what is epidemiology? › ... › handouts › lecture06.pdf · epidemiology – disease mapping...

What is epidemiology?

• Part 1. GIS and health geography• Some of the major applications for GIS

• Part 2. Applications of GIS in health research or spatial epidemiology

– Disease Mapping– Cluster Detection– Spatial Exposure Assessment– Assessment of Risk of Disease

• Part 3. Exposure Assessment • Part 4. An example of exposure to PM25 and NO2 in Canada

• A GIS can be a useful tool for health researchers and planners because, as expressed by Scholten and Lepper(1991):

• Health and ill-health are affected by a variety of life-style and environmental factors, including where people live. Characteristics of these locations (including socio-demographic and environmental exposure) offer a valuable source for epidemiological research studies on health and the environment. Health and ill-health always have a spatial dimension, therefore. More than a century ago, epidemiologists and other medical scientists began to explore the potential of maps for understanding the spatial dynamics of disease.

1. Spatial epidemiology

2. Environmental hazards

3. Modeling Health Services

4. Identifying health inequalities

• Spatial epidemiology is concerned with describing and understanding spatial variation in disease risk.

• Individual level data• Counts for small areas

• Recent developments owe much to:• Geo-referenced health and population data• Computing advances • Development of GIS• Statistical methodology

• Population is unevenly distributed geographically.• People move around (day-to-day movements; longer

term movements including migration).• People possess relevant individual characteristics (age,

sex, genetic make-up, lifestyle, etc).• People live in communities (small areas).

• Provides a qualitative answer about the existence of an association (e.g. between environmental variable and health outcome).

• May provide evidence that can be followed up in other ways.

These studies typically involve examining geographical variations in exposure to environmental variables (air, water,

soil, etc.) and their association with health outcomes while controlling for other relevant factors using regression.

• Frequency and quality of population data (e.g. Census every 5/10 years).

• Spatial compatibility of different data sets.• Availability of data on population movements.• Measuring population exposure to the environmental

variable.• Environmental impacts are often likely to be quite small

(relative to, for example, lifestyle effects) and there may be serious confounding effects.

• Cannot estimate strength of an association.• Ecological (or aggregation) bias.

• Allow for heterogeneity of exposure.• Use well defined population groups.• Use survey data to help obtain good

exposure data.• Allow for latency times.• Allow for population movement

effects.

(Richardson 1992)

Hazard Surveillance

• Hazardous agent present in the environment

• Route of exposure exists

Exposure Surveillance

• Host exposed to agent• Agent reaches target tissue• Agent produces adverse effect

Outcome Surveillance

• Effect clinically apparent

GIS: Identify causal and mitigating factors

• A generic index of accessibility/ remoteness for all populated places in non-metropolitan Australia.

• A model which allows accessibility to any type of service to be calculated from all populated places in Australia.

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Metro.

Rural

Remote

Geo

grap

hica

l loc

atio

n

Mortality Rate / 1000 live births

non-AboriginalAboriginal

“Where do infants and children die in WA? 1980-2002” Jane Freemantle, PhD. November 2004

Chart5

Metro.Metro.

