the health effects of heat waves: present and future health effects of heat waves: present and...
TRANSCRIPT
TheHealthEffectsofHeatWaves:PresentandFuture
RogerD.Peng,PhD
DepartmentofBiosta/s/csJohnsHopkinsBloombergSchoolofPublicHealth
WorkshoponEnvironmetrics,NCAR2010
(JointworkwithJFBobb,CTebaldi,LMcDaniel,MLBell,FDominici)
ClimateChangeandHealth
• ClimatechangeisthoughttoaffecthealthbychangingthedistribuPonofknownriskfactors(droughts,floods,heatwaves,diseasevectors,aero‐allergens)
• DesigningintervenPonsandmiPgaPonstrategiesrequiresunderstandingwhatpopulaPonsaremostvulnerabletoclimate‐relatedriskfactors
ClimateandHealthPathways
8.2 Current sensitivity and vulnerability
Systematic reviews of empirical studies provide the bestevidence for the relationship between health and weather orclimate factors, but such formal reviews are rare. In this section,we assess the current state of knowledge of the associationsbetween weather/climate factors and health outcome(s) for thepopulation(s) concerned, either directly or through multiplepathways, as outlined in Figure 8.1. The figure shows not onlythe pathways by which health can be affected by climate change,but also shows the concurrent direct-acting and modifying(conditioning) influences of environmental, social and health-system factors.Published evidence so far indicates that:• climate change is affecting the seasonality of some allergenicspecies (see Chapter 1) as well as the seasonal activity anddistribution of some disease vectors (see Section 8.2.8);
• climate plays an important role in the seasonal pattern ortemporal distribution of malaria, dengue, tick-borne diseases,cholera and some other diarrhoeal diseases (see Sections8.2.5 and 8.2.8);
• heatwaves and flooding can have severe and long-lastingeffects.
8.2.1 Heat and cold health effects
The effects of environmental temperature have been studiedin the context of single episodes of sustained extreme
temperatures (by definition, heatwaves and cold-waves) and aspopulation responses to the range of ambient temperatures(ecological time-series studies).
8.2.1.1 HeatwavesHot days, hot nights and heatwaves have become more
frequent (IPCC, 2007a). Heatwaves are associated with markedshort-term increases in mortality (Box 8.1). There has been moreresearch on heatwaves and health since the TAR in NorthAmerica (Basu and Samet, 2002), Europe (Koppe et al., 2004)and East Asia (Qiu et al., 2002; Ando et al., 2004; Choi et al.,2005; Kabuto et al., 2005).A variable proportion of the deaths occurring during
heatwaves are due to short-term mortality displacement (Hajatet al., 2005; Kysely, 2005). Research indicates that thisproportion depends on the severity of the heatwave and thehealth status of the population affected (Hemon and Jougla,2004; Hajat et al., 2005). The heatwave in 2003 was so severethat short-term mortality displacement contributed very little tothe total heatwave mortality (Le Tertre et al., 2006).Eighteen heatwaves were reported in India between 1980 and
1998, with a heatwave in 1988 affecting ten states and causing1,300 deaths (De and Mukhopadhyay, 1998; Mohanty andPanda, 2003; De et al., 2004). Heatwaves in Orissa, India, in1998, 1999 and 2000 caused an estimated 2,000, 91 and 29deaths, respectively (Mohanty and Panda, 2003) and heatwavesin 2003 inAndhra Pradesh, India, caused more than 3000 deaths(Government of Andhra Pradesh, 2004). Heatwaves in SouthAsia are associated with high mortality in rural populations, and
Human Health Chapter 8
396
Figure 8.1. Schematic diagram of pathways by which climate change affects health, and concurrent direct-acting and modifying (conditioning)influences of environmental, social and health-system factors.
AR4,WGII,ch.8,2007
NIHWorkingGroupReportandPAR
1
Published by Environmental Health Perspectives andthe National Institute of Environmental Health Sciences
A Human Health Perspective On Climate Change
A Report Outlining the Research Needs on the Human Health Effects of Climate Change
APRIL 22, 2010
NIHPAR‐10‐235:ClimateChangeandHealth:AssessingandModelingPopula?onVulnverabilitytoClimateChange(R21)
ChallengesinClimateChangeandHealthResearch
• Workishighlyinterdisciplinary,requiresexperPsefromavarietyofdomains
• Dataneedtobemerged/integratedfromavarietyofsources(neverdesignedtodothat)
• NeedtounderstandmagnitudesofuncertainPesinthedatafromdifferentdomains
• Notobviouswheretopublishresults!• Fundingmechanismsonlyrecentlydeveloped
• ButstaPsPcianscan/shouldplayabigrole!
HeatWavesandClimateChange
• High/extremeambienttemperaturesareassociatedwithmortalityandotherhealthoutcomesintheNorthAmerica(Basu&Samet2002,Anderson&Bell2010)
• WillclimatechangeaffectthedistribuPonofheatwaves?
HeatWavesandClimateChange
• TherehasbeenanincreaseinthefrequencyofheatwavesinrecentPmes(“Likely”,IPCC,2007)
• Thefrequencyandseverityofheatwaveswillincreaseinthefuture(“VeryLikely”,IPCC,2007;Meehl&Tebaldi2004)
Howwillanychangeinthedistribu/onofheatwavesaffectmortalityandmorbidity?
