PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 24, 2014PLoS ONE75 citationsOpen Access

Epidemiological Analysis, Detection, and Comparison of Space-Time Patterns of Beijing Hand-Foot-Mouth Disease (2008–2012)

JWJiaojiao WangJiangsu UniversityZCZhidong CaoChinese Academy of SciencesDZDaniel ZengShanghai International Studies University

Key Result

The most likely space-time cluster of hand, foot, and mouth disease in Beijing was located in the mid-east part of the Fangshan district, with a relative risk of 2.18.

Study Design

Type

Observational (n=157,707)

Structured PICO

P
Population
157,707 reported cases of hand, foot, and mouth disease in Beijing from 2008 to 2012, predominantly affecting children aged 0-4 years.
O
Outcome
Epidemiological features and high relative risk space-time HFMD clusters

HFMD in Beijing from 2008-2012 showed steady space-time patterns with high-risk populations mainly in urban-rural transition zones.

Main Result

Relative Risk: 2.18

p-value: p=0.001

Limitations

  • Only 4.59% of reported HFMD cases were tested for the associated pathogen, which may have reduced the power of the virological surveillance data analysis.
  • Missing spatial location information for some cases may have reduced the accuracy of the results.
  • The spatial scan statistics method assumes circular or cylinder scanning windows, which may not represent the actual shapes of the clusters.

Abstract

BACKGROUND: Hand, foot, and mouth disease (HFMD) mostly affects the health of infants and preschool children. Many studies of HFMD in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level HFMD cases in a major city such as Beijing are unknown. The objective of this study was to investigate epidemiological features and identify high relative risk space-time HFMD clusters at a fine spatial scale. METHODS: Detailed information on age, occupation, pathogen and gender was used to analyze the epidemiological features of HFMD epidemics. Data on individual-level HFMD cases were examined using Local Indicators of Spatial Association (LISA) analysis to identify the spatial autocorrelation of HFMD incidence. Spatial filtering combined with scan statistics methods were used to detect HFMD clusters. RESULTS: A total of 157,707 HFMD cases (60.25% were male, 39.75% were female) reported in Beijing from 2008 to 2012 included 1465 severe cases and 33 fatal cases. The annual average incidence rate was 164.3 per 100,000 (ranged from 104.2 in 2008 to 231.5 in 2010). Male incidence was higher than female incidence for the 0 to 14-year age group, and 93.88% were nursery children or lived at home. Areas at a higher relative risk were mainly located in the urban-rural transition zones (the percentage of the population at risk ranged from 33.89% in 2011 to 39.58% in 2012) showing High-High positive spatial association for HFMD incidence. The most likely space-time cluster was located in the mid-east part of the Fangshan district, southwest of Beijing. CONCLUSIONS: The spatial-time patterns of Beijing HFMD (2008-2012) showed relatively steady. The population at risk were mainly distributed in the urban-rural transition zones. Epidemiological features of Beijing HFMD were generally consistent with the previous research. The findings generated computational insights useful for disease surveillance, risk assessment and early warning.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wang et al. (2014) conducted an observational in Hand, foot, and mouth disease (HFMD) (n=157,707). Geographic location (urban-rural transition zones) vs. Other areas in Beijing was evaluated on Most likely space-time cluster of HFMD (RR 2.18, p=0.001). The most likely space-time cluster of hand, foot, and mouth disease in Beijing was located in the mid-east part of the Fangshan district, with a relative risk of 2.18.

synapsesocial.com/papers/6a387b8c41436a8b20ce6e99https://doi.org/10.1371/journal.pone.0092745
Ask AI
Helpful
Bookmark
Share
View Full Paper