Expanded HIV Screening in the US: W

It Cost, and Who Will Pay? A Budget 

Analysis

Erika G. Martin, PhD MPH

Rockefeller College of Public Affairs & Nelson A. Rockefeller 

Government, SUNY‐Albany

Health Economics Resource Center ( ) HERC S

September 21, 2011
Poll 1: Why are you attending 

cyber‐seminar?

• I am interested in the methods (budget imp

analysis)

• I am interested in the topic (HIV screening)• I am interested in both the methods and to

• At the time it seemed like a good idea to at

I’m not sure why I am here
Poll 2: What is your comfort lev

cost analysis?

• I take primary responsibility for conducting 

analyses

• I work on collaborative teams that conduct 

anal i ys s, but I d ’ on t do the number crunchi

• I understand the main concepts but I do no

this type of research

• I am a newbie!
Research paper details

• Martin EG, Paltiel AD, Walensky RP, Schackman BR

HIV Screening in the US: What Will It Cost Governm

Discretionary Discretionary and Entitlement Entitlement Programs? Programs? A Budge

Analysis. Value in Health 2010; 13(8): 893‐902

• Funding sources: NIDA (R01DA015612), NIAID (R37

AHRQ (T32HS017589), NIMH (R01MH65869, R01M

Doris Duke Charitable Foundation (Clinical Scientis

Development Award)
Cyber‐Seminar Objective

• By the end of this session, you should be ab

– Articulate the major national policy initiative to 

HIV screening, and its rationale

– Explain the difference between cost effectivene

budget impact analysis

– Id if ent y how you could apply this tool to your o

research area
Are current HIV screening prac

working?

• 1 in 5 HIV+ Americans unaware of serostatu• 2 in 5 of newly identified HIV+ cases receive 

diagnosis within a year of HIV detection
The majority of “late testers” receive an H

because of illness, not through routine 

Branson B, Journal of Medical Virology 2007
Revised CDC HIV screening guide

(September 2006)

• Key revi is ons

– Testing in general populations (previous: high‐r

populations and high‐prevalence prevalence areas)

– Opt‐out testing (previous: signed opt‐in consen

• Public health motivations

– Earlier Earlier detection detection and referral referral to care saves lives– Cost effectiveness– Potential Potential way to reduce secondary secondary transmission transmission
Common misconception

• “Cost effective” = “cheap” = “saves mon

Æ Cost effectiveness analysis compares rela

• If a policy intervention is If a policy intervention is “cost effective

cost effective

we should be willing to pay for it

Æ Cost effectiveness analysis considers soci

Cost effectiveness analysis considers soci

perspective; ignores issue of who pays and benefits
Affordability ≠ efficienc

• Under-funding HIV programs may yield 

numbers of newly-identified cases who 

unable to receive care

• Fragmented US healthcare system mak

difficult to apply societal perspective usedifficult to apply societal perspective use

effectiveness analysis
HIV treatment is financed through a fra

system of care

Figure reproduced from Kaiser Family FoundRyan White Program Factsheet, 2009
HIV treatment is financed through a fra

system of care

- Low-income and disabled- Entitlement program

Figure reproduced from Kaiser Family FoundRyan White Program Factsheet, 2009
HIV treatment is financed through a fra

system of care

- Low-income and disabled- Entitlement program

- Permanent disability- Age 65+- Entitlement program

t t e e t p og a

Figure reproduced from Kaiser Family FoundRyan White Program Factsheet, 2009
HIV treatment is financed through a fra

system of care

- “Payer 

for un(d

especiaespecia

have noto AIDS

- Include

Include

Assista(ADAP)

- Discret

Discret

with sta

- Flat-fun

- Low-income and disabled- Entitlement program

- Permanent disability- Age 65+- Entitlement program

t t e e t p og a

Figure reproduced from Kaiser Family FoundRyan White Program Factsheet, 2009
Budget impact analysis

• Payer perspective

• Budget outlays of policy/technology uptake

• Assesses affordability (not efficiency)• Tailor analyses analyses to interests interests and needs of de

de

maker:

– Time horizon ‐ Assumptions Assumptions– Data ‐ Scenarios– Costs (undiscounted) Costs (undiscounted)

For additional additional details details see Mauskopf Mauskopf et al. 2007, Principles Principles of Good Practice Practice f

Analysis: Report of the ISPOR Task Force of Good Research Practices – Budg

Analysis, Value in Health 10(5): 336‐347. 
Research questions

• How much will expanded HIV screening cos

payers over 5 years?

