Forecasting for Decision Making in International Public Health

Vineet Prabhu, PhDHIV Market Intelligence, CHAI

Presented at:Improving the Response of Global Public Health in a Fast-changing WorldJoint UNICEF, UNFPA and WHO meeting with manufacturers and suppliers of in vitro diagnostic products, vaccines & immunization devices,finished pharmaceutical products, active pharmaceutical ingredients, contraceptive devices and vector control productsUN City, Copenhagen, Denmark

December 3, 2019

• Highlight challenges both demand and supply side face in serving public health needs

• Encourage both sides to work together – flexibility and transparency are key

• Urge continuous improvement including learning from other disease areas

Objective of presentation

(With apologies for the heavy reliance on HIV examples as that is the world I live in day to day)2

Fact:

All forecasts are wrong!!

(forecasters are simply aiming to minimize how wrong they are)

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At the very outset, important to consider what decision the forecasting exercise is informing

Timescale of the forecasting exercise becomes paramount – next 12 mo. or 3-5 yrs from now?

DevelopmentCapacity

scale planning

SourcingActual

production

• What is the investment case for a manufacturer?

• What is the public health impact for potential funders?

Stage

Relevant Questions

• How quickly can production be scaled if demand truly takes off?

• What factory or machinery (and potentially regulatory) limitations exist?

• What lead times, shelf life, and inventory cost considerations exist for upstream raw materials?

• What lead times, shelf life, and inventory cost considerations exist for finished product?

Forecast Type

Market need sizing(e.g. PLHIV)

Addressable market(e.g. PLHIV on ART)

Demand forecast(e.g. likely market share)

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For ensuring sufficient supply (and thus confidence on the demand side), rough order of magnitude of market size (need) is most relevant pre-commercialization

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5K 50Kvs. vs.

Albeit actual production will be proportional to actual demand (i.e. orders) in real time, production capacitycannot be rapidly changed on a sliding scale of in-between figures; a major learning from LPV/r pellet roll-out

500K

Different types of forecasting methodologies – think about context for appropriateness

Different contexts call for different approaches to be taken

Consumption based Morbidity based

E.g. malaria drugs (ACTs)

• Often inappropriately taken when fever develops – easily bought over-the-counter

• Consumption may not have any relation to actual malaria prevalence

• Past trends/seasonality reasonable inference of “demand”

Self-diagnosis

Self-medication

Self-diagnosis

Self-medication

E.g. ARVs for HIV

• Will reflect programmatic targets for ART scale-up, retention, and treatment optimization

• Based on national prevalence, with ART clinics as gatekeepers

• Past consumption levels may only reflect a “floor” for demand

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Multiple steps to introduce new product introduction, each with varying probabilities of success and timing across countries and even within a country in different years

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High level of uncertainty created – any global forecast must be grounded in quality country intelligence following the 80/20 principle

Beware false precision!

Uncertainty comes from underlying data quality – unnecessarily complicated models won’t fix that.Understand the level of precision that is reasonably possible and be transparent about it.

1,424

Pediatric population (ages 0-14) living with HIV in 26 high-burden LMICs

Differences in CLHIV estimates would mask whatever assumptions one might make on ART coverage, 1st- vs. 2nd-line numbers, and individual API or formulation market shares

(<20K annual new infections by 2020)

(<20K annual new infections by 2022)

-

200

400

600

800

1,000

1,200

1,400

1,600

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027P

ed

iatr

ic P

op

ula

tio

n (

age

s 0

-14

) liv

ing

wit

h H

IV

"Business as usual"Scenario

Moderate Scenario

Super Fast-track Scenario

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Source: Prabhu VR, McGovern S, Domanico P. IAS, July 2017, Paris. Oral abstract WEAD0203

• In many LMICs, record keeping is poor or requires (error-ridden) manual compilation for central level visibility

• Particularly acute for pediatrics where formulation and dosing is age and weight dependent

• Thus, it can be very difficult to have an accurate baseline view of the situation in country(s)

• Forecast output cannot be more precise than the baseline input

Success/failure in one programmatic area can affect market dynamics for downstream commodities

Don’t do things in a vacuum – underlying numbers for one commodity need to inform forecast(s) for related commodities

Increasing # on ART # of viral load tests needed

to monitor ART patients

(if CD4 testing is available)

# patients identified with Advanced HIV Disease

(AHD)

ARV procurement

(if done with more testing)

(if done with high yield testing only)

# of HIV RDTs procured

# of TB LAM dipsticks procured

# of CrAg LFA tests procured

TB treatment and prophylaxis procurement

CM treatment and prophylaxis procurement

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• Funding – how much of the need can be supported? At what price point(s)?– e.g. underlying assumption on ARV forecasts is that drug treatment funding is the last thing to be cut if HIV funding

decreases vs. for oral pre-exposure prophylaxis (PrEP) for prevention in otherwise healthy clients?