MoreIntense,FrequentHeatWaves
Meehl&Tebaldi2004
25%(Chicago)and31%(Paris)increaseinheatwavefrequencyin2080—2099
shift in the model to more and longer livedheat waves in future climate.
Heat waves are generally associated withspecific atmospheric circulation patterns repre-sented by semistationary 500-hPa positiveheight anomalies that dynamically produce sub-sidence, clear skies, light winds, warm-air ad-vection, and prolonged hot conditions at thesurface (15, 19). This was the case for the 1995Chicago heat wave and 2003 Paris heat wave(Fig. 3, A and B), for which 500-hPa heightanomalies of over !120 geopotential meters(gpm) over Lake Michigan for 13 to 14 July1995 and !180 gpm over northern France for 1to 13 August 2003 are significant at greater thanthe 5% level according to a Student’s t test. Astratification based on composite present-dayheat waves from the model for these twolocations over the period of 1961 to 1990(Fig. 3, C and D) shows comparable ampli-tudes and patterns, with positive 500-hPaheight anomalies in both regions greater than!120 gpm and significance exceeding the5% level for anomalies of that magnitude.
There is an amplification of the positive500-hPa height anomalies associated with agiven heat wave for Chicago and Paris forfuture minus present climate (Fig. 4, A andB). Statistically significant (at greater thanthe 5% level) ensemble mean heat wave 500-hPa differences for Chicago and Paris in thefuture climate compared with present-day arelarger by about 20 gpm in the model (com-paring Fig. 4, A and B, with Fig. 3, C and D).
The future modification of heat wave char-acteristics with a distinct geographical pattern(Fig. 1, E and F) suggests that a change inclimate base state from increasing greenhousegases could influence the pattern of those chang-es. The mean base state change for future climateshows 500-hPa height anomalies of nearly !55gpm over the upper Midwest, and about !50gpm over France for the end of the 21st century(Fig. 4, C and D, all significant above the 5%level). The 500-hPa height increases over theMediterranean and western and southern UnitedStates for future climate are directly associatedwith more intense heat waves in those regions(Fig. 1, E and F), thus confirming the link be-tween the pattern of increased 500-hPa heightsfor future minus present-day climate and in-creased heat wave intensity in the future climate.A comparable pattern is present in an ensembleof seven additional models for North Americafor future minus present-day climate, with some-what less agreement over Europe (fig. S1). Inthat region, there is still the general character oflargest positive anomalies over the Mediterra-nean and southern Europe regions, and smallerpositive anomalies to the north (fig. S1), butlargest positive values occur near Spain, as op-posed to the region near Greece as in our model(Fig. 4D). This also corresponds to a similarpattern for increased standard deviations of bothsummertime nighttime minimum and daytime
Fig. 2. Based on the threshold definition of heat wave (16), mean number of heat waves per yearnear Chicago (A) and Paris (B) and mean heat wave duration near Chicago (C) and Paris (D) areshown. In each panel, the blue diamond marked NCEP indicates the value computed fromNCEP/NCAR reanalysis data. The black segment indicates the range of values obtained from thefour ensemble members of the present-day (1961 to 1990) model simulation. The red segmentindicates the range of values obtained from the five ensemble members of the future (2080 to2099) model simulation. The single members are marked by individual symbols along the segments.Dotted vertical lines facilitate comparisons of the simulated ranges/observed value.
-50
0
50
100
150
Observed Heat Wave 500hPa Height Anomalies July 13-14, 1995, minus July 1948-2003
0
50
100
150
200
Observed Heat Wave 500hPa Height Anomalies August 1-13, 2003, minus August 1948-2003
-50
0
50
100
150
Simulated Composite Heat Wave 500 hPa Height Anomalies (JJA, 1961-1990)
0
50
100
150
200
Simulated Composite Heat Wave 500 hPa Height Anomalies (JJA, 1961-1990)
48.8°N
48.8°N
23.7°N
23.7°N
65.5°N
65.5°N
26.5°N
26.5°N
123.7°W
123.7°W
70.3°W
70.3°W
16.8°W
16.8°W
30.9°E
30.9°E
A B
C D
Fig. 3. Height anomalies at 500 hPa (gpm) for the 1995 Chicago heat wave (anomalies for 13 to 14 July1995 from July 1948 to 2003 as base period), from NCEP/NCAR reanalysis data (A) and the 2003 Parisheat wave (anomalies for 1 to 13 August 2003 from August 1948 to 2003 as base period), fromNCEP/NCAR reanalysis data (B). Also shown are anomalies for events that satisfy the heat wave criteriain the model in present-day climate (1961 to 1990), computed at grid points near Chicago (C) and Paris(D). In both cases, the base period is summer [ June, July, August (JJA)], 1961 to 1990.
