Browsing by Author "Acquah, S."
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Item Levels of serum alanine/aspartate aminotransferase and urea in apparently healthy rural community in Ghana: A case study in Sabin-Akrofrom and Trede in the Ashanti region(Journal of the Ghana Science Association, 2009) Boampong, J.N.; Acquah, S.; Mate-Siakwa, P.; Osei-Bonsu, M.D.; Nyarko, A.Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and urea levels were assessed in 27 males (mean age 32.33 yrs) and in 34 females (mean age 27.85yrs) Ghanaian rural dwellers to determine the functional status of their liver (ALT/AST) and kidney (Urea). No significant (P≥0.05) differences were observed between the sexes in all the assessed parameters. Mean values of 28.92 U/L, 31.64 U/L, 9.04 mmol/L for males and 30.09 U/L, 33.92 U/L, 8.72 mmol/L for females were obtained respectively for ALT, AST and Urea. The serum levels of ALT, AST and AST to ALT ratio indicated that both groups had normal functioning liver but the urea levels for both sexes appear to suggest renal impairment. Further investigations are needed to establish the underlying pathology.Item Modeling the relationship between precipitation and malaria incidence in children from a holoendemic area in Ghana.(2011) Kriefis, A.C.; Schwarz, N.G.; Kruger, A.; Fobil, J.N.; Nkrumah, B.; Acquah, S.; Loag, W.; Sarpong, N.; Adu-Sarkodie, Y.; Ranft, U.; May, J.Climatic factors influence the incidence of vector-borne diseases such as malaria. They modify the abundance of mosquito populations, the length of the extrinsic parasite cycle in the mosquito, the malarial dynamics, and the emergence of epidemics in areas of low endemicity. The objective of this study was to investigate temporal associations between weekly malaria incidence in 1,993 children < 15 years of age and weekly rainfall. A time series analysis was conducted by using cross-correlation function and autoregressive modeling. The regression model showed that the level of rainfall predicted the malaria incidence after a time lag of 9 weeks (mean = 60 days) and after a time lag between one and two weeks. The analyses provide evidence that high-resolution precipitation data can directly predict malaria incidence in a highly endemic area. Such models might enable the development of early warning systems and support intervention measures.