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GIS using in Epidemiology and Risk Assessment

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Title: GIS using in Epidemiology and Risk Assessment


1
GIS using in Epidemiology and Risk Assessment
2
Content
  • GIS and Epidemiology
  • GIS and Risk Assessment
  • Examples

3
John Snow (1813-1858)
  • GP based in Soho section of London
  • Insight into communication of cholera by water

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Health Risk Assessment- Modified from Health
Risk Assessment, Dr. Bill Hartley
  • 2006/05/09

1
ENHS 762 Health Risk Assessment, Dr. Bill Hartley
18
Basic Concepts and Definitions
  • Risk Assessment
  • The scientific estimation (qualitative or
    quantitative) of hazard which is obtained by
    combining the results of an exposure assessment
    with the results of the toxicity assessment for
    the subject chemical.

Modified from Health Risk Assessment, Dr. Bill
Hartley
19
Basic Concepts and Definitions
  • Risk Management
  • The judgment and analysis that combine the
    scientific results of a risk assessment with
    economic, political, legal and social factors to
    produce a decision about environmental action.

20
Basic Concepts and Definitions
  • Risk Communication
  • Communication of risk assessment/management
    information to the public, reporters, and
    state/local officials.

21
What is Risk?
  • the likelihood of injury, disease, or death
  • What is Environmental Risk?
  • The likelihood of injury, disease, or death
    resulting from human exposure to a potential
    environmental hazard

22
Comparative Risks of Death
23
Comparative Risks Pesticides
24
Risk
  • Subjective
  • Not a risk-free world
  • Uncertainty complexity
  • Probabilistic
  • Benchmarks
  • Product of probability consequence

25
Risk Assessment
dose-response assessment
hazard identification
risk characterization
exposure assessment
26
Risk Management and Risk Assessment Working
Together
  • Risk Assessment
  • Risk Management

dose-response assessment
regulatory decision
hazard identification
risk characterization
control options
exposure assessment
non-risk analyses
27
Non-risk Analyses
economics
regulatory decision
risk characterization
politics
control options
statutory legal considerations
non-risk analyses
social factors
28
Risk Management and Risk Assessment working
together
  • Risk Assessment

Risk Management
dose-response assessment
hazard identification
regulatory decision
risk characterization
exposure assessment
control options
non-risk analyses
29
The Components of Risk Assessment
  • Hazard Identification
  • Dose-Response Evaluation
  • Human Exposure Evaluation
  • Risk Characterization

30
Component 1 Hazard Identification
?
  • Agent

Effect
31
Human Evidence
  • Sufficient
  • Limited
  • Inadequate
  • No Data
  • No evidence

32
Advantages Uncertainties of Animal Data
Dolly
33
Weigh All the Evidence
Human Evidence
34
Hazard Identification
  • Review analyze toxicity data
  • Weigh the evidence that a substance causes
    various toxic effects
  • Evaluate whether toxic effects in one setting
    will occur in other settings

35
Component 2 Dose-Response Assessment
?
Dose
Response
36
Dose-Response
  • Dancing with proper limitations is a salutary
    exercise, but when violent and long continued in
    a crowded room it is extremely pernicious, and
    has hurried many young people to the grave.
  • A.
    Murray, M.D. 1826

37
Sources of Toxicity Data
  • Human Studies
  • Case reports
  • Epidemiological studies
  • retrospective
  • prospective
  • case-control
  • Animal Studies
  • General toxicity studies
  • acute sub-chronic
  • sub-acute chronic
  • Specialized toxicity studies
  • teratology
  • genotoxicity
  • developmental
  • male/female reproductive
  • cancer
  • endocrine disruption

38
Animal Data
Approximate Oral LD50 in rats for a group of
well-known chemicals Chemical
LD50(mg/kg) Sucrose (table sugar) 29,700 Ethyl
alcohol 14,000 Sodium chloride 3,000
(table salt) Vitamin A 2,000 Aspirin
1,000 Chloroform 800 Copper Sulfate
300 Caffeine 192 Phenobarbital
162 DDT 113 Nicotine 53
39
Dose-Response Evaluation
  • Threshold
  • Non-
  • Threshold

Cancer
Organ Damage
No effect
40
Dose-Response Evaluation
Threshold-Systemic Toxicity (Non-cancer)
responding
MFO Slim
Fatty Change
Decreased Dye Clearance
Mortality
Dose (mg/kg)
mixed function oxidase
41
Dose-Response Evaluation
Non-threshold--Cancer
Performed to estimate the incidence of the
adverse effect as a function of the magnitude of
human exposure to a substance
42
Dose-Response Curve
Non-Threshold--Cancer
43
Dose-Response Curve
Non-Threshold--Cancer
Observable range
Response
Range of inference
0
Dose
44
Dose-Response Evaluation
  • All substances are poisons there is none which
    is not a poison. The right dose differentiates a
    poison and a remedy. Paracelsus
    (1495-1541)