R E P O R T S
13 AUGUST 2004 VOL 305 SCIENCE www.sciencemag.org996
on
De
ce
mb
er
4,
20
08
w
ww
.scie
nce
ma
g.o
rgD
ow
nlo
ad
ed
fro
m
Chicago1995HeatWave
• AmajorheatwaveoccurredinChicagoinJuly,1995
• >700excessdeathswereajributedtotheheatwaveina1‐weekperiod
• Elderlyandhome‐boundweremostsuscepPble• TheheatwaveaffectedsurroundingmidwesternciPesaswell
• AsecondmajorheatwaveoccurredinJuly1999withlessdevastaPngconsequences
Whitmanetal.1997
Chicago1999HeatWave
0
10
20
30
40
50
(a)
He
at
str
oke
ad
mis
sio
ns
May 29 Jun 18 Jul 08 Jul 28 Aug 17
60
65
70
75
80
85
(b)
Te
mp
era
ture
(°°F
)
May 29 Jun 18 Jul 08 Jul 28 Aug 17
Figure 3: (a) Numbers of emergency hospital admissions for heat stroke among Medicare enrollees inChicago, IL, June–August, 1999; (b) Daily 24-hour average temperature, June–August, 1999. Light graylines indicate values in years 2000–2006.
assumes that the time series of the outcome of interest Yt is Poisson distributed with mean µt where
log µct = !c + "c xc
t + #c zct + $c xc
t · zct + log N c
t + s(t, d)
The time series xct is the indicator of heat wave days (xc
t = 1 for a heat wave day) and zct is the daily time
series of an air pollutant. We can include multiple pollutants if necessary, in which case we will have theterms #c
1 zc1t, #c
2 zc2t, etc. By fitting the model above, we can examine estimates of $c to determine if heat
waves have a synergistic relationship with ambient air pollution exposure. The estimates of $c can becombined using the Bayesian hierarchical models described earlier to borrow strength from neighboringlocations and obtain more precise esitmates.
3.2 Predicting Future Health Impacts of Heat Waves
For evaluating the health impacts of heat waves in the future, we will link our risk estimates with dataobtained from the WCRP CMIP3 Multi-Model Dataset [12]. This dataset contains multi-model ensemblesof global forecasts from coupled atmosphere-ocean general circulation models run for a present daycontrol experiment and for 21st century experiments with various plausible forcing parameters, includingranges of CO2 concentration. For each scenario, the multi-model ensemble represents a set of possibleoutcomes, that can ideally be combined to represent the best estimate for the probability distribution of thefuture climate. This dataset contains several variables pertinent to our heat wave model, including indicesof heat wave events and daily mean/max estimates of temperature and humidity to which we could applythe model of heat waves developed in Aim 1. The availability of model predictions of these parameterswill allow us to explore the range of possible future health impacts. While there is substantial uncertaintyregarding the nature of future emissions scenarios and climate-related interventions, we will use theprobability distribution of heat waves estimated from the ensemble of forecasts to provide quantitativeupper and lower bounds on the future health impacts of heat waves.
As a product of Aim 1, we will obtain estimates of "c, the relative risk between heat waves and mortal-ity/morbidity, for different US locations. We can calculate the average number of excess deaths (ED) on aheat wave day as N c ! ("c " 1) where N c is the average number of daily deaths on a non-heat wave dayin location c. Then we can compute the annual average number of excess deaths per year by multiplying
8
European2003HeatWave
• August4—13,2003• EsPmated14,800excessdeathsinFranceduringthis9dayperiod
• Temperaturesover99oFfor9consecuPvedays
• Minimumtemperaturewas78oFduringthatperiod
• ExcessmortalityalsoseeninItaly,Spain,Portugal,UK
Bouchama2004
WhatisaHeatWave?
• ThereisnogenerallyagreedupondefiniPonofaheatwave
• ExceedancesofpercenPlesofthetemperaturedistribuPon
• Exceedancesofspecificabsolutetemperaturelevels
• ConPnuousstretchesofhightemperature
• Highhumidity
Present‐dayextremeheatrisk
ProjecPon1
ProjecPon2
FutureExtremeHeatExcessMortality/Morbidity(2081—2100)
DatabasesforcurrentcondiPons
Globalclimatemodels(IPCCCMIP3)
.
.
.ProjecPonN
PopulaPongrowth,agestructure(IIASA)
Weather(NCDC)
AirPolluPon(EPA)
EnvironmentalcondiPons
LocalprojecPon1
LocalprojecPon2
LocalprojecPonN
.
.
SpaPaldownscaling
FuturecondiPons
Futureextremeheatepisodes
Baselinemortality/morbidityrate
AdaptaPon
.
