Study protocol for a mixed methods approach to optimize colorectal cancer screening in Malaysia: Integrating stakeholders insights and knowledge-to-action framework.

INTRODUCTION: Colorectal cancer is a growing global health concern and the number of reported cases has increased over the years. Early detection through screening is critical to improve outcomes for patients with colorectal cancer. In Malaysia, there is an urgent need to optimize the colorectal cancer screening program as uptake is limited by multiple challenges. This study aims to systematically identify and address gaps in screening service delivery to optimize the Malaysian colorectal cancer screening program. METHODS: This study uses a mixed methods design. It focuses primarily on qualitative data to understand processes and strategies and to identify specific areas that can be improved through stakeholder engagement in the screening program. Quantitative data play a dual role in supporting the selection of participants for the qualitative study based on program monitoring data and assessing inequalities in screening and program implementation in healthcare facilities in Malaysia. Meanwhile, literature review identifies existing strategies to improve colorectal cancer screening. Additionally, the knowledge-to-action framework is integrated to ensure that the research findings lead to practical improvements to the colorectal cancer screening program. DISCUSSION: Through this complex mix of qualitative and quantitative methods, this study will explore the complex interplay of population- and systems-level factors that influence screening rates. It involves identifying barriers to effective colorectal cancer screening in Malaysia, comparing current strategies with international best practices, and providing evidence-based recommendations to improve the local screening program.

Effectiveness of COVID-19 vaccines among children 6-11 years against hospitalization during Omicron predominance in Malaysia.

There is currently limited data on the effectiveness of COVID-19 vaccines for children aged 6-11 years in Malaysia. This study aims to determine vaccine effectiveness (VE) against COVID-19-related hospitalization after receipt of one- and two-doses of BNT162b2 mRNA (Comirnaty-Pfizer/BioNTech) vaccine over a duration of almost 1 year in the predominantly Omicron period of BA.4/BA.5 and X.B.B sub lineages. This study linked administrative databases between May 2022 and March 2023 to evaluate real-world vaccine effectiveness (VE) for the BNT162b2 mRNA (Comirnaty-Pfizer/BioNTech) vaccine against COVID-19-related hospitalization in the Omicron pre-dominant period with BA.4/BA.5 and X.B.B sub lineages. During the Omicron-predominant period, the cumulative hospitalization rate was almost two times higher for unvaccinated children (9.6 per million population) compared to vaccinated children (6 per million population). The estimated VE against COVID-19 hospitalization for one dose of BNT162b2 was 27% (95% CI - 1%, 47%) and 38% (95% CI 27%, 48%) for two doses. The estimated VE against hospitalization remained stable when stratified by time. VE for the first 90 days was estimated to be 45% (95% CI 33, 55%), followed by 47% (95% CI 34, 56%) between 90 and 180 days, and 36% (95% CI 22, 45%) between 180 and 360 days. Recent infection within 6 months does not appear to modify the impact of vaccination on the risk of hospitalization, subject to the caveat of potential underestimation. In our pediatric population, BNT162b2 provided moderate-non-diminishing protection against COVID-19 hospitalization over almost 1 year of Omicron predominance.

Rapidly scalable and low-cost public health surveillance reporting system for COVID-19.

OBJECTIVE: Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting. METHODS: A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources. RESULTS: Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability. CONCLUSION: This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.

Does Awareness of Malaysian Healthy Plate Associate with Adequate Fruit and Vegetable Intake among Malaysian Adults with Non-Communicable Diseases?

The healthy eating plate concept has been introduced in many countries, including Malaysia, as a visual guide for the public to eat healthily. The relationship between Malaysian Healthy Plate (MHP) and adequate fruit and vegetable (FV) intake among morbid Malaysian adults is unknown. Hence, we investigated the relationship between awareness of the MHP and FV intake among morbid Malaysian adults. National survey data on 9760 morbid Malaysian adults aged 18 years and above were analyzed. The relationship between awareness of MHP and FV intake among Malaysian adults with obesity, diabetes mellitus, hypertension, and hypercholesterolemia were determined using multivariable logistic regression controlling for sociodemographic characteristics and lifestyle risk factors. Our data demonstrated that MHP awareness is associated with adequate FV intake among the Malaysian adults with abdominal obesity (adjusted odds ratio (aOR): 1.86, 95% confidence interval (CI): 1.05-3.29), diabetes mellitus (aOR: 4.88, 95% CI: 2.13-22.18), hypertension (aOR: 4.39, 95% CI: 1.96-9.83), and hypercholesterolemia (aOR: 4.16, 95% CI: 1.48-11.72). Our findings indicated the necessity for ongoing efforts by policymakers, healthcare professionals, and nutrition educators to promote the concept of MHP and ensure that morbid Malaysian adults consume a sufficient intake of FV or adopt a healthy eating pattern to achieve and maintain optimal health.