45
Dose-Two types of measurement
  • Type I
  • Measurement of the amount of the substance in the
    medium (air, diet, etc.) in which it is present
    or administered.
  • NOT DOSE
  • Type II
  • Measurement of the amount absorbed by the
    subject, whether human or animal.
  • DOSE

46
Dose
  • Example
  • Chemical HC-BAD is present in drinking water at a
    concentration of 10mg/L (Type I measurement)
  • What is the dose (DAILY) for an adult (Type II)?
  • Exposure Assumptions Body weight 70kg (150
    lbs)
  • Water consumption 2L water/day

by convention
or .29mg/kg ? day
47
Component 3 Exposure Assessment
  • ?

Agent
People
48
Exposure Issues Assumptions
  • Extent frequency of human exposure
  • how much?
  • how often?
  • how certain?
  • Number of people exposed
  • Degree of absorption by various routes of
    exposure
  • Use of average or typical individual
  • Use of high risk groups

49
Routes of Exposure
  • Ingestion (Drinking, Food)
  • Skin Absorption (Water, Soil)
  • Inhalation
  • Total Dose is equal to the sum of the doses from
    the above routes ( pathways)

50
Estimation of exposure levels
  • Direct
  • Measure the actual concentration of chemical in
    the air, water or food used by an individual.
  • Measure the actual amount of air, water or food
    used by the individual.
  • Calculate the actual dose to the individual.

51
Estimation of exposure levels
  • Reconstructive
  • Measure the level of the chemical, its
    metabolites, or some other chemical-specific
    change (biomarker) in the body.
  • Based on toxicokinectic models, calculate the
    dose to the individual from the level of the
    biomarker.

52
Estimation of exposure levels
  • Predictive
  • Measure or calculate the concentration of
    chemical in environmental media around the
    source.
  • Estimate human contact with the media.
  • Estimate the resulting dose to the average person.

53
Component 4 Risk Characterization
Hazard Identification
Risk Characterization
Dose-Response
Exposure
54
Risk Assessment Issues
Hazard Animal data Identification
Epidemiological Studies
Weight of Evidence Dose-Response
Extrapolation from high to low Assessment
dose Extrapolation from
animals to humans Exposure
Assessment Modeling vs. monitoring vs.
biological monitoring Risk Characterization
Qualitative or quantitative
55
Quantitative Analysis of Cancer Risk
  • In Air (mg/kg-day)-1 -gt ? (mg/m3)-1
  • -gt Unit from (kg-day/mg) to (m3/mg)
  • Exposure Assumption (adult only)
  • Respiratory rate 20 m3/day
  • Body weight 70 kg
  • Absorption rate 100

56
Quantitative Analysis of Cancer Risk
  • In Drinking water (mg/kg-day)-1 -gt ? (ug/L)-1
  • -gt Unit from (kg-day/mg) to (L/ug)
  • Exposure Assumption (adult only)
  • Drinking water 2 L/day
  • Body weight 70 kg
  • Absorption rate 100

57
Risk Analysis (Systematic and Cancer)
  • Step 1. Dose Calculation
  • Child and Adult
  • Step 2. Margin of Exposure (MOE) (Threshold
    model NOAEL/LOAEL with UFs and MF)
  • Children and Adults
  • Step 3. Cancer risk (No child cancer model yet,
    Non-threshold model currently)
  • Cancer Potency Factor (q1) dose (adult only)
  • Cancer Risk (no unit) (mg/kg-day)-1
    (mg/kg-day)
  • Unit Risk Concentration
  • Cancer Risk (no unit) (mg/m3)-1 (mg/m3) (air)
  • or (ug/L)-1 (ug/L) (drinking water)
  • (General speaking, acceptable cancer risk 10-4
    10-6)
  • Step 4. Size of population (estimate how many
    excess cancer cases will be caused by exposure to
    chemical in drinking water and/or air
    (life-time))
  • Step 5. Fact Sheet for public (systematic and
    cancer)

58
Key Variables in Exposure Assessment
  • Population

Visitors/Trespassers
Current and Future Residents
Workers
68
Sensitive Groups
59
Key Variables in Exposure Assessment
  • Location
  • On-site
  • Off-site

