Spatial Point Pattern Analysis of Valley Fever in the Phoenix Metropolitan Area

Using a novel Arizona Medicaid data set, we model Valley fever (VF) cases in the Phoenix Metropolitan Area as a spatial point process during six month intervals between 2013-2023. We estimate the intensity function of VF cases, observed at residential locations of patients, as a function of environmental covariates available at high spatial resolutions, including land cover type, change in land cover type, Normalized Difference Vegetation Index (NDVI), change in NDVI, and a Habitat Suitability Index (HSI) for the Coccidioides fungus. Models with all covariates included and quadratic terms for NDVI, NDVI difference, and HSI tend to perform the best. We show that land cover change and HSI measured at the residential (point) level are useful for understanding VF risk. Furthermore, we find that NDVI and its semesterly changes appear to have a nonlinear relationship with the intensity of VF cases. Despite more pronounced peaks in predicted intensities for our most complex model compared to our simplest model, we show that even our best, most complex model underestimates the intensity in portions of the study region. Thus, our results provide initial evidence that spatially localized environmental information is useful for examining VF incidence among Medicaid patients in the study area.