.Mortality(1987—2005)Morbidity(1999—2009)
NCHSandMedicare
Biological,environmental,socio‐economicmodifiersofvulnerability(Census)
ClimateScenario
MortalityData
• ObtainedfromtheNaPonalCenterforHealthStaPsPcsfortheperiod1987—2005
• DeathcerPficatedatacontaininganindividual’sdateandcauseofdeath,locaPonofresidence,age
• WeconstructadailyPmeseriesofmortalityfornon‐accidentalcausesanddifferentagecategories
• Dataareavailablefor>100metropolitanareas
Present‐dayTemperatureData
• HourlytemperatureanddewpointtemperatureavailablefromNCDC
• Maximumovera24hourperiodisused
• MaximumovermulPplemonitorvaluesinacityistaken,ifavailable
NMMAPS data: 105 Cities, 1987-2005
Y ct : Number of deaths from all causes on day t in city c,
stratified by three age categories
temperaturect
confoundersct
7 / 25
NCHS105CiPes,1987‐2005
ChicagoMortalityData,1987—2005
HeatWaveDefiniPon
• Threshold1(T1)isthe97.5thpercenPleofthedistribuPonofdailymaximumtemperatures
• Threshold2(T2)isthe81stpercenPleofdailymaxtemperatures
• AheatwaveisdefinedasthelongestperiodofconsecuPvedayssaPsfying:– DailymaxtempisaboveT1for>3days– DailymaxtempisaboveT2fortheenPreperiod– Averageofdailymaxtempovertheperiodis>T1
Huthetal.2000;Meehl&Tebaldi2004
ChicagoMaxTemperatureApril—September,1987—2005
StaPsPcalModelforPresent‐dayRisk
where we allow Yt to be a member of the quasipoisson family and f (·) and g(·) are unknown
smooth functions. Here, weather variables may include one or more of the following covari-
ates: current day’s maximum temperature, average of previous three days’ maximum daily
temperature, and current day’s dew point temperature. The potential confounding variables we
accounted for were current day’s mean ozone and particulate matter levels and smooth tempo-
ral fluctuations in time. The smooth function of time was included in the model to remove any
medium- and long-term trends in the data because we are only interested in examining short-
term effects of heat waves. We also stratified our analysis by three age categories (< 65 years
of age, 65–74, and ! 75) and therefore included an intercept for age and interactions of the
weather variables with age category, in the model. Interactions with age categories are needed
because of the differing temporal trends in mortality by age category. The final model was of
the form
logE[Yt ] = !1 +3
!i=2
!iI(aget = i)+3
!i=1
fi(weathert)I(aget = i)+g(confounderst) (1)
We fit several models of the form of (1), where the models differed based on which weather
covariates were included. The models were fit with the gam() function in the R package mgcv.
The final exposure-response model was selected based on the generalized cross validataion
(GCV) criterion. The next step was to apply this full model of the weather-mortality relationship
to the half of the year containing the summer season to estimate the relative risk of mortality
comparing heat wave days to non-heat wave days in Chicago for the period 1987–2005. Using
quasi-likelihood procedures (7), we obtain f̂i, the estimate of the exposure-response function for
weather and mortality. We also obtain an estimate of g, but because g is a nuisance parameter
its specific value is not of primary interest.
Relative risks were calculated separately for each age category as well as a pooled relative
risk across the three age groups. For the ith age group, the expected mortality on day t given
5
Agecategory‐specificdailymortalitymodel
SmoothsplinefuncPonoftemperaturevariables
Expectedagecategory‐specificmortalitycountondayt
SmoothfuncPonsofairpolluPonlevels(ozone,PM),temporaltrends
RangeofModelsModels f (temperature;!) df of natural
cubic splines1 !1tmax
2 !1tmax(3)
3 !1dptp
4 !1tmax+ !2tmax(3)
5 !1tmax+ !2dptp
6 !1tmax(3) + !2dptp
7 !1tmax+ !2tmax(3) + !3dptp
8–12 ns(tmax;!,") " ! {2, . . . , 6}13–17 ns(tmax(3);!,") " ! {2, . . . , 6}18–22 ns(dptp;!,") " ! {2, . . . , 6}23–27 ns(tmax;!,") + ns(tmax(3);!,") " ! {2, . . . , 6}
28 ns(tmax;!,") + ns(dptp;!,") " = 329 ns(tmax(3);!,") + ns(dptp;!,") " = 330 ns(tmax;!,") + ns(tmax(3);!,") + ns(dptp;!,") " = 331 ns(tmax;!,")" ns(tmax(3);!,") " = 332 ns(tmax;!,")" ns(dptp;!,") " = 333 ns(tmax(3);!,")" ns(dptp;!,") " = 3
12 / 25
RelaPveRiskEsPmate
the weather variables and confounders on that day is
E[Yt | weathert ,confounderst ] = exp{!i + fi(weathert)+g(confounderst)}
It follows that the relative risk associated with a heat wave day for age group i is given by
RRi =E[Yt | hwt = 1,confounderst ]E[Yt | hwt = 0,confounderst ]
We estimate this quantity by
!RRi =1n1
!t exp{ f̂i(weathert)}I(hwt = 1)1n0
!t exp{ f̂i(weathert)}I(hwt = 0),
where n1 is the number of heat wave days and n0 is the number of non-heat wave days in
Chicago during this period and I(·) is an indicator function. Similarly, the relative risk pooled
across age categories is estimated by
!RR =1n1
!t !i exp{ f̂i(weathert)}I(hwt = 1)1n0
!t !i exp{ f̂i(weathert)}I(hwt = 0)
We calculate variances and asympototic 95% confidence intervals for the relative risk estimates
by applying the delta method (8).
The expected number of excess deaths on a heat wave day compared to a non-heat wave day
was calculated as EDhw = N! (RR" 1) where N is the number of daily deaths on a non-heat
wave day averaged across the study period and RR is the relative risk of mortality associated
with heat wave days, i.e. the ratio of the rate of mortality on a heat wave day and the rate of mor-
tality on a non-heat wave day. Annual excess heat wave deaths were obtained by multiplying
EDhw by the average number of heat waves days per year.