Outside
Inside
69
ENHS 762 Health Risk Assessment, Dr. Bill Hartley
60
Key Variables in Exposure Assessment
  • Exposure Medium

Food
Air
Soil/sediments
House dust
Surface Ground Water
70
61
Key Variables in Exposure Assessment
  • Duration
  • acute
  • sub-chronic
  • chronic
  • lifetime
  • Exposure Route
  • oral
  • inhalation
  • dermal

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ENHS 762 Health Risk Assessment, Dr. Bill Hartley
62
Estimation of Exposure Levels
  • Direct
  • Measure the actual concentration of chemical in
    the air, water or food used by an individual.
  • Measure the actual amount of air, water or food
    used by the individual.
  • Calculate the actual dose to the individual.

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ENHS 762 Health Risk Assessment, Dr. Bill Hartley
63
Estimation of Exposure Levels
  • Reconstructive
  • Measure the level of the chemical, its
    metabolites, or some other chemical-specific
    change (biomarker) in the body.
  • Based on Toxicokinetic models, calculate the dose
    to the individual from the level of the biomarker.

73
ENHS 762 Health Risk Assessment, Dr. Bill Hartley
64
Estimation of Exposure Levels
  • Predictive
  • Measure or calculate the concentration of
    chemical in environmental media around the
    source.
  • Estimate human contact with the media.
  • Estimate the resulting dose to the average person.

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ENHS 762 Health Risk Assessment, Dr. Bill Hartley
65
Identify Major Uncertainty Factors in Risk
Estimates
  • Assumptions variables that contribute most to
    uncertainty in risk estimates
  • Site specific factors monitoring data quality,
    likelihood of exposure scenarios occurring,
    chemicals not included in risk estimates, key
    assumptions for contaminant transport fate
    models
  • Toxicity values weight of evidence, adjustment
    factors, dose additivity/extrapolation, less than
    lifetime cancer risk estimates

75
ENHS 762 Health Risk Assessment, Dr. Bill Hartley
66
Evaluate Uncertainty Factors
  • Identify data gaps default values
  • Provide distributions for parameter values, where
    possible
  • Statistical treatment of uncertainty possible in
    very few situations
  • A qualitative evaluation of uncertainty factors
    is typical
  • Describe impacts (bias) on risk estimates
    (direction order of magnitude)
  • Qualify risk estimates for risk management
    decisions

76
ENHS 762 Health Risk Assessment, Dr. Bill Hartley
67
GIS and Risk AssessmentA Fruitful Combination
  • Results from evaluations of human health risks
    associated with environmental contamination are
    traditionally presented non-spatially.
  • Because of the expense associated with sampling
    and analysis, samples of environmental
    contaminants are often taken from relatively few
    spatial locations, such as test wells.
  • Moreover, in many cases, the environmental
    gradient of contamination is known to be
    anisotropic due to directional forces like
    groundwater flow or wind.
  • In an effort to avoid presenting misleading
    information, non-spatial tabular reporting of
    single values, such as increased incidence of
    human cancer, has become the widely-accepted
    convention for communication of human health risk
    results.

68
GIS and Risk AssessmentA Fruitful Combination
  • Ultimately, interpolation of contaminant data
    which are rare in space and time is necessary for
    full evaluation of human risks.
  • We present an alternative spatial format for the
    communication of contamination and risk an array
    of maps which we call a 'Map Spreadsheet.' The
    Map Spreadsheet is analogous to a data
    spreadsheet, except that each cell is a spatially
    co-registered interpolated map of contaminant
    concentration.
  • Columns and rows in the Map Spreadsheet could
    represent alternative ingestion pathways,
    chemical classes and/or species of contaminants,
    or years through time.
  • The viewer of a Map Spreadsheet obtains a
    perspective of the combined risk from exposure to
    multiple contaminants, as well as an idea of the
    heterogeneity of contamination across space.

69
The Spatial Nature of Risk
  • Environmental risk professionals typically report
    risk as single values or tables of values. Often,
    separate numbers are calculated and reported for
    different pathways, each of which are considered
    independently.
  • Yet it is clear that risk assessments have an
    important spatial component. Risk evaluations are
    generated by reference to specific environmental
    data collected from specific locations.
  • We argue that the presentation of risk benefits
    greatly from a spatial approach, and that the
    marriage of GIS and risk analysis is a sensible
    one. Although difficulties and problems certainly
    exist in the spatial analysis of risk, we suggest
    that it is a better - and more conservative -
    strategy to openly present these details and
    assumptions spatially rather than allowing them
    to remain implicit. Risk professionals will not
    mislead by presenting maps - they mislead by not
    presenting maps.

