DOI 10.1515/reveh-2014-0029 Rev Environ Health 2014; 29(1-2): 119–123
Tania Busch Isaksen, Michael Yost*, Elizabeth Hom and Richard Fenske
Projected health impacts of heat events in Washington State associated with climate change Abstract: Climate change is predicted to increase the frequency and duration of extreme-heat events and associated health outcomes. This study used data from the historical heat-health outcome relationship, and a unique prediction model, to estimate mortality for 2025 and 2045. For each one degree change in humidex above threshold, we find a corresponding 1.83% increase in mortality for all ages, all non-traumatic causes of death in King County, Washington. Mortality is projected to increase significantly in 2025 and 2045 for the 85 and older age group (2.3–8.0 and 4.0–22.3 times higher than baseline, respectively). Keywords: climate change; heat events; Pacific Northwest region. *Corresponding author: Michael Yost, Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA, E-mail: [email protected] Tania Busch Isaksen, Elizabeth Hom and Richard Fenske: Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
Background Climate change is projected to have serious and long-term consequences for public health. One of the more important and measurable impacts is health outcomes associated with extreme heat events, particularly in moderate climates. We sought to investigate the regional historical heat-health outcomes, develop a unique model predicting heat-health impacts from climate change, and validate the model performance. This analysis builds upon previous work that found significant increases in non-traumatic mortality associated with heat days compared to non-heat days in the Pacific Northwest region (1). Rather than using a binary heat exposure variable for heat day vs. non-heat day, this study quantified the 1980–2006 historical relationship between humidex as a continuous heat exposure variable and non-traumatic mortality for various age groups and select causes of death. This analysis shows results for King, the most populous county in Washington State. The temperature-mortality relationship was combined with population and climate projections to estimate future non-traumatic heat-related mortality.
Methods The historical relationship, represented by the changes in daily mortality per degree C humidex increase above county-specific thresholds, was modeled using Poisson regression and a distributed-lag time series model developed by Armstrong (2) to study the effect of temperature and mortality in London. Outcomes for specific diseases were adjusted for age and temporal trends. The results were applied to varying downscaled climate warming scenarios, along with population projections, to predict future heat-related mortality.
Meteorology data Gridded meteorology data containing daily maximum temperature and humidity values were obtained from the University of Washington Climate Impacts Group. These data were produced by the variable infiltration capacity (VIC) macroscale hydrologic simulation model for the Pacific Northwest and were provided at a 1/16 spatial scale (∼4.5 km × 7 km resolution); these location indices are referred to as VIC points. The average daily maximum humidex was computed at each VIC point and averaged to a county-wide daily maximum humidex as a single daily exposure measure. Humidex is an apparent temperature index that measures the combined effects of temperature and humidity on the human body. It is defined by: 7.5 T
5 H Humidex =T + ×( v − 10 ), where v=(6.112×10 237.7 +T )× 100 9
(1) Here T is the air temperature (°C), H is the relative humidity (%), and v is the vapor pressure (kPa) (3). For abbreviation, “humidex” refers to the “county-wide average daily maximum humidex” in this analysis.
Death certificate data Death certificate data for 1980–2006 were obtained from the Washington State Department of Health. The analysis only included deaths, which occurred during the warmer months of May through September, which is 153 days
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120 Busch Isaksen et al.: Health impacts of heat events in Washington State per year (4,131 days for the entire study period). This minimized potential confounding by infectious diseases, such as cold and influenza, typically seen during colder months. The study focused on all non-traumatic deaths (ICD-9 codes 001–799; ICD-10 codes A00-R99), including separate categories of respiratory (ICD-9 codes 460–519; ICD-10 codes J00–J99) and circulatory (ICD-9 codes 390– 459; ICD-10 codes I00–I99, G45, G46) deaths. Additionally, cardiovascular (a subset of circulatory) deaths (ICD-9 codes 393–429; ICD-10 codes I05–I52) and ischemic (a subset of cardiovascular) deaths (ICD-9 codes 410–414; ICD-10 codes I20–I25) were investigated separately. Causeof-death groupings were defined a priori from a Toronto study analyzing heat mortality (4). Death certificates were coded using ICD-9 from 1980 to 1998 and using ICD-10 from 1999 to 2006. Some characteristics coded on death certificates were used to identify individuals potentially more vulnerable to heat-related mortality: elderly, nonWhite race, occupation primarily outdoors, less than high school education, Hispanic origin, and having tobacco contribute to death.
produced low, medium, and high warming scenarios for the Pacific Northwest Region for the 2025 and 2045 time periods, each date representing the midpoint of a decade. Three climate change scenarios were selected: (1) high warming scenario according to the HADCM-A1B model, (2) low warming scenario according to the PCM1-B1 model, (3) a middle warming scenario averaging the high and low warming scenarios. The details for these climate models are discussed elsewhere (6). At the time of our analysis, these future scenarios were the most current, plausible projections of future climate change for the Pacific Northwest.
