Transcript Slide 1

What is epidemiology?
• GIS and health geography
• Major applications for GIS
• Epidemiology
• What is health (and how location matters)
• What is a disease (and how to identify one)
• Quantifying disease occurrence
• Incidence vs prevalence
• Identifying the population
• Working with small area data
• 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 lifestyle and environmental factors, including where people
live. Characteristics of these locations (including sociodemographic 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:
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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)
• Dr. John Snow’s Map
of Cholera Deaths in
the SOHO District of
London, 1854
1. Spatial epidemiology
2. Environmental hazards
3. Modeling Health Services
4. Identifying health inequalities
Hazard
Surveillance
Exposure
Surveillance
Outcome
Surveillance
• Hazardous agent present in the
environment
• Route of exposure exists
• Host exposed to agent
• Agent reaches target tissue
• Agent produces adverse effect
• Effect clinically apparent
GIS: Identify causal and mitigating factors
1. Spatial epidemiology
2. Environmental hazards
3. Modeling Health Services
4. Identifying health inequalities
• 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.
Geographical location
“Where do infants and children die in WA? 1980-2002”
Jane Freemantle, PhD. November 2004
Remote
non-Aboriginal
Rural
Aboriginal
Metro.
0
2
4
6
8 10 12 14 16 18 20 22 24 26
Mortality Rate / 1000 live births
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
• GIS and health geography
• Major applications for GIS
• Epidemiology
• What is health (and how location matters)
• What is a disease (and how to identify one)
• Quantifying disease occurrence
• Incidence vs prevalence
• Identifying the population
• Working with small area data
• The study of the distribution and determinants of
health and disease-related states in populations,
and the application of this study to control health
problems.
• ‘the product of [epidemiology] is research and
information and not public health action and
implementation’ (Atwood et al. 1997)
• ‘epidemiology’s full value is achieved only when its
contributions are placed in the context of public
health action, resulting in a healthier populace.’
(Koplan et al. 1999)
• … are like bookies of disease, stalking the globe to
determine point-spreads on which groups of people
are most likely to get which diseases.
• Part detective and part statistician, part
anthropologist and part physician, epidemiologists
hope to track down the agents of illness by
deducing which of the differences between peoples
lie at the root of their distinctive disease patterns.
(H. Shodell, Science ’82, September, p. 50)
DESCRIPTIVE
Health and disease in the community
What?
Who?
When?
Where?
What are the
health problems
of the
community?
What are the
attributes of
these illnesses?
ANALYTIC
Why?
What are the
causal agents?
What factors
affect outcome?
How many people
are affected?
What are the
attributes of
affected persons?
Over what
period of time?
Where do the
affected people
live, work or
spend leisure
time?
Etiology, prognosis and program evaluation
How?
By what mechanism
do they operate?
Dorland's Illustrated Medical Dictionary (28th ed.):
Health – "a state of optimal physical, mental, and social
well-being, and not merely the absence of disease and
infirmity.“
Disease – "any deviation from or interruption of the
normal structure or function of any part, organ, or
system (or combination thereof) of the body that is
manifested by a characteristic set of symptoms and
signs . . .".
• Health, as defined in the World Health Organization's
Constitution, is "a state of complete physical, mental and social
well-being and not merely the absence of disease or
infirmity."
• Health is seen as more than just the absence of disease, and
depends upon a complex suite of factors, with location taking
the lead. A location is more than just a position within a spatial
frame (e.g., on the surface of the Earth or within the human
body).
• Different locations on Earth are usually associated with
different profiles: physical, biological, environmental,
economic, social, cultural and possibly even spiritual profiles,
that do affect and are affected by health, disease and
healthcare.
• An example of how location matters and carries with
it other factors into play:
• The body weight of infants at birth is one readily available piece
of data, and the relationship between low birth-weight and
maternal and child health is a continuing line of research.
• In New York City, Sara McLafferty and Barbara Tempalski have
studied the spatial distribution of low birth-weight infants and
identified areas in which the number of low birth-weight infants
increased sharply during the 1980s.
• Their results indicated that the rise in low birth-weight was closely
linked to women's declining economic status, inadequate insurance
coverage and prenatal care, as well as the spread of
crack/cocaine.
