Tracking: Success through Partnerships

1. Tracking: Success through Partnerships Evelyn Talbott, Dr. P.H. University of Pittsburgh Dan Wartenberg, Ph.D, University of Medicine and Dentistry of NJ. Lu Ann White, Ph.D. Tulane University Jennifer Mann, Ph.D. John Balmes, MD University of California at Berkeley Academic Partners for Excellence in Environmental Public Health Tracking

2. Academic Partner for Excellence in Environmental Public Health Tracking

3. Importance of Collaborations Academic Partner for Excellence in Environmental Public Health Tracking

4. University of Pittsburgh Transfer of asthma ER Data from a Hospital System to a Local Health Department Evelyn O. Talbott, Dr.P.H., M.P.H LuAnn L. Brink, Ph.D. Academic Partner for Excellence in Environmental Public Health Tracking

5. Asthma Expected Achievements • Innovative, cost-effective surveillance strategy • Near real-time surveillance • Manageable amount of information (~50 asthma cases/day) • Based upon a diagnosis, not free-text • Mechanism to collect other information of public health importance Academic Partner for Excellence in Environmental Public Health Tracking

6. What Can Be Achieved with Surveillance • Asthma Trends over time by zipcode, county, CT ( i.e. coke oven closes, new technology is put in place, etc.) provide a baseline from which to follow • To become alerted concerning a putative hazard in the environment • Identify high risk groups for further follow up and public health measures

8. Cooperation • Data provided to Allegheny County Health Department as part of Public Health Surveillance • Data provided by the University of Pittsburgh Medical Center to improve Public Health • Only de-identified data will be provided to investigators

9. Via site-to-site VPN, • UPMC will send: • patient name, • SSN, • race • date of visit, • time of visit, • address, • date of birth, • gender, • type of insurance, • Chief complaint • ICD9 diagnosis code • disposition • For all ED visits with a final diagnosis code of 493.x from Allegheny County hospitals Matched respiratory data ACHD will purge all ED data that do not match a corresponding discharge code of ICD 9: 460-520 Irrelevant data see full talk Wednesday at 4:00PM

10. iSOVAT Spatial OLAP Visualization and Analysis Tool Bambang Parmanto, PhD Ravi Sharma, PhD Evelyn O. Talbott, Dr.P.H., M.P.H University of Pittsburgh In collaboration with : Cliff Mitchell, M.D. John Braggio, PhD Maryland State Dept of Health Academic Partner for Excellence in Environmental Public Health Tracking

11. Interface Academic Partner for Excellence in Environmental Public Health Tracking

12. iSOVAT • Provides linkage and integration of data sets from various sources, including spatial data (for example, industrial or mining locations, rivers and lakes), health data (e.g., cancer registry), and demographic data (e.g., population, age structure, income). • iSOVAT is capable of integrating all these complex data sets into a multidimensional database that can be viewed easily from multiple angles and in visual forms (maps and charts). Academic Partner for Excellence in Environmental Public Health Tracking

13. Potential collaborations • Collaborations with Maryland DOH to provide stand alone package with following capabilities: • Provide socio-demographic information by state, county, Zip code, etc as needed • Show age specific and both crude and age adjusted rates (95% CI) of outcomes • Provide layering of environmental variables and health outcomes Academic Partner for Excellence in Environmental Public Health Tracking

14. An Example of Numbers of Inpatient asthma and MI primary admissions by County Academic Partner for Excellence in Environmental Public Health Tracking

15. Maryland Portal • Will link health outcome (asthma and MI) with environmental (PM2.5), demographic, and socio-economic data. Inpatient counts due to asthma and MI: male and female by County

16. Inpatient counts due to asthma and MI: by race by County

17. Jennifer Mann, Ph.D.John Balmes, MD University of California, Berkeleyin collaboration with Tim Tyner, MS and Fresno Unified School District School-based asthma surveillance Academic Partner for Excellence in Environmental Public Health Tracking

18. School-based asthma surveillance Academic Partner for Excellence in Environmental Public Health Tracking • School-based classroom survey • Given to all 7th and 9th graders in Fresno • Assesses symptom level among diagnosed • can be used to triage level of and need for intervention • Both prevalence and severity level could be used for EPHT • Identifies “possible” asthmatics = undiagnosed who report severe asthma symptoms

