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.

Background Clinical inertia can lead to poor glycemic control among type 2 diabetes patients. However, there is paucity of information on clinical inertia in low-and middle-income countries including Malaysia. This study aimed to determine the time to treatment intensification among T2D patients with HbA1c of �7% (�53 mmol/mol) in Malaysian public health clinics. The proportion of patients with treatment intensification and its associated factors were also determined. Material and methods This was a five-year retrospective open cohort study using secondary data from the National Diabetes Registry. The study setting was all public health clinics (n = 47) in the state of Negeri Sembilan, Malaysia. Time to treatment intensification was defined as the number of years from the index year until the addition of another oral antidiabetic drug or initiation of insulin. Life table survival analysis based on best-worst case scenarios was used to determine the time to treatment intensification. Discrete-time proportional hazards model was fitted for the factors associated with treatment intensification. Results The mean follow-up duration was 2.6 (SD 1.1) years. Of 7,646 patients, the median time to treatment intensification was 1.29 years (15.5 months), 1.58 years (19.0 months) and 2.32 years (27.8 months) under the best-, average-and worst-case scenarios respectively. The proportion of patients with treatment intensification was 45.4% (95% CI: 44.2-46.5), of which 34.6% occurred only after one year. Younger adults, overweight, obesity, use of antiplatelet medications and poorer HbA1c were positively associated with treatment intensification. Patients treated with more oral antidiabetics were less likely to have treatment intensification.

Good control of glycosylated haemoglobin A1C in diabetes patients prevents cardiovascular complications. We aim to describe the A1C trend and determine the predictors of the trend among type 2 diabetes patients in Malaysia. Longitudinal data in the National Diabetes Registry from 2013 to 2017 were analysed using linear mixed-effects modelling. Among 17,592 patients, 56.3% were females, 64.9% Malays, and the baseline mean age was 59.1 years. The U-shaped A1C trend changed marginally from 7.89% in 2013 to 8.07% in 2017. The A1C excess of 1.07% as reported in 2017 represented about 22% higher risk of diabetes-related death, myocardial infarction, and stroke, which are potentially preventable. The predictors for higher baseline A1C were non-Chinese ethnicity, younger age groups, longer diabetes duration, patients on insulin treatment, polypharmacy use, patients without hypertension, and patients who were not on antihypertensive agents. Younger age groups predicted a linear increase in the A1C trend, whereas patients on insulin treatment predicted a linear decrease in the A1C trend. Specifically, the younger adults and patients of Indian and Malay ethnicities had the poorest A1C trends. Targeted interventions should be directed at these high-risk groups to improve their A1C control.

Background This study aimed to describe changes in body mass index, glycosylated hemoglobin A1C, blood pressure, and low‐density lipoprotein (LDL)‐cholesterol among type 2 diabetes patients in Malaysia. Methods A five‐year retrospective cohort study was conducted using data from the National Diabetes Registry. Type 2 diabetes patients aged ≥18 years and had ≥2 clinical audits between 2013 and 2017 were included in the analysis. The first audit information formed the baseline characteristics, and the last audit information was used for comparison. Individualized A1C, blood pressure, and LDL‐cholesterol goals were adapted from Malaysian Clinical Practice Guidelines on Type 2 Diabetes Management 2020, American Diabetes Association 2020, and European Association for the Study of Diabetes 2019. Results Of the 18 341 patients, 55.8% were female and 64.9% Malay ethnicity. The baseline mean age was 59.3 ± 10.6 years. During an average of 2.5 person‐years of follow‐up, the mean body mass index dropped by 0.16 kg/m2 to 27.9 kg/m2, A1C increased by 0.16% to 8.0%, systolic blood pressure increased by 1.4 mm Hg to 136.2 mm Hg, diastolic blood pressure decreased by 1.0 mm Hg to 77.3 mm Hg and LDL‐cholesterol reduced by 0.12 mmol/L to 2.79 mmol/L, P < 0.001 for all. Out of eight categories of individualized goals, most patients achieved the A1C goal of ≤8.0%. The new LDL‐cholesterol goal of <1.4 mmol/L was least likely to be achieved. Conclusions The body mass index, A1C, blood pressure, and LDL‐cholesterol performance remained suboptimal. Standards of care for these clinical parameters remain to be achieved by the majority of diabetes patients.

DISCLAIMER This paper was submitted to the Bulletin of the World Health Organization and was posted to the COVID-19 open site, according to the protocol for public health emergencies for international concern as described in Vasee Moorthy et al. (

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.

No abstract