Description of the COVID-19 epidemiology in Malaysia

IntroductionSince the COVID-19 pandemic began, it has spread rapidly across the world and has resulted in recurrent outbreaks. This study aims to describe the COVID-19 epidemiology in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate for each outbreak from the beginning of the pandemic in 2020 till endemicity of COVID-19 in 2022 in Malaysia.MethodsData was sourced from the GitHub repository and the Ministry of Health’s official COVID-19 website. The study period was from the beginning of the outbreak in Malaysia, which began during Epidemiological Week (Ep Wk) 4 in 2020, to the last Ep Wk 18 in 2022. Data were aggregated by Ep Wk and analyzed in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate by years (2020 and 2022) and for each outbreak of COVID-19.ResultsA total of 4,456,736 cases, 35,579 deaths and 58,906,954 COVID-19 tests were reported for the period from 2020 to 2022. The COVID-19 incidence rate, death rate, CFR and test positivity rate were reported at 1.085 and 0.009 per 1,000 populations, 0.80 and 7.57%, respectively, for the period from 2020 to 2022. Higher cases, deaths, testing, incidence/death rate, CFR and test positivity rates were reported in 2021 and during the Delta outbreak. This is evident by the highest number of COVID-19 cases, ICU admissions, ventilatory requirements and deaths observed during the Delta outbreak.ConclusionThe Delta outbreak was the most severe compared to other outbreaks in Malaysia’s study period. In addition, this study provides evidence that outbreaks of COVID-19, which are caused by highly virulent and transmissible variants, tend to be more severe and devastating if these outbreaks are not controlled early on. Therefore, close monitoring of key epidemiological indicators, as reported in this study, is essential in the control and management of future COVID-19 outbreaks in Malaysia

The effects of the COVID-19 pandemic on dengue cases in Malaysia

BackgroundGlobally, the COVID-19 pandemic has affected the transmission dynamics and distribution of dengue. Therefore, this study aims to describe the impact of the COVID-19 pandemic on the geographic and demographic distribution of dengue incidence in Malaysia.MethodsThis study analyzed dengue cases from January 2014 to December 2021 and COVID-19 confirmed cases from January 2020 to December 2021 which was divided into the pre (2014 to 2019) and during COVID-19 pandemic (2020 to 2021) phases. The average annual dengue case incidence for geographical and demographic subgroups were calculated and compared between the pre and during the COVID-19 pandemic phases. In addition, Spearman rank correlation was performed to determine the correlation between weekly dengue and COVID-19 cases during the COVID-19 pandemic phase.ResultsDengue trends in Malaysia showed a 4-year cyclical trend with dengue case incidence peaking in 2015 and 2019 and subsequently decreasing in the following years. Reductions of 44.0% in average dengue cases during the COVID-19 pandemic compared to the pre-pandemic phase was observed at the national level. Higher dengue cases were reported among males, individuals aged 20–34 years, and Malaysians across both phases. Weekly dengue cases were significantly correlated (ρ = −0.901) with COVID-19 cases during the COVID-19 pandemic.ConclusionThere was a reduction in dengue incidence during the COVID-19 pandemic compared to the pre-pandemic phase. Significant reductions were observed across all demographic groups except for the older population (>75 years) across the two phases

Correlation between Population Density and COVID-19 Cases during the Third Wave in Malaysia: Effect of the Delta Variant

In this study, we describe the incidence and distribution of COVID-19 cases in Malaysia at district level and determine their correlation with absolute population and population density, before and during the period that the Delta variant was dominant in Malaysia. Methods: Data on the number of locally transmitted COVID-19 cases in each of the 145 districts in Malaysia, between 20 September 2020 and 19 September 2021, were manually extracted from official reports. The cumulative number of COVID-19 cases, population and population density of each district were described using choropleth maps. The correlation between population and population density with the cumulative number of COVID-19 cases in each district in the pre-Delta dominant period (20 September 2020–29 June 2021) and during the Delta dominant period (30 June 2021–19 September 2021) were determined using Pearson’s correlation. Results: COVID-19 cases were strongly correlated with both absolute population and population density (Pearson’s correlation coefficient (r) = 0.87 and r = 0.78, respectively). A majority of the districts had higher numbers of COVID-19 cases during the Delta dominant period compared to the pre-Delta period. The correlation coefficient in the pre-Delta dominant period was r = 0.79 vs. r = 0.86 during the Delta dominant period, whereas the pre-Delta dominant population density was r = 0.72, and in the Delta dominant period, r = 0.76. Conclusion: More populous and densely populated districts have a higher risk of transmission of COVID-19, especially with the Delta variant as the dominant circulating strain. Therefore, extra and more stringent control measures should be instituted in highly populated areas to control the spread of COVID-19

