Geographically weighted regression analysis to assess hotspots of early sexual initiation and associated factors in Ethiopia.

Background: Early sexual initiation (ESI) causes unintended pregnancy, sexually transmitted infections (STI), high risk of depression and anxiety, developmental delays, lack of emotional maturity, and difficulty in pursuing education. This study aims to analyze the geographically weighted regression and associated factors of ESI of women in Ethiopia. Methods: The study utilized data from the Ethiopian Demographic and Health Survey, 2016. It included a weighted sample of 11,775 women. Spatial regression was carried out to determine which factors are related to hotspots of ESI of women. To identify the factors associated with ESI, a multilevel Poisson regression model with robust variance was conducted. An adjusted prevalence ratio (APR) with its 95 % confidence interval was presented. Results: The prevalence of ESI was 75.3 % (95%CI: 74.6 %, 76.1 %), showing notable spatial variation across different regions of Ethiopia. Areas of significant hotspots of ESI were identified in Western and Southern Tigray, most parts of Amhara, Southern, Central and Western Afar, Eastern Gambella, and North Western SNNPR. The significant variables for the spatial variation of ESI were; being single, rural residence, and having no formal education of the women. Factors including; wealth index, marital status, khat chewing, education level, residence, and region were associated significantly with ESI in the multilevel robust Poisson analysis. Conclusion: A higher proportion of ESI in women was found. Public health interventions must be made by targeting hotspot areas of ESI through increasing health care access and education (specifically among rural residents), developing a comprehensive sexual education, implementing policies and laws that outlaw early marriage, and mass community-based programs to increase awareness about the importance of delaying sexual activity.

Nomogram to predict risk of neonatal mortality among preterm neonates admitted with sepsis at University of Gondar Comprehensive Specialized Hospital: risk prediction model development and validation.

BACKGROUND: Mortality in premature neonates is a global public health problem. In developing countries, nearly 50% of preterm births ends with death. Sepsis is one of the major causes of death in preterm neonates. Risk prediction model for mortality in preterm septic neonates helps for directing the decision making process made by clinicians. OBJECTIVE: We aimed to develop and validate nomogram for the prediction of neonatal mortality. Nomograms are tools which assist the clinical decision making process through early estimation of risks prompting early interventions. METHODS: A three year retrospective follow up study was conducted at University of Gondar Comprehensive Specialized Hospital and a total of 603 preterm neonates with sepsis were included. Data was collected using KoboCollect and analyzed using STATA version 16 and R version 4.2.1. Lasso regression was used to select the most potent predictors and to minimize the problem of overfitting. Nomogram was developed using multivariable binary logistic regression analysis. Model performance was evaluated using discrimination and calibration. Internal model validation was done using bootstrapping. Net benefit of the nomogram was assessed through decision curve analysis (DCA) to assess the clinical relevance of the model. RESULT: The nomogram was developed using nine predictors: gestational age, maternal history of premature rupture of membrane, hypoglycemia, respiratory distress syndrome, perinatal asphyxia, necrotizing enterocolitis, total bilirubin, platelet count and kangaroo-mother care. The model had discriminatory power of 96.7% (95% CI: 95.6, 97.9) and P-value of 0.165 in the calibration test before and after internal validation with brier score of 0.07. Based on the net benefit analysis the nomogram was found better than treat all and treat none conditions. CONCLUSION: The developed nomogram can be used for individualized mortality risk prediction with excellent performance, better net benefit and have been found to be useful in clinical practice with contribution in preterm neonatal mortality reduction by giving better emphasis for those at high risk.

Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021.

BACKGROUND: Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians. OBJECTIVE: Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021. METHODS: A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model. RESULTS: The incidence proportion of diabetic neuropathy among DM patients was 21.29% (95% CI; 18.59, 24.25). In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment, white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2% (95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7 (95% CI; 67.2%, 75.9%). It had less optimism coefficient (0.015). To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2% (95% CI; 65.8%, 74.6%). The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. CONCLUSION: The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.

Prevalence and antimicrobial susceptibility profile of bacteria isolated from the hands of housemaids in Jimma City, Ethiopia.

