METHODS: This study utilised data from the 2016 National Health and Morbidity Survey (NHMS). Multiple logistic regression was used to determine the factors associated with malnutrition among non-stunted and stunted children.
RESULTS: The proportion of stunting among children below 5 years old in this East Coast region was 26.2%. When divided by state, Kelantan had the highest proportion of stunting, followed by Pahang and Terengganu, at 28.8%, 26.2% and 23.4%, respectively. In this study, the factors associated with stunting were children aged 24 months old-59 months old (adjusted odds ratio [aOR]: 1.52; 95% CI: 1.26, 1.83; P < 0.001), male children (aOR: 1.47; 95% CI: 1.23, 1.76; P < 0.001), Orang Asli children (aOR: 2.84; 95% CI: 1.86, 4.32; P < 0.001), children with low birth weight from 1,500 g to 2,499 g (aOR: 1.86; 95% CI: 1.36, 2.55; P < 0.001) and children from households that practice unsanitary waste disposal (aOR: 1.42; 95% CI: 1.16, 1.74; P = 0.001).
CONCLUSION: Stunting among children under the age of 5 years old on the East Coast of Peninsular Malaysia remains a public health problem. To reduce the prevalence of stunting in this region, intervention programmes should be intensified. Emphasis should be placed on public health programmes that target the associated factors, such as dietary habits, Orang Asli children, low birth weight and unsanitary waste disposal.
METHODS: This secondary dataset analysis used data from the National Health and Morbidity Survey (NHMS) 2018. Data from 3914 participants were collected on elderly health in the Malaysian population. Sociodemographic characteristics were recorded. Smoking status was grouped as current smokers, former smokers, and non-smokers. A validated Malay language version of the Geriatric Depression Scale (M-GDS-14) was used to screen for depression among the elderly.
RESULTS: There was a significant association between smoking status with location, gender, employment status, marital status, ethnicity, education level, income, and depression. Current smokers are significantly higher in rural than urban areas. Among depressed participants, 65.7%, 17.1% and 17.2% were non-smokers, former smokers and current smokers, respectively. Multiple logistic regression showed that single (unmarried/separated/ divorced/widowed) participants were more likely to be depressed compared to married participants (AOR=1.68; 95% CI: 1.16-2.43). Whilst unemployed participants were more likely to be depressed than those who were employed (AOR=1.72; 95% CI: 1.22-2.44). Other Bumiputras were more likely to have depression compared to Malay, Chinese and Indian participants. Participants without formal education were more likely to be depressed compared to those having tertiary education. These participants have a 2-fold increased risk of depression (AOR=2.13; 95% CI: 1.02-4.45). Participants whose monthly salaries were <2000 MYR (AOR=3.67; 95% CI: 1.84-7.31) and 1000-1999 MYR (AOR=2.71; 95% CI: 1.23-5.94) were more likely to have depression compared with those who had received ≥3000 MYR. Ever smokers were more likely to be depressed than non-smokers (AOR=1.68; 95% CI: 1.23-2.29).
CONCLUSIONS: Elderly Malaysians are indeed at risk of developing depression particularly if they had ever smoked. Public health awareness and campaigning are pertinent to disseminate these outcomes in order to spread the awareness associated with smoking-related depression.
METHODS: Data on 123 obese and overweight housewives in the intervention group from the MyBFF@home study were utilised. A validated Malaysian Malay version of Obesity Weight Loss Quality of Life (OWLQOL) questionnaire was administered at baseline and 6 months after intervention. Descriptive analysis, univariate analysis, paired t-test and multiple logistic regression were performed using SPSS Version 22.
RESULTS: Mean body mass index (BMI) was 31.5 kg/m2 (SD:4.13), with 51 participants classified as overweight (41.5%) while 72 were obese (58.5%). About 72% of the housewives experienced weight reduction (62% reduced weight less than 5% and 11% reduced weight more than 5% of their baseline weight). There was a significant improvement in HRQOL with a pre-intervention total mean score of 59.82 (SD: 26.60) and post-intervention of 66.13 (SD: 22.82), p-value
PATIENTS AND METHODS: The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study.
RESULTS: With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression.
CONCLUSION: These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.
METHODS: The survey included 3977 community-dwelling respondents, aged ≥60 years across Malaysia. Two-stage stratified random sampling with selection of enumeration blocks from both urban and rural areas ensured the findings were representative of the Malaysian older population.
RESULTS: Complex sampling analyses ensured that findings on various geriatric syndromes, non-communicable diseases, nutritional status, functional status, social well-being and quality of life are representative of the Malaysian older population. Among the limitations are that the composition of older persons varied slightly from the general older population.
CONCLUSIONS: This article presents the methodology of the survey, and a baseline of the major health issues for older persons in Malaysia to provide clear guidance to researchers, program managers and policymakers to fully utilize the data from the Malaysian National Health and Morbidity Survey toward achieving healthy aging. The findings might stimulate more research on the health problems faced by older people to provide inputs for policymaking and program implementation. Geriatr Gerontol Int 2020; 20: 7-15.