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

Patrick KinneyProfessor of Environmental Health SciencesDirector, Columbia Climate and Health Program

Mailman School of Public Health, Columbia University, NY

August 25, 2013

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

Pathways linking climate change and health

McMichael et al. 2003a

Methods for assessing public health aspects of climate change:

1. The Epidemiologic Approach Quantify historical relationships between climate metrics and human health outcomes…The goal is to identify and quantify “exposure-response” functions

2. 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

STRATUS CONSULTING

Case Study: Assessing historical and potential future mortality

effects of warming temperatures in Manhattan

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

Exposure-Response Function

Warm and cold effects fitted separately:

Lag 0 for warm effectLag 2 for cold effect

Assumed no effect where 95% conf. intervals crossed 0

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”)

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

Annual temperature-related deaths in baseline and future periods

Li, Horton and Kinney, Nature Climate Change, 2013

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

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