Research Article: Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic

Date Published: April 16, 2019

Publisher: Public Library of Science

Author(s): Jia-Hong Tang, Tzu-Jung Tseng, Ta-Chien Chan, Ram K. Raghavan.

http://doi.org/10.1371/journal.pone.0215434

Abstract

A resurgence of scarlet fever has caused many pediatric infections in East Asia and the United Kingdom. Although scarlet fever in Taiwan has not been a notifiable infectious disease since 2007, the comprehensive national health insurance data can still track its trend. Here, we used data from the open data portal of the Taiwan Centers for Disease Control. The scarlet fever trend was measured by outpatient and hospitalization rates from 2009 to 2017. In order to elucidate the spatio-temporal hotspots, we developed a new method named the spatio-temporal Gi* statistic, and applied Joinpoint regression to compute the annual percentage change (APC). The overall APCs in outpatient and hospitalization were 15.1% (95% CI: 10.3%-20.2%) and 7.7% (95%CI: 4.5% -10.9%). The major two infected groups were children aged 5–9 (outpatient: 0.138 scarlet fever diagnoses per 1,000 visits; inpatient: 2.579 per 1,000 visits) and aged 3–4 (outpatient: 0.084 per 1,000 visits; inpatient: 1.469 per 1,000 visits). We found the counties in eastern Taiwan and offshore counties had the most hotspots in the outpatient setting. In terms of hospitalization, the hotspots mostly occurred in offshore counties close to China. With the help of the spatio-temporal statistic, health workers can set up enhanced laboratory surveillance in those hotspots.

Partial Text

Streptococcus pyogenes causes a variety of human diseases, including relatively mild skin infections as well as severe invasive diseases [1]. Among the diseases caused by this pathogen, scarlet fever, characterized by a sore throat, fever, headaches, swollen lymph nodes, and a characteristic rash, is predominantly an infectious disease of childhood, though it can also occur in older children and adults [2]. With improved nutrition and widespread use of antibiotics, scarlet fever is now a common, mild contagious disease. However, it is still a notifiable disease in many countries and regions. During the last decade, sporadic outbreaks and reemerging epidemics have been recorded worldwide, including in Vietnam [3], the Republic of Korea [4], China [5–7], Hong Kong [8, 9], Australia [10], Poland [11], and the United Kingdom [12, 13]. In Taiwan, scarlet fever was removed in 2007 from the list of notifiable diseases because of improved medical care capacities [14]. However, through using national health insurance data, the Taiwan Centers for Disease Control (Taiwan CDC) can still monitor the morbidity and hospitalization trends of scarlet fever.

In this article, we have proposed a new spatio-temporal Gi* statistic to cope with the question associated with the Gettis-Ord Gi* statistic where the time-to-time autocorrelation of spatio-temporal data could not be taken into account in hotspot detection. Many hotspot detection approaches first perform a spatial characterization of the data then find the temporal pattern. The work presented in this paper sets itself apart from other studies by finding temporal intervals in the dataset. The temporal neighborhood was defined as a certain time window which is determined by the number of consecutive time-lagged significant correlation coefficients. Then a modified weighted function in the proposed spatio-temporal Gi* statistic was used to precisely reflect the correlation between space and time in the data.

A spatial-temporal Gi* statistic was proposed in this paper to detect hotspots in the space-time domain. A ring map was used to summarize the Z scores calculated by the spatial-temporal Gi* statistic and to present an array of regional attributes in a single spatio-temporal reference graphic.

 

Source:

http://doi.org/10.1371/journal.pone.0215434

 

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