Prevalence and associated factors of HIV-TB co-infection among HIV patients: a retrospective Study

Background: TB/HIV co-infection is a major public health problem in many parts of the world. But the prevalence of co-infection was varies among countries. This study was designed to assess prevalence of TB/HIV co-infection and to determine its factors. Methods: A retrospective study was done among HIV-positive patients at Hiwot Fana hospital from December, 2014 to 2018. The study participants were selected by simple random sampling. Patients with incomplete chart reviews were excluded and demographic, clinical and laboratory information were analyzed using SPSS and STATA. Uni-vitiate and bivariate logistic regressions were applied. Results: Five hundred fourteen patients were enrolled in this study. Of these, 187(37.4%) had TB. Bivariate logistic analysis showed that HIV patients with regards to marital status[AOR = 2.6; 95%CI = 1.19- 2.89] , education status [AOR = 3.74; 95%CI = 2.47\u20135.66], weight less than 50kg [AOR = 2.54; 95% CI = 1.35 \u2013 4.81], CD4 level < 200cells/mm3 [AOR = 4.57; 95%CI = 2.38\u2013 6.86] and patient who were at WHO clinical stage III [AOR = 7.8; 95%CI = 5.15 \u2013 8.55] were significantly associated with TB/HIV co-infection. Conclusion: The prevalence of TB among HIV patients was high and predicted by marital, education status, weight, CD4 cell count and WHO clinical stage III

Joint binary response modelling for childhood comorbidity in Ethiopia.

Childhood diarrhea and Acute Respiratory Infection (ARI) are two diseases with similar risk factors in tropical developing regions. The objective of this study was to employ a joint binary response model and identify risk factors for childhood diarrhea and ARI in children under the age of five. A joint binary response model that takes into account the interdependence of the two diseases was used. Explanatory variables such as residence, vaccination, mother's education, and antenatal care visits during pregnancy were found to be statistically significant risk factors for diarrhea in the joint model, whereas residence, the number of children ever born, vaccination, mother's education, and wealth index were found to be statistically significant risk factors for childhood Acute Respiratory Infection. We discovered a common odds ratio value (4.30) greater than one, indicating a positive relationship between the two childhood diseases. As a result, using a joint model to assess the risk factors for diarrhea and acute respiratory infection (ARI) was reasonable. Furthermore, the standard errors of the parameter estimates in the joint response model were found to be smaller than the corresponding standard errors in the separate models. The risk factors such as residence, vaccination, and mother's education all had a significant effect on the two correlated dichotomous response variables, diarrhea and ARI

Spatial pattern and predictors of malaria in Ethiopia: Application of auto logistics regression.

IntroductionMalaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia.MethodsA weighted sample of 15,239 individuals with rapid diagnosis test obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. The auto logistics spatial binary regression model was used to investigate the predictors of malaria.ResultsThe final auto logistics regression model was reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR = 2.401, 95% CI: (2.125-2.713) ]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR = 52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR = 34.95, 95%CI: (27.159-44.963)]. Similarly, individuals in Amhara [AOR = 0.243, 95% CI:(0.195-0.303], Oromiya [AOR = 0.197, 955 CI: (0.158-0.244)], Dire Dawa [AOR = 0.064, 95%CI(0.049-0.082)], Addis Ababa[AOR = 0.057,95%CI:(0.044-0.075)], Somali[AOR = 0.077,95%CI:(0.059-0.097)], SNNPR[OR = 0.329, 95%CI: (0.261-0.413)] and Harari [AOR = 0.256, 95%CI:(0.201-0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for one meter increase in altitude, the odds of positive rapid diagnostic test (RDT) decreases by 1.6% [AOR = 0.984, 95% CI: (0.984-0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR = 1.671, 95% CI: (1.504-1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression.ConclusionsThis study found that the incidence of malaria in Ethiopia had a spatial pattern which is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who lived in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramics tiles roof houses. Moreover, using a protected anti-mosquito net was reducing the risk of malaria incidence

Prevalence and associated factors of neonatal mortality in Ethiopia

Abstract Neonatal mortality is the death of a live-born baby within the first 28 days of birth. For the selected households, neonatal mortality was collected from children aged 0–28 days and women aged 15–49. The neonatal period is a significant 4-week period in human life because it carries a greater mortality risk. To identify the determinant factors of neonatal mortality in Ethiopia based on EDHS 2016 data with the application of count regression models. In this study, all neonates in Ethiopia were born within the 5 years preceding EDHS 2016 of the source population in the selected EAs from September to December 2015. Count regression models were used to analyze the data. A total of 10,641 live-born neonates within the previous 5 years of EDHS 2016 had neonatal mortality of women aged 15–49, which was considered in the study to be 7193. The data were found to have excess zeros (96.6%), and the variance (0.052) was higher than its mean (0.04). The count regression model (ZINB) was best fitted to the data with maximum likelihood parameter estimation methods. The average neonatal mortality difference in multiple births was increased by IRR = 8.53 times compared with a single birth. The average number of neonatal deaths experienced during breastfeeding was lower (IRR = 0.38) than that experienced by mothers who did not experience breastfeeding their child. The average neonatal mortality difference in rural residences was increased by IRR = 3.99 times compared to urban mothers' residences. In this study, the prevalence of Neonatal mortality in Ethiopia was higher. For selected ZINB count regression models of explanatory variables, such as multiple birth types, having rural residence factors of neonatal mortality increased the risk of death. However, having early breastfeeding, a female household head, and antenatal visits (1–4) and (5–10) during pregnancy decrease the risk of neonatal death