RuralRural

RemoteRemote

Aboriginal

non-Aboriginal

Geographical location

Mortality Rate / 1000 live births

17.9

5.9

18.5

6.5

23.9

6.8

infan_Ab status ALL_inf nn pnn

Infant mortality 1980-2002, by Aboriginal status

Aboriginalnon-AboriginalRR

1980-1984258.42.98

1985-1989257.63.27

1990-199420.15.63.61

1995-199716.94.43.83

1998-200116.13.74.4

ALL

infantneonatalpostneonatal

1980-19849.25.53.5

1985-19898.65.43.3

1990-19946.43.62.7

1995-19975.13.22.1

1998-20014.52.71.8

ALL inf neo post mortality 1980-2002

infan_Ab status ALL_inf nn pnn

Aboriginal

non-Aboriginal

RR

Birth year group

Rate/1000 live births

nn pnn_Ad stats

infant

neonatal

postneonatal

Birth year groups


location

NND and PND by Abstatsus 1980-2002

Ab.-neonatenon-Ab.-neonateAb.-postneonon-Ab.-postneo

1980-198415.55.259.723.14

1985-198910.225.0814.732.61

1990-19948.53.4711.922.15

1995-19977.222.959.71.6

1998-20017.42.38.81.3

location

Ab.-neonate

non-Ab.-neonate

Ab.-postneo

non-Ab.-postneo

Birth year groups


ALL

MetroRuralRemote

1980-19848.39.415

1985-19897.98.512.9

1990-19945.87.19.7

1995-19974.95.17.1

1998-20013.7512.3

Ab.metronon-Ab.metroAb.ruralnon-Ab.ruralAb.remotenon-Ab.remote

1980-198418.58.117.48.932.610

1985-198923.27.624.87.4268.7

1990-199419.35.4186.3225.7

1995-199716.64.610.54.720.32.5

1998-200112.13.518.33.918.14.9

RRnon-Aboriginal cf non-Aboriginal

Remote cf metroRemote cf ruralRural cf metro

1980-19841.241.131.1

1985-19891.171.221

1990-19941.10.91.2

1995-19970.60.50.3

1998-20011.41.31.1

RRAboriginal cf Aboriginal

Remote cf metroRemote cf ruralRural cf metro

1980-19841.81.90.9

1985-19891.21.11.1

1990-19941.11.20.9

1995-19971.220.6

1998-20011.511.5

CMR - 1980-2001

Aboriginalnon-AboriginalRR

Metro.17.95.93

Rural18.56.52.9

Remote23.96.83.5

Metro

Rural

Remote

Birth year group


Ab.metro

non-Ab.metro

Ab.rural

non-Ab.rural

Ab.remote

non-Ab.remote

Birth year group


Ab.metro

non-Ab.metro

Ab.rural

non-Ab.rural

Ab.remote

non-Ab.remote

Birth year groups


*

Remote cf metro

Remote cf rural

Rural cf metro

Birth year groups

Relative risk of death

Remote cf metro

Remote cf rural

Rural cf metro

Birth year groups

Relative risk of death

Aboriginal

non-Aboriginal

RR

Geographical location

Rate/1000 live births& RR

Identifying health inequalities:Well-known relationship• 25% – 50% of observed gradient due to risk factors like smoking,

hypertension and diabetes in lower socio-economic groups (Marmot et al.,1997)

• Access to healthcare (Bosma et al., 2005)• Imbalance between workplace demands and economic reward (Lynch

et al.,1997)• Poor education, lower levels of health literacy, low birth weight

(Marmot, 2000)

Relationship may vary with gender, with the association thought to be stronger in males (Thurston, 2005)

• Number of daily hospital discharges (Y) with Ischemic Heart Disease (IHD) where admission had been via emergency room for• 591 postcodes in NSW• Every day from July 1, 1996

to June 30, 2001• Males and females• 5-year age increments

• Denominator (N) obtained from census• Social disadvantage measured at postal area level

using the census-derived SEIFA (Socio-Economic Indexes for Areas) index

High values indicate social advantage

• Part 1. GIS and health geography• Major applications for GIS

• Part 2. Applications of GIS in Health research or spatial epidemiology



“The study of the distribution and determinants of health-related states in populations, and the application of this study to control health problems”.

SPATIAL

• There’s always a ‘where’.• The importance of the ‘where’ varies depending on what

you’re examining.• ‘Where’ is a major way statistics are organized - and can be

further organized, processed and analyzed.

A. Disease Mapping

B. Cluster Detection

C. Spatial Exposure Assessment

D. Assessment of Risk of Disease

Disease Mapping

Statistics Canada

Disease Mapping

The British Columbia Wellness Atlas - http://www.geog.uvic.ca/wellness/

Determinant of Disease Mapping

• Describe patterns of disease.• Explore and analyze spatial patterns.• Portray messages and disseminate information on health and

health determinants.• Hypothesize about possible causal relationships• However, the following needs to be addressed:

• The selection of areal unit (e.g. DAs vs. CTs vs. Health Regions)• Small number problems: Rates calculated from small populations-at-risk

tend to be unstable, because small changes in the count or in the population lead to wide variance in the rate.

• The interpretation of patterns without information on other confounding factors.

B. Cluster Detection

• Clusters are geographically and/or temporally bounded groups of occurrences of sufficient size and concentration unlikely to have occurred by chance.

• Clusters are either related to each other through some social or biological mechanism or they have a common relationship with some other event or circumstance.

• Incidence of Childhood Leukaemia in Northern England 1968-85.

• Openshaw (1987) Geographical Analysis Machine (GAM).

• Cluster detection generally driven by public/media concern

• Use ESRI’s Optimized hotspot analysis tool

S Openshaw et al. 1987. International Journal of Geographical Information Science, 1987 Vol 1, No.4, 335-358

http://sk.sagepub.com/reference/geoinfoscience/n80.xmlhttp://sk.sagepub.com/reference/geoinfoscience/n80.xmlhttps://desktop.arcgis.com/en/arcmap/10.7/tools/spatial-statistics-toolbox/optimized-hot-spot-analysis.htm

• Composition: Similar people tend to aggregate and their shared characteristics explain in part the health and place association (e.g., neighbourhoods).

