METHODS: Six hundred and sixty two (662) international students participated in this study. A cluster sampling method was employed and data was generated using self-administered questionnaire, which was validated and its reliability checked.
RESULTS: Normality test was conducted followed by descriptive statistics, spearman's correlation and Chi-square tests to explore associations between variables in the study. The response rate was 71.49 %. Of these, 50.3 % of the respondents had better knowledge of hepatitis B; 52.7 % had better knowledge of hepatitis C; 54.8 % had positive attitude towards hepatitis B and C and 77.6 % had safer practices towards hepatitis B and C. Positive correlations were found between knowledge of hepatitis B and knowledge of hepatitis C; knowledge hepatitis B and attitude; knowledge hepatitis C and attitude; knowledge hepatitis B and practice; knowledge hepatitis C and practice; and attitude and practice regarding hepatitis B and C. Similarly, some socio-demographic variables and history of hepatitis were found to be associated with knowledge, attitude and practice related to hepatitis B and C.
CONCLUSION: The levels of knowledge and attitude towards hepatitis B and C were low among respondents but majority of them exhibited safe practices. The study level, faculty, age, nationality, marital status and gender of the respondents were significantly associated with their levels of knowledge, attitude and practices towards the disease. These findings imply that there is need for hepatitis health promotion among the international students of UPM and possibly other international students across the globe. It will serve to improve their levels of knowledge, attitude and practices in short term and get them protected against the disease in the long run.
METHODS: Two electronic academic databases were searched: Scopus and Web of Science (WoS) using specific keywords as search terms derived from the PCC framework with no specific time limit. The search strategy was developed based on the JBI Manual for Evidence Synthesis and utilised the PRISMA-ScR guidelines. Data on the risk of violence, intervening factors, and aggressive behavior were extracted from the included studies. Further analysis was performed whereby similar data were grouped and synthesised together.
RESULTS: The initial search produced 342 studies. However, only nine studies fulfilled the inclusion criteria. The nine studies included 1,068 adult forensic inpatients from various psychiatric hospitals. Only mediation studies reported significant mechanisms of influence between the risk of violence and aggressive behavior. It is postulated that the human agency factor may be the underlying factor that influences a person's functioning and the subsequent series of events between the risk of violence and aggression.
CONCLUSIONS: In light of the paucity of evidence in this area, a generalised conclusion cannot be established. More studies are warranted to address the gaps before conclusive recommendations can be proposed to the relevant stakeholders.
Methods: A cross sectional study was conducted in Sep 2017 using data from registered TB cases in Kelantan state, Malaysia from 2012 to 2016. The profile of TB patients with and without DM were compared in univariable analysis. Multiple logistic regression was used to determine association between DM and unsuccessful treatment outcomes.
Results: A total of 1854 TB patients were diagnosed with DM. The annual proportion was ranging from 26 to 29%. TB patients with DM had an older age, live single, low educational status, poor chest x ray finding and diagnosed with smear positive sputum compared to TB patients without DM. TB patients with DM had three times higher risk to develop unsuccessful TB treatment outcomes compared to TB patients without DM (95% CI 2.47-3.58; P = 0.012) in multivariable analysis.
Conclusion: Those with DM had the worst prognosis of TB outcomes among the significant risk factors. TB control program in Malaysia will need to expand efforts to focus on treatment of TB-DM patients to improve their cure rates in order to achieve the goals of tuberculosis elimination.
Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).
Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.
Conclusion: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.