In the second stage of our approach we obtained estimates of future heat waves from 11
different climate model simulations of temperature from the Program for Climate Model Di-
agnosis and Intercomparison (PCMDI) as part of the Coupled Model Intercomparison Project
(CMIP3) (9). The frequency and length of heat waves for the 2081–2100 period was estimated
6
the weather variables and confounders on that day is
E[Yt | weathert ,confounderst ] = exp{!i + fi(weathert)+g(confounderst)}
It follows that the relative risk associated with a heat wave day for age group i is given by
RRi =E[Yt | hwt = 1,confounderst ]E[Yt | hwt = 0,confounderst ]
We estimate this quantity by
!RRi =1n1
!t exp{ f̂i(weathert)}I(hwt = 1)1n0
!t exp{ f̂i(weathert)}I(hwt = 0),
where n1 is the number of heat wave days and n0 is the number of non-heat wave days in
Chicago during this period and I(·) is an indicator function. Similarly, the relative risk pooled
across age categories is estimated by
!RR =1n1
!t !i exp{ f̂i(weathert)}I(hwt = 1)1n0
!t !i exp{ f̂i(weathert)}I(hwt = 0)
We calculate variances and asympototic 95% confidence intervals for the relative risk estimates
by applying the delta method (8).
The expected number of excess deaths on a heat wave day compared to a non-heat wave day
was calculated as EDhw = N! (RR" 1) where N is the number of daily deaths on a non-heat
wave day averaged across the study period and RR is the relative risk of mortality associated
with heat wave days, i.e. the ratio of the rate of mortality on a heat wave day and the rate of mor-
tality on a non-heat wave day. Annual excess heat wave deaths were obtained by multiplying
EDhw by the average number of heat waves days per year.
In the second stage of our approach we obtained estimates of future heat waves from 11
different climate model simulations of temperature from the Program for Climate Model Di-
agnosis and Intercomparison (PCMDI) as part of the Coupled Model Intercomparison Project
(CMIP3) (9). The frequency and length of heat waves for the 2081–2100 period was estimated
6
the weather variables and confounders on that day is
E[Yt | weathert ,confounderst ] = exp{!i + fi(weathert)+g(confounderst)}
It follows that the relative risk associated with a heat wave day for age group i is given by
RRi =E[Yt | hwt = 1,confounderst ]E[Yt | hwt = 0,confounderst ]
We estimate this quantity by
!RRi =1n1
!t exp{ f̂i(weathert)}I(hwt = 1)1n0
!t exp{ f̂i(weathert)}I(hwt = 0),
where n1 is the number of heat wave days and n0 is the number of non-heat wave days in
Chicago during this period and I(·) is an indicator function. Similarly, the relative risk pooled
across age categories is estimated by
!RR =1n1
!t !i exp{ f̂i(weathert)}I(hwt = 1)1n0
!t !i exp{ f̂i(weathert)}I(hwt = 0)
We calculate variances and asympototic 95% confidence intervals for the relative risk estimates
by applying the delta method (8).
The expected number of excess deaths on a heat wave day compared to a non-heat wave day
was calculated as EDhw = N! (RR" 1) where N is the number of daily deaths on a non-heat
wave day averaged across the study period and RR is the relative risk of mortality associated
with heat wave days, i.e. the ratio of the rate of mortality on a heat wave day and the rate of mor-
tality on a non-heat wave day. Annual excess heat wave deaths were obtained by multiplying
EDhw by the average number of heat waves days per year.
In the second stage of our approach we obtained estimates of future heat waves from 11
different climate model simulations of temperature from the Program for Climate Model Di-
agnosis and Intercomparison (PCMDI) as part of the Coupled Model Intercomparison Project
(CMIP3) (9). The frequency and length of heat waves for the 2081–2100 period was estimated
6
RelaPveriskforindividualagecategories
OverallrelaPveriskacrossallagecategories
(Varianceobtainedviadeltamethod)
PriorDistribuPonsPrior selection
Adopted class of prior distributions for GLMs from Raftery (1996)
!(!k ,"k | Mk )
Accounts for nesting of models, e.g.
!(("0,"1) | "1 = 0) = !("0)
Depends on three hyperparameters #, $, and %
Only # has significant impact on inference !" reportinferences across a range of values of #
Var(RR | Mk ) # #2&k
15 / 25
PosteriorDistribuPonsforLogRRResults
Atlanta
!
-0.02 0.00 0.02 0.04 0.06 0.08
010
20
30
Chicago
!
0.09 0.10 0.11 0.12 0.13 0.14
020
40
60
Cleveland
!
-0.02 0.00 0.02 0.04 0.06 0.08
05
15
25
Detroit
!
0.06 0.08 0.10 0.12
010
20
30
Dallas/Fort Worth
!
-0.01 0.00 0.01 0.02 0.03
020
40
60
Houston
!
-0.02 -0.01 0.00 0.01 0.02
0100
200
Los Angeles
!
0.020 0.030 0.040 0.050
020
40
Miami
!
-0.01 0.00 0.01 0.02 0.030
40
80
Minneapolis/St. Paul
!
-0.02 0.00 0.02 0.04
020
40
New York
!
0.080 0.090 0.100 0.110
020
40
60
80
Oakland
!
0.02 0.04 0.06 0.08 0.10 0.12
05
15
25
Philadelphia
!
0.02 0.04 0.06 0.08 0.10
010
20
30
40
Phoenix
!
