Background There has been a significant increase in the incidence of cardiovascular disease (CVD) in Malaysia. It is important to identify the group at high risk of CVD. This study aimed to assess the population distribution and factors associated with 10-year CVD risk among adults aged 40 to 74 years in Malaysia. Methods This study used secondary data from the NHMS 2019, a nationally representative cross-sectional population study. The following measurements were collected: anthropometric, systolic blood pressure, fasting blood glucose, total cholesterol, smoking, and sociodemographic. The 2019 WHO Southeast Asia laboratory-based charts were used to estimate individuals’ CVD risk. These charts predict significant cardiovascular events over ten years. Multiple logistic regression analysis was employed to ascertain the factors that are linked to elevated or extremely elevated risk of CVD. Results A total of 5,503 respondents were included in the analysis. Less than one-quarter of the respondents were current smokers and obese. Approximately 41.7%, 30.9%, and 22.5% of the participants had extremely low risk (less than 5%), low risk (between 5% and less than 10%), and moderate risk (between 10% and less than 20%), respectively. A total of 4.9% of the participants were categorised as having high (20% to < 30%) or very high (CVD) risk (≥ 30%). This classification was more prevalent among males (7.3%) than among females (2.5%; p < 0.001). The factors associated with high/very high CVD risk were unemployment (aOR = 1.88, 95% CI = 1.47–2.40), those with non-formal and primary education level (aOR = 2.36, 95% CI = 1.36 − 4.12 and aOR = 3.28, 95% CI = 2.10 − 5.12, respectively), and being physically inactive with obesity (aOR = 2.19, 95% CI = 1.18 − 4.08). Conclusions This study revealed that almost 5% of the population in Malaysia has a high 10-year CVD risk. These findings highlight Malaysia’s urgent need for comprehensive CVD prevention efforts.

Diabetes is one of the quickest-growing global health emergencies of the twenty-first century, and data-driven care can improve the quality of diabetes management. We aimed to describe the formation of a 10-year retrospective open cohort of type 2 diabetes patients in Malaysia. We also described the baseline treatment profiles and HbA1c, blood pressure, and lipid control to assess the quality of diabetes care. We used 10 years of cross-sectional audit datasets from the National Diabetes Registry and merged 288,913 patients with the same identifying information into a 10-year open cohort dataset. Treatment targets for HbA1c, blood pressure, LDL-cholesterol, HDL-cholesterol, and triglycerides were based on Malaysian clinical practice guidelines. IBM SPSS Statistics version 23.0 was used, and frequencies and percentages with 95% confidence intervals were reported. In total, 288,913 patients were included, with 62.3% women and 54.1% younger adults. The commonest diabetes treatment modality was oral hypoglycaemic agents (75.9%). Meanwhile, 19.3% of patients had ≥ 3 antihypertensive agents, and 71.2% were on lipid-lowering drugs. Metformin (86.1%), angiotensin-converting enzyme inhibitors (49.6%), and statins (69.2%) were the most prescribed antidiabetic, antihypertensive, and lipid-lowering medications, respectively. The mean HbA1c was 7.96 ± 2.11, and 31.2% had HbA1c > 8.5%. Only 35.8% and 35.2% attained blood pressure < 140/80 mmHg and LDL-cholesterol < 2.6 mmol/L, respectively. About 57.5% and 52.9% achieved their respective triglyceride and HDL-cholesterol goals. In conclusion, data integration is a feasible method in this diabetes registry. HbA1c, blood pressure, and lipids are not optimally controlled, and these findings can be capitalized as a guideline by clinicians, programme managers, and health policymakers to improve the quality of diabetes care and prevent long-term complications in Malaysia.

