GIS in Spatial Epidemiology: small area studies of exposure outcome relationships

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GIS in Spatial Epidemiologysmall area studies
of exposure- outcome relationships

• Robert Haining
• Department of Geography
• University of Cambridge

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• Spatial epidemiology
• Some definitions
• Geographical correlation studies
• Framework for analysis
• Problems with small area analysis
• Reasons for conducting small area analysis
• Good practice
• Regression models
• Reference to a case study
• Data issues
• Statistical modelling

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• 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.

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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 whilst
  controlling for other relevant factors using
  regression.

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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)
• Live in communities

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Problems with small area analyses

• 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.

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Reasons for conducting small area analysis

• 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.

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Good practice (Richardson 1992)

• 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

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Regression model specification Oi denotes the
number of cases for area i. i 1,,n. If the
outcome is rare, typically, it is assumed that Oi
is Poisson distributed with parameter ?i. The
expected value of Oi is written EOi ?i
Eiri i 1, , n, where ri is the unknown
area-specific relative risk in area i, and Ei
defines the expected number of cases for i given
the size of the population and its age and sex
composition.
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ln?i lnEi lnri .
ln?i lnEi ? ?1X1,i ?2X2,i .....
?kXk,i ?Zi

• This defines a Poisson regression model where ?
  is the intercept parameter, and ?1, ?2,, ?k and
  ? are regression parameters. lnEi is an offset.
  
• The area-specific relative risk at i is
  associated with attributes of the population
  X1,,Xk and the environmental exposure Z at i.
• Adjustment for overdispersion is necessary
  because of population heterogeneity at the scale
  of the individual small areas (see, for example,
  Manton and Stallard 1981).
• Allowance for data uncertainty arising from the
  use of sample data

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A short case study I Data Issues and GIS

• Demographic and social and economic data
• Pre-2001 Census
• Enumeration Districts (EDs)
• Wards.
• 2001 Census
• Output Areas (OAs)
• Super Output Areas (SOAs)
• Health data (Heart disease stroke mortality
  admissions)
• Individual records geo-referenced to ED
• Postcoded counts
• Environmental data (NOx PM10 CO)
• Grided

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• Problem obtain a measure of air pollution
  exposure at the ED level.

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Step 1 Measuring NOx exposure. The Indic-Airviro
model
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Average annual mean pollution levels 1994-9 (exc
1998) a) NOx (ug/m3) b) PM10 (ug/m3)
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Comparing modelled and monitored values for NOx.
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Step 2 Transferring the gridded data to the ED
framework. Areal Interpolation i Area weighting
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Areal Interpolation (from grid to EDs) ii point
in polygon ED centroid
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Areal Interpolation (from grid to EDs) iii point
in polygon weighted PostPoint
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Weighted PostPoint and ED centroid exposure
measures are very similar areal weighting
different
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Weighted PostPoint differs from both ED centroid
and areal weighting
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Where all three methods will give the same or
similar results
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Step 3 Making allowance for population movements

• Long term population movement
• Sheffield Health and Illness Prevalence study
• 12,239 representative individuals 18-94 tracked
  from 1994-2002
• 1491 died 1572 left Sheffield.
• Of the 9176 remaining
• 70 did not move
• 23 made 1 move
• 5 made 2 moves
• Just over 1 made 3 moves
• Under 1 made 4 or more moves.
• gt significant risk of misclassification of
  exposure level.

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2. Short term population movement
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Spatially smoothed CO average of the annual mean
pollution levels (1994-1999, excluding 1998) for
Sheffield enumeration districts (ug/m3)(i) 1km
(ii) 2km (iii) 4km
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Comparing indoor and outdoor air pollution
exposure

• People spend between 75 and 90 of their time
  indoors.
• Indoor pollution levels depend not only on
  outdoor emissions but on housing conditions
  (cooking, heating, ventilation etc).
• Evidence on relationship between indoor and
  outdoor pollution levels

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Statistical modelling issues

• ln?i lnEi ? ?1X1,i ?2X2,i ...
  ?kXk,i ?Zi
• 1.Overdispersion linked to spatially correlated
  missing covariates.
• 2.Sampling errors where data are based on
  surveys (e.g lifestyle data).
• Fitted spatially structured random effects models
  in WinBUGS (MCMC estimation) to handle
  overdispersion
• Used posterior densities for some of the
  lifestyle covariates (e.g. smoking prevalence)
• WinBUGS output sent to GIS to map model output
  (e.g. area specific risks).

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Map of excess relative risks of coronary heart
disease. An area (i) is considered to have excess
relative risk when 97.5 of the simulated values
of relative risk of area i (ri) are greater than
1.
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References

• P.Brindley, R.Maheswaran, T.Pearson, S.Wise and
  R.Haining (2004) Using modelled outdoor air
  pollution data for health surveillance. In
  R.Maheswaran and M.Craglia (eds) GIS in Public
  Health Practice. Taylor and Francis, London,
  p.125-149.
• P.Brindley, S.Wise, R.Maheswaran, and R.Haining.
  (2005) The effect of alternative
  representations of population location on the
  areal interpolation of air pollution exposure.
  Computers, Environment and Urban Systems, Vol 29,
  455-469.
• R.Maheswaran, R.Haining, P.Brindley, J.Law,
  T.Pearson, N.Best (2006) Outdoor NOx and stroke
  mortality adjusting for small area level
  smoking prevalence using a Bayesian approach.
  Statistical Methods in Medical Research, 2006,
  15, 499-516.