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Comprehensive Assessment of Rural Health in Iowa (CARHI)

Comprehensive Assessment of Rural Health in Iowa (CARHI). A Partnership Between: The University of Iowa The Iowa Department of Public Health The Centers for Disease Control and Prevention. Rural Environmental Health Surveillance. CARHI Staff:

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Comprehensive Assessment of Rural Health in Iowa (CARHI)

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  1. Comprehensive Assessment of Rural Health in Iowa (CARHI) A Partnership Between:The University of IowaThe Iowa Department of Public HealthThe Centers for Disease Control and Prevention Rural Environmental Health Surveillance CARHI Staff: Kelley Donham, Ken Sharp, Lauren Lewis, Peg Buman, Gerard Rushton, Pete Weyer, Paul James, Eileen Fisher, Meggan Fisher

  2. Why CARHI ? • Lack of Comprehensive Rural Environmental Health Surveillance • New tools are now available to link possible environmental contaminants to health outcomes; • These tools use geographic information systems and geocoded data. • Near Real-Time

  3. Purpose of CARHI • To demonstrate the feasibility of assessing the health effects—if any--of environmental contaminants in a typical rural county. • Provide proof-of-concept for an innovative approach to rural health surveillance. • Innovation is in: • linking existing administrative health records—”health encounters,”—to measured and modeled estimates of environmental contamination; • Close to real-time health surveillance; • database and analysis system in a comprehensive geographic information system.

  4. Health Encounter Database Environmental Exposure Database CAFO’s Toxic Waste Sites Water Quality Sewage Plants Leaking Storage Tanks Components of the CARHI project • Secondary Health Databases

  5. Overview of the CARHI Process--1 • IDPH receives selected ICD9 diagnoses from providers at health care clinics in the pilot county. • UI organizes geospatial data coverages of possible environmental contaminants including positionally accurate geocodes of all residential locations in the county. • UI attaches estimated contaminant measure to the address and readies a masking algorithm for IDPH use that will jitter the measure so that at least k other residence locations have contaminant measures within the range of the mask. • UI organizes spatial data analysis programs so that IDPH can examine disease patterns in relation to environmental exposures. • IDPH receives training and technical support from UI. • IDPH will organize follow-up studies if relationships are suspected. • UI does not have access to personal identifying data from IDPH diagnosis file.

  6. Demonstration of the CARHI GIS Analysis System in Carroll County, Iowa • LAYERS • Streets • E911 Addresses • E911 geocodes and residence geocodes • Towns • CAFOs (confined animal feedlots) • Other Feedlots • Orthophotos at 2 feet and six inch per pixel resolution • Airborne pollutants dispersal Model

  7. Why Carroll County ? • Rural • Varied Environmental Exposures • 84% of Residents get Health Care in County • Excellent Support from County Health Department • Cooperative Health Care Community

  8. Iowa: Primary Care Service Areas—proportion of primary care patient visits made inside the local service area Sioux Center Hampton Maquoketa Carroll proportion Manning Miles Source: data files from pcsa.hrsa.gov See Goodman et al. 2003

  9. Orthophoto map of a portion of Carroll County showing 14 houses and 20 addresses. Each house has a corresponding E911 address point located where the entrance road to the house leaves the public road.

  10. The distance between the E911 address point and the improved point in this case is approximately 955 feet *The orthophoto displayed is at the 2 ft resolution E911 address points ‘Improved’ address points Roads 955feet

  11. Surveillance of Rural Environmental Exposures

  12. Confined Animal Feedlots by Permitted Size

  13. Roads, E911 Addresses geocoded to residence locations, and CAFOs

  14. Yearly Average Wind Rose • National Weather Service cumulative wind rose for years 1987-1991, Sioux Falls, SD

  15. Plume from a single CAFO • Based on EPA Plume Dispersion Model • Values are relative to a unit release at the site • All modelled values are relative measures—no on-site exposure measurements have been made and no remedial mitigation measures have been modelled.

  16. Footprint of Plume Values from One CAFO

  17. Plume from a single CAFO Contamination values for a CAFO of “Unit Size”

  18. CAFO and address data Animal Units = “Size of CAFO” * 9520 enhanced E-911 addresses for Carroll County

  19. Contamination at 9520 addresses

  20. From one to many If contamination can be calculated for 9520 address location, it can be calculated for any location. If each location is a point on a 50 meter grid over Carroll County, we effectively have a visualization of the CAFO plume ‘surface’ over Carroll County.

  21. Contaminant Exposures from Permitted CAFO’s in Carroll County Based on Model

  22. Contaminant Exposures from Permitted CAFO’s in Carroll County Values based on plume model centered on each CAFO and adjusted for number of animal equivalents permitted at the site

  23. Estimated Exposure Values for Carroll County Residence

  24. Illustration of the GIS buffer function for a section of Carroll County. This map shows a section of Carroll County with some addresses and 1250 feet buffers around CAFOs smaller than 1000 animal units (one of the legal categories for distance separation). Buffers 1,2 and 3 have addresses in them , while buffer 4 does not.

  25. Illustration of the GIS buffer function for a section of Carroll County. This map shows a section of Carroll County with some addresses and 1250 feet buffers around CAFOs smaller than 1000 animal units (one of the legal categories for distance separation) overlaid on CAFO plumes. Buffers 1,2 and 3 have addresses in them , while buffer 4 does not.

