Flexible Scan Statistics for Detecting Spatial Disease Clusters: The rflexscan R Package

The spatial scan statistic is commonly used to detect spatial disease clusters in epidemiological studies. Among the various types of scan statistics, the flexible scan statistic proposed by Tango and Takahashi (2005) is one of the most promising methods to detect arbitrarily-shaped clusters. In this paper, we introduce a new R package, rflexscan (Otani and Takahashi 2021), that provides efficient and easy-to-use methods for analyses of spatial count data using the flexible spatial scan statistic. The package is designed for any of the following interrelated purposes: to evaluate whether reported spatial disease clusters are statistically significant, to test whether a disease is randomly distributed over space, and to perform geographical surveillance of disease to detect areas of significantly high rates. The functionality of the package is demonstrated through an application to a public-domain small-area cancer incidence dataset in New York State, USA, between 2005 and 2009

Dynamical Networks in Function Dynamics

As a first step toward realizing a dynamical system that evolves while spontaneously determining its own rule for time evolution, function dynamics (FD) is analyzed. FD consists of a functional equation with a self-referential term, given as a dynamical system of a 1-dimensional map. Through the time evolution of this system, a dynamical graph (a network) emerges. This graph has three interesting properties: i) vertices appear as stable elements, ii) the terminals of directed edges change in time, and iii) some vertices determine the dynamics of edges, and edges determine the stability of the vertices, complementarily. Two aspects of FD are studied, the generation of a graph (network) structure and the dynamics of this graph (network) in the system.Comment: 29 pages, 10 figure

Multiple-cluster detection test for purely temporal disease clustering: integration of scan statistics and generalized linear models

The spatial scan statistic is commonly used to detect spatial and/or temporal disease clusters in epidemiological studies. Although multiple clusters in the study space can be thus identified, current theoretical developments are mainly based on detecting a ‘single’ cluster. The standard scan statistic procedure enables the detection of multiple clusters, recursively identifying additional ‘secondary’ clusters. However, their p-values are calculated one at a time, as if each cluster is a primary one. Therefore, a new procedure that can accurately evaluate multiple clusters as a whole is needed. The present study focuses on purely temporal cases and then proposes a new test procedure that evaluates the p-value for multiple clusters, combining generalized linear models with an information criterion approach. This framework encompasses the conventional, currently widely used detection procedure as a special case. An application study adopting the new framework is presented, analysing the Japanese daily incidence of out-of-hospital cardiac arrest cases. The analysis reveals that the number of the incident increases around New Year’s Day in Japan. Further, simulation studies undertaken confirm that the proposed method possesses a consistency property that tends to select the correct number of clusters when the truth is known

The daily incidence of out-of-hospital cardiac arrest unexpectedly increases around New Year's Day in Japan

Background: Over 100,000 patients are diagnosed every year as out-of-hospital cardiac arrest (OHCA) cases in Japan and their number has continued to rise for the last decade, presenting a challenge for preventive public health research as well as emergency medical care. The purpose of this study was to identify whether there are any temporal patterns in daily OHCA presentations in Japan. Methods: Records of OHCA patients (n=701,651) transported by ambulance over the course of six years (1st January 2005 to 10th March 2011) in Japan were obtained from the All-Japan Utstein registry data of cardiopulmonary arrest patients. Time periods within which the incidence of OHCA significantly increased were identified by a temporal cluster detection test using scan statistics. The risk ratios of OHCA for the detected periods were calculated and adjusted according to a Poisson regression model accounting for effects of other factors. Results: The risk of OHCA significantly rises 1.3-1.6 times around New Year's Day in Japan. Conclusions: Our analysis revealed the increased daily incidence of OHCA around every New Year's Day in Japan

Detecting multiple spatial disease clusters: information criterion and scan statistic approach

Background: Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specifc epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods.Methods: A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters.Results: A case study and simulation studies conducted both confrmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity.Conclusions: We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.</div

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>Based on the <it>flexible purely spatial scan statistic</it>, we propose a flexibly shaped space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic using benchmark data. In order to compare their performances, we have developed a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic.</p> <p>Conclusion</p> <p>The flexible space-time scan statistic is well suited for detecting and monitoring disease outbreaks in irregularly shaped areas.</p

A two-item version of the Japanese Consultation and Relational Empathy measure:A pilot study using secondary analysis of a cross-sectional survey in primary care

BACKGROUND: The Consultation and Relational Empathy (CARE) measure is a patient-reported measure of physician empathy which is widely used internationally. The Japanese version of the CARE measure has very high internal reliability, suggesting that a shorter version may have adequate validity and reliability. OBJECTIVE: To investigate a valid shorter version of the Japanese CARE measure. METHODS: We conducted a pilot study using secondary analysis of previous data obtained from 9 general practitioners and 252 patients and used to develop the Japanese CARE measure. All 1,023 possible combinations of the Japanese CARE items (n = 1–10) were candidates for the short measure. The internal consistency (Cronbach’s alpha) and the correlations between candidate short questionnaires and the original questionnaire were calculated. After selecting the most valid short questionnaire, inter-rater reliability was determined using generalizability theory, and construct validity (Spearman’s rho) was determined using patient satisfaction. RESULTS: Two items were selected for a pilot shorter version: item 6 “Showing care and compassion” and item 9 “Helping you to take control.” These showed high internal consistency and correlations with the 10-item measure (Cronbach’s alpha = 0.920, correlation = 0.979). Forty-five questionnaires per doctor allowed us to reliably differentiate between practitioners. The construct validity for the pilot short measure was high (Spearman’s rho 0.706, P < 0.001). CONCLUSION: We generated a pilot 2-item version of the Japanese CARE measure. This pilot 2-item version provides a basis for future validation studies of short CARE measures in other languages