Background: Point-of-care testing (POCT) is commonly used in epidemiological surveys due to its various advantages, such as portability and immediate test results. The CardioChek® PA analyser 3-in-1 lipid panel measures total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol. This study tested the reliability and diagnostic accuracy of the CardioChek® PA analyser using a 3-in-1 lipid panel. Methods: A cross-sectional study design with quota sampling was used. A total of 203 respondents aged 18 years and above from a research centre in the Ministry of Health, Malaysia, were recruited. Venous blood was sent to the laboratory and tested with Siemens Atellica CH, while a POCT analyser was used for capillary blood measurements. Intraclass coefficient correlation (ICC) analysis was employed to determine the agreement between capillary and venous blood parameters. The diagnostic performance of the evaluated tests was evaluated using STATA version 12. Results: The agreement between capillary and laboratory venous blood was moderate (0.64–0.67) for TC and HDL, good (0.75) for LDL and excellent (0.91) for TG). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were as follows: TC, 57.1%, 94.3%, 92.3% and 64.8%; TG, 76.0%, 100%, 100%, and 96.6%; HDL, 96.2%, 83.2%, 47.2% and 99.3%; and LDL, 81.0%, 100%, 100% and 68.3%, respectively. Conclusions: The CardioChek® PA analyser showed acceptable diagnostic accuracy for screening high-risk individuals more often in places where laboratories are inaccessible. It could also be used in clinical settings where patients would benefit from swift treatment decisions.

Mental health has become a growing concern in the wake of the COVID-19 pandemic. We sought to determine the prevalence of mental health symptoms 18 months after the pandemic's declaration. Our cross-sectional study conducted among 18- to 65-year-old adults (N = 33,454) in October 2021 using the Depression, Anxiety and Stress Scales (DASS-21) found a high prevalence of severe to extremely severe anxiety (49%), depression (47%) and stress (36%) symptoms in Malaysia, Indonesia, Thailand, and Singapore. Multiple logistic regression showed that female and non-binary genders were associated with increased odds of severe/extremely severe symptoms of anxiety (female: aOR 1.44 [95% CI 1.37–1.52]; non-binary aOR 1.46 [1.16–1.84]), depression (female: aOR 1.39 [1.32–1.47]; non-binary aOR 1.42 [1.13–1.79]), and stress (female: aOR 1.48 [CI 1.40–1.57]; non-binary aOR 1.42 [1.12–1.78]). In all three symptom domains, the odds of severe/extremely severe symptoms decreased across age groups. Middle- and high-income respondents had lower odds of reporting severe/extremely severe anxiety (middle-income: aOR 0.79 [0.75–0.84]; high-income aOR 0.77 [0.69–0.86]) and depression (middle-income: aOR 0.85 [0.80–0.90]; high-income aOR 0.84 [0.76–0.94]) symptoms compared to low-income respondents, while only middle-income respondents had lower odds of experiencing severe/extremely severe stress symptoms (aOR 0.89 [0.84–0.95]). Compared to residents of Malaysia, residents of Indonesia were more likely to experience severe/extremely severe anxiety symptoms (aOR 1.08 [1.03–1.15]) but less likely to experience depression (aOR 0.69 [0.65–0.73]) or stress symptoms (aOR 0.92 [0.87–0.97]). Respondents living in Singapore had increased odds of reporting severe/extremely severe depression symptoms (aOR 1.33 [1.16–1.52]), while respondents residing in Thailand were more likely to experience severe/extremely severe stress symptoms (aOR 1.46 [1.37–1.55]). This study provides insights into the impacts of the COVID-19 pandemic on the point prevalence of psychological distress in Southeast Asia one and a half years after the beginning of the pandemic

