Allocative Efficiency Modelling:
Session on TB
Nicole Fraser
Improving Efficiency in Health Conference
4th February 2016, World Bank Washington
DC
Outline of the session
• Optima TB: Context & progress (Nicole Fraser)
• Technical presentation Optima TB (Tan Doan)
• Overview on other TB models (Nick Menzies)
• General discussion
The greatest public health contribution of the early
models in tuberculosis was that they generated a
scientific attitude towards the whole decision-
making process in control programmes by
employing the techniques of systems analysis and
cost/benefit analysis, thereby making the planner
realize that medicosocial, economic, and operational
parameters were just as important to determine as
epidemiological parameters in the formulation of an
optimum tuberculosis programme.
Halfdan Mahler, 1973
value for money
End TB Strategy
Global Plan to Stop TB
results
resource constraints
cost-effectiveness
a world free of TB equity
patient-centered
integration
accountability
political commitment
universal health coverage
innovation
quality
reach
catastrophic costs
investment case
elimination targets
scope
sustainability
value for money
End TB Strategy
Global Plan to End TB
results
resource constraints
cost-effectiveness
a world free of TB equity
patient-centered
integration
accountability
political commitment
universal health coverage
innovation
quality
reach
catastrophic costs
investment case
elimination targets
scope
sustainability
value for money
End TB Strategy
Global Plan to Stop TB
results
resource constraints
cost-effectiveness
a world free of TB equity
patient-centered
integration
accountability
political commitment
universal health coverage
innovation
quality
reach
catastrophic costs
investment case
elimination targets
scope
sustainability
help understand
heterogeneous
epi settings
use TB data
better
know the costs of
implementation
modes forecast
impact of
NTPs
provide
policy
analysis
identify
opportunities
for better
allocations
Drake et al.
Systematic review,
Health economics 2016
TB incidence has not responded to
TB funding increase
Source: Based on Global TB report 2015, WHO
MDGs SDGs
Stop TB Strategy End TB Strategy 2016-35
2016
High Burden
Country concept
Financial burden of TB on patients…
• Patients risk incurring catastrophic costs esp. if NTP poorly
funded and implemented
• Average total costs to TB patients US$ 847, ~20% are medical
costs, ~20% non-medical costs, and 60% to income loss
• Half of costs incurred before initiating TB treatment
• Equals on average 39% of household income
• higher among the poor and those with MDR-TB
• Financial burden of TB illness greater for patients in poorer
countries without UHC
• OOP expenses typically an inefficient way of buying services
• Need efficient NTPs which minimizes direct and indirect
costs to TB patients
Costs per TB patient highly variable - Within regions (typically higher in former Soviet Union)
- Within GDP bands (China, India, SA, Indonesia, Bangladesh, Pakistan
relatively low)
Launch of the Optima TB partnership: 1 Dec 2015
Partnership for Improving Allocative Efficiency in TB
Title of Presentation 17
Steering
group
Strategic need and
direction, review,
dissemination, scale-up,
advisory
Global Fund, USAID,
Stop TB, Burnet
Institute, World Bank
Technical
group
Sharing of expertise,
identification of priorities
on data and model
development, review,
learning
TB modellers (various
levels of engagement),
economists, public
health experts
National TB
programs,
ministries of
health &
finance
Optima-TB Pilot countries
Subsequent phases of
Optima-TB applications
with NTPs
Mix of TB settings,
includes ex-USSR
country, HIV/TB co-
epidemic, SE Asia, LAC
A software tool that works with selected existing TB epidemic
models to provide evidence for better allocative efficiency in NTPs.
1. TB epidemic model(s) to calibrate to data and project TB trends
with different program/funding combinations or policy scenarios
2. relationships costs of delivering TB services - attained coverage
3. strategic epidemiological objectives and constraints
4. a formal mathematical optimization algorithm to assess optimal
allocation of resources to best achieve the objectives.
AuTuMN is the initial epidemiological model within Optima TB
TB modelling groups have been invited to consider inclusion of their
models in the Optima TB infrastructure and thus be an
epidemiological model selection within Optima TB.
