Background. Atherogenic index of plasma (AIP) was found to be one of the strongest markers in predicting the cardiovascular disease (CVD) risk. This study was to determine the AIP and its relationship with other CVD risk factors. Materials and Methods. This cross-sectional study was done among 349 staff of a public university in Sarawak. Data were collected using questionnaire, blood sampling, and anthropometric and blood pressure measurement. Data were analyzed using IBM SPSS version 20. Results. A total of 349 respondents participated with majority females (66.8%), aged 38.5 ± 7.82 years. Nearly 80% of the respondents were overweight and obese, 87.1% with high and very high body fat, and 46.9% with abnormal visceral fat. For AIP category, 8.9% were found to be in intermediate and 16.4% were at high risk. Elevated lipid profile showed that total cholesterol (TC) is 15.5%, low density lipoprotein (LDL) is 16.1%, and triglyceride (TG) is 10.6%. AIP was significantly correlated with body mass index (r=0.25), visceral fat (r=0.37), TC (r=0.22), LDL (0.24), HDL (r=−0.72), TG (r=0.84), glucose (r=0.32), systolic blood pressure (r=0.22), and diastolic blood pressure (r=0.28). Conclusion. It indicated that AIP is associated with other CVD risk factors. Modification of lifestyle is strongly recommended.

As there are few food frequency questionnaires (FFQ) to assess the dietary intake of preschool children, this study examined the validity and reliability of an FFQ for this purpose. A total of 210 preschoolers aged 4 to 6 years participated in the validation study, while a subsample of 66 participants joined the reliability study. The FFQ is modified from the ToyBox-study and South East Asian Nutrition Surveys (SEANUTS), and comprised 108 food items from 13 food groups. A three-day estimated dietary record (3DR) was used as reference and reliability was assessed through a second administration of the FFQ (FFQ2), four weeks after the first administration (FFQ1). For the validation study, Spearman’s correlation coefficients showed moderate to high correlations (p < 0.001) between FFQ and 3DR. Cross-classification of quartile analysis showed moderate agreement between the two methods. As for reliability, Spearman’s correlation coefficients showed moderate to high correlations (p < 0.001) between FFQ1 and FFQ2. Cronbach’s alpha values (0.708 to 0.824) and intraclass correlation coefficients (0.710 to 0.826) showed good agreement between repeated FFQs. The results suggest that the FFQ has acceptable validity and good reliability. Hence, the FFQ can be used to assess preschool children’s food intake.

Nondisclosure of traditional and complementary medicine (T&CM) use may cause individual to be at risk of undue harm. This study aimed to explore patient’s experience and views on their decision to disclose the use of T&CM to the doctor. An exploratory qualitative study using in‐depth interview involving 10 primary care clinics attendees in Kuching was conducted. The results indicated that disclosure of T&CM use will motivate them to get information, increase doctor’s awareness, and get support from family and friends for disclosure. Fear of negative relationship and negative response from doctors was a barrier for disclosure. Doctor’s interpersonal and communication skills of being involved, treating patients respectfully, listening attentively, respecting privacy, and taking time for the patient were a critical component for disclosure. Intrapersonal trust regarding doctor influences their satisfaction on healthcare. Women are more open and receptive to a health concern and expressing negative emotions and tend to share problems, whereas men always described themselves as healthy, tended to keep their own personal feeling to themselves, and tended to not share. The doctor should consider gender differences in disclosure, their attitude towards T&CM use, and gained patient’s trust in the delivery of healthcare services. Good interpersonal and communication skills must be maintained between doctor and patients.

Diabetes mellitus is a public health concern in Malaysia. Treatment of diabetes is costly and can lead to complications if disease is poorly controlled. Diabetes self-management (DSM) is found to be essential for optimal glycemic control. This cross-sectional study was conducted among samples from four randomly selected diabetes clinics in Sarawak, Malaysia. The aim was to determine the predictors for DSM. Face-to-face interview using questionnaire was used to collect data. Four hundred respondents with type 2 diabetes mellitus (T2DM) were recruited. Majority of the respondents were Sarawak Bumiputra (Iban and Bidayuh, 48.6%) and female (68.6%). The mean age was 58.77 years (SD = 11.46) and approximately half of the respondents (50.6%) had T2DM for six years (SD = 4.46). The mean fasting blood glucose (FBG) was 8.06 mmol/L (SD = 2.94), with majority (76.1%) having the level higher than 6.1 mmol/L. Multiple logistic regression tests showed significant linear relationship between DSM and belief in treatment effectiveness (p=0.001), family support (p=0.007), and self-efficacy (p=0.027). Health care personnel must convince patients with T2DM of the effectiveness of the treatment, empower and enhance their self-efficacy, and enlist the family support so as to ensure patients sustain their DSM efforts.

Background.Recognizing the needs of cancer survivors is one of the important aspects in healthcare delivery. This study aimed to determine the prevalence of unmet supportive care needs and its associated factors among the breast cancer survivors of community-based support group in Kuching, Sarawak.Materials and Methods.This was a cross-sectional study using Supportive Care Needs Survey (SCNS-SF34). All the members of community-based breast cancer support groups in Kuching were invited. A total of 101 respondents were face-to-face interviewed after the consent was obtained. Data was entered and analyzed using SPSS version 20.Results.The respondents endorsedhealth system and informationdomain with the highest mean score (2.48; 95% CI: 2.32–2.64). Top 10 items with “moderate to high” level unmet needs had a prevalence of 14.9% to 34.7% of respondents indicating need. Significantly higher level of unmet needs was associated with survivors who were younger (less than 60 years old), had higher education attainment, were unemployed, had survival duration of up to 5 years, and were undergoing active treatment.Conclusion. Systematic delivery of health information which is targeted, culturally sensitive, and linguistically appropriate for addressing younger age, education level, employment status, length of survivorship, and treatment stage should be considered not only at hospital-based setting but also at the community-based support groups.

The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes.