Introduction: The Philadelphia Hot Weather-Health Watch/Warning System was initiated in 1995 to alert the city’s population to take precautionary actions when hot weather posed risks to health. This system is the basis for a number of other heat-health watch warning systems being instituted in cities worldwide. This is the first attempt to calculate the number of lives saved and the economic benefit of these systems in reducing heat related mortality.

Methods: Two types of hot weather patterns, maritime tropical and dry tropical, have been historically associated with elevated mortality in Philadelphia; we refer to these weather patterns as "heatwaves." Our analyses included daily data for all such heatwaves during 1995-1998, plus up to three additional days after the end of each heatwave. The three days following a heatwave were included because mortality effects can lag heatwaves by up to three days. We estimated the number of lives saved and the economic benefit of this system. We focused on excess mortality in people 65 years of age and older. For the days in our sample, mortality data were obtained from the National Center for Health Statistics for the Philadelphia SMSA. Excess mortality was defined as reported mortality minus mortality predicted by a historical trend line developed over the period 1964-1988. We develop a statistical explanation of excess mortality using the following explanatory variables: daily weather variables, the duration in days of each heatwave, the daily sequence number indicating the time of the 139-day summer season on which each heatwave commenced (Time of Season), the classification of each heatwave day as maritime tropical or dry tropical, and an indicator variable to show whether a heatwave warning was issued (Warning Indicator). Weather data for the time period 1995-1998 included temperature at 5AM, and temperature and dew point at 5PM.

Results: There were 45 days during 1995-1998 when warnings were issued (or the effect of warnings persisted over the three-day lag after the warning ended). We explained excess mortality during heatwaves using linear regression, and found two variables convincingly associated with mortality: the Time of Season when a particular heatwave started, and a Warning variable indicating whether or not a heatwave warning had been issued. The estimated coefficient of the Warning variable was about –2.6, suggesting that when a warning was issued, 2.6 lives were saved, on average, for each warning day and for three days after the warning ended. Therefore, we estimated that the warning system saved a total of 117 lives. We also estimated the dollar costs of the system, and reviewed the literature on the value of life.

Conclusions: We estimated that the net dollar benefits of the system were on the order of $468 million over the three-year period.