HCV elimination model

The aim of the HCV elimination model is to allow users to investigate the relative value of different routes towards the WHO Hepatitis C incidence and mortality elimination targets. Based on a previously published and validated disease transmission model that has been cited over 150 times in the peer-reviewed literature [ Scott ]. The model calls upon the most recent literature on trends in both HCV prevalence across multiple strata of society and risky behaviors known to drive Hep C infection such as injection drug use, and estimates time to elimination based on treatment reach and access and take-up of treatment options in a combined population of a general, PWID and prison sub-populations. It also can be used to estimate the combinations of these inputs for a particular elimination goal.

Model

Directions

For combined gen pop/PWID/prison populations: Three sub-populations model
For DAA and NSP/OST intervention: Intervention PWID and Intervention gen pop
For precomputed scenario outputs: Automated output
For model parameters and refernces: Model parameters
For model fitting and validation: Epidemic data
For baseline models: Fit model

Application Contact

warren.stevens@medicuseconomics.com

Version Control

Version 2, Spetember 12, 2021

Security and License

Copyright 2021
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1. Total population
  • In 2016, 3.5 million people were chronically infected with HCV
2. Current PWID
  • (38.1% to 68%) of current PWID are infected with HCV in 2016
3. Scaling to a population (assuming a growing PWID population)
  • In 2007 there were 2,248,500 PWID (current)

  • Allow PWID (current) population to grow with two model fitting parameters
4. Fit multiplier parameter for force of infection to model increasing incidence of HCV
  • Flat prior to 2009
  • 3 to 4 fold increase in incidence between 2010 and 2018

  • Assume increasing incidence and flattening in 2030 and steady incidence until 2040
5. Fit epidemic model parameters for best fit to 2 to 4. Allow the following model parameters to vary
  • Calibration multiplier for force of infection

  • Relapse (former to current) rate and Injecting career length (current to former)
6. Split of HCV case for PWID (current and former) and general population
  • Fit scenarios around a 60%/20% split

  • Adjust the split such that current PWID account for (74% to 82%) of incident cases

  • MSM and prison populations are not included in this model
7. Number of former PWID
  • Former PWID population with HCV = PWID% (point 6) * 3.5million - fitted pop in 2016 (point 3) * fitted prevalence in point 2
8. Baby boomer effect (contaminated blood) and endemic trend for general population
  • Subjects born in 1945 to 1964 infected from 1970 to 1990

  • Subjects in these age groups have 5 times HCV prevalence compared to other general population age groups

  • Chronic HCV exposure reduces life expectancy on average by 5 years

  • Converting life expectancy reduction to mortality rates

  • Assume that in the general population HCV is endemic, but not an infectious disease

  • So infection is for example from long term blood dialysis, needle sticks, sharps, or mucosal exposures rather than person-to-person exposure (in PWID)
9. Fitting prison population
  • Extract the PWID (format and current) population for 2015

  • Scale the population such that % of incarcerated with HCV who are PWID is 50% with the remainder in the former population

  • Assume 20% (this has to be consistent point 6(i) above) of 3,500,000 HCV positive subjects in 2015 are incarcerated

  • Scale the starting prison population such that the total population is 2,173,800 in 2015

  • This gives an incarcerated HCV population percentage of 32%, consistent with

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