Discussion: Statistical Cluster Detection, Epidemiologic Interpretation, and Public Health Policy

Waller, Lance A.

doi:10.1080/2330443x.2015.1026621

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…To identify meaningful clusters, in other words, to investigate a regional or temporal tendency in the presence of certain diseases, whether the disease risk is relatively high to other surrounding regions or subsequent time periods, a number of statistical tests have been proposed and are widely used [ 5 ]. These tests are classified based on their purpose.…”

Section: Introductionmentioning

confidence: 99%

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

Takahashi

Shimadzu

2018

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Takahashi

Shimadzu

2018

PLoS ONE

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“…There have been many statistical tests widely used [3] for identifying meaningful spatial clusters. Amongst those techniques, a class called the general test [4] searches for clusters without any preconceived assumptions on their locations.…”

Section: Introductionmentioning

confidence: 99%

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

Takahashi

Shimadzu

2020

Int J Health Geogr

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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 specific 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 confirmed 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.

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“…However, some extensions to those methods using

L_{1}

distance have the potential to capture sharp changes in infection rates in space (see, eg, Arnold et al ., ). More recent advances in spatial cluster detection can be found in a special edition of the journal Statistics and Public Policy (Waller, ).…”

Section: Introductionmentioning

confidence: 99%

A heterogeneity measure for cluster identification with application to disease mapping

Lin

Zhu

2019

Biometrics

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Mapping of disease incidence has long been of importance to epidemiology and public health. In this paper, we consider identification of clusters of spatial units with elevated disease rates and develop a new approach that estimates the relative disease risk in association with potential risk factors and simultaneously identifies clusters corresponding to elevated risks. A heterogeneity measure is proposed to enable the comparison of a candidate cluster and its complement under a pair of complementary models. A quasi‐likelihood procedure is developed for estimating the model parameters and identifying the clusters. An advantage of our approach over traditional spatial clustering methods is the identification of clusters that can have arbitrary shapes due to abrupt or noncontiguous changes while accounting for risk factors and spatial correlation. Asymptotic properties of the proposed methodology are established and a simulation study shows empirically sound finite‐sample properties. The mapping and clustering of enterovirus 71 infections in Taiwan are carried out for illustration.

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Discussion: Statistical Cluster Detection, Epidemiologic Interpretation, and Public Health Policy

Cited by 16 publications

References 18 publications

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

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

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

A heterogeneity measure for cluster identification with application to disease mapping

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