Download - Hope in the Pipeline: Virtual Implementation to Assess New Diagnostic Tools

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


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


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



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%



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


……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


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


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


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,


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



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



Virtual implementation – What is it? - example screen layout – District diagnostic facility



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



SET-UP

Virtual implementation – What is it? - example screen layout– District diagnostic laboratory



affect transmission

Virtual implementation – What is it?- common features in transmission model



• 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



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



• 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?



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



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



TRANSMISSION MODEL Community & DiseaseTransmission Impacts


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


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


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


Virtual implementation – Case Study – Tanzania - example patient output – impact on time to start treatment

ZN Microscopy

LED Fluorescence

LED Same Day





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


Virtual implementation – Case Study – Tanzania - example health system output – impact on TB treatment

ZN Microscopy

LED Fluorescence

LED Same Day





Xpert full roll-out

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000

Test+ve TB

Test-ve TB

MDR-TB


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%


Virtual implementation – Case Study – Tanzania - example output – impact on TB cases over time taking into account transmission


Virtual implementation – Case Study – Tanzania - example transmission output – impact on HIV prevalence of Xpert vs. ZN microscopy

Incremental increase in HIV prevalence


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


• 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


• 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)


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


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.


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


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


ANY QUESTIONS?


Virtual implementation – What is it?-what is modelling?

• A model is a simplified representation of a complex system

• The more realistic (complex) the better?

Download - Hope in the Pipeline: Virtual Implementation to Assess New Diagnostic Tools

Top Related