Association between humidex and mortality Poisson regression was used to explore the relationship between daily humidex and mortality rates, with a nonparametric adjustment for the seasonal effects and longterm trend in mortality over time. Specifically, it was assumed that:
Population data Age-specific annual population data for 1980–2006 and future population projections for 2010, 2015, 2020, and 2025 for 5-year age categories (from 0–4 to 85+) were obtained from the Washington State Office of Financial Management (OFM) for King, Clark, and Spokane Counties (5). The model to develop these population projections took into account the 2000 census count, the “aging forward” of this population, estimates of annual population growth (including births and deaths) based on actual data, predicted net migration based on an econometric model, as well as forecasted fertility rates and life expectancy. The rate of population increase was obtained from the OFM for historical estimates from 1980 through 2006 plus future projections for 2010, 2015, 2020, and 2025. Interpolation between 1980 and 2025 showed a linear population increase relationship that was then extrapolated to generate age-specific population estimates for 2045.
Future meteorology projections Future climate projections were produced by the University of Washington’s Climate Impacts Group (CIG) using recent, publicly available global climate model simulations. These
log( µ j ) = β0 + s( hj ) + s( t j ) + ∑ l=6 βl I{ month =l } 9
j
(2)
where Yj is approximately Poisson, the observed mortality count on day j, Pj is the population on day j, and s(hj) and s(tj) are natural cubic splines over daily humidex and time; fixed effects ( β′l s ) for the month also were included. Similar to other studies, we used non-parametric splines to model the log mortality rate over time to adjust for longterm changes in population, health behaviors such as smoking, changes in medical practice patterns, and access to health care (7–9). The number of degrees of freedom for the splines modeling long-term time trend were selected to minimize the cross-validation error to avoid overfitting. Modeling was implemented by the “mgcv” package in the statistical software R version 2.14.1 (10). The humidex-mortality relationship with the spline model from 1980 to 2006 was J-shaped, suggesting a positive relationship above a specific humidex threshold (Figure 1). This relationship was simplified to a piecewise linear form with two knots: the first set at the 50th humidex percentile. The second knot, or “optimal alert threshold,” was set to 35.7°C humidex by maximizing the fitted-model likelihood as the upper threshold changed from 20° to 40°C, in 0.1° increments. A similar approach was used to study heat and hospitalizations in New York City (11). A heat day was defined as a day in which the county-wide daily maximum humidex was above the optimal alert threshold; the impact of humidex intensity
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Busch Isaksen et al.: Health impacts of heat events in Washington State 121
A 0.2
-10.5
0.1
log(daily mortality rate)
Smoothed trend on log moratlity rate
King County
B
0.0
-11.0
-11.5
-0.1
-12.0 10
20
30
40
10
Humidex
20
30
40
County-wide daily max humidex
Figure 1 (Left) Non-parametric spline model of non-traumatic log mortality rate and humidex relationship; (right) corresponding piecewise linear approximation using two knots at 22.6°C and 35.7°C humidex.
was assessed by the slope of the line above the threshold. The piecewise model is: log( µ j ) = β0 + β 1 ( hj − hq 50 )+ + β 2 ( hj − hˆ0 )+ + s( t j )
9
+ ∑ l=6 βl I { monthj = l }
(3)
where hj = county-wide average daily maximum humidex value, hq50 = the 50th percentile of humidex from January to December 1980–2006, and hˆ0 = the optimal alert threshold, Imonth = indicator variable for months May through September. For King County, hq50 = 22.6, and hˆ0 = 35.7 and s(tj) has df = 5.
Evaluation of other effects of heat on mortality The data were evaluated to see if there was a “cool down effect,” such that elevated minimum humidex on a hot day prevented cooling down and, thus, increased mortality. The data also were examined for increases in mortality with a greater number of consecutive days of a heat event (lag effect), which has been found previously (7). The mortality rate was not significantly affected by the lagged humidex, while the acute association between excess humidex above the upper threshold and mortality on the same day remained statistically significant. This study found no cool down or duration effect modification on mortality rates so these covariates were not included in the final model.
