Background: The spatial scan statistic proposed by Kulldorff has been applied to a wide variety of epidemiological studies for cluster detection. This scan statistic, however, uses a circular window to define the potential cluster areas and thus has difficulty in correctly detecting actual noncircular clusters. A recent proposal by Duczmal and Assunção for detecting noncircular clusters is shown to detect a cluster of very irregular shape that is much larger than the true cluster in our experiences.Methods: We propose a flexibly shaped spatial scan statistic that can detect irregular shaped clusters within relatively small neighborhoods of each region. The performance of the proposed spatial scan statistic is compared to that of Kulldorff's circular spatial scan statistic with Monte Carlo simulation by considering several circular and noncircular hot-spot cluster models. For comparison, we also propose a new bivariate power distribution classified by the number of regions detected as the most likely cluster and the number of hot-spot regions included in the most likely cluster.Results: The circular spatial scan statistics shows a high level of accuracy in detecting circular clusters exactly. The proposed spatial scan statistic is shown to have good usual powers plus the ability to detect the noncircular hot-spot clusters more accurately than the circular one.Conclusion: The proposed spatial scan statistic is shown to work well for small to moderate cluster size, up to say 30. For larger cluster sizes, the method is not practically feasible and a more efficient algorithm is needed.

Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic.

Background: The use of a statistical approach to analyze cumulative adverse event (AE) reports has been encouraged by regulatory authorities. However, data variations affect statistical analyses (eg, signal detection). Further, differences in regulations, social issues, and health care systems can cause variations in AE data. The present study examined similarities and differences between two publicly available databases, ie, the Japanese Adverse Drug Event Report (JADER) database and the US Food and Drug Administration Adverse Event Reporting System (FAERS), and how they affect signal detection. Methods: Two AE data sources from 2010 were examined, ie, JADER cases (JP) and Japanese cases extracted from the FAERS (FAERS-JP). Three methods for signals of disproportionate reporting, ie, the reporting odds ratio, Bayesian confidence propagation neural network, and Gamma Poisson Shrinker (GPS), were used on drug-event combinations for three substances frequently recorded in both systems. Results: The two databases showed similar elements of AE reports, but no option was provided for a shareable case identifier. The average number of AEs per case was 1.6±1.3 (maximum 37) in the JP and 3.3±3.5 (maximum 62) in the FAERS-JP. Between 5% and 57% of all AEs were signaled by three quantitative methods for etanercept, infliximab, and paroxetine. Signals identified by GPS for the JP and FAERS-JP, as referenced by Japanese labeling, showed higher positive sensitivity than was expected.
Conclusion: The FAERS-JP was different from the JADER. Signals derived from both datasets identified different results, but shared certain signals. Discrepancies in type of AEs, drugs reported, and average number of AEs per case were potential contributing factors. This study will help those concerned with pharmacovigilance better understand the use and pitfalls of using spontaneous AE data.

Objectives: Occurrence of delirium in the ICU is associated with a longer stay in the ICU. To examine whether the use of ramelteon, a melatonin agonist, can prevent delirium and shorten the duration of ICU stay of critically ill patients. Design: A single-center, triple-blinded, randomized placebo-controlled trial. Setting: ICU of an academic hospital. Patients: Eligible patients were ICU patients who could take medicines orally or through a nasogastric tube during the first 48 hours of admission. Interventions: The intervention group received ramelteon (8 mg/d), and the control group received placebo (1 g/d of lactose powder) at 20:00 hours every day until discharge from the ICU. Measurements and Main Results: A total of 88 subjects were randomized to the ramelteon group (45 subjects) or the placebo group (43 subjects). As the primary endpoint, there was a trend toward decrease in the duration of ICU stay (4.56 d) in the ramelteon group compared with the placebo group (5.86 d) (p = 0.082 and p = 0.028 before and after adjustments). As the secondary endpoints, statistically significant decreases in the occurrence rate (24.4% vs 46.5%; p = 0.044) and duration (0.78 vs 1.40 d; p = 0.048) of delirium were observed in the ramelteon group. The nonintubated patients of the ramelteon group showed statistically significantly fewer awakenings per night and a higher proportion of nights without awakenings. Conclusions: Ramelteon tended to decrease the duration of ICU stay as well as decreased the occurrence rate and duration of delirium statistically significantly.