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Abstract A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder. The UAV picture first had to be converted to grayscale, then its contrast was amplified. Image contrast was improved using Optimum Wavelet-Based Masking and the Enhanced Cuckoo Methodology (ECM). According to the contrast-enhanced image, Gabor-Transform (GT) and Stroke Width Transform (SWT) methods are used to derive attributes that help categorise mask-wearers and non-mask-wearers. Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. When the system recognises unmasked individuals, it sends their information to the doctor and the nearby police station. One unmanned aerial vehicle’s automated system alert people via speakers, ensuring social spacing. The problem involves a large percentage of appropriate and incorrect face mask wear using data from GitHub and Kaggle, including a training repository of 16,000 images and a testing data set of 12,751 images. To enhance the performance of the model’s learning, the methodology of 10-fold cross-validation will be used. Precision, recall, F1-score, and speed are then measured to determine the efficacy of the suggested approach.

Abstract Globally, the prevalence of multidrug-resistant tuberculosis (MDR-TB) has been increasing recently. This is a major public health concern, as MDR-TB is more difficult to treat and has poorer outcomes compared to drug-sensitive tuberculosis. The main objective of the study was to identify risk factors for recurrent multidrug-resistant tuberculosis, at Alert Specialized Hospital, Addis Ababa, by using different parametric shared frailty models. From January 2016 to December 2021, a retrospective study was conducted on MDR-TB patients at Alert Specialized Hospital in Addis Ababa. The data for the study were collected from the medical records of MDR-TB patients at the hospital during this time period. Gamma and inverse-Gaussian shared frailty models were used to analyze the dataset, with the exponential, Weibull, and lognormal distributions included as baseline hazard functions. The data were analyzed using R statistical software. The median recurrence time of the patients was 12 months, and 149 (34.3%) had recurrences. The clustering effect was statistically significant for multiple drug-resistant tuberculosis patients’ recurrence. According to the Weibull-Inverse-Gaussian model, factors that reduced time to MDR-TB recurrence included lower weight (ɸ = 0.944), smoking (ɸ = 0.045), alcohol use (ɸ = 0.631), hemoptysis (ɸ = 0.041), pneumonia (ɸ = 0.564), previous anti-TB treatment (ɸ = 0.106), rural residence (ɸ = 0.163), and chronic diseases like diabetes (ɸ = 0.442) were associated with faster recurrence. While, higher education (ɸ = 3.525) and age (ɸ = 1.021) extended time to recurrence. For weight increment, smokers and alcohol users, clinical complications of hemoptysis and pneumonia, patients with pulmonary disease who had a history of previous anti-TB treatment, and being rural residents are prognostic factors. There was a significant clustering effect at the Alert Specialized Hospital in Addis Ababa, Ethiopia. The Weibull-Inverse Gaussian Shared Frailty Model was chosen as the best model for predicting the time to recurrence of MDR-TB.

Abstract Nowadays, Tuberculosis remains the major cause of HIV-associated mortality, which accounts for 1 out of every 5 HIV-related mortality worldwide. This study aimed to determine the survival rate and predictors of mortality among TB/HIV co-infected patients. An institution-based retrospective cohort study was undertaken on adult TB/HIV co-infected individuals between 1st February 2014 and 30th January 2022 at Mettu Karl Referral Hospital. A Cox regression model was used to identify predictors of survival time to death among TB/HIV co-infected patients. This study comprised 402 TB and HIV co-infected adult patients. Among these, 84 (20.9%) died, and 318 (79.1%) were censored. The study subjects have been followed up for 6920 person-months with an overall median survival time of 17.6 months. The overall incidence rate was 12.1 per 1000 person months [95% CI: 9.77–14.98]. The results of a multivariable Cox regression analysis showed that being at an older age, urban residence, WHO clinical stage II & IV, CD4 count of ≥ 200 cells/mm3, bedridden functional status, using INH, and using CPT were associated with the survival time of TB and HIV co-infected patients at a significance level of alpha = 0.05. This retrospective study found that high mortality of TB/HIV co-infected patients occurred in the earlier months of treatment initiation. Close monitoring of patients with low CD4, who do not utilize CPT, who are in advanced WHO stages, and who have poor functional levels can help them improve their health and live longer.

