Space–time pattern of hepatitis A in Spain, 1997–2007

Abstract

In Spain hepatitis A is a compulsory notifiable disease and individual cases are reported to the national epidemiological surveillance network. Incidence rates show variations in different regions. The aim of this study was to analyse the space-time pattern of hepatitis A risk at municipal level in Spain and at global and local levels during the period 1997-2007. At global level we used two estimates of risk: the standardized incidence ratio (SIR) and the posterior probability that the smoothed relative risk is >1 (PP). At local level we used the scan statistic method to analyse the space-time clusters. The SIR and significant PP (>0·8) showed the highest risk concentrated in areas of the Mediterranean coast. The most likely cluster gave a relative risk of 53·530. These spatial statistics methodologies can be complementary tools in the epidemiological surveillance of infectious diseases.

Epidemiological and Etiological Characteristics of Hand, Foot and Mouth Disease in Henan, China, 2008–2013

“…In this study, Scan statistics were used to detect spatial-temporal clusters. This method has been widely used to detect space-time clusters for various diseases, including other infectious diseases and cancers 26 27 28 29 30 . Space-time cluster detection is an important tool in HFMD surveillance to identify various size, areas and duration of elevated risk 31 32 33 .…”

Spatiotemporal Analysis of Infant Measles Using Population Attributable Risk in Shandong Province, 1999–2008

“…The analysis of spatial and temporal variations in the incidence of measles can be useful for optimizing measles surveillance and control efforts in areas where they are most needed [10] . The spatial scan has been increasingly used as a useful tool to explore the spatial and temporal clusters and to formulate new etiological hypothesis [11] – [13] . However, it has difficulty in distinguishing statistical and public health significant regions when multiple clusters are detected.…”

Spatial distribution and cluster analysis of a leishmaniasis outbreak in the south-western Madrid region, Spain, September 2009 to April 2013

“…We chose Kulldorff's method for the spacial cluster analysis because it has several advantages [34]: it adjusts for population density and confounding variables (e.g. age and sex); there is no pre-selection bias since the clusters are selected without prior hypothesis on their location, size or time period; the statistical test takes into account multiple testing and delivers a single p value; if a cluster is detected, its location is specified.…”