GLI Partners Meeting 18 th April Ivor Langley (LSTM) & Afrânio Kritski (Rede-TB)
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1 Xpert MTB/RIF - Operational Research Initiatives GLI Partners Meeting 18 th April 212 Ivor Langley (LSTM) & Afrânio Kritski (Rede-TB)
2 Agenda Introduction Example Initiatives Virtual Implementation Prove-IT pragmatic trial in Brazil - Ivor Langley, LSTM - Afrânio Kritski, Rede-TB Acknowlegements 2
3 Introduction Operational research 1 projects are conducted with the aim of 1. Improving programme performance and outcomes 2. Assessing the feasibility, effectiveness or impact of new strategies or interventions on TB control 3. Collecting data to guide policy recommendations on specific interventions 1 Priorities in Operational Research to Improve Tuberculosis Care and Control, World Health Organisation (211), ISBN
4 Introduction Operational Research Process and Scope Feasibility DEFINE the problem & questions to be addressed Impact/ effectiveness Observational Qualitative DESIGN the study and agree the analysis to be conducted Interventional Quantitative Routine Setting Specific DO the change, collect the data, and complete the analysis Research Across settings Local DISSEMINATE the results and learning s from the study and analysis Global 4
5 Introduction - Two Example Studies work in progress Virtual Implementation Modelling of TB diagnostic tools including Xpert MTB/RIF Impact & Effectiveness Observational Quantitative Across many settings with example from Tanzania Global Implications PROVE-IT, Brazil Pragmatic controlled trial of Xpert & LPA for drug sensitivity testing Impact & Effectiveness Interventional Qualitative & Quantitative Across a number of settings in Brazil Local with Global Implications 5
6 Virtual Implementation of new TB diagnostics projecting impacts Data on current diagnostics in Context A Impacts of new diagnostics in ContextA Data on new diagnostics in Context B 6
7 Virtual Implementation DEFINE using the Impact Assessment Framework (IAF) Impact Assessment Framework 2 Patients Health System Community EFFICACY - How well does it work? EQUITY - Who benefits and why? HEALTH SYSTEM - Operational effects? SCALE-UP - Impacts of national rollout? What is the increase in patients diagnosed and cured? Do HIV+ patients benefit? Will it benefit the poor? Will drug resistant Will it reduce patients patient benefit? visits and waiting time? How much quicker will patients be treated? How many patients will benefit if rolled out? How many more TB treatments required? Will it reduce wastage false positive? How will staff be impacted? 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 Where to start? How much will it cost? Is it cost effective? What will the impact be on TB incidence and prevalence? HORIZON SCANNING - How does it compare to other technologies? 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 215/ 25 millennium development goals for TB? 2 Mann G, Squire SB, Bissell K, Eliseev P, Du Toit E, Hesseling A, et al. (21), Beyond accuracy: creating a comprehensive evidence base for TB diagnostic tools. Int J Tuberc Lung Dis. 21; 14(12):
8 Virtual Implementation DEFINE understanding the process 8
9 Virtual Implementation - DESIGN the key requirements The model needed to be:- A. Sufficiently detailed - totake 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 5-1 years of TB diagnosis in under an hour of real time F. Comprehensive to model both the shorter term operational characteristics of the process and the longer term disease transmission impacts 9
10 Virtual Implementation DESIGN the conceptual model 4 Patient & Health System Impacts Turnaround time Default Rates TB Incidence Transmission Impacts 4 Lin HH, Langley I, Mwenda R, et al. (211), A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis 15(8):996 14, doi:1.5588/ijtld
11 Virtual Implementation DO the model development - INPUTS DIAGONSTIC CENTRE CURRENT PERFORMANCE Annual Smear Positive TB Cases 255 Annual Smear Negative TB Cases 64 Annual Retreatment Cases 91 HIV+ rate in TB cases 42.% Proportion of new suspects Smear Poitive 11.6% The Diagnostic Options DIAGNOSTIC DEMAND MATRIX EXCEL WORKBOOK DISTRICT1 Parameters marked in yellow must be input SMEAR +ve -ve -ve -ve -ve Default N. X-RAY No Yes Yes No Diagnosis Seeking TREATMENT Yes Yes Yes No No No TOTAL PATIENT CATEGORY NEW/ RETREAT HIV NEW +ve ,86 -ve , ,5 TOTAL , ,586 RETREAT +ve ve TOTAL Assumptions validated with experts, published literature, and subject to sensitivity analysis e.g. Accuracy (Sensitivity and Specificity of each test) TB suspect and treatment default rates Diagnostic processes and treatment times Costing information 11
12 Virtual Implementation DO the model development Diagnostic Centre A detailed model was developed using the WITNESS Simulation Software. Patients are modelled through each process from Health Clinics into a TB Diagnostic Centre and through to Treatment where appropriate. 12
13 Virtual Implementation DO the model development Diagnostic Lab Sputum samples are modelled through the lab process. The lab model shows a schematic of the process and includes microscopy and Xpert MTB/RIF with flows defined for each sample by rules defined in an input data files Lab technicians and assistants as well as clinical staff are modelled as resources 13
14 LAB TESTS COMPLETED TOTAL NEW SUSPECTS RETREAT TREATMENT MONITORING TOTAL Micrsoscopy GeneXpert Micrsoscopy GeneXpert Micrsoscopy NEW SUSPECTS RETREAT TREATMENT MONITORING TOTAL Year TB Positive TB Negative Positive Negative TB Positive TB Negative Positive Negative TB Positive TB Negative Micro GX Total Micro GX Total Micro Micro GX Total RIF Sensitive RIF Resistant RIF Sensitive RIF Resistant RIF Sensitive RIF Resistant RIF Sensitive RIF Resistant +ve rate +ve rate RIF+% +ve rate +ve rate +ve rate RIF+% +ve rate +ve rate +ve rate +ve rate RIF+% +ve rate QTR % %.4% %.% %.% % % % %.4% % QTR % %.4% %.% %.% % % % %.4% % LAB TESTS COMPLETED QTR % %.3% %.% %.% % % % %.3% % TOTAL QTR % %.2% %.% %.% % % % %.2% % QTR NEW SUSPECTS RETREAT TREATMENT MONITORING.% %.2% %.% %.% % TOTAL % % %.2% % QTR Micrsoscopy GeneXpert 76 Micrsoscopy 6354 GeneXpert Micrsoscopy.% %.2% 463 NEW SUSPECTS 18.7%.% %.% 6354 RETREAT 1.% % TREATMENT MONITORING 1.6% %.2% TOTAL11.9% QTR3 1 Year TB Positive 8787 TB Negative Positive 77 Negative TB Positive 7774 TB Negative 23 Positive 3321 Negative TB Positive TB.% Negative Micro 18.8%.2% GX 18.8% Total.% 7797 Micro 1.%.3% 7797 GX 1.% Total 1.7% Micro 1.7% Micro 2.2%.2% GX 11.8% Total QTR RIF Sensitive RIF Resistant 785 RIF Sensitive RIF Resistant RIF Sensitive RIF Resistant RIF Sensitive 4222 RIF Resistant % % +ve rate.2% ve rate 19.1% RIF+%.% +ve rate % +ve rate.3% 856 +ve rate 1.% RIF+% ve 1.8% rate % +ve rate % +ve rate.2% ve rate 11.7% RIF+% +ve rate QTR1 2 QTR % %.%.2% % 19.2%.4% 6144.% 17.3% %.%.2% % 1.%.% % 1.7% % 13.% % 13.%.2% % 11.6%.4% % QTR2 2 QTR % %.%.2% % 19.2%.4% % 18.7% %.%.2% % 1.%.% % 1.6% % 5.2% % 5.2%.2% % 11.4%.4% % LAB TESTS COMPLETED QTR3 2 QTR % %.%.2% % 19.1%.3% % 19.% %.%.2% % 1.%.% % 1.6% % 2.6% % 2.6%.2% % 11.3%.3% % TOTAL QTR4 2 QTR % %.%.2% % 19.1%.2% % 18.7% %.%.2% % 1.%.% % 1.7% % 1.8% % 1.8%.2% % 11.2%.2% % QTR1 3 QTR NEW SUSPECTS RETREAT % TREATMENT MONITORING 18.9%.%.2% % 18.9%.2% % 18.8% %.%.2% % 1.%.% % TOTAL 1.7% % 1.8% % 1.8%.2% % 11.2%.2% % QTR2 3 QTR Micrsoscopy GeneXpert Micrsoscopy GeneXpert % Micrsoscopy 19.%.%.2% % 19.%.2% 463 NEW SUSPECTS.% 18.7% %.%.2% % 1.%.% RETREAT 1.% 1.7% % 1.6% TREATMENT MONITORING 2.3% 1.6%.2% % 11.1%.2% TOTAL11.9% QTR3 3 QTR3 1 Year TB Positive TB Negative Positive 77 Negative TB Positive TB Negative 23 Positive Negative.% TB Positive 1877 TB 19.%.% Negative.2% Micro % 19.%.2% GX.% 18.8% Total 1.%.%.2% 7797 Micro % 1.%.3% GX 1.% 1.6% Total % 1.7% Micro 2.3% 1.7%.2% Micro % 11.1%.2% GX 11.8% Total QTR4 3 QTR RIF Sensitive RIF Resistant 785 RIF Sensitive RIF Resistant RIF Sensitive RIF Resistant 879 RIF Sensitive RIF Resistant.% %.%.2% % +ve rate 19.%.2% ve rate.% 19.1% RIF+% %.% +ve rate.2% % +ve rate 1.%.3% ve rate 1.% RIF+% 1.6% ve % 1.8% rate % 1.8% +ve rate.2% % +ve rate 11.%.2% ve rate 11.7% RIF+% +ve rate QTR1 4 QTR1 2 QTR % %.% 144.2% %.% 19.%.2% %.% 19.2%.4% %.% 17.3%.4% %.% 1.%.2% % 1.%.% 1.6% % % 1.7% % 1.7% 13.%.2% % 13.% 11.%.2% % 11.6%.4% % QTR2 4 QTR2 2 QTR % %.% % %.% 18.9%.2% %.% 19.2%.4% %.% 18.7%.4% %.% 1.%.2% % 1.%.% 1.6% % % 1.6% % 1.6% 5.2%.2% % 5.2% 1.9%.2% % 11.4%.4% % LAB TESTS COMPLETED QTR3 4 QTR3 2 QTR % %.% % %.% 18.9%.2% %.% 19.1%.3% %.% 19.%.3% %.% 1.%.2% % 1.%.% 1.7% % % 1.6% % 1.6% 2.6%.2% % 2.6% 1.9%.2% % 11.3%.3% % TOTAL QTR4 4 QTR4 2 QTR % %.% % %.% 18.9%.2% %.% 19.1%.2% %.% 18.7%.3% %.% 1.%.2% % 1.%.% 1.6% % % 1.7% % 1.7% 1.8%.2% % 1.8% 1.9%.2% % 11.2%.2% % QTR1 5 QTR1 3 QTR NEW SUSPECTS RETREAT % %.% TREATMENT.2% MONITORING %.% 18.9%.2% %.% 18.9%.2% %.% 18.8%.3% %.% 1.%.2% % 1.%.% 1.7% % TOTAL % 1.7% % 1.7% 1.8%.2% % 1.8% 1.9%.2% % 11.2%.2% % QTR2 5 QTR2 3 QTR Micrsoscopy GeneXpert Micrsoscopy GeneXpert % %.% % 9415 Micrsoscopy %.% 19.%.2% %.% 19.%.2% NEW SUSPECTS 1.%.% 18.7%.3% %.% 1.%.2% % 1.%.% 1.7% RETREAT 1.% % 1.7% % 1.7% 1.6% TREATMENT.2% MONITORING % 1.6% 1.9%.2% % 11.1%.2% TOTAL11.9% QTR3 5 QTR3 3 QTR Year TB Positive TB Negative Positive 77 Negative TB Positive TB Negative Positive % Negative %.% TB Positive.2% 1877 TB %.% Negative 19.%.2% Micro %.% 19.%.2% GX 1.%.% 18.8%.3% Total %.% 1.%.2% 7797 Micro % 1.%.3% 1.7% GX 1.% % 1.6% Total % 1.6% 1.7%.2% Micro % 1.7% 1.8%.2% Micro % 11.1%.2% GX 11.8% Total QTR4 5 QTR4 3 QTR RIF Sensitive RIF Resistant 785 RIF Sensitive RIF Resistant RIF Sensitive RIF Resistant RIF Sensitive.% RIF Resistant %.% % %.% 18.9%.2% % +ve rate.% 19.%.2% ve rate 1.%.% 19.1% RIF+%.3% %.% +ve rate 1.%.2% % +ve rate 1.%.3% 1.7% ve rate 1.% RIF+% % 1.6% ve % 1.6% 1.8% rate.2% % 1.8% +ve rate 1.8%.2% % +ve rate 11.%.2% ve rate 11.7% RIF+% +ve rate QTR1 6 QTR1 4 QTR QTR % %.% % %.% %.2% %.%.% 19.%.2% % 1.%.% 19.2%.4%.3% %.% 17.3% 1.%.4% %.% 1.%.2% % % 1.%.% % 1.6% % % 1.6% 1.7%.2% % 1.7% 13.% 1.8%.2% % 13.% 11.%.2% % 11.6%.4% % QTR2 6 QTR2 4 QTR QTR % %.% % %.% %.2% %.%.% 18.9%.2% % 1.%.% 19.2%.4%.2% %.% 18.7% 1.%.4% %.% 1.%.2% % % 1.%.% % 1.6% % % 1.6% 1.6%.2% % 1.6% 5.2% 1.8%.2% % 5.2% 1.9%.2% % 11.4%.4% % QTR3 6 QTR3 4 QTR QTR % %.% % %.% %.2% %.%.% 18.9%.2% % 1.%.% 19.1%.3%.3% %.% 19.% 1.%.3% %.% 1.%.2% % % 1.%.% % 1.7% % % 1.7% 1.6%.2% % 1.6% 2.6% 1.8%.2% % 2.6% 1.9%.2% % 11.3%.3% % QTR4 6 QTR4 4 QTR QTR % %.% % %.% %.2% %.%.% 18.9%.2% % 1.%.% 19.1%.2%.2% %.% 18.7% 1.%.3% %.% 1.%.2% % % 1.%.% % 1.6% % % 1.6% 1.7%.2% % 1.7% 1.8% 1.8%.2% % 1.8% 1.9%.2% % 11.2%.2% % QTR1 7 QTR1 5 QTR1 3 QTR % %.% % %.% 18.9%.2% %.% 18.9%.2% %.% 18.8%.3% %.% 1.%.2% % 1.%.% 1.7% % % 1.7% % 1.7% 1.8%.2% % 1.8% 1.9%.2% % 11.2%.2% % QTR2 7 QTR2 5 QTR2 3 QTR % %.% % %.% 19.%.2% %.% 19.%.2% %.% 18.7%.3% %.% 1.%.2% % 1.%.% 1.7% % % 1.7% % 1.7% 1.6%.2% % 1.6% 1.9%.2% % 11.1%.2% % QTR3 7 QTR3 5 QTR3 3 QTR % %.% % %.% 19.%.2% %.% 19.%.2% %.% 18.8%.3% %.% 1.%.2% % 1.%.3% 1.7% % % 1.6% % 1.6% 1.7%.2% % 1.7% 1.8%.2% % 11.1%.2% % QTR4 7 QTR4 5 QTR4 3 QTR % %.% % %.% 18.9%.2% %.% 19.%.2% %.% 19.1%.3% %.% 1.%.2% % 1.%.3% 1.7% % % 1.6% % 1.6% 1.8%.2% % 1.8% 1.8%.2% % 11.%.2% % QTR1 8 QTR1 6 QTR1 4 QTR % %.% % %.% 18.9%.2% %.% 19.%.2% %.% 19.2%.3% %.% 1.%.4% % 1.%.2% 1.7% % % 1.6% % 1.6% 1.7%.2% % 1.7% 1.8%.2% % 11.%.2% % QTR2 8 QTR2 6 QTR2 4 QTR % %.% % %.% 18.9%.2% %.% 18.9%.2% %.% 19.2%.2% %.% 1.%.4% % 1.%.2% 1.7% % % 1.6% % 1.6% 1.6%.2% % 1.6% 1.8%.2% % 1.9%.2% % QTR3 8 QTR3 6 QTR3 4 QTR % %.% % %.% 18.9%.2% %.% 18.9%.2% %.% 19.1%.3% %.% 1.%.3% % 1.%.2% 1.7% % % 1.7% % 1.7% 1.6%.2% % 1.6% 1.8%.2% % 1.9%.2% % QTR4 8 QTR4 6 QTR4 4 QTR % %.% % %.% 19.%.2% %.% 18.9%.2% %.% 19.1%.2% %.% 1.%.3% % 1.%.2% 1.7% % % 1.6% % 1.6% 1.7%.2% % 1.7% 1.8%.2% % 1.9%.2% % QTR1 9 QTR1 7 QTR1 5 QTR % %.%.2% % 18.9%.2% % 18.9% %.%.3% % 1.%.2% % 1.7% % 1.7% % 1.7%.2% % 1.9%.2% % QTR2 9 QTR2 7 QTR2 5 QTR % %.%.2% % 19.%.2% 9415.% 19.% %.%.3% % 1.%.2% % 1.7% % 1.7% % 1.7%.2% % 1.9%.2% % QTR3 9 QTR3 7 QTR3 5 QTR % %.%.2% % 19.%.2% 1877.% 19.% %.%.3% % 1.%.2% % 1.7% % 1.6% % 1.6%.2% % 1.8%.2% % QTR4 9 QTR4 7 QTR4 5 QTR % %.%.2% % 18.9%.2% % 19.% %.%.3% % 1.%.2% % 1.7% % 1.6% % 1.6%.2% % 1.8%.2% % QTR1 1 QTR1 8 QTR1 6 QTR % %.%.2% % 18.9%.2% % 19.% %.%.3% % 1.%.4% % 1.7% % 1.6% % 1.6%.2% % 1.8%.2% % QTR2 1 QTR2 8 QTR2 6 QTR % %.%.2% % 18.9%.2% % 18.9% %.%.2% % 1.%.4% % 1.7% % 1.6% % 1.6%.2% % 1.8%.2% % QTR3 1 QTR3 8 QTR3 6 QTR % %.%.2% % 18.9%.2% % 18.9% %.%.3% % 1.%.3% % 1.7% % 1.7% % 1.7%.2% % 1.8%.2% % QTR4 1 QTR4 8 QTR4 6 QTR % %.%.2% % 19.%.2% % 18.9% %.%.2% % 1.%.3% % 1.7% % 1.6% % 1.6%.2% % 1.8%.2% % QTR1 9 QTR1 7 QTR % %.2% %.% %.3% % % % %.2% % QTR2 9 QTR2 7 QTR % %.2% %.% %.3% % % % %.2% % QTR3 9 QTR3 7 QTR % %.2% %.% %.3% % % % %.2% % QTR4 9 QTR4 7 QTR % %.2% %.% %.3% % % % %.2% % QTR1 1 QTR1 8 QTR % %.2% %.% %.3% % % % %.2% % QTR2 1 QTR2 8 QTR % %.2% %.% %.2% % % % %.2% % QTR3 1 QTR3 8 QTR % %.2% %.% %.3% % % % %.2% % QTR4 1 QTR4 8 QTR % %.2% %.% %.2% % % % %.2% % QTR1 9 QTR1 7 QTR2 9 QTR2 7 QTR3 9 QTR3 7 QTR4 9 QTR4 7 QTR1 1 QTR1 8 QTR2 1 QTR2 8 QTR3 1 QTR3 8 QTR4 1 QTR4 8 QTR1 9 QTR2 9 QTR3 9 QTR4 9 QTR1 1 QTR2 1 QTR3 1 QTR4 1 Virtual Implementation DO the model development OUTPUTS EXCEL WORKBOOK Data is output to an Excel Workbook every Quarter in line with the World Health Organisation requirements for trials of TB diagnostics 14
15 Virtual Implementation DO the model development Calibration in Tanzania Testing approach in Tanzania. Measure Population New TB cases per year Count 44million 8k p.a. TB cases Treated 64k p.a. HIV+% in TB Incidence 47% Treatment Cure Rate 87% Health Facility Count Central TB Reference Lab 1 Zonal Laboratories 2 Regions 27 Districts 168 TB Laboratories >9 Sources Basra Doulla, Ministry of Health, Dar es Salaam, Tanzania 15
16 SET-UP 16
17 SET-UP 17
18 Virtual Implementation DO the options to be considered A. Base Case ZN Microscopy with 2 samples B. LED Fluorescence Microscopy C. Same Day Fluorescence Microscopy D. Xpert MTB/RIF as secondary test for HIV+, Smear-ve new TB suspects- part of current Xpert trials in Tanzania (a. 1% HIV status known, b. 9% HIV status known) E. Xpert MTB/RIF as primary test for HIV+ new TB suspects (a. 1% HIV+ status known, b. 9% HIV+ status known) F. Full rollout of Xpert MTB/RIF to all new TB suspects 18
19 Virtual Implementation DO the example results - DRAFT 19
20 Virtual Implementation DO the example results - DRAFT 2
21 Virtual Implementation DO the example results - DRAFT 21
22 Virtual Implementation DO the example results Cost Effectiveness& Sustainability Rank by cost (High- Low) Site A Currently using ZN Microscopy Site B Currently using LED Fluor. Microscopy Incremental Health System Costs ($kp.a.) 1 Xpert for all Xpert for all Xpert for all Xpert -HIV Xpert -HIV+ Xpert for all Xpert for all No Change Xpert -HIV+ Xpert -HIV Xpert -HIV+ No Change LED Fluor. Xpert for all LED Fluor. Xpert -HIV LED Fluor. No Change.5 22
23 Virtual Implementation DO the example results Cost Effectiveness& Sustainability Rank by cost (High- Low) Site A Currently using ZN Microscopy Site B Currently using LED Fluor. Microscopy DALY s averted per year Incremental Health System Costs ($kp.a.) 1 Xpert for all Xpert for all 1, Xpert for all Xpert -HIV+ 1, Xpert -HIV+ Xpert for all 1, Xpert for all No Change 1, Xpert -HIV+ Xpert -HIV+ 1, Xpert -HIV+ No Change LED Fluor. Xpert for all 1, LED Fluor. Xpert -HIV LED Fluor. No Change
24 Virtual Implementation DO the example results Cost Effectiveness& Sustainability Rank by cost (High- Low) Site A Currently using ZN Microscopy Site B Currently using LED Fluor. Microscopy DALY s averted per year Incremental Health System Costs ($kp.a.) 1 Xpert for all Xpert for all 1, Xpert for all Xpert -HIV+ 1, Xpert -HIV+ Xpert for all 1, Xpert for all No Change 1, X 5 Xpert -HIV+ Xpert -HIV+ 1, Xpert -HIV+ No Change X 7 LED Fluor. Xpert for all 1, Ineffective due to no benefit over 5 Ineffective due to no benefit over 7 8 LED Fluor. Xpert -HIV LED Fluor. No Change
25 Virtual Implementation DO the example results Cost Effectiveness& Sustainability Rank by cost (High- Low) Site A Currently using ZN Microscopy Site B Currently using LED Fluor. Microscopy DALY s averted per year Incremental Health System Costs ($kp.a.) ICER to base case ($) ICER(2) to the next effective intervention ($) 1 Xpert for all Xpert for all 1, Xpert for all Xpert -HIV+ 1, Xpert -HIV+ Xpert for all 1, Xpert for all No Change 1, X 5 Xpert -HIV+ Xpert -HIV+ 1, Xpert -HIV+ No Change X 7 LED Fluor. Xpert for all 1, Ineffective due to no benefit over 5 Ineffective due to no benefit over 7 8 LED Fluor. Xpert -HIV LED Fluor. No Change
26 Virtual Implementation DO the example results Cost Effectiveness& Sustainability Rank by cost (High- Low) Site A Currently using ZN Microscopy Site B Currently using LED Fluor. Microscopy DALY s averted per year Incremental Health System Costs ($kp.a.) ICER to base case ($) ICER(2) to the next effective intervention ($) 1 Xpert for all Xpert for all 1, Xpert for all Xpert -HIV+ 1, Xpert -HIV+ Xpert for all 1, Xpert for all No Change 1, X 5 Xpert -HIV+ Xpert -HIV+ 1, Xpert -HIV+ No Change X 7 LED Fluor. Xpert for all 1, Comment (ICER threshold of $15 per DALY averted) Cost effective $79k pa Ineffective due to high ICER(2) Ineffective due to no benefit over 5 Ineffective due to high ICER(2) Ineffective due to no benefit over 7 8 LED Fluor. Xpert -HIV LED Fluor. No Change
27 Virtual Implementation DO the example results Cost Effectiveness& Sustainability Rank by cost (High- Low) Site A Currently using ZN Microscopy Site B Currently using LED Fluor. Microscopy DALY s averted per year Incremental Health System Costs ($kp.a.) ICER to base case ($) ICER(2) to the next effective intervention ($) 1 Xpert for all Xpert for all 1, Xpert for all Xpert -HIV+ 1, Xpert -HIV+ Xpert for all 1, Xpert for all No Change 1, X 5 Xpert -HIV+ Xpert -HIV+ 1, Xpert -HIV+ No Change X 7 LED Fluor. Xpert for all 1, LED Fluor. Xpert -HIV Comment (ICER threshold of $15 per DALY averted) Cost effective $79k pa Ineffective due to high ICER(2) Cost effective $55k pa Ineffective due to no benefit over 5 Ineffective due to high ICER(2) Ineffective due to no benefit over 7 Cost effective $27k pa Cost effective $19k pa 9 LED Fluor. No Change Cost effective $1k pa 27
28 Virtual Implementation DOING the next steps IMPACT ASSESSMENT IN TANZANIA Of scale-upof the options Of the proposed Xpert MTB/RIF implementations in the 5 diagnostic centres (supporting Tanzanian staff to conduct their own evaluations) WIDER APPLICATION Apply the models to other settings e.g. Malawi Develop models for MDR-TB diagnosis e.g. Brazil 28
29 Virtual Implementation DISSEMINATION the next steps Local Presentation to a forum of policy makers in Tanzania Pier reviewed publications Concept Paper 4 Results orientated paper Health Journal Methodology orientated publications Management Science International Conferences Union conferences Post Graduate Courses Guidelines Modelling guidelines 4 Lin HH, Langley I, Mwenda R, et al. (211), A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis 15(8):996 14, doi:1.5588/ijtld
30 Summary Virtual implementation of options for TB diagnostics :- is a practical and effective approach engages policy makers in validation and questioning enables complex interactions to be taken into account provides comprehensive information on impacts that can be useful in policy decisions 3
31 PROVE-IT, BRAZIL Afrânio Kritski PROVE-IT Policy Relevant Outcomes from Validating Evidence on ImpacT of Xpert MTB/RIF and Line Probe Assay on Presumptive Diagnosis of DR-TB in Brazil REDE-TB: Afranio Kritski, Martha Oliveira, Monica Kramer, Claudia Vater, Fatima Carparo, Jose Ueleres Braga The Union: Anne Detjen, Paula Fujiwara MRC UK: Patrick Phillips LSTM: Bertie Squire, Ivor Langley, Kerry Millington 31
32 PROVE-IT in Brazil - DEFINE GOAL: To evaluate Xpert MTB/RIF, Line Probe Assay (LPA), and MGIT96, performance for DR/MDR TB diagnosis in different provinces with distinct epidemiological and health systems backgrounds in order to identify which one(s) will be most appropriate for the Brazilian public health system (SUS) To determine the most appropriate we need to understand effects/impactsof the change 32
33 PROVE-IT in Brazil - DEFINE A Framework for Impact Assessment for New Diagnostics 2 Layer of Assessment Layer 1: EFFECTIVENESS Howwelldoesitwork? Kinds of question(s) being answered Layer 2: EQUITY -Whobenefitsandwhy? Layer 3: HEALTH SYSTEM - Operational effects? Layer4: SCALEUP Impacts of national rollout? Layer 5: HORIZON SCANNING -How does it compare to other technologies? 2 Mann G, Squire SB, Bissell K, Eliseev P, Du Toit E, Hesseling A, et al. (21), Beyond accuracy: creating a comprehensive evidence base for TB diagnostic tools. Int J Tuberc Lung Dis. 21; 14(12):
34 PROVE-IT in Brazil - DEFINE A Framework for Impact Assessment for New Diagnostics 2 Layer of Assessment Layer 1: EFFECTIVENESS Howwelldoesitwork? Layer 2: EQUITY -Whobenefitsandwhy? Layer 3: HEALTH SYSTEM - Operational effects? Layer4: SCALEUP Impacts of national rollout? Layer 5: HORIZON SCANNING -How does it compare to other technologies? Kinds of question(s) being answered Time from sputum collection to detect DR/MDR-TB cases and to start the appropriate DR-TB treatment - PRIMARY OUTCOME Proportion smear conversion rate at 2 months Proportion culture conversion rate at 6 months - SECONDARY OUTCOME All-cause mortality Costs to the patients (by poverty status/income) Who is benefiting? Subgroup analyses: poor/non-poor, men/women, adults HIV+/HIV-, inpatients vs. outpatients. Costs to the health system (per case started on appropriate regimen) Lab system requirements (infrastructure, HR, procurement, quality assurance etc) Clinical system requirements HR and training requirements Patient management requirements (drugs, treatment facilities etc) Projection of data from Layers for national scale-up. Projection of likely impact on transmission Comparison of LPA/GeneXpert/MGIT96 impact with models of impact expected from other new tools/approaches and packages of tools/approaches. 2 Mann G, Squire SB, Bissell K, Eliseev P, Du Toit E, Hesseling A, et al. (21), Beyond accuracy: creating a comprehensive evidence base for TB diagnostic tools. Int J Tuberc Lung Dis. 21; 14(12):
35 PROVE-IT in Brazil - DESIGN a pragmatic, cluster-randomised, implementation trial Site 4 Site 3 Site 2 Site 1 35
36 PROVE-IT in Brazil - DESIGN a pragmatic, cluster-randomised, crossover design Site 1 Xpert traini Implementation 9 mths MGIT follow-up 6 mths traini Implementation 9 mths follow-up 6 mths Site2 MGIT trainin Implementation 9 mths LPA follow-up 6 mths train nin Implementation follow-up 9 mths 6 mths Site 3 trainin g MGIT Implementation 9 mths Xpert follow-up 6 mths trainin Implementation 9 mths follow-up 6 mths Site 4 traini ng LPA Implementation 9 mths MGIT follow-up 6 mths traini Implementation 9 mths follow-up 6 mths 36
37 PROVE-IT in Brazil - DESIGN a pragmatic, cluster-randomised, implementation trial Implementation e.g. Site 4, 1 st 9 months a) Site accounts health system costs b) Routine registers (clinical & lab) c) Subset of patients patients costs LPA LJ and DST (PM/MGIT) Expected ~3 days 37
38 PROVE-IT in Brazil - DESIGN a pragmatic, cluster-randomised, implementation trial Implementation e.g. Site 1, 2 nd 9 months a) Site accounts health system costs b) Routine registers (clinical & lab) c) Subset of patients patients costs MGIT 96 LJ and DST (PM/MGIT) Expected ~15 days 38
39 PROVE-IT in Brazil - DESIGN a pragmatic, cluster-randomised, implementation trial Implementation e.g. Site 3, 1 st 9 months a) Site accounts health system costs b) Routine registers (clinical& lab) c) Subset of patients patients costs MTB/RIF LJ and DST (PM/MGIT) Expected ~2 days 39
40 PROVE-IT in Brazil - DESIGN inclusion criteria and data collection 1. Inclusion criteria - DR/MDR TB suspects presenting with a) TB in the past i. Suspected re-treatment failure or ii. Smear positive at 2 months b) Without previous TB treatment i. HIV positive or ii. Close contact with MDR-TB case or iii. Homeless iv. Inmates/assylum/penitentiary 2. DR/MDR TB patients at each site and during each phase interviewed for to collect cost and socio-economic data 3. Health system costs collected across all sites for each intervention 4. Active involvement of community through Community Advisory Boards (CAB s) at all stages in all sites or 4
41 PROVE-IT in Brazil - DESIGN Policy Transfer Analysis Policy transfer anaysis will interview: Laboratory professionals Physicians that take care of DR-TB suspects Other health care professionals that are part of the local health teams Representatives of Community Advisory Board Managers of TB Reference Hospitals Managers of Health Services(Unified Health System/ SUS) Objective: To derive constructive lessons that will be applicable for future activity in introducing/piloting/modifying other new diagnostics. 41
42 PROVE-IT in Brazil DO base line recruitment 1. Baseline (Feb September sites and sentinel Units) Rio de Janeiro (x2), São Paulo, Ceará Total number of patients enrolled at implementation phase: 269 Excluded: 11 Included: 258 Required numbers to power trial 63 over 7months 2. Problem: Low recruitment of eligible patients 3. Strategies adopted for increasing recruitment Added sites and sentinel Units Ongoing discussion in Municipalities to increase engagement. 42
43 PROVE-IT in Brazil DO increased sites Site 4 Site 8 (?) PARÁ Site 6 (Added) Site 7 (?) Site 2 Site 3 Site 1 Site 5 (Added) 43
44 PROVE-IT in Brazil DO recruitment since intervention Results: Number of eligible DR TB suspects have increased Site 3: Reference Center Helio Fraga Site 1: Clemente Ferreira Institute N abs Nov Dec Jan Feb March Dec Jan Feb March April 44
45 PROVE-IT in Brazil DOING/DISSEMINATION 1. Recruitment ongoing to first intervention 2. Switch to second intervention August 212 December Complete recruitment to second intervention - April Ongoing patient, health system, and policy transfer data collection throughout intervention 5. Complete Analysis September Publish results
46 Acknowledgment USAID YaDiul Mukadi Tanzania NTLP Dr Saidi Egwaga Basra Doulla The Union (Treat-TB) I.D. Rusen Anne Detjen Paula Fujiwara MRC UK Patrick Phillips Liverpool School of Tropical Medicine Bertie Squire Kerry Millington National Taiwan University Hsien-Ho Lin Harvard School of Public Health Ted Cohen Megan Murray Lanner Group Geoff Hook 46
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