Citation
Abdillah, Azline and Muthiah, Sri Ganesh and Kadir @ Shahar, Hayati
(2020)
Predictors and association of hepatitis C virus infections among people who injects drug in Negeri Sembilan.
Malaysian Journal of Medicine and Health Sciences, 16 (1).
pp. 261-269.
ISSN 1675-8544; ESSN: 2636-9346
Abstract
Introduction: Hepatitis C virus (HCV) infection is known as contributing to high morbidity and mortality globally. Major liver complications such as liver failure and liver cancer which can lead to fatality have been associated with persistent HCV infection. Globally, it is estimated that 5.6 million chronically infected HCV are among people who inject drugs (PWID). Malaysia has estimated that 59% HCV infections were among PWID. The aim of this study is to determine the prevalence of HCV infection and its predictors among PWID in Negeri Sembilan. Methods: A cross-sectional study based on random proportion to size sampling was conducted among 212 out of 1414 registered Methadone Maintenance Therapy (MMT) clients with PWID attending health clinics in Negeri Sembilan from February 2018 to July 2018. Data were collected using questionnaires administered through face-to-face interviews. Data were analyzed using Statistical Package of IBM SPSS Statistics Version 23 and p-value of <0.05 is considered significant. Independent T test and Chi-square test(χ2) were used to determine the associations between the variables, and multiple logistic regressions for the predictors. Results: Majority of the respondents were infected with HCV infection (89%). HCV infection were associated with their age(p<0.001), low education level(p=0.022), HIV infection(p=0.001), and higher frequency(p=0.001) with longer duration(p=0.026) of drug injections and needle sharing(p=0.001). The predictors of HCV were older age [AOR 1.07, 95% CI(1.032, 1.110)] and higher frequency of injections[AOR 5.98, 95% CI(3.110,11.476)]. Conclusion: HCV infection is prevalent among PWIDs. Hence, effective and efficient preventive measures should be targeted to the identified predictors.
Download File
Additional Metadata
Actions (login required)
|
View Item |