Evaluating Mortality through Composite Mapping: A Seasonal Perspective

Heat waves and cold spells have been shown to influence human health. Factors related to increases in mortality often include socio-economic status, race, and education. The impact of heat is often more acute whereas cold-related mortality is lagged and complex. Consequently, fewer studies examine cold-related mortality due to the enhanced lagged effect and other confounding factors such as disease prevalence. This study examines the relationship of short-term weather variability on human health during the winter season. Data used in this research includes the NCAP/NCEP reanalysis (NNR) and daily mortality counts from the National Center for Health Statistics (1975-2004). Daily mean mortality is computed across various Metropolitan Statistical Areas and used to define seasonal high-mortality days. These are days which represent dramatic increases in mortality as well as the days with the highest seasonal mortality. Using this subset of days, surface temperature and pressure composite maps were created using the ArcGIS Krging interpolation tool. Seven days preceding a high-mortality event were examined as well as Day 0 (high-mortality day). Various anomalous techniques were used and tested for significance to examine the role of short-term weather variability. While acknowledging that other confounding factors such as socioeconomic status and and the lagged physiological response plays an important role in the human-health relationship, the purpose of this study was to examine the specific weather conditions that impact human health. Particularly, locations dominated by cold air for a sustained period of time often see increases in mortality during the winter season. This increase in mortality is often preceded by a warming period and associated change in pressure. While the relationship was consistent across various locations, spatial and temporal variability was observed.