Gender Differences in Factors Associated with the Total Delay in Treatment of Pulmonary Tuberculosis Patients: A Cross-Sectional Study in Selangor, Malaysia

Background: Gender plays a significant role in health-care-seeking behavior for many diseases. Delays in seeking treatment, diagnosis, and treatment for pulmonary tuberculosis (pTB) may increase the risk of transmission in the community and lead to poorer treatment outcomes and mortality. This study explores the differences in factors associated with the total delay in treatment of male and female pTB patients in Selangor, Malaysia. Methods: A cross-sectional study was conducted from January 2017 to December 2017. Newly diagnosed pTB patients (≥18 years) were recruited from selected government health clinics and hospitals in Selangor during the specified study period. An interviewer-administered questionnaire was used to collect information on sociodemographic characteristics, lifestyle, knowledge about pTB, stigma, distance to the nearest health facility, and chronology of pTB symptom onset, diagnosis, and treatment. The total delay was measured as the length of time between the onset of pTB symptoms to treatment initiation. Factors significantly associated with a longer total delay among men and women were identified using binary logistic regression. Results: A total of 732 patients (61.5% men, 38.5% women) were enrolled in the study. The median total delay was 60 days. Men who have weight loss as a symptom (AOR: 1.63, 95%CI: 1.10–2.41) and are employed (1.89, 1.15–3.11) were more likely to have a longer total delay, while those who know others who have had pTB (0.64, 0.43–0.96) were less likely to have a longer total delay. On the other hand, among women, having a stigma towards TB (0.52, 0.32–0.84) and obtaining a pTB diagnosis at the first medical consultation (0.48, 0.29–0.79) were associated with a shorter total delay. Conclusion: Factors associated with the total delay in pTB treatment were different for male and female pTB patients. Increasing awareness of pTB symptoms and the importance of seeking early medical consultation and a prompt diagnosis among the general public may reduce total delay in pTB treatment

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 (β), 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 (β), (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

COVID-19 in Malaysia:Descriptive Epidemiologic Characteristics of the First Wave

This study aimed to describe the characteristics of COVID-19 cases and close contacts during the first wave of COVID-19 in Malaysia (23 January 2020 to 26 February 2020), and to analyse the reasons why the outbreak did not continue to spread and lessons that can be learnt from this experience. Characteristics of the cases and close contacts, spatial spread, epidemiological link, and timeline of the cases were examined. An extended SEIR model was developed using several parameters such as the average number of contacts per day per case, the proportion of close contact traced per day and the mean daily rate at which infectious cases are isolated to determine the basic reproduction number (R0) and trajectory of cases. During the first wave, a total of 22 cases with 368 close contacts were traced, identified, tested, quarantine and isolated. Due to the effective and robust outbreak control measures put in place such as early case detection, active screening, extensive contact tracing, testing and prompt isolation/quarantine, the outbreak was successfully contained and controlled. The SEIR model estimated the R0 at 0.9 which further supports the decreasing disease dynamics and early termination of the outbreak. As a result, there was a 11-day gap (free of cases) between the first and second wave which indicates that the first wave was not linked to the second wave

