IntroductionA major challenge to the provision of health care worldwide is the irrational use of antibiotics. To help promote rational use of drugs, standard treatment guidelines (STG) and essential medicine lists and facility-specific formularies have been developed to be used by clinicians. This study assessed the prescription pattern of antibiotics and explored the use of STG by clinicians.MethodsA prospective cross sectional study that made use of seven core drug use indicators was conducted from February, 2017 to July, 2017. Prescribing indicators were assessed using 388 prescriptions that were submitted for filling and dispensing at the pharmacy unit of the hospital. Clinicians were interviewed to assess their use of STG. Data were analyzed using SPSS version 20.0 with a p-value of < 0.05 considered as statistically significant.ResultsA total of 1351 drugs were prescribed for 388 patients. The average number of medicines per prescription was 3.5. Of the 388 prescriptions, 55.2% bore antibiotics, with amoxicillin (22.5%) and ciprofloxacin (18.4%) being the most prescribed antibiotics. Patients' knowledge about their medications was found to be significantly associated with the number of drugs per prescription (p = 0.001), age (p = 0.015) and educational level (p = 0.001). Only 41.7% of prescribers had copies of STG and used them.ConclusionThe prescribing and dispensing practices in the hospital were generally not satisfactory with a low patronage of STG among prescribers. In order to improve the situation, clinicians should practice evidence based medicine rather than empirical treatment of conditions as well as use the STG in practice.

Summary Background Urinary tract infection (UTI) is a major global public health issue. The gold standard for diagnosing UTI is urine culture. This is however labour intensive and time consuming. Many prescribers therefore rely on urinalysis in diagnosing UTI. This study sought to evaluate the performance of some parameters of urinalysis as predictors of urine culture positivity. The common causative agents and their antibiotic susceptibility patterns were also determined. Methods A cross sectional study was carried out at the University of Cape Coast Hospital from July 2017 – December 2017 among out-patients. The performance characteristics of leukocyte esterase (3+) and nitrite reactions were estimated and compared with urine culture. Antimicrobial susceptibility tests were done using disc diffusion technique described by Kirby-Bauer. Results Prevalence of UTI in this study was 30.0% (64/213). The most prevalent pathogen was E. coli (20, 31.2%), followed by S. saprophyticus (9, 14.1%). Most of the bacteria (52, 94.5%) were sensitive to amikacin, followed by ciprofloxacin (42, 76.3%). The most sensitive (94.4%) of the parameters was pus cells [>5 white blood cells (WBC) per high power field (HPF)] and the least sensitive was the nitrite test (21.0%). The leukocyte esterase test showed the highest accuracy of 91.1%. Conclusion The study supports the recommendation of the use of oral ciprofloxacin as the first line treatment of uncomplicated UTI by the Ghana Standard Treatment Guidelines (2017). Funding No funding was provided for this study.

Background: Modelling and forecasting demand for future emergency healthcare services is increasingly gaining wide attention in the emergency healthcare industry worldwide. This aids hospital managers in looking into various options to appropriately plan and allocate available scarce resources for optimal and swift performance. Despite its importance, our knowledge of daily patient flow into the Accident and Emergency Department (AED) of the University of Cape Coast Hospital is incomprehensive, and even the model that best explains its movements remains unknown. Methods: Using daily periodicity of 517 time-series observations on daily patient arrivals sourced from the AED register over January 2020 through May 2021 the autoregressive integrated moving average (ARIMA) of the classical Box-Jenkins methods of time series analysis was used to analyse the data. Results: This study revealed twenty-five non-seasonal candidate models for the hospital AED and ARIMA (0, 1, 2) emerged as the best fitting model. The study results showed that the daily patient arrivals at the AED section of the University Hospital witnessed a 50% decline on average within the study period and a further 33% decline in the forecast region. The findings also revealed very high volatility in daily patient arrivals with an average of eight patient arrivals per day. Conclusion Non-seasonal ARIMA (0, 1, 2) was identified as the best model. Thus, for policy, intervention, and future research direction, it is recommended that steps are taken to investigate the highly volatile nature of patient arrivals as well as the steady downward trending of the daily patient flow at the Accident and Emergency Department of the University Hospital

Background: The healthcare needs of under-fives and infants create major public health and development challenges in Ghana. Though some level of progress has been achieved in the area, there still exist several challenges to be addressed with regard to the survival and development of these vulnerable age groups. One module used in this domain is to monitor, assess, and evaluate the healthcare needs of infants and under-fives in healthcare facilities. Although the literature has dealt greatly with child healthcare in Ghana very little has been done in the area of modelling and forecasting the prevalence of infant and under-five outpatients’ healthcare needs across hospitals in the country. Purpose: This study was conducted to assess, model, and probably predict the outpatient performance variable for infants and under-fives in the University of Cape Coast Hospital. Methodology: Quantitative methods and a longitudinal research design were used in this study. Monthly data of infant and under-five outpatients covering January 2012 through December 2021 was sourced from the District Health Information Management System (DHIMS II) constituting a total sample size of 120 observations. The classical Box-Jenkins method of applied univariate statistical modelling was used to analyze the data. Findings: This unbiased study revealed in its findings 100 competitive models for the data. A seasonal autoregressive moving average model SARIMA (2, 0, 3)(1, 0, 1)12 emerged as the best fitting model for the data with the lowest AIC value. The Ljung-Box Q-test for serial correlation indicated p-values > 0.05 meaning that the model exhibited white noise in its residuals. The findings revealed seasonality but no trend with seasonal peaks in March, July, and November every year. Unique contribution to theory and practice: A seasonal autoregressive moving average model SARIMA (2, 0, 3)(1, 0, 1)12 emerged as the best fitting model predicting under-five and infant visits to the outpatient department. This model will logistically guide the management of the hospital to prudently and effectively allocate human and material resources to the paediatric unit of the hospital. Further studies are recommended to investigate why there are seasonal peaks in the months of March, July, and November every year so that appropriate public health interventions can be applied to mitigate the situation.