Disease Mapping: A Renewed Opportunity for Epidemiologic Investigation

Dan WartenbergUMDNJ-RW Johnson Medical School, Piscataway, NJand the Cancer Institute of New Jersey

Research supported by: • R01 CA92693-02 from the National Cancer Institute • U61/ATU272387 from ATSDR (CDC) • New Jersey Legislature

dan.wartenberg@umdnj.edu

Epidemiology and Disease Mapping

Disease and Exposure Mapping Descriptive studies: Issues of scale, accuracy, missing data, interpretation Cluster Detection and Disease Surveillance Assessment and Availability of Health Services: Screening, access to care Exposure Assessment: Traffic density and Pesticide Studies

Data Linkage Geographical Correlation Studies and Hypothesis Generation The Assessment of Risk from a Point or Local Source Lead poisoning: Linking risk factors, case identification and intervention

Data Integration Habitat Suitability for Disease Vectors—Lyme disease, West Nile,… Infection and Cancer: ‘Population Mixing’ and the role of hygiene/SES

Novel Epidemiologic Study Designs: Population identification and recruitment Rare Exposures AND Outcomes: Magnetic fields and Childhood leukemia

Why has disease mapping growing so rapidly?

Development of GIS Geographically indexed relational database Computer program to map and analyze

spatial data Increasing availability of georeferenced

data Ability to geocode, use GPS Demographics, disease outcomes,

environmental quality, health services

Disease Mapping

The history of disease mapping Several excellent reviews, e.g.,

Cliff and Haggett (1988) Howe (1989) Walter and Birnie (1991) Lawson et al. (1999) Walter (2000) Bithell (2000)

Evolution of Data Displays Univariate mapping

Cases (dot or spot maps) Area counts—aggregate to numerators Rates (choropleth maps)—added denominator Adjusted rates—accommodate confounding Smoothed rates—easier interpretation 3-Dimensional plots—alternative displays

Multivariate maps Bivariate displays Maps of multivariate analyses (e.g., PCA)

Cholera in London, 1849Dot Map+Marginal Histograms

From Cliff and Haggett 1988

Thematic and Rate Maps

From Cliff and Haggett 1988

Three-Dimensional View

From Cliff and Haggett 1988

Space-Time Maps

From Cliff and Haggett 1988

Cartograms

Effect of Age Adjustment

From Monmonier 1997

Displaying Multiple Variables

Aggregation Bias

Rate Instability Rate estimates for areas with small

populations often are unstable Previous approaches

Smoothing (e.g., empirical Bayes) Can miss important peaks

Aggregation of geographic units Need consistent rule to identify grouping criteria Need replicable algorithm for aggregation process

Current research is evaluating alternatives

Interactive MappingFemale Childhood Leukemia 1970-1994 (ages 0-19)

From Biomedware 2003

Disease Clusters

Two Cluster Dot Maps Woburn, Ma Toms River, NJ

Why study clusters? Public concern

Clarify of misconceptions Allay concerns that are unfounded Initiate study when concerns are well founded

Encourage Remediation and Prevention Determine if situation is a sentinel of a larger

problem Identify unknown exposure situations

Facilitate Scientific Discovery Identify new exposure-disease link Identify new carcinogens

New Opportunities Rapid response to resident concerns (e.g., SAHSU)

Online, real-time display of cancer data, known point sources, other environmental data

Space-time pattern of cancer incidence at small scale

How many ‘clusters’ are there? Where are the lows as well as the highs, and why?

Prioritize potential investigations What warrants follow up?

Implementation of prospective surveillance Active vs. Passive approach

Exposure Assessment/Estimation

An Epidemiologist’s Perspective on Exposure Assessment

A tool rather than an end in and of itself

User rather than developer Specific uses

Conduct otherwise undoable epidemiology

Improve scope, focus or interpretability

Point and Nonpoint Source Pollution Point source pollution

Toxics, birth defects and low birth weight (Geschwind et al. 1992; Stallones et al. 1992; Berry

and Bove 1997; Croen et al. 1997) Incinerators and cancer (Gatrell and Dunn

1995) Nonpoint source pollution

Drinking Water (Nuckols et al. 1995) Pesticides (Nuckols et al. ) Traffic derived air pollution