• GIS and health geography
• Major applications for GIS
• Epidemiology
• What is health (and how location matters)
• What is a disease (and how to identify one)
• Quantifying disease occurrence
• Incidence vs prevalence
• Identifying the population
• Working with small area data
Manifestional criteria:
Manifestational criteria refer to symptoms, signs, and other
manifestations of the condition. Defining a disease in terms
of manifestational criteria relies on the proposition that
diseases have a characteristic set of manifestations. This
defines disease in terms of labeling symptoms.
Causal criteria:
Causal criteria refer to the etiology of the condition, which
must have been identified in order to be employed. This
defines disease in terms of underlying pathological etiology.
• How do you identify a disease?
• The Acquired Immunodeficiency Syndrome (AIDS) was initially
defined by the CDC in terms of manifestational criteria as a
basis for instituting surveillance.
• The operational definition grouped diverse manifestations –
Kaposi's sarcoma outside its usual subpopulation, PCP and
other opportunistic infections in people with no known basis
for immunodeficiency.
• This was based on similar epidemiologic observations (similar
population affected, similar geographical distribution) and a
shared type immunity deficit (elevated ratio of T-suppressor
to T-helper lymphocytes).
• Human immunodeficiency virus (HIV, previously called
human lymphotrophic virus type III) was discovered and
demonstrated to be the causal agent for AIDS.
• AIDS could then be defined by causal criteria.
• A single causal agent may have multiple clinical effects.
• Multiple etiologic pathways may lead to apparently
identical manifestations, so that a manifestationallydefined disease entity may include subgroups with
differing etiologies.
• Multi-causation necessitates a degree of arbitrariness in
assigning a causative versus a contributing factor to a
disease.
• Not all persons with the causal agent develop the disease.
Onset of
disease
Underlying
Genetic
Susceptibility
Physiologic
Abnormalities
Diagnosis
of disease
Sub-clinical disease
Cause-specific
mortality
Clinical disease
Environmental & Behavioral Factors
(Spatial dependence)
X
• GIS and health geography
• Major applications for GIS
• Epidemiology
• What is health (and how location matters)
• What is a disease (and how to identify one)
• Quantifying disease occurrence
• Incidence versus prevalence
• Identifying the population
• Working with small area data
• To study disease, we need measures of its
occurrence.
• Some measures of disease occurrence
•
•
•
•
Counts
Prevalence
Incidence
Mortality
DESCRIPTIVE
What?
What are the
health problems
of the
community?
What are the
attributes of
these illnesses?
Health and disease in the community
Who?
When?
Where?
How many people
are affected?
What are the
attributes of
affected persons?
Over what
period of time?
Where do the
affected people
live, work or
spend leisure
time?
Each of the measures can be calculated for different combinations
of What? Who? When? and Where?
Each of the W’s needs to be defined carefully to get comparable
measures across a province or state, a nation, the world.
• The prevalence of a disease is the proportion of
individuals in a population with the disease (cases) at
a specific point in time:
Number cases in population at specified time
Number of persons in population at that specified time
• Prevalence is a proportion – range of 0 to 1
• Removes the effect of total population size – makes
estimates from different populations or over time
more comparable.
• Often expressed as a percent (%) – Prevalence * 100
• Also often expressed as the prevalence per 1,000 or 10,000
or 100,000.
• Prevalence * 1,000 = prevalence per 1,000.
(*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” woman)
1991
1995
2002
No Data
<10%
10%–14%
2006
15%–19%
20%–24%
≥25%
Cases infected with the outbreak strain of Salmonella Saintpaul,
as of July 15, 2008 9 pm EDT. We would need to know the
population in each state in order to determine the prevalence.
Number of NEW cases in population DURING specified time
Number of persons AT RISK of disease in population during that specified time
If population size is
3.81 million, then
652
100,000
3,810,000
 .00017 100,000
I
 17.1
The incidence of a disease is the rate at which new
cases occur in a population during a specified period.
Incidence of cases of infection with the outbreak strain as of
July 15, 2008 9pm EDT
Cases infected with the outbreak strain of Salmonella Saintpaul,
as of July 15, 2008 9pm EDT
• Incidence and prevalence measure different aspects
of disease occurrence
Prevalence
Incidence
Numerator:
All cases, no matter how
long diseased
Only NEW cases
Denominator:
All persons in pop
Only persons at risk of
disease
Measures:
Presence of disease
Risk of disease
Most useful:
Resource allocation
Risk, etiology
Etiology: the study of a disease’s causes.
• Numerator
• Number of deaths
• Denominator
• Number of individuals in
population (how defined?)
• Time interval
• 1-year: Annual Mortality Rate
• (typical to use an annual rate)
• Specifier
• age, sex, race, etc.
