Assessment of climate-coccidioidomycosis model: model sensitivity for assessing climatologic effects on the risk of acquiring coccidioidomycosis

Abstract

Understanding the predictive relationships between climate variability and coccidioidomycosis is of great importance for the development of an effective public health decision-support system. Preliminary regression-based climate modeling studies have shown that about 80% of the variance in seasonal coccidioidomycosis incidence for southern Arizona can be explained by precipitation and dust-related climate scenarios prior to and concurrent with outbreaks. In earlier studies, precipitation during the normally arid foresummer 1.5-2 years prior to the season of exposure was found to be the dominant predictor. Here, the sensitivity of the seasonal modeling approach is examined as it relates to data quality control (QC), data trends, and exposure adjustment methodologies. Sensitivity analysis is based on both the original period of record, 1992-2003, and updated coccidioidomycosis incidence and climate data extending the period of record through 2005. Results indicate that models using case-level data exposure adjustment do not suffer significantly if individual case report data are used "as is." Results also show that the overall increasing trend in incidence is beyond explanation through climate variability alone. However, results also confirm that climate accounts for much of the coccidioidomycosis incidence variability about the trend from 1992 to 2005. These strongly significant relationships between climate conditions and coccidioidomycosis incidence obtained through regression modeling further support the dual "grow and blow" hypothesis for climate-related coccidioidomycosis incidence risk.