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AbstractSub-Saharan Africa faces a number of essential issues, including water quality. As such, evaluating the surface water quality of lakes and reservoirs is a crucial part of environmental monitoring and management. Especially in a region where these water bodies serve as a source of livelihood for communities living around them. Water quality parameters (WQPs) are usually taken from the site and sent to the laboratory for measurement and analysis. However, this traditional method is time-consuming, costly, and labor-intensive. Combining geographic information system and remote sensing (RS) allows researchers to analyze WQPs more conveniently. This study, therefore, used RS technology to map and estimate WQPs and correlated it with in-situ measurement. Using the empirical regression model and Landsat 8, WQPs such as chlorophyll-a (Chl-a), total suspended solids (TSS) and turbidity were estimated. The results from RS were correlated with the in-situ measurements of water quality. The results showed that the in-situ Chl-a levels varied from 0.206 to 13.5 mg/L, averaging 5.1 mg/L. The Chl-a values estimated from Landsat 8 had R2 of 0.883 and 0.853, respectively, for both periods (17 December 2022 and 16 March 2023). The green band (B3) was more instrumental in detecting Chl-a. The in-situ measurement for TSS ranged between 18 and 48 mg/L, with a mean value of 28.7 mg/L. These readings were low and within tolerable bounds of 50 mg/L. High TSS concentrations were found near farms and communities with a significant influx of silt into the surrounding lake. The comparison of in-situ water quality and the reflectance from satellite data showed that the turbidity estimated from the sensor from the two periods has R2 > 0.65. The study showed that the combination of the Landsat image and in-situ measurement offers great ways to provide timely and affordable estimation from WQPs.

Background: The addition of dairy products to a meal high in carbohydrates or fat may potentially result in reductions in acute postprandial glycemic responses. There is limited data on the postprandial effects of local Ghanaian meals and this potential role of milk in attenuating acute postprandial glycemia changes have not been previously tested among Ghanaians. Objectives: The overall objective of the study is to determine the effects of low-fat whole milk on postprandial glycemia after consumption of two common Ghanaian local meals among healthy young adults. Methods: The study included 10 healthy, non-obese young adults. Two common local Ghanaian meals (Waakye and Ga Kenkey) were consumed on separate days with water and low-fat whole milk. Glycemic responses were assessed at fasting and 30-minutes postprandial point intervals for 3 hours. Postprandial glycemia, was assessed by the incremental Area Under the Curve (iAUC). Percentage postprandial glycemia peak was assessed as the percentage glycemia difference between peak glycemia point and baseline. Percentage postprandial glycemia retention. Student t-test was used to compare differences in postprandial responses with and without milk. Results: On consumption of Waakye, there were statistically non-significant decreases in mean postprandial iAUC, and percentage postprandial glycemia retention with low-fat whole milk. However, a relatively lower but statistically non-significant percentage postprandial glycemia peak was observed with the low-fat whole milk. With Kenkey, the results showed a relatively lower postprandial iAUC, percentage postprandial glycemia peak and percentage postprandial glycemia retention on consumption with the low-fat whole milk. However, these differences were not observed to be statistically significant. Conclusion: There are variations in the effects of low-fat whole milk on postprandial glycemia response between different meal types. No statistically significant differences were observed in the effects of low-fat whole milk in limiting postprandial glycemia response in both test meals. The findings point to further research on the subject in different targeted populations, including type-2 diabetes mellitus (T2DM) clients.

Background: Falls risk assessment tools are used in hospital inpatient settings to identify patients at increased risk of falls to guide and target interventions for fall prevention. In 2022, Western Health, Melbourne, Australia, introduced a new falls risk assessment tool, the Western Health St. Thomas’ Risk Assessment Tool (WH-STRATIFY), which adapted The Northern Hospital’s risk tool (TNH-STRATIFY) by adding non-English speaking background and falls-risk medication domains to reflect patient demographics. WH-STRATIFY replaced Peninsula Health Risk Screening Tool (PH-FRAT) previously in use at Western Health. This study compared the predictive accuracy of the three falls risk assessment tools in an older inpatient high-risk population. Aims: To determine the predictive accuracy of three falls risk assessment tools (PH-FRAT, TNH-STRATIFY, and WH-STRATIFY) on admission to Geriatric Evaluation Management (GEM) units (subacute inpatient wards where the most frail and older patients rehabilitate under a multi-disciplinary team). Method: A retrospective observational study was conducted on four GEM units. Data was collected on 54 consecutive patients who fell during admission and 62 randomly sampled patients who did not fall between December 2020 and June 2021. Participants were scored against three falls risk assessment tools. The event rate Youden (Youden IndexER) indices were calculated and compared using default and optimal cut points to determine which tool was most accurate for predicting falls. Results: Overall, all tools had low predictive accuracy for falls. Using default cut points to compare falls assessment tools, TNH-STRATIFY had the highest predictive accuracy (Youden IndexER = 0.20, 95% confidence interval CI = 0.07, 0.34). The PH-FRAT (Youden IndexER = 0.01 and 95% CI = −0.04, 0.05) and WH-STRATIFY (Youden IndexER = 0.00 and 95% CI = −0.04, 0.03) were statistically equivalent and not predictive of falls compared to TNH-STRATIFY. When calculated optimal cut points were applied, predictive accuracy improved for PH-FRAT (Cut point 17, Youden IndexER = 0.14 and 95% CI = 0.01, 0.29) and WH-STRATIFY (Cut point 7, Youden IndexER = 0.18 and 95% CI = 0.00, 0.35). Conclusions: TNH-STRATIFY had the highest predictive accuracy for falls. The predictive accuracy of WH-STRATIFY improved and was significant when the calculated optimal cut point was applied. The optimal cut points of falls risk assessment tools should be determined and validated in different clinical settings to optimise local predictive accuracy, enabling targeted fall risk mitigation strategies and resource allocation.

