BackgroundExclusive breastfeeding is defined as feeding infants only breast milk, be it directly from breast or expressed, except drops or syrups consisting of vitamins, mineral supplements or medicine. Exclusive breastfeeding is one of the essential actions for infant development and survival. However, the prevalence of exclusive breastfeeding in Ethiopia has been estimated at 52% which is far less than the World Health Organization (WHO) recommendations. Moreover, there are inconsistencies among estimates in different districts of the country. Therefore, this study aimed to assess the prevalence and associated factors of exclusive breastfeeding among mothers in Gozamin district, northwest Ethiopia.MethodsUsing the simple random sampling technique, seven kebeles (lowest administrative units) were selected as the primary sampling unit of the district. Sample mother-infant pairs were then selected using the systematic random sampling technique that involved our moving from house to house in each village. Data were collected from 506 mother-infant pairs using interviews. Factors associated with exclusive breastfeeding were determined using logistic regression. The measure of association used was the odds ratio, and statistical tests with p-values of less than 0.05 were considered as statistically significant.ResultsIn this study, the prevalence of exclusive breastfeeding among mothers was 74.1% (95% CI 70.80, 79.10). For government employee mothers, the odds of exclusive breastfeeding were reduced by half compared to housewives (AOR 0.49, 95% CI 0.26, 0.94). Mothers who did not receive breastfeeding counseling after delivery were 0.43 times less likely to practice exclusive breastfeeding compared with mothers who received the services (AOR 0.43, 95% CI 0.25, 0.72). Mothers who gave birth at health institutions were more likely to practice exclusive breastfeeding.ConclusionEven though the estimated prevalence is relatively high, more effort to meet WHO recommendations is still necessary. Therefore, we suggest health institutions encourage hospital birthing and increase breastfeeding counseling after delivery, and employers needs to give longer maternity leave to improve exclusive breastfeeding practice.

BackgroundThe response of HIV patients to antiretroviral therapy could be measured by its strong predictor, the CD4+ T cell (CD4) count for the initiation of antiretroviral therapy and proper management of disease progress. However, in addition to HIV, there are other factors which can influence the CD4 cell count. Patient’s socio-economic, demographic, and behavioral variables, accessibility, duration of treatment etc., can be used to predict CD4 count.MethodsA retrospective cohort study was conducted to examine the predictors of CD4 count among ART users enrolled in the first 6 months of 2010 and followed upto mid 2014. The covariance components model was employed to determine the predictors of CD4 count over time.ResultsA total of 1196 ART attendants were used to analyze their data descriptively. Eight hundred sixty-one of the attendants had two or more CD4 count measurements and were used in modeling their data using the linear mixed method. Thus, the mean rates of incensement of CD4 counts for patients with ambulatory/bedridden and working baseline functional status were 17.4 and 30.6 cells/mm3 per year, respectively. After adjusting for other variables, for each additional baseline CD4 count, the gain in CD4 count during treatment was 0.818 cells/mm3 (p value <0.001). Patient’s age and baseline functional status were also statistically significantly associated with CD4 count.ConclusionIn this study, higher baseline CD4 count, younger age, working functional status, and time in treatment contributed positively to the increment of the CD4 count. However, the observed increment at 4 year was unsatisfactory as the proportion of ART users who reached the normal range of CD4 count was very low. To see their long term treatment outcome, it requires further research with a sufficiently longer follow up data. In line with this, the local CD4 count for HIV negative persons should also be investigated for better comparison and proper disease management.

Background In Ethiopia, despite considerable improvement of measles vaccination, measles outbreaks is occurring in most parts of the country. Understanding the neighborhood variation in childhood measles vaccination is crucial for evidence-based decision-making. However, the spatial pattern of measles-containing vaccine (MCV1) and its predictors are poorly understood. Hence, this study aimed to explore the spatial pattern and associated factors of childhood MCV1 coverage. Methods An in-depth analysis of the 2016 Ethiopia demographic and health survey data was conducted, and a total of 3722 children nested in 611 enumeration areas were included in the analysis. Global Moran’s I statistic and Poisson-based purely spatial scan statistics were employed to explore spatial patterns and detect spatial clusters of childhood MCV1, respectively. Multilevel logistic regression models were fitted to identify factors associated with childhood MCV1. Results Spatial hetrogeniety of childhood MCV1 was observed (Global Moran’s I = 0.13, p -value < 0.0001), and seven significant SaTScan clusters of areas with low MCV1 coverage were detected. The most likely primary SaTScan cluster was detected in the Afar Region, secondary cluster in Somali Region, and tertiary cluster in Gambella Region. In the final model of the multilevel analysis, individual and community level factors accounted for 82% of the variance in the odds of MCV1 vaccination. Child age (AOR = 1.53; 95%CI: 1.25–1.88), pentavalent vaccination first dose (AOR = 9.09; 95%CI: 6.86–12.03) and third dose (AOR = 7.12; 95%CI: 5.51–9.18, secondary and above maternal education (AOR = 1.62; 95%CI: 1.03–2.55) and media exposure were the factors that increased the odds of MCV1 vaccination at the individual level. Children with older maternal age had lower odds of receiving MCV1. Living in Afar, Oromia, Somali, Gambella and Harari regions were factors associated with lower odds of MCV1 from the community-level factors. Children far from health facilities had higher odds of receiving MCV1 (AOR = 1.31, 95%CI = 1.12–1.61). Conclusion A clustered pattern of areas with low childhood MCV1 coverage was observed in Ethiopia. Both individual and community level factors were significant predictors of childhood MCV1. Hence, it is good to give priority for the areas with low childhood MCV1 coverage, and to consider the identified factors for vaccination interventions.

