Identification of Local Clusters of Hand Foot and Mouth Disease Based on Local Geary C Statistic

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Identification of Local Clusters of Hand Foot and Mouth Disease Based on Local Geary C Statistic

1Thi-Quynh Nguyen, 2Thi-Tuyet-Mai Nguyen
1Faculty of Nursing, East Asia University of Technology, Hanoi, Vietnam
2Faculty of Pharmacy, East Asia University of Technology, Hanoi, Vietnam


ABSTRACT:

Background: Hand, foot, and mouth disease (HFMD) is a common childhood illness caused by enteroviruses. Increasingly, the disease has a substantial burden throughout Vietnam. To better understand of the epidemiology of HFMD, this study aims to investigate the use of local Geary’s C statistic in the identification of local clusters of HFMD in Ho Chi Minh city, Vietnam. Methods: Histogram was first used to study the distribution of HFMD cases the incidence of HFMD. Local Geary C statistics was then applied to identify the spatial clusters of HFMD cases and HFMD incidence including high-high, low-low, negative and other positive clusters. Finally, HFMD cases and infection rates collected in Ho Chi Minh were used to explore the spatial clusters of HFMD.
Results: It was found that, in the case of using HFMD cases, two high-high clusters in District 12 and Binh Tan in the city center, one low-low cluster in District 5 in the east of the city center and two negative clusters in Binh Thanh and District 7 in the west of the city were successfully detected. On the other hand, in the case of using HFMD infection rates, a total of three high-high clusters, one low-low cluster, one negative and one other positive clusters were successfully detected, in which, the three high-high clusters were found in the districts of Binh Tan (289 cases/100,000 inhabitants), Binh Chanh (283 cases/100,000 inhabitants) and Tan Phu (281 cases/100,000 inhabitants) in the east of the city.
Conclusions: findings in this study confirm the usefulness of local Geary’s C statistic in the identification of spatial clusters of HFMD.

  

KEYWORDS:

Hand, foot, and mouth disease, Spatial distribution, Local clusters, Local Gearry’s C statistic.

 

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