Defining heat waves and extreme heat events using sub-regional meteorological data to maximize benefits of early warning systems to population health.

BACKGROUND

Extreme heat events have been consistently associated with an increased risk of hospitalization for various hospital diagnoses. Classifying heat events is particularly relevant for identifying the criteria to activate early warning systems. Heat event classifications may also differ due to heterogeneity in climates among different geographic regions, which may occur at a small scale. Using local meteorological data, we identified heat waves and extreme heat events that were associated with the highest burden of excess hospitalizations within the County of San Diego and quantified discrepancies using county-level meteorological criteria.

METHODS

Eighteen event classifications were created using various combinations of temperature metric, percentile, and duration for both county-level and climate zone level meteorological data within San Diego County. Propensity score matching and Poisson regressions were utilized to ascertain the association between heat wave exposure and risk of hospitalization for heat-related illness and dehydration for the 1999-2013 period. We estimated both relative and absolute risks for each heat event classification in order to identify optimal definitions of heat waves and extreme heat events for the whole city and in each climate zone to target health impacts.

RESULTS

Heat-related illness differs vastly by level (county or zone-specific), definition, and risk measure. We found the county-level definitions to be systematically biased when compared to climate zone definitions with the largest discrepancy of 56 attributable hospitalizations. The relative and attributable risks were often minimally correlated, which exemplified that relative risks alone are not adequate to optimize heat waves definitions.

CONCLUSIONS

Definitions based on county-level defined thresholds do not provide an accurate picture of the observed health effects and will fail to maximize the potential effectiveness of heat warning systems. Absolute rather than relative risks are a more appropriate measure to define the set of criteria to activate early warnings systems and thus maximize public health benefits.