In developing countries including Ethiopia stunting remained a major public health burden. It is associated with adverse health consequences, thus, investigating predictors of childhood stunting is crucial to design appropriate strategies to intervene the problem stunting. The study uses data from the Ethiopian Demographic and Health Survey (EDHS) conducted from January 18 to June 27, 2016 in Ethiopia. A total of 8117 children aged 6–59 months were included in the study with a stratified two stage cluster sampling technique. A Bayesian multilevel logistic regression was fitted using Win BUGS version 1.4.3 software to identify predictors of stunting among children age 6–59 months. Adjusted odds ratio (AOR) with 95% credible intervals was used to ascertain the strength and direction of association. In this study, increasing child’s age (AOR = 1.022; 95% CrI 1.018–1.026), being a male child (AOR = 1.16; 95%CrI 1.05–1.29), a twin (AOR = 2.55; 95% CrI 1.78–3.56), having fever (AOR = 1.23; 95%CrI 1.02–1.46), having no formal education (AOR = 1.99; 95%CrI 1.28–2.96) and primary education (AOR = 83; 95%CrI 1.19–2.73), birth interval less than 24 months (AOR = 1.40; 95% CrI 1.20–1.61), increasing maternal BMI (AOR = 0.95; 95% CrI 0.93–0.97), and poorest household wealth status (AOR = 1.78; 95% CrI 1.35–2.30) were predictors of childhood stunting at individual level. Similarly, region and type of toilet facility were predictors of childhood stunting at community level. The current study revealed that both individual and community level factors were predictors of childhood stunting in Ethiopia. Thus, more emphasize should be given by the concerned bodies to intervene the problem stunting by improving maternal education, promotion of girl education, improving the economic status of households, promotion of context-specific child feeding practices, improving maternal nutrition education and counseling, and improving sanitation and hygiene practices.

Background Second line anti-tuberculosis drugs are substantially complex, long term, more costly, and more toxic than first line anti-tuberculosis drugs. In Ethiopia, evidence on the incidence and predictors of adverse drug events has been limited. Thus, this study aimed at assessing incidence and predictors of major adverse drug events among drug resistant tuberculosis patients on second line tuberculosis treatment in Amhara Regional State public hospitals, Ethiopia. Methods A multi-center retrospective cohort study was conducted on 570 drug resistant tuberculosis Patients. Data were entered in to EPI-Data version 4.2.0.0 and exported to Stata version 14 for analysis. Proportional hazard assumption was checked. The univariate Weibull regression gamma frailty model was fitted. Cox-Snell residual was used to test goodness of fit and Akaike Information Criteria (AIC) for model selection. Hazard ratio with 95% CI was computed and variables with P -value < 0.05 in the multivariable analysis were taken as significant predictors for adverse drug event. Results A total of 570 patients were followed for 5045.09 person-month (PM) observation with a median follow-uptime of 8.23 months (Inter Quartile Range (IQR) =2.66–23.33). The overall incidence rate of major adverse drug events was 5.79 per 100 PM (95% CI: 5.16, 6.49). Incidence rate at the end of 2nd, 4th, and 6th months was 13.73, 9.25, 5.97 events per 100 PM observations, respectively. Age at 25–49 (Adjusted Hazard Ratio (AHR) = 3.36, 95% CI: 1.36, 8.28), and above 50 years (AHR = 5.60, 95% CI: 1.65, 19.05), co-morbid conditions (AHR = 2.74 CI: 1.12, 6.68), and anemia (AHR = 3.25 CI: 1.40, 7.53) were significant predictors of major adverse drug events. Conclusion The incidence rate of major adverse drug events in the early 6 months of treatment was higher than that of the subsequent months. Age above 25 years, base line anemia, and co-morbid conditions were independent predictors of adverse drug events. Thus, addressing significant predictors and strengthening continuous follow-ups are highly recommended in the study setting.