OBJECTIVE: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10.
METHODS: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview.
RESULTS: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88).
CONCLUSIONS: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.
STUDY QUESTION: Whether FDA death data in the PLATO trial matched the local site records.
STUDY DESIGN: The NDA spreadsheet contains 938 precisely detailed PLATO deaths. We obtained and validated local evidence for 52 deaths among 861 PLATO patients from 14 enrolling sites in 8 countries and matched those with the official NDA dataset submitted to the FDA.
MEASURES AND OUTCOMES: Existence, precise time, and primary cause of deaths in PLATO.
RESULTS: Discrepant to the NDA document, sites confirmed 2 extra unreported deaths (Poland and Korea) and failed to confirm 4 deaths (Malaysia). Of the remaining 46 deaths, dates were reported correctly for 42 patients, earlier (2 clopidogrel), or later (2 ticagrelor) than the actual occurrence of death. In 12 clopidogrel patients, cause of death was changed to "vascular," whereas 6 NDA ticagrelor "nonvascular" or "unknown" deaths were site-reported as of "vascular" origin. Sudden death was incorrectly reported in 4 clopidogrel patients, but omitted in 4 ticagrelor patients directly affecting the primary efficacy PLATO endpoint.
CONCLUSIONS: Many deaths were inaccurately reported in PLATO favoring ticagrelor. The full extent of mortality misreporting is currently unclear, while especially worrisome is a mismatch in identifying primary death cause. Because all PLATO events are kept in the cloud electronic Medidata Rave capture system, securing the database content, examining the dataset changes or/and repeated entries, identifying potential interference origin, and assessing full magnitude of the problem are warranted.
RESULTS: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.
CONCLUSION: The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.
METHODS: A mixed-method research design will be employed, combining quantitative surveys and qualitative interviews. Stakeholder theory and policy change models will form the theoretical framework of the study. Participants from various stakeholder groups will be recruited using purposive sampling. Data collection will involve surveys and one-on-one semi-structured interviews. Descriptive statistics, inferential analysis and thematic analysis will be used to analyse the data. Integration of quantitative and qualitative data will be used to provide a comprehensive understanding of the data.
DISCUSSION: This study will shed light on factors influencing policy decisions related to dental education and workforce development in Malaysia. The findings will inform evidence-based decision-making, guide the enhancement of dental education programmes and improve the quality of oral healthcare services. Challenges related to participant recruitment and data collection should be considered, and the study's unique contribution to the existing body of knowledge in the Malaysian context will be discussed.
AIMS: This study aimed to explore the postgraduate students' perspective on using Twitter as a learning resource.
SUBJECTS AND METHODS: This qualitative study was conducted as part of a postgraduate program at a university in the United Kingdom. A focus group discussion and five in-depth interviews were conducted after receiving the informed consent. The qualitative data were analyzed by R package for Qualitative Data Analysis software.
ANALYSIS USED: Deductive content analysis was used in this study.
RESULTS: Qualitative analysis revealed four salient themes, which were (1) background knowledge about Twitter, (2) factors influencing the usage of Twitter, (3) master's students' experiences on using Twitter for education, and (4) potential of using Twitter in the postgraduate study. The students preferred to use Twitter for sharing links and appreciated the benefit on immediate dissemination of information. Meanwhile, privacy concern, unfamiliarity, and hesitation to participate in discussion discouraged the students from using Twitter as a learning platform.
CONCLUSIONS: Using social media platforms in education could be challenging for both the learners and the educators. Our study revealed that Twitter was mainly used for social communication among postgraduate students however most could see a benefit of using Twitter for their learning if they received adequate guidance on how to use the platform. The multiple barriers to using Twitter were mainly related to unfamiliarity which should be addressed early in the learning process.
Objectives: The objective of the present study was to demonstrate water quality modelling methodology in reviewing existing policies for Malaysian river catchments based on an example case study.
Methods: The MIKE 11 software developed by the Danish Hydraulic Institute was used to model the main pollutant point sources within the study area - sand mining and aquaculture. Water quality data were obtained for six river stations from 2000 to 2015. All sand mining and aquaculture locations and approximate production capacities were quantified by ground survey. Modelling of the sand washing effluents was undertaken with the advection-dispersion module due to the nature of the fine sediment. Modelling of the fates of aquaculture deposits required both advection-dispersion and Danish Hydraulic Institute ECO Lab modules to simulate the detailed interactions between water quality determinants.
