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HMG-CoA reductase inhibitors (statins) are extensively used in treating hypercholesterolemia. The statins available in Malaysia include atorvastatin, lovastatin, pravastatin, rosuvastatin, simvastatin, and fluvastatin. Over the years, they have accumulated in the National Drug Formulary; hence, the need for review. Effective selection of the best drugs to remain in the formulary can become complex due to the multiple drug attributes involved, and is made worse by the limited time and resources available. The multiattribute scoring tool (MAST) systematizes the evaluation of the drug attributes to facilitate the drug selection process. In this study, a MAST framework was developed to rank the statins based on their utilities or benefits. Published literature on multicriteria decision analysis (MCDA) were studied and five sessions of expert group discussions were conducted to build the MAST framework and to review the evidence. The attributes identified and selected for analysis were efficacy (clinical efficacy, clinical endpoints), safety (drug interactions, serious side effects and documentation), drug applicability (drug strength/formulation, indications, dose frequency, side effects, food-drug interactions, and dose adjustments), and cost. The average weights assigned by the members for efficacy, safety, drug applicability and cost were 32.6%, 26.2%, 24.1%, and 17.1%, respectively. The utility values of the attributes were scored based on the published evidence or/and agreements during the group discussions. The attribute scores were added up to provide the total utility score. Using the MAST, the six statins under review were successfully scored and ranked. Atorvastatin scored the highest total utility score (TUS) of 84.48, followed by simvastatin (83.11). Atorvastatin and simvastatin scored consistently high, even before drug costs were included. The low scores on the side effects for atorvastatin were compensated for by the higher scores on the clinical endpoints resulting in a higher TUS for atorvastatin. Fluvastatin recorded the lowest TUS. The multiattribute scoring tool was successfully applied to organize decision variables in reviewing statins for the formulary. Based on the TUS, atorvastatin is recommended to remain in the formulary and be considered as first-line in the treatment of hypercholesterolemia.

Poor adherence to prescribed medications is a major cause for treatment failure, particularly in chronic diseases such as hypertension. This study was conducted to assess adherence to medications in patients undergoing hypertensive treatment in the Primary Health Clinics of the Ministry of Health in Malaysia. Factors affecting adherence to medications were studied, and the effect of nonadherence to blood pressure control was assessed. This was a cross-sectional study to assess adherence to medications by adult patients undergoing hypertensive treatment in primary care. Adherence was measured using a validated survey form for medication adherence consisting of seven questions. A retrospective medication record review was conducted to collect and confirm data on patients' demographics, diagnosis, treatments, and outcomes. Good adherence was observed in 53.4% of the 653 patients sampled. Female patients were found to be more likely to adhere to their medication regime, compared to their male counterparts (odds ratio 1.46 [95% confidence intervals [CI]: 1.05-2.04; P < 0.05]). Patients in the ethnic Chinese were twice as likely (95% CI: 1.14-3.6; P < 0.05) to adhere, compared to those in the Indian ethnic group. An increase in the score for medicine knowledge was also found to increase the odds of adherence. On the other hand, increasing the number of drugs the patient was taking and the daily dose frequencies of the medications prescribed were found to negatively affect adherence. Blood pressure control was also found to be worse in noncompliers. The medication adherence rate was found to be low among primary care hypertensive patients. A poor adherence rate was found to negatively affect blood pressure control. Developing multidisciplinary intervention programs to address the factors identified is necessary to improve adherence and, in turn, to improve blood pressure control.

Rapid deployment of COVID-19 vaccines is challenging for safety surveillance, especially on adverse events of special interest (AESIs) that were not identified during the pre-licensure studies. This study evaluated the risk of hospitalisations for predefined diagnoses among the vaccinated population in Malaysia. Hospital admissions for selected diagnoses between 1 February 2021 and 30 September 2021 were linked to the national COVID-19 immunisation register. We conducted self-controlled case-series study by identifying individuals who received COVID-19 vaccine and diagnosis of thrombocytopenia, venous thromboembolism, myocardial infarction, myocarditis/pericarditis, arrhythmia, stroke, Bell’s Palsy, and convulsion/seizure. The incidence of events was assessed in risk period of 21 days postvaccination relative to the control period. We used conditional Poisson regression to calculate the incidence rate ratio (IRR) and 95% confidence interval (CI) with adjustment for calendar period. There was no increase in the risk for myocarditis/pericarditis, Bell’s Palsy, stroke, and myocardial infarction in the 21 days following either dose of BNT162b2, CoronaVac, and ChAdOx1 vaccines. A small increased risk of venous thromboembolism (IRR 1.24; 95% CI 1.02, 1.49), arrhythmia (IRR 1.16, 95% CI 1.07, 1.26), and convulsion/seizure (IRR 1.26; 95% CI 1.07, 1.48) was observed among BNT162b2 recipients. No association between CoronaVac vaccine was found with all events except arrhythmia (IRR 1.15; 95% CI 1.01, 1.30). ChAdOx1 vaccine was associated with an increased risk of thrombocytopenia (IRR 2.67; 95% CI 1.21, 5.89) and venous thromboembolism (IRR 2.22; 95% CI 1.17, 4.21). This study shows acceptable safety profiles of COVID-19 vaccines among recipients of BNT162b2, CoronaVac, and ChAdOx1 vaccines. This information can be used together with effectiveness data for risk-benefit analysis of the vaccination program. Further surveillance with more data is required to assess AESIs following COVID-19 vaccination in short- and long-term.