Tan Doan, PhD
Improving Efficiency in Health Washington DC, 3-4 February 2016
with the AuTuMN transmission dynamic model
More to be achieved with each dollar
Governments should provide services that are safe, timely, effective, efficient, equitable, and patient-focused
Allocative efficiency
Maximization of health outcomes using the most cost-effective mix
of health interventions
Efficient allocation of health resources for TB
The following types of questions need to be answered: Knowing your TB epidemics
What are my country’s TB trends and how will these change under different funding scenarios?
How many TB cases and TB-related deaths have been averted through past TB investments?
Planning and prioritizing TB programs
For a certain level of funding, how should resources be allocated across different programs to maximize health outcomes?
What is the expected epidemic trajectory of TB incidence, prevalence and deaths?
Targeting areas of need
How can targeting national resources to sub-national regions and special population groups improve outcomes?
Predicting outcomes
To achieve predefined TB targets, how much funding will be required, and how should it be allocated?
The approach
Phase 4. Apply optimization algorithm and interpret results
Phase 1. Assess burden of disease
Define diseases, health states and model structure
Compile estimates of prevalence and incidence of disease
Determine baseline projections for each disease state (calibration)
Phase 2. Intervention module
Define clinical interventions to reduce burden of disease
Determine cost functions associated with intervention delivery
Determine effect of increasing coverage
Phase 3. Define objectives and constraints
Set political and strategic objectives
Set time horizons for attaining objectives
Set logistic, ethical, political and other constraints
Institutionalization for sustainability
Transmission dynamic model
TB is a highly complex disease
Transmission dynamic model needs to incorporate the following features:
Three strains of TB: DS-TB, MDR-TB, XDR-TB
Misidentification of strain type due to lack of available drug-resistance testing
Amplification of resistance through default from treatment
Immunity by past treatment and vaccination history
Differential infectiousness by smear status
Latent infection and treatment of latent infection
Sensitivity of diagnostic algorithms for TB
Declining infectiousness with treatment
The ability to distinguish previously treated patients
AuTuMN (Australian Tuberculosis Modelling Network)
TIs
Is
Dssasdfasdf
Lk;asjdfl;as
TIx TIs
Is
Dssadfa
Askldjf;as
TIx On appropriate regimen
Undiagnosed active disease in the community
Correctly diagnosed
On inappropriate regimen
Susceptible compartments
Retreatment cases have same structural links between compartments, but different inter-compartmental flow rates
Defaults and failures pass to re-treatment undiagnosed active disease in the community compartments
Birth
New infection
Reinfection Stabilisation Rapid progression
Late reactivation
Prophylactic treatment
Re-treatment compartments
Extra-pulmonary Smear-negative
Smear-positive
Spontaneous recovery
Treatment commencement by regimen availability
Dss Dss TB diagnosis missed Dssasdfasdf
Lk;asjdfl;as
Dssadfa
Askldjf;as
Strain incorrectly identified
Correct identification as TB and by strain
Correct identification as TB, but strain not identified
Failure of health system to identify patient
Late latent compartments
Early latent compartments
Drug-susceptible TB Multidrug-resistant TB
Extensively drug-resistant TB
Transmission dynamic model
Emma McBryde James Trauer
Transmission dynamic model
Age: children, elderly
HIV status (CD4 count >300, 200-300, <200)
Comorbidity: diabetes mellitus, malnutrition, etc.
