Heat Watch/Warning Systems Save
Lives: Estimated Costs and Benefits for Philadelphia 1995-1998
Thomas J. Teisberg1, Kristie L. Ebi2*,
Laurence S. Kalkstein3, Lawrence Robinson4, Rodney
F. Weiher5
1 Teisberg
Associates
1475 Ingleside Drive
Charlottesville, VA 22901
(434) 293-4003
2 EPRI
3412 Hillview Ave.
Palo Alto, CA 94304
(650) 855-2735
* Corresponding author
3 Center
for Climatic Research
University of Delaware
Newark, DE 19702
(302) 831-8269
4 Deputy
Health Commissioner
City of Philadelphia
1101 Market St
Philadelphia, PA 19107
(215) 685-5350
5 NOAA
Office of Policy & Strategic Planning
HCHB, Rm 6117
US Department of Commerce
14th and Constitution Ave, NW
Washington, DC 20230
(202) 482-0636
Dateline:____________________________________________________________
Heat Watch/Warning Systems Save
Lives: Estimated Costs and Benefits for Philadelphia 1995-1998
Running title: Heat Wave Warning Systems Save Lives
Keywords:
Heat
Mortality
Warning Systems
Value of Information
Methods
Abbreviations:
PWWS = Philadelphia Hot Weather-Health Watch/Warning System
NWS = National Weather Service
VSL = Value of a Statistical Life
Abstract
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. The number of lives saved and the economic
benefit of this system were estimated using data from 1995-1998. Excess mortality in people 65 years of age and older was
defined as reported mortality minus mortality predicted by a historical trend
line developed over the period 1964-1988. Excess mortality
during heatwaves was explained using linear regression. Two
variables were 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. Given
the number of warnings issued over the three-year period, the system saved
an estimated 117 lives. Estimated dollar costs for
running the system were small compared with estimates of the value of a life.
1.
Introduction
Severe and sustained episodes of summer heat are
associated with increased morbidity and mortality, particularly in temperate
regions. The death toll in an unprepared region can
be substantial. For example, during July 1-14, 1993,
the eastern United States experienced a severe heatwave with high temperatures
(33.9 – 38.3 ºC) and high humidity (36 – 58%) (US CDC 1994). During July 6-14, the Philadelphia Medical Examiners determined
118 deaths were heat related (either a core body temperature of 40.6 ºC or
higher, or a body found in a hot, unventilated environment). This is certainly an underestimate, as heat is associated
with increased mortality from a number of causes other than heat stroke (Shen
et al. 1998). Historically, cardiovascular diseases
have accounted for 13% to 90% of the increase in overall mortality during
and following a heatwave, cerebrovascular disease 6% to 52%, and respiratory
diseases 0% to 14% (Kilbourne 1997). Heatwaves also
increase the rate of nonfatal illnesses.
Partly in response to heatwaves in 1993 and 1994,
the Philadelphia Hot Weather-Health Watch/Warning System (PWWS) was initiated
in 1995 to alert the city’s population when weather conditions posed risks
to health (Kalkstein et al. 1996, Mirchandani et al. 1996, Sheridan and Kalkstein
1998). Appendix 1 includes a description of the PWWS,
including the measures taken during a heatwave to reduce morbidity and mortality. The National Weather Service (NWS) advises the Philadelphia
Department of Health of potentially dangerous weather based on the forecasts
generated by the PWWS and other information. The Philadelphia
Department of Health then implements emergency precautions and mitigation
procedures to reduce mortality risk (Kalkstein et al. 1996). This system is the basis for more than a dozen other heat-health
watch warning systems being instituted in cities worldwide (Kalkstein 2003;
Sheridan and Kalkstein 1998). This is the first attempt
to calculate the number of lives saved and the economic benefit of these
systems in reducing heat related mortality.
2. Methods
Kalkstein et al. identified two types of hot weather
patterns, maritime tropical and dry tropical, which have been historically
associated with elevated mortality in Philadelphia (Kalkstein et al. 1996). 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 (Curriero
et al. 2002, Laschewski and Jendritzky 2002).
