Distribution and in-vitro bioaccessibility of potentially toxic metals in surface soils from a mining and a non-mining community in Ghana: implications for human health.

The concentration and bioaccessibility of potentially toxic metals, including As, Cd, Cr, Cu, Mn, Ni, Pb and Zn, were determined in surface soil samples from a mining community (Kenyasi) and a non-mining community (Sunyani) in Ghana, to investigate the contribution of mining activities to the environmental burden of potentially toxic metals. The study found significant differences in metal concentrations (p < 0.05) in As, Cd, Cu, Mn, Ni, and Zn, but no significant difference (p > 0.05) in Pb and Cr between the two communities. The study found a moderate correlation between pH and metal concentrations in the mining community and a moderate positive correlation with As, Cd, Cr, Cu, Ni, and Zn in the non-mining community. The distribution pattern revealed elevated levels of toxic metals in the southeastern corridor of the mining community, which is close to a gold mine. Most heavy metals were concentrated in the commercial community's southern zone, with more residents and private elementary schools. Metal bioaccessibility was variable, and except for Cu and Zn, the mean bioaccessibility was less than 50% for a given metal. Contamination factor, geoaccumulation index, and soil enrichment factor suggested very high contamination of Cd, and a considerable to moderate contamination of As, Ni, Zn, and Cu at both the mining and non-mining communities. The above observations and the pollution and risk indices employed in this study confirmed that the mining community was more polluted (PLI = 2.145) than the non-mining community (PLI = 1.372). The total metal hazard (HI) exceeded thresholds by three and four times at non-mining and mining sites. Regular monitoring is necessary, especially in the mining community, to prevent soil metal accumulation.

Retrospective Post-Hospitalisation COVID-19 Mortality Risk Assessment of Patients in South Africa.

Background: This study explores the determinants impacting the mortality risk of COVID-19 patients following hospitalisation within South Africa's Limpopo province. Methods: Utilising a dataset comprising 388 patients, the investigation employs a frailty regression model to evaluate the influence of diverse characteristics on mortality outcomes, contrasting its performance against other parametric models based on loglikelihood measures. Results: The findings underscore diabetes and hypertension as notable contributors to heightened mortality rates, underscoring the urgency of effectively managing these comorbidities to optimise patient well-being. Additionally, regional discrepancies come to the fore, with the Capricorn district demonstrating elevated mortality risks, thereby accentuating the necessity for precisely targeted interventions. Medical interventions, particularly ventilation, emerge as pivotal factors in mitigating mortality risk. Gender-based distinctions in mortality patterns also underscore the need for bespoke patient care strategies. Conclusions: Collectively, these outcomes supply practical insights with implications for healthcare interventions, policy formulation, and clinical strategies aimed at ameliorating COVID-19 mortality risk among individuals discharged from hospitals within South Africa's Limpopo province.

Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana.

The use of principal component analysis (PCA) for soil heavy metals characterization provides useful information for decision making and policies regarding the potential sources of soil contamination. However, the concentration of heavy metal pollutants is spatially heterogeneous. Accounting for such spatial heterogeneity in soil heavy metal pollutants will improve our understanding with respect to the distribution of the most influential soil heavy metal pollutants. In this study, geographically weighted principal component analysis (GWPCA) was used to describe the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. The results from the conventional PCA revealed that three principal components cumulatively accounted for 86% of the total variation in the soil heavy metals in the study area. These components were largely dominated by Fe and Zn. The results from the GWPCA showed that the soil heavy metals are spatially heterogeneous and that the use of PCA disregards this considerable variation. This spatial heterogeneity was confirmed by the spatial maps constructed from the geographically weighted correlations among the variables. After accounting for the spatial heterogeneity, the proportion of variance explained by the three geographically weighted principal components ranged between 85% and 89%. The first three identified GWPC were largely dominated by Fe, Zn and As, respectively. The location of the study area where these variables are dominated provides information for remediation.

Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation.

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

Using the SITAR Method to Estimate Age at Peak Height Velocity of Children in Rural South Africa: Ellisras Longitudinal Study.

Various studies have been conducted on children using the Ellisras Longitudinal Study Survey in South Africa, but none of these has addressed growth variations of children in this rural location. The aim of this paper is to assess the age at peak height velocity using the superimposition by translation and rotation (SITAR) method for both boys and girls in rural South Africa. The study is part of the on-going Ellisras Longitudinal Study, and has employed secondary data during the period from November 1996 to November 2003. The data was collected biannually in May and November each year. The Ellisras study initially followed a cluster sampling method. All participating children underwent a series of anthropometric measurements of height and weight according to the standard procedures recommended by the International Society for the Advancement of Kinanthropometry. The analysis was done using the SITAR model. Age at peak height velocity for Ellisras rural children was at 14.45 years for boys at 11.82 years for girls. Ellisras rural girls had their age at peak height velocity way earlier than Ellisras rural boys did by an estimated 2.63 years. Ellisras rural children and their growth variations were comparable to other studies.

A comparative study of different laboratory storage conditions for enhanced DNA analysis of crime scene soil-blood mixed sample.

Crime scene investigation is an important step in the entire criminal investigation process because this is where evidence is gathered. Blood from the perpetrator or victim of a crime can be left at crime scenes or transferred to other materials such as clothing, knives and guns. Most often, this body fluid is contaminated with soil at outdoor crime scenes but this might be the only or the most important evidence in solving a crime. This work aimed at identifying the most appropriate method of storing crime scene soil-blood mixed sample prior to analysis at the laboratory. Human blood was mixed with soil and stored at three different storage conditions (i.e., Room temperature/25°C, 4°C and -20°C). Samples stored at room temperature saw significant reduction in DNA concentration as storage time increased (P=0.001). Samples stored at 4°C saw a drastic decrease in DNA concentration just after two weeks of storage. By the eighth week of storage at 4°C, there was no detectable DNA (P=0.000). Samples stored at -20°C recorded no specific pattern in decrease or increase in DNA concentration for the entire 12week storage (P=0.324). There were full STR Profiles generated for room temperature stored samples and -20°C stored samples throughout the study. There were full, partial and null Profiles generated for 4°C stored samples depending on the sample storage duration. In conclusion, -20°C was identified as the best storage condition for soil-blood mixed sample followed by room temperature and 4°C, respectively.