Access to medicines: problems

Global disease: Disease affecting both those in rich and in poor countries (e.g.: cancer)

Problem: medicines too expensive for poor
Neglected disease: Disease primarily affecting people in poor countries (e.g.: malaria, diarrheal diseases)

Problem: pharma companies have little incentive to produce

Selgelid: A Full-Pull Program for the Provision of Pharmaceuticals

Critically evaluates Pogge’s Health Impact Fund (HIF): Seeks to increase access to medicines by incentivizing pharma companies to produce and deliver affordable medications to the poor by rewarding them according to the “health impact” their drugs actually end up having.
Selgelid mentions two problems with this idea:

1.Patent-reform (which HIF ultimately requires) is only part, possibly only a very small part, of what causes global health crisis. Poor patients cannot even afford very cheap medications

2.The HIF is designed to address the development of new drugs. It would be more effective if it applied its reward system to already existing, off-patent drugs and other determinants of health
Alternative (Kremer and Glennerster):
- To supplement “push programs” (which pay money to companies upfront to stimulate research and development but do nothing to incentivize drugs actually getting to the market) by making “advanced purchase commitments” so that it makes sense for companies to make dugs market-ready.
- Problem: still lacks incentive to actually get marketed drug to patients (last mile problem). (136)
Objection to (both original and expanded) HIF:
- Real problem is unwillingness of wealthy countries and donors to spend much money on global health. If there were more money for that end (which HIF assumes) then we would not need HIF.
  - “If large sums of money were spent on improving global health, then the new market that the full-pull program aims to create would already exist—and the incentive to innovate would already be in place.”(137)
- Yes, pharma companies would probably be more excited about governments generally pouring more money into global health, but “governments …would be more likely to support funding of global health via the full-pull program …given that
  - (1) there would be no monopoly pricing for the drugs in question and that (2) spending would be tied to impact—and also because (3) a fullpull program would provide health benefits to citizens of wealthy countries themselves.” (137)
Practical problems with (original or expanded HIF):
- Measurement. It is unclear how to precisely measure the health impact of particular drugs in poor countries.
  - “The impact of greater drug provision will depend on other, natural and non-natural, factors affecting the population in question. Fluctuations in climate, nutritional status, water supply, education, economic status, behaviours related to health risk, availability of other drugs and medical care, and so on, may all affect the impact that increased provision of any particular drug will have in any given population.” (138)
- It is also unclear what health metric to use:
  - DALY’s” Disability Adjusted Life Years”:
    
    “In the case of life expectancy, the ideal…is 83 years. If a woman succumbs to a disease that kills her 10 years earlier…then 10 DALYs are attributed to her premature death from this disease. If a person spends 10 years with a disability that reduces the functioning or quality of her life to half of perfect health, then five DALYs are attributed to the disability.” (138)
Problems with DALY’s:

1.How should severity of a disability be measured?

2.Age-weighing (life-years lost at younger age weigh more than life-years lost at later age)?

3.Time-discounting (life lost in future is weighed less than life threatened here and now)

Selgelid: While 2. and 3. may be appropriate when it comes to allocation questions, they are not appropriate when it comes to measuring disease burden.
Problems of Causation:

1.Disease burden measurement: if 40 year old women dies in Africa from TB, then TB is assigned 43 DALYs.
- But: living in Africa, this woman would have probably died by the age of 50 anyway. So while it may be correct to assign 43 DALYs, it is wrong to assign these to this particular TB infection.
- It would thus be wrong to reward company that had developed successful TB drug for health impact of saving 43 years of life.
Possible solution:
- take local life-expectancy into account - either in the measure of DALYs or in the measurement of global disease burden more generally.
  - Implication: It would weigh much more heavily if a woman in Japan died of TB at 40 than if a woman in Africa did.
  - But very few 40 year olds fall ill of TB in Japan
Most serious measurement difficulty:
- Causal attribution: “This is the problem of determining the extent to which any reduction in GDB—or the burden of any particular disease—is the result of one intervention as opposed to another.” (140)
- “Imagine that someone who would have died from malaria ends up living because she receives a partially effective vaccine and gains access to a mosquito net. Even with perfect data availability, it may then be dubious to say that it was either the vaccine or the mosquito net that saves her life—and it may be dubious to say that there are some numbers X and Y such that her survival is X% caused by the vaccine and Y% … caused by the net.” (14)
Possible solution: counterfactual analysis:
- We ask how many more deaths would have occurred, holding everything else equal, had particular drug or intervention not been present.
- But: problem of not always achieving “additive decomposition”: the number of lives attributed to different causes need not add up to the total number of lives that are saved” (140-1)
- Example: Drug A saves 20 %, drug B 30 % of given in isolation. Together, they save 100%.
  - Counterfactual analysis impact of drug (post-intervention): if A had not been provided 70 people (of 100) would have died
  - Counterfactual analysis impact of drug B (post-intervention): if B had not been provided 80 people (of 100) would have died
  - Number of averted deaths of A + B= 150 (!)
- Way around this problem of over-counting is to use ratio between A and B as basis for research : 7 to 8. Given $100 reward, this would translate in t $47 for providers of drug A and $53 for providers of drug B.
  - Which Baseline
  - Example above uses post-intervention baseline. What if we used pre-intervention baseline?
    
    Drug A averts 20 death, drug B 30 deaths (although 100 would be saved if both were given together)
    
    Ratio 2:3. Providers drug A: $ 40, Providers drug B: $60.
Time of Intervention Baseline?

Drug A at time t1: saves 20.

Drug B at time t2: saves 80

Drug A + drug B= save 100.
- Problem: incentive to hold off on providing treatment because benefits of synergistic effects would be attributed solely to later provider.
On the table: three sorts of baseline.

1.Pre-Intervention baseline (2:3)

2.Post-Intervention baseline (7:8)

3.Time of Intervention baseline (20, 80)
Post intervention baseline:

Drug A: 5%

Drug B: 5%

A+B: 100%

Drug C: 100%

Drug A: 95%

Drug B: 95%

Drug C: 100%

Better: Drug A (if provided with B): 50%

Drug B (if provided with A): 50%
Pre-Intervention Baseline

Drug A: 5%

Drug B: 5%

A+B: 100%

Drug C: 100%

Drug A: 5%

Drug B: 5%

Drug C: 100%

Problem: No incentive to give A and B together. Synergistic effects ignored.

General lesson from these cases:

1.Examples can be construed that have counter-intuitive results

2.Depending on which baseline is used, profits would be divided very differently

●Selgelid thinks time-of intervention baseline holds most promise: incentive it provides is proportional to value of intervention

Since problem is so serious, we should just try to do the best we can.

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