Individual-level characteristics obtained from death certificates were evaluated for changes in the risk of heatrelated mortality. These covariates included age (5-year increments), gender, race, primarily outdoor occupation, high school graduation, Hispanic origin, and tobacco use. Effect modification was examined by adding each covariate as a separate term and adding an interaction term. Mortality rates for all covariate groups, except age, were not adjusted by population size of covariate groups because we did not have access to this data. With the exception of the age of the decedent, we did not find that any other individual-level characteristics altered the risk of dying on a heat day.
Projection of future excess mortality due to heat The projected future climate data was generated by adding the predicted change in monthly mean temperature for each future projection scenario to each year based on a historical 30-year climate reference period of 1970– 2000. Parameter estimates for the King County-specific temperature-mortality relationships were applied to the projected future climate scenarios and the future population to estimate the annual excess deaths from heat events in 2025 and 2045. The process was repeated for each of an ensemble of 30 possible realizations of future temperatures in 2025 and in 2045, respectively, in order to account for the year-to-year variation of temperatures under a
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122 Busch Isaksen et al.: Health impacts of heat events in Washington State Table 1 Historical mortality (1980–2006) in King County due to heat events, by underlying cause of death and age group; values are the observed% Change (95% CI) in mortality per degree humidex above a 35.7°C threshold. Age
All non-traumatic
Respiratory
Circulatory
Cardiovascular
Ischemic
All ages 1.83% (0.77%, 2.91%) 3.21% (–0.09%, 6.63%) 2.3% (0.68%, 3.94%) 1.64% (–0.27%, 3.58%) 1.19% (–1.25%, 3.7%) 0–64 0.6% (–1.64%, 2.9%) 0.47% (–9.69%, 11.78%) –0.75% (–5.1%, 3.81%) –1.38% (–6.2%, 3.68%) –0.78% (–7.14%, 6.01%) 65–84 0.95% (–0.57%, 2.49%) 2.88% (–1.69%, 7.66%) 1.27% (–1.05%, 3.65%) 0.75% (–1.94%, 3.52%) 1.29% (–1.97%, 4.66%) 85+ 4.21% (2.32%, 6.14%) 4.46% (–0.96%, 10.18%) 4.8% (2.2%, 7.46%) 4.22% (1.11%, 7.43%) 1.9% (–2.25%, 6.23%)
low, medium, or high warming scenario. The ensemble was then averaged to calculate the mean annual excess mortality and confidence intervals for each future climate scenario. The excess mortality due to heat in 2025 and 2045 was calculated as the increased mortality above a designated baseline period of 2002–2006. To establish the baseline mortality rate, all days below the alert threshold were considered non-heat days. Although the estimation of the baseline mortality rate is uncertain, this uncertainty is very small compared to other sources of variability and was regarded as negligible.
Results King County, located in Western Washington, is characterized by relatively cool summers. From 2002 to 2006, King
County averaged 4.4 heat days per year above the alert threshold of 35.7°C, with an average county-wide humidex during heat days of 38.6°C. Future projections for the medium scenario indicate the annual number of heat days is expected to increase to 9.3 (4.3–16.3 low and high) per year in 2025 and grow to 12.9 (8.5–28.7 low and high) per year in 2045; the average temperature during heat days will be slightly higher (about 1°C) in each period. Figure 1 illustrates the underlying relationship between apparent temperature measured by humidex and the trend in daily non-traumatic mortality. The figure on the left shows the non-parametric spline fit with log non-traumatic mortality using the time-series distributedlag Poisson model. The spline curve shows a smooth and increasing trend in daily mortality associated with humidex values above the median, with confidence bands that do not overlap with zero. The figure on the right shows
Table 2 Estimated mean annual excess heat-related deaths (95% CI) during 2002–2006 and projected annual excess heat-related deaths (95% CI) in 2025 and 2045. Age
Time period 2002–2006
