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Dr. Presented by Ivor Langley, Liverpool School of Tropical Medicine and Dr. Hsien-Ho, National Taiwan University
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Hope in the Pipeline: Virtual Implementation to assess new diagnostic tools
43rd Union World Conference on Lung Health – November 2012J2J Lung Health Media Training
Ivor Langley, Liverpool School of Tropical Medicine, UKHsien-Ho Lin, National Taiwan University
2© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Agenda
Background – Tuberculosis diagnostics-Some challenges and opportunities-How can modelling help?
Virtual implementation – What is it?-Operational modelling-Transmission modelling-Linking operational and transmission models
Virtual Implementation – Case Study from Tanzania
Next Steps
3© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
TB diagnostics – some challenges and opportunities - the challenges for TB diagnosis
ACCESS TO DIAGNOSISCase detection is only around 45-80% in many parts of the world
McNerney R, and Daley P (2011), Towards a point-of-care test for active tuberculosis: obstacles and opportunities, March 2011 | Volume 9 www.nature.com/reviews/micro
4© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
SPEED OF DIAGNOSIS Many visits required
to provide sputum samples, receive diagnosis, and commence treatment
Leads to high diagnostic default rate
MDR-TB diagnosis will take a lot longer ~2-4 months
Home
TB Diagnostic
Centre
Becomes Sick with cough
TB Suspect
Provide Sputum
Sample 1
Health Clinic
Home
TB Diagnostic
CentreHome
TB Diagnostic
Centre
Home
ProvideSputum
Sample 2
Return Home
Receive Diagnosis
TB Diagnostic
Centre
ReceiveTreatmentMedicine
Home
TB Diagnostic
Centre
Treatment Monitoring
Return Home
Return Home
Return Home Returning
Every 2 wks for Medicines
At end of intermediate
phases if smear negative
SmearPositive
TB Diagnostic
Centre
Smear Negative
TB Diagnosed
At end of intermediatephases if smear Positive – TestFor Drug Resistance and put on
MDR -TB Treatment if found
Home
No TB Found
TB Cure
TB diagnostics – some challenges and opportunities - the challenges for TB diagnosis
5© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
ACCURACY OF DIAGNOSISSputum Smear Microscopy only detects around 30 – 70% of TB casesAccuracy is much worse for HIV+ patients (typically 40% of cases)
Secondary techniques such as chest x-ray and short course antibiotics treatment are likely to have high false positive rate 25-50%
TB diagnostics – some challenges and opportunities - the challenges for TB diagnosis
6© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
The introduction and scale-up of new tools for the diagnosis of Tuberculosis (TB) in developing countries has the potential to make a huge difference to the lives of millions of people particularly those living in poverty e.g.
LED FluorescenceMicroscopy GeneXpert MTB/RIF
Small PM, Pai M. (2010), Tuberculosis diagnosis - time for a game change. N Engl J Med. 2010; 363(11): 1070-1.
Ziehl NeelsenMicroscopy
Sensitivity 35-75%Specificity 99-100%Turnaround 24-48hrsCost per test $1.50Time per test ~60minsExtra Investment nil
Sensitivity 40-80%Specificity 99-100%Turnaround 24-48hrsCost per test $1.50Time per test ~55minsExtra Investment $1,250
Sensitivity 80-95%Specificity 98-99%Turnaround <12hrsCost per test $10-$17Time per test ~2hrsExtra Investment $9k-18k
Point of Care?
?Sensitivity ?Specificity ?Turnaround <1hrCost per test ?Time per test ?Extra Investment ?
TB diagnostics – some challenges and opportunities - hope in the pipeline
7© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
……to identify the most effective, sustainable, and appropriate TB diagnostic technology and algorithm for each individual context
…. by projecting the impacts on patients, the health system, and the community.
TB diagnostics – how modelling can help? - the challenge for policy makers
Frank Cobelens, Susan van den Hof, Madhukar Pai, S. Bertel Squire, Andrew Ramsay, Michael E. Kimerling (2012); Which New Diagnostics for Tuberculosis, and When?, The Journal of Infectious Diseases, DOI: 10.1093/infdis/jis188
8© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Patients Health System Community
EFFICACY- How well does
it work?
EQUITY- Who benefits
and why?
SCALE-UP- Impacts of
national rollout?
HEALTH SYSTEM- Operational
effects?
HORIZON SCANNING- How does it compare
to other technologies?