BackgroundInsomnia is a prevalent sleep disorder that affects people all over the world. Creating suitable interventions will require a better understanding of the magnitude and determinants of insomnia. This study aimed to assess the prevalence and associated factors of insomnia symptoms among residents of Mettu town during the pandemic lockdown.MethodologyA community-based cross-sectional study was conducted among residents of Mettu town from October 1st to October 15th, 2020. Residents who lived in Mettu town at least for six months were included. To determine the prevalence and determinants of insomnia symptoms, both descriptive and inferential analyses were used. The chi-squared test of association and logistic regression was used to identify predictors of insomnia symptoms among residents of Mettu town. We used SPSS version 25 for all statistical analyses.Principal findingsThe prevalence of depressive symptoms among residents of Mettu town was 52.6%. According to results of multivariable binary logistic regression, being female [AOR = 3.677, 95%CI: 2.124-6.365], being aged between 19 and 40 [AOR = 13.261, 95%CI: 6.953-25.291], being aged above 41 [AOR = 2.627, 95%CI: 1.120-6.159], smoking [AOR = 15.539, 95%CI: 7.961-30.329], satisfaction with information available [AOR = 0.310, 95%CI: 0.168-0.570], fear Corona Virus Disease 2019 (COVID-19), [AOR = 2.171, 95%CI: 1.262-3.733], feeling alienated from others [AOR = 3.288, 95%CI: 1.897-5.699], having somatic symptoms [AOR = 2.298, 95% CI: 1.360-3.884], having depressive symptoms [AOR = 1.841, 95% CI: 1.073-3.160], and experiencing psychological distress [AOR = 1.962, 95% CI: 1.173-3.281] were significantly associated with insomnia symptoms.ConclusionIn this study, the prevalence of insomnia symptoms was found to be high among residents of Mettu town. Being female, being aged between 19 and 40, being aged above 41 years, smoking, fear of Corona Virus Disease 2019, feeling alienated from others, having somatic symptoms, having depressive symptoms, and experiencing psychological distress were all associated with an increased risk of developing insomnia symptoms while being satisfied with the information available decreased the risk of insomnia symptoms among residents of Mettu town. Interventions should be put in place to promote healthy sleep among residents of Mettu town.

Background. Socioeconomic status (SES) refers to an individual’s or group’s social position or class, which is often determined by a combination of education, income, and occupation. Knowing factors that affect the SES of the society might help to take action and improve their economy. In addition, using an ordinal logistic regression model for ordered SES outcomes will yield suitable results and conclusions. This study aimed to utilize an ordinal logistic regression model to find the factors associated with SES for households in Tepi town, Southwest Ethiopia. Methods. The community-based cross-sectional study was carried out in Tepi town, southwest Ethiopia, with data collected from 382 households using a simple random sample technique. The ordinal logistic regression models were evaluated and contrasted for proper accounting of ordinal form. In addition, to come up with a better model, we compared fitted ordinal logistic models with the likelihood-ratio test and AIC criteria. We performed data analysis using STATA version 16. Results. Of all 382 household heads, 170 (45.5%), 120 (31.4%), and 92 (24.1%) were at low, medium, and high SES of households, respectively. According to the result of the multivariable, partial proportional odds model (PPOM), age, education level, family size, and the saving habit were significantly associated with the SES of households at a 5% level of significance. Conclusions. According to the findings of this study, ordinal regression may be a better option in the event of the ordinal form of the outcome. Furthermore, PPOM may be a preferable option if any of the covariates violate the proportionality requirement. Based on the result of this study, the most likely associated indicators with the SES of families in Tepi town, southwest Ethiopia, were family size, age, saving habit, and education level. It is recommended that action should be taken to improve the SES of households.