All non-traumatic deaths 0–64 0.4 (–1.3, 2) 65–84 1.5 (–0.9, 3.7) 85+ 5.2 (3, 7.2) Respiratory 0–64 0 (–0.9, 0.5) 65–84 0.4 (–0.3, 1) 85+ 0.6 (–0.2, 1.1) Circulatory 0–64 –0.1 (–0.6, 0.3) 65–84 0.7 (–0.6, 1.9) 85+ 2.4 (1.2, 3.5) Cardiovascular 0–64 –0.1 (–0.6, 0.3) 65–84 0.3 (–1, 1.5) 85+ 1.6 (0.5, 2.6) Ischemic 0–64 0 (–0.3, 0.2) 65–84 0.5 (–0.8, 1.5) 85+ 0.4 (–0.6, 1.2)
2025 Low
2025 Middle
2025 High
2045 Low
2045 Middle
1 (–2, 4) 5 (–3, 13) 12 (6, 20)
1 (–3, 6) 7 (–4, 19) 18 (9, 29)
3 (–7, 14) 16 (–9, 44) 42 (21, 68)
1 (–3, 5) 9 (–5, 25) 21 (10, 34)
2 (–5, 11) 17 (–10, 48) 40 (20, 65)
6 (–14, 30) 48 (–26, 132) 116 (59, 187)
0 (–1, 1) 1 (–1, 4) 1 (0, 3)
0 (–1, 2) 2 (–1, 7) 2 (0, 5)
0 (–2, 4) 5 (–3, 16) 4 (–1, 12)
0 (–1, 2) 3 (–1, 9) 2 (0, 6)
0 (–1, 3) 6 (–3, 17) 4 (–1, 11)
0 (–2, 1) 2 (–2, 7) 6 (2, 11)
0 (–2, 2) 3 (–2, 10) 9 (4, 16)
–1 (–5, 5) 7 (–5, 23) 22 (9, 38)
0 (–2, 2) 4 (–3, 13) 11 (4, 19)
–1 (–4, 4) 8 (–6, 24) 21 (9, 37)
–2 (–10, 10) 22 (–16, 68) 61 (25, 107)
0 (–1, 1) 1 (–2, 4) 4 (1, 7)
0 (–2, 2) 1 (–3, 7) 6 (1, 11)
–1 (–4, 4) 3 (–7, 15) 13 (3, 26)
0 (–2, 1) 2 (–4, 9) 6 (2, 13)
–1 (–4, 3) 3 (–8, 16) 12 (3, 25)
0 (–1, 1) 1 (–2, 4) 1 (–1, 4)
0 (–2, 2) 2 (–2, 6) 2 (–2, 6)
0 (–3, 4) 4 (–5, 15) 4 (–4, 14)
0 (–1, 2) 2 (–3, 8) 2 (–2, 7)
0 (–3, 3) 4 (–5, 16) 4 (–4, 13)
–1 (–7, 9) 11 (–15, 45) 10 (–10, 38)
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2045 High
0 (–4, 10) 16 (–8, 50) 12 (–2, 35)
–2 (–9, 8) 9 (–21, 47) 36 (9, 72)
Busch Isaksen et al.: Health impacts of heat events in Washington State 123
the piecewise linear approximation fitted to this data, to provide a threshold for declaring heat events. We selected this approach because a study goal was to find a threshold at which public health actions could be initiated and to estimate mortality rates for heat events for use in future projections. The slope of the line above the threshold measures the intensity of the (linear) heat effect. In King County, we found an increase of 1.83% in daily non-traumatic mortality per degree change in humidex (95% CI: 0.77%, 2.91%) above a threshold of 35.7°C humidex. A significant increase in the risk of mortality was found for older age groups, in particular, those 85 years and older. Table 1 shows the results of the time series risk estimates for specific causes of mortality, separated by age groups. The pattern of increasing risk with age in nontraumatic causes also held for specific causes of death such as circulatory disease. For most causes, the risk estimate approximately doubles for the most elderly population compared to all ages. Table 2 presents our projection model estimates for the mean annual non-traumatic heat-related mortality in King County for the baseline and future periods.
Those age 85 and older are expected to have 2.3–8.0 (2025) and 4.0–22.3 times higher (2045) mortality for low compared to high warming scenarios, compared to the annual average from 2002 to 2006. Similar trends in excess future mortality also were projected for circulatory causes of death.
Conclusions High humidex levels above county-specific thresholds were associated with increases in mortality. Heat-related outcomes among seniors in King County are predicted to increase in 2025 and 2045 under future climate warming scenarios. Public Health planning, even in moderate climate areas, should begin to prepare for increased mortality due to future heat events.
Received January 17, 2014; accepted January 17, 2014; previously published online March 22, 2014
References 1. Jackson JE, Yost MG, Karr C, Fitzpatrick C, Lamb BK, et al. Public health impacts of climate change in Washington State: Projected mortality risks due to heat events and air pollution. Clim Change 2010;102:159–86. 2. Armstrong B. Models for the relationship between ambient temperature and daily mortality. Epidemiology 2006;17: 624–31. 3. Masterton, Richardson. Canadian Centre for Occ Health and Safety 2011-07-04. Env. Canada 1979;2012-02-02. 4. Cheng CS, Campbell M, Li Q, Li G, Auld H, et al. Differential and combined impacts of winter and summer weather and air pollution due to global warming on human mortality in southcentral Canada. 2010. 5. Washington State Office of Financial Management. Washington state growth management projections for counties: 2000 to 2030 (2007 Projections). 2007. In: Washington State Office of Financial Management. Available at: http://www.ofm.wa.gov/pop/gma/ projections07.asp.
