MATERIALS AND METHODS: We present a detailed step-by-step protocol for performing and interpretating PVSA testing, along with recommendations for proficiency testing, competency assessment for performing PVSA, and clinical and laboratory scenarios. Moreover, we conducted an analysis of 1,114 PVSA performed at the Cleveland Clinic's Andrology Laboratory and an online survey to understand clinician responses to the PVSA results in various countries.
RESULTS: Results from our clinical experience showed that 92.1% of patients passed PVSA, with 7.9% being further tested. A total of 78 experts from 19 countries participated in the survey, and the majority reported to use time from vasectomy rather than the number of ejaculations as criterion to request PVSA. A high percentage of responders reported permitting unprotected intercourse only if PVSA samples show azoospermia while, in the presence of few non-motile sperm, the majority of responders suggested using alternative contraception, followed by another PVSA. In the presence of motile sperm, the majority of participants asked for further PVSA testing. Repeat vasectomy was mainly recommended if motile sperm were observed after multiple PVSA's. A large percentage reported to recommend a second PVSA due to the possibility of legal actions.
CONCLUSIONS: Our results highlighted varying clinical practices around the globe, with controversy over the significance of non-motile sperm in the PVSA sample. Our data suggest that less stringent AUA guidelines would help improve test compliance. A large longitudinal multi-center study would clarify various doubts related to timing and interpretation of PVSA and would also help us to understand, and perhaps predict, recanalization and the potential for future failure of a vasectomy.
METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).
RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.
CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.
AIMS: The present study intends to develop, implement, and evaluate the effectiveness of a theory-based integrated dengue education and learning (iDEAL) module in improving the KAP, environmental cleanliness index, and dengue index among schoolchildren in Selangor and Kuala Lumpur.
METHODS: This study is a single-blinded, cluster randomised controlled trial to be conducted from 1 September 2023 to 31 August 2025. The study will involve 20 primary and 20 secondary schools in Selangor and Kuala Lumpur. The 1600 participants will be randomly allocated to intervention and control groups based on selected clusters to avoid contamination. A cluster is a comparable school that fulfils the inclusion and exclusion criteria. The intervention group will receive the iDEAL module, while the control group will receive standard education. The iDEAL module will be developed following a systematic procedure and delivered in-person by trained researchers to the participants. The outcome will be measured using validated, self-administered questionnaires at baseline (T0), immediately (T1), one month (T2), and three months (T3) post-intervention to measure the intervention module effectiveness. The data will be analysed using IBM Statistical Package for Social Science (SPSS) version 28 and descriptive and inferential statistics. Within-group changes over time will be compared using one-way repeated measure analysis of variance for continuous and normally distributed variables. Within-group analysis of categorical data will use Cochran's Q test. The main effect and interaction between and within the intervention and control groups at T0, T1, T2, and T3 will be tested using the generalised linear mixed model (GLMM). Hypothetically, the KAP, environmental cleanliness index, and dengue index among the intervention group will be significantly improved compared to the control group. The hypothesis will be tested using a significance level with a p-value of 0.05 and a confidence interval of 95%.
CONCLUSIONS: The study protocol outlines developing and testing an iDEAL module for schoolchildren in Selangor and Kuala Lumpur, with no socio-demographic differences expected. The intervention aims to improve KAP, environmental cleanliness index, and dengue index, potentially reducing dengue risk. Results could inform public health policies, emphasizing school-based interventions' importance in combating diseases like dengue.
METHODS: We report on the development of the EWARS tool, based on users' recommendations into a convenient, user-friendly and reliable software aided by a user's workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico.
FINDINGS: 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion.
CONCLUSION: EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.