Accelerating Policy Decisions to Adopt Haemophilus influenzae Type b Vaccine: A Global, Multivariable Analysis

Jessica Shearer and colleagues analyze data from 147 countries to identify factors that influence the time taken to introduce routine vaccination against Haemophilus influenzae type b (Hib)

Funding gap for immunization across 94 low- and middle-income countries

Novel vaccine development and production has given rise to a growing number of vaccines that can prevent disease and save lives. In order to realize these health benefits, it is essential to ensure adequate immunization financing to enable equitable access to vaccines for people in all communities. This analysis estimates the full immunization program costs, projected available financing, and resulting funding gap for 94 low- and middle-income countries over five years (2016–2020). Vaccine program financing by country governments, Gavi, and other development partners was forecasted for vaccine, supply chain, and service delivery, based on an analysis of comprehensive multi-year plans together with a series of scenario and sensitivity analyses. Findings indicate that delivery of full vaccination programs across 94 countries would result in a total funding gap of $7.6 billion (95$4.6–$11.8 billion) over 2016–2020, with the bulk (98$5.0 billion ($2.7–$8.4 billion) with an additional $1.1 billion ($0.9–$2.7 billion) needed for vaccines and$1.5 billion ($1.1–$2.0 billion) for supply chain. When viewed as a percentage of total projected costs, the funding gap represents 66% of projected supply chain costs, 30% of service delivery costs, and 9% of vaccine costs. On average, this funding gap corresponds to 0.2% of general government expenditures and 2.3% of government health expenditures. These results suggest greater need for country and donor resource mobilization and funding allocation for immunizations. Both service delivery and supply chain are important areas for further resource mobilization. Further research on the impact of advances in service delivery technology and reductions in vaccine prices beyond this decade would be important for efficient investment decisions for immunization

During the 'decade of vaccines,' the lives of 6.4 million children valued at $231 billion could be saved

Governments constantly face the challenge of determining how much they should spend to prevent premature deaths and suffering in their populations. In this article we explore the benefits of expanding the delivery of life-saving vaccines in seventy-two low- and middle-income countries, which we estimate would prevent the deaths of 6.4 million children between 2011 and 2020. We present the economic benefits of vaccines by using a "value of statistical life" approach, which is based on individuals' perceptions regarding the trade-off between income and increased risk of mortality. Our analysis shows that the vaccine expansion described above corresponds to $231 billion (uncertainty range:$116-$614 billion) in the value of statistical lives saved. This analysis complements results from analyses based on other techniques and is the first of its kind for immunizations in the world's poorest countries. It highlights the major economic benefits made possible by improving vaccine coverage

Estimated economic benefits during the 'decade of vaccines' include treatment savings, gains in labor productivity

In 2010 the Bill & Melinda Gates Foundation announced a $10 billion commitment over the next ten years to increase access to childhood vaccines in the world's poorest countries. The effort was labeled the "Decade of Vaccines." This study estimates both the short- and long-term economic benefits from the introduction and increased use of six vaccines in seventy-two of the world's poorest countries from 2011 to 2020. Increased rates of vaccination against pneumococcal and Haemophilus influenzae type b pneumonia and meningitis, rotavirus, pertussis, measles, and malaria over the next ten years would save 6.4 million lives and avert 426 million cases of illness,$6.2 billion in treatment costs, and $145 billion in productivity losses. Monetary estimates based on this type of analysis can be used to determine the return on investment in immunization from both the international community and local governments, and they should be considered in policy making

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future