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Evaluation 3 of “Advance Market Commitments: Insights from Theory and Experience” by Joel Tan

Evaluation 3 of “Advance Market Commitments: Insights from Theory and Experience” by Joel Tan

Published onMar 20, 2023
Evaluation 3 of “Advance Market Commitments: Insights from Theory and Experience” by Joel Tan

Summary Measures

Overall Assessment

Answer: 79

90% CI: (59,94)

Quality Scale Rating

“On a ‘scale of journals’, what ‘quality of journal’ should this be published in?: Note: 0= lowest/none, 5= highest/best”

Answer: 5

Confidence: High

See here for a more detailed breakdown of the evaluators’ ratings and predictions.

Written report

Link to spreadsheet calculations

Summary of Kremer, Levin & Snyder's Primary Findings

Kremer, Levin & Snyder (KLS) find that the pneumococcal vaccine (PCV) advanced market commitment (AMC) probably resulted in around 700,000 lives saved that would otherwise have been lost.

Counterfactual increase in coverage due to pneumococcal vaccine advanced market commitment

To estimate the counterfactual impact of the PCV AMC, KLS compare the actual impact of the PCV AMC rollout to the rotavirus (RV) non-AMC rollout. This choice of comparator does make sense; as KLS writes: "We selected rotavirus from the six global vaccine initiatives proceeding around that time for the following reasons. Three of them (IPV, second dose of measles, birth dose of hepatitis) involved early-vintage rather than new vaccines. The yellow-fever vaccine was not rolled out in many high-income countries, leaving no good base rate for coverage speed comparison. We conjecture the results would be stronger using HPV, the remaining candidate apart from rotavirus, for comparison, but any slow rollout of HPV vaccine in GAVI countries could be attributed to its administration to older children, slowing coverage expansion."

That said, there is a reasonable worry over whether the results are robust to a different comparator class of vaccines when estimating the counterfactual impact of the pneumococcal vaccine (PCV) advanced market commitment (AMC). To check this point, I ran a rough quantitative analysis of my own. I use vaccination data from the following sources:

  • For RCV and RV vaccination, I use the International Vaccine Access Center & John Hopkins Bloomberg School of Public Health's View-Hub database (last access, 2023-02-02), which draws on WUENIC estimates. WUENIC estimates are official WHO/UNICEF estimates of national immunization coverage, created by drawing on country-reported data as well as published and grey literature, even while correcting for potential biases

  • For human papillomavirus vaccine (HPV) vaccination, I pull from UNICEF (last access, 2023-02-02.

  • For yellow fever (YF) vaccination, I rely on Shearer et al’s (2017) estimate of global yellow fever vaccination coverage (last access, 2023-02-03).

The disparate datasets are not ideal, but no single source I found while undertaking this quick review provides comprehensive data on all vaccines of relevance. Calculations were done in the linked Google sheet, last edited 2023-02-05.

Using this data, I first look at the proportion of PCV coverage with the AMC (across GAVI countries, for the first 12 years from introduction) relative to full coverage. This was calculated by taking a population-weighted average of coverage rates per country-year across all 54 current GAVI countries across 12 years (from introduction in 2010 up to 2021). Based on the aforementioned data and this specific methodology, overall coverage for GAVI countries across this time period was 49%.

Second, I look at three different comparator classes: (a) rotavirus vaccine (RV) coverage, (b) human papillomavirus vaccine (HPV) coverage, as well as yellow fever (YF) vaccine coverage (similarly looking across GAVI countries across for the first 12 years from introduction or equivalent). 1 2 These three classes were used as the vaccines in question are either newer vaccines with a more recent introduction date (RV & HPV), or else an older vaccine where there appears to be a discrete recent push for vaccination by GAVI (YF) – allowing for us to perhaps observe the counterfactual world in which PVC was rolled out without an AMC but with standard GAVI support. In each case, I look at the proportion of the comparator vaccine's coverage across GAVI countries for the first 12 years. And again, this is calculated by taking a population-weighted average of coverage rates per country-year across all 54 current GAVI countries across 12 years (n.b. RV: from introduction in 2006 up to 2017, for comparability; for HPV, from introduction in 2010 up to 2021; and for YF, from GAVI making a concerted effort on YF in 2001 up to 2012, for comparability).

