The Health Impacts of Climate Change:

The Evidence & The Uncertainty

”Francesca Dominici”Harvard School of Public Health

The National Medicare Cohort Study, 1999-2010 (MCAPS)

• Medicare data include: – Billing claims for everyone over 65

enrolled in Medicare (~48 million people), • date of service• disease (ICD 9)• age, gender, and race• place of residence (zip code)

NationalMorbidityMortalityAir PollutionStudy1987—2006

Conducted by Dockery and Schwartz at HSPH

Chicago 1999 Heat Wave

Statistical Model for Present-day Risk(May – October, 1987-2005)

Age category-specific daily mortality model

Smooth spline function of temperature variables

Expected age category-specific mortality count on day t

Smooth functions of air pollution levels (ozone, PM), temporal trends

Relative Risk Estimate

Relative risk for individual age categories

Average number of deaths on heat wave daysAverage number of death on non-heat wave days

BMA

Analysis Schematic and Data Integration

(Chicago,1987—2005)

(Chicago,2081—2100)

Assumptions for Future Estimates

• Constant heat wave relative risk over time• No adaptation to extreme heat• Constant rate of mortality on non-heat

wave days• Minimal short-term mortality displacement

Are we becoming less vulnerable to heat? A national analysis of temporal changes in heat-related mortality in the US

Bobb, Peng, Bell, and Dominici

Mortality Data

• Obtained from the National Center for Health Statistics for the period 1987—2005

• Death certificate data containing an individual’s date and cause of death, location of residence, age

• We construct a daily time series of mortality for non-accidental causes and different age categories

• Data are available for > 100 metropolitan areas

Present-day Temperature Data

• Hourly temperature and dew point temperature available from NOAA

• Maximum over a 24 hour period is used• Maximum over multiple monitor values in a

city is taken, if available

What is a Heat Wave?

• There is no generally agreed upon definition of a heat wave

• Exceedances of percentiles of the temperature distribution

• Exceedances of specific absolute temperature levels

• Continuous stretches of high temperature• High humidity

Heat Wave Definition

• Threshold 1 (T1) is the 97.5th percentile of the distribution of daily maximum temperatures

• Threshold 2 (T2) is the 81st percentile of daily max temperatures

• A heat wave is defined as the longest period of consecutive days satisfying:– Daily max temp is above T1 for > 3 days– Daily max temp is above T2 for the entire period– Average of daily max temp over the period is > T1

Huth et al. 2000; Meehl & Tebaldi 2004

Accounting for Model Uncertainty in the temperature-mortality exposure-

response

Log Relative Risks of Mortality and 95% HPD Associated with a Heat Wave Day: Resultsare shown for the largest 105 cities using data for the period 1987 to 2005. Results are grouped by geographical region and within each group are plotted as a function of the longitude (south to north)

A National Study of Heat Waves and Mortality

Bobb, Dominici, Peng, Biometrics 2011

Zanobetti et al PNAS 2012

2) Climate Change, Heat Waves, and Human Health

Motivation

• While the present-day health effects of hot temperatures have been fairly well characterized, the extent to which future changes in the heat wave distribution will affect human health has not been as extensively studied

• We estimate the excess mortality associated with heat waves in Chicago for 2081– 2100 under several global climate change scenarios

• We are interested in partitioning the uncertainty into: 1) statistical variation; 2) climate models, and 3) and climate change scenarios

How will any change in the distribution of heat waves affect mortality and morbidity?

Heat Waves and Climate Change

• High/extreme ambient temperatures are associated with mortality and other health outcomes in the North America

• Will climate change affect the distribution of heat waves?

Expected number of excess deaths during a given heat wave period

Expected number of excess deaths during a given heat wave period

Expected daily number of deaths on a non-heat wave day (estimated from present-day data)

Relative risk associated with heat wave days(estimated frompresent data)

length of the heat wave period in days(estimated from future data)

For Chicago there were 0.7 heat waves per year with an average length of 9.2 days (a total of 14 heat waves) during the period 1987-2005

Heat waves for the period 2081-2100 (IPCC, 2000)

• Heat wave summary statistics for the 2081–2100 period were calculated using the CMIP3 multi-model daily maximum temperature output under the B1, A1B, and A2 scenarios

• The A1 family of scenarios assumes rapid economic growth, an increase in world population until mid-century followed by a decrease, and the introduction of more efficient energy sources and conversion technologies.

• The A1B scenario in particular assumes a mix of energy sources that is balanced across fossil fuel and alternative sources.

• The B1 scenario assumes a highly convergent world with moderate population growth (as with A1), a reduction in material intensity, and the introduction of clean and resource-efficient technologies.

• The A2 scenario assumes a very heterogeneous world with little convergence between nations, regionally oriented economic development, and continuously increasing global population 

IPCC, SRES, 2000

Annual Number of Heat Waves and AverageLength (in days) for Chicago, 2081—2100

Accounting for changing population growth and age structure

• To estimate future excess mortality, we assumed the same non-heat wave rate of mortality as the 1987–2005 period and projected population growth using the B1, A1, and A2 age-stratified population estimates for the 2081-2100 (Lutz, 1996)

• When computing the future excess mortality attributable to heat waves, we take into account the changing age structure of the population by applying age category-specific relative risks estimated from the age-stratified time series models.

