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Risks versus benefits related to the possible implementation of a malaria blood-screening test

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Risks versus benefits related to the possible implementation of a malaria blood-screening test

FDA Workshop on Testing for Malarial Infections in Blood Donors
July 12, 2006

Steve Anderson
Office of Biostatistics & Epidemiology
FDA-Center for Biologics Evaluation and Research

Probabilistic Modeling Risk/Benefit of New Donor Populations

  • Current U.S. policy includes deferral of:

     

    • Travelers - malaria endemic countries in last year
    • Immigrants - from malaria endemic countries < 3 yrs
    • Donors that had malaria - asymptomatic < 3yrs

     

     

  • Goal: Use probabilistic model to evaluate potential risks / benefits and uncertainties of:

     

     

    • Current policy
    • Universal NAT Testing Scenario
    • Universal Antibody Testing Scenario

     

Probabilistic Modeling

  • Rather than single numbers or "point estimates"

     

     

  • Employs statistical distributions for INPUT PARAMETERS - represents uncertainty of data

     

     

  • Monte Carlo method chooses a value from each distribution as the "single number" for ONE iteration and generates OUTPUT as distributions

     

     

  • Model is run thousands or millions of iterations and single "aggregate" OUTPUT distributions reflecting uncertainty and variability are generated

     

Uncertainty

 

  • Arises from lack of or limited data for an input parameter(s)

  • Assumptions used in model - add to uncertainty

     

  • Lack of information or data for estimating -

     

     

    • Self deferral for travelers to / immigrants from malaria areas,
    • effectiveness malaria deferrals,
    • Donation rates of travelers / immigrants,
    • NAT test sensitivity,
    • Antibody test sensitivity, etc.

     

  • Uncertainty represented as confidence intervals about mean estimated outcomes

 

Malaria Risk in the United States

  • 1,325 reported cases of Malaria identified in the U.S. in 2004 (CDC, MMWR 2006)

     

     

  • All but 4 cases imported

     

     

  • ~ 50% cases were Plasmodium falciparum

     

     

  • Transfusion transmitted malaria (TTM) rate is low

     

     

    • ~ 0.25 cases per million units collected

     

Possible Risks (Costs) and Benefits of Malaria testing of blood

  • Risks (Costs)

     

    • Additional malaria units, transfusion transmitted malaria (TTM), etc.
    • Costs of testing entire supply (>14 million units / yr)
    • Costs of re-testing units
    • Loss of blood donors and blood units
    • Costs of recruiting donors

     

     

  • Benefits

     

     

    • Number of additional donors gained
    • Detection of additional malaria units from non-deferred donors

     

Overview of Model Components

Estimation Size of Donor Pool

INPUT DATA:

 

  • ~ 8 - 9 million Total Annual number blood donors
  • ~ 27.4 million US travelers to malaria countries
  • ~ 382,000 Immigrants from malaria countries
  • ~ 60 % Population qualified to donate
  • 5 % Donation rate general population
  • 1.7 Annual donations per donor per yr
  • ~ 14 million Total number blood donations per yr

 

OUTPUTS:

 

  • > 880,000 Donors per year travel to malaria country
  • > 730,000 Donors - self defer for malaria risk
  • > 150,000 Donors - deferred by questionnaire

 

Estimation of malaria infection prevalence potential new donor groups

INPUT DATA :

 

  • 95 - 99% Effectiveness of Questionnaire screen
                     (effectively lowers malaria prevalence in donors)

 

OUTPUTS:

 

  • ~ 42 Potential mean malaria donors per year*
  • ~ 71 Potential mean malaria donations per yr*
  • ~ 3 Malaria units - not deferred per yr

 

*Most are removed by donor screening

Testing Scenarios: Universal Nucleic Acid Test (NAT)

  • Test all donations using NAT

     

     

  • Travelers (< 1yr) and Immigrants (< 3yr) to Malaria endemic countries

     

