Cardiovascular disease (CVD) leads to high morbidity and mortality rate worldwide. Therefore, it is important to determine the risk of CVD across the sociodemographic factors to strategize preventive measures. The current study consisted of 53,122 adults between the ages of 35 and 65 years from The Malaysian Cohort project during recruitment phase from year 2006 to year 2012. Sociodemographic profile and physical activity level were assessed via self-reported questionnaire, whereas relevant CVD-related biomarkers and biophysical variables were measured to determine the Framingham Risk Score (FRS). The main outcome was the 10-year risk of CVD via FRS calculated based on lipid profile and body mass index (BMI) associated formulae. The BMI-based formula yielded a higher estimation of 10-year CVD risk than the lipid profile-based formula in the study for both males (median = 13.2% and 12.7%, respectively) and females (median = 4.3% and 4.2%, respectively). The subgroup with the highest risk for 10-year CVD events (based on both FRS formulae) was the Malay males who have lower education level and low physical activity level. Future strategies for the reduction of CVD risk should focus on screening via BMI-based FRS in this at-risk subpopulation to increase the cost-effectiveness of the prevention initiatives.
Background.This study aimed to identify the factors of CAM usage for general health and to determine the factors associated with the usage of different types of CAM after the diagnosis of chronic diseases among The Malaysian Cohort participants.Methods.This was a cross-sectional study derived from The Malaysian Cohort (TMC) project, a prospective population-based cohort aged between 35 to 65 years old that recruited from April 2006 to September 2012. Association between the CAM usage and contributing factors were determined via logistic regression.Results.The sample were mostly female (58.1%), Malays (43.1%), came from urban (71.9%), aged 44 years and below (26.8%) and had secondary education (45.9%). The prevalence of CAM usage varied across diseases; 62.8% in cancer patients, 53.3% in hypercholesterolemia, 49.4% in hypertensives and 48.6% in diabetics. General CAM usage was greater among female (OR: 1.54, 95% CI: 1.49, 1.59), Chinese (OR: 1.15, 95% CI: 1.12, 1.19), those with higher education (OR: 3.12, 95% CI: 3.00, 3.25), urban residents (OR: 1.55, 95% CI: 1.50, 1.61) and older people (OR ranging from 1.15 to 1.75) while for post-diagnosis of chronic diseases usage, the odds were higher among those with lower education and living in rural areas.Conclusion.Health status, educational level, age, living location and types of chronic diseases were significant factors that influence CAM usage for the intent of either health maintenance or disease treatment. Further exploration on CAM safety and benefit are crucial to minimize the adverse effect and to ensure the efficacy of CAM product.
Malaysia is a country with an intermediate endemicity for hepatitis B. As the country moves toward hepatitis B and C elimination, population-based estimates are necessary to understand the burden of hepatitis B and C for evidence-based policy-making. Hence, this study aims to estimate the prevalence of hepatitis B and C in Malaysia. A total of 1458 participants were randomly selected from The Malaysian Cohort (TMC) aged 35 to 70 years between 2006 and 2012. All blood samples were tested for hepatitis B and C markers including hepatitis B surface antigen (HBsAg), anti-hepatitis B core antibody (anti-HBc), antibodies against hepatitis C virus (anti-HCV). Those reactive for hepatitis C were further tested for HCV RNA genotyping. The sociodemographic characteristics and comorbidities were used to evaluate their associated risk factors. Descriptive analysis and multivariable analysis were done using Stata 14. From the samples tested, 4% were positive for HBsAg (95% CI 2.7–4.7), 20% were positive for anti-HBc (95% CI 17.6–21.9) and 0.3% were positive for anti-HCV (95% CI 0.1–0.7). Two of the five participants who were reactive for anti-HCV had the HCV genotype 1a and 3a. The seroprevalence of HBV and HCV infection in Malaysia is low and intermediate, respectively. This population-based study could facilitate the planning and evaluation of the hepatitis B and C control program in Malaysia.
Prediction model has been the focus of studies since the last century in the diagnosis and prognosis of various diseases. With the advancement in computational technology, machine learning (ML) has become the widely used tool to develop a prediction model. This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) patients using machine learning. A systematic search on Scopus and Web of Science (WoS) was conducted to look for relevant articles based on the research question. The risk of bias (ROB) for all articles were assessed based on the Prediction model Risk of Bias Assessment Tool (PROBAST) statement. Neural network with 76.6% precision, 88.06% sensitivity, and area under the curve (AUC) of 0.91 was found to be the most reliable algorithm in developing prediction model for cardiovascular disease among type 2 diabetes patients. The overall concern of applicability of all included studies is low. While two out of 10 studies were shown to have high ROB, another studies ROB are unknown due to the lack of information. The adherence to reporting standards was conducted based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) standard where the overall score is 53.75%. It is highly recommended that future model development should adhere to the PROBAST and TRIPOD assessment to reduce the risk of bias and ensure its applicability in clinical settings. Potential lipid peroxidation marker is also recommended in future cardiovascular disease prediction model to improve overall model applicability.
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