Technical Supplement

Technical Supplement

CONCEPTUAL MODEL

A discrete time Markov model was developed using R programming language to simulate HCV progression and treatment (see one-year cycle schematic in Figure S1)[1]. The model simulated repeatedly over 20 years and population outcomes (e.g., the number of people in each disease state) were collected at the end of each cycle.

The states modeled include (1) states in which the population is not infected (i.e., “susceptible”) or cured, (2) states in which the population is infected, and (3) states in which patient is treated. The model defines the infectious disease states as acute or chronic, where chronic consists of seven stages of liver damage: fibrosis stages F0-F4 using the METAVIR scoring system, decompensated cirrhosis (DC), and hepatocellular carcinoma (HCC). The METAVIR scoring system quantifies the degree of liver fibrosis in patients with liver diseases such as HCV. F0 is classified as having no fibrosis; F1, periportal fibrotic expansion; F2, periportal septae; F3, porto-central septae; and F4, compensated cirrhosis [2].

POPULATIONS MODELED

Three main subpopulations are modeled, categorized by their HCV exposure risk:

1)People who inject drugs (PWID), who have the highest risk of contracting HCV; between 54% to 77% of new HCV infections are in individuals who have reported injection drug use[3,4];

2)HIV-infected men who have sex with men (MSM-HIV), who are at increased risk for HCV infection through sexual transmission, exacerbated by HIV co-infection[5,6]; and

3)Other Adults (OA), a “catch-all” population comprised of adults born before 1992 (when systematic testing of the blood supply was implemented in the U.S.). This group includes those who were infected by modes of transmission other than high-risk behaviors, including needle-stick pricks and blood transfusions. Approximately 70% of infected individuals in this group are “baby boomers” (those born between 1946-1965) who were primarily infected via blood transfusions[7].In addition, persons infected with HCV in an occupational setting (e.g., healthcare workers) are represented in this group, albeit at a small percentage (3%)[8]. Finally, HCV-infected persons with unknown exposure are included in this group, which is estimated at 20-30% of HCV cases[8,9]. We note that while this group has minimal infectivity, it represents a large number of prevalent cases in the population[10]. Given the minimal infectivity in this group, we assume that there are no new infections in this cohort. Note that allowing a small level of infectivity in this group does not substantively change the model results.

Figure S1: Simulation schematic for model of transmission and progression of Hepatitis C virus among high-risk populations

Note: DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; Trans, transplant

Although an individual belongs to only one risk cohort throughout the simulation, individuals in the high-risk cohorts (PWID and MSM-HIV) are allowed to interact with individuals from other risk cohorts if they have both risk factors for acquisition of HCV infection, namely PWID and MSM-HIV. An important dynamic in the transmission of HCV arises from the interaction between MSM-HIV and PWID groups. Therefore, we separate the high-risk cohorts into three groups: PWID who are not MSM-HIV, MSM-HIV individuals who are not PWID, and the overlap (OV) group that consists of individuals who are both PWID and MSM-HIV.

By allowing for this interaction among the highest risk cohorts, our model better captures the burden from HCV due to infectivity and transmission of the disease. We assumed that the OA group does not interact directly with the high-risk cohorts. We assumed that the high-risk cohorts have constant mortality rates and experience ongoing entry and exit into the model, such that their size and age distributions remained constant over the simulation. Given the no infectivity assumption, the infected population in the OA group is allowed to decline, i.e., the infected population is not replenished to account for mortality. However, the susceptible population in the OA cohort is allowed to replenish to keep this population constant as the susceptible population dies.

The three most common HCV genotypes in the U.S. (genotypes 1, 2, and 3) were modeled[11]. This allows for more precision in modeling HCV by accounting for differences between genotypes in mortality rates[12], prevalence[13], and progression[14]. Individuals in the model may only be infected with one genotype at a time, but if cured and re-infected, they may re-enter the model with any of the three genotypes.