Prisoners
Indigenous population
Urban and rural poor
Geographical locations
AuTuMN model is able to stratify population by any demographic and risk profile grouping
AuTuMN successfully implemented in PNG, China, SA, India
Example: Papa New Guinea (PNG)
Transmission dynamic model
Inci
den
ce
% In
cid
ence
at
trib
uta
ble
to
MD
R-T
B
Mo
rtal
ity
DOTs, GeneXpert, pilot PMDT
Scale-up DOTs, GeneXpert, PMDT
in 1 district
Scale-up DOTs, GeneXpert,
province-wide PMDT
AuTuMN successfully implemented in PNG, China, SA, India
Example: India, China, South Africa
Transmission dynamic model
In addition to AuTuMN, other models may be included in Optima TB through re-coding of model into the Optima infrastructure
Models to be provided with optimization algorithm in their existing structures and programming languages
Transmission dynamic model
The approach
Phase 4. Apply optimization algorithm and interpret results
Phase 1. Assess burden of disease
Define diseases, health states and model structure
Compile estimates of prevalence and incidence of disease
Determine baseline projections for each disease state (calibration)
Phase 2. Intervention module
Define clinical interventions to reduce burden of disease
Determine cost functions associated with intervention delivery
Determine effect of increasing coverage
Phase 3. Define objectives and constraints
Set political and strategic objectives
Set time horizons for attaining objectives
Set logistic, ethical, political and other constraints
Institutionalization for sustainability
AuTuMN transmission dynamic model
Epidemic outputs: prevalence, incidence, mortality
Intervention module
Define clinical interventions to reduce TB burden
Measure current levels of activity
Determine the cost-coverage-outcome functions associated with intervention delivery
Interpret programs and coverage into model parameters
Calibration of the TB transmission dynamic model to the epidemic context under consideration
Intervention module
AuTuMN is complex and flexible enough to simulate all known and future interventions
Increased coverage of DOTs
Improved diagnostic algorithms
Programmatic management of MDR-TB and XDR-TB
Active and intensified case finding
Addressing HIV-TB co-infection
Preventive therapy for latent infection
Short course for MDR-TB
New diagnostics (e.g. GeneXpert)
Laboratory strengthening
Unit cost for well-established programs; total cost for new programs
Countries need to have reasonable cost data accessible for analyses
Cost data can also be obtained from budget planning analysis through existing partnerships between Optima TB and Global Fund, USAID and Stop TB
Costs of TB programs
How should the budget be allocated amongst these ‘n’
programs, modalities, and delivery options, considering their
interactions with synergies and limitations?
Resource allocation
How should the budget be allocated
among these 10 programs?
We have:
Cost-outcome curves that relate spending to model parameters
An transmission dynamic model that translates the parameters into epidemic outcomes
Next question:
Optimization
Aim: for a given amount of money, how should it be allocated to achieve the best outcome?
“Best” could mean:
• Fewest infections
• Fewest deaths
• All of the above
Formally: For resource vector R such that 𝑅 = const. and outcome 𝑂 = 𝑓 𝑅 , find R that minimizes O.
Philippines: already started, to be completed May 2016
Fiji, PNG: early discussion stage, to be completed Dec 2016
4 other countries: planning to be completed Sep 2016
Country implementation
Dec 2015 Feb 2016 Mar 2016 Apr 2016 May 2016
Initial dialogue:
GF, NPT, AuTuMN
Stakeholder engagement
Data collection
and quality check
Preliminary results Final results
Today Philippines
Acknowledgements
AuTuMN Emma McBryde James Trauer Tan Doan Nick Scott Romain Ragonnet Justin Denholm Bosco Ho
David Wilson and team
www.tb-mac.org
Modelling and methods to improve the allocative efficiency of TB programs
Nicolas A Menzies
Harvard T.H. Chan School of Public Health
www.tb-mac.org
• Aim • To improve global TB control by coordinating and promoting mathematical
modelling and other research activities to provide scientific support for policy decisions and implementation
• Objectives • Identify research questions concerning TB control that require input from
mathematical modelling or other quantitative research
• Facilitate sharing of data, information and expertise to achieve consensus on current knowledge and knowledge gaps, methodological standards and current best practice for TB control decision-making
• Fund small analytical/modelling research projects
• Disseminate results and tools to key stakeholders including TB control programmes and donors
www.tb-mac.org
TB MAC Organisation • Consortium
• Open to anyone using mathematical models or other quantitative methods to answer TB control questions
• Committee • Anna Vassall (LSHTM), Katherine Floyd (WHO), David
Dowdy (JHU), Ted Cohen (Yale), Philip Eckhoff (IDM), Michael Kimerling (KNCV), Geoff Garnett (BMGF), Damian Walker (BMGF), Richard White (Chair, LSHTM)
• Secretariat • Rein Houben • Christina Albertsen
• Advisory Panel • Mario Raviglione (WHO/GTB), Lucica Ditiu (STB
Partnership), Jane Carter (Union), Jaap Broekmans (KNCV), Mehran Hosseini (Global Fund), Amy Bloom (USAID), Bruce Levin (Emory), Phil LoBue (CDC), Peter Kim (NIAID/NIH)
www.tb-mac.org
TB MAC activities
• Thematic meetings on modelling issues (TB/HIV, diagnostics, novel drug regimens, UHC & socio-economic determinants)
• Support to foster new entrants to the field (modelling courses, small grants)
• Scientific input to high-level policy meetings or planning processes
• Promote methods development and their application in the service of policy-making
Example:
Modelling the Post-2015 WHO Targets
www.tb-mac.org
• Post-2015 End TB Targets demand TB incidence reduced by 50%, TB mortality reduced by 75% by 2025
Need major gains in high-burden countries
• TB-MAC convened a collaboration of TB modelling groups: can intensified action on TB meet the global TB targets in China, India and South Africa?