70
Maps with Icons
  • This series of maps shows the spatial
    relationships bewteen chemical concentrations in
    a series of test wells and a manufacturing
    facility, a series of buried pipelines, and a
    group of holding ponds. Concentration data are
    presented spatially for 1,1-dichloroethene,
    trichloroethene, and vinyl chloride in this
    K-1420 industrial area. Although we chose to plot
    each contaminant on separate maps, the icons
    could have been combined on a single map, and
    differentiated by the shape of the icons used.
  • Only slightly more sophisticated is the plotting
    not of simple contaminant concentrations, but of
    risk due to the contamination at that
    concentration. In this map of total human health
    risk due to vinyl chloride contamination, human
    risk values calculated for the water ingestion
    pathway are mapped directly. Mapped units are in
    terms of likelihood of excess'' human cancers.

71
Map and Chart Hybrids
  • The combination of charts with maps allows for
    the inclusion of additional information regarding
    contaminants. The simple maps above presented a
    static snapshot of contamination no information
    was included about the way that contaminant
    concentrations changed through time (figure
    left).
  • All maps to this point have shown total
    contamination or risk. By plotting size-dependent
    pie diagrams at sampled locations, we can
    communicate not only the total but the proportion
    of total contamination or risk contributed by
    each of several contaminants. This map of the
    X-10 area shows proportional human health risk by
    all pathways posed by test well concentrations.
    The size of each pie reflects total relative
    human health risk in terms of increased incidence
    of human cancer, while the color and proportion
    of each pie slice indicate each contaminants'
    contributions. The locations of each data point
    are maintained, so that the spatial distribution
    of the test wells and their proximity to
    prominent features is obvious. (figure right)

72
GIS and Risk AssessmentA Fruitful Combination (
CONCLUSIONS )
  • It is clearly not sufficient to report risk
    non-spatially. Risk is an inherently spatial
    phenomenon. A risk map should be considered the
    ultimate product of any risk investigation, and
    should be the first resource sought for any risk
    decision or evaluation. GIS techniques can be
    central to these important and critical processes
    of risk identification, quantification, and
    evaluation.

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Case Study I An investigation of the association
between traffic exposure and thediagnosis of
asthma in children
  • Gordian ME, Haneuse S, Wakefield J.
  • J Expo Sci Environ Epidemiol. 2006
    Jan16(1)49-55.
  • ??????? ??
  • ??????

106
Introduction
  • Several studies have indicated that children who
    live near traffic have greater symptoms and
    increased hospitalizations for asthma (Edwards et
    al., 1994, Brauer et al., 2002).
  • The severity of asthma symptoms is also
    associated with ambient benzene concentrations
    (Thompson et al., 2001 Delfino et al., 2003).

107
Objective
  • This paper reports the results of a study that
    investigated the association between a
    traffic-density related measure of exposure and
    the prevalence of diagnosed asthma among school
    children5 to 7 years of age in Anchorage, Alaska
    (an area with high ambient benzene
    concentrations), adjusting for gender, parental
    asthma, a smoker in the home, and family income.

108
Exposure Assessment
  • Traffic counts on roadways were obtained from the
    Alaska Department of Transportation and the city
    Planning Office.
  • We used the GIS to map the coordinates of the
    nearest cross streets to each childs home.
  • We calculated a traffic exposure variable by
    drawing a 100-m buffer zone around each
    intersection of interest.

109
Materials Methods
  • All the road segments that fell within the 100-m
    buffer were multiplied by the traffic count of
    that road at that point and then summed to create
    a measure of traffic exposure.
  • A total of 1106 surveys were received. The
    overall return rate by school for surveys was
    75, with a range of 5492 across schools.

110
Statistical Analysis
  • We used logistic regression to model the lifetime
    probability of being diagnosed with asthma, as a
    function of the traffic count in vehicle-meters
    (vm), and additional confounder and precision
    variables that were known to be associated with
    diagnosis of asthma.

111
Conclusion
  • We have evidence of a weak association between
    asthma prevalence in children 57 years of age
    exposed to traffic in an area where the primary
    air pollutants are VOCs and coarse fraction
    particulate matter(2.5µm lt PM lt 10µm).
  • Children without genetic predisposition to asthma
    appear to be most at risk.
  • More epidemiological studies are needed regarding
    the effects of traffic pollutants, especially
    VOCs.

