期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 21, 期 5, 页码 457-477出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280212446319
关键词
Disease surveillance; multiple diseases; shared component model; conditional predictive ordinate
类别
资金
- NCI NIH HHS [R03CA162029, R03 CA162029] Funding Source: Medline
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.
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