Estimating excess mortalities due to the COVID-19 pandemic in Malaysia between January 2020 and September 2021.

Excess mortalities are a more accurate indicator of true COVID-19 disease burden. This study aims to investigate levels of excess all-cause mortality and their geographic, age and sex distributions between January 2020-September 2021. National mortality data between January 2016 and September 2021 from the Department of Statistics Malaysia was utilised. Baseline mortality was estimated using the Farrington algorithm and data between 1 January 2016 and 31 December 2019. The occurrence of excess all-cause mortality by geographic-, age- and sex-stratum was examined from 1 January 2020 to 30 September 2021. A sub-analysis was also conducted for road-traffic accidents, ethnicity and nationality. Malaysia had a 5.5-23.7% reduction in all-cause mortality across 2020. A reversal is observed in 2021, with an excess of 13.0-24.0%. Excess mortality density is highest between July and September 2021. All states and sexes reported excess trends consistent with the national trends. There were reductions in all all-cause mortalities in individuals under the age of 15 (0.4-8.1%) and road traffic accident-related mortalities (36.6-80.5%). These reductions were higher during the first Movement Control Order in 2020. Overall, there appears to be a reduction in all-cause mortality for Malaysia in 2020. This trend is reversed in 2021, with excess mortalities being observed. Surveillance of excess mortalities can allow expedient detection of aberrant events allowing timely health system and public health responses.

Vaccines alone are no silver bullets: a modeling study on the impact of efficient contact tracing on COVID-19 infection and transmission in Malaysia.

BACKGROUND: The computer simulation presented in this study aimed to investigate the effect of contact tracing on coronavirus disease 2019 (COVID-19) transmission and infection in the context of rising vaccination rates. METHODS: This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted. RESULTS: At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases. CONCLUSIONS: While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control.

Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia.

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.

Estimating the infection burden of COVID-19 in Malaysia.

Malaysia has reported 2.75 million cases and 31,485 deaths as of 30 December 2021. Underestimation remains an issue due to the underdiagnosis of mild and asymptomatic cases. We aimed to estimate the burden of COVID-19 cases in Malaysia based on an adjusted case fatality rate (aCFR). Data on reported cases and mortalities were collated from the Ministry of Health official GitHub between 1 March 2020 and 30 December 2021. We estimated the total and age-stratified monthly incidence rates, mortality rates, and aCFR. Estimated new infections were inferred from the age-stratified aCFR. The total estimated infections between 1 March 2020 and 30 December 2021 was 9,955,000-cases (95% CI: 6,626,000-18,985,000). The proportion of COVID-19 infections in ages 0-11, 12-17, 18-50, 51-65, and above 65 years were 19.9% (n = 1,982,000), 2.4% (n = 236,000), 66.1% (n = 6,577,000), 9.1% (n = 901,000), 2.6% (n = 256,000), respectively. Approximately 32.8% of the total population in Malaysia was estimated to have been infected with COVID-19 by the end of December 2021. These estimations highlight a more accurate infection burden in Malaysia. It provides the first national-level prevalence estimates in Malaysia that adjusted for underdiagnosis. Naturally acquired community immunity has increased, but approximately 68.1% of the population remains susceptible. Population estimates of the infection burden are critical to determine the need for booster doses and calibration of public health measures.

Correction: Implementation of a COVID-19 surveillance programme for healthcare workers in a teaching hospital in an upper-middle-income country.

[This corrects the article DOI: 10.1371/journal.pone.0249394.].

Propagation of a hospital-associated cluster of COVID-19 in Malaysia.

BACKGROUND: Hospitals are vulnerable to COVID-19 outbreaks. Intrahospital transmission of the disease is a threat to the healthcare systems as it increases morbidity and mortality among patients. It is imperative to deepen our understanding of transmission events in hospital-associated cases of COVID-19 for timely implementation of infection prevention and control measures in the hospital in avoiding future outbreaks. We examined the use of epidemiological case investigation combined with whole genome sequencing of cases to investigate and manage a hospital-associated cluster of COVID-19 cases. METHODS: An epidemiological investigation was conducted in a University Hospital in Malaysia from 23 March to 22 April 2020. Contact tracing, risk assessment, testing, symptom surveillance, and outbreak management were conducted following the diagnosis of a healthcare worker with SARS-CoV-2 by real-time PCR. These findings were complemented by whole genome sequencing analysis of a subset of positive cases. RESULTS: The index case was symptomatic but did not fulfill the initial epidemiological criteria for routine screening. Contact tracing suggested epidemiological linkages of 38 cases with COVID-19. Phylogenetic analysis excluded four of these cases. This cluster included 34 cases comprising ten healthcare worker-cases, nine patient-cases, and 15 community-cases. The epidemic curve demonstrated initial intrahospital transmission that propagated into the community. The estimated median incubation period was 4.7 days (95% CI: 3.5-6.4), and the serial interval was 5.3 days (95% CI: 4.3-6.5). CONCLUSION: The study demonstrated the contribution of integrating epidemiological investigation and whole genome sequencing in understanding disease transmission in the hospital setting. Contact tracing, risk assessment, testing, and symptom surveillance remain imperative in resource-limited settings to identify and isolate cases, thereby controlling COVID-19 outbreaks. The use of whole genome sequencing complements field investigation findings in clarifying transmission networks. The safety of a hospital population during this COVID-19 pandemic may be secured with a multidisciplinary approach, good infection control measures, effective preparedness and response plan, and individual-level compliance among the hospital population.

