Recognizing the impact of endemic hepatitis D virus on hepatitis B virus eradication.
Hepatitis delta virus (HDV) in conjunction with hepatitis B virus (HBV) increases adult morbidity and mortality. A number of studies have performed cost-benefit analyses for HBV interventions, but they have ignored the impact of HDV on these outcomes.



Using a mathematical model of HBV-HDV epidemiology, we compare health benefits and cost outcomes of four interventions: testing with HBV adult vaccination (diagnosis), diagnosis with antiviral treatment for HBV infections (mono-infections), diagnosis with antiviral treatment for HBV-HDV infections (dual-infections), and awareness programs. The relationship between optimal levels and outcomes of each of these interventions and HDV prevalence in HBV infected individuals ranging from 0 to 50% is determined.



Over a 50 year period under no intervention, HBV prevalence, per capita total cost and death toll increase by 2.25%, -$11 and 2.6-fold respectively in moderate HDV endemic regions compared to mono-infected regions; the corresponding values for high HDV endemic regions are 4.2%, -$21 and 3.9-fold. Optimal interventions can be strategized similarly in mono and dually endemic regions. Only implementation of all four interventions achieves a very low HBV prevalence of around 1.5% in a moderate HDV endemic region such as China, with 2.8 million fewer deaths compared to no intervention. Although the policy of implementation of all four interventions costs additional $382 billion compared to no intervention, it still remains cost-effective with an incremental cost-effectiveness ratio of $1400/QALY. Very high efficacy awareness programs achieve less prevalence with fewer deaths at a lower cost compared to treatment and/or vaccination programs.



HDV substantially affects the performance of any HBV-related intervention. Its exclusion results in over-estimation of the effectiveness of HBV interventions.
School of Mathematics and Statistics, UNSW Australia, Sydney, NSW 2052, Australia. Electronic address: ashish.goyal@student.unsw.edu.au.
Ashish
A
Goyal
School of Mathematics and Statistics, UNSW Australia, Sydney, NSW 2052, Australia.
John M
JM
Murray
D000998:Antiviral Agents / Q000627:therapeutic use*; D002681:China / Q000453:epidemiology; D060085:Coinfection / Q000188:drug therapy / Q000191:economics / Q000453:epidemiology / Q000821:virology*; D006509:Hepatitis B / Q000175:diagnosis / Q000188:drug therapy* / Q000191:economics / Q000453:epidemiology*; D006515:Hepatitis B virus / Q000187:drug effects* / Q000302:isolation & purification; D019694:Hepatitis B, Chronic; D003699:Hepatitis D / Q000175:diagnosis / Q000188:drug therapy* / Q000191:economics / Q000453:epidemiology*; D003698:Hepatitis Delta Virus / Q000187:drug effects* / Q000302:isolation & purification; D006801:Humans; D008962:Models, Theoretical; D010043:Outcome and Process Assessment, Health Care / Q000191:economics*; D015995:Prevalence
D000998:Antiviral Agents
Awareness programs; HBV adult vaccination; HBV antiviral treatment; Hepatitis B virus; Hepatitis delta virus; Monitoring