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Going Beyond GIS for Environmental Health

Going Beyond GIS for Environmental Health. Frank C. Curriero fcurrier@jhsph.edu Environmental Health Sciences and Biostatistics Bloomberg School of Public Health EnviroHealth Connections Summer Institute 2006. Bio. Joint appt. in Env Health Sci and Biostatistics PhD in Statistics

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Going Beyond GIS for Environmental Health

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  1. Going Beyond GIS for Environmental Health Frank C. Curriero fcurrier@jhsph.edu Environmental Health Sciences and Biostatistics Bloomberg School of Public Health EnviroHealth Connections Summer Institute 2006

  2. Bio • Joint appt. in Env Health Sci and Biostatistics • PhD in Statistics • Research agenda is spatial statistics Statistics Geography (GIS) Env Health Spatial Statistics

  3. Objectives • Provide exposure to the field of spatial statistics. • Keep it simple (non-technical) • Applications of GIS in Environmental Health • Beyond GIS, maps make you think/question • Current research topics • Geography (location) is a source of variation worth • considering in environmental health investigations.

  4. What is Spatial Statistics? Statistics for the analysis of spatial data “spatial” “geographic” What is Spatial Data? The “where” in addition to the “what” was observed or measured is important and recorded with the data. Location information (the “where”) can vary. What is GIS? Stands for Geographic Information System Anything more depends on who you ask!

  5. What is a GIS? One word def: Database Two word def: Visual Database Visual database for geographic data • Stores • Manipulates • Analysis • Queries • Creates • Displays . . . . MAPS “Layer cake of information”

  6. What else: - A computer system (piece of software) with a tremendous amount of capability for storing, querying, combining, presenting, . . . , spatial data. - GIS is designed specifically for spatial data and hence built to handle all of its complicated features. - GIS is a generic name like word processor. ArcGIS, MapInfo, Idrisi are examples of different GIS. - The earth does not have to be the backdrop for every GIS application, but certainly most common.

  7. What else (cont.) - Public health was not the first and probably not be the last application of GIS and spatial statistics. - GIS as a mechanism for generating hypotheses (exploratory spatial data analysis). - GIS is a tool, a very powerful and valuable tool when working with spatial data.

  8. Applications in Spatial Statistics and GIS • Waterborne disease outbreaks • DDE soil contamination • Lyme Disease • Prostate cancer mapping • Chesapeake Bay water quality assessment

  9. US Waterborne Disease Outbreaks, 1948-1994 Outbreak Data Location Longitude Latitude Month Year AL, Anniston -85.83 33.65 Oct 1953 AL, Center Pt. -86.68 33.63 Nov 1958 WY, Cody -109.06 44.53 July 1986 . . . . . . . . .

  10. US Waterborne Disease Outbreaks, 1948-1994 Substantive Questions Do outbreaks occur at random across the US? Are outbreaks preceded by extreme precipitation events? Does the risk of an outbreak vary spatially and related to watershed vulnerability?

  11. Objective: Association between extreme prcip. and outbreaks Methods: Overlay map of outbreaks and extreme precip events 2,105 watersheds (USGS) 16,000+ weather stations (NCDC) define extreme precipitation aggregate precip and outbreak to watershed Results: 51% of outbreaks were coincident with extreme levels of precip within a 2 month lag preceding the outbreak month. Conclusion: Is this evidence of an association?

  12. 16,000+ Weather Stations Reporting Monthly Precipitation

  13. 2105 US Watersheds

  14. US Waterborne Disease Outbreaks, 1948-1994 Results: 51% of outbreaks were coincident with extreme levels of precip within a 2 month lag preceding the outbreak month. Conclusion: Is this evidence of an association?

  15. US Waterborne Disease Outbreaks, 1948-1994 • Map generation included many involved GIS tasks • on numerous data sources, GIS Spatial Analysis. • Statistically speaking though it represents risk • factor data. • Spatial statistics often considers the map as a • starting point, which in GIS is often an endpoint.

  16. Western Maryland Superfund Site DDE Soil Sample Data Sample # Easting Northing DDE (ppm) 1 1108420 725173 160 2 1108300 725378 4 110 1108490 725038 92 . . . . . . . . .

  17. Substantive Questions Does the site exceed regulated levels of DDE contamination and in need of remediation? What is the level of DDE in my backyard?

  18. Kriged DDE Predictions Kriging: Spatial prediction at unsampled locations based on data from sampled locations. Environmental health applications of kriging exposure maps

  19. Baltimore County Lyme Disease: 1989-1990 Lyme Case Lyme Control Lyme Disease Cases and Controls Cases Controls Longitude Latitude Longitude Latitude -76.4047 39.3421 -76.4054 39.3419 -76.3433 39.3736 -76.3522 39.3718 -76.7592 39.3265 -76.7665 39.3119 . . . . .

  20. Baltimore County Lyme Disease: 1989-1990 Lyme Case Lyme Control Substantive Questions Do cases of Lyme Disease tend to cluster, generally or as localized “hot spots?” Does risk of Lyme Disease vary spatially over Balt. County? Identify and quantify environmental risk factors associated with Lyme Disease.

  21. Baltimore County Lyme Disease Risk: 1989-1990 Spatial Case/Control Analysis • Spatial density estimate of cases divided by spatial density • estimate of controls (nonparametric kernel approach). • Logistic regression approach to include covariates.

  22. Statistical Methods Exist to Address • Do cases (events) show a tendency to cluster? • Identifying “clusters” or “hot spots.” • Does risk of disease (or outcome of interest) vary • spatially? • Is disease risk elevated near a particular point • source? • Spatial prediction of outcomes at unobserved • locations. • Risk factor estimation in the presence of residual • spatial variation.