(English et al. 1999; Ritz et al. (2000, 2002, 2003; Brauer et al. 2003)

Air Pollution from Traffic

From Wilhelm and Ritz 2003

Data Linkage

Map-Based Correlational Studies

Various historical efforts New impetus triggered by NCI Atlas (1970s)

Compared mortality maps to possible exposures Then validated with traditional epidemiology

Bladder cancer and chemical manufacturing Nasal adenocarcinoma and furniture manufacturing Lung cancer and shipyards Oral cancers among women and snuff use

Approach reinvigorated in past few years New tools and geocoded databases

Despite the ‘Bad Press’ these can be useful Must be careful of limitations of ‘ecologic analysis’

Preventing Childhood Lead Poisoning

Screening data readily available Using the GIS

screening assessing the impact of targeted screening evaluating predictive equations

Are SES, housing age, other factors predictive

surveillance combining maps of exposure and disease

target educational and remediation programs

Preliminary Lead Study Proposed strategy for targeted screening

use demographics and local hazards to predict lead poisoning rates

map cases validate by comparing cases to predictions and

then adjust prediction equation Developed hypothetical model and data Implement appropriate

screening/intervention (Wartenberg 1992)

Childhood Lead Exposure:Advantages of Using a GIS

Can expand to nationwide demographic evaluation

Can explore prediction models using individual data

Can look for other risk factors Can adjust for bias due to spatial

autocorrelation Can design/evaluate screening and

interventions

Population Mapping

Magnetic Fields and Childhood Leukemia

Do magnetic fields cause cancer? Blue Ribbon panels--possible Combined Analyses of Studies Show

Small but consistent elevations of risk A moderate exposure-response gradient Few subjects and “high” exposures

Exposure Metric Pooled Analysis Meta-Analysis Ahlbom et al. (2000) Greenland et al. (2000) Wartenberg (2001)

Measured 1.2 (1.0-1.3) 1.2 (1.0-1.5) Continuous Analysis (per 0.2 uT) Calculated 1.1 (0.9-1.3) 1.4 (1.1-2.0)

Dichotomous Analysis

Measured or Calculated

2.0 (1.3-3.1) (>0.4 uT) 1.7 (1.2-2.3) (>0.3 uT) 1.3 (1.1-1.7) (>0.2 uT)

Rare Exposure, Rare Disease Proximity to high voltage transmission

lines Background exposure ~1 mG Exposure over 10 mG are very rare

Most studies have low exposure (0-5 mG) Homes close to high voltage lines can be 50 mG

Childhood leukemia rare (1 in 30,000 per year) Proximity to high voltage lines rare (<2%) Following Feychting and Ahlbom (1993), design

nested case control study in US, we don’t have the luxury of a population

registry

NYS Power Lines Mapping Study

Geolocated all 345+ kV power lines Overlaid on Residential Data

digital orthophotographs US Census data NYS RPS data

Goals Estimate population exposures near lines Assess risk among most highly exposed

High Voltage Electric Power Transmission Lines in NY

Exposure Buffer Areas

From Monmonier 1991

#

#

#

#

#

##

#

#

#

#

#

#

#######

#

#

#

#

#

#

#

#####

#

#

#

#

#

#

#

###

#

#

#

#

#

# #

#

#

# ##

#

#

#

#

#

# # #

#

#

#

#

#

#

##

# # #

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#####

#

#

#

#

#

# #

#

#

#

# #

#

#

#

#

#

#

#

#

#

##

#

# # # # #

#

##

#

##

## #

# # ##

#

#

#

# # # # # # # # # # ##

#

## # #

#

#

#

#

#

#

# # # # # #

#

#

#

#

#

#

#

#

#

## # # #

#

#

#

#

#############

# # # # # # # # # # #

##

#

#

#

#

#####

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

Ë

Ë

Ë

Ë

Ë

Ë

Ë

Ë

Ë

Ë

Ë

Ë

Census Boundaries# RPS Housing Data

Power LinesNYPA Line Estimate

N

EW

S

HenriettaTownship

New York State Orthophoto

Case Location Problems Map NYS childhood leukemia cases 1976-1994

2.3% of population inside buffer

Reasons for Uncoded Missing address Not in Tiger Files No house number Rural Delivery only P.O Box

Location of Subjects (2000 ft. Buffer)