For the days in our sample, mortality data were
obtained from the National Center for Health Statistics for the 65 and older
age group in the Philadelphia SMSA for 1995 through 1998, the last year for
which mortality data were available (National Center for Health Statistics
2000). We chose to limit our analyses to this age
group because they are vulnerable to excessive heat; therefore the statistical
evidence regarding the effectiveness of the warning system should be strongest
for this age group (Semenza et al. 1996, Smoyer 1998, US CDC 1995). The analyses were based on excess mortality, where excess
mortality was calculated as the difference between reported mortality and
the underlying mortality trend estimated from years prior to 1995, as described
in Appendix 1 (Kalkstein et al. 1996).
We developed 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 (US Department of Commerce 2002).
A heatwave warning was issued only if the local
NWS office concurred with a PWWS recommendation that a warning be issued. Before the development of the PWWS, the NWS issued most
heat/health alerts based on guidelines that relied heavily on the computation
of the “heat index” (NOAA 1994). An excessive heat
warning was issued when daytime heat index values were expected to exceed
40.5 °C for more than three hours a day on two consecutive days, or when
the daytime heat index was expected to exceed 46 °C for any length of time
(Kalkstein et al. 1996).
During the time period of the study, the local
NWS office agreed to use the PWWS for guidance in the issuance of heat advisories
and excessive heat warnings. However, forecasters
did not completely rely on the system and were often conservative in issuing
advisories and warnings. In some cases, forecasters
would not call an advisory or warning if the heat index was below 38 °C,
even if the system called for such an issuance. Some
forecasters felt that the system called too many advisories and warnings
and were concerned that the public might become less responsive to subsequent
warnings (Kalkstein, personal communication). An early
evaluation of the system indicated that, because of this policy, warnings
were not called frequently enough and that heat-related deaths occurred on
days when the system called for a warning and the forecasters did not issue
one (Kalkstein et al. 1996). More recently, forecasters
are using the PWWS as primary guidance, although they are advised to use
discretion and not call advisories and warnings based exclusively on the
system.
The NWS concurred with a PWWS recommendation that
a warning be issued less than one-quarter of the time; during the period 1995-1998,
there were 70 days the PWWS suggested a heat warning, but only 16 days when
the NWS concurred and warnings were issued. In addition,
there were 5 days on which the NWS issued warnings, even though the PWWS
did not recommend one, making a total of 21 warning days.
Counting the days immediately following the end of each of these warnings,
our sample included a total of 45 days for which a warning could potentially
have affected excess mortality during 1995-1998.
Excess mortality was linearly regressed against
these 45 heatwave days using the linear regression capabilities provided
in MicroSoft Excel.
3. Results
a. Estimation of Lives Saved
Among the explanatory variables considered for inclusion
in the regression model, only Time of Season and Warning Indicator were convincingly
associated with excess mortality. The omission of
the daily weather variables might seem surprising, but is less so when one
recognizes that all the days in our statistical sample were identified as
potentially life-threatening based on weather variables.
Thus, within this subset of days, weather differences might be relatively
small in terms of their effects on mortality. Other
variables that did not significantly contribute to the explanation of excess
mortality were the duration of the heatwave and airmass type (maritime tropical
or dry tropical). The latter means that there were
no significant differences between the airmasses; obviously, both explain
excess mortality, as they were the basis for warnings.
Excluding variables not significantly associated
with mortality left the following regression:
Excess Mortality = 3.27 - 0.049 x Time of Season
– 2.58 x Warning Indicator
The t-statistics were
(-2.55) for Time of Season and (-1.43) for the Warning Indicator. A low R-squared value of 0.04 suggests that mortality
depends on weather to only a minor extent, which is not too surprising because
omitted disease processes and accidents would be expected to be the primary
determinants of morality. The coefficient for Time
of Season means that excess mortality (assuming no warning) could range from
+3.22 to –3.54 lives depending on whether a heatwave occurred on the first
or last day of the season.
The estimated coefficient of the Warnings Indicator
meant that when a warning was issued, assuming no mortality displacement,
2.6 lives were saved, on average, for each day a warning was issued as well
as for the three days immediately following the warning.
Mortality displacement results when deaths are brought forward in time;
e.g., some individuals who died during a heatwave would have died anyway within
a short period of time after the heatwave. Because
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), the
estimated total number of lives saved was 117.