• For any measure, carefully defining both the
numerator and denominator is crucial for
interpretation.
• In order for measures to be comparable across
studies, need consistent definition and reporting
strategies for numerator.
• Also need consistent approaches for counting (or
estimating) the persons or person-time for the
denominator.
AIDS cases, United States 1984-2000
Result of
new definition
1st Quarter of 1993:
Expansion of
surveillance case
definition
Understanding population dynamics is crucial to epidemiology.
Demography = the study of population dynamics including
fertility, mortality and migration
Greek
English
epi
among
demos
people
logy
study
• GIS and health geography
• Major applications for GIS
• Epidemiology
• What is health (and how location matters)
• What is a disease (and how to identify one)
• Quantifying disease occurrence
• Incidence vs prevalence
• Identifying the population
• Working with small area data
• Developing multi-level models for spatially-correlated
data requires confidence in the dependent data.
• Data for disease mapping often consists of disease
counts and exposure levels
in small adjacent geographical areas.
• The analysis of disease rates or counts for small areas
often involves a trade-off between statistical stability
of the estimates and geographic precision.
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• Disease caused by a deficient diet or
failure of the body to absorb B complex
vitamins or an amino acid.
• Common in certain parts of the world (in
people consuming large quantities of corn),
the disease is characterized by scaly skin
sores, diarrhea, mucosal changes, and
mental symptoms (especially a
schizophrenia-like dementia). It may
develop after gastrointestinal diseases or
alcoholism.
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• A case study:
• They considered approximately 800 counties clustered within 9 states in
southern US
• For each county, data consisted of observed and expected number of
pellagra deaths
• For each county, they also had several county-specific socio-economic
characteristics and dietary factors
• % acres in cotton
• % farms under 20 acres
• Dairy cows per capita
• Access to mental hospital
• % Afro-American
• % single women
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• Which social, economical, behavioral, or dietary factors
best explain spatial distribution of pellagra in southern
US?
• Which of the above factors is more important for
explaining the history of pellagra incidence in the US?
• To what extent have state-laws affected the incidence of
pellagra?
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• For small areas, the Standardized Mortality Ratio
(SMR) can be very instable and maps of SMR can
be misleading
• Spatial smoothing can improve stability
• SMR are spatially correlated
• Spatially correlated random effects
• Covariates available at different level of spatial
aggregation (county, State)
• Multi-level regression structure
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• Spatial smoothing can reduce the random noise in
maps of observable data (or disease rates)
• Trade-off between geographic resolution and the
variability of the mapped estimates
• Spatial smoothing as method for reducing random
noise and highlight meaningful geographic patterns
in the underlying risk
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• Shrinkage methods can be used to take into account
instable SMR for the small areas
• Idea is that:
• smoothed estimates for each area “borrow strength” (precision)
from data in other areas, by an amount dependent on the
precision of the raw estimate of each area
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• When population in area A is large
• Statistical error associated with observed rate is small
• High credibility (weight) is given to observed estimate
• Smoothed rate is close to observed rate
• When population in area A is small
• Statistical error associated with observed rate is large
• Little credibility (low weight) is given to observed estimate
• Smoothed rate is “shrunk” towards mean rate of
surrounding areas
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Crude SMR
Smoothed SMR
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• In epidemiology and demography, most
rates, such as incidence, prevalence,
mortality, are strongly age-dependent,
with risks rising (e.g. chronic diseases) or
declining (e.g. measles) with age. In part
this is biological (e.g. immunity
acquisition), and in part it reflects the
hazards of cumulative exposure, as is the
case for many forms of cancer. For many
purposes, age-specific comparisons may
be the most useful.
• However, comparisons of crude age-specific rates
over time and between populations may be very
misleading if the underlying age composition differs in
the populations being compared. Hence, for a variety
of purposes, a single age-independent index,
representing a set of age-specific rates, may be more
appropriate. This is achieved by a process of age
standardization or age adjustment.
• The age-standardized mortality
rate is a weighted average of
the age-specific mortality rates
per 100 000 persons, where
the weights are the proportions
of persons in the corresponding
age groups of the standard
population.
• Spatial Analytic Techniques
for Medical Geographers
(Albert et al., 2000)
• GIS and health geography
• Major applications for GIS
• Epidemiology
• What is health (and how location matters)
• What is a disease (and how to identify one)
• Quantifying disease occurrence
• Incidence versus prevalence
• Identifying the population
• Working with small area data