apheo pres may2011 ejh
DESCRIPTION
daedaTRANSCRIPT
Small area mapping, spatial analysis
and public health
Building on the
Ontario Health and Environment
Spatial Surveillance (OHEIS)
ProjectProject
Eric J. Holowaty,Professor,
Dalla Lana School of Public Health,University of Toronto.
May 15, 2011.
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Geographic Information Systems
and Population Health
� interest and use of GIS
� computing power and software availability
� georeferenced data
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� georeferenced data
� rapid hazard appraisal and more granularity in community health profiling
� advances in spatial analysis
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How can GIS help Public Health?
Research, Surveillance and Planning� Hypothesis gen./testing – maps, correlations, clusters
� Spatial and S–T models of disease risk
� Service planning and optimisation
� Making predictions e.g. Health Impact Ass’t
Spatial Decision Support SystemsSpatial Decision Support Systems� Infrastructure – roads, towns, HC services/avail.
� Census – population statistics; socio-demographics
Emergency/Pandemic Response Systems� 911 services
� Disease/event registers, incl. infectious diseases
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Deprivation
Census
geography
Area
classification
Popn
counts/estimates
Cancer
Deaths
Births
Hospital admissions
Congenital anomalies
Stillbirths and perinatal
deaths
Health
event
data
Oracle/AccessOracle/Access, GIS
Postcode EA/DA,CT
Link data
Postcode – EA/DA
Integrated GIS
Name
Street address
Postal code
Municipality
Historic Pop’n
Res. Files Census
data
(1986, 1991,
1996, 2001,
2006)
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Environmental and
geographic data
Roads, Railways,
Rivers
Road Traffic
Chemical Release
Radiation release or
exposure
Locations of refineries,
incinerators, dumps
Water supplies
Oracle/Access, GIS
Oracle/Access Postcode – EA/DA
CT, CMA/CA
CSD, CD boundaries
airborne
waterborne
foodborne
soil
Pathway Analysis
and
Exposure Modeling
Smoking
Screening ConfoundersTools
Methods
RIF
ArcGIS
R
WinBUGS
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Estimating Disease Risk in Small Areas
� Small areas with counts of 0 or 1 produce highly variable/implausible SIRs.
� Spatial dependence : areas close together have similar risks.
� Detecting areas at truly higher risk:
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� Detecting areas at truly higher risk:� Must allow for uncertainty due to low counts;
� Use spatial dependence to pool info. from neighbouring areas.
� One solution: Hierarchical random effects models : actual risk is unobserved, and case counts are Poisson distributed.
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Deterministic model
Stochastic uncertainty
Fixed vs. Random Effects
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And now, with some trepidation: a Bayesian Mapping Model!
From Besag, York and Mollie, 1991.
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Markov Chain Monte Carlo Sampling
How to solve such a complex hierarchical model?
Area of Circle (est.) = Area of Square X dots inside circle
all dots
(20X20) X 39 = 31210 cm.
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From: Brighton Statistical and Data Services. Accessed at:
http://www.brighton-webs.co.uk/montecarlo/concept.asp
(20X20) X 39 = 31250
Area of Circle (exact) = ππππr2
(3.1416) X 102 = 314.16
10 cm.
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What if the formula for the area of
a circle was unknown?
Consider 10 simulations
of 10,000 dots each
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Consider 100 simulations
of 10,000 dots each
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Fundamental Spatial Surveillance Questions
� What is the risk of PH outcomes among residents of a particular geographic area or neighbourhood?
� Are there areas of unusually high (or low) risk?
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� Are there areas of unusually high (or low) risk?
� Is the observed pattern of risk similar to the pattern of known RFs and other antecedent determinants?
From Pickle, 2002.
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CDNAME Rate SIR Obs
Algoma District 73.29 1.21 571
Brant County 65.74 1.08 443
Bruce County 57.78 0.96 253
Chatham-Kent Division 62.06 1.02 386
Cochrane District 84.52 1.41 370
Dufferin County 59.93 0.98 128
Durham Regional Municipality 62.11 1.03 1329
Elgin County 67.67 1.11 299
Essex County 69.67 1.15 1330
Frontenac County 70.97 1.17 566
Greater Sudbury Division 81.78 1.36 714
Grey County 55.94 0.91 341
Middlesex County 61.45 1.02 1283
Muskoka District Municipality 65.51 1.08 246
Niagara Regional Municipality 62.25 1.03 1646
Nipissing District 75.70 1.25 367
Northumberland County 76.79 1.27 404
Ottawa Division 63.80 1.06 2350
Oxford County 54.75 0.90 311
Parry Sound District 70.43 1.15 208
Peel Regional Municipality 46.97 0.76 1710
Perth County 58.45 0.96 240
Peterborough County 69.16 1.14 589
Tabular Summary of
Rates and SIRs
Grey County 55.94 0.91 341
Haldimand-Norfolk RM 68.46 1.13 421
Haliburton County 74.45 1.19 101
Halton Regional Municipality 49.39 0.81 919
Hamilton Division 64.52 1.07 1767
Hastings County 83.38 1.38 657
Huron County 57.20 0.93 222
Kawartha Lakes Division 74.41 1.21 365
Kenora District 69.76 1.14 195
Lambton County 73.75 1.22 579
Lanark County 75.03 1.24 281
Leeds and Grenville United Cnt. 74.42 1.23 462
Lennox and Addington County 67.88 1.13 164
Manitoulin District 52.18 0.89 45
Peterborough County 69.16 1.14 589
Prescott and Russell United Counties 85.54 1.43 314
Prince Edward Division 63.00 1.02 120