MODEL TRANSITIONS

We model the possible transitions from each disease state, represented by arrows in the schematic. The probabilities of transition along each of these arrows were computed based on the peer-reviewed literature, public databases such as NHANES or Census, or were assumed based on theoretical foundations of economics and epidemiology. We report these below in Tables S3 – S6. The model assumes that transition to death is possible from all states, but to simplify the schematic, Figure S1 suppresses arrows representing the transition to death.

Upon initial infection, patients enter an “acute” phase, which they must leave after one model cycle. Patients may die, spontaneously clear the disease without treatment, or progress to “chronic” disease. We assumed that between 12-29% of acute infections clear spontaneously, depending on risk cohort[15-17]. Patients may stay in any disease state for more than one cycle, with the exception of the acute phase. Consistent with our assumption of no infectivity in the OA cohort, we assume transition from susceptible to acute is zero for this cohort. Therefore, the OA susceptible population may either transition to death or remain susceptible.

We assumed that patients in stage F0 or above may receive HCV treatment during the simulation, depending on a particular policy scenario (see further description below). If not cured, we assumed that treated patients progress at the same rate as infected and untreated patients. If HCV is cured in stages F0-F2, liver damage was assumed to be reversed and patients returned to the susceptible population with healthy livers[18]. Patients cured of HCV in stages F3 and higher are no longer infectious, but may progress to additional liver damage more slowly than patients with active HCV; we did not assume that their liver damage is reversed[19]. These patients are also susceptible to reinfection at the same rate as patients without liver damage, but if re-infected, they re-enter the infected population at the same fibrosis stage as when they were cured. Patients with DC or HCC who are cured of HCV are eligible for liver transplant, which resolves their liver damage; if re-infected, they re-enter the infected population with healthy livers. Patients co-infected with HIV are not eligible for liver transplants[20,21].

TRANSMISSION FUNCTION

Because the OV cohort interacts with the PWID and MSM-HIV cohorts, the infection risk among the PWID, MSM-HIV, and OV groups is higher than treating these groups in isolation, as the infectivity depends on the size of the infected population in all three groups.

In the PWID, MSM-HIV and Overlap risk groups, for each genotype, the rate at which individuals are infected is modeled dynamically as a function of the number in the risk group who are currently infected. Individuals who are not infected (i.e., susceptible) at the beginning of year t are at risk of becoming infected. To describe the susceptible-to-infected transition probabilities used in the model, denote the risk groups by P, M, and OV, for PWID, MSM-HIV, and Overlap, respectively. In each of these three groups, infections occur through interactions with susceptible individuals in the group (e.g., Overlap) and infected individuals in all three of the groups. We let denote the number of susceptible (uninfected) individuals in risk group X at time t and let denote the number of infected individuals in group X at time t, where X refers to either P, M, or OV. If denotes that an individual in group X is susceptible (not infected) at time t and denotes that an individual in group X is infected at time t, then the probabilitythat a susceptible individual in risk group X becomes infected during year t is given by:

(1) /
(2) /
(3) /

The constants k1 and k2 represent the rates at which susceptible individuals in the PWID and MSM-HIV risk groups, become infected by interactions with infected individuals. For the Overlap group, we derive the probability of becoming infected as a combination of the probability for PWID and MSM-HIV.

The probability that an infected person in a group at time t becomes infected at t+1 is assumed to be proportional to the fraction of persons he interacts with who are infected. The transmission model specified by equations (1) – (3) assumes that the incidence rate is proportional to the fraction of individuals in a risk group who are infected. The proportionality constants k1 and k2are calibrated from the ratio of the incidence divided by the prevalence at baseline.

Incidence rates for each risk cohort were derived from the literature. Table S1 below shows incidence rates and the value of the transmission rates k for each risk group. To compute the incidence by risk group, we obtained data from CDC[22] on HCV incidence and allocated among the PWID and MSM-HIV risk groups based on Williams & Bell[8].

Throughout this study, the initial genotype distribution for each risk group is assumed to be 72%, 16%, and 12% for genotypes 1, 2, and 3, respectively. This implies that within each risk cohort, the rates of infection for each genotype remain the same.