Because serial correlation of errors is a potential
statistical problem in time series analysis, we also estimated the association
using heatwaves as the sampling unit. For this purpose,
excess mortality was defined as average daily excess mortality for the days
of the heatwave (and three days after), and the Warning Indicator was set
to one whenever there was a warning issued on at least one of the heatwave
days. The results obtained were very similar to those
presented above; in particular the coefficient of the Warnings Indicator
was the same, i.e. a savings of 2.6 lives per day.
b. Estimation of the Benefits of the PWWS
For some public policy decisions, lives are assigned
monetary value. The USEPA, for example, places values
on lives (value of a statistical life or VSL) to assess the benefits of policies
that reduce pollution (Smith et al. 2001). VSL are
in units of dollars per life saved. The key piece
of data underlying the VSL is the value of mortality risk reduction. It has been estimated by analyzing wage rates in occupations
with differing mortality risks, to identify the wage differential due to
mortality risk, and by contingent valuation, i.e. asking a sample of people
to assess their own willingness to pay to reduce risks. The
results of both methods are usually similar.
Two recent studies addressed the question of whether
the value of a statistical life is lower for older people or people with
health problems (Krupnick et al. 2000, Smith et al. 2001).
This is of particular interest in connection with the PWWS, because
the population most at risk from heat is older and frequently suffers from
health problems. Smith et al. estimated VSLs for older
Americans who were still working, and reported a range of $5.3 - $6.6 million
(Smith et al. 2001). While the people in the Smith
study were younger (generally 50-65 years old) and probably healthier than
the people most at risk from heatwaves, Smith et al. found no indication
that the VSL fell with age over this range, nor that the VSL was lower for
those with serious illness. Krupnick et al. estimated
the VSL for Canadians 40 to 75 years old, and reported a range of $0.82 to
$2.6 million (US dollars) (Krupnick et al. 2000). Krupnick
et al. also found that poor health did not lower the VSL.
However, they concluded that Canadians 70-75 years old were willing
to pay about a third less for mortality risk reductions.
We decided to use the USEPA estimate of $6.12 million
for the value of a statistical life (Smith et al. 2001) and accepted the
Krupnick et. al. finding that VSLs eventually fall off with age, being a third
lower for the 70-75 age group. Thus, we assumed $4
million for the VSL among people 65 years of age or older in Philadelphia. This implies that the gross benefits of the Philadelphia
heatwave warning system could be on the order of $468 million (117 lives
saved times $4 million).
c. Estimation of the Costs of the PWWS
As discussed in detail in Appendix 1, when a heatwave
warning is declared in Philadelphia, the city takes a number of actions to
reduce the risks of heat related mortality. Most of
these actions do not have direct monetary costs, including actions taken
by city employees as a normal part of their jobs, actions taken by volunteers,
and delayed actions (i.e. halting service suspensions). A
few of the actions taken do have direct costs, including the Heatline and
additional EMS crews. Costs for these actions are primarily
additional wages. We estimated these costs to be $1000/day
on weekdays and $3000/day on weekends for the Heatline, and $4000/day for
EMS crews. Because we don’t have full information
on all direct costs, we assumed they would be on the order of $10,000 per
day when a heatwave warning is issued. During 1995-1998,
there were 21 such days. This implies that the total
cost of the Philadelphia system was on the order of $210,000 over the three-year
period.
The Philadelphia system depends on weather forecasts
generated by the NWS. One might want to allocate some
portion of the costs of those forecasts, and count it as part of the costs
of the system. Obviously, making such an allocation
would be very difficult and we did not attempt to do so.
Instead, the net benefit estimate reported below may be interpreted
as one of many societal benefits derived from weather forecasting systems.
d. Estimation of the Net Benefits of the PWWS
Obviously, if the Philadelphia system saves any
lives at all, it will have large estimated dollar benefits. This is because the VSL, for even one life, is bigger
than the costs of running the system. In fact, system
costs are so far into the “noise” of the estimate that they are essentially
irrelevant. Thus, based on this analysis, we would
conclude that the net benefits of the Philadelphia heatwave warning system
were around $468 million over the three-year period, 1995-1998.
4. Conclusions
The results suggest that issuing a warning lowered
daily mortality by about 2.6 lives on average, based on a number of assumptions. One is that the baseline used to generate the estimates
of excess mortality is valid. Excess mortality was
calculated as the difference between reported mortality and the underlying
mortality trend estimated from years prior to 1995. The
mortality trend line directly reflects changes in population, and indirectly
reflects secular time trends in the housing stock, peoples’ lifestyles, access
to health care, quality of health care, and any other underlying factors
that would tend to change mortality rates over time. We
believe that variations around such a mortality trend line are a better indicator
of the effects of daily weather, and of efforts to counter those effects,
than are medical examiners’ determinations that deaths were caused by extreme
temperatures.