• What will be the budget impact to:

– Discretionary programs (e.g., Ryan White HIV/A

Prog , ram $2.2 billion, “flat‐funded”)

– Entitlement programs (e.g., Medicaid/Medicare

billion federal share)

– Prevention programs (CDC, local health departm
Budget impact analysis analysis met

met

• Computer simulation model of HIV screenin

clinical HIV disease (CEPAC model)

• Extrapolate Extrapolate costs to public programs programs based 

national data on program enrollment and e

pat ays hw

• Forecast Forecast costs to different different programs programs and co

co

current budgets
Cost‐effectiveness of Preventin

Complications (CEPAC)

• Simulation Simulation model of HIV disease disease that captur

captur

status (CD4 cell count), HIV viral load, HIV tr

opportunistic infections

• HIV testing module simulates screening prog• Data sources: sources: public use datasets datasets, published published observational cohorts, clinical trials

• Model outcomes: outcomes: life expectancy expectancy, quality quality‐ad

ad

expectancy, cost, cost‐effectiveness

• Recent analyses using the CEPAC model:

– Lifetime costs of HIV care: Schackman et al, Med Care, 2006– HIV testing: Paltiel et al, N Eng J Med, 2005– Pre‐exposure p py ro h laxis: Paltiel et al., CID, 2009– Test and treat in DC: Walensky et al., CID 2010
CEPAC Investigators: U

Massachusetts General Hospital/

Harvard Medical School

Harvard Sch

Public He

June ParkMai Pho, MDMary Pisculli MD MPH

Kara CotichSue Goldie, MDGeorge Seage

Aima Ahonkai, MD, MPHIngrid Bassett, MD, MPHJessica Becker Mary Pisculli, MD, MPH

Lynn Ramirez, MDErin Rhode, MSN S th

George SeageMilton WeinsteCornell

Jessica BeckerMelissa Bender, MD, MPHJohn ChiosiJ if Ch

Narayan 

Sampath, 

MBA

Paul Sax, MD

April Kimmel, PBruce Schackm

Y l

Jennifer Ch

u

Andrea Ciaranello, MD, MPHKenneth Freedberg, MD, MSc

Callie Scott, MScAdam Stoler, MALauren Uhler

Yale A. David PaltieLille and Bor

Julie Levison, MD, MPhilBen Linas, MD, MPHSarah Lorenzana

Rochelle Walensky, 

MD, MPH

Bin

gxia Wang, PhD

Lille and BorYazdan YazdanDelphine GabilSylvie Deuffic

-B

Elena Losina, PhDZhigang Lu, MDBethany Morris g g,

Angela Wong

Bethany Morris Sylvie Deuffic BFarzad Noubary

Supported by NIAID, NIMH, CDC, DDCF, EGPAF 
CEPAC disease disease model

Enter:

Age, Sex, CD4, RNA

CHRONIC ACUTE

DEATH
State space

• stage of disease

chronic / acute / death

• HIV‐1 RNA (current / “set point”)

>100,000 / 30,001‐100,000 / 10,001 ‐ 30,000 / 3,00

500‐3 000 , / <500

• CD4 (current / nadir)

>500 / 301‐500 / 201‐300 / 101‐200 / 51‐100 / 0‐5

• acute infections

PCP / toxoplasmosis / Mycobacterium avium Com

/ fungal / “other”

• history of each acute event type

• time on therapy therapy / time to treatment treatment failure failure

• cause of death

acute OI / chronic AIDS / non‐AIDS
Patient trace

900 CD4

1st-line ART 2nd-line ART

3rd- and 4thline ART

700800900

l)

HVL

Start Therapy

400500600

unt (cells/u 

Start PCP prophylaxis Start MAC prophylaxis

200300

CD4 co

p py CMV infect

Pneumon

0

100

Time (years)

5 10 15 20 25

Death chron

(y )
Mechanisms of HIV detect

detect

Undiagnosed HIV-infected patient

Detection via Screening Modul

development of an 

opportunistic 

infection

(HIV counseling

testing, and referr

program)

HIV therapy (ART and OI prophylaxis)
Three cohorts

• “Prevalent aware” cases

– Aware of infection at start of simulation

– Immediately eligible for linkage to care

– Modeled in disease module only

• “Prevalent unaware” cases

– Unaware of infection at start of simulation

– Only eligible for linkage to care upon diagnosis

– Modeled in screening module and disease mod

• Incident cases

– Only eligible for linkage to care upon diagnosis

– Modeled in screening module and disease mod
Three cohorts

• “Prevalent aware” cases

– Aware of infection at start of simulation

– Immediately eligible for linkage to care

– Modeled in disease module only

• “Prevalent unaware” cases

– Unaware of infection at start of simulation

– Only eligible for linkage to care upon diagnosis

– Modeled in screening module and disease mod

• Incident cases

– Only eligible for linkage to care upon diagnosis

– Modeled in screening module and disease mod
Pathway of care

DiscretionaryRyan White

EntitlemenMedicare

Uncompensated care Medicaid
Pathway of care

Disability

DiscretionaryRyan White

EntitlemenMedicare

DisabilityAge 65

y

Uncompensated care Medicaid
Pathway of care

Some prevcases curre

f i f ti

Some prevalent cases currently aware of infection

of infection

Disability

DiscretionaryRyan White

EntitlemenMedicare

DisabilityAge 65

y

Uncompensated care Medicaid
Pathway of care

Some prevcases curre

f i f ti

Some prevalent cases currently aware of infection

+

of infection All newly diagnosed cases

Disability

DiscretionaryRyan White

EntitlemenMedicare

DisabilityAge 65

y

Uncompensated care Medicaid
Key input parameters

• Number eligible for discretionary and entitl

programs

– National HIV epidemiology data; ages 19+– National data on health insurance coverage