Introduction: Bacterial pathogens continue to be a major cause of foodborne gastroenteritis in humans and remain a public health problem. Housemaids operating inside a kitchen could be the source of infection and may transmit disease-inflicting pathogens through contaminated hands. Objective: This study aimed to assess the prevalence and antimicrobial susceptibility profile of bacteria isolated from the hands of housemaids in Jimma City, Ethiopia. Methods: A laboratory-based cross-sectional study was employed among 234 housemaids. Hand swab samples from the dominant hand of the study participants were collected under sterile conditions following standard operating procedures. Then, in the laboratory, the swabs were inoculated aseptically using streak-plating methods on the growth media, such as mannitol salt agar [Staphylococcus aureus and coagulase-negative staphylococci], MacConkey agar [Klebsiella species and Proteus species], salmonella-shigella agar [Salmonella species and Shigella species], and eosin methylene blue agar [Escherichia coli (E. coli)]. In addition, a set of biochemical tests was applied to examine bacterial species. Data were double-entered into EpiData version 3.1 and then exported to the Statistical Package for Social Science (SPSS) version 26 for further analysis. Descriptive analyses were summarized using frequency and percentage. Results: The proportion of housemaids' hands containing one or more positive bacterial isolates was 72% (95% CI: 66.2, 77.8). The dominant bacterial isolates were Staphylococcus aureus (31.6%), Escherichia coli (21.3%), Salmonella species (1.3%), Shigella species (6.7%), Klebsiella species (23.1%) and Proteus species (14.7%). Fingernail status (AOR =15.31, 95% CI: 10.372, 22.595) and the removal of a watch, ring, and bracelet during hand washing (AOR = 20.844, 95% CI: 2.190, 9.842) were significantly associated with the prevalence of bacterial isolation. Most Staphylococcus aureus isolates were susceptible to chloramphenicol (98.6%). Escherichia coli isolates were susceptible to tetracycline (75%), ceftriaxone (79.2%), chloramphenicol (87.5%), and ceftazidime (77.1%). Eighty percent of isolated Shigella species were susceptible to chloramphenicol and gentamicin respectively. In addition, Klebsiella and Proteus species exhibited high susceptibility to chloramphenicol. However, their isolates showed resistance against a number of the tested antimicrobials. Staphylococcus aureus isolates (28.2%) were resistance to tetracycline. Moreover, One-quarter of Escherichia coli isolates were resistance to tetracycline, ceftriaxone, chloramphenicol, and ceftazidime. Whereas 46.7% and 48.5% of isolated Shigella species and Proteus species were resistance to tetracycline and ceftriaxone. Conclusion: The hands of housemaids are important potential sources of pathogenic bacteria that would result in the potential risk of foodborne diseases. Most bacteria isolates were resistant to tetracycline, ceftriaxone, and ceftazidime. Therefore, practicing good hand hygiene helps to prevent and control the spread of antimicrobial-resistant microbes.

Exploring the spatial variation and associated factors of childhood febrile illness among under-five children in Ethiopia: Geographically weighted regression analysis.

BACKGROUND: The global burden of febrile illness and the contribution of many fever inducing pathogens have been difficult to quantify and characterize. However, in sub-Saharan Africa it is clear that febrile illness is a common cause of hospital admission, illness and death including in Ethiopia. Therefore the major aim of this study is to explore the spatial variation and associated factors of childhood febrile illness among under-five children in Ethiopia. METHODS: This study were based on the 2016 Ethiopian Demographic health survey data. A total weighted sample of 10,127 under- five children was included. Data management was done using Stata version-14, Arc-GIS version-10.8 and SatsScan version- 9.6 statistical software. Multi-level log binomial model was fitted to identify factors associated with childhood febrile illness. Variables with a p-value < 0.2 in the bi-variable analysis were considered for the multivariable analysis. In the multivariable multilevel log binomial regression analysis p-value< 0.05, the APR with the 95% CI was reported. Global spatial autocorrelation was done to assess the spatial pattern of childhood febrile illness. Spatial regression was done to identify factors associated with the spatial variations of childhood febrile illness and model comparison was based on adjusted R2 and AICc. RESULT: The prevalence of febrile illness among under-five children was 13.6% (95% CI: 12.6%, 14 .7%) with significant spatial variation across regions of Ethiopia with Moran's I value of 0.148. The significant hotspot areas of childhood febrile illness were identified in the Tigray, Southeast of Amhara, and North SNPPR. In the GWR analysis, the proportion of PNC, children who had diarrhea, ARI, being 1st birth order, were significant explanatory variables. In the multilevel log binomial regression age of children 7-24 months(APR = 1.33, 95% CI: (1.03, 1.72)), maternal age 30-39 years (APR = 1.36 95% CI: 1.02, 1.80)), number of children (APR = 1.78, 95% CI: 0.96, 3.3), diarrhea(APR = 5.3% 95% CI: (4.09, 6.06)), ARI (APR = 11.5, 95% CI: (9.2, 14.2)) and stunting(APR = 1.21; 95% CI: (0.98, 1.49) were significantly associated with childhood febrile illness. CONCLUSION: Childhood febrile illness remains public health problem in Ethiopia. On spatial regression analysis proportion of women who had PNC, proportion of children who had diarrhea, proportion of children who had ARI, and proportion of children who had being 1st birth order were associated factors. The detailed map of childhood febrile illness and its predictors could assist health program planners and policy makers to design targeted public health interventions for febrile illness.