Patient prioritization in disease -specific treatment budgets: the case of chronic ... · Patient...
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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
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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
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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
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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 $54,600
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Incremental cost effectiveness ratio ($/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)
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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
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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
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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
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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
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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
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Results
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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
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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
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Population health outcomes
FCFS ICER Opt. S
Within 10 years
Within 5 years
FCFS ICER Opt. S
ESLD and cancer QALYs
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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
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Population health outcomes
FCFS ICER Opt. S
Within 5 years
Within 10 years
FCFS ICER Opt. S
ESLD and cancer QALYs
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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
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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
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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?