gis and health geography

GIS and Health Geography

What is epidemiology?

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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

GIS and health Geography

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.

Major applications for GIS

1. Spatial epidemiology

2. Environmental hazards

3. Modeling Health Services

4. Identifying health inequalities

Spatial epidemiology

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

Framework for analysis

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).

Why small area analyses?

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.

Geographical correlation studies

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.

Issues: Spatial misalignment

Issues: Uncertainty

Frequency and quality of population data (e.g. Census every 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.

Issues: Best practices

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)

Spatial epidemiology

Dr. John Snow’s Map of Cholera Deaths in the SOHO District of London, 1854

Environmental hazards

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

Environmental hazards

GIS: Identify causal and mitigating factors

ARIA (Accessibility/Remoteness Index of Australia)

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

Mortality rate of infants (1980-2001)

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

SES and Heart disease

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)

The Data

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 censusSocial disadvantage measured at postal area

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

High values indicate social advantage

SEIFA distribution in NSW

NSW IHD rates

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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)

What is epidemiology?

(H. Shodell, Science ’82, September, p. 50)

Epidemiologists . . .

… 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.

Epidemiologic approaches

DESCRIPTIVEHealth and disease in the community

What? Who? When? Where?What are thehealth problemsof the community?

What are theattributes of these illnesses?

How many peopleare affected?

What are theattributes ofaffected persons?

Over whatperiod of time?

Where do theaffected peoplelive, work orspend leisuretime?

ANALYTICEtiology, prognosis and program evaluation

Why? How?What are the causal agents?

What factorsaffect outcome?

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 . . .".

What are “disease” and “health”?

What is ‘health’

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.

Location and health

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.

Location and health

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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.

What is ‘disease’

Manifestational Criteria

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).

Causal Criteria

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.

Challenges with Disease Classifications

A single causal agent may have multiple clinical effects.

Multiple etiologic pathways may lead to apparently identical manifestations, so that a manifestationally-defined 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.

UnderlyingGeneticSusceptibility

Onset ofdisease

Diagnosisof disease

Environmental & Behavioral Factors(Spatial dependence)

PhysiologicAbnormalities Clinical disease

Cause-specificmortality

XSub-clinical disease

The natural history of disease

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Incidence versus prevalence Identifying the population Working with small area data

Measures of disease occurrence

To study disease, we need measures of its occurrence.

Some measures of disease occurrence◦ Counts◦ Prevalence ◦ Incidence◦ Mortality

Epidemiologic approaches

DESCRIPTIVE Health and disease in the community

What? Who? When? Where?What are thehealth problemsof the community?What are theattributes of these illnesses?

How many peopleare affected?

What are theattributes ofaffected persons?

Over whatperiod of time?

Where do theaffected peoplelive, work orspend leisuretime?

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.

Prevalence

The prevalence of a disease is the proportion of individuals in a population with the disease (cases) at a specific point in 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.

Number cases in population at specified timeNumber of persons in population at that specified time

Prevalence

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.

1991 1995

2002

No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

(*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” woman)

2006

Obesity Trends Among U.S. Adults

Salmonella cases: Infected

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.

Incidence

Number of NEW cases in population DURING specified timeNumber of persons AT RISK of disease in population during that specified time

If population size is 3.81 million, then

652 100,0003,810,000

.00017 100,00017.1

I

The incidence of a disease is the rate at which new cases occur in a population during a specified period.

Salmonella cases: Incidence

Incidence of cases of infection with the outbreak strain as of July 15, 2008 9pm EDT

Cases and Incidence – Salmonella

Cases infected with the outbreak strain of Salmonella Saintpaul, as of July 15, 2008 9pm EDT

Incidence and Prevalence

Incidence and prevalence measure different aspects of disease occurrence

Prevalence Incidence

Numerator:

Denominator:Measures:

Most useful:

All cases, no matter how long diseased

Only NEW cases

All persons in pop Only persons at risk of disease

Presence of disease Risk of disease

Resource allocation Risk, etiology

Etiology: the study of a disease’s causes.

Mortality Rate – Incidence of death

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.

Mortality rates

Importance of defining terms

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.

Prevalence numerator – case definition

Result ofnew definition

1st Quarter of 1993:Expansion of

surveillance casedefinition

AIDS cases, United States 1984-2000

Understanding population dynamics is crucial to epidemiology.

Demography = the study of population dynamics including fertility, mortality and migration

The “demi” in Epidemiology

Greek English

epi among

demos people

logy study

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Data considerations

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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An example: Pellagra in the US

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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Multi-level data in spatial epidemiology

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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Scientific Questions

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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Definition of Standardized Mortality Ratio

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Definition of the expected number of deaths

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Crude Standardized Mortality Ratio (Observed/Expected) of Pellagra Deaths in Southern USA in 1930 (Courtesy of Dr Harry Marks)

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Statistical Challenges

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

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 Estimation

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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Shrinkage Estimation

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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Raw and smoothed SMR

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SMR of pellagra deaths for 800 southern US counties in 1930

Crude SMR Smoothed SMR

Ensuring comparability

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.

Ensuring comparability

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.

Standardizing

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.

Methodological toolboxes

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

Summary



Incidence versus prevalence Identifying the population Working with small area data

gis and health geography

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