Prospective surveillance of multivariate spatial disease data.
Stat Methods Med Res
; 21(5): 457-77, 2012 Oct.
Article
em En
| MEDLINE
| ID: mdl-22534429
ABSTRACT
Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Vigilância da População
/
Surtos de Doenças
País/Região como assunto:
America do norte
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Espanha