Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data

Spatial scan statistics are widely used as a technique to detect geographical disease clusters for different types of data. It has been pointed out that the Poisson-based spatial scan statistic tends to detect rather larger clusters by absorbing insignificant neighbors with non-elevated risks. We suspect that the spatial scan statistic for ordinal data may also have similar undesirable phenomena. In this paper, we propose to apply a restricted likelihood ratio to spatial scan statistics for ordinal outcome data to circumvent such a phenomenon. Through a simulation study, we demonstrated not only that original spatial scan statistics have the over-detection phenomenon but also that our proposed methods have reasonable or better performance compared with the original methods. We illustrated the proposed methods using a real data set from the 2014 Health Screening Program of Korea with the diagnosis results of normal, caution, suspected disease, and diagnosed with disease as an ordinal outcome. (C) 2018 Elsevier B.V. All rights reserved.