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


1
GIS in Spatial Epidemiologysmall area studies
of exposure- outcome relationships
  • Robert Haining
  • Department of Geography
  • University of Cambridge

2
  • 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

3
  • 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.

4
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.

5
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

6
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.

7
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.

8
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

9
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.
10
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

11
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

12
  • Problem obtain a measure of air pollution
    exposure at the ED level.

13
Step 1 Measuring NOx exposure. The Indic-Airviro
model
14
Average annual mean pollution levels 1994-9 (exc
1998) a) NOx (ug/m3) b) PM10 (ug/m3)
15
Comparing modelled and monitored values for NOx.
16
Step 2 Transferring the gridded data to the ED
framework. Areal Interpolation i Area weighting
17
Areal Interpolation (from grid to EDs) ii point
in polygon ED centroid
18
Areal Interpolation (from grid to EDs) iii point
in polygon weighted PostPoint
19
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20
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21
Weighted PostPoint and ED centroid exposure
measures are very similar areal weighting
different
22
Weighted PostPoint differs from both ED centroid
and areal weighting
23
Where all three methods will give the same or
similar results
24
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.

25
2. Short term population movement
26
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
27
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

28

29
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).

30
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.
31
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.
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