Relative risk estimation of tuberculosis disease mapping with stochastic equations - Ijlal, Mohd Diah (2017)
Tuberculosis (TB) is one of the death leading causes in developing countries. The current monitoring of the disease is based only on the total cases reported. Alternatively, a better approach called disease mapping offers geographic distribution of the disease relative risk. Previous studies used Standard Mortality Ratio (SMR) and Poisson-gamma models to estimate relative risk but these models have several drawbacks. SMR model cannot detect relative risk for small areas while Poisson-gamma model cannot allow for covariate adjustments. Hence, the objective of this study is to develop an alternative statistical model in estimating the relative risk called stochastic Susceptible-Latently infected-Infectious-Recovered (SLIR). There are four phases in this study. Firstly, the deterministic SLIR model for TB disease transmission is developed. Then, the stochastic SLIR model is constructed. Next, the stochastic SLIR model is used to estimate the relative risk for the disease. Later, the performance of the stochastic SLIR model is compared with other existing models based on relative risk values. Finally, the TB risk maps are constructed. For numerical analysis, this study used a data set of Malaysia TB cases reported from 2008 to 2015. Findings show that there is a large difference of relative risk estimation values when using stochastic SLIR model compared to existing models. This is clearly visible through disease mapping as some locations change colour from low tone (low risk) to darker tone (higher risk). This is due to the inclusion of latent component in the stochastic SLIR model. As a conclusion, this study offers a better model in estimating relative risk for TB disease. The findings may assist the government in prioritizing locations which need further attention especially in terms of health policy and financial support.