  23. Types of Spatial Data 1. Geostatistical Data Basic structure is data tagged with locations. Locations can essentially exist anywhere. Referred to as continuous spatial variation. Example:MD Superfund Site DDE

  24. 2. Point Pattern Data Locations are the data denoting occurrence of events. Common to aggregate to area-level data. Example: Baltimore County Lyme Disease Cases Baltimore County Lyme Disease Controls 3. Area-level Data Data summarized to an area unit. Rarely arises naturally. Often an aggregate form of point pattern data. Referred to as discrete spatial variation. Example: Maryland prostate cancer by zip code

  25. Why Collect Locations as Part of Data? • Sometimes locations are the only data (as in point patterns). • Risk (or outcome of interest) may vary spatially. • Location can serve as an information gatewayto other • linked data sources: environmental • demographic • social • etc. • Data are spatially dependent and locations are used in • statistical methods that account for this dependence. • In general things can vary spatially and geography (location) • maybe a source of variation worth considering.

  26. Temporal Dependence • Time series or longitudinal data. • Past/present direction inherent in temporal data. Spatial Dependence • Dimensions > 1 and loss of directional component. • Observations closer together in space are more • similar than observations further away (clustering). “in space” “on the earth”

  27. Spatial Dependence (clustering) in Environmental Health Data Could be due to: • A contagious agent of the outcome under • investigation. • The spatial variation in the population at risk. • An underlying shared environmental characteristic, • measured or unmeasured, that also varies spatially • (Shared Environment Effect).

  28. What GIS is Not • A complete system for statistical or scientific inference. • Maps, most basic and fundamental concepts in GIS, • are not statistical inference. • A GIS map of • one variable is analogous to a histogram display • two variables overlayed is analogous to an x-y • scatterplot or 2x2 table. • In statistics we go beyond histograms and • scatterplots.

  29. An Important Distinction In the GIS literature analysis or spatial analysis often means spatial data manipulation which is something different than statistical analysis.

  30. Two Current Research Problems in Spatial Statistics and GIS Non-geocoded Data Non-Euclidean Distance

  31. Geographic Analysis of Prostate Cancer in Maryland PI: Ann Klassen (HPM & Oncology) Collaborators: Margaret Ensminger, Chyvette Williams, JeanHeeHong (HPM) Frank Curriero (Biostat), Anthony Alberg (Epi) Martin Kulldorff (Harvard), Helen Meissner (NCI) Cooperative Agreement from Association of Schools of Public Health and Centers for Disease Control Data Agreement with the Maryland Cancer Registry One of six CDC projects investigating geography and prostate cancer, including NY, CT/MA, NJ, Kansas/Iowa, and Louisiana.

  32. Prostate Cancer Reported to MD Cancer Registry 1992-1997 Proportion of an Outcome of Interest * Legend No Data 0 - 12 13 - 30 31 - 67 68 - 100 * All geocoded cases Outcomes of Interest Include • Incidence • Stage at diagnosis • Tumor grade at diagnosis • Failure to stage or grade • Treatment and mortality

  33. * Proportion of an Outcome of Interest Legend No Data 0 - 12 13 - 30 31 - 67 68 - 100 * All geocoded cases

  34. What is Geocoding? GIS process of translating mailing address information to coordinates on a map, such as with longitude and latitude 16 Goucher Woods Ct Towson, MD 21286 (-76.5883, 39.4005) Nongeocoded Data Mailing addresses that could not be geocoded 8123 Rose Haven Road Rosedale, MD 21237 Nongeocoded

  35. Reasons for Nongeocodes Address error PO Box Rural routes Base maps out of date

  36. Legend 0 - 8 9 - 12 13 - 30 31 - 67 68 - 100 Proportion of Outcome of Interest Geocoded Cases (15,585) Legend No Data 0 - 12 13 - 30 31 - 67 68 - 100 All Cases (17,091)

  37. Statistical Issues (1) Common to just ignore nongeocodes What's the Consequence? Historically not well documented in publications (2) Level of aggregation for analyses? Zip code level Census tract, county, etc.

  38. Statistical Issues (cont.) (3) Nongeocodes represent missing data and most likely not missing at random MD Prostate Cancer Proportion of NonGeocodes % Nongeocoded 0 - 9 10 - 25 26 - 47 48 - 75 76 - 100

  39. Statistical Issues (cont.) (3) Nongeocodes carry plenty of information Known Information(fictitious example) Age = 72 Race = White Year of Diagnosis = 1991 Stage at Diagnosis = Late Tumor Grade = Aggressive Zip Code = 21237

  40. Statistical Solutions (a) Impute a location for nongeocodes Determine the age-race distribution within known zip codes Weighted random selection based on known age and race Sampling with and without replacement Multiple imputation to assess bias (Joint work with Ann Klassen, HPM) (b) Develop statistical models for outcomes at different levels of aggregation Spatial variation in risk model for geocoded household level data and nongeocoded zip code level data (Joint work with Peter Diggle, Biost)

  41. Chesapeake Bay Water Quality Assessment Data Temperature Turbidity Dissolved Oxygen Chlorophyll a Needed Assessments at unsampled locations

  42. Kriging A spatial regression method that provides optimal prediction at unsampled locations. Kriged predictions are weighted averages of sampled data, higher weights given to data closer to the prediction site. Proximity is measured by the straight line Euclidean distance (“as the crow flies”).

  43. Chesapeake Bay Fixed Station Data Euclidean distance may not be appropriate. Propose a water metric Currently kriging only works for Euclidean distance. New methods needed.

  44. Closing Remarks • GIS for spatial database management and • hypothesis generation (posing the questions) • Spatial Statistics for inferential methods • (answering the questions) • Why consider location • Scientific inference may depend on it • Gateway to environmental data • Source of variation worth considering • Biography and Geography of Public Health

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