• Context: Ecological attributes (e.g. physical, social, economical, political) influence whole groups and affect health over and above aggregate individual characteristics.

• Spatial epidemiology provides a framework to examine the influences of space and place on health.

• Space and place can often be used as surrogates for influences on health, for example:– Access to primary healthcare.– Exposure to environmental pollutants.– Social networks and capital.– Physical activity opportunities.

Vinceti et al. International Journal of Health Geographics 2009 8:8 doi:10.1186/1476-072X-8-8

-Exposure through air -Living conditions-Exposure through water

http://www.ij-healthgeographics.com/content/8/1/8/figure/F1http://www.ij-healthgeographics.com/content/8/1/8/figure/F1

• Examined exposure to emissions from a municipal solid waste incinerator and risk of birth defects in a northern Italy community.

• Modelled incinerator emissions for three dioxin exposure levels.

• Mapped congenital anomalies observed over 1998–2006 and matched controls at postal code level.



http://www.ij-healthgeographics.com/content/8/1/8/figure/F1http://www.ij-healthgeographics.com/content/8/1/8/figure/F1http://www.ij-healthgeographics.com/content/8/1/8/figure/F2http://www.ij-healthgeographics.com/content/8/1/8/figure/F2

• No significant increase in total congenital anomalies, or in specific types of anomalies with exposure.

• Odds ratio for congenital anomalies did not decrease during a prolonged shut-down period of the plant (important sensitivity test).

• Evaluated the relationship between the built environment around each participant's place of residence and self-reported travel patterns (walking and time in a car), body mass index (BMI)

Frank et al., (2004) Obesity relationships with community design, physical activity, and time spent in cars . American Journal of Preventative Medicine. 27(2): 87-96.

Derived Exposures


• Land-use mix had the strongest association with obesity (BMI≥30 kg/m2), with each quartile increase being associated with a 12.2% reduction in the likelihood of obesity.

• Strong relationship with time spent in car

• TTHM is a chemical used for cooling, it is also a disinfection by-product (result from the reaction of chlorine with organic matter present in the water being treated)

• Modeled estimates of quarterly TTHM concentrations in water zones

• Linked to one million birth and stillbirth records using maternal residence at time of birth.

Relation of Trihalomethane Concentrations in Public Water Supplies to Stillbirth and Birth Weight in Three Water Regions in England Environmental Health Perspectives • VOLUME 113 | NUMBER 2 | February 2005

Water company supply regions.Water supply-zone-level TTHM exposure categories for each quarter, Severn Trent Water, 1997: (A) January–March; (B) April–June; (C) July–September; (D) October–December.


• Found a significant association of stillbirths with maternal residence in areas with high TTHM exposure.


• What is spatial epidemiology?• The study of the spatial distribution of health-related states and health determinants in

populations.

• Applications of GIS in Health Research?• Disease mapping• Cluster detection• Spatial exposure assessment and the assessment of risk

• Why are space and place important health determinants?• Similar individuals tend to cluster together.• Health determinants (e.g. behaviors, environmental risks, health resources, socio-

economic conditions) vary across space and place.

• Exposure assessment is the study of human contact with chemical, physical, biological or social agents occurring in their environments.

• Exposure assessment examines the mechanisms and dynamics of events and how they relate to health outcomes.

• Direct Methods– Biomonitoring (blood, urine samples)– Personal exposure monitoring (personal monitors)– Self-reporting

• Indirect Methods– Spatial modeling (GIS)

• A. Proximity (the simplest case)• B. Interpolation (creating a surface out of a set of

known points)• C. Landuse Regression Spatial Modeling (Prediction

based on geographic factors)• D. Satellite data

• Is the simplest form of spatial exposure assessment.• Assumes all individuals within a specific distance to a source

have the same exposure.• Commonly used for measuring access to resources and

exposure to environmental hazards (e.g. air pollution).

• Examined how access to health foods are related to diabetes in Toronto.

• Identified proximity to health food and density of health food outlets by dissemination areas.

• Caution! Simple proximity is often a poor surrogate for resource access.

• Enhanced proximity by modeling walking times to health food outlets.

http://www.ices.on.ca/file/TDA_Chp8_PartA_press.pdf

http://www.ices.on.ca/file/TDA_Chp8_PartA_press.pdf

• Spatial interpolation is used to estimate a value of a variable at an un-sampled location from measurements made at other sites.

• Spatial interpolation is based on the notion that points which are close together in space tend to have similar attributes.