Background: Patients with diabetes have increased risks of cardiovascular diseases (CVD), and their LDL-cholesterol (LDL-C) has to be treated to target to prevent complications. We aim to determine the LDL-C trend and its predictors among patients with type 2 diabetes (T2D) in Malaysia. Methods: This was a retrospective open cohort study from 2013 to 2017 among T2D patients in public primary health care clinics in Negeri Sembilan state, Malaysia. Linear mixed-effects modelling was conducted to determine the LDL-C trend and its predictors. The LDL-C target for patients without CVD was <2.6 mmol/L, whereas <1.8 mmol/L was targeted for those with CVD. Results: Among 18,312 patients, there were more females (55.9%), adults ≥60 years (49.4%), Malays (64.7%), non-smokers (93.6%), and 45.3% had diabetes for <5 years. The overall LDL-C trend reduced by 6.8% from 2.96 to 2.76 mmol/L. In 2017, 16.8% (95% CI: 13.2–21.0) of patients without CVD and 45.8% (95% CI: 44.8–46.8) of patients with CVD achieved their respective LDL-C targets. The predictors for a higher LDL-C trend were younger adults, Malay and Indian ethnicities, females, dyslipidemia, and diabetes treatment with lifestyle modification and insulin. Longer diabetes duration, obesity, hypertension, retinopathy, statin therapy, achievement of HbA1c target and achievement of BP target were independent predictors for a lower LDL-C trend. Conclusions: The LDL-C trend has improved, but there are still gaps between actual results and clinical targets. Interventions should be planned and targeted at the high-risk populations to control their LDL-C.

Background: Long COVID is new or ongoing symptoms at four weeks or more after the start of acute COVID-19. However, the prevalence and factors associated with long COVID are largely unknown in Malaysia. We aim to determine the proportion and factors associated with long COVID among COVID-19 patients in Port Dickson, Malaysia. The positive outcomes of our long COVID active detection initiative were also described. Methods: This was a retrospective analysis of long COVID data collected by the Port Dickson District Health Office between 1 September 2021 to 31 October 2021. Monitoring long COVID symptoms was our quality improvement initiative to safe guard residents’ health in the district. The study population was patients previously diagnosed with COVID-19 who resided in Port Dickson. The inclusion criteria were adults aged 18 years and above and were in the fifth week (day 29 to 35) post-COVID-19 diagnosis during the data collection period. We called all consecutive eligible patients to inquire regarding long COVID symptoms. Long COVID was defined as new or ongoing symptoms lasting more than 28 days from the date of positive SARS-CoV-2 by polymerase chain reaction test. Binary multivariate logistic regression was conducted to determine factors associated with long COVID. Results: Among 452 patients, they were predominantly male (54.2%), Malays (68.8%) and aged 18–29 years (58.6%). A total of 27.4% (95% CI [23.4–31.8]) of patients experienced long COVID symptoms and were referred to government clinics. The most frequent long COVID symptoms experienced were fatigue (54.0%), cough (20.2%), muscle pain (18.5%), headache (17.7%) and sleep disturbance (16.1%). Females, patients with underlying cardiovascular disease, asthma and chronic obstructive airway disease, those who received symptomatic care, and patients with myalgia and headaches at COVID-19 diagnosis were more likely to have long COVID. Three patients with suspected severe mental health problems were referred to the district psychologist, and ten patients with no/incomplete vaccination were referred for vaccination. Conclusion. Long COVID is highly prevalent among COVID-19 patients in Port Dickson, Malaysia. Long-term surveillance and management of long COVID, especially among the high-risk groups, are needed as we transition to living with COVID-19.

The prevalence of diabetes in Malaysia is increasing, and identifying patients with higher risk of complications is crucial for effective management. The use of machine learning (ML) to develop prediction models has been shown to outperform non-ML models. This study aims to develop predictive models for Type 2 Diabetes (T2D) complications in Malaysia using ML techniques.Design and methods:This 10-year retrospective cohort study uses clinical audit datasets from Malaysian National Diabetes Registry from 2011 to 2021. T2D patients who received treatment in public health clinics in the southern region of Malaysia with at least two data points in 10 years are included. Patients with diabetes complications at baseline are excluded to ensure temporality between predictors and the target variable. Appropriate methods are used to address issues related to data cleaning, missing data imputation, data splitting, feature selection, and class imbalance. The study uses 7 ML algorithms, including logistic regression, support vector machine, k-nearest neighbours, decision tree, random forest, extreme gradient boosting, and light gradient boosting machine, to develop predictive models for four target variables: nephropathy, retinopathy, ischaemic heart disease, and stroke. Hyperparameter tuning is performed for each algorithm. The model training is performed using a stratified k-fold cross-validation technique. The best model for each algorithm is evaluated on a hold-out dataset using multiple metrics. Expected impact of the study on public health: The prediction model may be a valuable tool for diabetes management and secondary prevention by enabling earlier interventions and optimal resource allocation, leading to better health outcomes.