6. Mote P, Salathe Jr. EP. Future climate in the Pacific Northwest. Clim Change 2010;102:29–50. 7. Anderson BG, Bell ML. Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States. Epidemiology 2009;20:205–13. 8. Baccini M, Biggeri A, Accetta G, Kosatsky T, Katsouyanni K, et al. Heat effects on mortality in 15 European cities. Epidemiology 2008;19:711–9. 9. Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, et al. Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol 2002;155:80–7. 10. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. ISBN 3-90005 1-07-0. URL: http://www.R-project. org/. 11. Lin S, Luo M, Walker RJ, Liu X, Hwang SA, et al. Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology 2009;20:738–46.
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Tania Busch Isaksen, Michael Yost*, Elizabeth Hom and Richard Fenske
Projected health impacts of heat events in Washington State associated with climate change Abstract: Climate change is predicted to increase the frequency and duration of extreme-heat events and associated health outcomes. This study used data from the historical heat-health outcome relationship, and a unique prediction model, to estimate mortality for 2025 and 2045. For each one degree change in humidex above threshold, we find a corresponding 1.83% increase in mortality for all ages, all non-traumatic causes of death in King County, Washington. Mortality is projected to increase significantly in 2025 and 2045 for the 85 and older age group (2.3–8.0 and 4.0–22.3 times higher than baseline, respectively). Keywords: climate change; heat events; Pacific Northwest region. *Corresponding author: Michael Yost, Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA, E-mail: [email protected] Tania Busch Isaksen, Elizabeth Hom and Richard Fenske: Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
Background Climate change is projected to have serious and long-term consequences for public health. One of the more important and measurable impacts is health outcomes associated with extreme heat events, particularly in moderate climates. We sought to investigate the regional historical heat-health outcomes, develop a unique model predicting heat-health impacts from climate change, and validate the model performance. This analysis builds upon previous work that found significant increases in non-traumatic mortality associated with heat days compared to non-heat days in the Pacific Northwest region (1). Rather than using a binary heat exposure variable for heat day vs. non-heat day, this study quantified the 1980–2006 historical relationship between humidex as a continuous heat exposure variable and non-traumatic mortality for various age groups and select causes of death. This analysis shows results for King, the most populous county in Washington State. The temperature-mortality relationship was combined with population and climate projections to estimate future non-traumatic heat-related mortality.
Methods The historical relationship, represented by the changes in daily mortality per degree C humidex increase above county-specific thresholds, was modeled using Poisson regression and a distributed-lag time series model developed by Armstrong (2) to study the effect of temperature and mortality in London. Outcomes for specific diseases were adjusted for age and temporal trends. The results were applied to varying downscaled climate warming scenarios, along with population projections, to predict future heat-related mortality.
Meteorology data Gridded meteorology data containing daily maximum temperature and humidity values were obtained from the University of Washington Climate Impacts Group. These data were produced by the variable infiltration capacity (VIC) macroscale hydrologic simulation model for the Pacific Northwest and were provided at a 1/16 spatial scale (∼4.5 km × 7 km resolution); these location indices are referred to as VIC points. The average daily maximum humidex was computed at each VIC point and averaged to a county-wide daily maximum humidex as a single daily exposure measure. Humidex is an apparent temperature index that measures the combined effects of temperature and humidity on the human body. It is defined by: 7.5 T
5 H Humidex =T + ×( v − 10 ), where v=(6.112×10 237.7 +T )× 100 9
(1) Here T is the air temperature (°C), H is the relative humidity (%), and v is the vapor pressure (kPa) (3). For abbreviation, “humidex” refers to the “county-wide average daily maximum humidex” in this analysis.
Death certificate data Death certificate data for 1980–2006 were obtained from the Washington State Department of Health. The analysis only included deaths, which occurred during the warmer months of May through September, which is 153 days
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120 Busch Isaksen et al.: Health impacts of heat events in Washington State per year (4,131 days for the entire study period). This minimized potential confounding by infectious diseases, such as cold and influenza, typically seen during colder months. The study focused on all non-traumatic deaths (ICD-9 codes 001–799; ICD-10 codes A00-R99), including separate categories of respiratory (ICD-9 codes 460–519; ICD-10 codes J00–J99) and circulatory (ICD-9 codes 390– 459; ICD-10 codes I00–I99, G45, G46) deaths. Additionally, cardiovascular (a subset of circulatory) deaths (ICD-9 codes 393–429; ICD-10 codes I05–I52) and ischemic (a subset of cardiovascular) deaths (ICD-9 codes 410–414; ICD-10 codes I20–I25) were investigated separately. Causeof-death groupings were defined a priori from a Toronto study analyzing heat mortality (4). Death certificates were coded using ICD-9 from 1980 to 1998 and using ICD-10 from 1999 to 2006. Some characteristics coded on death certificates were used to identify individuals potentially more vulnerable to heat-related mortality: elderly, nonWhite race, occupation primarily outdoors, less than high school education, Hispanic origin, and having tobacco contribute to death.