0.00 0.02 0.04 0.06
010203040
Riverside
!
0.00 0.02 0.04 0.06
010
20
30
40
San Antonio
!
-0.04 -0.02 0.00 0.02 0.04
010
30
San Bernardino
!
0.00 0.02 0.04 0.06 0.08 0.10
010
20
30
San Diego
!
0.00 0.01 0.02 0.03 0.04 0.05 0.06
020
40
60
San Jose
!
0.05 0.06 0.07 0.08 0.09 0.10
020
40
Seattle
!
0.03 0.04 0.05 0.06 0.07 0.08
010203040
Santa Ana/Anaheim
!
-0.01 0.00 0.01 0.02 0.03 0.04
020
40
Figure: Histograms and kernel density estimates of 2000 samples fromthe posterior P(!c | yc) for the 20 largest cities, where ! is the logrelative risk of mortality comparing heat wave to non-heat wave days.
19 / 25
PosteriorDistribuPonsforLogRR(105CiPes,1987—2005)Results
Figure: 95% highest posterior density intervals for the log relative riskof mortality. Cities categorized into 7 regions: southeast (SE),southwest (SW), southern California (SC), northeast (NE), uppermidwest (UM), industrial midwest (IM), and northwest (NW). Withinregions, cities listed from left to right in order of decreasing latitude.
20 / 25
PosteriorModeforLogRR(105CiPes,1987—2005)
Results
<20th percentile
20th-40th percentile
40th-60th percentile
60th-80th percentile
>80th percentile
Figure: Estimated % increase in mortality associated with a heat waveday (posterior mode). The 20th, 40th, 60th, and 80th percentiles are0.2%, 1.1%, 2.2%, 4.4%, and 12.4%, respectively. Size ! !̂!1
BMA21 / 25
FutureTemperature
• GCMoutputobtainedfromtheWCRPCMIP3mulP‐modeldatasetarchiveatPCMDI
• Dailymax(surface)temperaturefrom7modelswereobtainedfor1981—2000and2081—2100
• EmissionsscenariosA1B,B1,andA2wereused
• Numberofheatwavesandlengthofheatwavescomputedforeachmodel
ClimateScenarios
IPCC,SRES,2000
Emissions Scenarios4
The main characteristics of the four SRES storylines and scenario families
By 2100 the world will have changed in ways that are difficult to imagine – as difficult as it would have been at the end of the
19th century to imagine the changes of the 100 years since. Each storyline assumes a distinctly different direction for future
developments, such that the four storylines differ in increasingly irreversible ways. Together they describe divergent futures that
encompass a significant portion of the underlying uncertainties in the main driving forces. They cover a wide range of key
“future” characteristics such as demographic change, economic development, and technological change. For this reason, their
plausibility or feasibility should not be considered solely on the basis of an extrapolation of current economic, technological,
and social trends.
• The A1 storyline and scenario family describes a future world of very rapid economic growth, global population that
peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major
underlying themes are convergence among regions, capacity building, and increased cultural and social interactions, with
a substantial reduction in regional differences in per capita income. The A1 scenario family develops into three groups
that describe alternative directions of technological change in the energy system. The three A1 groups are distinguished
by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources
(A1B).3
Figure 1: Schematic illustration of SRES scenarios. Four qualitative storylines yield four sets of scenarios called “families”:
A1, A2, B1, and B2. Altogether 40 SRES scenarios have been developed by six modeling teams. All are equally valid with
no assigned probabilities of occurrence. The set of scenarios consists of six scenario groups drawn from the four families:
one group each in A2, B1, B2, and three groups within the A1 family, characterizing alternative developments of energy
technologies: A1FI (fossil fuel intensive), A1B (balanced), and A1T (predominantly non-fossil fuel). Within each family and
group of scenarios, some share “harmonized” assumptions on global population, gross world product, and final energy.
These are marked as “HS” for harmonized scenarios. “OS” denotes scenarios that explore uncertainties in driving forces
beyond those of the harmonized scenarios. The number of scenarios developed within each category is shown. For each of
the six scenario groups an illustrative scenario (which is always harmonized) is provided. Four illustrative marker scenarios,
one for each scenario family, were used in draft form in the 1998 SRES open process and are included in revised form in
this Report. Two additional illustrative scenarios for the groups A1FI and A1T are also provided and complete a set of six
that illustrates all scenario groups. All are equally sound.
3 Balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar improvement rates apply
to all energy supply and end use technologies.
IPCCA1Family
“TheA1storylineandscenariofamilydescribesafutureworldofveryrapideconomicgrowth,globalpopula?onthatpeaksinmid‐centuryanddeclinesthereaqer,andtherapidintroducPonofnewandmoreefficienttechnologies.Majorunderlyingthemesareconvergenceamongregions,capacitybuilding,andincreasedculturalandsocialinteracPons,withasubstanPalreducPoninregionaldifferencesinpercapitaincome.”
“TheA1scenariofamilydevelopsintothreegroupsthatdescribealternaPvedirecPonsoftechnologicalchangeintheenergysystem.ThethreeA1groupsaredisPnguishedbytheirtechnologicalemphasis:fossilintensive(A1FI),non‐fossilenergysources(A1T),orabalanceacrossallsources(A1B)”
IPCC,SRES,2000
PopulaPonTrends
IPCC,SRES,2000
ChangingAgeStructureofPopulaPon
IPCC,SRES,2000
EsPmateofRRforagecategory>65was~30%largerthanoverallRR
ChicagoGridCell
!