ImpactAssessmentFramework*
* Mann G, Squire SB, Bissell K, Eliseev P, Du Toit E, Hesseling A, et al. (2010), Beyond accuracy: creating a comprehensive evidence base for TB diagnostic tools. Int J Tuberc Lung Dis. 2010; 14(12): 1518-24.
Do HIV+ patients benefit? Will it benefit the poor? Will drug resistant patients benefit?
How many more TB treatments required? Will it reduce wastage – false positive?
How much will it cost? What is the affect on the number of samples collected? Will it overcome bottlenecks or just move them on? Where to place the new test in the diagnostic algorithm
What will the impact be on TB incidence and prevalence?
What is the increase in patients diagnosed and cured?
How many patients will benefit if rolled out?
Where to start? How much will it cost? Is it cost effective?
How will staff be impacted?
Will it reduce patient visits and waiting time? How much quicker will patients be treated?
Will it mean more patients seek diagnosis?
What if? - New test performance changes, targeted differently, numbers grow or fall?
Will it contribute to achieving the 2015/ 2050 millennium development goals for TB?
TB diagnostics – how modelling can help? - the projected impacts that policy makers want to understand
9© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Critical evidence is provided by :- Laboratory TestsDemonstration StudiesExplanatory Trials (Does it work?)Pragmatic Trials (Does it work in normal practice in a particular context?)
Modelling (Virtual Implementation) complements trials by applying the evidence to other contexts to predict impactsProjecting patient effects across a wide spectrum of measuresProjecting health system effects and costsProjecting impacts of scale-upAssessing cost effectiveness and sustainabilityProjecting TB incidence and other transmission impacts
SB. Squire, ARC. Ramsay, S. van den Hof, KA. Millington, I. Langley, G. Bello, A. Kritski,A. Detjen, R. Thomson, F. Cobelens, GH. Mann, Making innovations accessible to the poor through
implementation research, INT J TUBERC LUNG DIS 15(7):862–870, doi:10.5588/ijtld.11.0161
TB diagnostics – how modelling can help? - complementing trials
10© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
OPERATIONAL MODELPatient & Health System Effects
Berkeley Madonna WITNESS simulation tool
Virtual implementation – What is it? - bringing together operational, transmission, and cost effectiveness modelling
Katsaliaki K, Mustafee N (2010), Applications of simulation within the healthcare context. Journal of the Operational Research Society. 2010; doi:10.1057/ jors.2010.20mall PM,
11© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Individuals home
TB patients home
Sputum Collection and Diagnosis
TB treatment dispensed
Microscopy, culture and drug susceptibility testing
Central Reference Lab(CTRL)
2. Return for further samples, diagnosis, or first treatment
3. Further sample required or not TB
8. Return for therapeutic monitoring
7. Return for next batch of medication
5. Start TB treatment
C. Drug resistance expected, Transport samples to CTRL
1. Individual becomes sick with TB symptoms and is referred to diagnostic centre
9. Continuation of treatment
D. Results
6. Return home with treatment
Sample PreparationZN or LED Microscopy
Automated Nucleic AcidAmplification test
(aNAAT)
Diagnostic Centre Lab
B. Results
A. Sputum sample for
testing
District DiagnosticCentre
LEGENDSolid Green Lines – Individuals
with suspected TBDash/Dot Red Lines -.-.-
Patients being treated for TBDash Blue Lines - - -
Sputum sample pathways
X-Ray
4. Smear-ve sentfor X-ray
DOTS training
Virtual implementation – What is it? - operational modelling
OPERATIONAL MODELPatient & Health System Effects
12© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
The operational component of virtual implementation is:-
A. Detailed - to take account of the complex interactions that affect outcomes, cause bottlenecks, and limit capacity
B. Visual- to give a representation of the operation that enables non modellers (e.g. policy makers) to engage with the modelling and assist in its validation – not a ‘black box’.
C. Flexible- so the effects of many new and existing diagnostics options and contexts can be modelled. Also enabling ‘what if?’ questions to be addressed.