The actual prevalence of diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D) in Malaysia is unknown. We aimed to determine the prevalence of DKD and its associated risk factors among T2D patients in Malaysia. An analytical cross-sectional study was conducted using the year 2022 clinical audit dataset from the National Diabetes Registry. DKD was defined as albuminuria, a decreased glomerular filtration rate, or both. Among 80,360 patients, 62.2% were female, 68.4% were Malay, and the mean age was 61.4 years. A total of 56.7% (95% CI 56.4–57.1%) of patients were found to have DKD. Increasing age, male sex, Malay ethnicity, longer duration of diabetes, overweight, obesity, hypertension, diabetic retinopathy, diabetic foot ulcer, nontraumatic lower-extremity amputation, ischaemic heart disease, stroke, insulin, higher numbers of antihypertensive agents, antiplatelet agents, poorer HbA1c control, higher systolic blood pressure, non-achievement of triglyceride target, and non-attainment of HDL-cholesterol goal were independent risk factors associated with DKD. Clinicians, program managers, and health policymakers should target modifiable factors to manage DKD and prevent its progression to end-stage kidney disease in Malaysia.

Introduction: Metabolic syndrome (MetS) is a cluster of cardio-metabolic dysfunctions characterised by increased fasting plasma glucose, waist circumference, blood pressure, triglycerides and reduction in high-density lipoprotein cholesterol. Meanwhile, metabolic dysfunction-associated fatty liver disease (MAFLD) is the new term for fatty liver associated with MetS. People with MetS or MAFLD have higher risks for adverse cardiovascular outcomes and mortalities. However, large-scale data on MetS and MAFLD prevalence in Malaysia is mainly unknown. This study aims to determine the prevalence of MetS and MAFLD among the general adult population in Malaysia. Methods and analysis: This is a community-based nationwide cross-sectional study in Malaysia. The data collection period is from July 2023 until September 2023, with a planned sample size of 1296 participants. We use a two-stage proportionate stratified random sampling method to ensure national representativeness. The definition of MetS follows the Harmonised Joint Interim Statement in 2009. A diagnosis of MAFLD is made if a participant has fatty liver, defined as having a Fatty Liver Index ≥60 and has type 2 diabetes, a body mass index ≥23 kg/m 2, or ≥2 metabolic risk abnormalities. Complex sample analysis will be conducted, and the disease prevalence will be reported with 95% CIs, unweighted counts and estimated populations. Ethics and dissemination: The protocol has been approved by the Medical Research and Ethics Committee of the Ministry of Health Malaysia (NMRR ID-22–02845-GUT). The findings will be disseminated through a formal report, policy brief, scientific publications, conference presentations, social media, print media and stakeholder engagement activities.

Abstract Treatment intensification is essential to ensure guideline targets are attained in diabetes patients. The failure to intensify treatment when the targets are not achieved is therapeutic inertia. This study aimed to determine the proportions and factors associated with treatment intensification and therapeutic inertia of antihypertensive therapy in type 2 diabetes patients with uncontrolled hypertension in Malaysia. A retrospective cohort analysis was conducted utilising registry data. Diabetes hypertensive patients with uncontrolled baseline systolic or diastolic blood pressure were included. Treatment intensification was the increase in the number of antihypertensive agents from the index treatment. Therapeutic inertia was the absence of treatment intensification when the second blood pressure reading was still uncontrolled. About 6956 patients were followed up over 2.5 ± 1.1 person-years. Treatment intensification was observed in 29.8% of patients, while 38.6% had therapeutic inertia. Chinese, Indian, and ‘others’ ethnic groups, retinopathy, more antihypertensive agents, and higher systolic blood pressure were associated with therapeutic inertia. Underweight, overweight patients and those with dyslipidaemia had lower risks for therapeutic inertia. The results indicate suboptimal quality of care in public health clinics in Malaysia. Further studies are needed to determine the underlying causes to formulate precise interventions to tackle the problem in Malaysia.