Annual Heat Wave Mortality for Chicago, 2081—2100

We estimated an annual excess mortality attributable to heat waves ranging between 166 to 2217 deaths per year 

Under the A2 scenario, 5 of the 7 climate models project that the annual mortality from heat waves will be similar to or greater than the mortality from the devastating 1995 heat wave.

Peng, Bobb, Tebaldi, McDaniel, Bell, Dominici, EHP,2010

Future Directions

• Methodology is reproducible across different locations using publicly available data

• Look nationally at variation in heat wave risk across locations via hierarchical modeling

• Look at morbidity effects of heat• Incorporate more realistic assumptions about

the future (adaptation)• Examine time-varying risk, relate to other factors• Distributed lag may estimate the “total effect” of

a heat wave better than single lag models (mortality displacement/harvesting)

Why the mortality risks are declining?

• Higher AC prevalence does not automatically imply higher usage • Air Pollution Levels have declined• Prevention and treatment of cardiovascular diseases have

improved• Extensive heat-health warning systems and public health response

programs have been implemented in several US cities• Other factors that might contribute the to declining risk of heat-

related mortality include: 1) changes in activity patterns (e.g., less time spent outside), changes in the built environment (e.g., more green spaces), physiological adaptations, and greater awareness of the dangers of extreme heat

3)Climate Change, Ozone, and Health

VOCs + NOx + heat / sunlight O3

Known:Temperature Affects O3 Formation

Precursors to ozone Secondary pollutant

Accelerated reaction rates

Biogenic VOCs

November 17 2004

O3 Mortality

Ozone Levels Likely to Increase

Bell et al. Climatic Change 2007

Forecasting fine particulate matter (PM2.5) across the United States in a changing

climate

Loretta J. Mickley

Wildfires in Quebec the same day.Haze over Boston on May 31, 2010

Dominick Spracklen, Jennifer A. Logan, Xu Yue, Amos P.K.A. Tai, Daniel J. Jacob, Rynda C. Hudman

Two constellations of studies

1. Sensitivity of PM2.5 to changing meteorology in the East.

2. Sensitivity of wildfires to changing climate in the West and the consequences for PM2.5.

Models show increased duration of stagnation in the East, with corresponding increases in annual mean PM2.5.We use observed relationships + climate models, no chemistry models.

2000-2050 climate change leads to increases in annual mean PM2.5 across much of the Eastern US.

Change in annual mean PM2.5 concentrations in 2050s relative to present-day

mg m-3

Tai et al. 2012

Particulate Matter and Health

• PM has been linked with health outcomes: hospitalization, mortality, decreased lung function, cardiac events

• Evidence of both short-term (acute) and long-term (chronic) effects of exposure to ambient PM

• Much recent work has examined ambient PM mass (PM10, PM2.5, PM10-2.5) indicator

• More studies now looking at health effects of PM constituents, sources

Climate Change

Temperature PM2.5 OzoneForest Fires

Human Health

• GCM Models• Atmospheric

Chemistry• Statistical

Models• Downscaling

Exposure ResponseJoint effectsAdaptationSusceptibility

Human Health

Air Quality Policies

Climate Change Policies

• Many policies to improve air quality would lower GHG emissions.

• Many policies to avoid/mitigate climate change would improve air quality in the short-term.

Co-Benefits (and Co-Costs) of Climate Change Policies

Relationship between Climate

Change and Air Quality Policies

Bell et al. Environmental Health 2008

Summary• What is known

– Climate is changing– Health Impact of Extreme temperatures (e.g.

heat waves)– Air pollution (especially O3) likely to be

affected• What is not known

– Changes in the pollutant mixture– Actual temperature levels (climate modeling)– Changing susceptibility/vulnerability– Changing adaptation (e.g., air conditioning)– Co-benefits (and co-costs) of policies

Questions?

References• Bobb, J.F., F. Dominici, and R.D. Peng, A Bayesian model averaging approach

for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities. Biometrics, 2011. 67(4): p. 1605-16.

• Dominici, F., et al., Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA, 2006. 295(10): p. 1127-34.

• Bell, M.L., et al., Ozone and short-term mortality in 95 US urban communities, 1987-2000. JAMA, 2004. 292(19): p. 2372-8.

• Tai, A., et al., Meteorological modes of variability for fine particulate matter (PM2. 5) air quality in the United States: implications for PM2. 5 sensitivity to climate change. Atmos. Chem. Phys, 2012. 12: p. 3131-3145.

• Barr, C.D. and F. Dominici, Cap and trade legislation for greenhouse gas emissions: public health benefits from air pollution mitigation. JAMA, 2010. 303(1): p. 69-70.

• Peng, R.D., et al., Toward a quantitative estimate of future heat wave mortality under global climate change. Environ Health Perspect, 2011. 119(5): p. 701.

• Zanobetti, A., O’Neill, M. S., Gronlund, C. J. & Schwartz, J. D. Summer temperature variability and long-term survival among elderly people with chronic disease. Proceedings of the National Academy of Sciences 1–6 (2012).

Expected number of deaths in a one-year period caused by all heat waves in that year

• We calculated this summary by computing for every heat wave in the respective time period (1987–2005 for present day, 2081–2100 for future period), summing the excess deaths across all heat waves and dividing by the total number of years.

• Using the daily maximum temperature output from each of the climate models, we calculated the number of heat waves per year and the length of each heat wave (in days) under a changing climate.

• Averaging each of these numbers over the length of the study period and multiplying them together provided the expected number of heat wave days per year for each set of climate change scenarios and climate models.