Bayesian modeling of quantiles of body mass index among under-five children in Ethiopia.

BACKGROUND: Body Mass Index (BMI) is a measurement of nutritional status, which is a vital pre-condition for good health. The prevalence of childhood malnutrition and the potential long-term health risks associated with obesity in Ethiopia have recently increased globally. The main objective of this study was to investigate the factors associated with the quantiles of under-five children's BMI in Ethiopia. METHODS: Data on 5,323 children, aged between 0-59 months from March 21, 2019, to June 28, 2019, were obtained from the Ethiopian Mini Demographic Health Survey (EMDHS, 2019), based on the standards set by the World Health Organization. The study used a Bayesian quantile regression model to investigate the association of factors with the quantiles of under-five children's body mass index. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the country-specific marginal posterior distribution estimates of model parameters, using the Brq R package. RESULTS: Out of a total of 5323 children included in this study, 5.09% were underweight (less than 12.92 BMI), 10.05% were overweight (BMI: 17.06 - 18.27), and 5.02% were obese (greater than or equal to 18.27 BMI) children's. The result of the Bayesian quantile regression model, including marginal posterior credible intervals (CIs), showed that for the prediction of the 0.05 quantile of BMI, the current age of children [ ß = -0.007, 95% CI :(-0.01, -0.004)], the region Afar [ ß = - 0.32, 95% CI: (-0.57, -0.08)] and Somalia[ ß = -0.72, 95% CI: (-0.96, -0.49)] were negatively associated with body mass index while maternal age [ ß = 0.01, 95% CI: (0.005, 0.02)], mothers primary education [ ß = 0.19, 95% CI: (0.08, 0.29)], secondary and above [ ß = 0.44, 95% CI: (0.29, 0.58)], and family follows protestant [ ß = 0.22, 95% CI: (0.07, 0.37)] were positively associated with body mass index. In the prediction of the 0.95 (or 0.85?) quantile of BMI, in the upper quantile, still breastfeeding [ ß = -0.25, 95% CI: (-0.41, -0.10)], being female [ ß = -0.13, 95% CI: (-0.23, -0.03)] were negatively related while wealth index [ ß = 0.436, 95% CI: (0.25, 0.62)] was positively associated with under-five children's BMI. CONCLUSIONS: In conclusion, the research findings indicate that the percentage of lower and higher BMI for under-five children in Ethiopia is high. Factors such as the current age of children, sex of children, maternal age, religion of the family, region and wealth index were found to have a significant impact on the BMI of under-five children both at lower and upper quantile levels. Thus, these findings highlight the need for administrators and policymakers to devise and implement strategies aimed at enhancing the normal or healthy weight status among under-five children in Ethiopia.

Spatiotemporal modeling of under-five mortality and associated risk factors in Ethiopia using 2000-2016 EDHS data.

BACKGROUND: The under-five mortality rate serves as a key indicator of the performance of a country's healthcare system. Despite a minor decline, Ethiopia continues to face a persistently high under-five mortality rate across different zones. Thus, this study aimed to identify the risk factors of under-five mortality and the spatiotemporal variation in Ethiopian administrative zones. METHOD: This study used the 2000-2016 Ethiopian Demographic and Health Survey (EDHS) data which were collected using a two-stage sampling method. A total of 43,029 (10,873 in 2000, 9,861 in 2005, 11,654 in 2011, and 10,641 in 2016) weighted sample under-five child mortality were used. The space-time dynamic model was employed to account for spatial and time effects in 65 administrative zones in Ethiopia. RESULTS: From the result of a general nesting spatial-temporal dynamic model, there was a significant space-time interaction effect [γ = -0.1444, 95% CI(-0.6680, -0.1355)] for under-five mortality. The increase in the percentages of mothers illiteracy [ß = 0.4501, 95% CI (0.2442, 0.6559)], not vaccinated[ß= 0.7681, 95% CI (0.5683, 0.9678)], unimproved water[ß= 0.5801, CI (0.3793, 0.7808)] were increased death rates for under five children while increased percentage of contraceptive use [ß= -0.6609, 95% CI (-0.8636, -0.4582)] and antenatal care unit visit > 4 times [ß= -0.1585, 95% CI(-0.1812, -0.1357)] were contributed to the decreased under-five mortality rate at the zone in Ethiopia. CONCLUSIONS: Even though the mortality rate for children under five has decreased over time, still there is higher in different zones of Ethiopia. There exists spatial and temporal variation in under-five mortality among zones. Therefore, it is very important to consider spatial neighborhood's and temporal context when aiming to avoid under-five mortality.

Spatial patterns and predictors of unintended pregnancy among reproductive age women in Ethiopia.

INTRODUCTION: Unintended pregnancy is amajor sexual and reproductive health problem that imposes substantial health, economical and psychosocial costs to individuals and society as well as significant emotional distress to women, families, and society. The main aim of this study was to investigate the spatial distribution and predictors of unintended pregnancy in Ethiopian regions and administrative zones. METHODS: This study was conducted based on data from 2016 Ethiopian Demographic and Health Survey. The prevalence of unintended pregnancy across regions and or zones was assessed using spatial analysis, and the effect of different factors on unintended pregnancy in Ethiopia was investigated using a generalized linear mixed model with a multistage clustered sampling strategy. The crude and best linear unbiased predictor estimations of zones were integrated with the shape file data to demonstrate the performance of each zone on maps. RESULTS: The prevalence of unintended pregnancy for reproductive women in Ethiopia was29.49%. The highest rates of unintended pregnancy were recorded in the North Gondar zone of the Amhara region and the Jima zone in the Oromiya region. The mixed effects model revealed that age [AOR = 0.78, 95% CI, 0.62-0.97], residence [AOR = 2.62, 95%CI, 1.94, 7.27], marital status [AOR = 0.05, 95%CI, 0.01-0.38], women education [AOR = 1.34, 95%CI, 0.75-2.39], smoking cigarettes [AOR = 3.67, 95CI, 1.17-11.56], and poorer wealth index [AOR = 1.89, 95% CI, 1.51-2.31] were significantly associated with unintended pregnancy. CONCLUSION: In Ethiopia, unintended pregnancy is a public health issue, and prevention stratagem for unintended pregnancy among reproductive women need to be focused based on the identified predictors. The spatial distribution of unintended pregnancy varied greatly at zonal and regional levels in Ethiopia. Hence, we recommended that, creating awareness of sexual and reproductive health with special priority to the identified hotspot areas (Amhara, Oromiya and SNN regions) to reduce unintended pregnancy. Emphasis on fertility and contraceptive techniques should be given to couples by health professionals.