Table S1: Starting incidence rates and values of proportionality constant k

Group / Annual Incidence Rate / Calculated K
PWID
Genotype 1 / 0.0065 / 0.025
Genotype 2 / 0.0014 / 0.025
Genotype 3 / 0.0011 / 0.025
MSM-HIV
Genotype 1 / 0.0034 / 0.021
Genotype 2 / 0.0007 / 0.021
Genotype 3 / 0.0006 / 0.021
Other Adults
Genotype 1 / 0 / 0
Genotype 2 / 0 / 0
Genotype 3 / 0 / 0

Note: MSM-HIV, human immunodeficiency virus-infected men who have sex with men; PWID, people who inject drugs

PARAMETERIZING MODEL

Starting Populations

At the beginning of model simulation, there are 3,639,339 HCV-infected persons across all risk cohorts and genotypes based on 2012 NHANES data[13]. Of this, 29,718 are incident cases reported by the CDC using 2013 data[23]. This incident population made up the starting acute cases in each risk cohort using the genotype distribution from Manos et al. [11]. The remaining 3,588,825 prevalent cases were distributed across risk cohorts, genotypes, and fibrosis stages[13,24,25]. The starting distributions are shown in Table S2.

Table S2: Starting population by risk cohort, genotype, and fibrosis stage

Disease Stage / PWID / MSM-HIV / Overlap / Other Adults / Total
Susceptible (% total) / 2,242,594 (1%) / 461,600 (0%) / 37,620 (0%) / 197,404,131 (99%) / 200,145,945
Infected (% total) / 1,267,862 (35%) / 135,000 (4%) / 20,796 (1%) / 2,215,681 (61%) / 3,639,339
Genotype 1 / Acute (% total) / 17,332 (81%) / 4,065 (19%) / 0 (0%) / 0 (0%) / 21,397
F0 (% total) / 137,855 (33%) / 12,459 (3%) / 2,545 (1%) / 271,199 (64%) / 424,058
F1 (% total) / 319,501 (35%) / 34,020 (4%) / 5,241 (1%) / 558,352 (61%) / 917,114
F2 (% total) / 200,829 (35%) / 21,384 (4%) / 3,294 (1%) / 350,964 (61%) / 576,471
F3 (% total) / 127,801 (35%) / 13,608 (4%) / 2,096 (1%) / 223,341 (61%) / 366,846
F4 (% total) / 54,772 (35%) / 5,832 (4%) / 898 (1%) / 95,717 (61%) / 157,219
DC (% total) / 27,386 (35%) / 2,916 (4%) / 449 (1%) / 47,859 (61%) / 78,610
HCC (% total) / 27,386 (35%) / 2,916 (4%) / 449 (1%) / 47,859 (61%) / 78,610
Genotype 2 / Acute (% total) / 3,851 (81%) / 903 (19%) / 0 (0%) / 0 (0%) / 4,754
F0 (% total) / 30,634 (33%) / 2,769 (3%) / 566 (1%) / 60,267 (64%) / 94,236
F1 (% total) / 71,000 (35%) / 7,560 (4%) / 1,165 (1%) / 124,078 (61%) / 203,803
F2 (% total) / 44,629 (35%) / 4,752 (4%) / 732 (1%) / 77,992 (61%) / 128,105
F3 (% total) / 28,400 (35%) / 3,024 (4%) / 466 (1%) / 49,631 (61%) / 81,521
F4 (% total) / 12,171 (35%) / 1,296 (4%) / 200 (1%) / 21,271 (61%) / 34,938
DC (% total) / 6,086 (35%) / 648 (4%) / 100 (1%) / 10,635 (61%) / 17,469
HCC (% total) / 6,086 (35%) / 648 (4%) / 100 (1%) / 10,635 (61%) / 17,469
Genotype 3 / Acute (% total) / 2,889 (81%) / 678 (19%) / 0 (0%) / 0 (0%) / 3,567
F0 (% total) / 22,976 (33%) / 2,076 (3%) / 424 (1%) / 45,200 (64%) / 70,676
F1 (% total) / 53,250 (35%) / 5,670 (4%) / 873 (1%) / 93,059 (61%) / 152,852
F2 (% total) / 33,471 (35%) / 3,564 (4%) / 549 (1%) / 58,494 (61%) / 96,078
F3 (% total) / 21,300 (35%) / 2,268 (4%) / 349 (1%) / 37,223 (61%) / 61,140
F4 (% total) / 9,129 (35%) / 972 (4%) / 150 (1%) / 15,953 (61%) / 26,204
DC (% total) / 4,564 (35%) / 486 (4%) / 75 (1%) / 7,976 (61%) / 13,101
HCC (% total) / 4,564 (35%) / 486 (4%) / 75 (1%) / 7,976 (61%) / 13,101

