icasa, 5 n 2011 - solthis · part 1 data & models. situation 1 – stability 5800 6000 6200...
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DEALING WITH UNCERTAINTY THE CHALLENGE OF LONG TERM PROJECTIONS
IN LOW DATA RESOURCES COUNTRIES
ICASA, 5TH NOVEMBER 2011
Mouslihou Diallo, Pharm.D.
Pharmaceutical & biological manager – Solthis
Grégoire Lurton
Health Information Systems specialist – Solthis
Etienne Guillard, Pharm.D. – MSc
Pharmacy coordinator & PHPM specialist - Solthis
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Forecasting process
Today
Future needs
Future Time
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Time framework
Future Time Today
Funding e.g. GF 5 years
1 to 2 years
2 years PSM plans & Procurement
Forecast
6 to 12 month
s
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Forecasting process
Today
Future needs Data Past & present
projection model
& hypothesis
Future Time
?
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Consequences of uncertain forecast
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
2008 2009 2010 2011 2012
Nb de patients réels
Nb de patients prévus
Actual number of patients
Forecasted number of patients
From 80 patients / month forecasted
to 150 patients / month actually
Consequence : stock outs
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Consequences of uncertain forecast
0
100
200
300
400
500
600
700
800
900
1000
Juillet Août Septembre Octobre Novembre Décembre
Forecasted
Used
Forecasted & used quantity of TDF+3TC+EFV, 2009
Consequence : stock outs
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Consequences of uncertain forecast
Forecasted & used quantity of D4T+3TC+NVP baby, 2009
0
50
100
150
200
250
Juillet Août Septembre Octobre Novembre Décembre
Forecasted
Used
Consequence : products expiration
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Forecasting process
Today
Future needs Data Past & present
projection model
& hypothesis
Future Time
Part 1 Questions of
numbers
Part 2 Questions of funding and procurement
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PART 1 DATA & MODELS
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Situation 1 – Stability
5800
6000
6200
6400
6600
6800
7000
7200
7400
7600
7800
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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5800
6000
6200
6400
6600
6800
7000
7200
7400
7600
7800
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
Situation 1 – Stability
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Situation 2 – Growth
3000
8000
13000
18000
23000
28000
33000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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Situation 2 – Growth
3000
8000
13000
18000
23000
28000
33000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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Situation 2 – Growth
3000
8000
13000
18000
23000
28000
33000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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Situation 2 – Growth
3000
8000
13000
18000
23000
28000
33000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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What do I need to make projections
• Baselines
Number of patients currently on treatment
• Hypotheses on my programme future performances
Rythm for initiation of new patients
• Parameters
Proportion of patients who should get treatment
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• Epidemiological data
– Local
HIV Prevalence
OI Incidence
– Generic
Proportion of patients in need of treatment
• Programmatic data Number of patients
ART schemes repartition
• Rarely updated
• Rarely available for target population
• Large confidence intervals
Type of data Issues
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• Epidemiological data
– Local
HIV Prevalence
OI Incidence
– Generic
Proportion of patients in need of treatment
• Programme data
Number of patients
ART schemes repartition
• Tendency to over / under estimate
• Very dependant on raw data
Type of data Issues
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Programmatic Data
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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Programmatic Data
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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Different types of models
• Simple models
• A bit more complex
• Very complex
Ft = Ft-1 + It – Ot
8000 patients at t=0
+ 300 new patients at t = 1
- 100 deaths and LTFU at t = 1
8200 followed up patients in t=1
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What kind of projections
• Simple models
• A bit more complex
• Very complex
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What kind of projections
• Simple models
• A bit more complex
• Very complex
Futures Institute / Unaids
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Futures Institute / Unaids
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Which model should I use ?
• Too simple = little more than a guess
• More parameters= more uncertainty
• Need for uncertainty and sensitivity analysis
• Need to adapt model to national specificity
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Programmatic Data
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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High Low
Baseline 8400 6400
Initiations 300
Retention M6 90% 80%
Retention M6 - M12 95% 87%
Yearly retention >M12
97% 95%
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Projection results
0
2000
4000
6000
8000
10000
12000
Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
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Things you can’t input in your projection
• Program hazard
• External factors
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Take away
• Improving Data availability
• Adapt model to situation
• Point estimate is not enough
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PART 2 FROM NUMBERS
TO FUNDING & PROCUREMENT
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The question
How can procurement systems be efficient
and provide sustained access
to treatment for patients
in this context of uncertainty
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Plan
How to manage uncertainty in funding & procurement systems?
1. At the step of forecasting
2. At the step of purchasing
3. At the step of implementation
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At the step of forecasting
Quantifying and budgeting the needs
a. Define various scenarios based on estimates dispersion
(low, middle & high)
The choice of the selected scenario is political
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At the step of forecasting
b. take a margin into account
(Buffer stock is the first simple way to manage uncertainty…)
For example on various products
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take a margin into account
Needs + CI x%
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Take a margin into account
It is impossible to forecast the unexpected without overestimate
and to accept the consequences
• increased funding needs • increased risk of loss by expiration
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At the step of purchasing & tendering
• Integrate a stock option mechanism:
establish contracts with suppliers based on
minimum quantity + margin
if necessary, margin can be ordered quickly
This option must be accepted by suppliers and donors and use sparingly
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From forecast to reality
Today
Est. needs
Future Time
Recommandation Changes
Programmatic Changes
Health system deficiency
External factors
Program hazard & external factors will affect the forecasts
and increase the uncertainty
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From forecast to reality
Today
Est. needs
Time
Real needs
Real needs
To ensure and anticipate the adequacy between forecasts & real needs
1st step : identify the risk
A clear view of the situation is needed (incl. data quality)
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Develop warning indicators & dashboards
Real
Est.
Real
0
100
200
300
400
500
600
700
800
900
1000
Forecasted
Used
To ensure and anticipate the adequacy between forecasts & real needs
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From forecast to reality
Today
Est. needs
Time
Real needs
Real needs
To ensure and anticipate the adequacy between forecasts & real needs
1st step : identify the risk
A clear view of the situation is needed (incl. data quality)
3rd step: launching procurement procedures
2nd step: take decision on rationalization
Political decision based on technical advice
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DISCUSSION
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CONCLUSION
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Key points
• Health information systems have to be strengthened for data quality and not only for M&E reporting
• In low data quality settings, we need to take into account uncertainty and to integrate forecasts range into subsequent process
• Bridges have to be built between PSM systems & HIS
• Consensus and political management on – Validation of hypothesis and targets
– Relay choices that have been made to field practionners
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• Harmonization of methods between stakeholders (both national and international levels)