Results: According to the Malaysian standard, biochemical oxygen command (BOD) and ammonium (NH4) parameters fell under Class IV at most of the river reaches, while the dissolved oxygen (DO) parameter varied between Classes II to IV. Total suspended solids (TSS) fell within Classes IV to V along the mid river reaches of the catchment.
Discussion: Comparison between corresponding constituents and locations showed that the water quality model reproduced the long-term duration exceedance for the main body of the curves. However, the water quality model underestimated the infrequent high concentration observations. A standard effluent disposal was proposed for the development of legislation and regulations by authorities in the district that could be replicated for other similar catchments.
Conclusions: Modelling pollutants enables observation of trends over the years and the percentage of time a certain class is exceeded for each individual pollutant. The catchment did not meet Class II requirements and may not be able to reach Class I without extensive improvements in the quality and reducing the quantity of both point and non-point effluent sources within the catchment.
Competing Interests: The authors declare no competing financial interests.
AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.
METHOD: This study proposes a combination of decision tree and logistic regression techniques to model crash severity (injury vs. noninjury), because the combined approach allows the specification of nonlinearities and interactions in addition to main effects. Both a scobit model and a random parameters logit model, respectively accounting for an imbalance response variable and unobserved heterogeneities, are tested and compared. The study data set contains a total of 5 years of crash data (2008-2012) on selected mountainous highways in Malaysia. To enrich the data quality, an extensive field survey was conducted to collect detailed information on horizontal alignment, longitudinal grades, cross-section elements, and roadside features. In addition, weather condition data from the meteorology department were merged using the time stamp and proximity measures in AutoCAD-Geolocation.
RESULTS: The random parameters logit model is found to outperform both the standard logit and scobit models, suggesting the importance of accounting for unobserved heterogeneity in crash severity models. Results suggest that proportion of segment lengths with simple curves, presence of horizontal curves along steep gradients, highway segments with unsealed shoulders, and highway segments with cliffs along both sides are positively associated with injury-producing crashes along rural mountainous highways. Interestingly, crashes during rainy conditions are associated with crashes that are less likely to involve injury. It is also found that the likelihood of injury-producing crashes decreases for rear-end collisions but increases for head-on collisions and crashes involving heavy vehicles. A higher order interaction suggests that single-vehicle crashes involving light and medium-sized vehicles are less severe along straight sections compared to road sections with horizontal curves. One the other hand, crash severity is higher when heavy vehicles are involved in crashes as single vehicles traveling along straight segments of rural mountainous highways.
CONCLUSION: In addition to unobserved heterogeneity, it is important to account for higher order interactions to have a better understanding of factors that influence crash severity. A proper understanding of these factors will help develop targeted countermeasures to improve road safety along rural mountainous highways.
MATERIALS AND METHODS: This descriptive study utilises a desk review approach and employs the WHO Data Quality Assurance (DQA) Tool to assess data quality of ASDK. The analysis involves measuring eight health indicators from ASDK and Survei Status Gizi Indonesia (SSGI) conducted in 2022. The assessment focuses on various dimensions of data quality, including completeness of variables, consistency over time, consistency between indicators, outliers and external consistency.
RESULTS: Current study shows that routine health data in Indonesia performs high-quality data in terms of completeness and internal consistency. The dimension of data completeness demonstrates high levels of variable completeness with most variables achieving 100% of the completeness.
CONCLUSION: Based on the analysis of eight routine health data variables using five dimensions of data quality namely completeness of variables, consistency over time, consistency between indicators, outliers. and external consistency. It shows that completeness and internal consistency of data in ASDK has demonstrated a high data quality.
METHODS: This quasi-experimental study was conducted in the private medical institution in Malaysia. The same questionnaire was used to administer twice, before and after the posting. Moreover, a qualitative question on the issues related to family planning and contraception utilizations in Malaysia was added to the after posting survey. The quantitative data were analyzed using IBM SPSS (version 20) and qualitative data by RQDA software.
RESULTS: A total of 146 participants were recruited in this study. Knowledge on contraception method before posting was 5.11 (standard deviation [SD] ±1.36) and after posting was 6.35 (SD ± 1.38) (P < 0.001). Thematic analysis of the students' answer revealed four salient themes, which were as follows: (1) cultural barrier, (2) misconception, (3) inadequate knowledge, and (4) improvement for the health-care services.
CONCLUSIONS: The teaching-learning process at the MCH posting has an influence on their perception and upgraded their knowledge. It also reflects the role of primary health-care clinics on medical students' clinical exposure and training on family planning services during their postings.