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Patient prioritization in disease-specific treatment budgets: the case of chronic hepatitis C treatment
Lauren E. Cipriano
Shan Liu Mark Holodniy
Kaspar S. Shahzada Jeremy D. Goldhaber-Fiebert
Funding & Disclosure • Funding
• Seth Bonder Foundation • Natural Sciences and Engineering Research Council of Canada • National Institutes of Health (JGF) • Veteran’s Health Administration (MH)
Conflict of interest
• Partner employed at Merck Research Labs
Hepatitis C • Progressive liver disease affecting 3-4 million Americans
• 66-75% of infected individuals were born between 1945 -1965
• Largely silent progression to ESLD and liver cancer • Hepatitis C related mortality: 17,000-53,000 per year • Most common reason for liver transplant in the US
• Significant underdiagnosis • 2001-2008: 50% of infected individuals were unaware • 2012: CDC and USPSTF recommended screening for individuals born between 1945-1965
Direct acting agents
Harvoni (Gilead) ledipasvir-sofosbuvir Genotypes 1, 4, 5, 6
$1125/day $63,000-$94,500
Daklinza+Sovaldi (BMS/Gilead) daclatasvir + sofosbuvir
Genotypes 1, 3 $1750/day $147,000
Viekira Pak (AbbVie) ombitasvir-paritaprevir- ritonavir, and dasabuvir
Genotype 1 $1000/day
$83,300-166,600
Olysio+Solvaldi (Janssen) simeprevir + sofosbuvir
Genotype 1 $1785/day
$150,000-300,000
Sovaldi (Gilead) sofosbuvir
Genotypes 1,2,3,4 $1000/day
$84,000-$168,000
Zepatier (Merck) Elbasvir-grazoprevir
Genotype 1, 4 $650/day $54,600
Incremental cost effectiveness ratio ($/QALY gained) Treatment vs. no treatment
F0 F1 F2 F3 F4 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79
< $25,000 per QALY gained
$25,000 – 50,000 per QALY gained $50,000 – 100,000 per QALY gained
Treating 20% of the treatment eligible population = $20-25 billion annually (25% of Medicare Part D)
Patient prioritization in action
April 19, 2016 Boston Globe December 1, 2015
February 2, 2016 Seattle Times
Medicaid issues warning to State Medicaid programs November 15, 2015
August 2015
Research question
Evaluate and compare population health outcomes for various hepatitis C patient treatment prioritization schemes
including to develop and evaluate a prioritization scheme with the
objective of maximizing net monetary benefit
HCV natural history model
No fibrosis Non-progr. (F0)
No fibrosis Progressor (F0)
Few septa (F2) Num. septa (F3)
Compensated cirrhosis (F4) No septa (F1) Remission
Liver cancer
Decompensated cirrhosis
Aged 80 years Dead
• 3 million people aged 40-79 are treatment eligible (NHANES) • Assumed 10% annual demand from “prioritized” groups • Annual treatment budget of $8.6 Billion
Patient priority optimization • Objective function
• Maximize NMB -- Lifetime discounted costs and QALYs for all cohorts between 40-79 years over the next 25 years WTP = $100,000 per QALY gained
• Decision variables • Which year to prioritize treatment offers to each of 40 subgroups
• Constraints • Each of 25 years: Amount spent on treatment in year x ≤ Annual budget constraint ($8.6 billion)
F0 F1 F2 F3 F4 40-44 5 3 2 1 0 45-49 5 3 2 1 0 50-54 5 3 2 1 0 55-59 5 3 2 1 0 60-64 5 3 2 1 0 65-69 5 3 2 1 0 70-74 5 3 2 1 0 75-79 5 3 2 1 0
Why is priority sequence on ICER different than maximizing NMB?
