortality.

Background Coronavirus disease is a major global public health problem. The contagious disease caused by a newly discovered coronavirus, coronavirus disease 2019 (COVID-19), was declared a pandemic following the outbreak of cases of respiratory illness in 2019. While studies assessed COVID-19 knowledge, attitude, and practice in Ethiopia the findings were highly variable and inconsistent. 1 , 2 , 3 , 4 Therefore, this study assessed the pooled status of knowledge, attitude, and prevention practices regarding COVID -19 in Ethiopia. Methods International and national electronic databases, including PubMed/MEDLINE, EMBASE, CINAHL, Google Scholar, Science Direct, and Google, were systematically searched. All observational studies on COVID-19 knowledge, attitude, and prevention practices in Ethiopia were included. We assessed heterogeneity among the included studies using the Cochrane Q test statistics and I 2 test. Lastly, a random-effects meta-analysis model was fitted to estimate the pooled proportion of knowledge, attitude, and prevention practices toward COVID-19 in Ethiopia. Results Our search identified 206 studies, 13 of which were included in the final analysis. Adequate knowledge, good attitude, and good prevention practice towards COVID-19 in Ethiopia were observed in 70.25% (95% confidence interval [CI], 61.82, 78.02), 69.08% (95% CI: 55.42, 81.24), and 41.62% (95% CI: 27.77, 56.17) of total participants across studies, respectively. Conclusion The results of this study revealed low proportions of adequate knowledge, attitudes, and preventive practices toward COVID-19 in Ethiopia. The lowest pooled proportion was observed in the Amhara region. These findings indicate the need to revise plans and policies to improve the knowledge, attitudes, and prevention practices of people towards COVID-19 in Ethiopia, especially in the Amhara region.

BackgroundDental caries are a significant public health problem. It is a disease with multifactorial causes. In Sub-Sahara Africa, Ethiopia is one of the countries with a high record of dental caries. This study was to determine the risk factors affecting dental caries using both Bayesian and classical approaches.MethodsThe study design was a retrospective cohort study in the period of March 2009 to March 2013 dental caries patients Hawassa Haik Poly Higher Clinic. The Bayesian logistic regression procedure was adapted to make inference about the parameters of a logistic regression model. The purpose of this method was generating the posterior distribution of the unknown parameters given both the data and some prior density for the unknown parameters.ResultsFrom this study the prevalence of natural dental caries was 87% and non-natural dental caries were 13%. The age group of 18–25 was higher prevalence of dental caries than the other age groups. From Bayesian logistic regression, we found out that rural patients, do not clean their teeth, patients from SNNPR and age group 18–25 are statistically significant. The finding from the Bayesian statistics approach is getting popular in data analysis than classical statistics because the technique is more robust and precise.ConclusionsBayesian approach was found to be better than classical method as the value of the standard errors in Bayesian approaches is smaller than that of classical logistic regression. The Bayesian credible interval is smaller than the length of the confidence interval for all significant risk factors. Age, sex, place of residence, region and habit of cleaning teeth was found to have a significant effect on dental caries patients.

Perinatal mortality is the total number of fetal death and early neonatal death. Perinatal mortality is a major public health problem, particularly in developing countries, and is used as an implication of the economic, social, and health status of the country. The analysis of count data with hurdle and zero-inflated count models are the most applicable methods to accommodate with excessive zero counts. Therefore, this study aimed to apply the Poisson logit hurdle model to identify the associated factors of perinatal mortality in Ethiopia. A cross-sectional study design was conducted in Ethiopia using EDHS 2016. The sample was multistage stratified and units selected in a two-stage cluster sampling design. The association between the outcome and the independent variables was determined using the Poisson logit hurdle model. A total of 7230 mothers were obtained from EDHS 2016 survey. Of these mothers, 95.27% of them never, 4.47% of them once, 0.26% twice, and 0.04% three times experienced perinatal mortality preceding 5 years of the survey. The main protective associated factors were 40–49 years age of mother, having long preceding birth interval, and secondary + husband education. Parity is greater than four, rural residence, Caesarean section delivery, multiple pregnancies, institutional delivery, having a history of abortion were increased perinatal mortality per mother. This study implies that intervention is needed on family planning and mode of delivery to minimize perinatal mortality in the country.