Environmental Public Health Tracking in California ( 加州环境公共卫生的监测 )

1. Environmental Public Health Tracking in California (加州环境公共卫生的监测) Eric M. Roberts, MD PhD (爱瑞克•罗佰兹) Paul English, PhD MPH, Principal Investigator Geoff Lomax, DrPH, Research Director Michelle Wong, MPH, Health Educator Craig Wolff, MS Eng, IT/GIS Manager

2. Background • Chronic disease accounts for significant morbidity and mortality among Californians (along with injuries, responsible for 75% of deaths) • Many chronic diseases are increasing in prevalence • Asthma • Auto-immune diseases • Autism and learning disabilities (maybe)

3. Asthma Neurodevelomental disorders Autoimmue diseases Cancer Alzheimer’s, Parkinson’s Endocrine disruption Endometriosis Heart disease Background • Diseases with known or suspected environmental links: • Besides pain and suffering, treatment of environmental diseases costs at least US$10 billion annually in California alone

4. Background • Problem: • We have limited data to track trends in health conditions which have suspected links to the environment • We have limited ability to monitor human exposure to toxic chemicals, and we know little about what the public is exposed to and at what level

5. Environmental Public Health Tracking • EPHT is the systematic, ongoing collection, collation, and analysis of data related to • Environmentally related disease • Environmental exposures • EPHT also includes the timely dissemination of information to those who need to know so that action can be taken

6. Functions of an EPHT System • Track environmental hazards, exposures, and diseases to help monitor ‘hotspots’ where exposure to environmental hazards is excessive and requires reduction; • Track trends over time to help evaluate the success of environmental protection and public health measures; • Link environmental hazard information and disease information to help generate hypotheses about possible connections; and • Provide the foundation researchers need in order to do scientific studies designed to identify the causes of disease.

7. Planning for an EPHT system Level of agency: Federal EPHT Program CDC funded program in EPHT Federal (美国) State (加州省) County, city, public (县,城市,公众) Legislature mandated that DHS develop program in EPHT EHIB, DHS Planning Consortium Interested parties convened by EHIB

8. The Process of EPHT • coordinate between agencies • develop IT infrastructure • format and process data Disparate sources of data • tabulation • statistical analysis • map making Useable datasets • stakeholder input • develop and field test materials • create mechanisms for access and dissemination Results Information for action

9. EPHT Process: Discussion • Assembling data from a variety of agencies through automated processes • Collaboration with private-sector data sources • Data visualization • As an analytic tool • For communication with community stakeholders

10. Center for Vital Statistics (Birth certificate records) Department of Pesticide Regulation Childhood Lead Poisoning Prevention Branch Air Resources Board Department of Disability Services Public and private sector providers of health services 1. Data Assembly • The keepers of information are spread out over many agencies, many of which may not be accustomed to working with each other

11. Inter-agency Data Communication Infratructure • California Air Resources Board (ARB) collects emissions data from thousands of industrial facilities in the state • Using geography, weather patterns, and dispersion modeling, ARB is attempting to model ground-level concentrations (GLCs) of air toxics Emissions (inventoried) Concentrations (modeled GLCs) GLC grid layer

12. Geographic Information Systems (GIS) data queries between agencies • Inputfrom DHS to ARB is circular buffer or polygon • Outputfrom ARB to DHS is proportional summation of metrics from all overlapping grids • Resolution varies; grid size (d) may be as small as 250 m d

13. Remember the limitations! • All modeling is based on inventories of air toxics released into the environment • Large-scale industries must report releases to the government, but what if they are inaccurate? • Small-scale industries are not required to report releases No amount of technical modeling will allow us to overcome inaccurate reporting

14. Remember the limitations! • The assumptions going into any modeling procedure are very numerous • The precision for any GLC estimate in any area is likely to be low • Exposure data to use are “low,” “medium,” and “high” rather than actual concentrations of air toxics Emissions (inventoried) Concentrations (modeled GLCs) GLC grid layer

15. 2. Partnering with private-sector data sources • Kaiser-Permanente is a very large private source of health services in Alameda County, California • In 2001, they provided services to 577,687 people, or 40% of the County population • Sample of patients is broadly representative of County population

16. Income group representation of Kaiser vs. General Population

17. Asthma-related health utilization • 577,687 Kaiser Permanente patients in Alameda County in 2001 • 587 hospitalizations • 2,694 emergency room visits • 51,087 outpatient visits • 218,205 prescriptions for asthma medications

18. EPHT Process: Discussion • Data visualization • As an analytic tool • For communication with community stakeholders

19. Geocoding health outcome data • Geocoding assigns to every address record: • The postal code of residence • The tract and block group usedby the US Census • x and y coordinates y x

20. Preterm singleton births by postal code: Alameda County, 2001

21. Problems with Postal Code maps • Small communities with very high or low rates do not show up within postal codes • Crossing the street from one postal code to another should not appear to take you from one level of risk to another • Some large codes have very few people living in them • Solution: “smoothed” maps based on geocoded address data

22. Smoothed maps • Ignore the postal code (or any other) boundaries • Calculate small area rates at regular intervals.

23. Preterm singleton births: Alameda County, 2001

24. Statistical significance on “smoothed surface” maps • Difficult to indicate which regions of map have statistically significant variations in preterm birth • Overlapping and adjoining circular areas violates assumption of independence of rates (spatial autocorrelation) • Must use Monte Carlo simulation approach to assess “real” distribution

25. Statistical significance on “smoothed surface” maps • Monte Carlo simulation • Assuming uniform distribution of preterm births, generate 1,000 hypothetical preterm birth rate “maps” • At each point can compare the measured rate to the distribution of hypothetical rates

26. Statistical significance on “smoothed surface” maps • Monte Carlo simulation • For p<0.05, expect about 5% of measured rates to appear “significant” • Therefore, this is a test to locate significant rates that you assume exist somewhere in the County • For health conditions with well established disparities in the United States (preterm birth, asthma) this assumption (of the existence of these significant rates) is valid

27. Elevated preterm singleton birth rates: Alameda County, 2001

28. Principle Investigator Paul English, PhD MPH Research Director Geoff Lomax, DrPH IT/GIS Manager Craig Wolff, MS Eng Administration Mailie Newman Community Health Education Michelle Wong, MPH Mimi Johnson, MPH Eddie Oh, MPH University of California Center for Excellence Jonathan Balmes, MD Ira Tager, PhD Amy Kyle, PhD 谢谢! Funding Centers for Disease Control and Prevention, Environmental Public Health Tracking Program