• Demographic and clinical characteristics of 

– HIV Research Network ( ), HIVRN), MACS cohort, p

studies of individuals with primary infection
Key input parameters

• Costs

– Undiscounted $2009

– Pharmaceuticals: Average wholesale prices, ad

average state Medicaid Medicaid discount discount

– Laboratory monitoring: CMS Clinical Laborator

Schedule

– HIV test costs: Adapted from Farnham et al. 20

• HIV test characteristics

– Sensitivity pre‐seroconversion 0.1%

– Specificity pre‐seroconversion 99.6%

– Specificity post‐seroconversion seroconversion 99 9%.– Rapid test return rate: HIV+ 97%; HIV‐ 97%– ELISA test return rate: HIV+ 75%; HIV‐ 67%
Key input parameters

• Screening strategies

– Current practice: every 10 years

– Expanded screening: every 5 years

– Sensitivity Sensitivity analysis: analysis: no screening screening – annual scre

scre

• Base case

– Rapid test, 80% linkage linkage to care, % return for res

res

on national data, no pre‐test counseling

• High cost scenario scenario

– Rapid test, 100% linkage to care, 100% return fo

pre‐test counseling included

• Low cost scenario

– Laboratory Laboratory test (ELISA) (ELISA), 50% linkage linkage to care, 50

results, no pre‐test counseling
Overview of main finding

finding

• $2.7 billion billion five‐year costs to government government t

t

discretionary, and entitlement programs

• Testing costs a small fraction of budget incr• Important budget concerns:

– Downstream care costs (esp. to discretionary b– Cost shifting between discretionary and entitle

programs
Expanded screening identifies indi

earlier in their infections

• B tt e er immune st t a us (hi hg er mean CD4 cell count) at d 

under expanded screening

– Prevalent cases: 122 (current practice); 140 (expanded)– Incident cases: 251 (current practice); 312 (expanded)

• Fewer cases detected due to opportunistic infection

– Prevalent Prevalent cases: 68 3%. (current (current practice); practice); 57 8%. (expanded

(expanded

– Incident cases: 49.0% (current practice); 32.3% (expanded)

• Fewer cases undetected over lifetime

– Prevalent cases: 12.0% (current practice); 9.1% (expanded)– Incident cases: 11.7% (current practice); 7.5% (expanded)

• Improved quality quality‐adjusted survival survival

– Prevalent cases: 2.0 QALYs

– Incident cases: 3.2 QALYs
Total five‐year budget impact of $2.7 bil

most costs due to downstream ca

• Total incremental costs $2.7 billion

– Sensitivity analysis: $1.9 – $3.4 billion

• Testing costs ($503 million) less than one‐fifth of tot

– Sensitivity analysis: $383 – $697 million

• Largest budget impact ($2.9 billion) to discretionary 

– Sensitivity analysis: $1.9 – $3.4 billion

• Slight short‐term cost savings ($624 million) to entit

programs

– Sensitivity analysis: $421 – $742 million
Incremental cost of expanded screening vers

practice: Discretionary programs will 

disproportionately affected by expanded sc
Pharmaceutical costs of expanded screening vers

practice practice: Pharmace tical Pharmaceutical costs, partic larl u y those 

discretionary programs, will be the main driver of bud
Limitations

• Assumptions

– No new drugs or technologies

– Constant % of individuals with private or VA he

insurance coverage

– No dropping out of care/treatment discontinua

– No financial benefits of preventing secondary in

• Data limitations: number with VA insuranc

estimated, HIVRN not nationally representa

• National aggregation aggregation ignores ignores interstate interstate vari

vari

• Does not incorporate Medicaid expansion a

health reform
Budget challenges

• Largest Largest impact to discretionary discretionary programs programs, w

less capacity to expand

• President Obama’s pledged $53 million incr

CDC for HIV prevention and surveillance un

cover t ti es ng costs

• Although Although health reform expands expands Medicaid Medicaid 

<133% FPL, many HIV‐infected individuals w

need additional services

• Important to consider downstream care co
Current HIV policy questions that could 

from budget impact analysis

• Health reform and shift in payers for HIV ca

– Medicaid expansion– Insurance exchange; high‐risk pools

• Recent clinical findings on “treatment as pr

(HPTN 052)
Whiteboard: Are there research questions (from your a

be addressed addressed using budget impact analysis analysis tech

tech
Questions?

If you have any follow‐up questions about the 

or the topic, feel free to email me at

emartin@albany.edu