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Transcript of John M Blandford, PhD Chief – Health Economics, Systems and Integration Branch Division of Global...
John M Blandford, PhDChief – Health Economics, Systems and Integration Branch
Division of Global HIV/AIDSU.S. Centers for Disease Control and Prevention
Nairobi, Kenya14 October 2011
Estimating Health Impact and Costs of Treatment in PEPFAR-
Supported Programs
Center for Global Health
Division of Global HIV/AIDS
2
Evolving Use of Cost Data under PEPFAR
Early emphasis on robust cost analyses and projections• Support planning and
efficient implementation• Focus on total and USG
costs for each patient-year of treatment
Need to account more fully for societal impact of treatment • Direct benefits to patient• Indirect benefits to
society• Averted costs
Note: Per-patient budget allocation is estimated as treatment allocation divided by lagged end-of-reporting direct patients.
2004 2005 2006 2007 2008 2009$0
$200
$400
$600
$800
$1,000
$1,200
0
500000
1000000
1500000
2000000
2500000
3000000
PEPFAR Per-Patient ART Cost No. of Direct ART Patients
Scale-Up of ART Access and Declining PEPFAR Per-Patient Costs, 2004-2009
3
Modeling the Impact and Costs of Treatment in PEPFAR-Supported
Programs
Two complementary analytic approaches:1. Estimation of health impact and net
societal cost of PEPFAR-supported treatment
2. Estimation of longer-term epidemic impact and costs of accelerated scale-up in light of HPTN 052
4
ESTIMATING HEALTH IMPACT AND SOCIETAL COST OF TREATMENT UNDER PEPFAR
Modeling the Impact and Costs of Treatment
5
PEPFAR ART Cost Model (PACM) Background
Developed to estimate resource requirements for treatment scale-up Designed to inform USG planning at
global and country levels Utilizes data from multi-country
PEPFAR ART Costing Project Study, other PEPFAR-supported studies
Open-cohort state-transition model Model projects estimates of patient
populations by patient type Size of patient population, by patient
type, recalculated on a quarterly basis
Direct treatment costs are estimated for each patient group
Model structure and assumptions reviewed by Government Accountability Office (GAO)
Summary Results for ART Patients: PEPFAR Global HIV Treatment Cost Projection Model
Projection Period, Countries, and Funding to Include Links: PRE-ART SUMMARY
DETAILED COST RESULTS
DETAILED PT RESULTS
SENS ANALYSIS PARAMETERS
SENS ANALYSISART
SENS ANALYSISPRE-ART
RAW COST DATA
Start Year (e.g. 2009): 2010
Costs Included: TRUE FALSE
TRUE TRUE
Total Annual Costs Over 10 Years
Time Period
Non-ARV Recurrent
Costs
Non-ARV Investment
CostsAntiretroviral
Drugs
Country-level Admin and
Support
Country Indirect Support
TOTAL ART COSTS
FY2010 451.1$M 63.9$M 456.0$M 257.5$M 150.0$M 1,378.6$M
FY2011 547.7$M 65.4$M 587.4$M 306.6$M 250.0$M 1,757.1$M
FY2012 628.4$M 65.6$M 698.5$M 329.6$M 300.0$M 2,022.1$M
FY2013 693.7$M 64.5$M 776.4$M 341.2$M 350.0$M 2,225.8$M
FY2014 742.1$M 62.0$M 847.5$M 341.7$M 400.0$M 2,393.3$M
FY2015 779.3$M 61.6$M 890.0$M 336.4$M 450.0$M 2,517.3$M
FY2016 815.6$M 64.3$M 914.8$M 352.0$M 450.0$M 2,596.7$M
FY2017 853.7$M 67.2$M 938.0$M 368.4$M 450.0$M 2,677.3$M
FY2018 893.1$M 70.1$M 956.1$M 385.3$M 450.0$M 2,754.6$M
FY2019 933.8$M 73.1$M 974.6$M 402.8$M 450.0$M 2,834.2$M
10-Year Total: 7,338.5$M 657.7$M 8,039.3$M 3,421.3$M 3,700.0$M 23,156.8$M
Total Patient Volume at End of Each Year
Time PeriodAdult
PatientsPediatric Patients
1st-Line Regimens
2nd-Line Regimens
ALL ARTPATIENTS
Start 2,286,493 198,825 2,361,052 124,266 2,485,318
End Of FY2010 2,856,840 288,478 2,940,240 205,078 3,145,318
End Of FY2011 3,322,837 362,481 3,383,982 301,336 3,685,318