Urbanisation in Ghana has been associated with significant effects on the land surface temperature which has its impacts on the natural environment. Conventional literature exhibits that land surface temperature associated with urbanisation have concentrated in the developed world leaving the African terrain unattended to. Hence, its knowledge and implications in Africa and for that matter Ghana is deficient. This study is two-pronged a) examines the relationship between land surface temperature coupled with its spatial patterns (b) examines the surface urban heat island and urban-rural gradient in Greater Accra region of Ghana. Landsat images for 1991, 2002, 2012 and 2020 were used where demarcations for spatial integrity were done. The use of machine learning algorithms were adopted in the land use classification. It was observed that Accra would experience high impervious surface mostly due to rural–urban migration purposely to seek for employment. It was concluded that in order to achieve the Sustainable Development Goals (especially goals 3,6,11 and 13) more efforts should be made to improve the conditions in the rural areas in order to curtail the migration drive which does not only put pressure on the social amenities in the urban areas but also exacerbate in the land surface temperature situation.

Sub-Saharan Africa faces a number of essential issues, including water quality. As such, evaluating the surface water quality of lakes and reservoirs is a crucial part of environmental monitoring and management. Especially in a region where these water bodies serve as a source of livelihood for communities living around them. Water quality parameters (WQPs) are usually taken from the site and sent to the laboratory for measurement and analysis. However, this traditional method is time-consuming, costly, and labor-intensive. Combining geographic information system and remote sensing (RS) allows researchers to analyze WQPs more conveniently. This study, therefore, used RS technology to map and estimate WQPs and correlated it with in-situ measurement. Using the empirical regression model and Landsat 8, WQPs such as chlorophyll-a (Chl-a), total suspended solids (TSS) and turbidity were estimated. The results from RS were correlated with the in-situ measurements of water quality. The results showed that the in-situ Chl-a levels varied from 0.206 to 13.5 mg/L, averaging 5.1mg/L. The Chl-a values estimated from Landsat 8 had R² of 0.883 and 0.853, respectively, for both periods (17 December 2022 and 16 March 2023). The green band (B3) was more instrumental in detecting Chl-a. The in-situ measurement for TSS ranged between 18 and 48 mg/L, with a mean value of 28.7 mg/L. These readings were low and within tolerable bounds of 50 mg/L. High TSS concentrations were found near farms and communities with a significant influx of silt into the surrounding lake. The comparison of in-situ water quality and the reflectance from satellite data showed that the turbidity estimated from the sensor from the two periods has R² > 0.65. The study showed that the combination of the Landsat image and in-situ measurement offers great ways to provide timely and affordable estimation from WQPs. [ABSTRACT FROM AUTHOR]

Urbanisation in Ghana has been associated with significant effects on the land surface temperature which has its impacts on the natural environment. Conventional literature exhibits that land surface temperature associated with urbanisation have concentrated in the developed world leaving the African terrain unattended to. Hence, its knowledge and implications in Africa and for that matter Ghana is deficient. This study is two-pronged a) examines the relationship between land surface temperature coupled with its spatial patterns (b) examines the surface urban heat island and urban-rural gradient in Greater Accra region of Ghana. Landsat images for 1991, 2002, 2012 and 2020 were used where demarcations for spatial integrity were done. The use of machine learning algorithms were adopted in the land use classification. It was observed that Accra would experience high impervious surface mostly due to rural–urban migration purposely to seek for employment. It was concluded that in order to achieve the Sustainable Development Goals (especially goals 3,6,11 and 13) more efforts should be made to improve the conditions in the rural areas in order to curtail the migration drive which does not only put pressure on the social amenities in the urban areas but also exacerbate in the land surface temperature situation. [ABSTRACT FROM AUTHOR]