GFDLCM2.0
HeatWaveMortalityEsPmate
the weather variables and confounders on that day is
E[Yt | weathert ,confounderst ] = exp{!i + fi(weathert)+g(confounderst)}
It follows that the relative risk associated with a heat wave day for age group i is given by
RRi =E[Yt | hwt = 1,confounderst ]E[Yt | hwt = 0,confounderst ]
We estimate this quantity by
!RRi =1n1
!t exp{ f̂i(weathert)}I(hwt = 1)1n0
!t exp{ f̂i(weathert)}I(hwt = 0),
where n1 is the number of heat wave days and n0 is the number of non-heat wave days in
Chicago during this period and I(·) is an indicator function. Similarly, the relative risk pooled
across age categories is estimated by
!RR =1n1
!t !i exp{ f̂i(weathert)}I(hwt = 1)1n0
!t !i exp{ f̂i(weathert)}I(hwt = 0)
We calculate variances and asympototic 95% confidence intervals for the relative risk estimates
by applying the delta method (8).
The expected number of excess deaths on a heat wave day compared to a non-heat wave day
was calculated as EDhw = N! (RR" 1) where N is the number of daily deaths on a non-heat
wave day averaged across the study period and RR is the relative risk of mortality associated
with heat wave days, i.e. the ratio of the rate of mortality on a heat wave day and the rate of mor-
tality on a non-heat wave day. Annual excess heat wave deaths were obtained by multiplying
EDhw by the average number of heat waves days per year.
In the second stage of our approach we obtained estimates of future heat waves from 11
different climate model simulations of temperature from the Program for Climate Model Di-
agnosis and Intercomparison (PCMDI) as part of the Coupled Model Intercomparison Project
(CMIP3) (9). The frequency and length of heat waves for the 2081–2100 period was estimated
6
Excessdeathsonaheatwaveday
Baseline#ofdeathsonanon‐heatwaveday(esPmatedfrompresent‐daydata)
RelaPveriskassociatedwithaheatwaveday
AssumpPonsforFutureEsPmates
• ConstantheatwaverelaPveriskoverPme• NoadaptaPontoextremeheat
• Constantrateofmortalityonnon‐heatwavedays
• Minimalshort‐termmortalitydisplacement
AnnualHeatWaveFrequencyandAverageHeatWaveLengthforChicago,2081—2100
A Tables
Table 1: Estimates of the number of heat waves per year (annual frequency), average heat wave
length (in days), and excess deaths per year from heat waves days for Chicago, 2081–2100.
Heat Waves Excess DeathsFrequency Length # per year 95% CI
Model (# per year) (days)
LASG/IAP FGOALS-g1.0 1.8 9.8 165 (130, 200)CCCMA CGCM 3.1 (T63) 1.4 12.5 170 (134, 206)CCCMA CGCM 3.1(T47) 1.4 13.1 172 (135, 208)CSIRO Mk 3.0 1.4 14.4 196 (154, 237)GFDL CM 2.0 2.1 14.6 286 (225, 348)GISS AOM 2.0 16.0 300 (236, 364)MRI CGCM 2.3.2a 2.5 16.4 392 (309, 477)CNRM CM3 3.3 25.9 800 (629, 971)MPI ECHAM5 5.2 17.6 855 (673, 1038)MIROC 3.2 (medres) 5.4 19.6 990 (779, 1203)MIROC 3.2 (hires) 6.3 22.8 1345 (1059, 1634)
11
ForChicago1987—2005therewere0.7heatwavesperyearwithanaveragelengthof9.2days
AnnualHeatWaveMortalityforChicago,2081—2100
!
!
!
!
!
!
0 500 1000 1500 2000 2500 3000
Annual heat wave mortality
SR
ES
Scenari
o
B1
A1B
A2
!
!
csiro.mk3.0
cccma.cgcm3.1
gfdl.cm2.0
mri.cgcm2.3.2a
mpi.echam5
cnrm.cm3
miroc3.2.medres
1995 heat !
wave!
1999 heat!
wave!
Summary
• Using19yearsofdailydatafor1987—2005,wefoundstrongevidencethatheatwavesareassociatedwithexcessmortalityinpresent‐dayChicago
• TheincidenceandlengthofheatwavesinChicagoisprojectedtoincreasein2081—2100;therewasawiderangeofvariaPonacrossacollecPonof7GCMs
• MortalityfromheatwavesisesPmatedtoincreasefrompresent‐daycondiPonsbyafactorrangingfrom3to25dependingontheGCMused
FutureDirecPons
• MethodologyisreproducibleacrossdifferentlocaPonsusingpubliclyavailabledata
• LooknaPonallyatvariaPoninheatwaveriskacrosslocaPonsviahierarchicalmodeling
• Lookatmorbidityeffectsofheat• IncorporatemorerealisPcassumpPonsaboutthefuture(adaptaPon)
• ExaminePme‐varyingrisk,relatetootherfactors• DistributedlagmayesPmatethe“totaleffect”ofaheatwavebejerthansinglelagmodels(mortalitydisplacement/harvesPng)
Collaborators
• JenniferF.Bobb(JHSPH)• FrancescaDominici(Harvard)
• ClaudiaTebaldi(ClimateCentral/UBC)
• MichelleL.Bell(Yale)
• LarryMcDaniel(NCAR)
JABESSpecialIssueonClimateChangeandHealth
• Co‐Editors:RogerPeng,BoLi• Submissiondeadline:~October2011
• Lookingforpaperson– Climatemodeling– Modelingofriskfactors/environmentalexposures– Healtheffectsofclimate‐relatedphenomena
– Predictorsofvulnerabilitytoclimatechange
SensiPvityAnalysisSensitivity Analysis
0.00 0.02 0.04 0.06
010203040
Atlanta
!
