Background: Fertility is the element of population dynamics that has a vital contribution towards changing population size and structure over time. The global population showed a major increment from time to time because of these dynamics, particularly in south Asia and sub-Saharan Africa including Ethiopia. So this study targeted on the factors affecting fertility among married women in Ethiopia through the framework of multilevel count regression analysis using EDHS 2016 data.Methods: The sampling design for EDHS 2016 was a two-stage stratified cluster sampling design, where stratification was achieved by separating every region into urban and rural areas except the Addis Ababa region. Results: Among the random sample of 6141 women in the country, 27150 births were recorded based on EDHS 2016 report. The histograms showed that the data has a positively skewed distribution extremely picked at the beginning. Two- level negative binomial regression model was fitted to spot out the determinants of fertility among married women in Ethiopia because it has the smallest value for the fit statistics and the variance of the data was higher than its mean.Conclusion: Findings from the study revealed that contraception method used, residence, educational level of women, women’s age at first birth, and proceeding birth interval were the major predictors of fertility among married women in Ethiopia. Moreover, the estimates from the random effect result revealed that there is more fertility variation between the enumeration areas than within the enumeration areas. Application of standard models by ignoring this variation ought to embrace spurious results, then multilevel modeling is recommended for such types of hierarchical data.

Background Fertility is the element of population dynamics that has a vital contribution toward changing population size and structure over time. The global population showed a major increment from time to time due to fertility. This increment was higher in south Asia and sub-Saharan Africa including Ethiopia. So this study targeted the factors affecting fertility among married women in Ethiopia through the framework of multilevel count regression analysis using the 2016 Ethiopian Demographic and Health Survey data. Methods Secondary data set on the birth records were obtained from the 2016 Ethiopia Demographic and Health Survey. The survey was a population-based cross-sectional study with a two-stage stratified cluster sampling design, where stratification was achieved by separating every region into urban and rural areas except the Addis Ababa region because it is entirely urban. A two-level negative binomial regression model was fitted to spot out the determinants of fertility among married women in Ethiopia. Results Among the random sample of 6141 women in the country, 27,150 births were recorded based on the 2016 Ethiopian Demographic and Health Survey report. The histograms showed that the data has a positively skewed distribution not extremely picked at the beginning. Findings from the study revealed that the contraception method used, residence, educational level of women, women’s age at first birth, and proceeding birth interval were the major predictors of fertility among married women in Ethiopia. Moreover, the estimates from the random effect result revealed that there is more fertility variation between the enumeration areas than within the enumeration areas. Conclusion Unobserved enumeration area fertility differences that cannot be addressed by a single-level approach were determined using a two-level negative binomial regression modeling approach. So, the application of standard models by ignoring this variation ought to embrace spurious results, then for such hierarchical data, multilevel modeling is recommended.

BACKGROUND Education is a vehicle for national economic development equally as for individual advancement. Historically, girls were denied opportunities for schooling in most of the regions and societies of Ethiopia. So this study geared towards the factors of women's education level in Ethiopia. METHODS Secondary data on women’s data sets were obtained from the 2016 Ethiopia Demographic and Health Survey. A population-based cross-sectional study design was used for the survey. The sampling technique used for the survey was the two-stage sampling technique, which is stratified in the first stage and equal probability systematic selection technique in the second stage. An ordinal logistic regression model was fitted to identify the determinants of women education in Ethiopia. RESULTS Among the random sample of 17137 women, the majority 7647(44.62%) were illiterate. This is evidence that most the women are still under the darkness of illiteracy and having meager participation in higher education. The odds ratios for women’s age at first birth, women’s age at marriage, women from rural areas, families wealth index: poorer, middle, richer, the richest, religion: Catholic, Muslim, and Protestant religions were given as 1.022 (p value: <0.0001), 1.02 (p value: <0.0001), 0.121 (p value: <0.0001), 1.492 (p value:=0.0235), 1.971 (p value: <0.0001), 3.072 (p value: <0.0001), 4.582(p value: <0.0001), 0.185 (p value: =0.0074), 0.762 (p value: =0.0175), and 0.75 (p value: =0.0444) respectively, and they are statistically significant predictors of education level among women in Ethiopia. CONCLUSION The results of this study showed that most of the women were illiterate due to different reasons. Thus, the federal government, the Ministry of Education, and the Regional Education Bureaus must enforce the legal age of marriage and increase the number of schools and other infrastructure in rural areas.

