MN EPHT Brownbag Series April 12, 2010 Development of Environmental Health Indicators of Climate Change
Road Map Background Building Capacity in MN EPH Tracking & Indicators Example Indicators (heat & pollen) Indicators of Potential Interest to MN Discussion & Questions
IPCC 4 th Assessment Changes in temperature, precipitation, and other weather variables due to climate change are likely to affect the health status of millions of people, particularly those with low adaptive capacity. (IPCC, 2007)
Exposures & Effects Climate Change Effects: Temperature Sea level Precipitation Heat Storms, coastal flooding Vector biology Air pollutants, allergens Food supply Civil conflict Morbidity/mortality Morbidity/mortality Infectious diseases Respiratory diseases Malnutrition Morbidity/mortality/ displacement
Kittson Marshall Polk Roseau Pennington Red Lake Beltrami Clear Water Lake of the Woods Koochiching St. Louis Lake High-Risk Areas for Tick-borne Diseases Cook in Minnesota Itasca Norman Mahnomen Hubbard Cass Clay Becker Aitkin Wadena Crow Wing Carlton Wilkin Ottertail Pine Todd Mille Lacs Kanabec Grant Douglas Morrison Benton Traverse Stevens Pope Stearns Isanti Big Stone Sherburne Chisago Swift Kandiyohi Anoka Meeker Wright Wash - ing- Chippewa Ram - ton Hennepin sey Lac Qui Parle McLeod Renville Carver Yellow Medicine Scott Dakota Sibley Lincoln Lyon Redwood Le Nicollet Sueur Rice Goodhue Wabasha Brown Pipestone Murray Cottonwood Olmsted Watonwan Blue Earth Waseca Steele Dodge Winona Tick-borne disease risk in Minnesota is highest in forested areas within the shaded zones. Blacklegged ticks may also be found at lower levels in some forested areas outside this zone. Known high risk areas for tick-borne diseases, before 2004 Rock Nobles Jackson Martin Faribault Freeborn Mower Fillmore Houston Known high risk areas for tick-borne diseases, added in 2004
Rare or Emerging Tick-borne Diseases (Minnesota) Agent Tick Vector Ehrlichiosis Ehrlichia spp. Lone star tick (Amblyomma americanum) Rocky Mountain spotted fever Rickettsia rickettsii Wood/dog tick (Dermacentor sp.) Powassan encephalitis Powassan virus (prototype and deer tick virus lineages) Blacklegged tick, Woodchuck tick (Ixodes spp.)
Building Capacity to Address PH Impacts of Climate Change Needs Assessment (survey) Training (4 webinars) Strategic Plan Web Site
Training (4 modules) Climate Change 101 Vector-Borne Disease Extreme Heat Vulnerable Populations
Ongoing Activities Seeking Funding Building Capacity (state & local levels) Fostering Collaboration & Communication LPHA, UMN, MPCA, DNR, MDA, CDC, EPA
What Are Indicators? Indicators are quantitative summary measures that may be used to track changes by population, location, & time Used to: Evaluate trends over time Identify areas for intervention & prevention Evaluate effectiveness of programs & policies
State Environmental Health Indicators Collaborative (SEHIC) State-level epidemiologists Voluntary (supported by CSTE & CDC) Climate Change Workgroups Morbidity & mortality Population vulnerability Air quality & respiratory morbidity Vector-borne diseases
Environmental Health Perspectives (November 2009) Evaluated strengths & limitations of data sources Developed a suite of indicators of climate change Environmental Health Perspectives: http://ehp03.niehs.nih.gov/home.action
EH Perspectives, 2009
EPHT Network Congress first appropriated funding to the CDC to plan and establish a national EPHT network in 2002. The national EPHT program will Link health and environmental data systems Bring together existing and new sources of data Draw data and information from state networks and from national data systems Provide data that are nationally consistent Make information available through a web-based, secure electronic network
National EPHT Network (2009) CDC currently funds 22 states, 1 city, and 4 academic partners to implement EPHT network. Minnesota joined the network as a funded state in 2009.
