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Climate data needs for assessing public health aspects of climate change: a research perspective

Climate data needs for assessing public health aspects of climate change: a research perspective. Patrick Kinney Professor of Environmental Health Sciences Director, Columbia Climate and Health Program Mailman School of Public Health, Columbia University, NY August 25, 2013.

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Climate data needs for assessing public health aspects of climate change: a research perspective

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  1. Climate data needs for assessing public health aspects of climate change: a research perspective Patrick Kinney Professor of Environmental Health Sciences Director, Columbia Climate and Health Program Mailman School of Public Health, Columbia University, NY August 25, 2013

  2. Outline of my remarks… • Pathways connecting climate and health • How can public health science inform climate adaptation and mitigation policies? • Case study assessing temperature-related deaths under climate change in Manhattan • Some thoughts about climate-health partnerships

  3. Pathways linking climate change and health McMichael et al. 2003a

  4. Methods for assessing public health aspects of climate change: • The Epidemiologic Approach Quantify historical relationships between climatemetrics and human health outcomes …The goal is to identify and quantify “exposure-response” functions • The Health Impact Assessment Approach Predict health impacts for hypothetical climate and/or policy scenarios. For example: …if climate changes by X amount, what would be the health impacts for a given region? This is key to adaptation planning in health sector

  5. Case Study: Assessing historical and potential future mortality effects of warming temperatures in Manhattan

  6. Epidemiologic Methods • Historical Data • Obtained data for 18 year period (1982-99) in Manhattan for: • daily maximum temperature • daily death counts • Statistical analysis using Poisson GLM daily mortality ~ natural spline(Tempmax_lag, 3df) + natural spline(time, 7df/year) + day of week indicator

  7. Exposure-Response Function Warm and cold effects fitted separately: Lag 0 for warm effect Lag 2 for cold effect Assumed no effect where 95% conf. intervals crossed 0

  8. Future temperature modeling: • Obtained projections of future Tmax using 32 combinations of global climate models and greenhouse gas emissions scenarios (Maurer et al., 2007). • Two IPCC emissions scenarios (A2 and B1) • 16 Global Climate Models from IPCC 4th assessment report • Statistical downscaling via BCSD to Central Park, NY station for 30 year periods centered on the 2020s, 2050s and 2080s. Baseline period is the 30 year climatological baseline of 1970 to 1999 (referred to here as “1980s”)

  9. Estimating daily deaths due to temperature: • Using observations in the baseline period, and downscaled data for each of the 32 climate ensemble members, we took the daily max temperature series, and • For each day in the baseline and future time slices, computed the % increase in temperature-related deaths using our exposure-response function • This was multiplied times the baseline daily death rate (deaths/day) for Manhattan (assumed constant over time) to get daily temperature-related deaths • We computed the mean annual heat, cold, and net temperature-related deaths in each time slice for each of 32 climates • We examined the distributions of these latter numbers over time

  10. Annual temperature-related deaths in baseline and future periods Li, Horton and Kinney, Nature Climate Change, 2013

  11. Observations on Temperature-Death Relationships • This case study was “simple” in that it involved direct health effects of daily temperatures • We ignored second-order effects from multi-day runs of heat (heat waves) • We used a simple exposure metric – daily max T • Research has shown that results do not vary appreciably for other metrics, e.g., min, mean, heat index etc. • We assumed no changes over time in the exposure-response function, baseline mortality rates, and population • Ignores adaptation • We need to develop plausible scenarios

  12. More general observations… • Other climate-related health impacts will usually require more sophisticated climate inputs: e.g., realistic temporal and spatial correlations structures; precipitation • Each of the many climate-health pathways requires specific epidemiologic modeling to reveal complex pathways • Climate scientists need to be part of the epidemiologic team, providing best available observational data • Climate scientists also key partners in assessing future health risks, providing projections at relevant spatial and temporal scales • Health scientists can also learn from other impact areas

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