Occup Environ Med 2001;58:315-320
( May )
Small area estimation of incidence of cancer around a known
source of exposure with fine resolution data
E Kokkia b, J Rantaa c, A Penttinenb, E Pukkalad, J Pekkanena
a Unit of
Environmental Epidemiology, National Public Health Institute, PO Box
95, FIN-70701 Kuopio, Finland, b Department of Statistics, University of
Jyväskylä, PO Box 35, FIN-40351 Jyväskylä, Finland, c Rolf Nevanlinna
Institute, University of Helsinki, PO Box 4, FIN-00014, University of
Helsinki, Finland, d Finnish
Cancer Registry, Liisankatu 21 B, FIN-00170 Helsinki, Finland
Correspondence to: Mr E Kokki
Esa.Kokki{at}ktl.fi
Accepted 15 January
2000
OBJECTIVES
To
describe the small area system developed in Finland. To illustrate the
use of the system with analyses of incidence of lung cancer around an
asbestos mine. To compare the performance of different spatial
statistical models when applied to sparse data.
METHODSIn the small
area system, cancer and population data are available by sex, age, and
socioeconomic status in adjacent "pixels", squares of size 0.5 km × 0.5 km. The study area was partitioned into sub-areas based on
estimated exposure. The original data at the pixel level were used in a
spatial random field model. For comparison, standardised incidence
ratios were estimated, and full bayesian and empirical bayesian models
were fitted to aggregated data. Incidence of lung cancer around a
former asbestos mine was used as an illustration.
RESULTSThe
spatial random field model, which has been used in former small area
studies, did not converge with present fine resolution data. The number
of neighbouring pixels used in smoothing had to be enlarged, and
informative distributions for hyperparameters were used to stabilise
the unobserved random field. The ordered spatial random field model
gave lower estimates than the Poisson model. When one of the three
effects of area were fixed, the model gave similar estimates with a
narrower interval than the Poisson model.
CONCLUSIONSThe
use of fine resolution data and socioeconomic status as a means of
controlling for confounding related to lifestyle is useful when
estimating risk of cancer around point sources. However, better
statistical methods are needed for spatial modelling of fine resolution data.
Keywords:
disease mapping;
point source;
spatial random field
model
© 2001 by Occupational and Environmental Medicine