produced low, medium, and high warming scenarios for the Pacific Northwest Region for the 2025 and 2045 time periods, each date representing the midpoint of a decade. Three climate change scenarios were selected: (1) high warming scenario according to the HADCM-A1B model, (2) low warming scenario according to the PCM1-B1 model, (3) a middle warming scenario averaging the high and low warming scenarios. The details for these climate models are discussed elsewhere (6). At the time of our analysis, these future scenarios were the most current, plausible projections of future climate change for the Pacific Northwest.
Association between humidex and mortality Poisson regression was used to explore the relationship between daily humidex and mortality rates, with a nonparametric adjustment for the seasonal effects and longterm trend in mortality over time. Specifically, it was assumed that:
Population data Age-specific annual population data for 1980–2006 and future population projections for 2010, 2015, 2020, and 2025 for 5-year age categories (from 0–4 to 85+) were obtained from the Washington State Office of Financial Management (OFM) for King, Clark, and Spokane Counties (5). The model to develop these population projections took into account the 2000 census count, the “aging forward” of this population, estimates of annual population growth (including births and deaths) based on actual data, predicted net migration based on an econometric model, as well as forecasted fertility rates and life expectancy. The rate of population increase was obtained from the OFM for historical estimates from 1980 through 2006 plus future projections for 2010, 2015, 2020, and 2025. Interpolation between 1980 and 2025 showed a linear population increase relationship that was then extrapolated to generate age-specific population estimates for 2045.
Future meteorology projections Future climate projections were produced by the University of Washington’s Climate Impacts Group (CIG) using recent, publicly available global climate model simulations. These
log( µ j ) = β0 + s( hj ) + s( t j ) + ∑ l=6 βl I{ month =l } 9
j
(2)
where Yj is approximately Poisson, the observed mortality count on day j, Pj is the population on day j, and s(hj) and s(tj) are natural cubic splines over daily humidex and time; fixed effects ( β′l s ) for the month also were included. Similar to other studies, we used non-parametric splines to model the log mortality rate over time to adjust for longterm changes in population, health behaviors such as smoking, changes in medical practice patterns, and access to health care (7–9). The number of degrees of freedom for the splines modeling long-term time trend were selected to minimize the cross-validation error to avoid overfitting. Modeling was implemented by the “mgcv” package in the statistical software R version 2.14.1 (10). The humidex-mortality relationship with the spline model from 1980 to 2006 was J-shaped, suggesting a positive relationship above a specific humidex threshold (Figure 1). This relationship was simplified to a piecewise linear form with two knots: the first set at the 50th humidex percentile. The second knot, or “optimal alert threshold,” was set to 35.7°C humidex by maximizing the fitted-model likelihood as the upper threshold changed from 20° to 40°C, in 0.1° increments. A similar approach was used to study heat and hospitalizations in New York City (11). A heat day was defined as a day in which the county-wide daily maximum humidex was above the optimal alert threshold; the impact of humidex intensity
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Busch Isaksen et al.: Health impacts of heat events in Washington State 121
A 0.2
-10.5
0.1
log(daily mortality rate)
Smoothed trend on log moratlity rate
King County
B
0.0
-11.0
-11.5
-0.1
-12.0 10
20
30
40
10
Humidex
20
30
40
County-wide daily max humidex
Figure 1 (Left) Non-parametric spline model of non-traumatic log mortality rate and humidex relationship; (right) corresponding piecewise linear approximation using two knots at 22.6°C and 35.7°C humidex.
was assessed by the slope of the line above the threshold. The piecewise model is: log( µ j ) = β0 + β 1 ( hj − hq 50 )+ + β 2 ( hj − hˆ0 )+ + s( t j )
9
+ ∑ l=6 βl I { monthj = l }
(3)
where hj = county-wide average daily maximum humidex value, hq50 = the 50th percentile of humidex from January to December 1980–2006, and hˆ0 = the optimal alert threshold, Imonth = indicator variable for months May through September. For King County, hq50 = 22.6, and hˆ0 = 35.7 and s(tj) has df = 5.