Sources: CDC [26]; NHANES [13]; Hagan et al. [24]; Jordan et al. [25]; Manos et al. [11]; and authors’ analysis

Note: DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; MSM-HIV, human immunodeficiency virus-infected men who have sex with men; PWID, people who inject drugs

Model Parameters

Model parameters were taken from the literature. Where possible, parameter estimates are specific to genotype, fibrosis stage, and risk cohort. Model parameters inputs and sources by risk cohort are shown in Tables S4 through S7.

Table S3: Model parameters for PWID cohort

Genotype 1 / Genotype 2 / Genotype 3
Annual Mortality Ratesb
Susceptible / 0.0139[27]
Acute, F0-F2 / 0.0330[27,28] / 0.0281[12,27,28] / 0.0307[12,27,28]
F3, F4 / 0.1246[12,27,28] / 0.1059[12,27,28] / 0.1159[12,27,28]
DC / 0.1350[29-31] / 0.1148[12,29-31] / 0.1256[29-31]
HCC / 0.4270[29-31] / 0.3630[12,29-31] / 0.3971[29-31]
Transplant / 0.1650[32]
Post-Transplant / 0.0526[12,27]
Annual Transition Probabilities
Acute  Spontaneous Clearance / 0.211[16]
F0  F1 / 0.116[33]
F1  F2 / 0.085[33]
F2  F3 / 0.085[33]
F3  F4 / 0.130[33]
F3  HCC / 0.008[29] / 0.014[14,29]
F4  DC / 0.039[29] / 0.027[14,29] / 0.051[14,29]
F4  HCC / 0.025[29] / 0.014[14,29] / 0.045[14,29]
DC  HCC / 0.025[29] / 0.014[14,29] / 0.045[14,29]
F3 Cure  F4 Cure / 0.038[19,29]
F3 Cure  HCC Cure / 0.003[19,29]
F4 Cure  DC Cure / 0.011[19,29]
F4 Cure  HCC Cure / 0.009[19,29]
DC Cure  HCC Cure / 0.007[19,29]
DC  Transplant / 0.031[34]
HCC  Transplant / 0.103[34]
QoL Weights
Susceptible/Acute / 0.85[35]
F0-F2 / 0.77[35]
F0 Fail-F2 Fail / 0.66[35]
F3 / 0.66[35]
F3 Fail / 0.55[35]
F4 / 0.55[35]
DC / 0.45[35]
HCC / 0.45[35]
F3 Cure, F4 Cure, DC Cure, HCC Cure / 0.72[35]
Transplant / 0.45[35]
Post-Transplant / 0.67[35]
Annual Medical Expenditures
Susceptible / $6,937[36]
Acute, F0, F1, F2, F3 / $17,061[37]
F0 Fail, F1 Fail, F2 Fail, F3 Fail, F3 Cure / $11,022[37,38]
F4 / $20,238[37]
F4 Fail, F4 Cure / $15,381[37,38]
DC / $56,514[37]
DC Fail, DC Cure / $38,995[37,38]
HCC / $125,385[37]
HCC Fail, HCC Cure / $86,516[37,38]
Transplant, Post-Transplant / $162,607[37]
Annual Short-Term Disability Costs
Susceptible / $326[39]
Infected / $674[39]
Probability of Employment
Susceptible / 0.820[40]
Infected / 0.630[41]
SVR Ratec
F0, Genotype 1 / 0.98
F1, Genotype 1 / 0.97
F2, Genotype 1 / 0.97
F3, Genotype 1 / 0.97
F4, Genotype 1 / 0.97
DC, Genotype 1 / 0.65
HCC, Genotype 1 / 0.65
F0, Genotype 2 / 0.97
F1, Genotype 2 / 0.97
F2, Genotype 2 / 0.97
F3, Genotype 2 / 0.97
F4, Genotype 2 / 0.87
DC, Genotype 2 / 0.87
HCC, Genotype 2 / 0.87
F0, Genotype 3 / 0.91
F1, Genotype 3 / 0.91
F2, Genotype 3 / 0.91
F3, Genotype 3 / 0.91
F4, Genotype 3 / 0.68
DC, Genotype 3 / 0.68
HCC, Genotype 3 / 0.68