     

    • Assumed there was a one month window period (WP) - donors with malaria not detected

     

     

  • All other donors

     

     

    • Assumed no window period

     

     

  • Test Sensitivity assumed 99% - 100% sensitive

     

Testing Scenarios: Universal Antibody testing

  • Travelers (≤ 3 months) to Malaria countries

     

    • Assumed a 3 month WP - test may not detect malaria

     

  • Travelers (> 3 months) to Malaria countries

     

    • Test sensitivity - assumed to vary by species

     

  • Immigrants (≤ 3yr) to Malaria countries

     

    • Assumed no WP

     

  • All other donors

     

    • Assumed no WP

Travelers (> 3 months) to Malaria countries

  • Adjust test sensitivities for (>3 mo) traveler population by occurrence of species in geographic regions traveled

(1) Assumed Test Sensitivity:

  • P. falciparum 94% - 99.5%
  • P. vivax 75% - 100%
  • Others 50% - 75%

(2) Occurrence of species in travelers by region

  Pf Pv Other
Africa 82% 10% 7%
Asia 11% 83% 6%
Americas 36% 57% 6%
Others 10% 76% 14%
All regions 63% 30% 7%

Results: potential risks and benefits of alternative screening methods

  Risks
(5th, 95th perc)		 Benefits
(5th, 95th perc)
CurrentPolicy Blood units lost Donors removed Malaria units - not removed Costs of screening Malaria units removed Potential donors gained
Self deferred 1,276,000 729,000 na

Assumed low

Costs for recruiting

 58
(48-79)		 na
Questionnaire deferred 207,000 150,000 3
(1 - 5)		 9
(3 - 18)		 na
Total: Self + Questionnaire 1,483,000 879,000 3
(1 - 5)		 67
(48 - 90)		 na

Blood units collected per year in US = ~ 14 million

Results: potential risks and benefits of alternative screening methods

  Risks (5th, 95th perc) Benefits (5th, 95th)
  Blood units collected Blood units lost Donors removed Malaria units - not removed Costs of screening Malaria units removed Potential donors gained
Current ~ 14 million 1,483,000 879,000 3
(1 - 5)		 Assumed low
Costs for recruiting		 67
(48 - 90)		 na
NAT testing 15,761,616 66
(46 - 87)
(benefit)		 40
(30 - 51)
(benefit)		 5
(2 - 9)		 Costs > 14 million tests
Re-testing of units		 66
(46 - 87)		 ~ 880,000
Antibody testing 15,760,264 1,418
(954 - 1912)
(benefit)		 890
(600- 1200)
(benefit)		 10
(4 - 16)		 Costs >14 million tests
Re-testing of units		 61
(43 - 81)		 ~ 880,000

Key Uncertainties

  • Overall there is uncertainty for many of model inputs

     

     

  • Would expect Malaria prevalence in donors with travel history (< yr) or immigrant - Malaria countries to be leading contributor to uncertainty

     

     

  • Variability in malaria species by region over time

     

     

  • Sensitivity of test that would by used

     

Conclusions from Malaria model

  • Current policy - many donors (~ 150,000) deferred or ~ 880,000 donors if include self-deferrals

     

     

  • Antibody testing - fewer donors deferred(~1,400)

     

     

  • NAT testing - even fewer deferred (66)

     

     

  • However, testing has significant costs associated with testing / re-testing >14 million units / yr

     

     

  • But, testing scenario there may be a net gain of ~ 880,000 donors

     

     

  • Need further exploration of costs of each option

     

    • Testing
    • Re-testing
    • Recruitment of donors

     

  • Validate assumptions (with data) on test sensitivities

     

     

  • Peer review of Model

     

    • Assumptions, data used, etc.

Acknowledgements

 

  • Hong Yang - CBER/OBE
  • Sanjai Kumar - CBER/OBRR
  • OBRR staff

 

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