Methods: A cross-sectional study was conducted in a single center in Malaysia via recruiting care providers of patients with TBI. The modified caregiver strain index (MCSI) questionnaires were utilized to ascertain the level of strain. The demographic data of informal care providers were also obtained. Independent sample t-test, analysis of variance (ANOVA), and a linear regression model were processed for data analysis.
Results: A total of 140 informal care providers were included in the study. More than half of informal care providers claimed to have strain (54.3%). Factors associated with increased strain include receiving tertiary education, being of Chinese background, and employed experience higher strain level. Informal care providers with characteristics such as being single, retired and provided care for 5 years experienced a lower level of strain.
Conclusion: Guidance on integrating the TBI knowledge into practice, assessing the care provider's level of strain regularly and providing supportive measures may aid in supporting informal care providers at risk.
METHOD: The model was formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear-quadratic with the repopulation model was coupled into the model to account for the cells' population decay due to radiation. The treatment process was then simulated with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which included the proliferation coefficients of the cells, competition coefficients of the cells, and the perturbation constant of the normal cells were obtained from previous literature. The radiation and numerical parameters were obtained from reported clinical data of six patients treated with radiotherapy. The patients had tumor volumes of 24.1cm3, 17.4cm3, 28.4cm3, 18.8cm3, 30.6cm3, and 12.6cm3 with fractionated doses of 2 Gy for the first two patients and 1.8 Gy for the other four. The initial tumor volumes were used to obtain initial populations of cells after which the treatment process was simulated in MATLAB. Subsequently, a global sensitivity analysis was done to corroborate the model with clinical data. Finally, 96 radiation protocols were simulated by using the biologically effective dose formula. These protocols were used to obtain a regression equation connecting the value of the Caputo fractional derivative with the fractionated dose.
RESULTS: The final tumor volumes, from the results of the simulations, were 3.58cm3, 8.61cm3, 5.68cm3, 4.36cm3, 5.75cm3, and 6.12cm3, while those of the normal cells were 23.87cm3, 17.29cm3, 28.17cm3, 18.68cm3, 30.33cm3, and 12.55cm3. The sensitivity analysis showed that the most sensitive model factors were the value of the Caputo fractional derivative and the proliferation coefficient of the cancer cells. Lastly, the obtained regression equation accounted for 99.14% of the prediction.
CONCLUSION: The model can simulate a cancer treatment process and predict the results of other radiation protocols.
AIMS: This cross-sectional study aims to determine the association between sociodemographic factors, parental factors, and lifestyle factors with autism severity in children with ASD.
METHODS AND PROCEDURES: A total of 224 children with ASD were included in this study. Their mothers completed a self-administered questionnaire on sociodemographic characteristics, autism severity, parenting style, parental feeding practices, parenting stress, child's sleep habits and eating behaviours.
OUTCOMES AND RESULTS: As high as 78.1 % of the children with ASD demonstrated a high level of autism severity. Multiple linear regression showed that father's employment status (B = 6.970, 95 % CI = 3.172, 10.768, p
METHODS: This cross-sectional study was conducted from March-November 2014 in the form of a telephone survey. Participants aged 40 years and above were randomly selected across Malaysia and interviewed using the validated Awareness Beliefs about Cancer (ABC) measurement tool. Linear regression was conducted to test the association between symptom and risk factor recognition and socio-demographic variables.
RESULTS: A sample of 1895 participants completed the survey. On average, participants recognised 5.8 (SD 3.2) out of 11 symptoms and 7.5 (SD 2.7) out of 12 risk factors. The most commonly recognised symptom was 'lump or swelling' (74.5%) and the most commonly recognised risk factor was 'smoking' (88.7%). Factors associated with prompted awareness were age, ethnicity, education and smoking status.
CONCLUSION: Recognition of symptom and risk factors for most cancers was relatively low across Malaysia compared to previous studies in high-income countries and to studies conducted in Malaysia. There is a need to conduct regular public health campaigns and interventions designed to improve cancer awareness and knowledge as a first step towards increasing the early detection of cancer.
METHODS: Diabetes data were derived from the Malaysian National Health and Morbidity Surveys conducted in 2006, 2011 and 2015. The air pollution data (NOx, NO2, SO2, O3 and PM10) were obtained from the Department of Environment Malaysia. Using multiple logistic and linear regression models, the association between long-term exposure to these pollutants and prevalence of diabetes among Malaysian adults was evaluated.
RESULTS: The PM10 concentration decreased from 2006 to 2014, followed by an increase in 2015. Levels of NOx decreased while O3 increased annually. The air pollutant levels based on individual modelled air pollution exposure as measured by the nearest monitoring station were higher than the annual averages of the five pollutants present in the ambient air. The prevalence of overall diabetes increased from 11.4% in 2006 to 21.2% in 2015. The prevalence of known diabetes, underdiagnosed diabetes, overweight and obesity also increased over these years. There were significant positive effect estimates of known diabetes at 1.125 (95% CI, 1.042, 1.213) for PM10, 1.553 (95% CI, 1.328, 1.816) for O3, 1.271 (95% CI, 1.088, 1.486) for SO2, 1.124 (95% CI, 1.048, 1.207) for NO2, and 1.087 (95% CI, 1.024, 1.153) for NOx for NHMS 2006. The adjusted annual average levels of PM10 [1.187 (95% CI, 1.088, 1.294)], O3 [1.701 (95% CI, 1.387, 2.086)], NO2 [1.120 (95% CI, 1.026, 1.222)] and NOx [1.110 (95% CI, 1.028, 1.199)] increased significantly from NHMS 2006 to NHMS 2011 for overall diabetes. This was followed by a significant decreasing trend from NHMS 2011 to 2015 [0.911 for NO2, and 0.910 for NOx].