Rainy River District 74.36 1.23 98
Renfrew County 71.47 1.18 417
Simcoe County 69.92 1.16 1399
Stormont, Dundas and Glengarry 84.88 1.39 571
Sudbury District 71.99 1.14 92
Thunder Bay District 74.07 1.23 622
Timiskaming District 102.7 1.71 231
Toronto Division 50.23 0.83 6626
Waterloo Regional Municipality 52.77 0.88 1063
Wellington County 52.52 0.87 493
York Regional Municipality 47.07 0.76 1405 11
Disease Mapping
Hamilton Census
Division
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Hamilton Steel & Iron Industry
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Disease Mapping: Female Lung Cancer
in Hamilton
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Disease Mapping:
Male Lung Cancer
in Hamilton
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SENES Approach to Dispersion
Model
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Risk Analysis: Modeled
Concentration Exposure Bands
Males - Unadjusted and Adjusted
1.75
Females - Unadjusted and Adjusted
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1.25
1.5
1.75
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SIR Females - unadjusted
Females - adjusted
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0.5
0.75
1
1.25
1.5
0 0.02 0.04 0.06 0.08 0.1
Average Concentration (ug/m3)
SIR
Males - unadjusted
Males - adjusted
0.5
0.75
0 0.02 0.04 0.06 0.08 0.1
Average Concentration (ug/m3)
Homogeneity χ2 p value Linearity χ2 p value Homogeneity χ2 p value Linearity χ2 p value
Males unadjusted 79.72 <0.0001 75.06 <0.0001 77.61 <0.0001 66.44 <0.0001
adjusted for QAIPPE 26.63 <0.0001 21.31 <0.0001 21.73 <0.0001 17.41 0.0006
Females unadjusted 67.79 <0.0001 65.40 <0.0001 54.51 <0.0001 31.26 <0.0001
adjusted for QAIPPE 36.38 <0.0001 33.45 <0.0001 22.47 <0.0001 5.85 0.02
Dispersion Model Distance as a Proxy for Exposure
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Thyroid cancer inwomen living in the
Greater Toronto area2004-2008 Raw SIRs
998 CTs
Overall SIR=1.18
4705 cases
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Thyroid cancer inwomen living in the
Greater Toronto area2004-2008
Smoothed SIRs
SaTScan cluster 243 CTs
Overall SIR=1.46
1582 cases
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Systematic approach to identifying “hot spots”
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WDG Lung Cancer in males 1999-2003
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WDG Lung Cancer in males 1999-2003
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WDG Prostate cancer 1999-2003
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WDG Prostate cancer 1999-2003
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Special challenges
� Accuracy, granularity and completeness of exposure,
health and population data, and boundary files
� Geocoding, i.e., accurately assigning spatial
coordinates to location info.
� Current place of residence may not be good proxy for
exposureexposure
� Problems adjusting for known confounders
� Necessity of using aggregated counts
� Scale/zone translation problems (MAUP)
� Spatial autocorrelation
� Data access and confidentiality restrictions
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Future enhancements
� Quality, timeliness and accessibility of georeferenced data
� Faster MCMC simulations
� Spatio-temporal analysis
� Modeling small area estimates of RF and service utilization
� Comparing similar maps – beyond visualization
� More flexibility within RIF
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� Effective stakeholder engagement
� Preparatory discussions/training; user readiness surveys
� Policy/legs./regs. changes may be required
� Significant “up front” work in data
Moving Forward - Key Issues
enhancement & harmonization
� Imp’t distinction between levels of complexity
- rapid surveillance vs. designed research
� Varying levels of stakeholder “readiness”
� technical infrastructure, data sharing, data discovery, stat. methods
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Resources• “The GIS Primer” web-based
http://www.innovativegis.com/basis/primer/primer.html
• “Health and Environment Information Systems for Exposure and Disease Mapping and Risk Assessment” –mini-monographs
Jarup et al. Environmental Health Perspectives. June 2004.
Vol. 112: 995-1045.
Elliott et al. Environmental Health Perspectives. Aug 2008. Vol. 116: 1098-1130.Vol. 116: 1098-1130.
• “GIS and Public Health”
Cromley EK and McLafferty SL. Guildford Press. 2002.
• “Feasibility and utility of mapping disease risk at the
neighbourhood level within a Canadian public health unit”
Holowaty et al. Inter. Journal Health Geogr. May 10, 2010.
http://www.ij-healthgeographics.com/content/9/1/21
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Resources (cont’d)• “Spatial Epidemiology : Methods and Applications”
Elliott P. et al. Oxford University Press. 2000.
• “Applied Spatial Statistics for Public Health”
Waller LA and Gotway CA. Wiley Interscience. 2004.
• “Geographic Information Systems and Public Health”
Richards TB et al. Public Health Reports Vol.114.1999.
http://www.healthgis-li.com/library/phr/phr.htm
• “Public Health and GIS”• “Public Health and GIS”
Rushton G et al. Annual Review of PH. Vol.24.2003.
• “Putting People on the Map : Protecting Confidentiality with Linked Socio-Spatial Data”
Gutmann MP et al. National Research Council. 2007.
http://books.napedu/catalog/11865.html
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For more information about the RIF, contact:
Dr. Judy Qualters, Chief, EPHTN Branch, CDC
Ms. Gonza Namulanda, Informatics, EPHTN, CDC
E-mail: [email protected]
Website: www.cdc.gov/nceh/tracking
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