Abstract:
In real life situations, the values of the response variable, which is the count data is
mostly under-reported. In this work, we develop a model to cater for under-reporting
in count data. In particular, we allow under-reporting to vary spatially by regions and
it is captured by a binomial probability. Poisson distribution is used in modeling the
count response under the assumption that over-dispersion does not exist. In the case of
under-reporting, it was made to also vary spatially from one unit to the other through
a probability captured by a binomial distribution.
The spatial variations of the disease were divided into correlated and uncorrelated
parts. When a Poisson Regression analysis was used, both the correlated and uncorrelated
parts were all found to share a significant relationship with the relative risk
for each region with more contribution coming from the uncorrelated part. The model
obtained was applied to diabetes data in Ghana. Disease maps for the diseases are
also developed for Ghana at administrative (district) level. These maps are critical and
informative to policy makers. These maps allow them to target policies and use the
already meagre resources well.