CONCLUSION: The findings of this study suggest that long-term exposure to O3 is an important associated factor of underdiagnosed DM risk in Malaysia. PM10, NO2 and NOx may have mixed effect estimates towards the risk of DM, and their roles should be further investigated with other interaction models. Policy and intervention measures should be taken to reduce air pollution in Malaysia.
Methods: The proposed study will be conducted in three phases: Phase I will involve the development of the item-pool to be included in the tool, followed by a face, content validity and construct validity. The tool reliability, readability and difficulty index will be determined. Phase II will involve the utilization of the tool to assess baseline SAV knowledge among the HCPs followed by an educational intervention. Multiple Linear Regression analysis will be used to determine the factors associated with SAV knowledge among the HCPs. Lastly, Phase III which will be a repeat of Phase II to assess and evaluate the knowledge after the intervention.
Discussion: The study design and findings may guide future implementation and streamline the intervention of improving SAV knowledge in HCPs training and practice.
Lay Summary: Knowledge assessment and educational intervention of snake antivenom among healthcare practitioners in northern Nigeria: a study protocol Snakebite envenoming (SBE) is an important occupational and public health hazard especially in sub-Saharan Africa. For optimum management of SBE, adequate knowledge of snake antivenom (SAV) is very critical among the healthcare practitioners. The baseline knowledge SAV dosage, mode of administration, availability, and logistics is very relevant among healthcare professionals, particularly those that are directly involved in its logistics. It is paramount that SAV is handled and used appropriately. The efforts and advocacy for the availability for more SAV will be in vain if not handled appropriately before they are used. This study protocol aims to develop a tool, to assess SAV knowledge and effects of educational interventions among healthcare professionals (HCPs) in northern Nigeria. This protocol suggests conducting studies in three phases: (a) Development and validation of SAV knowledge assessment tool, (b) Baseline assessment of SAV knowledge assessment tool among HCPs, and (c) Development, implementation and evaluation of an educational intervention to improve SAV knowledge among HCPs in northern Nigeria.
Objectives: This study aimed to determine post-treatment oral cancer patients' concerns and its relationship with patients' clinical characteristics, health-related quality of life (HRQoL), psychological distress and patient satisfaction with the follow-up consultation.
Methods: A total of 85 oral cancer patients were recruited from a three-armed pragmatic RCT study on the patient concerns inventory for head and neck cancer (PCI-H&N), which was conducted at six hospital-based oral maxillofacial specialist clinics throughout Malaysia. Malaysians aged 18 years and above and on follow-ups from 1 month to 5 years or more were eligible. Patients completed the PCI-H&N, functional assessment of cancer therapy -H&N v4.0 and Distress Thermometer at pre-consultation and satisfaction questionnaire at post-consultation. The data were analysed descriptively; multiple linear regression and multivariate logistic regression analyses were used to determine possible predictors of patients' HRQoL and psychological distress.
Results: 'Recurrence or fear of cancer coming back' (31.8%) was most frequently selected. 43.5% of patients selected ≥4 concerns. A significantly high number of concerns were associated with patients of '1-month to 1-year post-treatment' (n = 84%; p = 0.001). A significant association existed between 'time after treatment completed' and patients' concerns of 'chewing/eating', 'mouth opening', 'swelling', 'weight', 'ability to perform', 'cancer treatment' and 'supplement/diet-related'. 'Chewing/eating' was predicted for low HRQoL (p < 0.0001) followed by 'appearance' and 'ability to perform recreation activities' (personal functions domain). Patients with high psychological distress levels were 14 times more likely to select 'ability to perform recreation activities' and seven times more likely to select 'feeling depressed'. No significant association was identified between patients' concerns and patients' satisfaction with the consultation.
Conclusion: Routine follow-up consultations should incorporate the PCI-H&N prompt list to enhance patient-centred care approach as the type and number of patients' concerns are shown to reflect their HRQoL and psychological distress.TRIAL REGISTRATION: NMRR-18-3624-45010 (IIR).
Purpose: To determine the level of adherence to opioid analgesics in patients with cancer pain and to identify factors that may influence the adherence.
Patient and Methods: This was a cross-sectional study conducted from March to June 2018 at two tertiary care hospitals in Malaysia. Study instruments consisted of a set of validated questionnaires; the Medication Compliance Questionnaire, Brief Pain Inventory and Pain Opioid Analgesic Beliefs─Cancer scale.
Results: A total of 134 patients participated in this study. The patients' adherence scores ranged from 52-100%. Factors with a moderate, statistically significant negative correlation with adherence were negative effect beliefs (rs= -0.53, p<0.001), pain endurance beliefs (rs = -0.49, p<0.001) and the use of aqueous morphine (rs = -0.26, p=0.002). A multiple linear regression model on these predictors resulted in a final model which accounted for 47.0% of the total variance in adherence (R2 = 0.47, F (7, 126) = 15.75, p<0.001). After controlling for other variables, negative effect beliefs were the strongest contributor to the model (β = -0.39, p<0.001) and uniquely explained 12.3% of the total variance.
Conclusion: The overall adherence to opioid analgesics among Malaysian patients with cancer pain was good. Negative effects beliefs regarding cancer pain and opioids strongly predicted adherence.