sources and effects of bias in investigating links between adverse health outcomes and environmental...
TRANSCRIPT
Sources and effects of bias in investigating links between adverse health outcomes and
environmental hazards
Frank Dunstan
University of Wales College of Medicine
The results are only as good as the data!
Outline of talk• Why do so many spatial studies fail to find evidence
of the effect of risk factors?
• Is it because exposure is usually measured inadequately?
• Consider a point source of risk and the effects ofDistance as a surrogate for exposureMigration
• More generally in looking at association between the spatial variation of disease incidence and risk factors, what is the effect of measurement error?
How do we measure exposure?
• Distance from focus is often used as a surrogate
• Algorithms of Stone, Bithell, Tango etc use different models under the alternative hypothesis – but the usual assumption is that risk decreases monotonically with distance.
• It is implicit that it is the same in all directions
• ‘Circles’ approach similar
• Models of transmission of risk make this implausible
‘Circles’ method
Example on congenital anomalies
• Data on all births in Wales in 15 year period, linked to records of congenital anomalies
• Locations obtained using a GIS
• Data on landfill sites which changed significantly in the period – 24 in all
• Individual data on maternal age, birthweight
• Census data on deprivation
• Is the opening of a site associated with an increased risk of anomalies?
Modelling
• Rate varied significantly between hospitals and by year of birth – adjustment for these needed
• Risk modelled as function of
Age of mother Hospital Gender
Year of birth Deprivation
• Calculate observed and expected for each square of side 250m (for example), then smooth standardised differences using kernel smoothing
Smoothed risks around 2 landfill sites, before and after opening
Standard
Astbury
Quarry
After opening
EAST
336000334000332000330000328000326000324000322000
NO
RT
H
372000
370000
368000
366000
364000
362000
360000
358000
high
medium
low
Before opening
EAST
340000338000336000334000332000330000328000326000
NO
RT
H
366000
364000
362000
360000
358000
356000
354000
352000
350000
medium
low
After opening
EAST
340000338000336000334000332000330000328000326000
NO
RT
H
364000
362000
360000
358000
356000
354000
352000
350000
high
medium
low
Before opening
EAST
336000334000332000330000328000326000324000322000
NO
RT
H
372000
370000
368000
366000
364000
362000
360000
358000
COL
high
medium
low
Trecatti
Nantygwyddon
Before opening
EAST
316000
314000
312000
310000
308000
306000
304000
302000
300000
NO
RT
H
214000
212000
210000
208000
206000
204000
202000
200000
High
Medium
Low
After opening
EAST
316000
314000
312000
310000
308000
306000
304000
302000
300000
NO
RT
H
214000
212000
210000
208000
206000
204000
202000
200000
High
Medium
Low
After opening
EAST
306000304000302000300000298000296000294000292000
NO
RT
H
202000
200000
198000
196000
194000
192000
190000
188000
186000
High
Medium
Low
Before opening
EAST
306000304000302000300000298000296000294000292000
NO
RT
H
202000
200000
198000
196000
194000
192000
190000
188000
186000
Medium
Low
Interpretation
• Need to consider the change in risk pattern, comparing before and after opening.
• Different sites have different risk patterns.
• Pooled results across sites must be interpreted carefully.
• Possibly due to geographical differences.
• Risk does not seem isotropic – possibly affected by wind, water flow, topography of site.
• What is the effect on tests and estimates of risk?
Simulation exercise
• Assume that a certain amount of pollutant is spread from a point source
• Consider different patterns of spread
– isotropic
– Concentration on direction of prevailing wind
– Non-monotonic
• Based on scenario of births to provide detailed data – but interested in relative magnitudes of power, etc, rather than absolute
• Does geography matter?