b Sources: McCombs et al.[12]; Mathers et al.[27]; El-Kamary et al.[28]; Younossi et al.[29]; Rein et al.[30]; Planas et al.[31]; Best et al.[32]; and authors’ calculations

c Sources: Efficacy for Genotype 1 based on Feld et al.[42]; Poordad et al.[43]; Afdhal et al.[44]; Gane et al.[45]; Kwo et al.[46]; Lawitz et al.[47]; Osinusi et al.[48] Efficacy for Genotypes 2 & 3 based on Foster et al.[49]; Zeuzem et al.[50]

Note: DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; PWID, people who inject drugs; QoL, quality of life; SVR, sustained virologic response

Table S4: Model parameters for MSM-HIV cohort

Genotype 1 / Genotype 2 / Genotype 3
Annual Mortality Ratesb
Susceptible / 0.0071[51]
Acute, F0-F2 / 0.0168[28,51] / 0.0156[12,28,51] / 0.0156[12,28,51]
F3, F4 / 0.0632[12,28,51] / 0.0538[12,28,51] / 0.0588[12,28,51]
DC / 0.1350[29-31] / 0.1148[12,29-31] / 0.1256[12,29-31]
HCC / 0.4270[29-31] / 0.3630[12,29-31] / 0.3971[12,29-31]
Transplant / 0.1650[52,53]
Post-Transplant / 0.0267[52,53]
Annual Transition Probabilities
Acute  Spontaneous Clearance / 0.286[15]
F0  F1 / 0.122[33]
F1  F2 / 0.115[33]
F2  F3 / 0.124[33]
F3  F4 / 0.115[33]
F3  HCC / 0.016[20,29] / 0.0288[20,29]
F4  DC / 0.078[20,29] / 0.053[20,29] / 0.101[20,29]
F4  HCC / 0.050[20,29] / 0.028[20,29] / 0.09[20,29]
DC  HCC / 0.050[20,29] / 0.028[20,29] / 0.09[20,29]
F3 Cure  F4 Cure / 0.032[19,29]
F3 Cure  HCC Cure / 0.006[19,29]
F4 Cure  DC Cure / 0.022[19,29]
F4 Cure  HCC Cure / 0.018[19,29]
DC Cure  HCC Cure / 0.014[19,29]
DC  Transplant / 0.000
HCC  Transplant / 0.000
QoL Weights
Susceptible/Acute / 0.870[52]
F0-F3 / 0.810[52]
F4 / 0.680[52]
DC / 0.480[52]
HCC / 0.230[52]
F3 Cure / 0.810[52]
F4 Cure / 0.680[52]
DC Cure / 0.480[52]
HCC Cure / 0.230[52]
Transplant / 0.810[52]
Post-Transplant / 0.810[52]
Annual Medical Expenditures
Susceptible / $6,937[36]
Acute, F0, F1, F2, F3 / $29,857[37]
F0 Fail, F1 Fail, F2 Fail, F3 Fail, F3 Cure / $19,288[37,38]
F4 / $35,417[37]
F4 Fail, F4 Cure / $26,917[37,38]
DC / $98,900[37]
DC Fail, DC Cure / $68,241[37,38]
HCC / $219,425[37]
HCC Fail, HCC Cure / $151,403[37,38]
Transplant, Post-Transplant / $284,563[37]
Annual Short-Term Disability Costs
Susceptible / $326[39]
Infected / $674[39]
Probability of Employment
Susceptible / 0.630[54]
Infected / 0.470[41]
SVR Ratec
F0, Genotype 1 / 0.96
F1, Genotype 1 / 0.96
F2, Genotype 1 / 0.96
F3, Genotype 1 / 0.96
F4, Genotype 1 / 0.91
DC, Genotype 1 / 0.65
HCC, Genotype 1 / 0.65
F0, Genotype 2 / 0.97
F1, Genotype 2 / 0.97
F2, Genotype 2 / 0.97
F3, Genotype 2 / 0.97
F4, Genotype 2 / 0.87
DC, Genotype 2 / 0.87
HCC, Genotype 2 / 0.87
F0, Genotype 3 / 0.91
F1, Genotype 3 / 0.91
F2, Genotype 3 / 0.91
F3, Genotype 3 / 0.91
F4, Genotype 3 / 0.68
DC, Genotype 3 / 0.68
HCC, Genotype 3 / 0.68

