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Using statistical models to assess medical cost of hepatitis C virus

Mohsen Vahedi1, Asma Pourhoseingholi2, Sara Ashtari3

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Gastroenterology and Hepatology From Bed to Bench
|May 17, 2014
PubMed

Insights

Negative binomial regression (NB) is more effective than Poisson regression (PR) for predicting Hepatitis C virus (HCV) patient costs in Iran. This finding aids in better financial planning for HCV treatment and management.

Area of Science:

  • Health Economics
  • Biostatistics
  • Infectious Diseases

Background:

  • Hepatitis C virus (HCV) poses a significant economic burden in Iran, with associated costs often poorly characterized.
  • Accurate cost data is crucial for effective budgeting and informed decision-making in healthcare.
  • Statistical regression models, specifically Poisson regression (PR) and negative binomial regression (NB), are commonly employed for predicting healthcare costs.

Purpose of the Study:

  • To compare the predictive accuracy of Poisson regression (PR) and negative binomial regression (NB) for estimating direct medical costs in Hepatitis C virus (HCV) patients in Iran.
  • To identify key factors influencing the direct costs associated with HCV patient treatment.

Main Methods:

  • A cross-sectional study was conducted from 2001 to 2010, analyzing data from the first treatment period of each patient.
  • Direct medical costs, including doctor visits, medications, hospitalization, and laboratory tests, were estimated in Purchasing Power Parity dollars (PPP$).
  • Both Poisson regression (PR) and negative binomial regression (NB) models, derived from generalized linear models (GLM), were utilized for cost prediction.

Main Results:

  • The Likelihood ratio test indicated that the negative binomial regression (NB) model was significantly more appropriate than the Poisson regression (PR) model for predicting HCV patient costs (P < 0.001).
  • Factors such as HCV genotype, marital status, specific medications (Pegasys), and sustained virologic response (SVR) were identified as significant predictors of cost.
  • Specifically, Genotype 3 was associated with lower costs compared to Genotype 1, while marriage, Pegasys medication, and achieving SVR were linked to increased costs.

Conclusions:

  • Selecting the appropriate statistical model is critical for accurately analyzing and predicting healthcare costs, especially given the unique characteristics of cost data.
  • The superior performance of the negative binomial regression (NB) model allows for more reliable prediction of future patient costs under various circumstances.
  • This study provides valuable insights for healthcare resource allocation and financial planning related to Hepatitis C virus management in Iran.
Abstract

Keywords:
Chronic hepatitis CCostsNegative binomial regressionPoisson regressionPredictive models

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