National EPHT Network Climate Change Team Adopted climate change as a developmental content area (Summer 2009) Established Climate Change Team (Fall 2009) Initial Focus: Heat-related mortality & morbidity
Collaboration National SEHIC National EPHT Network, CDC NOAA, US EPA State & Local ASTHO Grantees (CA, MI, NH, FL, MN) MPCA, DOC, DNR, MDH, UMN (ICAT) Local health departments
Extreme Heat Events Heat waves in MN (recent) 1983, 1995, 1999, 2001, 2005, 2006 Consecutive days of abnormally high temps combined with humidity Heat Index
Extreme Heat: Health Effects Primary Heat stroke & heat exhaustion Acute dehydration Secondary Cardiovascular disease & heart attacks Kidney failure Respiratory illness
Extreme Heat: Indicator Development Define extreme heat period Heat Index > 105 F for 2 or more days Maximum ambient temperature & relative humidity Define referent period Equivalent number of days and distribution of days of the week (exclude holidays) Close to time period of heat event
Areas of Analysis Climate Change Team California Louisiana Maine Massachusetts Minnesota Missouri New Hampshire New York City Oregon Utah Washington Wisconsin
Extreme Heat: Mortality Indicator Sum of daily counts of all-cause mortality during heat wave & referent periods Calculate rate ratio & confidence interval Deaths (Heat wave) / Deaths (Referent period) Assume population is constant over time
Extreme Heat: Preliminary Results Minnesota July 20 August 2, 2006 (2 weeks) 7 county metro area Referent period June 24-31 and August 17-23 Deaths during heat wave: 547 Deaths during referent period: 509 Rate ratio: 1.07 (0.95, 1.21)
Preliminary Results Rate Ratio (Confidence Interval) California 1.06 (1.03, 1.09) Louisiana 3.40 (1.68, 6.88) Maine 1.32 (0.88, 1.97) Massachusetts --- Minnesota 1.07 (0.95, 1.21) Missouri --- New Hampshire --- New York City 1.09 (1.01, 1.18) Oregon 1.00 (0.78, 1.28) Utah 0.90 (0.57, 1.42) Washington --- Wisconsin ---
Extreme Heat: Morbidity Indicator Sum of daily counts of specific health conditions Hospitalizations or ED visits during heat wave period and referent period Primary & secondary diagnoses (Knowlton, 2009) Calculate rate ratio & confidence interval
Data Issues Heat Index Some states rarely experience > 90 F or high humidity Daily maximum temp/humidity to calculate Heat Index May be better to calculate paired temp & humidity Requires hourly data
Data Issues May want to evaluate accounting for low temps during heat waves Night-time cooling off period is crucial Referent period Close to heat event so temps are still relatively high
Data Issues Mortality data Not heat-specific More easily obtained than hospitalization or ED data Difficult to create a national indicator that is consistent and meaningful for all geographic areas
Pollen & Climate Change Increased pollen production Change in pollen species observed at particular location Longer pollen season
Proposed Pollen Indicator Measures Pollen load: Percentage of days with pollen levels higher than the action level per population in a calendar year Pollen type: Pollen species measured in a calendar year Length of pollen season: Number of days
National Allergy Bureau Monitoring Stations - Midwest Source: http://www.aaaai.org/nab/index.cfm
Next Steps: Pollen Indicator Develop template and how-to guide Pilot-test using NAB data Present results at conference roundtable in June Pilot-test measures (other states) Post final documents on CSTE website
Indicators of Potential Interest in MN Extreme weather events (e.g., heat waves, floods) Population vulnerability Vector-borne diseases Aero-allergens Air quality (ozone, fine particles) Others?
Tracking in Action Public Health Action Data Collection Data Dissemination Data Analysis & Integration Tracking = public health surveillance
Next Steps MDH Strategic Plan CDC (funding opportunity) National Tracking Network Joint meeting w/sehic (April 29) Climate Change Team (ongoing)
Climate Change Indicators & Tracking: Chuck Stroebel Wendy Brunner Paula Lindgren Jean Johnson Collaborators: University of MN Extension MPCA LPHA Building Capacity: Lynne Markus Kristin Raab Myrlah Olson David Neitzel Dan Symonik Web Site: www.health.state.mn.us/tracking/
.the most important question we must ask ourselves is, Are we being good ancestors? Jonas Salk