Another assumption is that there was no mortality
displacement. The assumption of no mortality displacement
is likely to have inflated the estimate of the number of lives saved. It was not possible to estimate mortality displacement
with the data available. There is a wide range of
estimates of mortality displacement following heatwaves, from very little
to explaining much of the net excess mortality (Huynen et al. 2001, Kalkstein
1998, Kunst et al. 1993, Laschewski and Jendritzky 2002, Rooney et al. 1998). On the other hand, because the NWS declared warnings only
about one-quarter of the time that the PWWS forecast suggested declaring
a warning, our estimate of the number of lives saved by the PWWS is likely
to be an underestimate.
As implied above, it would seem that more lives
might have been saved if more warnings had been declared.
However, there is a question about whether the daily number of lives
saved might be different between days when only the PWWS recommended a warning
and those days when both the PWWS and the NWS concurred on declaring a warning. To address this question, we looked at temperatures on
different subsets of days. On the 70 days the PWWS
suggested a warning, the average temperature was 32.68 ºC.
On 16 of these days, the NWS concurred and a warning was issued; on
these days plus the additional five days the NWS issued a warning independent
of the PWWS, the average temperature was 32.77 ºC. On
the remaining 54 days when the PWWS suggested a warning and the NWS did not
concur, the average temperature was 32.34 ºC.
While these data suggest that the NWS was marginally
more conservative about declaring warnings, the temperature differences were
very small. This supports the view that the number
of lives saved per day might have been about the same if warnings had been
declared on additional days. Moreover, the low estimated
cost of warnings argues for being more liberal about issuing warnings. In support of this, recent analyses in London and Toronto
suggest that excess mortality begins at relatively low temperatures (Hajat
et al. 2002, Semenza et al. 1999, Smoyer-Tomic and Rainham 2001).
However, there remains some risk that if too many
warning are declared, they may become less effective, especially with regard
to the voluntary actions taken when a warning is issued.
The value of the t-statistic suggests that there
is about an 8% probability of our estimate of a reduction in excess mortality
of 2.6 lives per day occurring by chance. We interpret
this as reasonably strong statistical confirmation of our prior expectation
that warnings would save lives.
The R-squared for the linear regression is low,
which implies that most of the variation in excess mortality is attributable
to variables not included in our model. This was expected
because weather is only one of a number of factors related to mortality. Indeed, it is reasonable to expect that disease processes
and accidents are the dominant causes of mortality in the 65 and older age
group; we do not have variables representing these phenomena in our model. However, as long as these omitted variables are uncorrelated
with our included variables (most importantly, uncorrelated with the warnings
indicator), the estimated coefficients of our included variables will be unbiased. Particularly in the case of the warnings indicator, it’s
unlikely that this variable is statistically correlated with whatever myriad
of other variables is driving the unexplained variation in daily excess mortality.
Therefore, over the period 1995-1998, we conservatively
estimate that the Philadelphia Hot Weather-Health Watch Warning System saved
approximately 117 lives. This estimate of total benefits
is likely to be low. First, it only considered mortality
reductions in the 65 and older population – if there were mortality reductions
in younger populations too, this would add to the estimated benefits. Second, we did not estimate any increased morbidity associated
with excess heat exposure. It is reasonable to assume
that morbidity also was reduced by the PWWS. Hospitalizations
were shown to increase in London and Chicago during the 1995 heatwaves (Rooney
et al. 1998, Semenza et al. 1999). Morbidity reduction
is an additional benefit not included in the analyses.
Evaluations are underway for heat watch warning
systems established in other cities, including Toronto, Dayton and Phoenix,
although there currently are no direct estimates of the number of lives saved. An observation support the value of such systems. However, an observation based on the first year of operation
of the Philadelphia system during the hot summer of 1995 supports our results. It was noted that, as the summer progressed, the mortality
algorithm within the system overestimated heat-related mortality with greater
regularity and by a greater amount. This supports
the suggestion that people respond to the heat advisories and warnings in
ways that save lives (Kalkstein et al. 1996). Further,
in a detailed evaluation of heat watch warning system operation in a number
of cities, it was clear that cities utilizing systems like the one in Philadelphia
were developing and instituting more sophisticated heat intervention plans
whenever warnings were called (Kalkstein 2003). These
improved plans may be a direct result of system development.