D. Output rich - so outcomes can be analysed using readily available database and statistical tools e.g. matching the WHO output requirements for monitoring implementations of Xpert MTB/RIF
E. Powerful – to enable many iterations of the process to be rapidly completed e.g. simulating 5-10 years of TB diagnosis in under an hour of real time
Virtual implementation – What is it? - operational modelling
SB. Squire, ARC. Ramsay, S. van den Hof, KA. Millington, I. Langley, G. Bello, A. Kritski, A. Detjen, R. Thomson, F. Cobelens, GH. Mann, Making innovations accessible to the poor through implementation research, Int J Tuberc Ling Dis
15(7):862–870, doi:10.5588/ijtld.11.0161
OPERATIONAL MODELPatient & Health System Effects
13© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – What is it? - example screen layout – District diagnostic facility
OPERATIONAL MODELPatient & Health System Effects
14© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Inputs
Diagnostic algorithm
Microscopy tool LED fluorescence microscopy
Diagnostic Tool used:For Diagnosis New Patients Retreat Patients
Primary tool HIV+ Xpert MTB/RIF Xpert MTB/RIFPrimary tool HIV- LED fluorescence microscopy Xpert MTB/RIF
Secondary tool HIV+ clinical diagnosis(used i f primary test resul t i s negative) HIV- clinical diagnosis
For treatment followup LED fluorescence microscopy LED fluorescence microscopyCheck the diagnostic tools selection for
Number of samples needed per test:For diagnosis (tick box for same day front loading)
ZN microscopy 2 1LED fluorescence microscopy 2 1Xpert MTB/RIF 1 1
Check the same day front loading selectionFor treatment:
LED fluorescence microscopy 1 1
Virtual implementation – What is it? - operational modelling – entering input parameters
OPERATIONAL MODELPatient & Health System Effects
15© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
SET-UP
Virtual implementation – What is it? - example screen layout– District diagnostic laboratory
OPERATIONAL MODELPatient & Health System Effects
16© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
affect transmission
Virtual implementation – What is it?- common features in transmission model
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
17© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
• Diagnostic accuracy is only one step in the whole diagnostic pathway
• In order to understand the transmission impact of a new tool, we have to understand the operational context where it is implemented
Dowdy DW, Cattamanchi A, Steingart KR, Pai M (2011), Is Scale-Up Worth It? Challenges in Economic Analysis of Diagnostic Tests for Tuberculosis. PLoS Med 8(7): e1001063
Virtual implementation – What is it?- from diagnostic tool to diagnostic pathway
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
18© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – What is it?-expanded transmission component
Sensitivity
Lin HH, Langley I, Mwenda R, et al. (2011), A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis 15(8):996–1004, doi:10.5588/ijtld.11.0062
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
19© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
• What will be the impact of new TB diagnostics on HIV epidemiology?-- Do we care?
• Better survival of TB-HIV co-infected patients Increased HIV prevalence Increased cost from expenditure on antiretroviral therapy?
Virtual implementation – What is it?-transmission modelling -- what about HIV?
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
20© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
TB disease States
TB disease States
S Lf
Isp
Ls
Isn
R
LTFU
Test +will treatTest +Result
s
Sputum
exam
Health centerSick1
Sick2
Test -
Treat
A
B
HIV -
HIV +CD4>350
HIV +CD4<350
ART
HIV +CD4<350No ART
Virtual implementation – What is it?- incorporating HIV epidemiology
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
21© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
TB/HIV epidemiology
S F IsnL R
Isp
TB/HIV model structure and parameters
TB/HIV natural history
Country epidemiology
data
Characteristicsof diagnostics
Health system context
0
20
40
60
80
100
120
project
Virtual implementation – What is it?-transmission modelling -- overall summary
TRAMSMISSION MODEL Community & DiseaseTransmission Impacts
22© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
TRANSMISSION MODEL Community & DiseaseTransmission Impacts
OPERATIONAL MODELPatient & Health System Effects
TB Incidence rate
Time to start treatmentDiagnostic default rateOutput Input
Input Output
Lin HH, Langley I, et al. (2011), A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis 15(8):996–1004, doi:10.5588/ijtld.11.0062
Virtual implementation – What is it? - bringing together operational, transmission, and cost effectiveness modelling
Combining the outputs to calculate the Incremental Cost Effectiveness Ratio (ICER)
Incremental Costs
Incremental DALY’s averted
23© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – What is it?-transmission modelling – what models cannot do
• Tell the future -- The future is molded by unpredictable events. -- Models seek to simplify a complex world. -- Comparisons are usually more useful than precise point estimates.
• Tell us which sets of assumptions are “right” -- Models can use different sets of assumptions to make different
projections, but cannot tell which projections are the right ones.
• Make decisions for people -- Decision-making is a political process; models seek only to bring
evidence into that process, and highlight where assumptions are being made.