Seroprevalence of diphtheria toxoid IgG antibodies in the Malaysian population

Abstract Background Despite high childhood immunization coverage, sporadic cases of diphtheria have been reported in Malaysia in recent years. This study aims to evaluate the seroprevalence of diphtheria among the Malaysian population. Methods A total of 3317 respondents age 2 years old to 60 years old were recruited in this study from August to November 2017. Enzyme-linked immunosorbent assay (ELISA) was used to measure the level of IgG antibody against the toxoid of C. diphtheriae in the blood samples of respondents. We classified respondent antibody levels based on WHO definition, as protective (≥0.1 IU/mL) and susceptible (< 0.1 IU/mL) to C. diphtheriae infection. Results Among the 3317 respondents, 57% were susceptible (38.1% of children and 65.4% of adults) and 43% (61.9% of children and 34.6% of adults) had protective antibody levels against diphtheria. The mean antibody level peaked among individuals aged 1–2 years old (0.59 IU/mL) and 6–7 years old (0.64 IU/mL) but generally decreased with age, falling below 0.1 IU/mL at around 4–6 years old and after age 20 years old. There was a significant association between age [Children: χ2 = 43.22(df = 2),p < 0.001)], gender [Adults: χ2 = 5.58(df = 1),p = 0.018] and ethnicity [Adults: χ2 = 21.49(df = 5),p = 0.001] with diphtheria toxoid IgG antibody level. Conclusions About 57% of the Malaysian population have inadequate immunity against diphtheria infection. This is apparently due to waning immunity following childhood vaccination without repeated booster vaccination in adults. Children at age 5–6 years old are particularly vulnerable to diphtheria infection. The booster vaccination dose normally given at 7 years should be given earlier, and an additional booster dose is recommended for high-risk adults

Development of growth chart for Malaysian children

Growth charts are internationally used as a tool for assessment of physical growth which reflects the nutritional status of children. It is used in public health for screening for malnutrition, and for monitoring children’s growth patterns. Poor physical growth is closely related to poor health status. Currently most countries are using WHO Child Growth Standards 2006 for children less than 5 years old and WHO Growth Reference 2007 for school-aged children and adolescents. Some countries have developed growth references for their population as an additional reference besides the WHO’s using nationally-representative data and revising them periodically. Differences in the socio-economic and health environment of each country may result in differences in the growth potential of its children. Therefore there is a need to develop current, country-specific growth references for children that can be used in public health screening for malnutrition. This report present the first growth chart developed for Malaysian children (length/height-for-age, weight-for-age, body mass index-for-age) and describes the methodological processes involved.The Malaysian Children Growth Chart (MyGC) was developed by using the nationally representative data from the Third National Health and Morbidity (NHMS III) conducted in 2006. The NHMS III was population based cross-sectional study using two-stage stratified sampling proportionate to population size throughout Malaysia. The weight and length/height measurements of all apparently healthy children (11,177 boys and 10,855 girls) age 0 to 18 years in selected households were taken.The data were screened for extreme values and outliers (biological implausible) and any extreme values and outliers were removed based on the recommendations of the WHO leaving a final sample of 10,454 boys and 10,259 girls. The LMS ChartMaker Pro software was used to derive age-related reference centiles and z-scores for the anthropometric data. This method is based on the assumption that anthropometric data can be converted to a standard normal distribution by a Box-Cox transformation for any given age. It summarizes the age-changing distribution by 3 curves, namely L (Box-Cox Power) which measures skewness (λ); M, the median at each age (μ); and S, the coefficient of variation by age (σ). Using penalised likelihood, the three curves are fitted as cubic splines by non-linear regression, and the extent of smoothing required is expressed in terms of smoothing parameters or equivalent degrees of freedom (edf). The optimal model was obtained by balancing smoothness of the curves (e.d.f) and the model goodness of fit (Q test of fit and detrended Q-Q plot). The sex-specific percentiles and z-score curves for, weight-for-age, length/height-for-age and BMI-for-age were generated. Weight-for-age. The weight observations of 5722 boys and 5550 girls aged 0 to 10 years were used in the final construction of the growth charts. We produced sex-specific weight-for-age percentile chart which comprised of 3rd, 15th, 50th, 85th and 97th percentile curves. For z-score charts, -3SD, - 2SD, -1SD, Median, 1SD and 2SD curves (+3SD z-scores for age 9 to 10 years could not be produced by the software therefore +3SD curve were not shown). Length/height-for-age. For both boys and girls birth to 2 years, recumbent length measurements were used to construct length-for-age percentile and z-score curves while standing height measurements were used for age 2 to 18 years old. The length measurements of 1018 boys and 981 girls aged 0 to 24 month were used to construct length-for-age percentile and z-score curves. For 24-216 months, height data of 9124 boys and 9083 girls were used. We present percentile and z-score curves for age ranges of birth to 2 years, 2 to 5 years and 5 to 18 years. BMI-for-age. BMI curves for birth to 2 years were constructed using length measurements, for 2-18 years using height measurements. For birth to 2 years, there were 1022 boys and 995 girls records with both weight and length and BMI observations. After data cleaning, BMI for 1018 boys and 995 girls records were used to generate BMI-for-age percentile and z-score curves. For 2 to 18 years, there were 9415 boys and 9225 girls records with both weight and length and BMI observations. After data cleaning, BMI for 9234 boys and 9070 girls records were used to generate BMI-for-age percentile and z-score curves. We present percentile and z-score curves for age ranges of birth to 2 years, 2 to 5 years and 5 to 18 years. Comparison of weight-for-age percentile between MyGC and WHO. From birth to 5 years, MyGC curves for boys and girls aged 0 to 5years were lower than WHO Growth Standard for all percentiles. From 5 to 10 years, the curves for 3rd, 15th, 50th and 85th percentiles for the MyGC were lower than the WHO corresponding percentiles. MyGC 97th percentile crossed the WHO 97th percentile at the age of between 5 to 6 years for boys and 7 years for girls. Therefore above these ages, if the MyGC cut-off point for obesity was used, children will be considerably less likely to be classified as obese. Comparison of length/height-for-age percentile between MyGC and WHO. All MyGC percentile curves were below their respective WHO curves except for median boys and girls below 1 year and the 97th percentile for boys age 2 to 5 years. Estimates of prevalence stunting (<3rd percentile) will be lower if MyGC is used compared to using WHO references. Comparison of BMI-for-age percentile between MyGC and WHO. All MyGC percentile curves were below their respective WHO curves except for the 97th percentile for boys and girls all ages and 85th percentile for boys and girls aged 5 to 18 years, MyGC percentile curve was above WHO growth reference. There is no apparent difference between MyGC 85th percentile curve for boys and girls 2 to 5 years and the corresponding WHO curve. In conclusion, there are differences between MyGC and WHO Child Growth Standards and WHO Growth Reference for children. WHO Child Growth Standards/Reference are likely to overdiagnose obesity, thinness/underweight and stunting for most age groups as compared to MyGC. Thus health practitioners who are using WHO Child Growth Standards/Reference should be aware of this possibility and exercise caution when assessing the children physical growth. MyGC is representative of the existing growth pattern of Malaysia children; therefore it can be used by nutritionists, dieticians, nurses and paediatricians and public health practitioners as an additional reference for screening and early management of malnutrition and for research purposes

Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models

OBJECTIVES This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak. METHODS SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison. RESULTS In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario). CONCLUSIONS Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia

Prevalence and Determinants of Depressive Symptoms among Young Adolescents in Malaysia: A Cross-Sectional Study

Depression is the most common mental health problem affecting adolescents globally, wherein its increasing prevalence together with the negative health impacts escalates the need for further research in this area. This work determined the prevalence and factors associated with depressive symptoms among young adolescents in Malaysia. A total of 1350 adolescent aged 13 to 14 years in school across nine secondary schools in Selangor state, Malaysia participated in a cross-sectional study. Independent variables were examined using the using the Global School-Based Student Health Survey included age, gender, ethnicity, alcohol intake, smoking and illicit drug use, loneliness, bullying, parental marital status, income and supervision; and the Health Literacy and Stigma questionnaire examined mental health literacy levels. Depressive symptoms were the dependent variable which was examined using the Center for Epidemiology Study Depression (CESD) instrument. Prevalence of depressive symptoms among all participants was 19 % (95% CI [16.9, 21.2]), with a higher prevalence of depressive symptoms being reported among females 26.3% (95% CI [23.0, 29.8]) compared to males 11.7% (95% CI [9.4, 14.4]). Determinants namely females (AOR = 3.83; 95% CI [2.66, 5.52]), smoking (AOR = 6.16; 95% CI [3.15, 12.05]), been bullied (AOR = 3.70; 95% CI [2.51, 5.47]), felt lonely (AOR = 10.46; 95% CI [7.09, 15.42]) and having no parental supervision (AOR = 1.79; 95% CI [1.26, 2.53]) significantly increased the odds of depressive symptoms among all adolescents in the multivariate model. In addition, feeling lonely, being bullied and smoking were identified as common significant determinants of depressive symptoms across both genders. Feeling lonely (65% to 71%) and being bullied (10% to 19%) were ranked as the most important determinants of depressive symptoms among young adolescents. Tackling these factors would be instrumental in helping decision makers formulate depression prevention strategies and activities for adolescents