b Sources: McCombs et al.[12]; Mathers et al.[27]; El-Kamary et al.[28]; Younossi et al.[29]; Rein et al.[30]; Planas et al.[31]; CDC[51]; Forman et al.[53]; Hornberger et al.[52]; Neal et al.[55]; van der Meer et al.[56]; Karch et al.[57]; and authors’ calculations

c Sources: Efficacy for Genotype 1 based on Feld et al.[42]; Poordad et al.[43]; Afdhal et al.[44]; Gane et al.[45]; Kwo et al.[46]; Lawitz et al.[47]; Osinusi et al.[48]; Sulkowski et al.[58] Efficacy for Genotypes 2 & 3 based on Foster et al.[49]; Zeuzem et al.[50]; Sulkowski et al.[59]

Note: DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; MSM-HIV, human immunodeficiency virus-infected men who have sex with men; QoL, quality of life; SVR, sustained virologic response

Table S5: Model parameters for Overlap cohort

Genotype 1 / Genotype 2 / Genotype 3
Annual Mortality Ratesb
Susceptible / 0.0139[27]
Acute, F0-F2 / 0.0330[27,28] / 0.0281[12,27,28] / 0.0307[12,27,28]
F3, F4 / 0.1246[12,27,28] / 0.1059[12,27,28] / 0.1159[12,27,28]
DC / 0.1350[29-31] / 0.1148[12,29-31] / 0.1256[12,29-31]
HCC / 0.4270[29-31] / 0.3630[12,29-31] / 0.3971[12,29-31]
Transplant / 0.1650[32]
Post-Transplant / 0.0526[12,27]
Annual Transition Probabilities
Acute  Spontaneous Clearance / 0.116[15]
F0  F1 / 0.122[33]
F1  F2 / 0.115[33]
F2  F3 / 0.124[33]
F3  F4 / 0.115[33]
F3  HCC / 0.016[20,29] / 0.029[14,20,29]
F4  DC / 0.078[20,29] / 0.053[14,20,29] / 0.101[14,20,29]
F4  HCC / 0.050[20,29] / 0.028[14,20,29] / 0.090[14,20,29]
DC  HCC / 0.050[20,29] / 0.028[14,20,29] / 0.090[14,20,29]
F3 Cure  F4 Cure / 0.032[19,29]
F3 Cure  HCC Cure / 0.006[19,29]
F4 Cure  DC Cure / 0.022[19,29]
F4 Cure  HCC Cure / 0.018[19,29]
DC Cure  HCC Cure / 0.014[19,29]
DC  Transplant / 0.000
HCC  Transplant / 0.000
QoL Weights
Susceptible/Acute / 0.850[52]