• Many different methods available:

• Exact or approximate• Deterministic or stochastic• Local or global• Gradual or abrupt

• For example: Inverse-distance weighted, spline, kriging (geostattistics)

www.geog.leeds.ac.uk/courses/postgrad/blgy5000/blgy5000_9.ppt

Original surface (a DEM for example)

Interpolation based on 10 points

Interpolation based on 100 points Error map

Low

High

Error map

• Predicts measured values based on geographic proximal variables.

• Used extensively to predict air pollution levels based on land use variables.• Land use• Traffic data• Population density

• Method has been expanded to examine neighborhood factors, such as:• Walkabilty• Resource access

Brauer et al. (2006) A Land Use Regression Road Map for the Burrard Inlet Area Local Air Quality Study

Landuse Regression

http://www.cher.ubc.ca/UBCBAQS/welcome.htm


Deriving Predictive Variables

Jerrett et al. (2005) A review and evaluation of intraurban air pollution exposure models. Exposure Assessment and Environmental Epidemiology. 15, 185–204

Variable Group Base File Type (Source) Buffer SizesSpatial Analyst Feature Used to Generate Grids

Road Length Line (Federal)100, 200, 300, 500, 750 and 1000

Neighborhood Statistics

Traffic Density Line (GVRD Model)

100, 200, 300, 500, 750 and 1000

Calculate Density

Population Density Polygon (Federal)750, 1000, 1250, 1500, 2000 and 2500

Calculate Density

Land Use Area Polygon (Federal) 300, 400, 500 and 750 Neighborhood Statistics

Geographic Grid (Provincial) n/a

Brauer et al. (2006) A Land Use Regression Road Map for the Burrard Inlet Area Local Air Quality Study

Sentinel-5P satellite

https://medium.com/sentinel-hub/measuring-air-pollution-from-space-7492f5dad7bchttps://medium.com/sentinel-hub/measuring-air-pollution-from-space-7492f5dad7bc

• Methods available for exposure calculation.

• Proximity• Interpolation• Spatial modeling (Land Use Regression)• Satellite data

Estimating Canadians’ Exposure to PM2.5 and NO2 Using National LanduseRegression Models: Implications of Scale and Population Location Measures

ISEE 2010, Seoul

Alejandro Cervantes, Perry Hystad, Eleanor Setton, Karla Poplawski, Steeve Deschenes, Paul A. Demers

Air Quality Monitoring Network

GIS processes

GIS processes

Known PM2.5 and NO2 Concentrations from monitors

Buffer50m to 50Km

Intersect withgeographic variables

Geographic Variables:Land useWeather (precip, temp)VegetationKnown industrial emitters Satellite dataRoad density

RegressionAnalysis

Create surface with significant

variables

Calculate Exposure(assign concentrations

to people)

Value SE t p

PM2.5 Model R2=0.46, RMSE=1.529

Intercept2.802 0.497 5.64

Canada NO2 Model

Relationship between Predicted NO2 from the national LUR model plus gradients and independent measurements of NO2 from seven Canadian cities.

To examine the influence of scale, resolution, and population location measures on exposure estimates, we calculated annual average PM2.5 and NO2 concentrations at centroids of 1,412 census tracts (CT), 10,357 dissemination areas (DA), and 41,139 street blocks in Vancouver and Toronto metropolitan areas combined. We also derived concentration surfaces at four different resolutions (100m, 500m, 1000m, and 2000m). Exposure estimates were then calculated at each level of population location with the four surface resolutions.

Census Tractscontain ~8000 people


Dissemination areascontain ~750 people

Blocks have on average ~150 people

At this aggregation Level, Stats Canada only reports pop. totals


Dissemination areascontain ~750 people

a) (100 m resolution) b) (200 m resolution) c) (600 m resolution)