  26. Cumulative percent of people in Carroll county with estimated relative airborne concentrations from CAFOs For any given contaminant value at an address, need to find the range of contaminant values within which a given number of people live. Percent people Estimated relative measure of airborne contaminants

  27. Locations of private wells in our data This map shows the locations of the 125 wells.

  28. Predicted nitrate concentrations in Carroll County This map shows a surface of predicted nitrate concentrations in Carroll County and 9520 addresses from the CARHI address masterlist. The nitrate concentration surface was obtained by ‘simple kriging’ of nitrate concentration data for 125 private wells in Carroll County.

  29. Analysis System for Relating Health and Environmental Data • UI will design, develop and test analysis system • use GIS functions to assemble health data for specified spatial relationships between environmental exposure/residential location • simulated health encounter data • real environmental data for Carroll County • System will be provided to IDPH • link environmental data to Carroll County health encounter data • Use epidemiologic models to compute statistics • UI will uses analysis system to • link secondary health datasets to environmental data • analyze limited dataset for Carroll County health encounter info

  30. Analysis System for Relating Health and Environmental Data • Descriptive statistics • Frequency distributions • health outcomes by age, sex, geographic area, time of occurrence • environmental data by type, geographic area, time of occurrence • Analytical statistics • prevalence ratios • incidence rates • relative risks • general regression analysis models

  31. Analysis System for Relating Health and Environmental Data • Geographic pattern analysis • Map producing capability

  32. Other Analyses will Compute Indirectly Adjusted Standardized Incidence Rates • In all of a certain kind of spatial buffer areas, how many cases of any particular disease did we observe? • In all of a certain kind of spatial buffer areas, how many cases of any particular disease did we expect? • Observed / Expected = Standardized Incidence Rate.

  33. The privacy protection problem IDPH has access to the individually identifiable health data in Carroll County. UI personnel may not access this data at this level. Example of individual-level health data: ADDRESS ICD9 CODE Environmental Contamination 15 Wood Street 140 5000.3241 This presentation describes a privacy protection method that allows data users to investigate the relationship between environmental exposure and health effects without accessing individually identifiable health data.

  34. The solution 1. IDPH uses the HIPAA law for public health surveillance to access health encounter data with health provider consent . It sends UI health encounter addresses (Just Addresses). 2. UI calculates environmental contamination at each address and sends the addresses with attached contamination values to IDPH. 3.IDPH then uses a privacy protection tool provided by UI to mask the contamination values.

  35. The solution-2 4. IDPH attaches the masked environmental contamination values for each health encounter address, to its corresponding ICD9 code. 5. IDPH strips off the addresses and sends UI just the masked contamination value at each address and a corresponding ICD-9 code. 6. UIcarries out analyses on this masked contaminant attached health data.

  36. 1.What health encounter data has IDPH collected? • Selected ICD-9 codes of reasons for clinic visit from: a) McFarland Clinic :  22,000 encounter addresses b) St Anthony’s Clinic :  50,000 encounter addresses •  Around 6,000 unique addresses. (Each address can be represented many times in the health encounter addresses by multiple persons at the same address or by the same person making a repeat visit)

  37. 2. UIcalculates environmental contamination at each addresses. E911 address points ‘Improved’ address points Roads A. UI has 9520 E911 Addresses geocoded to residence locations.B. UI has 55 CAFO locations. 955feet

  38. 2a. UI uses a plume dispersal model and a spreadsheet program to evaluate contamination at each 9520 E-911 addresses

  39. Illustration of health encounter data sent by IDPH to UI

  40. 2b. UI attaches the calculated contamination value to each health encounter address. UI matches addresses in the health encounter data from IDPH to the 9520 enhanced E-911 addresses, and thus links the calculated contamination value, to these addresses.

  41. 3 IDPH then uses a privacy protection tool provided by UI to mask the contamination values. Why use a mask? Supposing IDPH does not mask the contamination value and proceeds to step 5 – sends the data ( contamination values and ICD-9 codes for health encounter addresses to UI)

  42. 4. IDPH attaches the unmasked environmental contamination values for each health encounter address, to its corresponding ICD9 code.

  43. 5. IDPH strips off the addresses and sends UI just the contamination value at each address and a corresponding ICD-9 code. This is what the UI receives But…………..

  44. 5.IDPH attaches health data and returns to UI High precision contaminant values give away the addresses the contaminant values belong to!

  45. ….Hence, the need for a mask Overall strategy: • There are various masking procedures for the kind of data we have. Some of them are rounding, swapping, suppression and random jittering. For our task we use a form of random jittering. • The modeled contamination values are deterministic. Add stochasticity to the predictions, utilizing this stochasticity to mask the original values.

  46. You don’t need X,Y to betray location!

  47. Relationship between health status and contamination is maintained.

  48. Overall strategy:  In reality, exposure measurements always have an element of stochasticity – ‘measurement error.’ It may be said that we are ‘introducing’ measurement error into our exposure estimates. • The ‘error’ that we introduce in the contaminant value for a record is such that the contamination value for that record can be ‘confused’ with at least k neighboring records (in a table sorted according to contamination) where k is an odd integral number the IDPH staff can choose.

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