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

2

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: EPHT@cdc.gov

Website: www.cdc.gov/nceh/tracking

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