19. School-based asthma surveillance • Project also develops models that are low-cost and do not violate school privacy law (FERPA) • FERPA does not have public health exclusion so researchers can not collect or enter data from schools • Use 12th graders in FUSD who are part of a “Medical Academy” to introduce survey and answer questions • After data processing by UCB, students do their own data analysis for report to FUSD. • Mapped 7th grade data at “city block” level in preparation for linkage with modeled pesticide use data for EPHT • FUSD sent out letter giving everyone right to refuse the mapping step to be compliant with FERPA

20. School-based asthma surveillance • In CT, survey results compared to “nurse-based” surveillance • Additional children were identified by the survey as asthmatic • Currently being tested in Massachusetts by tracking partners

21. Tulane Center for Applied Environmental Health CDC Tracking Conference February 25, 2009

22. Health and Air Quality Tulane – Missouri partnership Demonstration project examining statistical methods for linking hospital discharge data and EPA air quality monitoring data in Missouri. Link PM2.5 data with health indices (MI, COPD, and Asthma) and develop a predictive model for these health indices Data sources Missouri Hospital Discharge data (2001-2005) EPA air monitoring data for PM2.5 and Ozone downloaded from AQS Data Mart (2001-2005)

23. Tulane - Missouri Missouri facilitated obtaining the hospital data Missouri IRB and other state approvals Data access and pulling required fields Tulane is conducting the analyses including: Developed algorithm to interpolate missing sample values Tested methods to assign exposure - distance between the health endpoint and air monitor The PM2.5 value was adjusted for distance from monitor by dividing observed PM2.5 value/distance from monitor. Methods of analysis: Time series analysis, Poisson regression analysis and Case Crossover analysis using conditional logistic regression.

24. Gateway Project “Proof of Concept” partnership between Florida, Missouri, Washington and Tulane EPHT demonstration project to utilize PHIN-MS nodes to actually network EPHT sites to each other and a "central repository Goals: Test the design and mechanics of the EPHTN Node Package Test the effectiveness of EPHT metadata template search tools

25. Gateway Project Partners gained experience creating, exchanging, and searching Tracking-based metadata Created a resource for grantee portal Metadata and Data Repositories for grantee data not on the National Portal Metadata search and data retrieval for grantee-housed data Created a multi-node PHIN-MS-based network to allow bi-directional data sharing between multiple partners

26. In collaboration with several states: Washington, Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York Using Routinely Collected Surveillance Data: Studies of Births and Environmental Exposures Daniel Wartenberg, PhD. University of Medicine and Dentistry of New Jersey Academic Partner for Excellence in Environmental Public Health Tracking

27. Overall Strategy • Adverse Birth Outcomes/Air Pollution as a Model for EPHT • Relevance: • Health People 2010: Births an indication of the nation’s health • Use routine surveillance (tracking) data/methodology • Develop collaboration among multiple institutions • Addressing cutting edge issues • Role of exposure misclassification in interpretation of results • Consideration of alternative proxies for exposure • Suggest application of approach to other issues in environmental epidemiology • Studies Underway • 1: 6-state collaboration • 2: In depth assessment of exposure assessment issue Academic Partner for Excellence in Environmental Public Health Tracking

28. The Collaborative Project:6-State Study of Air Pollution and Births • Goal • Demonstrate issues in multistate collaboration (6 NE states) • Address local/regional/cross border concerns • Process • Request data separately from each state • Analyze individually and jointly • Assess local and regional patterns and impacts • Use of 6 states’ data Increases sample size (power) and relevance • Status • Analysis of 2 states underway • Additional data in request process Academic Partner for Excellence in Environmental Public Health Tracking

29. Air Pollution, Birth Outcomes and Maternal Change of Residence • Goal • Assess frequency and consequences of mother’s change in residence during pregnancy • Process • Use mobility data recorded on Washington birth certificate • Compare birth outcomes of stable vs. mobile mothers with respect to air pollution exposures, adjusted for known risk factors • Results • Stable mothers show stronger associations • May be due to exposure misclassification, sociodemographics of movers, stress of moving and other factors (see full talk Thursday at 8 AM Track 2a)

30. Future Goals of Academic Partners • Overall strategic goal of the academic partners as we move forward in implementation: • Development of more “real time” Surveillance capabilities (secure portals) to create more effective environmental public health surveillance programs • Provide enhanced software and tools to use and interpret these data in a meaningful way • Carryout sensitivity analysis of secondary data sources (eg. Birth certificates )for their use in the tracking network. Academic Partner for Excellence in Environmental Public Health Tracking

31. Thank you Academic Partner for Excellence in Environmental Public Health Tracking