METHODS: A retrospective review of reports received from 1 January 2009 to 31 December 2012 was undertaken. Descriptive statistics method was applied.
RESULTS: A total of 17,357 MEs reported were reviewed. The majority of errors were from public-funded hospitals. Near misses were classified in 86.3 % of the errors. The majority of errors (98.1 %) had no harmful effects on the patients. Prescribing contributed to more than three-quarters of the overall errors (76.1 %). Pharmacists detected and reported the majority of errors (92.1 %). Cases of erroneous dosage or strength of medicine (30.75 %) were the leading type of error, whilst cardiovascular (25.4 %) was the most common category of drug found.
CONCLUSIONS: MERS provides rich information on the characteristics of reported MEs. Low contribution to reporting from healthcare facilities other than government hospitals and non-pharmacists requires further investigation. Thus, a feasible approach to promote MERS among healthcare providers in both public and private sectors needs to be formulated and strengthened. Preventive measures to minimise MEs should be directed to improve prescribing competency among the fallible prescribers identified.
METHODS: 5 radiologists read 1 identical test set of 200 mammographic (180 normal cases and 20 abnormal cases) 3 times and were requested to adhere to 3 different recall rate conditions: free recall, 15% and 10%. The radiologists were asked to mark the locations of suspicious lesions and provide a confidence rating for each decision. An independent expert radiologist identified the various types of cancers in the test set, including the presence of calcifications and the lesion location, including specific mammographic density.
RESULTS: Radiologists demonstrated lower sensitivity and receiver operating characteristic area under the curve for non-specific density/asymmetric density (H = 6.27, p = 0.04 and H = 7.35, p = 0.03, respectively) and mixed features (H = 9.97, p = 0.01 and H = 6.50, p = 0.04, respectively) when reading at 15% and 10% recall rates. No significant change was observed on cancer characterized with stellate masses (H = 3.43, p = 0.18 and H = 1.23, p = 0.54, respectively) and architectural distortion (H = 0.00, p = 1.00 and H = 2.00, p = 0.37, respectively). Across all recall conditions, stellate masses were likely to be recalled (90.0%), whereas non-specific densities were likely to be missed (45.6%).
CONCLUSION: Cancers with a stellate mass were more easily detected and were more likely to continue to be recalled, even at lower recall rates. Cancers with non-specific density and mixed features were most likely to be missed at reduced recall rates. Advances in knowledge: Internationally, recall rates vary within screening mammography programs considerably, with a range between 1% and 15%, and very little is known about the type of breast cancer appearances found when radiologists interpret screening mammograms at these various recall rates. Therefore, understanding the lesion types and the mammographic appearances of breast cancers that are affected by readers' recall decisions should be investigated.
METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.
RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.
CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.
METHODS: A cross-sectional survey was conducted between March and May 2019 among the elderly aged ≥60 years old. The participants with the following criteria were included in the study: aged ≥60 years, residing in Puncak Alam and able to understand Malay or English language. Data were collected using a pre-validated questionnaire. All statistical analysis was conducted using IBM SPSS ver. 23.
RESULTS: Overall, 336 out of 400 elderly responded to the survey, achieving a response rate of 84%. This study observed that almost 50% of the respondents were using at least one type of HDS in the past one month of the survey. Among HDS non-users, most of them preferred to use modern medicines (62.6%, 114/182). Among the HDS users, 75.3% (116/154) were using at least one type of modern medicine (prescription or over-the-counter medicine). Multivariate analysis showed that having good to excellent perceived health (adjusted OR = 2.666, 95% CI = 1.592-4.464), having felt sick at least once in the past one month (adjusted OR = 2.500, 95% CI = 1.426-4.383), and lower body mass index (adjusted OR = 0.937, 95% CI = 0.887-0.990) were associated with HDS use. It was noted that only a small percentage of HDS users (16.2%, 25/154) had informed healthcare providers on their HDS use.
CONCLUSION: The use of HDS is common among the elderly sampled. Hence, healthcare providers should be more vigilant in seeking information of HDS use for disease management in their elderly patients. Campaigns that provide accurate information regarding the appropriate use of HDS among the elderly are pertinent to prevent misinformation of the products.