Epidemiological characteristics and spatial epidemiology of hand-foot-mouth disease in Hunan Province, China, from 2008 to 2019.

Abstract

To investigate the epidemic situation of hand-foot-mouth disease (HFMD) in Hunan Province, China, from 2008 to 2019, as well as its spatial autocorrelation characteristics and spatial-temporal clustering, and to provide a reference for the prevention and control of HFMD in Hunan Province. Spatial autocorrelation and spatial-temporal clustering analyses were used to analyze the monitoring data of HFMD in Hunan Province from 2008 to 2019. The epidemic situation of HFMD in Hunan Province from 2008 to 2019 showed obvious seasonal distribution, with a low incidence rate in January to March and a high incidence rate in April to July. As for population distribution, children aged 0-5 years had the highest number of HFMD cases and accounted for 95.89% (1 460 391/1 522 910) of all cases, with a mean annual incidence rate of 2 197.784/100 000, and scattered children had the highest number of cases and accounted for 82.59% (1 257 739/1 522 910) of all cases. The global spatial autocorrelation analysis showed that the onset of HFMD in Hunan Province showed a significant clustering distribution, and the local spatial autocorrelation analysis showed that the high clustering areas of HFMD were mainly the districts and counties of Changsha, Zhuzhou, and Yueyang cities. Time-space scanning showed that clustering time was mainly April to July; the cases were clustered in the northeast of Hunan Province from 2008 to 2010 and in the central part of Hunan Province from 2011 to 2019. The high incidence rate of HFMD is observed in April to July in Hunan Province. Children under 5 years of age are at a high risk of this disease. Spatial-temporal clustering is observed for the epidemic of HFMD, mainly clustered in the northeastern and central areas of Hunan Province. It is suggested that the results may be used as guidance to determine the key areas for HFMD prevention and control in Hunan Province and optimize the allocation of health resources.