Introduction: A scaled-up treatment cascade with direct-acting antiviral (DAA therapy is necessary to achieve global WHO targets for hepatitis C virus (HCV) elimination in Malaysia. Recently, limited access to sofosbuvir/daclatasvir (SOF/DAC) is available through compulsory licensing, with access to sofosbuvir/velpatasvir (SOF/VEL) expected through voluntary licensing due to recent agreements. SOF/VEL has superior clinical outcomes but has higher drug acquisition costs compared to SOF/DAC. A stratified treatment cascade might be the most cost-e cient approach for Malaysia whereby all HCV patients are treated with SOF/DAC except for patients with cirrhosis who are treated with SOF/VEL. Methods: This study aimed to conduct a 5-year budget impact analysis of the proposed stratified treatment cascade for HCV treatment in Malaysia. A disease progression model that was developed based on model-predicted HCV epidemiology data was used for the analysis, where all HCV patients in scenario A were treated with SOF/DAC for all disease stages while in scenario B, SOF/DAC was used only for non-cirrhotic patients and SOF/VEL was used for the cirrhotic patients. Healthcare costs associated with DAA therapy and disease stage monitoring were included to estimate the downstream cost implications. Results: The stratified treatment cascade with 109 in Scenario B was found to be cost-saving compared to Scenario A. The cumulative savings for the stratified treatment cascade was USD 1.4 million over 5 years. Discussion: A stratified treatment cascade with SOF/VEL was expected to be cost-saving and can result in a budget impact reduction in overall healthcare expenditure in Malaysia.

Economic burden issues in SARS-CoV-2 patients with underlying co-morbidities are enormous resources for patient treatment and management. The uncertainty costs for clinical management render the healthcare system catatonic and incurs deficits in national annual budgets. This article focuses on systematic steps towards selecting and evaluating literature to uncover gaps and ways to help healthcare stakeholders optimize resources in treating and managing COVID-19 patients with multi-morbidity. A systematic review of all COVID-19 treatment procedures with co-morbidities or multi-morbidity for the period from 2019 to 2022 was conducted. The search includes studies describing treatment costs associated with multi- or co-morbidity cases for infected patients and, if concurrently reported, determining recurring expenses. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Galbraith plots and I2 statistics will be deployed to assess heterogeneity and to identify potential sources. A backward elimination process will be applied in the regression modelling procedure. Based on the number of studies retrieved and their sample size, the subgroup analysis will be stratified on participant disease category, associated total costs, and degree of freedom in cost estimation. These studies were registered in the PROSPERO registry (ID: CRD42022323071).

Haze imposes a substantial disease burden on the human population especially in the Southeast Asia region due to the high frequency of haze episodes. The reduction of air quality levels by haze has resulted in a substantial disease burden and an increase in healthcare utilization (HU). This study aims to determine the association between haze and HU of haze-related respiratory illnesses with a focus given on the acute exacerbation of bronchial asthma (AEBA) and chronic obstructive pulmonary disease (AECOPD). A cross-sectional study was conducted through secondary data collection of haze/non-haze episodes as the study exposures and HU related to the exacerbation of bronchial asthma and COPD as the study outcomes. Data on haze/non-haze episodes and HU for four consecutive years (2012–2015) were retrieved from the Department of Environment and Ministry of Health Malaysia, respectively. In the four consecutive years, the percentage of haze episodes recorded in all stations was higher (67%) as compared to non-haze (33%) episodes. Means (SD) of patients diagnosed with AEBA and AECOPD were also significantly higher (p < 0.05) for inpatient 74 (62.1) and outpatient 320 (650.1) cases during haze episodes as compared to inpatient 34 (16.5) and outpatient 146 (170.5) cases during non-haze episodes. Findings from this study indicated that haze episodes incurred a significant healthcare burden due to an increase in HU. The evidence from this study will help the policymakers to prepare and allocate resources to control future implications of haze-related illnesses.

The rising healthcare demand during COVID-19 outbreak may endanger patients and forces hospital to plan for future needs. Predictive analyses were conducted to monitor hospital resources at one of the gazetted COVID-19 hospitals in Malaysia. Simultaneously, a real-time observation on patient’s volume was conducted to understand the actual trend of healthcare resource utilisations. All the projections were directly compared to the actual number of patients in-care. This predictive study was done at University Malaya Medical Centre (UMMC) using various sources of data. The projections revealed a steady increase in the number of cumulative cases until April 2020 followed by an exponential increase in the number of cumulative positive cases in Malaysia. When a comparison between the projection and actual data was done, it was found that the initial projections estimated a range that is 50% to 70% higher during the first three phases of Movement Control Order (MCO) compared to the actual number of COVID-19 patients at UMMC. Subsequent projections were done by using recent estimations from the national database and it was estimated that the number of patients treated will be less than 10 each day up until the end of May 2020. The accuracy of this estimation is 95% when compared to the actual number of COVID-19 patients in care. In conclusion, the practice of continuous projections and real-time observation through predictive analysis using mathematical calculations and algorithms is one of the useful tools to facilitate hospital management to allocate adequate resource allocations.

The sudden surge in the number of healthcare utilizations compels the hospital to plan for its future needs. Several time-series projections of Covid-19 were conducted to forecast the disease burden and resources utilization through simple predictive analytics. The projections revealed a rapid increase in the number of cases and patient in care at the hospital. It was estimated that the number of patients in care to range from 62 to 81 and 89 to 121 patients daily in the second and third phase of movement control order respectively. It was estimated that more than 100,000 plastic aprons, 80,000 sterile and non-sterile isolation gowns, 40,000 masks N95 and face shields, 30,000 gloves and nearly 17,000 bottles of hand sanitizers are needed until late May. Hence, a simple mathematical algorithm is a helpful tool to manage hospital resources during the pandemic.