Evaluation of the interventions on HIV case management and its association with cART adherence and disclosure of the disease status among HIV-positive adults under treatment.

The rate of prevalence of HIV among adults has been increasing in sub-Saharan African countries over the last decade. The objective of this study was to evaluate the interventions on HIV case management based on cART adherence and disclosure of HIV disease status among HIV-positive adults under treatment. A retrospective cohort longitudinal data was conducted on 792 randomly selected patients in the study area. Engagement of HIV-positive persons into care and achieving treatment outcomes such as the disclosure of HIV status and cART adherence were fundamental for HIV prevention strategy. The two response variables under the current investigation were evaluation of intervention on HIV case management interims cART adherence and disclosure of HIV status. Binary logistic regression was conducted for separate models. Among the predictors, age of patients (AOR = 1.020, 95% CI (1.016, 1.191); p value = 0.005), the number of follow-up (AOR = 1.014, 95% CI (1.023, 1.030); p value < 0.0001). CD4 cell count (AOR = 0.981; 95% CI (0.765, 0.971), p value < 0.01), Marital status (AOR = 1.013; 95% CI (1.002, 1.015), p value = 0.006), female patients (AOR = 1.014; 95% CI (1.001, 1.121), p value < 0.007), rural (AOR = 0.982; 95% CI (0.665, 0.998), p value = 0.004), non-educated adult patients (AOR = 0.950, 95% CI (0.92. 0.98). p value = 0.003), Non-existence of social violence (AOR = 1.012, 95% CI (1.008, 1.234), p value < 0.01), adult with non-opportunistic diseases (AOR = 1.021, 95% CI (1.002. 1.042). p value = 0.001) significantly affected the two response variables jointly. Interventions on HIV case management lead to an efficient continuum of successful treatment outcomes like disclosure of HIV status and cART adherence. Hence, HIV case management intervention and the two results had a positive association. HIV case management intervention should be given to younger patients, rural residents, and non-educated patients to disclose the disease status and to have a long life with the virus. Health-related education should be conducted for the community in general and for patients in particular on how HIV is transferred from an infected person to an uninfected one. This helps to reduce the stigma of patients and to deliver social support to patients.

Prevalence and associated factors of neonatal mortality in Ethiopia.

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.

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

INTRODUCTION: Malaria 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: This 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.

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.

Prevalence and risk factors of type-2 diabetes mellitus in Ethiopia: systematic review and meta-analysis.

Diabetes mellitus (DM) is a public health problem in developing as well as developed nations. DM leads to many complications that are associated with higher morbidity and mortality worldwide. Therefore, the current study was planned to assess the prevalence and risk factors of type-2 DM in Ethiopian population. Six electronic databases such as: PubMed, Scopus, Hinari, Web of science, Google Scholar, and African Journals Online were searched for studies published in English up December 30, 2020. Newcastle-Ottawa Scale was used for quality assessment of the included studies. The data was extracted by Microsoft excel and analyzed through Stata version 16 software. The random effect meta-regression analysis was computed at 95% CI to assess the pooled prevalence and risk factors of type-2 DM. Forty observational studies were included in this systematic review and meta-analysis. The pooled prevalence of DM in Ethiopia was 6.5% (95% CI (5.8, 7.3)). The sub-group analysis revealed that the highest prevalence of DM was found in Dire Dawa city administration (14%), and the lowest prevalence was observed in Tigray region (2%). The pooled prevalence of DM was higher (8%) in studies conducted in health facility. Factors like: Age ≥ 40 years ((Adjusted Odds Ratio (AOR): 1.91 (95% CI: 1.05, 3.49)), Illiterate (AOR: 2.74 (95% CI: 1.18, 6.34)), Cigarette smoking (AOR: 1.97 (95% CI: 1.17, 3.32)), Body mass index (BMI) ≥ 25 kg/m2 (AOR: 2.01 (95 CI: 1.46, 2.27)), family history of DM (AOR: 6.14 (95% CI: 2.80, 13.46)), history of hypertension (AOR: 3.00 (95% CI: 1.13, 7.95)) and physical inactivity (AOR: 5.79 (95% CI: 2.12, 15.77)) were significantly associated with type-2 DM in Ethiopian population. In this review, the prevalence of type-2 DM was high. Factors like: Older age, illiteracy, cigarette smoking, MBI ≥ 25, family history of DM, history of hypertension and physical inactivity were an identified risk factors of type-2 DM. Therefore, health education and promotion will be warranted. Further, large scale prospective studies will be recommended to address possible risk factors of type-2 DM in Ethiopian population.

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 = 2.47-5.66], weight less than 50kg [AOR = 2.54; 95% CI = 1.35 - 4.81], CD4 level < 200cells/mm3 [AOR = 4.57; 95%CI = 2.38- 6.86] and patient who were at WHO clinical stage III [AOR = 7.8; 95%CI = 5.15 - 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.