23 Courtesy of Dr. David Dowdy
24© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example patient output – impact on TB cases cured
ZN Microscopy
LED Fluorescence
LED Same Day
Xpert for Sm- HIV+ & Retreat
Xpert for Sm- HIV+ Known & Retreat
Xpert for HIV+ and retreat
Xpert for HIV+ known and retreat
Xpert full roll-out
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000
New TB Cures
Retreat TB Cures
MDR-TB
Treatment Fail
Untreated TB
25© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example patient output – impact on time to start treatment
ZN Microscopy
LED Fluorescence
LED Same Day
Xpert for Sm- HIV+ & Retreat
Xpert for Sm- HIV+ Known & Retreat
Xpert for HIV+ and retreat
Xpert for HIV+ known and retreat
Xpert full roll-out
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00
Complete Initial Diagnosis
Start TB Treatment
26© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example health system output – impact on TB treatment
ZN Microscopy
LED Fluorescence
LED Same Day
Xpert for Sm- HIV+ & Retreat
Xpert for Sm- HIV+ Known & Retreat
Xpert for HIV+ and retreat
Xpert for HIV+ known and retreat
Xpert full roll-out
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000
Test+ve TB
Test-ve TB
MDR-TB
27© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example transmission output – impact on TB incidence of Xpert vs. ZN microscopy
Decline in TB incidence
*2.4% *
4.0%
28© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example output – impact on TB cases over time taking into account transmission
29© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example transmission output – impact on HIV prevalence of Xpert vs. ZN microscopy
Incremental increase in HIV prevalence
30© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - example transmission output-impact on ART requirements of Xpert vs. ZN microscopy
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 0
2000
4000
6000
8000
10000
12000
14000
16000
31© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
• A summary measure of population health that combines:•Years of life lost from premature death (YLL): impact on
mortality •Years of life lost due to disability (YLD): impact on
incidence/prevalence
• DALY = YLL + YLD
• Includes the health impact on both TB and HIV
Virtual implementation – Case Study – Tanzania - calculating Disability adjusted life years (DALY’s) averted
32© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
• A measure of the cost effectiveness of a new intervention which enables interventions to be compared and prioritised.
• ICER = Incremental costs of the interventionIncremental DALY’s averted
• The ICER can be compared to theo ICER of alternative interventions in TB diagnosticso ICER of other health interventions (e.g. in malaria diagnosis)o Willingness to pay threshold of countries e.g. some have proposed
using the GDP per capita as a threshold (Tanzania $529)
Virtual implementation – Case Study – Tanzania - calculating the Incremental Cost Effectiveness Ratio (ICER)
33© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - Cost effectiveness and sustainability analysis
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
LED
Additional Annual Cost to Health Service (Sustainable?)
NOTE: The size of the circle and the num-ber in the circle represent the benefits measured in DALY's averted per year of the new tool relative to LED fluorescence microscopy (Benefit)
Incr
emen
tal C
ost E
ffec
tiven
ess
Rat
io (
Cos
t Eff
ectiv
e?)
Xpert forall suspects
XpertAll HIV+
XpertSm- & HIV+
Xpert for Sm- known HIV+
Xpert for known HIV+
SameDay LED
LED
34© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – Case Study – Tanzania - Summary
Virtual implementation for TB diagnostics in Tanzania will help:-
• guide where to implement Xpert MTB/RIF
• guide which algorithm to use with Xpert MTB/RIF • guide which alternatives to Xpert to use in districts where Xpert
roll-out is currently unsustainable
• evaluate future tools for TB diagnostics as they become available.
35© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Tanzania Finalise analysis of scale-up of the options including additional ART costs
and sensitivity analysis Consider which centres should be priority for Xpert implementation and
which algorithms Implement virtual implementation tool in the NTLP
Wider Application Apply the models to other settings Develop models for MDR-TB diagnosis
Publications Peer reviewed publication Brochure
– Treat TB Symposium – 15th November- 5pm – Conf Hall 1
Virtual Implementation the next steps
36© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Acknowledgment
USAIDYa Diul Mukadi
Tanzania NTLPSaidi EgwagaBasra DoullaRaymond ShirimaRiziki Kisonga
Malawi MOHReuben Mwenda
The Union (Treat-TB)I.D. RusenAnne Detjen
Liverpool School of Tropical MedicineBertie SquireKerry Millington
Harvard School of Public HealthTed CohenMegan Murray
Lanner Group Geoff Hook
37© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
ANY QUESTIONS?
38© Liverpool School of Tropical Medicine, National Taiwan University, and NTLP Tanzania
Virtual implementation – What is it?-what is modelling?
• A model is a simplified representation of a complex system
• The more realistic (complex) the better?
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