Targets modelling collaboration
www.tb-mac.org
• Countries need to understand cost-effectiveness and affordability before committing to efforts
• In planning expanded action on TB, what to do?
Multiple approaches which could be adopted, useful to compare relative costs and health impact of each
• TB places major economic burden on households, how will expanded TB control affect these costs?
Addressing economic questions
www.tb-mac.org
Form of the analytic model (example)
Uninfected,
Susceptible
Core TB Subdivision
TB Treatmnt
Smear-Neg
1st-Line
TB Treatmnt
Smear-Pos
1st-Line
Latent Slow
/ Recovered
TB Treatmnt
Smear-Neg
2nd-Line
Uninfected,
Partially
Immune
Latent
Fast
Smr-Pos
Active TB
Smr-Neg
Active TB
LTBI
Treatment
TB Treatmnt
Smear-Pos
2nd-Line
Model entry for
foreign born
Basic state
transition
(1) Exits due to
mortality not
shown.
(2) Foreign born
enter distributed
across non-
treatment states.
Model entry for
native born
www.tb-mac.org
HIV
Neg
HIV Subdivision Drug Resistance Subdivision
Pan-
Sensitive
RIF
Resistance
INH
Resistance
MDR-TB MDR+ /
XDR-TB
Treatment
Naive
Treatment
Experienced
Treatment History Subdivision
Basic state
transition
(1) Exits due to mortality not shown.
(2) Foreign born enter distributed across all drug resistance, treatment history and age states, not shown.
ART2
HIV CD4 < 350
ART 1
HIV CD4 > 350
Risk Group Subdivision
Low-Risk High-Risk Long-term
Residents
Age Group Subdivision
0-4 Years 95+ Years 85-94 Years
Model entry for
native born
Model entry for
foreign born
Recent
Arrivals
Native-Born Foreign-Born
10 year age bands
Form of the analytic model (example)
www.tb-mac.org
Small details important
• Example: mechanism for accessing care:
No TB
TB
No TB
TB
No TB
TB
No TB
TB
Presentation Clinical
suspicion Diagnostic
tests
TREA
TMEN
T
www.tb-mac.org
Collaborating models Model Model type Age structure Population strata Countries AuTuMN DC <15 and 15+ MDR/non-MDR-TB, care access. For
South Africa: HIV/ART/CD4 status CH, IN, SA
Harvard DC 15+ HIV/ART/CD4 status, drug resistance, tx history, TB care access
CH, IN, SA
Hopkins DC 15+ HIV/ART/CD4 status, MDR/non-MDR-TB SA
ICPHFI DC 15+ MDR/non-MDR-TB, tx history IN
IDM SM By month MDR/non-MDR-TB, provider and tx history
CH
NTU DC 15+ MDR/non-MDR-TB, health care system, tx history
CH
STAMP SM By month Sex, tx history and type, time since infection and activation
IN
TIME DC <15 and 15+ HIV/ART/CD4 status, MDR/non-MDR-TB, tx history
CH, IN, SA
UGA DC <15 and 15+ HIV/ART status, MDR SA
Planning to serve country needs at scale
Microsims: model details very different
www.tb-mac.org
Defining intervention scenarios • Sought input from program experts in each country to
define scenarios for expanded TB control
e.g. Reduce default between diagnosis and treatment from 10% to 5%
• Worked with country experts to define activities required to achieve scenario goals
e.g. Compensation for patient expenses assoc with diagnosis and treatment, follow-up of defaulters in community
www.tb-mac.org
Analytic approach
• Considered outcomes realized over 20 years
• DALYs used as summary measure of health burden
• Costs assessed from multiple perspectives
TB health services (relevant for affordability)
= Diagnosis, 1st line tx, 2nd line tx, other costs, program overheads
Patients and families (relevant for economic burden)
= Productivity costs, patient medical + non-medical costs
www.tb-mac.org
Cost-effectiveness, South Africa
0 5 10 15
0
5000
10000
15000
Incre
men
tal C
osts
(U
S$
, m
il.)
