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The modifiable areal unit problem (MAUP) is a cause of statistical and visual bias when aggregating data according to spatial units, particularly when spatial units may be changed arbitrarily. The MAUP is a concern in vector-borne disease research when entomological metrics gathered from point-level sampling data are related to epidemiological data aggregated to administrative units like counties or ZIP Codes. Here, we assess the statistical impact of the MAUP when calculating correlations between randomly aggregated cases of anaplasmosis in New York State during 2017 and a geostatistical layer of an entomological risk index for Anaplasma phagocytophilumin blacklegged ticks (Ixodes scapularisSay, Acari: Ixodidae) collected during the fall of 2017. Correlations were also calculated using various administrative boundaries for comparison. We also demonstrate the impact of the MAUP on data visualization using choropleth maps and offer pycnophylactic interpolation as an alternative. Polygon simulations indicate that increasing the number of polygons decreases correlation coefficients and their variability. Correlation coefficients calculated using ZIP Code tabulation area and Census tract polygons were beyond 4 standard deviations from the mean of the simulated correlation coefficients. These results indicate that using smaller polygons may not best incorporate the geographical context of the tick-borne disease system, despite the tendency of researchers to strive for more granular spatial data and associations.

Human granulocytic anaplasmosis (HGA) and human babesiosis are tick-borne diseases spread by the blacklegged tick (Ixodes scapularisSay, Acari: Ixodidae) and are the result of infection with Anaplasma phagocytophilumand Babesia microti, respectively. In New York State (NYS), incidence rates of these diseases increased concordantly until around 2013, when rates of HGA began to increase more rapidly than human babesiosis, and the spatial extent of the diseases diverged. Surveillance data of tick-borne pathogens (2007 to 2018) and reported human cases of HGA (n= 4,297) and human babesiosis (n= 2,986) (2013–2018) from the New York State Department of Health (NYSDOH) showed a positive association between the presence/temporal emergence of each pathogen and rates of disease in surrounding areas. Incidence rates of HGA were higher than human babesiosis among White and non-Hispanic/non-Latino individuals, as well as all age and sex groups. Human babesiosis exhibited higher rates among non-White individuals. Climate, weather, and landscape data were used to build a spatially weighted zero-inflated negative binomial (ZINB) model to examine and compare associations between the environment and rates of HGA and human babesiosis. HGA and human babesiosis ZINB models indicated similar associations with forest cover, forest land cover change, and winter minimum temperature; and differing associations with elevation, urban land cover change, and winter precipitation. These results indicate that tick-borne disease ecology varies between pathogens spread by I. scapularis.