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Identification of epidemiological risk factors for hepatitis C in Punjab, Pakistan

Muhammad Ghias1, Muhammad Khalid Pervaiz

  • 1Services Institute of Medical Sciences/Services Hospital, Lahore, Pakistan. ghiasgcu@yahoo.com

Journal of Ayub Medical College, Abbottabad : JAMC
|June 8, 2010
PubMed

Insights

Hepatitis C virus (HCV) infection in Punjab, Pakistan, is significantly associated with factors like age, blood transfusions, hospitalization, tattooing, family history of hepatitis, and surgical operations. Understanding these risk factors aids in predicting and managing HCV prevalence.

Area of Science:

  • Hepatology
  • Public Health
  • Epidemiology

Background:

  • Hepatitis C virus (HCV) poses a significant global health challenge, affecting approximately 3% of the world's population.
  • Punjab, Pakistan, experiences a notable burden of HCV infection, necessitating targeted research into its contributing factors.

Purpose of the Study:

  • To identify demographic, socio-economic, and clinical factors significantly associated with Hepatitis C virus infection in Punjab, Pakistan.
  • To determine the predictive strength of these associated factors using a logistic regression model.

Main Methods:

  • A hospital-based case-control study was conducted involving 400 patients (281 HCV-positive cases, 119 HCV-negative controls).
  • Data on risk factors were collected through interviews with patients admitted to gastroenterology wards in Lahore city hospitals.
  • Data collection occurred over a six-month period from April 2006 to September 2006.

Main Results:

  • Logistic regression analysis revealed significant positive associations between HCV infection and age (OR=1.035), history of blood transfusion (OR=9.204), history of hospitalization (OR=2.979), tattooing (OR=27.484), family history of hepatitis (OR=4.069), and surgical operation (OR=4.290).
  • All identified factors demonstrated statistically significant associations with HCV infection (p<0.05).

Conclusions:

  • The developed logistic regression model effectively predicts the likelihood of Hepatitis C virus infection based on the presence or absence of identified significant risk factors.
  • These findings can inform public health strategies for HCV prevention and control in the region.
Abstract

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