ABSTRACT
This study observed the pattern of reported dengue infections, clinical manifestations, and circulating dengue serotypes in Negeri Sembilan, Malaysia. The aim of this study was to determine the co-circulation of the four different dengue virus serotypes in Negeri Sembilan. We analyzed the surveillance data (VEKPRO) from Negeri Sembilan State Health Department and National Public Health Laboratory, Malaysia on reported dengue infections from 1st January 2010 to 31st December 2010. There were 1466 reported dengue infections, 1342 (91.5%) cases were dengue fever (DF) and 124 (8.5%) were dengue hemorrhagic fever (DHF). The mean age was 32.2± 15.8 years old and most were young adults, aged 15 years old and older. Males (p < 0.05), and those residing in Seremban district (p < 0.05) were more likely to get dengue infections. Symptoms presented upon admission were fever (100%), headache (99.9%), myalgia and arthralgia (98.8%), rash(24.2%), petechiae (16.0%),bleeding tendencies (7.0%) and neurological deficits(1.2%). All four dengue serotypes (DEN 1 – 4) were present, the pre-dominant serotype was DEN-3, noted in January, then existed together with DEN-2 until around May. DEN-1 was the most pre-dominant circulating dengue serotype afterwards, reaching a peak in December 2010. Dengue affected all age groups particularly young adults and males. Most cases reported were in urban areas and Seremban district. Most of the dengue infections occurred in the first half of the year, with the DEN-2 and DEN-3 serotypes being the most predominant.
ABSTRACT
Analysis of count event data such as mortality cases, were often modelled using Poisson regression model. Maximum likelihood procedures were used by using SAS software to estimate the model parameters of a Poisson regression model. However, the Negative Binomial distribution has been widely suggested as the alternative to the Poisson when there is proof of overdispersion phenomenon. We modelled the mortality cases as the dependent variable using Poisson and Negative Binomial regression and compare both of the models. The procedures were done in SAS by using the function PROC GENMOD. The results showed that the mortality data in Poisson regression exhibit large ratio values between deviance to degree of freedom which indicate model misspecification or overdispersion. This large ratio was found to be reduced in Negative Binomial regression. The Normal probability plot of Pearson residual confirmed that the Negative Binomial regression is a better model than Poisson regression in modelling the mortality data. The objective of this study is to compare the goodness of fit of Poisson regression model and Negative Binomial regression model in the application of air pollution epidemiologic time series study by using SAS software.