Spatial-temporal clusters and risk factors of hand, foot, and mouth disease at the district level in Guangdong Province, China.

OBJECTIVE: Hand, foot, and mouth disease (HFMD) has posed a great threat to the health of children and become a public health priority in China. This study aims to investigate the epidemiological characteristics, spatial-temporal patterns, and risk factors of HFMD in Guangdong Province, China, and to provide scientific information for public health responses and interventions.

METHODS: HFMD surveillance data from May 2008 to December 2011were provided by the Chinese Center for Disease Control and Prevention. We firstly conducted a descriptive analysis to evaluate the epidemic characteristics of HFMD. Then, Kulldorff scan statistic based on a discrete Poisson model was used to detect spatial-temporal clusters. Finally, a spatial paneled model was applied to identify the risk factors.

RESULTS: A total of 641,318 HFMD cases were reported in Guangdong Province during the study period (total population incidence: 17.51 per 10,000). Male incidence was higher than female incidence for all age groups, and approximately 90% of the cases were children [Formula: see text] years old. Spatial-temporal cluster analysis detected four most likely clusters and several secondary clusters (P<0.001) with the maximum cluster size 50% and 20% respectively during 2008-2011. Monthly average temperature, relative humidity, the proportion of population [Formula: see text] years, male-to-female ratio, and total sunshine were demonstrated to be the risk factors for HFMD.

CONCLUSION: Children [Formula: see text] years old, especially boys, were more susceptible to HFMD and we should take care of their vulnerability. Provincial capital city Guangzhou and the Pearl River Delta regions had always been the spatial-temporal clusters and future public health planning and resource allocation should be focused on these areas. Furthermore, our findings showed a strong association between HFMD and meteorological factors, which may assist in predicting HFMD incidence.