Results
• Show power – Stone’s method for simplicity (patterns the same for others)
• Mean estimated odds ratio if using ‘circles’ with correct threshold
• These vary between sites because of the distribution of the population
Results on power – 3 sites
0
20
40
60
80
100
0.01 0.02 0.03 0.04Rate at focus (background 0.01)
Pow
er
0
20
40
60
80
100
0.01 0.02 0.03 0.04Rate at focus (background 0.01)
Pow
er
0
20
40
60
80
100
0.01 0.02 0.03 0.04Rate at focus (background 0.01)
Pow
er
Isotropic
Not isotropic
Not isotropic
Not monotonic
Results on odds ratio – 3 sites
1
1.4
1.8
0.01 0.02 0.03 0.04Rate at focus (background 0.01)
Mea
n od
ds ra
tio
1
1.4
1.8
0.01 0.02 0.03 0.04Rate at focus (background 0.01)
Mea
n od
ds ra
tio
1
1.4
1.8
0.01 0.02 0.03 0.04Rate at focus (background 0.01)
Mea
n od
ds r
atio Isotropic
Not isotropic
Not isotropic
Not monotonic
Migration
• Large numbers of people move house each year
• Many diseases associated with environmental risks are believed to be due to long term exposure
• Taking place of residence at diagnosis as representing exposure is potentially misleading – exposure may have arisen from previous locations
• Effect will be to weaken the apparent risk
• We planned to use the NHSAR to identify appropriate models – but the data are not yet available
Migration model• Based on the population around a site, divided
into census EDs (between 150 and 200, depending on the site)
• Assume a fixed probability of moving each year
• Probability of destination of move decreases with distance
• Assume the background rate varies across EDs according to a log-normal distribution
• Assume a monotonically-decreasing risk from the source
• Monitor total number of exposure-years on each individual
• Assume a logistic model for the risk of a case as a function of exposure-years
• Use ‘circles’ method for simplicity to assess effect
• Estimate effect on odds ratio and power
Typical results from a site
• Odds ratio and power decrease markedly as migration increases.
• Absolute values depend on parameters – pattern seems to be preserved but local geography matters
1
1.05
1.1
1.15
1.2
1.25
1.3
0 0.1 0.2
Annual migration rate
Mean odds ratio
0
20
40
60
80
100
0 0.1 0.2
Annual migration rate
Power
Errors in variables
• Ecological studies by administrative area• Take area-based disease rates (mortality,
incident cancer cases etc.)• Risk factors also defined at area level
– Often from census data– Also from irregularly measured factors
• So these are unlikely to be reported at correct levels
• Typical problem – leukaemia incidence against ionising radiation
Wales.shp26 - 3031 - 3536 - 4041 - 4546 - 5051 - 55
Wales wards.shp0 - 0.50.5 - 0.80.8 - 1.251.25 - 22 - 4
Simulation
• Poisson regression & spatial models – only Poisson results shown for brevity
• Measure of deprivation used as covariate
• Spatial correlation induced
• Classical measurement error model
• Interested in bias and in the estimate of the SD of the regression coefficient
Typical simulation results – effect on bias
• Based on all-Wales (908 wards) and a sub-region (111 wards)
• Bias increases with error SD as in other contexts
• Effect of correlation more on the estimated SD
• Spatial model (BYM) gives similar parameter estimates but with better estimate of SE
Rho=0
Error SD
2.01.51.0.50.0
Re
gre
ssio
n c
oe
ffic
ien
t
.21
.20
.19
.18
.17
.16
.15
.14
N
908
111
Rho=0.15
Error SD
2.01.51.0.50.0
Re
gre
ssio
n c
oe
ffic
ien
t
.21
.20
.19
.18
.17
.16
.15
.14
N
908
111
Conclusion• In investigating the risk around a source we need a proper
measure of exposure; distance is not enough.
• Methods which assume the risk decreases monotonically with distance lack power.
• Effects will vary with geographical location and account must be taken of local conditions.
• Migration can have a considerable effect on the extent of exposure. This is particular important when distance is used a surrogate for exposure. More work is needed on better models.
• A proper investigation requires detailed studies at individual level, of locations and people to assess exposure accurately.