The Epidemiology of COVID-19 in Malaysia.

BACKGROUND: COVID-19 has rapidly spread across the globe. Critical to the control of COVID-19 is the characterisation of its epidemiology. Despite this, there has been a paucity of evidence from many parts of the world, including Malaysia. We aim to describe the epidemiology of COVID-19 in Malaysia to inform prevention and control policies better. METHODS: Malaysian COVID-19 data was extracted from 16 March 2020 up to 31 May 2021. We estimated the following epidemiological indicators: 7-day incidence rates, 7-day mortality rates, case fatality rates, test positive ratios, testing rates and the time-varying reproduction number (Rt). FINDINGS: Between 16 March 2020 and 31 May 2021, Malaysia has reported 571,901 cases and 2,796 deaths. Malaysia's average 7-day incidence rate was 26•6 reported infections per 100,000 population (95% CI: 17•8, 38•1). The average test positive ratio and testing rate were 4•3% (95% CI: 1•6, 10•2) and 0•8 tests per 1,000 population (95% CI: <0•1, 3•7), respectively. The case fatality rates (CFR) was 0•6% (95% CI: <0•1, 3•7). Among the 2,796 cases who died, 87•3% were ≥ 50 years. INTERPRETATION: The public health response was successful in the suppression of COVID-19 transmission or the first half of 2020. However, a state election and outbreaks in institutionalised populations have been the catalyst for more significant community propagation. This rising community transmission has continued in 2021, leading to increased incidence and strained healthcare systems. Calibrating NPI based on epidemiological indicators remain critical for us to live with the virus. (243 words). FUNDING: This study is part of the COVID-19 Epidemiological Analysis and Strategies (CEASe) Project with funding from the Ministry of Science, Technology and Innovation (UM.0000245/HGA.GV).

Implementation of a COVID-19 surveillance programme for healthcare workers in a teaching hospital in an upper-middle-income country.

INTRODUCTION: The reporting of Coronavirus Disease 19 (COVID-19) mortality among healthcare workers highlights their vulnerability in managing the COVID-19 pandemic. Some low- and middle-income countries have highlighted the challenges with COVID-19 testing, such as inadequate capacity, untrained laboratory personnel, and inadequate funding. This article describes the components and implementation of a healthcare worker surveillance programme in a designated COVID-19 teaching hospital in Malaysia. In addition, the distribution and characteristics of healthcare workers placed under surveillance are described. MATERIAL AND METHODS: A COVID-19 healthcare worker surveillance programme was implemented in University Malaya Medical Centre. The programme involved four teams: contact tracing, risk assessment, surveillance and outbreak investigation. Daily symptom surveillance was conducted over fourteen days for healthcare workers who were assessed to have low-, moderate- and high-risk of contracting COVID-19. A cross-sectional analysis was conducted for data collected over 24 weeks, from the 6th of March 2020 to the 20th of August 2020. RESULTS: A total of 1,174 healthcare workers were placed under surveillance. The majority were females (71.6%), aged between 25 and 34 years old (64.7%), were nursing staff (46.9%) and had no comorbidities (88.8%). A total of 70.9% were categorised as low-risk, 25.7% were moderate-risk, and 3.4% were at high risk of contracting COVID-19. One-third (35.2%) were symptomatic, with the sore throat (23.6%), cough (19.8%) and fever (5.0%) being the most commonly reported symptoms. A total of 17 healthcare workers tested positive for COVID-19, with a prevalence of 0.3% among all the healthcare workers. Risk category and presence of symptoms were associated with a positive COVID-19 test (p<0.001). Fever (p<0.001), cough (p = 0.003), shortness of breath (p = 0.015) and sore throat (p = 0.002) were associated with case positivity. CONCLUSION: COVID-19 symptom surveillance and risk-based assessment have merits to be included in a healthcare worker surveillance programme to safeguard the health of the workforce.

Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia.

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (ß), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (ß), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

Developing a dengue prediction model based on climate in Tawau, Malaysia.

Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.