End Of FY2012 3,684,169 421,149 3,697,909 407,409 4,105,318
End Of FY2013 3,940,826 464,492 3,887,864 517,454 4,405,318
End Of FY2014 4,092,799 492,519 3,958,995 626,323 4,585,318
End Of FY2015 4,192,579 512,739 3,975,596 729,722 4,705,318
End Of FY2016 4,292,654 532,664 3,999,477 825,841 4,825,318
End Of FY2017 4,393,012 552,306 4,030,135 915,183 4,945,318
End Of FY2018 4,493,643 571,675 4,066,762 998,556 5,065,318
End Of FY2019 4,594,533 590,785 4,108,648 1,076,670 5,185,318
Annual Per-Patient Costs for Different Patient Groups* Annual Per-Patient Cost, Disaggregated by Cost Category*
Time PeriodAdult
PatientsPediatric Patients
1st-Line Patients
2nd-Line Patients
ALL ART PATIENTS Time Period
Non-ARV Recurrent
Costs
Non-ARV Investment
CostsAntiretroviral
Drugs
Country-level Admin and
SupportALL ART
PATIENTS
FY2010 431$ 489$ 405$ 942$ 436$ FY2010 160$ 23$ 162$ 91$ 436$
FY2011 438$ 474$ 406$ 887$ 441$ FY2011 160$ 19$ 172$ 90$ 441$
FY2012 440$ 462$ 401$ 856$ 442$ FY2012 161$ 17$ 179$ 85$ 442$
FY2013 438$ 465$ 394$ 828$ 441$ FY2013 163$ 15$ 182$ 80$ 441$
FY2014 441$ 465$ 391$ 800$ 443$ FY2014 165$ 14$ 189$ 76$ 443$
FY2015 443$ 461$ 388$ 776$ 445$ FY2015 168$ 13$ 192$ 72$ 445$
FY2016 449$ 466$ 390$ 760$ 450$ FY2016 171$ 13$ 192$ 74$ 450$
FY2017 454$ 469$ 393$ 745$ 456$ FY2017 175$ 14$ 192$ 75$ 456$
FY2018 459$ 471$ 396$ 731$ 460$ FY2018 178$ 14$ 191$ 77$ 460$
FY2019 464$ 474$ 401$ 718$ 465$ FY2019 182$ 14$ 190$ 79$ 465$
10-Year Average: 447$ 469$ 396$ 773$ 449$ 10-Yr Average: 169$ 15$ 186$ 79$ 449$ *Excludes Country Indirect Support*Excludes Country Indirect Support
INPUTS:ARV REGIMENS
OTHER DATAINPUTS: PARAMETERS
INPUTS:ARV PRICES
INPUTS: NON-ARV COSTS
STATE TRANS. MODEL
$M-
$M500.0
$M1,000.0
$M1,500.0
$M2,000.0
$M2,500.0
$M3,000.0
FY2010 FY2011 FY2012 FY2013 FY2014 FY2015
Total Annual Treatment Cost, by Fiscal Year
Country Indirect Support
Country-level Admin and SupportAntiretroviral Drugs
0500,000
1,000,0001,500,0002,000,0002,500,0003,000,0003,500,0004,000,0004,500,0005,000,000
Start FY2010 FY2011 FY2012 FY2013 FY2014 FY2015
Tota
l Dire
ct P
atien
ts
Year
Total Direct Patients at End of Year
Pediatric Patients
Adult Patients
$-
$100
$200
$300
$400
$500
$600
$700
$800
$900
$1,000
FY2010 FY2011 FY2012 FY2013 FY2014 FY2015 FY2016 FY2017 FY2018 FY2019
Adult Patients Pediatric Patients 1st-Line Patients 2nd-Line Patients
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
Start FY2010 FY2011 FY2012 FY2013 FY2014 FY2015 FY2016 FY2017 FY2018 FY2019
2nd-Line Regimens 1st-Line Regimens
$-
$50
$100
$150
$200
$250
FY2010
NonAntiretroviral Drugs
LOCAL GOVT AND OTHER DONORSPEPFAR FUNDING
LOW-INCOME COUNTRIES MIDDLE-INCOME COUNTRIES
6
Estimation of Health Impact and Net Societal Cost of PEPFAR-Supported
Treatment
Patient population and cost estimates are inputs for model of health impact and societal cost Estimates the broad health of PEPFAR-supported
treatment programs for patients and others who are impacted
Estimates societal cost of treatment, considering costs that are averted through effective treatment
Counterfactual: no program or program of different scale
7
Estimation of Health Impact and Net Societal Cost of PEPFAR-Supported
Treatment
Estimation of health impact from treatment Direct benefit (to treatment patients)
HIV-attributable deaths averted Life-years saved
Indirect benefit (to others) Averted secondary infections
• Sexual• Vertical: women who become pregnant while on ART
Averted orphanhood Life-years saved
Notes: A discount rate of 3% is used for calculation of future benefits and costs . Life-years saved do not currently account for quality or disability adjustments.