1
1.65
3
5
BIC
0.09 0.10 0.11 0.12 0.13 0.14
020
40
60
Chicago
!
-0.02 0.00 0.02 0.04 0.06 0.08
010
30
50
Cleveland
!
0.06 0.08 0.10 0.12
020
40
60
Detroit
!
-0.01 0.00 0.01 0.02 0.03
020
40
60
Dallas/Fort Worth
!
-0.015 -0.005 0.005 0.015
0100
300
Houston
!
0.020 0.030 0.040 0.050
040
80
Los Angeles
!
0.000 0.010 0.020
0100
200
Miami
!
-0.02 0.00 0.02 0.04
020
40
60
Minneapolis/St. Paul
!
0.085 0.095 0.105 0.115
020
60
New York
!
0.02 0.04 0.06 0.08 0.10 0.12
010
20
30
Oakland
!
0.04 0.06 0.08 0.10
010
30
Philadelphia
!
0.00 0.02 0.04 0.06
020
40
Phoenix
!
0.00 0.02 0.04 0.06
010203040
Riverside
!
-0.02 0.00 0.02 0.04
010
30
50
San Antonio
!
0.02 0.04 0.06 0.08 0.10
010
20
30
San Bernardino
!
0.00 0.01 0.02 0.03 0.04 0.05
020406080
San Diego
!
0.05 0.06 0.07 0.08 0.09 0.10
020
40
San Jose
!
0.02 0.04 0.06 0.08
010
30
50
Seattle
!
-0.01 0.00 0.01 0.02 0.03 0.04
020
40
60
Santa Ana/Anaheim
!
Figure: Kernel density estimates of the posterior P(! | y) under BMAfor 4 values of hyperparameter ", and of the posterior under theBIC-selected model P(! | MBIC , y).
24 / 25
Time‐varyingRelaPveRisk
1980 to 2001 show that air conditioning use has been steadilyincreasing in all areas of the United States.22 Although anincrease in air conditioning is a plausible explanation of thedecrease in heat-related cardiovascular deaths, it is con-founded with other changes over time, such as improvedhealth care.
Cold-Related MortalityWhile an increase in air conditioning over time may
have affected heat-related cardiovascular deaths, nothing haschanged the impact of cold temperatures on mortality. Themechanism by which cold temperatures lead to increasedcardiovascular deaths is most likely via blood pressure.23
Susceptibility to cold-related mortality has been associated
with race,24,25 education24 and female sex.3,25 The sex differ-ence suggests either that clothing is an important modifier26 orthat there is a biologic difference between the ability tothermoregulate. Body temperature is regulated by the hypo-thalamus neurons, which are directly influenced by estrogenthrough estrogen receptors. The associations with race andeducation suggest a socioeconomic effect, although results onthe socioeconomic effect on cold-related deaths have beenmixed. Of 2 large UK studies, one found an associationbetween cold homes and increased risk of death,10 but an-other found that deprivation in an area was not related to riskof excess winter all-cause mortality.27
It is plausible that improvements over time in thestandard of living (specifically housing quality and heating)would reduce the number of cold-related deaths. The resultsfrom this study suggest either that improvements in the USstandard of living were insufficient or that such improve-ments are in fact not protective. Another possible pathway toprotection of the elderly from low temperatures is more andbetter clothes in cold weather.26 The results shown heresuggest that protective measures need to be taken not just inwinter, but also in relatively cold days spring and fall.However, there is no direct evidence in the literature tosupport an intervention of increased clothing. Basu andSamet1 have provided guidelines for the future research intoheat-related mortality. The results from the present studyindicate that new studies of cold-related cardiovasculardeaths are also needed. To date most research has analyzedtemperature at a population level, using temperature measure-ments obtained from outdoor monitors. Although logisticallymore difficult, a cohort study that monitored temperature insubjects’ homes and collected details on subjects’ clothingwould have much greater power to detect differences in riskrelated to individual and socioeconomic factors.
A successful intervention for cold-related mortalitycould have a substantial public health impact. Using the data
FIGURE 2. Mean changes in daily cardiovascular deaths (%)due to a 10°F increase temperature in summer and winter byregion.
FIGURE 1. Mean changes in daily cardiovasculardeaths (%) and 95% posterior intervals due to a10°F increase in temperature by year and season.
Epidemiology • Volume 18, Number 3, May 2007 Temperature and Cardiovascular Deaths in the US Elderly
© 2007 Lippincott Williams & Wilkins 371
Barnej,2007