[Forum SIS] PHD Seminars @Sapienza

[Marco Alfo']

We are glad to announce the following Seminar

friday, November 22, 12.00 am

 Room 34, Statistical Sciences Building CU002, Sapienza Universita' di Roma
Garyfallos Konstantinoudis (Imperial College)

*Discrete versus continuous domain models for disease mapping *
*and applications on childhood cancers*

The main goals of disease mapping is to calculate disease risk and identify
high-risk areas. Such analyses are hampered by the limited geographical
resolution of the available data. Typically data are counts of cases per
spatial unit and the most common approach is the Besag-York-Molli e model
(BYM). Less frequently, exact geocodes are available, allowing modelling a
disease as a point process through Log-Gaussian Cox processes (LGCPs). The
objective of this study is to examine in a simulation the performance of
BYM and LGCPs for
disease mapping. We simulated data in the Canton of Zurich in Switzerland
sampling cases from the true population mimicking the childhood leukaemia
incidence (n=334 during 1985-2015). We considered 39 di erent scenarios
varying in the risk generating function (step-wise, smooth,  at risk), the
size of the high-risk areas (1, 5 and 10km radii), the risk increase within
the high-risk areas (2 and 5-fold) and the number of cases (n, 5n and 10n).
We used the root mean integrated square error (RMISE) to examine the
ability of the models to recover the true risk surface and their
sensitivity/speci city in identifying high-risk areas. We found that, for
larger radii, LGCPs recover the true risk surface with lower error across
almost all scenarios (median RMISE: 9.17-27.0) compared to the BYM (median
RMISE: 9.12-35.6). For radii = 1km and at risk surfaces BYM performs
better. In terms of sensitivity and speci city across almost all scenarios
the median area under the curve (AUC) for LGCPs was higher (median AUC:
0.81-1) compared to the BYM (median AUC: 0.65-0.93).
We applied these methods to childhood leukaemia incidence in the canton of
Zurich during 1985-2015 and identi ed two high-risk spatially coherent
areas. Our fi ndings suggest that there are important gains to be made from
the use of LGCP models in spatial epidemiology.
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