Evaluation of other effects of heat on mortality The data were evaluated to see if there was a “cool down effect,” such that elevated minimum humidex on a hot day prevented cooling down and, thus, increased mortality. The data also were examined for increases in mortality with a greater number of consecutive days of a heat event (lag effect), which has been found previously (7). The mortality rate was not significantly affected by the lagged humidex, while the acute association between excess humidex above the upper threshold and mortality on the same day remained statistically significant. This study found no cool down or duration effect modification on mortality rates so these covariates were not included in the final model.
Individual-level characteristics obtained from death certificates were evaluated for changes in the risk of heatrelated mortality. These covariates included age (5-year increments), gender, race, primarily outdoor occupation, high school graduation, Hispanic origin, and tobacco use. Effect modification was examined by adding each covariate as a separate term and adding an interaction term. Mortality rates for all covariate groups, except age, were not adjusted by population size of covariate groups because we did not have access to this data. With the exception of the age of the decedent, we did not find that any other individual-level characteristics altered the risk of dying on a heat day.
Projection of future excess mortality due to heat The projected future climate data was generated by adding the predicted change in monthly mean temperature for each future projection scenario to each year based on a historical 30-year climate reference period of 1970– 2000. Parameter estimates for the King County-specific temperature-mortality relationships were applied to the projected future climate scenarios and the future population to estimate the annual excess deaths from heat events in 2025 and 2045. The process was repeated for each of an ensemble of 30 possible realizations of future temperatures in 2025 and in 2045, respectively, in order to account for the year-to-year variation of temperatures under a
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122 Busch Isaksen et al.: Health impacts of heat events in Washington State Table 1 Historical mortality (1980–2006) in King County due to heat events, by underlying cause of death and age group; values are the observed% Change (95% CI) in mortality per degree humidex above a 35.7°C threshold. Age
All non-traumatic
Respiratory
Circulatory
Cardiovascular
Ischemic
All ages 1.83% (0.77%, 2.91%) 3.21% (–0.09%, 6.63%) 2.3% (0.68%, 3.94%) 1.64% (–0.27%, 3.58%) 1.19% (–1.25%, 3.7%) 0–64 0.6% (–1.64%, 2.9%) 0.47% (–9.69%, 11.78%) –0.75% (–5.1%, 3.81%) –1.38% (–6.2%, 3.68%) –0.78% (–7.14%, 6.01%) 65–84 0.95% (–0.57%, 2.49%) 2.88% (–1.69%, 7.66%) 1.27% (–1.05%, 3.65%) 0.75% (–1.94%, 3.52%) 1.29% (–1.97%, 4.66%) 85+ 4.21% (2.32%, 6.14%) 4.46% (–0.96%, 10.18%) 4.8% (2.2%, 7.46%) 4.22% (1.11%, 7.43%) 1.9% (–2.25%, 6.23%)
low, medium, or high warming scenario. The ensemble was then averaged to calculate the mean annual excess mortality and confidence intervals for each future climate scenario. The excess mortality due to heat in 2025 and 2045 was calculated as the increased mortality above a designated baseline period of 2002–2006. To establish the baseline mortality rate, all days below the alert threshold were considered non-heat days. Although the estimation of the baseline mortality rate is uncertain, this uncertainty is very small compared to other sources of variability and was regarded as negligible.
