What Is the Estimated COVID-19 Reproduction Number and the Proportion of the Population That Needs to Be Immunized to Achieve Herd Immunity in Malaysia? A Mathematical Epidemiology Synthesis

We aimed to determine Malaysia’s COVID-19 reproduction number and herd immunity threshold through a mathematical epidemiology synthesis. Using time-series incidence data, the time-dependent reproduction number (Rt) was yielded over time during the COVID-19 containment measures in Malaysia. The value of Rt at the beginning of the epidemic and prior to any interventions in place was used to determine the proportion of the population that needs to be immunized to achieve herd immunity. Rt was strongly influenced by interventions being put in place. We established that at least 74% of the Malaysian population needs to be vaccinated to achieve herd immunity against COVID-19. This threshold estimate is somewhat influenced by the availability of an efficacious vaccine. A vaccine with 95% efficacy would approximately synthesize a herd immunity threshold of 78%. We conclude that Rt is a valid estimator to determine the effectiveness of control measures and a parameter of use to synthesize herd immunity thresholds in the current COVID-19 pandemic

Clinicians’ Perceived Understanding of Biostatistical Results in the Medical Literature: A Cross-Sectional Study

Background and objectives: The continuum of evidence-based medicine (EBM) depends solely on clinicians’ commitment to keep current with the latest clinical information. Exploration on clinicians’ understanding of biostatistical results in the medical literature is sparse to date. This study aimed to evaluate clinicians’ perceived understanding of biostatistical results in the medical literature and the factors influencing them. Materials and Methods: A cross-sectional study was conducted among 201 clinicians at the Seberang Jaya Hospital, a cluster-lead research hospital in Northern Malaysia. A self-administered questionnaire that consisted of items on sociodemographics, validated items on clinicians’ confidence level in interpreting statistical concepts, perceived understanding of biostatistics, and familiarity with different statistical methods were used. Descriptive, univariate, and multivariate analyses were conducted. Results: Perceived understanding of biostatistical results among clinicians in our sample was nearly 75%. In the final regression model, perceived understanding was significantly higher among clinicians who were able to interpret p-values with complete confidence (AOR = 3.0, 95% CI 1.1−8.1), clinicians who regularly encounter measures of central tendencies (AOR = 2.3, 95% CI 1.1−5.2), and clinicians who regularly encounter inferential statistics (AOR = 2.2, 95% CI 1.1−4.5) while appraising the medical literature. Conclusions: High perceived understanding was significantly associated with clinicians’ confidence in interpreting statistical concepts and familiarity with different statistical methods. Our findings form a platform to understand clinicians’ ability to appraise rigorous biostatistical results in the medical literature for the retrieval of evidence-based data to be used in routine clinical practice

Influence of Population Density for COVID-19 Spread in Malaysia: An Ecological Study

The rapid transmission of highly contagious infectious diseases within communities can yield potential hotspots or clusters across geographies. For COVID-19, the impact of population density on transmission models demonstrates mixed findings. This study aims to determine the correlations between population density, clusters, and COVID-19 incidence across districts and regions in Malaysia. This countrywide ecological study was conducted between 22 January 2021 and 4 February 2021 involving 51,476 active COVID-19 cases during Malaysia’s third wave of the pandemic, prior to the reimplementation of lockdowns. Population data from multiple sources was aggregated and spatial analytics were performed to visualize distributional choropleths of COVID-19 cases in relation to population density. Hierarchical cluster analysis was used to synthesize dendrograms to demarcate potential clusters against population density. Region-wise correlations and simple linear regression models were deduced to observe the strength of the correlations and the propagation effects of COVID-19 infections relative to population density. Distributional heats in choropleths and cluster analysis showed that districts with a high number of inhabitants and a high population density had a greater number of cases in proportion to the population in that area. The Central region had the strongest correlation between COVID-19 cases and population density (r = 0.912; 95% CI 0.911, 0.913; p < 0.001). The propagation effect and the spread of disease was greater in urbanized districts or cities. Population density is an important factor for the spread of COVID-19 in Malaysia

Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia

As COVID-19 dispersion occurs at different levels of gradients across geographies, the application of spatiotemporal science via computational methods can provide valuable insights to direct available resources and targeted interventions for transmission control. This ecological-correlation study evaluates the spatial dispersion of COVID-19 and its temporal relationships with crucial demographic and socioeconomic determinants in Malaysia, utilizing secondary data sources from public domains. By aggregating 51,476 real-time active COVID-19 case-data between 22 January 2021 and 4 February 2021 to district-level administrative units, the incidence, global and local Moran indexes were calculated. Spatial autoregressive models (SAR) complemented with geographical weighted regression (GWR) analyses were executed to determine potential demographic and socioeconomic indicators for COVID-19 spread in Malaysia. Highest active case counts were based in the Central, Southern and parts of East Malaysia regions of Malaysia. Countrywide global Moran index was 0.431 (p = 0.001), indicated a positive spatial autocorrelation of high standards within districts. The local Moran index identified spatial clusters of the main high–high patterns in the Central and Southern regions, and the main low–low clusters in the East Coast and East Malaysia regions. The GWR model, the best fit model, affirmed that COVID-19 spread in Malaysia was likely to be caused by population density (β coefficient weights = 0.269), followed by average household income per capita (β coefficient weights = 0.254) and GINI coefficient (β coefficient weights = 0.207). The current study concluded that the spread of COVID-19 was concentrated mostly in the Central and Southern regions of Malaysia. Population’s average household income per capita, GINI coefficient and population density were important indicators likely to cause the spread amongst communities

Mapping the Scientific Landscape of Diabetes Research in Malaysia (2000–2018): A Systematic Scientometrics Study

The escalated burden of diabetes on the population’s health has catalyzed rigorous scientific research to produce appropriate evidence for treatment and control. Malaysia suffers from the leading diabetes epidemic within the Western Pacific region. It is crucial to map the scientific landscape of diabetes research for the country to identify trends in productivity and determine whether research efforts are directed toward the needs-gaps priority for evidence synthesis that could be used for the drafting of policies and guidelines. This systematic scientometrics study was conducted to map the scientific research output (trends and distribution, citation frequency, keywords link visualization, and thematic cluster conceptualization) related to diabetes between 2000–2018 in Malaysia. Using three international databases (PubMed, EMBASE, Scopus) and one local database (MyCite), scientific publication records related to diabetes in Malaysia between 2000 and 2018 were retrieved and analyzed using quantitative and qualitative methodologies. Microsoft Excel 2016, EndNote X9.2, BibExcel 2016, GraphPad Prism 8.0.1, VOS viewer software 1.6.13, and R software version 1.3.959 were used to analyze the trend and contents of diabetes publications. A total of 2094 publication records that accounted for 35,497 citations were analyzed. Kuala Lumpur was the most scientifically productive state in Malaysia, contributing 754 papers. Medical Journal of Malaysia had the highest number of publications. The inflection point of the Malaysian diabetes research output was in 2013, with most publications being non-collaborative research works. Most publications originated from academia, especially from local public universities. The overall publication productivity of diabetes research in Malaysia was conceptualized into eleven thematic clusters, with clinical and animal studies being the most prevalent themes. The diabetes literature in Malaysia has grown steadily over the past 19 years. However, the cumulative evidence remains inadequate and is insufficiently powered to guide policymaking and the control of diabetes. It does not yet seem feasible to direct the diabetes epidemic curve to a plateau for the Malaysian population based on Malaysian diabetes publications

Validation of the Visual Cognitive Assessment Test (VCAT) for the Early Diagnosis of Cognitive Impairment in Multilingual Population in Malaysia

As Malaysia undergoes a demographic transformation of population aging, the prevalence of dementia is expected to rise, posing a major public health threat issue. Early screening to detect cognitive impairment is important to implement appropriate clinical interventions. The Visual Cognitive Assessment Test (VCAT) is a language-neutral cognitive assessment screening tool suitable for multilingual populations. This study was aimed to validate the VCAT screening tool for the detection of cognitive impairment amongst the population of Malaysia. A total of 184 participants were recruited, comprising 79 cognitively healthy participants (CHP), 46 mild cognitive impairment (MCI) patients, and 59 mild dementia (Alzheimer’s disease and Vascular Dementia) patients from five hospitals between May 2018 and December 2019 to determine the usefulness of VCAT. Diagnostic performance was assessed using area under the curve (AUC), and receiver operating characteristic (ROC) analysies was performed to determine the recommended cutoff scores. ROC analyses for the VCAT was comparable with that of MoCA (Montreal Cognitive Assessment) in differentiating between CHP, MCI, and mild dementia (AD and VaD) participants. The findings of this study suggest the following optimal cutoff score for VCAT: Dementia 0–19, MCI 20–23, Normal 24–30. The mean ± SD time to complete the VCAT was 10.0 ± 2.75 min in the CHP group and 15.4 ± 4.52 min in the CI group. Results showed that 76.0% of subjects thought that the instructions in VCAT were similar or easier to understand compared with MoCA. This study showed that the VCAT is a valid and useful screening tool for patients with cognitive impairment in Malaysia and is feasible to be used in the clinical settings