BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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.

Background The coronavirus disease 2019 (COVID-19) has severely impacted the lives of children and adolescents. School closure, one of the critical changes during the first COVID-19 wave, caused decreases in social contacts and increases in family time for children and adolescents. This can have both positive and negative influences on suicide, which is one of the robust mental health outcomes. However, the impact of the COVID-19 crisis on children and adolescents in terms of suicide is unknown. Objective This study investigates the acute effect of the first wave of the COVID-19 pandemic on suicide among children and adolescents during school closure in Japan. Data Total number of suicides per month among children and adolescents under 20 years old between January 2018 and May 2020. Methods Poisson regression was used to examine whether suicide increased or decreased during school closure, which spanned from March to May 2020, compared with the same period in 2018 and 2019. Robustness check was conducted using all data from January 2018 to May 2020. Negative binomial regression, a model with overdispersion, was also performed. Results We found no significant change in suicide rates during the school closure (incidence rate ratio (IRR) = 1.15, 95% confidence interval (CI): 0.81 to 1.64). We found the main effect of month, that is, suicides significantly increased suicides in May (IRR: 1.34, 95% CI: 1.01 to 1.78) compared to March, but the interaction terms of month and school closure were not significant (p > 0.1). Conclusions As preliminary findings, this study suggests that the first wave of the COVID-19 pandemic has not significantly affected suicide rates among children and adolescents during the school closure in Japan.

Background: 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.

Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan.

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