Diabetes mellitus is a growing global health problem that affects patients of all ages. Even though diabetes mellitus is recognized as a major chronic illness, adherence to antidiabetic medicines has often been found to be unsatisfactory. This study was conducted to assess adherence to medications and to identify factors that are associated with nonadherence in type 2 diabetes mellitus (T2DM) patients at Primary Health Clinics of the Ministry of Health in Malaysia. The cross-sectional survey was carried out among T2DM patients to assess adherence to medication in primary health clinics. Adherence was measured by using the Medication Compliance Questionnaire that consists of a total of seven questions. Other data, such as patient demographics, treatment, outcome, and comorbidities were also collected from patient medical records. A total of 557 patients were recruited in the study. Approximately 53% of patients in the study population were nonadherent. Logistic regression analysis was performed to predict the factors associated with nonadherence. Variables associated with nonadherence were age, odds ratio 0.967 (95% confidence interval [CI]: 0.948-0.986); medication knowledge, odds ratio 0.965 (95% CI: 0.946-0.984); and comorbidities, odds ratio 1.781 (95% CI: 1.064-2.981). Adherence to medication in T2DM patients in the primary health clinics was found to be poor. This is a cause of concern, because nonadherence could lead to a worsening of disease. Improving medication knowledge by paying particular attention to different age groups and patients with comorbidities could help improve adherence.

This study assessed the association between COVID-19 vaccines, SARS-CoV-2 infection and the risk of thrombocytopenia and venous thromboembolism (VTE). This self-controlled case series study used hospital records between 1st February 2021 and 28th February 2022 linked to the national immunisation registry and COVID-19 surveillance data in Malaysia. Conditional Poisson regression was used to estimate incidence rate ratios (IRR) of events in the risk period (day 1–21 post-exposure) relative to control period with the corresponding 95% confidence interval (CI) adjusted for calendar period. We found no significant increased risk of thrombocytopenia in 1–21 days following BNT162b2, CoronaVac and ChAdOx1 vaccines while the risk was increased following SARS-CoV-2 infection (IRR 15.52, 95% CI 13.38–18.00). Similarly, vaccination with BNT162b2, CoronaVac, or ChAdOx1 was not associated with an increased risk of VTE during the 1–21 days risk period. SARS-CoV-2 infection was associated with increased risk of VTE (IRR 39.84, 95% CI 27.45–32.44). Our findings showed low event rates of thrombocytopenia and VTE following booster vaccination with comparable safety profiles between those who received homologous and heterologous booster combinations. Our findings showed the risk of thrombocytopenia and VTE was not increased after COVID-19 vaccination while the risks were substantially higher after SARS-CoV-2 infection.

The objective of this study is to analyze the possibilities of increasing energy efficiency in the central region wastewater treatment plant by focusing on two aspects: biogas production and prediction of energy production. The analysis is based on one of the biggest central region wastewater treatment plants in Malaysia. After studying the energy efficiency, which consists of optimization of energy consumption and enhancing gas generation, the prediction of power consumption is performed using an autoregressive integrated moving average (ARIMA) model. The prediction results are compared with the linear regression method. Comparison shows that even though the total cost of savings is greater by using linear regression, the prediction through ARIMA is more accurate and has smaller root mean square error. The implementation of these two aspects managed to increase energy efficiency by 10% of energy recovery that could further reduce electricity cost and reduction of sludge cake disposal off site. The study recommends other aspects, such as modification in setting up the frequency of variable speed drive for aerators and blowers and optimizing number of feeds into train unit processes within aeration tanks in increasing energy efficiency.

The occurrence of the power outage caused inconvenience to the customers including the energy suppliers. There are various factors that can trigger the power outage such as lightning, weather or animal. In this paper, the power outage prediction has been performed by using the datasets provided which are lightning data and tripping report. The machine learning method was carried out to predict the power outage occurrence by using the Classification Learner App in MATLAB. Before performing the machine learning method, the data went through the data pre-processing to ensure the data is clean and the significant feature for prediction can be selected to run in the Classification Learner App. The results of this research have shown that Fine Tree is the most suitable model to be used for the prediction of power outage. The results have been compared by using the Area Under Curve (AUC) in Receiving Operating Characteristic (ROC). Logistic Regression and Coarse Tree shows the lowest value of AUC compared to other model and Fine Tree has the highest value of AUC.

Is the prediction accuracy affected by the method used in the ensemble of the classifiers? This paper is a sequel of our experiment in order to find an answer for such question. Previously, we had conducted an experiment by using single classifiers in the machine learning against traditional statistical methods. The results showed that single classifiers in machine learning perform well compared to the traditional statistical methods. Still, we believe that there is another way to increase the prediction accuracy of these classifiers. In this paper, we conducted another experiment by combining these classifiers in predicting currency crisis of 25 countries. The combined classifiers are support vector machine with k-nearest neighbor, logistic regression with k-nearest neighbor and finally LADTree with k-nearest neighbor. These three combined classifiers are tested on 13 chosen macroeconomic indicators which the data is taken from first quarter 1980 to third quarter 2012. The results of this experiment showed that these three different combined classifiers averagely have same higher accuracy and quite comparable. Our proposed method, nearest neighbor tree has the highest area under ROC curve number among these three combined classifiers although in terms of computational time it took longer running times than the others.

Doc number: A21