DALYs Averted (mil.)
Health System Perspective
● ● ● ●IPT for HIV−positives Expand access Improve treatment Combination
● 1000
● 1300
● 850
● 1400
0 5 10 15
0
2000
4000
6000
8000
10000
12000
14000
Incre
men
tal C
osts
(U
S$
, m
il.)
DALYs Averted (mil.)
Societal Perspective
● 730
● 1100
● 600
● 1100
www.tb-mac.org
Cost-effectiveness, China
0 1 2 3 4
0
2000
4000
6000
8000
10000
12000
Incre
men
tal C
osts
(U
S$
, m
il.)
DALYs Averted (mil.)
Health System Perspective
● ● ● ●Expand access Introduce Xpert Improve treatment Combination
●
470
● 16000
●
450
● 3700
0 1 2 3 4
−5000
0
5000
10000
Incre
men
tal C
osts
(U
S$
, m
il.)
DALYs Averted (mil.)
Societal Perspective
● Dom.
● 23000
● Dom.
● 1100
www.tb-mac.org
Cost-effectiveness, India
0 20 40 60
−2000
0
2000
4000
6000
8000
Incre
men
tal C
osts
(U
S$
, m
il.)
DALYs Averted (mil.)
Health System Perspective
● ● ● ● ●Expand access Active case−finding Introduce Xpert Improve treatment Combination
● Dom.
●
3700● 760
● 220
● 94
0 20 40 60
−25000
−20000
−15000
−10000
−5000
0
5000
Incre
men
tal C
osts
(U
S$
, m
il.)
DALYs Averted (mil.)
Societal Perspective
● Dom.
●
2300● 330
● Dom.
● Dom.
www.tb-mac.org
Rankings: impact, cost-effectiveness
Introduce Xpert Expand Access Improve Treatment
Improve Treatment Expand Access IPT for HIV-positives
Introduce Xpert Expand Access Improve Treatment Active Case Finding
DALYs Averted
Introduce Xpert Improve Treatment Expand Access
Expand Access Improve Treatment IPT for HIV-positives
Introduce Xpert Expand Access Improve Treatment Active Case Finding
Cost per DALY Averted
CHINA
INDIA
SOUTH AFRICA
CHINA
INDIA
SOUTH AFRICA
more less
lower higher
= approximately equal
www.tb-mac.org
Conclusions
• Details of expanding TB services likely very different between countries: relevant for structure and process
• Expanding access to care generally impactful and efficient
• Impact of Xpert contingent on DR-TB treatment quality
• In general, could see large reductions in patient econ. burden
• Substantial variation across models used for analysis
What we don’t know matters to decision-making
• Need to ask questions in context of local policy process, more detailed examination of policy space
www.tb-mac.org
Thanks to many Economists
Ines Garcia Baena, Fiammetta Bozzani, Yoko Laurence, Susmita Chatterjee, Sun Qiang, Nicola Foster, Andrew Siroka
Modellers
Rein Houben, Tom Sumner, Grace Huynh, Nimalan Arinaminpathy, Jeremy Goldhaber-Fiebert, Hsien-Ho Lin, Chieh-Yin Wu, Sandip Mandal, Surabhi Pandey, Sze-chuan Suen, Eran Bendavid, Andrew Azman, David Dowdy,Marcus Feldman, Andreas Handel, Christopher Whalen, Stewart Chang, Bradley Wagner, Philip Eckhoff, James Trauer, Justin Denholm, Emma McBryde, Ted Cohen, Joshua Salomon
Country experts, other experts
Carel Pretorius, Marek Lalli, Jeffrey Eaton, Delia Boccia, Mehran Hosseini, Suvanand Sahu, Colleen Daniels, Lucica Ditiu, Daniel Chin, Lixia Wang, Vineet Chadha, Kiran Rade, Puneet Dewan, Piotr Hippner, Salome Charalambous, Alison Grant, Gavin Churchyard, Yogan Pillay, David Mametja, Michael Kimerling, Richard White
www.tb-mac.org
Health service costs
0
5000
10000
15000
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Expand access Introduce Xpert Improve treatment Combination
Incr
em
en
tal C
ost
s (U
SD
, m
il.) China
−2000
0
2000
4000
6000
8000
10000
Au
TuM
N
Harv
