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Background: An increased risk of diabetes mellitus (DM) after COVID-19 has been reported in the United States, Europe, and Asia. The burden of COVID-related DM has yet to be described in Africa, where the overall risk of DM has been increasing rapidly. Our objective was to compare the prevalence of pre-DM and DM in Nigerian individuals with a history of COVID-19 to individuals without known COVID-19 infection. Methods: We undertook a retrospective cohort study with 256 individuals with a past medical history of COVID-19 with no history of pre-DM or DM and 256 individuals without a history of COVID-19 or pre-DM/DM. Participants were categorized as pre-DM (fasting capillary glucose 100–125 mg/dL) or DM (fasting capillary glucose ≥ 126 mg/dL). We employed univariate and multivariable logistic regression to identify key predictors and adjust for confounders related to hyperglycaemia risk factors. Additionally, we used multinomial logistic regression to analyze the relationship between COVID-19 history and diabetes status, distinguishing between normal, pre-diabetic, and diabetic glucose levels. All models were adjusted for age, gender, hypertension, physical activity, central adiposity, and family history of DM. Results: Compared to the control group, those with a history of COVID-19 had a similar median age (38 vs. 40 years, p = 0.84), had a higher proportion of men (63% vs. 49%), and had a lower prevalence of central adiposity (waist: hip ratio ≥ 0.90 for males and WHR ≥ 0.85 for females) (48% vs. 56.3%, p = 0.06). Of the 256 with a history of COVID-19, 44 (17%) required in-patient care. The median (interquartile range) time interval between COVID-19 diagnosis and the glycaemic assessment was 19 (IQR: 14, 24) months. Pre-DM prevalence was 27% in the post-COVID-19 group and 4% in the control group, whereas the prevalence of DM was 7% in the post-COVID-19 group and 2% in the control group. After multivariable adjustment, the odds of pre-DM were 8.12 (95% confidence interval (CI): 3.98, 16.58; p < 0.001) higher, and the odds of DM were 3.97 (95% CI: 1.16, 13.63) higher in those with a history of COVID-19 compared to controls. In the adjusted multinomial logistic regression analysis, individuals with a history of COVID-19 exhibited significantly elevated risks for pre-diabetes (RRR = 7.55, 95% CI: 3.76–15.17) and diabetes (RRR = 3.44, 95% CI: 1.01–11.71) compared to those without COVID-19. Conclusion: Previous COVID-19 was found to be a risk factor for prevalent pre-diabetes and diabetes mellitus in Nigeria. More intensive screening for DM in those with a history of COVID-19 should be considered. [ABSTRACT FROM AUTHOR]

The Sine-Exponential (Sine-E) distribution is a probability distribution that combines the periodic behavior of the sine function with the decay characteristic of the exponential function. This study addresses the problem of identifying the most accurate and reliable estimation method for the parameter of the Sine-E distribution. The objective is to evaluate various parameter estimation techniques, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), Weighted Least Squares Estimation (WLSE), Maximum Product of Spacing Estimation (MPSE), Cramer-von-Mises Estimation (CVME), and Anderson-Darling Estimation (ADE), using Mean Square Error (MSE) as the criterion for determining the technique with the minimum error. The study's findings reveal that as sample size increases, the parameter estimates for all techniques converge to the true parameter value, with decreases in bias, MSE, and mean relative estimates. Among the techniques evaluated, the MPSE method consistently provides estimates closest to the true parameter value and exhibits the least bias and lowest MSE across small, moderate, and large sample sizes, making it the best estimator for the Sine-E distribution. [ABSTRACT FROM AUTHOR]

This paper proposed a novel distribution parameterized by four parameters. This is achieved by compounding the potentials properties of the Type I half logistic topp-leone generalized distribution family with the properties of the inverse lomax distribution to form the novel Type I half logistic topp-leone inverse lomax distribution. The novel distribution is potentially capable of extending classical inverse lomax distribution. The potentiality of the shape of the probability density function of the novel distribution is worth recognizing since it produces right skewed, approximately normal, left skewed and a reverted J-shaped. Decreasing life failure shape is also observed. Distinctive features of the novel distribution such as moments, entropy, moment generating function, reliability and hazard function were derived. The estimation method explored in this study is maximum likelihood estimation. It is adopted to estimate the novel distribution unknown parameters. Real life data set was adopted to investigate the potentiality and applicability of the novel model. The type I half logistic topp-leone inverse lomax distribution outperform the recent models. [ABSTRACT FROM AUTHOR]

Sabore field is characterised by a high level of artisan activities that contributes to water pollution by heavy metals; these heavy metals when present in drinking water can be deleterious to human health. Consequently, in 2018 we investigated the seasonal variations of the water samples from the Sabore vicinity to determine its suitability for drinking. Six metals (Mn, Cu, Cd, Zn, Ni, Pb) were studied, the health risk assessment was investigated using Hazard Index (HI), Hazard Quotient (HQ) and Cancer Risk (CR). The results showed that water from this locality have HI values > 1, Pb and Cd were top contributors to non-carcinogenic risk while Ni and Cd caused concern for carcinogenic health risk making it unsuitable for consumption. The seasonal spread of the metals shows that Mn varied from 148 to 976 µg/L; Cu from 923 to 1676 µg/L; Cd from 1.167 to 22.5 µg/L; Zn from 63 to 118 µg/L, Ni from 37 to 95 µg/L and Pb from 128 to 185 µg/L in the rainy season. For the dry season, Mn shows a spread of 1442–1802 µg/L; 344–776 µg/L for Cu; 57–82 µg/L for Cd; 48–187 µg/L for Zn; 147–242 µg/L for Ni and 79–256 µg/L for Pb. The increased concentration of some metals during the dry seasons could be attributed to the increased human activities around the field, increased evaporation rate, absence of rainfall and runoff water. The heavy metal content of water from some of the sites exceeded the stipulated permissible limits and poses serious health risk to its consumers. [ABSTRACT FROM AUTHOR]