8
Estimation of Health Impact and Net Societal Cost of PEPFAR-Supported
Treatment
Estimation of net societal costs Net costs = treatment costs – averted costs
Treatment costs• Total program costs• All sources of support (e.g., PEPFAR, GFATM, national)
Averted costs• Medical costs for HIV-related illness• ART for secondary infections• Orphan care • Note: Averted productivity losses are not currently
estimated in the model
Note: A discount rate of 3% is used for calculation of future benefits and costs . Life-years saved do not currently account for quality or disability adjustments.
9
Key Preliminary Findings of PACM Estimates
1. For every one patient-year of HIV treatment provided, 2.2 discounted life-years are gained for society
2. For every 1000 patient-years of PEPFAR-supported HIV treatment provided: 228 HIV patients avert death 449 children avert orphanhood 61 sexual transmissions are averted 26 vertical (mother-to-child) transmissions are averted
10
Key Preliminary Findings of PACM Estimates
3. Cost savings to society that result from averted negative outcomes equal 59% of total treatment program costs
4. The net societal cost of treatment is $147 per discounted life-year gained when the indirect benefits and averted costs from treatment are considered Based on WHO standards for cost-effectiveness, ART
may potentially be highly cost-effective in most of sub-Saharan Africa
1 year
5 years
10 years
Infections Averted per 1,000 Patient-Years of Treatment
Source: Tim Hallett, Imperial College; The Impact of Treatment on HIV Incidence: Perspective from Epidemiology & Modelling
12
Current Limitations of PACM Estimates
Input parameter for secondary sexual transmissions that occur from non-acute PLWHA not on treatment requires further validation Base-case: Rate of 0.070 implies $147 per life-year saved CI: Rate of 0.560 – 0.930 implies $172 - $110 per life-year saved
Model does not capture dynamic effects of increased treatment coverage reducing community infectiousness Inclusion would likely improve estimated cost-effectiveness
No quality or disability adjustments currently estimated for life-years saved Inclusion would worsen estimated cost-effectiveness
Productivity gains from averted mortality and morbidity not currently estimated Inclusion would improve estimated cost-effectiveness
13
PRELIMINARY PROJECTIONS: EPIDEMIC IMPACT AND COST OF ACCELERATED SCALE-UP
Modeling the Impact and Costs of Treatment
14
Modeling the Impact and Costs of Treatment in PEPFAR-Supported
Programs
Two complementary analytic approaches:1. Estimation of health impact and net
societal cost of PEPFAR-supported treatment
2. Estimation of longer-term epidemic impact and costs of accelerated scale-up in light of HPTN 052
15
Modeled Example: Accelerated Treatment Scale-Up in Kenya
Desire to understand the potential epidemic impact and resource implications of accelerated treatment scale-up, in light of HPTN 052 findings What might be done in light of global health resource constraints? Understand the magnitude of economies required to allow
accelerated scale-up Collaboration with John Stover (Futures Institute)
Model based on AIDS Impact Model (AIM)/Spectrum to estimate epidemic impact and cost
Cost parameters derived from CDC’s PEPFAR ART Cost Model Kenya chosen as an example setting
16
Modeled Scenario: Rapid expansion of ART to patients already identified as
HIV-infected
Priority groups for accelerated access in scenario:1. Patients with CD4 <500 cells/µl already on
waiting lists for ART or in pre-ART care 2. Lifelong ART to pregnant and breastfeeding
women regardless of CD4 cell count3. Patients with active tuberculosis (TB)4. Persons known to be in serodiscordant
couples regardless of CD4 count
17
Modeled Scenario: Rapid expansion of ART to patients already identified as
