Page 309 - Contributed Paper Session (CPS) - Volume 2

CPS1871 Natividad J.M. et al.
was introduced and found out to exhibit significant spatial autocorrelation.
This indicates that HIV cases in a certain area are correlated with the incidences
of nearby areas. Bayesian CAR is found to be the better model taking into
account spatial autocorrelation and having the lowest value of DIC. Under the
Bayesian CAR, HIV relative risk estimates shrunk in majority of the cities and
municipality based on the neighborhood structure.
The map of Bayesian CAR model revealed that Mandaluyong and Makati
appeared to be the areas with highest relative risk of infection. The reasons as
to why these cities possess the highest estimates of HIV relative risk are
because of the numerous number of clubs, motels, affordable condominiums,
call centers, companies, and industries found in these areas. Furthermore, it is
assumed that due to a high cost of living in Makati, people tend to find houses
in its neighboring cities resulting to these areas to have high HIV relative risk
as well. These are followed by Pasig, Pasay, Marikina, Manila, San Juan, and
Pateros. Moreover, as cities and municipality go farther from these areas, the
relative risks of HIV lessen. Health organizations may be able to use these
results and start to provide health programs and to allocate funds more in
areas with high HIV relative risks.

References
1. Lawson, A. B. (2013). Bayesian disease mapping: hierarchical modeling in
spatial epidemiology. CRC press.
2. Lawson, A. B. (2013). Statistical methods in spatial epidemiology. John
Wiley & Sons.
3. Lawson A., Biggeri A., Bohning D., Lesaffre E., Biggeri A., Viel J.F., & Best
N. ( 1999). Disease mapping and risk assessment for public health. John
Wiley & Sons.
4. Marchetti, S., Dolci, C., Riccadonna, S., & Furlanello, C. (2010). Bayesian
Hierarchical Model for Small Area Disease Mapping: a Breast Cancer
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5. Neyens, T., Faes, C., & Molenberghs, G. (2012). A generalized Poisson-
gamma model for spatially overdispersed data. Spatial and spatio-
temporal epidemiology, 3(3), 185-194.
6. Rappler (2018) 32 Filipinos Test Positive for HIV-AIDS Everyday. Retrieved
from https://www.rappler.com/nation/212851-daily-hiv-aids-new-cases-
2018-philippines
7. Rappler (2015, May) WHO: PH has the fastest growing HIV epidemic in
the world. Retrieved from https://www.rappler.com/nation/93839-who-
ph-response-hiv.
8. Regencia, T. (2018, February). Philippines: HIV Cases Up to 3,147 percent
in 10 Years. https://www.aljazeera.com/news/2018/02/philippines-hiv-
cases-3147-percent-10-years-180219090618348.html.

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