Low high

87

100m 200m

1000m 2000m

Vancouver NO2 Model - Surface Resolution

Vancouver - people exposed to PM25 top quartile

Census aggregation unit

Model Resolution Block DA CT Maximum change

Model at centroid 517,198 545,658 534,617 5.50%

100m surface 519,068 549,049 524,659 5.78%

500m surface 553,706 563,722 533,950 5.58%

1000m surface 581,206 569,483 529,452 9.78%

2000m surface 586,071 567,208 535,023 9.54%

Maximum change 13.32% 4.37% 1.98%

Toronto - people exposed to PM25 top quartile



model at centroid 1,219,094 1,213,498 1,195,799 1.95%

100m surface 1,215,309 1,209,869 1,196,345 1.59%

500m surface 1,229,614 1,220,979 1,206,565 1.91%

1000m surface 1,305,337 1,262,870 1,192,247 9.49%

2000m surface 1,288,144 1,221,355 1,171,081 10.00%

Maximum Change 7.41% 4.38% 3.03%

Vancouver - people exposed to NO2 top quartile



model at centroid 488,423 533,155 532,235 9.16%

100m surface 489,240 533,930 521,414 9.13%

500m surface 553,909 537,161 521,535 6.21%

1000m surface 581,080 534,203 529,345 9.77%

2000m surface 586,071 529,904 534,633 10.60%

Maximum change 19.99% 1.37% 2.54%

Toronto - people exposed to NO2 top quartile



model at centroid 1,329,643 1,271,271 1,198,196 10.97%

100m surface 1,329,544 1,271,835 1,204,096 10.42%

500m surface 1,321,044 1,263,793 1,190,772 10.94%

1000m surface 1,345,987 1,248,178 1,186,843 13.41%

2000m surface 1,319,145 1,239,843 1,176,717 12.10%

Maximum change 2.03% 2.58% 2.33%

1. As expected PM25 is more homogenous, so less dependent on scale. Not the case for NO2.

2. There is no “right” way of calculating exposure

3. In general the finer the resolution of the surface, the better. But it is not feasible for large areas (a 10m surface for all Canada would take several months of computing power!)

4. Important to perform sensitivity analyses when assigning concentration values to people

Thanks for providing material for the slides:

• Alejandro Cervantes-Larios, ex-PhD student, Geography department, University of British Columbia

• Perry Hystad, (former PhD student in epidemiology) for sharing some of the materials in this ppt

• Spatial Analytic Techniques for Medical Geographers (Albert et al., 2000)

• Part 1. GIS and health geography• Major applications for GIS

• Part 2. Applications of GIS in Health research or spatial epidemiology



GIS and Health GeographyTOCGIS and Health GeographyMajor applications for GISSpatial epidemiologyFramework for analysisWhy small area analyses?Geographical correlation studiesIssues: Spatial misalignmentIssues: UncertaintyIssues: Best practicesMajor applications for GISEnvironmental hazardsEnvironmental hazardsMajor applications for GISARIA �(Accessibility/Remoteness Index of Australia)AIRAMortality rate of infants (1980-2001)SES and Heart diseaseThe DataSEIFA distribution in NSWNSW IHD ratesTOCPart 2: What is Epidemiology?Spatial Epidemiology / Health Geography: the ‘Where”Components of Spatial EpidemiologySlide Number 27Slide Number 28Slide Number 29Disease (and determinant) mapping allows us to:Slide Number 31Cluster analysisWhy are there Spatial Patterns of Health?Need for a Geographic Study of HealthExamples of Spatial Epidemiology StudiesExample 1: Risk of Congenital Anomalies Around a Municipal Solid Waste Incinerator: �A GIS Based Case-Control StudySlide Number 37ResultsExample 2: Obesity Relationships with Community Design, Physical Activity, and Time Spent in CarsSlide Number 40Slide Number 41ResultsExample 3: Relation of Trihalomethane (TTHM) Concentrations in Public Water Supplies to Stillbirth and Birth Weight in Three Water Regions in EnglandTTHM Exposure AssessmentResultsPart 2: SummaryPart 3: Exposure AssessmentHow Can We Assess Exposures?Exposure assessment in GISA. Proximity Example: Toronto Neighbourhood Environments – A Focus on DiabetesToronto Neighbourhood Environments – A Focus on DiabetesToronto Neighbourhood Environments – A Focus on DiabetesB. InterpolationTypes of InterpolationEffects of Data UncertaintyC. Landuse Regression Spatial ModelingSlide Number 60Physical Air Pollution Measurements NOx (nitrogen dioxide)Slide Number 62Slide Number 63Final Predictive Surface of NOx PollutionD. Satellite Data: Global Ozone MappingSlide Number 66Part 3: SummaryPart 4: An Example of Exposure Modeling in GISSlide Number 69Slide Number 72Slide Number 75Carcinogens in Outdoor Air – NO2 and PM25NO2 ModelSlide Number 79Carcinogens in Outdoor Air – NO2 and PM25Next step >> Assigning Concentration Values to Population LocationsCanada’s Census Geography – Census Tracts (CT)Canada’s Census Geography – Dissemination areas (DA)Canada’s Census Geography - BlocksThe issue of the resolution of the pollutant concentration surfaceSlide Number 87Results – Exposure to PM25 in VancouverResults – Exposure to PM25 in TorontoResults – Exposure to NO2 in VancouverResults – Exposure to NO2 in TorontoConclusionsSlide Number 95Methodological toolboxesSummarySlide Number 98

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