Background: Many nations collect data on adverse events (AEs) associated with the use of drugs using what is generally referred to as the Spontaneous Reporting System (SRS) [1,2,3]. Analysis of such data is important in discovering hitherto unknown problems associated with drug use and in understanding the features of the variables related to the problem of adverse drug reactions (ADRs) [4,5,6]. The SRS of the Food and Drugs Administration (FDA) of the United States of America (US), known as the FDA Adverse Event Reporting System (FAERS) [3], is probably the largest system for collecting data on AEs associated with drug use. Objectives: (i) Find any trends in the variables associated with the problem of adverse events in drug use, (ii) Elucidate some of the issues raised in the literature by way of the evidence provided by the data, (iii) Find the drugs that were most cited as principal suspect in adverse events and (iv) Examine the data for any other notable attributes. Methods: Quarterly Extracts from the FAERS database covering the period 2007 to 2012, which is publicly available on the website of the Food and Drugs Administration (FDA, US), were analysed. Out of the over fifty (50) variables contained in the extracts, fourteen (14) of them, which were thought to be relevant to the objectives of the study, were examined. Owing to the nature of the data, the tools of frequencies, proportions and averages were used in the analysis of it. Results: The results of the analysis revealed that for the period 2007 – 2012, the reported cases of adverse events almost tripled (2.7 times), with annual growth rate of 22.1%. Reports on female subjects dominated throughout the period, accounting for a little over two-thirds of the reported cases annually and in the overall number of reports for the period. The proportion of cases that resulted in death appeared to be increasing over time. Non-health professionals are almost as likely as health professionals to report adverse events. Expedited reports (concerning events that are unexpected, from the perspective of the known pharmacology of the suspect drug(s)) accounted for the highest number of cases throughout the period. A large proportion of the cases were reported electronically with an indication of increasing trend over the period under review and in the years following. The age group most involved in adverse events associated with drug use is 45 – 64, followed by the age groups 65 and over, 45 – 59, 18 – 44 and 0 – 17 in descending order of involvement when looked at from the point of view of number of reported cases. However the results of the analysis show that susceptibility to adverse events increases with age; the older one gets the more vulnerable one becomes to adverse events involving drug use. The analysis also revealed that some of the problems that prevent the best use of SRS data, such as missing values for age and sex, mentioned in the literature, existed during the period under consideration [7,8,9]. Conclusion: It is essential to encourage reporting of adverse events, especially accurate and prompt reporting. This is indispensable in dealing with the problem of adverse events in medication use comprehensively; as it not easy to obtain data on the variables involved with the problem through other means and SRS data provide useful insights, especially when keying out factors that contribute to the occurrence of adverse events associated with drug use.
This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non stationary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box–Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.
The evidence of rising numbers of multidrug-resistant organisms requires the implementation of effective stewardship programs. However, this should be informed by evidence-based knowledge of local antimicrobial resistance patterns. The current study aims to establish the prevalence of common pathogenic microbes including their antimicrobial susceptibility patterns and distribution in the Cape Coast Metropolis. This was a retrospective study where microbial culture and antimicrobial susceptibility records for 331 patients were reviewed from January to December 2019, at a private health centre. All data were analysed using Excel (Microsoft Office, USA), SPSS and GraphPad Prism 8 software programs. Among the samples tested, 125 (37.76%) were positive for microbes with high vaginal swab (HVS) samples recording the highest number of pathogens (44%), followed by urine (40%) and both pleural and semen samples having the least (0.3% each). Again, gram-negative isolates were more prevalent than the gram-positive isolates. The prevalence of antimicrobial resistance was very significant with isolates resistant to more than one antibiotic (P < 0.05). Escherichia coli showed the highest level of resistance, followed by Citrobacter spp. These were followed by Klebsiella spp., Staphylococcus spp., Coliforms, Pseudomonas spp., Commensals and Candida spp. The high resistance pattern suggests an inevitable catastrophe requiring continuous monitoring and implementation of effective antibiotic stewardship.
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