Abstract
The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP). In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states) by Moran's I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran's I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.
Highlights
Infections with feline immunodeficiency virus (FIV) and feline leukemia virus (FeLV) have been reported from a number of countries and are important conditions in cats [1]
This study showed that commonly used spatial epidemiological methods (Moran’s I, spatial scan test, (a) and spatial regression modeling) are sensitive to choice of the spatial aggregation scale for analysis, that is, affected by the modifiable areal unit problem (MAUP)
This study demonstrated the importance of study design in the context of spatial epidemiological studies
Summary
Infections with feline immunodeficiency virus (FIV) and feline leukemia virus (FeLV) have been reported from a number of countries and are important conditions in cats [1]. Geographic correlation studies can be important tools to evaluate the association of spatial or environmental risk factors with the occurrence of health events after adjusting for confounders The identification of such spatial patterns may provide clues for further testable hypotheses about an unknown disease etiology [12]. Ecological studies, such as geographic correlation studies, are valuable when an individual level association between infection and risk factors is evident and a group-level association is assessed to determine the population health impact [13]. Spatial analysis of FIV and FeLV infections can be a valuable tool in epidemiological understanding of these infections
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