– At its most basic level, (available funding) ÷ (unit cost) = number of units that can be bought

• Finding the patients/clients – do they naturally interact with the health system? – E.g. Zn/ORS for childhood diarrhea was scaled up through consumer marketing and retail shop sales

• How much demand generation is required?– Is a market being created from scratch (e.g. oral PrEP), or is it more a question of changing market shares to a more optimal

product (e.g. TLD rollout)?

• What infrastructure is required to support market growth?– e.g. number of molecular diagnostic tests (HIV/HCV/HPV/TB) that can be run is limited by number of labs/devices in country

and their accessibility

What are the constraints and enablers?

Market context is a critical qualitative input into any forecast, including informing where one lies on the market sizing vs. demand forecasting continuum

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Consider the bigger picture of market dynamics that can affect your sub-market of interest

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2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

New 3L PI Pts(post-DTG)

New 2L PI Pts(post-DTG)

New 2L PI Pts(post-NNRTI)

Existing 2L PI Pts

How durable will DTG be in 1L? How many years before we start to see failures?

How quickly will existing 2L PI patients be proactively switched to DTG?

Nu

mb

er o

f Pa

tien

ts o

n P

rote

ase

Inh

ibit

ors

Question of when not if PI market shrinks in short termQuestion of when not if PI market grows in long term

Protease Inhibitor (PI) Market Over Time with Dolutegravir IntroductionBefore dolutegravir

1st-line NNRTI

Question: what will be the market shares between different PIs given a constant flow of patients from 1st-line, which in turn in growing?

2nd-line PI

NNRTI: non-nucleoside reverse transcriptase inhibitor; PI: protease inhibitor; DTG: dolutegravir

Treatment failure

After dolutegravir (more durable than NNRTIs)

1st-line NNRTI 1st-line DTGProactive Switch Trea

tmen

t fa

ilure

1st-line DTG

2nd-line PI

Treatment failure

2nd-line PI

2nd-line NNRTI 2nd-line DTGTreatm

ent

failu

re

3rd-line PI

Question: what will the overall PI market look like?

Proactive Switch

Source: Prabhu VR, McGovern S, Panos Z. HIV Glasgow, Oct 2018, Poster 281

It is important to adapt and change how one forecasts for a given market rather than accept the status quo approach

• Clarity from demand side on what type of forecast is being presented and underlying uncertainty

• Transparency from suppliers on realistic production capacity, lead times etc.

Being flexible and understanding the constraints of the other side to create a win-win

DemandSupply

Supply Demand

Seeking actual order commitments to sustain and de-risk business investments

Seeking supply security and affordable prices before committing

Chicken and egg

cycle

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Example of supply and demand side working together – ARV Procurement Working Group (APWG)

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Quarterly Order Cycles Monthly Business Calls

Biannual Newsletters Quarterly Demand Forecasts Annual KPI reviews

Ad-Hoc Market Support Product Availability Dashboards

Consolidating orders and coordinating timing Market intelligence sharing Monthly calls with suppliers for challenging products

Broad dissemination of market info Best available picture of demand from countries Continuous improvement

Creating a common platform for understanding, communication, and transparency while maintaining confidentiality as appropriate

• Be clear on what decision is being informed by a forecasting exercise – this affects what level of precision is needed

• Understand interdependencies of commodities – you are talking about the same patient/client!

• Understand limitations of your data sources and beware false precision

• Don’t accept the status quo – push for continuous improvement in forecasting

• Learn new approaches from other disease areas

• While uncertainty will never go away, supply- and demand-side transparency can help greatly reduce it

Parting thoughts…

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Acknowledgements

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CHAI’s market shaping work on HIV commodities is made possible through the generous support of Unitaid, with complementary support from the

UK Department for International Development (DFID) and the Bill & Melinda Gates Foundation