Background Women’s education is the base for faster economic growth, longer life expectancy, lower population growth, improved quality of life, and a high rate of investment return in developing countries. Historically, girls were denied opportunities for schooling in most of the regions and societies of Ethiopia. So this study targeted a multilevel analysis of women’s education in Ethiopia using the 2016 Ethiopian Demographic and Health Survey data. Methods Secondary data on women’s data sets were obtained from the 2016 Ethiopia Demographic and Health Survey. A population-based cross-sectional study design was used for the survey. The sampling technique used for the survey was the two-stage sampling technique, which is stratified in the first stage and equal probability systematic selection technique in the second stage. The multi-level ordinal logistic regression model was fitted to identify the determinants of women’s education in Ethiopia. Results Among the random sample of 17137 women, the majority, 65.6 percent were rural residents. Somali regional state (75.3 percent) and the capital city Addis Ababa (8.6 percent) had the highest and lowest percentages of women illiteracy respectively than the remaining administrative units of Ethiopia. The minimum values for the fit statistics and the indicative value of the intra-class correlation (68.3%) of the multilevel model showed its appropriateness to the data. Among the predictors in the final multilevel ordinal logistic regression analysis, women’s age at first marriage, residence, and family’s wealth index were significant predictors of women’s education in Ethiopia. Moreover, the estimates from the random effect result revealed that there is more variation in women’s education between the enumeration areas than within the enumeration areas. Conclusion A multi-level ordinal logistic regression analysis has determined higher-level differences in women's education that could not be addressed by a single-level approach. So, the application of standard models by ignoring this variation ought to embrace spurious results, then for such hierarchical data, multilevel modeling is recommended.

Backgrounds Congestive heart failure is a serious chronic condition when the heart’s muscles become too damaged and a condition in which one or both ventricles cannot pump sufficient blood to meet the metabolic needs of the body. This study aimed to identify factors affecting the complications time of congestive heart failure patients treated from January 2016 to December 2019 at Felege Hiwot comprehensive specialized referral hospital in Bahir Dar, Ethiopia. Methods A hospital-based retrospective data collection was collected from the medical charts of 218 randomly selected congestive heart failure patients. The Kaplan-Meier curve and the Cox proportional hazards model were used to compare and identify the factors associated with time to complication in patients with congestive heart failure. Results The median complication time of congestive heart failure patients was 22 months [95% CI: 21.98–28.01]. About 194 (88.99%) of the patients were complicated. The Kaplan-Meier curve depicts the survival probability of complicated patients decreasing as the complication time increases. The hazard ratios for serum sodium concentration, left ventricular ejection fraction, patients from rural areas, age of patients, serum hemoglobin concentration, and New York heart association classes I, II, and III were given 0.94 [95% CI: 0.90–1.00], 0.74 [95% CI: 0.65–0.85], 0.75 [95% CI: 0.68–0.84], 1.28 [95% CI: 1.12–1.46], 0.89 [95% CI: 0.85–0.94], 0.44 [95% CI: 0.36–0.53], 0.54 [95% CI: 0.47–0.62] and 0.73 [95% CI: 0.65–0.81] respectively, and they are statistically associated with the complication time of congestive heart failure patients. Conclusions The median complication time of congestive heart failure patients was 22 months. This study strongly suggests that healthcare awareness should be strengthened earlier about the potential complications for patients with lower serum sodium concentrations below the threshold and aged congestive heart failure patients to reduce the risk of developing complications.