Page 303 - Contributed Paper Session (CPS) - Volume 2

CPS1871 Natividad J.M. et al.
syndrome progresses. The virus is transferred from one person to another
through blood-to-blood, sexual contact, and infected pregnant women to
their babies.
The Philippines had one of the ‘fastest growing HIV epidemics in the world’
according to the World Health Organization (Rappler, 2015). It has
tremendously increased by 3,147% increase in 10 years from 342 reported
HIV infections in 2007 to 11,103 new HIV reported cases in 2017 (Regencia
2018). It is further reported that as many as 32 people were diagnosed with
HIV-AIDS every day in the Philippines (Rappler, 2018). From January 1984 to
June 2016, the region with the most number of reported cases was National
Capital Region (NCR) with 15,053 (43%) cases. Majority (95%) of those HIV
infected in the said years were male. It is alarming in 2018 that many of the
new HIV infections were reported likely to be young adults and teen-agers
aged 15 to 24 years old and were transmitted sexually (Rappler, 2018).
Despite that the number of HIV new cases had gone down worldwide from
2.1 million to 1.8 million according to WHO, the number of HIV new cases in
the Philippines had surged up which would likely reach up to 133,300 by 2022
(Rappler, 2015) and might even exceed a quarter of a million by 2030 (Rappler,
2018). Hence, there is a continuous need to study spatial patterns of HIV
infections in order to control and to stop it from spreading.
In line with the United Nations (UN) sustainable development goals (SDG)
3 on good health and well-being, this study aims to provide a Bayesian
Conditional Autoregressive (CAR) model in estimating the relative risks of
infection in each city/municipality of NCR. Moreover, a comparison between
the mixed effect Poisson Gamma model with covariate and the Bayesian CAR
is presented for this serves as a support that Bayesian CAR is the better model.
Bayesian Conditional Autoregressive model also provides a clear
representation of relative risks through maps.
Performing Bayesian analysis intends to assist HIV prevention policy
formulation in the Philippines as this could help the local government towards
its goal of declining the incidence of HIV cases in the country. Knowing where
the needing high risk areas of HIV are, government officials would have an
idea where to allocate health funds more. Additionally, it would provide better
attention on the distribution of HIV cases in NCR compared to the distribution
of cases in the entire Philippines since it only covers a smaller area being
observed. It would give a better implementation in controlling the prevalence
of HIV cases because of the spatial patterns shown by the models. The scope
includes only the sexually active population (15 to 85 years old) and is only
limited on the 2014 HIV count data/cases in each city/municipality in NCR
which was the available data during the time of this study.

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