I find that KLS's results should be fairly robust, insofar as the coverage rates for alternative comparator vaccines are in fact lower than the mainline RV comparator KLS chose: coverage was 12% for RV, 2% for HPV, and 9% for YF.

Going further, I estimate the probable proportion of PCV coverage across GAVI countries across 12 years from introduction without the AMC, by creating a weighted average of the 3 comparator classes. In doing this, I firstly penalize dissimilarity in target demographics (n.b. PCV and RV are both targeted for <1 year olds, while HPV is for older individuals and the YF vaccine is for 9 months+ individuals) – this matters insofar as the former two vaccines will more likely be deployed as part of post-birth immunization schedules. Secondly, I penalize data limitations (e.g. the uncertain YF extrapolations). Weights are assigned subjectively and fairly aggressively, with each penalty leading to a comparator class being weighed a magnitude less than it otherwise would have. In all, the weighted average is 11% – which is my best-guess estimate of PCV coverage in the absence of the AMC.

Putting this together, the counterfactual impact of the PCV AMC as a proportion of total disease burden avertable by vaccinations in the relevant time period is around 38% relative to total coverage. Importantly, this estimate here would not differ by much (~2%) even if we only used RV as the comparator to estimate the non-AMC counterfactual – which suggests that KLS's results are robust to comparator class. That said, I would caution against using these results for direct comparisons to KLS's findings – this analysis is very rough, and given the different datasets/methodologies, I would be wary of utilizing these results for anything except a sense-check for the matter of comparator class robustness.

There is a further caveat to note – the datasets used see a considerable amount of missing data; in such cases, I made the methodological choice to treat missing data for country-years as 0% coverage. The idea is that (a) any country lacking the state capacity to report is unlikely to be doling out vaccines; and (b) such GAVI countries by definition have GAVI support, and GAVI does publish its data (which then feeds into the WUENIC estimates) – so unless GAVI were failing to report vaccinations (unlikely), it seems reasonable to think that unreported country-years do in fact suffer 0% coverage.

Robustness of headline DALY estimates

In any case, there are other issues that may affect the accuracy of the final estimates of DALYs averted:

  1. The estimate relies on Tasslimi et al's (2011) calculations of DALYs averted per PCV shot – and this in turn relies on O'Brien et al's (2009) estimate of the global burden of disease caused by streptococcus pneumoniae. However, DALYs lost per capita to pneumococcus were declining in poor countries year-on-year for two decades even before the AMC, possibly due – at least in part – to economic growth bringing improvements in sanitation/nutrition/access to healthcare. Hence, projections on future DALYs averted based on past disease burden data may overstate the benefit. Theoretically, a way to account for this would be to re-run KLS's analysis but discounting each year's DALY per dose estimate using the rate at which pneumococcus DALY burden per capita was experiencing a secular decline in the two decades before the introduction of the vaccine.

  2. On the other hand, KLS do not model the speeding up of the development of existing vaccines – hypothetically, the credible commitment provided by the AMC will have a dynamic effect not just on new entrants (to enter) but also on existing pharmaceuticals with nearly-licensed vaccines (to speed up their activities). The idea here is that with guaranteed profits on the horizon, existing pharmaceuticals will be willing to expand more resources and make greater efforts at bringing the nearly-licensed vaccine to market faster than they would otherwise have, thus bringing forward the date of introduction relative to a counterfactual world where the AMC was not made. Notably, the PCV-13 vaccine was licensed in 2010, while the AMC was made in 2009 – it is theoretically conceivable that licensure would have been later absent the AMC. That said, this is speculative, and hard to test besides – no obvious way presents itself to me at this juncture, and more research on this point would be both valuable and interesting.

My sense is that effect 1 would outweigh effect 2, such that the true effect of the PCV AMC is lower than currently estimated, but it is hard to say by how much, if at all.


Overall, relative to the null hypothesis (i.e. the AMC did nothing), I would (a) have extremely high confidence that the AMC made a difference and saved a significant number of lives; and (b) only moderate confidence that at least 700,000 lives were saved, per KLS's original estimate.

Evaluator details

How long have you been in this field?

1 year for cause prioritization, 5 years for broader economic research and analysis

How many proposals and papers have you evaluated?

Zero in an academic context

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