F0-F2 / 0.770[35]
F0-F2 Fail / 0.660[35]
F3 / 0.660[35]
F3 Fail / 0.550[35]
F4 / 0.550[35]
DC / 0.450[35]
HCC / 0.230[52]
F3 Cure / 0.720[35]
F4 Cure / 0.680[52]
DC Cure / 0.480[52]
HCC Cure / 0.230[52]
Transplant / 0.450[35]
Post-Transplant / 0.670[35]
Annual Medical Expenditures
Susceptible / $6,937[36]
Acute, F0, F1, F2, F3 / $29,857[37]
F0 Fail, F1 Fail, F2 Fail, F3 Fail, F3 Cure / $19,288[37,38]
F4 / $35,417[37]
F4 Fail, F4 Cure / $26,917[37,38]
DC / $98,900[37]
DC Fail, DC Cure / $68,241[37,38]
HCC / $219,425[37]
HCC Fail, HCC Cure / $151,403[37,38]
Transplant, Post-Transplant / $284,563[37]
Annual Short-Term Disability Costs
Susceptible / $326[39]
Infected / $674[39]
Probability of Employment
Susceptible / 0.630[54]
Infected / 0.470[41]
SVR Ratec
F0, Genotype 1 / 0.96
F1, Genotype 1 / 0.96
F2, Genotype 1 / 0.96
F3, Genotype 1 / 0.96
F4, Genotype 1 / 0.91
DC, Genotype 1 / 0.65
HCC, Genotype 1 / 0.65
F0, Genotype 2 / 0.97
F1, Genotype 2 / 0.97
F2, Genotype 2 / 0.97
F3, Genotype 2 / 0.97
F4, Genotype 2 / 0.87
DC, Genotype 2 / 0.87
HCC, Genotype 2 / 0.87
F0, Genotype 3 / 0.91
F1, Genotype 3 / 0.91
F2, Genotype 3 / 0.91
F3, Genotype 3 / 0.91
F4, Genotype 3 / 0.68
DC, Genotype 3 / 0.68
HCC, Genotype 3 / 0.68

b Sources: McCombs et al.[12]; Mathers et al.[27]; El-Kamary et al.[28]; Younossi et al.[29]; Rein et al.[30]; Planas et al.[31]; Best et al.[32]; and authors’ calculations

c Sources: Efficacy for Genotype 1 based on Feld et al.[42]; Poordad et al.[43]; Afdhal et al.[44]; Gane et al.[45]; Kwo et al.[46]; Lawitz et al.[47]; Osinusi et al.[48]; Sulkowski et al.[58]. Efficacy for Genotypes 2 & 3 based on Foster et al.[49]; Zeuzem et al.[50]; Sulkowski et al.[59]

Note: DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; QoL, quality of life; SVR, sustained virologic response