Cases Population TOTAL 1531 1,948,791 Inside 12 45,575 Outside 1082 1,903,216 Uncoded 437 --

Preliminary ResultsAge Relative Risk (95% CI) [n]Group 0-1000 ft 0-2000 ft 0-4 0.52 (0.17, 1.62)[3] 0.46 (0.19, 1.10)[5]0-14 0.36 (0.13, 0.96)[4] 0.51 (0.28, 0.93)[7]0-19 0.38 (0.16, 0.91)[5] 0.47 (0.27, 0.83)[7]Conclusions

Method works well Substantive results inconclusive (too small n) Need to conduct study in more densely populated state

Data Integration

Introduction to Lyme Disease

Natural History Bacterium Borrelia bugdorferi transmitted by ticks of

genus Ixodes Small mammals are reservoir Prime habitat is damp wooded areas

Barbour et al. 2001 Used Fire Model as proxy for I. scapularis

vegetation type, density, and ground moisture influence off-host survival, deer host presence

Used seroprevalence in dogs as surrogate for human risk

Combined to predict overall human risk

Lyme Disease RiskLyme Disease Risk based on co-Kriging

Habitat Suitability and Seroprevalence Data in Dogs

Lyme Disease in NE US

FIGURE 2: Habitiat Suitability Spatial Structure

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Average Distance Between Observations (KM)

Mo

ran

's I

Estimated Tick Establishment

Childhood Leukemia and Clusters Observed cluster of childhood cancer in

Seascale, UK near nuclear power station Few causes known of childhood leukemia Government concerned (much publicity)

Commissioned series of studies Results suggest only risk was paternal

exposure Seemed contrary to radiation risk

Others sought alternative explanation

Hypotheses

Kinlen—Population Mixing Children from isolated populations, with

decreased exposure to infections, when exposed to others from regions of greater population density are at increased risk of leukemia, likely due to viral etiology

Greaves—SES and Hygiene Delay of exposure to infections from infancy to

each childhood, such as due to improvements in socioeconomic status and hygiene, puts children at increased risk of leukemia and lymphomas.

Evaluations of Hypotheses Several studies in UK, generally supportive Few in other countries, mixed This study: Analysis of SEER Data

Use data from states with rural counties Test hypothesis for rates of ALL Compare rates by

Size of population change Size of income

Compare to pattern of CNS cancers as reference (to control for methodology)

Results for IA, NM, UTPercentage Change in Population Size

>0%-10% >10%-20% >20%

Base CaseALL 1.7 (0.8-3.5) 2.1 (1.2-3.9) 2.5 (1.4-4.5)CNS 1.1 (0.5-2.4) 1.3 (0.7-2.4) 0.6 (0.3-1.6)

Birth Location=Any StateALL 1.6 (1.0-2.5) 1.3 (0.8-2.0) 1.4 (0.9-2.3)CNS 1.3 (0.7-2.3) 1.2 (0.7-2.1) 0.9 (0.5-1.7)

Income$22,001-$25,500 $25,501-$27,500 >$27,500

ALL 1.3 (0.8-2.1) 0.8 (0.5-1.3) 0.9 (0.6-1.3)CNS 0.9 (0.5-1.9) 1.1 (0.6-2.0) 1.3 (0.8-2.3)

(with D. Schneider and S. Brown)

The Future

Epidemiologic Prospects—1

Improved descriptive studies Improved cluster identification and response

Rapid response, not limited by geopolitical boundaries correlate with possible environmental exposures prospective surveillance

Improved exposure assessment Use of geographic models and indices

proximity/dispersion from point/non-point sources (e.g., dump sites, stacks) (e.g., wind, groundwater models)

adjustments for spatial autocorrelation (e.g., time, space series)

Epidemiologic Prospects—2

New Data Linkages and Hypotheses Correlation analyses

Improved Data Integration Habitat identification for risk prediction

Lyme disease, cholera, West Nile disease Complex analyses (multivariate assessment)

New investigation strategies Population identification and recruitment

(controls?) Nested case control studies Hypothesis generation

Epidemiologic Prospects—3

Environmental Justice Investigations transcend geopolitical boundaries conduct multi-site studies for similar communities

Improved public communication maps communicate more clearly than numbers or

words this is a double edged sword