The methods described here can be used to evaluate
the costs and benefits of implementing heatwave early warning systems. Our results suggest that the benefits in terms of lives
saved will far outweigh the operational costs of such systems, at least for
cities located in temperate regions.
Appendix
1. Philadelphia hot weather-health watch/warning
system (PWWS)
Dr. Laurence S. Kalkstein designed the Philadelphia
Hot Weather-Health Watch/Warning System to predict periods when there is
high risk of heat-related mortality (Kalkstein et al. 1996). Past weather was classified into more or less homogenous
categories (synoptic categories), referred to as airmass types, based on
air temperature, dewpoint temperature, cloud cover, sea level pressure, wind
speed, and wind direction. Associations were determined
between airmass types and mortality. Cause of death,
place and date of death, age and race were extracted from National Center
for Health Statistics files for the Philadelphia Standard Metropolitan Statistical
Area for the years 1964-1966, 1973-1976, 1978 and 1980-1988; these were the
years for which the date of death was available (Kalkstein et al. 1996). The analysis used the total number of deaths per day. Direct standardization was used to adjust all mortality
data for changes in the total population over time. A
mortality trend line was constructed for each time period and daily mortality
expressed as deviations around this baseline. The
mean daily mortality for each synoptic air mass category was determined,
along with the standard deviation, to ascertain whether particular categories
were associated with high or low mortality. Potential
lag times of one to three days were accounted for in the analysis; the highest
mortality was found with no lag time.
Two airmass types were associated with elevated
mortality in Philadelphia: maritime tropical and dry tropical. As not all days within these air masses resulted in elevated
mortality, a stepwise multiple regression analysis was used to identify which
days within the air mass were associated with increased mortality. The regression identified several variables that were
predictive of elevated mortality. These were the number
of consecutive days the air mass was present, the maximum temperature, and
the time of season (e.g., whether the oppressive air mass occurs early or
late within the summer season) (Kalkstein et al. 1996).
The accuracy of the forecast data and the performance
of the categorizations were verified by backcasting, with archived model
forecasts used as predictors. For the summer of 1988,
the 24-hour forecast identified 89% of the oppressive days and the 48-hour
forecast identified 71%. The system was designed to
operate from May 15 through September 30 (139 days).
Each day the PWWS forecasts the air mass category
for the current day and the subsequent two (Kalkstein et al. 1996). The beginning and end of a heatwave are determined by
air mass type and by the mortality predictive equations associated with each
air mass. The system was designed to generate a health
watch, a health alert or a health warning based on air mass type. The criteria for a health warning are that a maritime
tropical or dry tropical air mass type is forecast for either that afternoon
or the following morning, and the predictive model associated with that particular
air mass forecasts four or more heat related deaths. The
local office of the National Weather Service determines whether or not to
issue a warning based on the PWWS forecasts, the heat index and other information. For example, in 1995 the NWS issued a heat warning on
9 of the 15 days recommended by the PWWS.
The City of Philadelphia and other agencies and
organizations institute a series of intervention activities when the NWS
issues a warning. Television and radio stations and
newspapers are asked to publicize the oppressive weather conditions, along
with information on how to avoid heat-related illnesses.
In addition, these media announcements encourage friends, relatives,
neighbors, and other volunteers (“buddies”) to make daily visits to elderly
persons during the hot weather. These buddies are
asked to ensure that the most susceptible individuals have sufficient fluids,
proper ventilation and other amenities to cope with the weather. A “Heatline” is operated in conjunction with the Philadelphia
Corporation for the Aging to provide information and counseling to the general
public on avoidance of heat stress. The Heatline telephone
number is publicized by the media and by a large display seen over much of
the center of Philadelphia. When a warning is issued,
the Department of Public Health contacts nursing homes and other facilities
boarding persons requiring extra care to inform them of the high-risk heat
situation and to offer advice on the protection of residents. The local utility company and water department halt service
suspensions during warning periods. The Fire Department
Emergency Medical Service increases staffing during warnings in anticipation
of increased service demand. The agency for homeless
services activates increased daytime outreach activities to assist those
on the streets. Senior centers extend their hours
of operation of air-conditioned facilities during warning periods.
Acknowledgements:
This work was funded
by EPRI under contract EP-P5431/C2688 and by NOAA under contract 40AANA1A1110. The
authors would like to thank Dr. Andrew Solow for his useful suggestions on
the statistical analysis.
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