Differentiating anomalous disease intensity with confounding variables in space.

ABSTRACT:

Background

The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can explain the occurrence of geographical clustering of disease incidence and to investigate other hidden spatially related risk factors if there still exist geographical disease clusters, after adjusting for risk factors.

Methods

We propose to develop statistical methods for differentiating inc

SUBMITTER: Wu CC 

PROVIDER: S-EPMC7489047 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature