1. HIV Modelling Consortium
Daniel Keebler DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) Stellenbosch University, South Africa
How Should HIV Programmes Monitor Adults on ART? A Combined Analysis of Three Mathematical Models

2. Acknowledgements
Paul Revill2, Scott Braithwaite3, Andrew Phillips4, Nello Blaser5, Annick Borquez6*, Valentina Cambiano4*, Andrea Ciaranello7*, Janne Estill5*, Richard Gray8*, Andrew Hill9*, Olivia Keiser5*, Jason Kessler3*, Nicolas A. Menzies10*, Kimberly A. Nucifora3*, Luisa Salazar Vizcaya5*, Simon Walker2*, Alex Welte1*, Philippa Easterbrook11, Meg Doherty11, Gottfried Hirnschall11 & Timothy Hallett6 for the HIV Modelling Consortium
*Listed in alphabetical order     2 Centre for Health Economics, University of York, York, UK 3 School of Medicine, New York University, New York, US 4 University College London, London, UK 5Division of International and Environmental Health, Institute of Social and Preventive Medicine (ISPM) University of Bern, Bern, Switzerland 6 Imperial College London, London, UK 7 Massachussetts General Hospital, Boston, MA, US 8 Kirby Institute, University of New South Wales, Sydney, Australia 9 University of Liverpool, Liverpool, UK 10 Centre for Health Decision Science, Harvard University 11 HIV Programme, World Health Organization, Geneva, Switzerland

3. The question addressed
"How can available resources for HIV treatment and monitoring be allocated among alternative adult patient monitoring strategies to maximize health benefits in a population?"
What constitutes “rational” viral load scale-up?

4. The question addressed
… considering:
the criteria used (clinical, immunological, virological)
the frequency of monitoring (6, 12, 36 months)
the decision rules applied (when to switch to 2nd-line)
Examining the:
Benefits (Life-years saved, infections averted, DALYS averted)
Costs (USD $ over short and long-terms)

5. Approach: model comparison
Previous modelling work has been done on this question.
Compare alternative potential monitoring strategies “head-to- head” in each model, with standardized cost inputs.
Focus on costs in three settings: South Africa, Zambia and Malawi
The aim of this document is to present new models results from three different models that compare alternative patient monitoring scenarios head-to-head within a standardized costing framework ‘Compiled Analysis’.

6. Cohort model, Monte Carlo progression
Models
Model
Group
Structure
Type
Time horizon
Patient benefit
Transmission
Adherence
Resistance
HIV Synthesis1
UCL
Individual based
Stochastic
15 years
YES
Braithwaite2
New York University
Cohort model, Monte Carlo progression
Lifetime NO
Estill3
University of Bern
Cohort model
Stochastic 
5 years
Emphasize: “modelling” is not just cost projections
Phillips AN, Pillay D, Miners AH, Bennett DE, Gilks CF, Lundgren JD. Outcomes from monitoring of patients on antiretroviral therapy in resource-limited settings with viral load, CD4 cell count, or clinical observation alone: a computer simulation model. Lancet. 2008; 371(9622): Epub 2008/04/29.
Braithwaite RS, Nucifora KA, Yiannoutsos CT, Musick B, Kimaiyo S, Diero L, et al. Alternative antiretroviral monitoring strategies for HIV-infected patients in east Africa: opportunities to save more lives? Journal of the International AIDS Society. 2011; 14:38. Epub 2011/08/02.
Estill J, Aubriere C, Egger M, Johnson L, Wood R, Garone D et al. Viral load monitoring of antiretroviral therapy, cohort viral load and HIV transmission in Southern Africa: a mathematical modelling analysis. AIDS. 2012; 26(11): Epub 2012/03/17.

7. Strategies investigated
Abbreviation
Scenario NM
No switching or monitoring
CM, S4
Switch on new stage 4 event
CM, S3/4
Switch on new stage 3 or 4 event
CD4<100/S4
Switch if CD4 <100 or new stage 4
CD4-CA
Currently widely-used CD4 monitoring algorithm
CD4/TGVL
Switch if CD4 a) falls >50% from peak on treatment, b) falls below baseline, with confirmation of viral load >1,000 copies.
CD4/TGVL+
Switch if CD4 a) falls >50% from peak on treatment, b) falls below baseline, with confirmatory VL >1,000, or new Stage 4, PLUS routine 12 monthly VL at 1,000 (CD4/VL mixed scenario)
VL36
36 monthly VL; switch if VL>1000
VL12
12 monthly VL; switch if VL>1000
VL6
6 monthly VL; switch if VL>1000
VL6/VL>1K
6 monthly VL; switch if VL>10000
VL6/VL>5K
6 monthly VL; switch if VL>5000
VL6/VL>500
6 monthly VL; switch if VL>500
Color-coding: none, clinical, immunologic, targeted, routine

8. Results

9. HIV Synthesis: Zambia Range of additional benefits modest
Six monthly VL greatest impact. Cost per DALY averted high
X-axis: DALYs averted (is good: avert loss of one year of perfectly healthy life)
Y-axis: costs
Red labels: cost per DALY averted
Clear pathway of prioritisation

10. Cost-Effectiveness Frontier Plots for Zambia (Cost per DALY Averted, 2012 USD)
Note qualitative distribution doesn’t shift across countries
6-monthly VL—greatest impact, but cost per DALY averted is high.
Clear pathway of prioritization
Range of additional benefits is modest
(a) Estill model; (b) Braithwaite et al. model; (c) HIV Synthesis model. Unfavoured (i.e. dominated/extendedly dominated; see Methods) strategies are shown in light grey while most efficient strategies are shown in black and their code is highlighted in bold. The frontier line that represents a most efficient pathway of spending as resources increase is shown in red together with the ICERs, i.e. the incremental cost per DALY averted of moving from one strategy to the next along the frontier.

11. Opportunity Costs
If ART coverage targets not met, resources allocated to increasing ART coverage would generate much greater benefits than if allocated to better patient monitoring.
Costs and benefits (DALYS averted) of alternative uses of resources (Braithwaite model). Results given are per 1 million HIV-infected persons with both benefits and costs discounted at 3%.

12. Opportunity Costs & Initiation Thresholds
Costs per patient lifetime and DALYs averted from alternative uses of resources (Braithwaite model).

13. Reduced 2nd-Line, Test Costs

15. Includes net effect of prevention of unneeded switch and earlier switching in failing patients

16. NB: Estill et al. not included owing to model structure

17. Interpretation
Not all models could run all scenarios, complicating comparisons (e.g. VL6/500)
Bulk of model scenarios more sensitive to test costs than to 2nd-line costs in sensitivity analyses
But proportion of total costs taken up by tests & 2nd-line, and life-years on 2nd-line, varied between models
Virological failure may also impact on results: if overlap is higher, then virological monitoring may convey less useful information in targeted VL scenarios
TGVL performed well in Braithwaite; not as well in Phillips
Life-years as proxy for predictive value of CD4  VL failure

18. Conclusions: Modelling Results

19. Conclusions: Modelling Results (1)
Regular viral load (every 6-12 months) provides the most benefits both in reducing patient morbidity and mortality and in reducing HIV transmission in the population…
… but is the most expensive strategy for monitoring patients.
Infrequent viral load monitoring (every 36 months) or conditional viral load measurement (for confirmation of immunologic failure suggesting a need for second line drugs) would have an intermediate cost but may maximise health benefits for that budget.
However, if infrequent/conditional viral load monitoring would lead to machines being used at less the full capacity then these alternatives are less likely to be cost-effective.

20. Conclusions: Modelling Results (2)
The question is not whether viral load provides greater benefit to individual patients than CD4 or clinical monitoring.
The question is whether, given available resources, the opportunity costs in morbidity and mortality of forgoing the use of these resources for other efforts is acceptable.
In particular, it is expected that resources would produce greater health benefits if they were committed to increasing the coverage of ART to those in need.
Equity should also be a concern: lower-cost but less-effective monitoring strategies may reach a larger number of people than high-cost, more-effective strategies.

21. Conclusions: Linking Modelling and Operational Research

22. Linking Modelling and Operational Research
While we expect the qualitative distribution of results to remain similar, when placed into real-world contexts, precise results given here will change
If I devote resources to patient monitoring, what benefits will it yield for program performance in my particular context?
The answer depends on…
Won’t exceed ART scaleup, but what if already have

23. Linking Modelling and Operational Research
Delays in getting results to lab, back from lab, and to the patient
Staff time & capacity for test processing & interpretation (can they do VL or CD4 better?)
Capacity for test coverage: centralization vs. de- centralization, lower-cost tests reaching more people
Role of monitoring in adherence interventions
Program-specific ART coverage and feasible targets for scale-up
Willingness-to-pay per DALY averted (level of resources)
And more…

24. Linking Modelling and Operational Research
Integration of operational research and modelling is key to assessing program-specific impacts, within a framework of opportunity costs
For interpreting and for generating models
Development of flexible modelling tools/user-friendly platforms that can incorporate all of the above, and be deployed to programs for their own use in planning, should be investigated

25. Linking Modelling and Operational Research
Modelling allows for the examination of strategies’ costs and health impacts over a longer timeframe than trials may allow, and without needing to withhold health interventions for the purpose of comparison
Modelling can provide a clear picture of which costs within a given intervention carry the greatest impact
Useful for advocates as well as program staff/operational researchers
Models can inform programming and operational research, but models must themselves be informed by this:
What factors impact how technologies work in the real world? How can these be incorporated into models?
Interpreting and building models requires strong linkage between modellers, operational researchers, implementers and advocates