112
Case Study II Cancer Risk Near a Polluted River
in Finland
  • Verkasalo PK, Kokki E, Pukkala E, Vartiainen T,
    Kiviranta H, Penttinen A, Pekkanen J.
  • Environ Health Perspect. 2004112(9)1026-31.
  • ??????? ??
  • ??????

113
PCDD/Fs
  • The surface sediment levels of PCDD/Fs are
    between 0.5 and 350 ng/g in dry weight as toxic
    equivalents and thus are among the highest
    sediment levels observed worldwide.
  • The most toxic congener, 2,3,7,8-tetrachlorodibenz
    o-p-dioxin (2,3,7,8-TCDD), has also been
    classified by the International Agency for
    Research on Cancer (IARC) as carcinogenic to
    humans in 1997.
  • Overall, the strongest epidemiologic evidence for
    the carcinogenicity of 2,3,7,8-TCDD is for all
    cancers combined rather than for any specific
    site.

114
  • In this study we investigated cancer risk in
    people living near the River Kymijoki (lt 20.0
    km).
  • We assumed that PCDD/Fs are mobilized from the
    river surface sediments and reach nearby
    residents via the food chain (e.g., by
    consumption of locally caught fish).
  • We hypothesized that cancer risk increases with
    decreasing distance to the river. Furthermore, we
    hypothesized that farmers show a higher risk than
    most other people.

115
Materials and Methods Small-Area Statistics on
Health System
  • The SMASH system has previously been used to
    investigate cancer risk near geographically
    defined exposure sources in Finland.
  • Data include population counts by age, sex,
    socioeconomic status (SES), and location
    coordinate of residence for 1980 and all cancer
    cases from 1981 to 2000.
  • The original data sets were linked using personal
    identification numbers unique to every resident
    in Finland. The data were available in 500 m
    500 m grid squares and were further aggregated
    according to our hypothesis on geographic
    reference to the river.

116
Small-Area Statistics on Health System
  • A total of 27 cancers were selected to be
    studied. They were classified traditionally
    according to the ICD-7(WHO1995).
  • Basal cell carcinomas (BCCs) of the skin were not
    included in the total numbers because there are
    large variations in the BCC rates by hospital
    catchment area, suggesting that many cases may
    remain undetected.

117
Exposure Assessment
  • The study population was defined as all people
    (farmers in particular) living within 20.0 km
    from the River Kymijoki (i.e., in a 500 m 500 m
    grid square at least partially located within
    20.0 km from the river shoreline) on 31 December
    1980.
  • The study area was divided into nine subareas
    according to increasing distance to the river
    downstream from the factory producing Ky-5 (lt 1.0
    km 1.04.9 km 5.019.9 km), and according to
    increasing distance to the sea (lt 20.0 km
    20.039.9 km 40.059.9 km) (Figure 1).

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Statistical Analyses
  • We estimated reference incidence rates separately
    for total Finnish population and Finnish farmers,
    dividing the number of new cases of cancer by the
    population at risk in 1980, by sex, age, time
    period, and SES (in analyses of all people but
    not farmers).
  • For the study area, we calculated expected
    numbers of cancers as the number of subjects
    multiplied by reference incidence rate for that
    cancer by sex, age, time period, SES, distance to
    sea, and distance to river.

120
Statistical Analyses
  • For distance to river comparisons, we used
    Poisson regression main-effect models for the
    observed numbers of cases in 3 3 contingency
    tables, where the classification is based on
    distance to river (three categories) and distance
    to sea (three categories).

121
Conclusions
  • This study cannot exclude the possibility that
    residence near the River Kymijoki may have
    contributed to a subtle increase in the risk of
    total cancer, especially among farmers.
  • This study can provide only first approximations
    of risks and tell only a little about causality.

122
Case Study III ??GIS???????
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  • ???????????????????
  • -??????????

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??GIS???????
  • ???????????,?????????????,?????????????????????,??
    ??????????,?????????????????,????????????????

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??GIS???????
  • ???????????????DEP(Department of Environmental
    Protection)?the Department of Health(DOH)?the
    Department of Agriculture(PDA)??????????????????,?
    ????????????????(database)?,??????????????????WN
    ????????????????,?????????????????DEP
    ????????????????????????,?????????????????

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??GIS???????
  • ???????????????????????,???ESRI
    ????????????????????????????????????????????????(C
    ampag)??????(handhelds)???????(Global Positioning
    System)?ESRI ???????(information
    mappingsoftware)ArcPad,??????????????????(Web)???,
    ???????????????????????????????????

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??GIS???????
127
??GIS???????
  • ?????????????????,?????????????????????,?????????
    ???????,???????????????????????

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??GIS???????
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