HIV-infected
Efficiency gains: Utilizing a public health approach to treatment, it is assumed that costs might be further reduced
Standardized package of care and treatment Increased task-shifting Decentralization of care Streamlined commodity procurement and management
For Kenya example Treatment cost decline is modeled to decrease from $668 to
$491, over 5 years (26.5% decrease compared to current) Base case: Maintenance of 2011 coverage, no efficiency
gains
18
Base-Case for Kenya Projects 70% Coverage of Those Eligible for
Treatment (CD4<350)
Based on PEFPAR 2011 APR data
2010 2011 2012 2013 2014 20150%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ART Coverage
<350 All HIV+
19
To Maintain Base Treatment Coverage, Continued Increase in Treatment
Required
2010 2011 2012 2013 2014 20150
100,000
200,000
300,000
400,000
500,000
600,000
700,000
20
Treatment Resources Would Need to Increase to Maintain Base Coverage
Levels
2011 2012 2013 2014 2015$0
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
Testing
PMTCT
Pre-ART
ART
21
With Accelerated Scale-Up an Additional 323,000 are Moved to Treatment
from Current Clinical Care and PMTCT
Based on population estimates in the following priority populations: patients in care with CD4<500, PMTCT patients, HIV patients with active TB, known PLHA in sero-discordant couples
2010 2011 2012 2013 2014 2015 -
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
Base Case Accelerated Scale-Up
22
Accelerated Scale-Up Results in Annual Decline in New HIV Infections
Under the base-case scenario, incident HIV infections remain relatively constant at or above 120,000 new cases per year. With accelerated treatment scale-up, incident HIV infections could be driven down to ~86,500 by 2015.
2010 2011 2012 2013 2014 20150
20,000
40,000
60,000
80,000
100,000
120,000
140,000
Accelerated Scale-upBase Case
23
Per Patient ART Costs ($/patient), under Base Case and Efficiency
Assumptions
2011 2012 2013 2014 2015$0
$100
$200
$300
$400
$500
$600
$700
$800
Base CaseEfficiency Scenario
24
Under Accelerated Scenario Annual Treatment Costs Reach Steady State
Over Time
Estimated costs to maintain current coverage levels in the Base Case and Accelerated Scale-Up Scenario. Flattened treatment costs in the accelerated scale-up scenario reflect effects of declining HIV incidence and additional implementation efficiency.
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020$0
$100
$200
$300
$400
$500
$600
$700
$800
Accelerated Scale-UpBase Case
Mill
ions
25
Preliminary Findings from Accelerated Treatment Projections for Kenya
Accelerated scale-up could reduce incident infections by 31% over five years A flattened program results in steady incidence and a
growing population of those in need of treatment With reasonable assumptions for continued efficiency
gains, accelerated scale-up possible within constrained budget
In the longer term, accelerated scale-up may be cost-saving
Over five years in the context of the Kenyan epidemic, 93 infections are projected to be averted for every additional 1000 patient-years of treatment provided
For more information please contact:
John Blandford; Chief, Health Economics, Systems and Integration Branch; CGH/DGHATelephone: (404) 639-8070 E-mail: [email protected]
Nalinee Sangrujee; Lead, Health Economics and Finance Team; CGH/DGHA/HESIBTelephone: (404) 639-0942 E-mail: [email protected]
AcknowledgmentsJohn Stover – Futures InstituteNalinee Sangrujee – CDC-AtlantaNick Menzies – Harvard, CDC-AtlantaJ. Michel Tcheunche – CDC-AtlantaVimalanand Prabhu – CDC-AtlantaKipruto Chesang – CDC-KenyaLucy Nganga – CDC-Kenya Andrea Kim – CDC-KenyaNancy Knight – CDC-KenyaJan Moore – CDC-AtlantaLaura Broyles – CDC-Atlanta
Center for Global Health
Division of Global HIV/AIDS