A Method of Creating Exposure Indicators for Examining the Potential Human Health Effects of Climate Change

摘要

Impact assessments of climate change on chronic diseases are less understood and existing assessments cover limited geographies showing mostly local, rather than national comparisons. Current knowledge gaps exist because metrics that capture individual level exposures to climate change with spatial and temporal consistency are lacking. We describe and evaluate a generic metric that captures exposure to climate variability and change with temporal and spatial resolutions that match the scale of routinely available national and state health data (county level) in the United States (US). Meteorological data was used to identify the number of county-specific temperature anomalies using location- and time-specific climatology. The metric, referred to as "exceedence days" (EDs), was defined as the number of days that exceeded the 95%-ile of the distribution of the observed daily maximum temperatures over a 1960-1989 baseline period and was calculated for a 51-year time period (1960-2010). We used negative binomial regression to investigate EDs across divisions, time and county-level urbanization. We then evaluated the ability of this approach to capture natural climate modes by incorporating La Nina (LN) and El Nino (EN) periods into the models. We observed significant increases in the annual mean number of EDs during the 1990s (16.4 EDs) and 2000s (18 EDs) compared to the reference period, 1960-1989, (15.2 EDs). Continentally, EN periods were associated with as much as a 24% decrease in EDs, while LN periods were associated with as much as a 29% increase in EDs, compared to neutral periods; and, both season and county-level urbanization were associated with EDs occurrences. The findings suggest that the ED metric captures long-term variability in temperature anomalies and subtle spatial and temporal variability with sensitivity to the natural climate modes. And, we have identified differences in EDs among different areas of the country, seasons, and overtime.