dc.description.abstract |
Cancer is an event associated with space and time. Counties relative risks esti-
mates can be obtained using Bayesian hierarchical models. The general objective
of the research was to obtain county based estimates through Bayesian hierarchi-
cal modeling of cervical, oesophageal and lung cancers in Kenya's counties from
2015 to 2016, period which complete data was available. Speci c objectives were:
to model over-dispersion and conduct spatial correlations tests in order to model
three cancer cases distribution in Kenya' counties; to model cervical cancer cases
using Poisson-Gamma and spatial-temporal models; to model the e ects of co-
variates on spatial-temporal distribution of oesophageal and lung cancer cases in
Kenya's counties. The data was obtained from National Cancer Registry (NCR)
which carried a 2 year retrospective study in ten counties. Cervical cancer cases
were 1064, oesophageal cancer cases 1599 while lung cancer cases were 256.
A
simple Poisson log-linear model dispersion parameter for cervical was 31.202, oe-
sophageal was 49.241 and lung cancer cases was 6.134 which were greater than
1 indicating over dispersion.
Spatial correlation tests conducted for the three
cancers revealed that there was no spatial auto correlations of the residuals since
for cervical cancer p-value=0.2104>0.05, oesophageal p-value= 0.4155>0.05 while
for lung cancer p-value=0.4120>0.05. The model revealed that the highest cervi-
cal cancer relative risk was in Embu=7.92 and lowest in Bomet which was 1.53.
The smoking and alcohol use interaction oesophageal cancer model revealed that
Bomet=11.16 had the highest risk while Kiambu had the lowest relative risk 0.6.
Smoking and alcohol use were signi cant risk factors of oesophageal cancer. The
multiplicative e ect of smoking was 1.012, thus 1.2 % higher to smokers compared
to non-smokers. Alcohol use was 1.0346 thus 3.5 % higher to alcohol users. The
interaction model revealed that oesophageal cancer was 16.88 % higher to alcohol
users while it was 4.60 % higher to smokers. The interaction model for lung cancer
revealed that in Nairobi=5.97 had highest risk while lowest in Kakamega=0.1. In
the lung cancer model the multiplicative e ect of smoking was 1.4021, indicating
40.21 % higher to smokers as compared to non-smokers, 1.3689 for alcohol use
variable that is 36.89 % higher to alcohol users. In interaction model the e ect
was 7.86 times higher for smokers. In conclusion, simple Poisson regression mod-
els were not appropriate to model the three cancers due to over dispersion nature
of the data sets. The spatial correlation tests revealed that there was no spatial
auto correlation for the three types of cancer. Application of Bayesian hierarchical
models enabled generation of relative risks and identi cation of the risk patterns
of various counties, a major milestone since previous studies focused on speci c
regions. We recommend that, since all counties had cervical cancer relative risk
greater than 1, step up screening and avail vaccines to the appropriate groups. To
mitigate oesophageal cancer, counties should create awareness on e ects of smok-
ing and alcohol use. In case of lung cancer, counties with relative risks greater than
1 should disseminate information elaborating the e ects of smoking and alcohol
use |
en_US |