Results King County, located in Western Washington, is characterized by relatively cool summers. From 2002 to 2006, King
County averaged 4.4 heat days per year above the alert threshold of 35.7°C, with an average county-wide humidex during heat days of 38.6°C. Future projections for the medium scenario indicate the annual number of heat days is expected to increase to 9.3 (4.3–16.3 low and high) per year in 2025 and grow to 12.9 (8.5–28.7 low and high) per year in 2045; the average temperature during heat days will be slightly higher (about 1°C) in each period. Figure 1 illustrates the underlying relationship between apparent temperature measured by humidex and the trend in daily non-traumatic mortality. The figure on the left shows the non-parametric spline fit with log non-traumatic mortality using the time-series distributedlag Poisson model. The spline curve shows a smooth and increasing trend in daily mortality associated with humidex values above the median, with confidence bands that do not overlap with zero. The figure on the right shows
Table 2 Estimated mean annual excess heat-related deaths (95% CI) during 2002–2006 and projected annual excess heat-related deaths (95% CI) in 2025 and 2045. Age
Time period 2002–2006
All non-traumatic deaths 0–64 0.4 (–1.3, 2) 65–84 1.5 (–0.9, 3.7) 85+ 5.2 (3, 7.2) Respiratory 0–64 0 (–0.9, 0.5) 65–84 0.4 (–0.3, 1) 85+ 0.6 (–0.2, 1.1) Circulatory 0–64 –0.1 (–0.6, 0.3) 65–84 0.7 (–0.6, 1.9) 85+ 2.4 (1.2, 3.5) Cardiovascular 0–64 –0.1 (–0.6, 0.3) 65–84 0.3 (–1, 1.5) 85+ 1.6 (0.5, 2.6) Ischemic 0–64 0 (–0.3, 0.2) 65–84 0.5 (–0.8, 1.5) 85+ 0.4 (–0.6, 1.2)
2025 Low
2025 Middle
2025 High
2045 Low
2045 Middle
1 (–2, 4) 5 (–3, 13) 12 (6, 20)
1 (–3, 6) 7 (–4, 19) 18 (9, 29)
3 (–7, 14) 16 (–9, 44) 42 (21, 68)
1 (–3, 5) 9 (–5, 25) 21 (10, 34)
2 (–5, 11) 17 (–10, 48) 40 (20, 65)
6 (–14, 30) 48 (–26, 132) 116 (59, 187)
0 (–1, 1) 1 (–1, 4) 1 (0, 3)
0 (–1, 2) 2 (–1, 7) 2 (0, 5)
0 (–2, 4) 5 (–3, 16) 4 (–1, 12)
0 (–1, 2) 3 (–1, 9) 2 (0, 6)
0 (–1, 3) 6 (–3, 17) 4 (–1, 11)
0 (–2, 1) 2 (–2, 7) 6 (2, 11)
0 (–2, 2) 3 (–2, 10) 9 (4, 16)
–1 (–5, 5) 7 (–5, 23) 22 (9, 38)
0 (–2, 2) 4 (–3, 13) 11 (4, 19)
–1 (–4, 4) 8 (–6, 24) 21 (9, 37)
–2 (–10, 10) 22 (–16, 68) 61 (25, 107)
0 (–1, 1) 1 (–2, 4) 4 (1, 7)
0 (–2, 2) 1 (–3, 7) 6 (1, 11)
–1 (–4, 4) 3 (–7, 15) 13 (3, 26)
0 (–2, 1) 2 (–4, 9) 6 (2, 13)
–1 (–4, 3) 3 (–8, 16) 12 (3, 25)
0 (–1, 1) 1 (–2, 4) 1 (–1, 4)
0 (–2, 2) 2 (–2, 6) 2 (–2, 6)
0 (–3, 4) 4 (–5, 15) 4 (–4, 14)
0 (–1, 2) 2 (–3, 8) 2 (–2, 7)
0 (–3, 3) 4 (–5, 16) 4 (–4, 13)
–1 (–7, 9) 11 (–15, 45) 10 (–10, 38)
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2045 High
0 (–4, 10) 16 (–8, 50) 12 (–2, 35)
–2 (–9, 8) 9 (–21, 47) 36 (9, 72)
Busch Isaksen et al.: Health impacts of heat events in Washington State 123
the piecewise linear approximation fitted to this data, to provide a threshold for declaring heat events. We selected this approach because a study goal was to find a threshold at which public health actions could be initiated and to estimate mortality rates for heat events for use in future projections. The slope of the line above the threshold measures the intensity of the (linear) heat effect. In King County, we found an increase of 1.83% in daily non-traumatic mortality per degree change in humidex (95% CI: 0.77%, 2.91%) above a threshold of 35.7°C humidex. A significant increase in the risk of mortality was found for older age groups, in particular, those 85 years and older. Table 1 shows the results of the time series risk estimates for specific causes of mortality, separated by age groups. The pattern of increasing risk with age in nontraumatic causes also held for specific causes of death such as circulatory disease. For most causes, the risk estimate approximately doubles for the most elderly population compared to all ages. Table 2 presents our projection model estimates for the mean annual non-traumatic heat-related mortality in King County for the baseline and future periods.
Those age 85 and older are expected to have 2.3–8.0 (2025) and 4.0–22.3 times higher (2045) mortality for low compared to high warming scenarios, compared to the annual average from 2002 to 2006. Similar trends in excess future mortality also were projected for circulatory causes of death.
Conclusions High humidex levels above county-specific thresholds were associated with increases in mortality. Heat-related outcomes among seniors in King County are predicted to increase in 2025 and 2045 under future climate warming scenarios. Public Health planning, even in moderate climate areas, should begin to prepare for increased mortality due to future heat events.
Received January 17, 2014; accepted January 17, 2014; previously published online March 22, 2014
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