• ICER is calculated as ‘treat now’ vs. ‘no treatment’ for each subgroup
• Consequences of waiting varies across subgroups • F3-F4 have higher short-term risk of ESLD / HCC (vs. F0-F2) • Younger people have lower competing mortality risk (vs. older people)
• Policy alternatives are the set of subgroup prioritization times
Results
Base case guidelines FCFS ICER
Severity only Optimize on NMB
Year Proportion of demand satisfied
0 63% 1 69% 2 76% 3 86% 4 97% 5 100%
6+ 100%
F0 F1 F2 F3 F4 40-44 5 3 1 0 0 45-49 5 3 1 0 0 50-54 5 3 1 0 0 55-59 5 3 1 0 0 60-64 5 3 1 0 0 65-69 5 3 1 0 0 70-74 5 3 1 0 0 75-79 5 3 1 0 0
F0 F1 F2 F3 F4 40-44 3 3 0 0 0 45-49 3 0 0 0 0 50-54 4 0 0 0 0 55-59 4 0 0 0 0 60-64 4 2 0 0 0 65-69 4 3 2 1 1 70-74 4 3 2 3 2 75-79 4 3 3 3 3
F0 F1 F2 F3 F4 40-44 0 0 0 0 0 45-49 1 0 0 0 0 50-54 2 0 0 0 0 55-59 2 0 0 0 0 60-64 4 0 0 0 1 65-69 5 2 2 2 3 70-74 5 3 3 3 4 75-79 5 4 4 4 4
Number treated: first 2 years • FCFS and ICER-order treats
more individuals with less severe disease
• Priority based on ICER treats the fewest patients in F4
• Compared to FCFS,
priority based on severity treats 85,000 more patients with F3-F4 disease
F0
F1
F2
F3
F4
FCFS ICER Opt. S
Population health outcomes
FCFS ICER Opt. S
Within 10 years
Within 5 years
FCFS ICER Opt. S
ESLD and cancer QALYs
2x demand for treatment FCFS ICER
Severity only Optimize on NMB
Year Proportion of demand satisfied
0-2 30-35% 3-4 40-45% 5 53% 6 62% 7 76% 8 96%
9+ 100%
F0 F1 F2 F3 F4 40-44 12 11 10 8 0
45-49 12 11 10 8 0
50-54 12 11 10 8 0
55-59 12 11 10 8 0
60-64 12 11 10 8 0
65-69 12 11 10 8 0
70-74 12 11 10 8 0
75-79 12 11 10 8 0
F0 F1 F2 F3 F4 40-44 6 5 3 0 0
45-49 6 4 2 0 0
50-54 6 5 3 0 0
55-59 7 6 3 0 0
60-64 7 5 4 1 0
65-69 7 6 4 1 2
70-74 7 6 7 5 8
75-79 7 7 7 6 8
F0 F1 F2 F3 F4 40-44 5 0 0 0 0
45-49 5 0 0 0 0
50-54 5 0 0 0 1
55-59 7 0 1 1 3
60-64 8 2 3 2 4
65-69 9 5 5 5 7
70-74 9 7 7 6 8
75-79 9 8 8 8 8
Population health outcomes
FCFS ICER Opt. S
Within 5 years
Within 10 years
FCFS ICER Opt. S
ESLD and cancer QALYs
Summary of results • Priority based on severity
• Most cases prevented ESLD/HCC • Fewest number in F4 over time • F3 and F4 have lowest average time to treatment
• First-come first-served & Priority based on ICER
• Least focused on patients with severe disease
• Priority based on maximizing NMB • Similar to priority based on severity, but delayed access to patients aged > 70 • Maximizes population QALYs • Focus on patients with ↓ competing mortality risk and ↑ disease progression risk
Limitations • Focus on the general population
• High incidence and prevalence in incarcerated population • High incidence and prevalence in people who inject drugs • Do not consider HIV co-infection
• Simplified model of hepatitis C
• HCV genotypes • Disease transmission • Re-treatment
• Do not consider the complexity of a multiple payer health system
• Different decision horizons
Value and affordability • DAAs for hepatitis C treatment are cost effective, but create a
significant affordability challenge
• Without substantial budget increases or explicit rules to the contrary, some form of explicit or implicit patient prioritization is likely to occur
• Likely to be a recurrent problem • Which (if any) patient characteristics can be used to prioritize patients? • Is transparency an important element of fair patient prioritization?