ard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Harv
ard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Harv
ard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Harv
ard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Harv
ard
ICP
HF
I
STA
MP
TIM
E
Me
an
Expand access Active case−finding Introduce Xpert Improve treatment Combination
Incr
em
en
tal C
ost
s (U
SD
, m
il.) India
0
5000
10000
15000
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
IPT for HIV−positives Expand access Improve treatment Combination
Incr
em
en
tal C
ost
s (U
SD
, m
il.) South Africa
●
Diagnosis
First−line treatment
MDR−TB treatment
Other provider costs
Program overheads
Total TB service costs
www.tb-mac.org
Patient costs
−10000
−5000
0
5000
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Au
TuM
N
Harv
ard
IDM
NT
U
TIM
E
Me
an
Expand access Introduce Xpert Improve treatment Combination
Incr
em
en
tal C
ost
s (U
SD
, m
il.) China
−30000
−20000
−10000
0
Au
TuM
N
Ha
rvard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Ha
rvard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Ha
rvard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Ha
rvard
ICP
HF
I
STA
MP
TIM
E
Me
an
Au
TuM
N
Ha
rvard
ICP
HF
I
STA
MP
TIM
E
Me
an
Expand access Active case−finding Introduce Xpert Improve treatment Combination
Incr
em
en
tal C
ost
s (U
SD
, m
il.) India
−5000
−4000
−3000
−2000
−1000
0
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
AuTu
MN
Harv
ard
Hop
kin
s
TIM
E
UG
A
Me
an
IPT for HIV−positives Expand access Improve treatment Combination
Incr
em
en
tal C
ost
s (U
SD
, m
il.) South Africa
●Patient productivity costs Patient non−medical costs Patient medical costs Total patient−incurred costs)
www.tb-mac.org
Budget impact
2020 2025 2030 2035
0
200
400
600
800
1000
1200
1400
China, Base Case Scenario
Tota
l Co
sts
(US
D,
mil.
)
2020 2025 2030 2035
China, Combination Scenario
2020 2025 2030 2035
0
500
1000
1500
2000
India, Base Case Scenario
Tota
l Co
sts
(US
D,
mil.
)
2020 2025 2030 2035
India, Combination Scenario
2020 2025 2030 2035
0
500
1000
1500
South Africa, Base Case Scenario
Tota
l Cost
s (U
SD
, m
il.)
2020 2025 2030 2035
South Africa, Combination Scenario
DiagnosisFirst−line treatment
MDR−TB treatmentOther provider costs
Program overheads
Title of Presentation 58
Discussion
Steering group: Coordination of AE analyses
Title of Presentation 59
• Coordination across pilot countries and modelling
initiatives
• Maximizing learning and application value, e.g.:
• National vs. provincial analyses
• Complementary objectives and research questions
• Implementation modes/program planning vs. investment
case focus
• Sharing of primary country data when appropriate
• Model comparisons
• Coordination of messages
Technical group: Joint work areas
60
1. Intervention and expenditure/budget categories
(nomenclature and what actually used in tools, this includes
non-service areas like training, infrastructure)
2. Costs
• Cost functions – need to collate existing cost work
• Patient-side costs – specific TB meeting focussing on this?
• Outcomes (incl. catastrophic costs)
• Understand costs & cost drivers, incl. broader system/operational costs
3. Intervention effectiveness
• Ongoing work at McGill
• Ongoing TB treatment systematic review by WHO
4. Other evidence for informing methods & parameterisation
61
Thank you