Table S6: Model parameters for Other Adults cohort

Genotype 1 / Genotype 2 / Genotype 3
Annual Mortality Ratesb
Susceptible (Background) / 0.0083[60]
Acute, F0-F2 / 0.0197[28] / 0.0167[12,28] / 0.0183[12,28]
F3, F4 / 0.0742[12,28] / 0.0631[12,28] / 0.0690[12,28]
DC / 0.1350[29-31] / 0.1148[12,29-31] / 0.1256[12,29-31]
HCC / 0.4270[29,61,62] / 0.3630[12,29,61,62] / 0.3971[12,29,61,62]
Transplant / 0.1650[32]
Post-Transplant / 0.0313[12,60]
Annual Background Mortality Growth Rate / 0.08[63]
Annual Transition Probabilities
Acute  Spontaneous Clearance / 0.180[17]
F0  F1 / 0.076[33]
F1  F2 / 0.095[33]
F2  F3 / 0.108[33]
F3  F4 / 0.134[33]
F3  HCC / 0.008[29] / 0.014[14,29]
F4  DC / 0.039[29] / 0.027[14,29] / 0.051[14,29]
F4  HCC / 0.025[29] / 0.014[14,29] / 0.045[14,29]
DC  HCC / 0.025[29] / 0.014[14,29] / 0.045[14,29]
F3 Cure  F4 Cure / 0.038[19,29]
F3 Cure  HCC Cure / 0.003[19,29]
F4 Cure  DC Cure / 0.011[19,29]
F4 Cure  HCC Cure / 0.009[19,29]
DC Cure  HCC Cure / 0.007[19,29]
DC  Transplant / 0.031[34]
HCC  Transplant / 0.103[34]
QoL Weights
Susceptible/Acute / 1.000
F0-F3 / 0.790[64]
F4 / 0.800[64]
DC / 0.600[64]
HCC / 0.720[64]
F3 Cure, F4 Cure, DC Cure, HCC Cure / 0.860[64]
Transplant / 0.730[64]
Post-Transplant / 0.850[64]
Annual Medical Expenditures
Susceptible / $6,937[36]
Acute, F0, F1, F2, F3 / $17,061[37]
F0 Fail, F1 Fail, F2 Fail, F3 Fail, F3 Cure / $11,022[37,38]
F4 / $20,238[37]
F4 Fail, F4 Cure / $15,381[37,38]
DC / $56,514[37]
DC Fail, DC Cure / $38,995[37,38]
HCC / $125,385[37]
HCC Fail, HCC Cure / $86,516[37,38]
Transplant, Post-Transplant / $162,607[37]
Annual Short-Term Disability Costs
Susceptible / $326[39]
Infected / $674[39]
Probability of Employment
Susceptible/Infected / 0.700[41]
SVR Ratec
F0, Genotype 1 / 0.98
F1, Genotype 1 / 0.97
F2, Genotype 1 / 0.97
F3, Genotype 1 / 0.97
F4, Genotype 1 / 0.97
DC, Genotype 1 / 0.65
HCC, Genotype 1 / 0.65
F0, Genotype 2 / 0.97
F1, Genotype 2 / 0.97
F2, Genotype 2 / 0.97
F3, Genotype 2 / 0.97
F4, Genotype 2 / 0.87
DC, Genotype 2 / 0.87
HCC, Genotype 2 / 0.87
F0, Genotype 3 / 0.91
F1, Genotype 3 / 0.91
F2, Genotype 3 / 0.91
F3, Genotype 3 / 0.91
F4, Genotype 3 / 0.68
DC, Genotype 3 / 0.68
HCC, Genotype 3 / 0.68

b Sources: McCombs et al.[12]; El-Kamary et al.[28]; Younossi et al.[29]; Rein et al.[30]; Planas et al.[31]; Best et al.[32]; U.S. Census Bureau[60]; Singer et al.[61]; Fattovich et al.[62]; Arias et al.[63]; and authors’ calculations. Annual growth in mortality rate is not applied to Susceptible group since this group is not becoming older on average.

c Sources: Efficacy for Genotype 1 based on Feld et al.[42]; Poordad et al.[43]; Afdhal et al.[44]; Gane et al.[45]; Kwo et al.[46]; Lawitz et al.[47]; Osinusi et al.[48] Efficacy for Genotypes 2 & 3 based on Foster et al.[49]; Zeuzem et al.[50]

Note: DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; QoL, quality of life; SVR, sustained virologic response

POLICY SCENARIOS

Six policy scenarios guided provision of HCV treatment in our analysis (see Table S7). The baseline policy scenario, Status Quo, was defined as treating HCV patients with advanced liver fibrosis (above stage F3) who are free of drug use. Five alternative treatment policy scenarios were compared relative to the Status Quo. The alternative treatment policies allow for treatment at earlier fibrosis stages as well as treatment of PWID. All baseline scenarios restrict treatment coverage to those who do not currently inject drugs, while the other three scenarios allow PWID to receive treatment.