How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials

Abstract

ObjectiveReports of efficacy, effectiveness and harms of COVID-19 vaccines have not used key indicators from evidence-based medicine (EBM) that can inform policies about vaccine distribution. This study aims to clarify EBM indicators that consider baseline risks when assessing vaccines’ benefits versus harms: absolute risk reduction (ARR) and number needed to be vaccinated (NNV), versus absolute risk of the intervention (ARI) and number needed to harm (NNH).MethodsWe used a multimethod approach, including a scoping review of the literature; calculation of risk reductions and harms from data concerning five major vaccines; analysis of risk reductions in population subgroups with varying baseline risks; and comparisons with prior vaccines.FindingsThe scoping review showed few reports regarding ARR, NNV, ARI and NNH; comparisons of benefits versus harms using these EBM methods; or analyses of varying baseline risks. Calculated ARRs for symptomatic infection and hospitalisation were approximately 1% and 0.1%, respectively, as compared with relative risk reduction of 50%–95% and 58%–100%. NNV to prevent one symptomatic infection and one hospitalisation was in the range of 80–500 and 500–4000. Based on available data, ARI and NNH as measures of harm were difficult to calculate, and the balance between benefits and harms using EBM measures remained uncertain. The effectiveness of COVID-19 vaccines as measured by ARR and NNV was substantially higher in population subgroups with high versus low baseline risks.ConclusionsPriorities for vaccine distribution should target subpopulations with higher baseline risks. Similar analyses using ARR/NNV and ARI/NNH would strengthen evaluations of vaccines’ benefits versus harms. An EBM perspective on vaccine distribution that emphasises baseline risks becomes especially important as the world’s population continues to face major barriers to vaccine access—sometimes termed ‘vaccine apartheid’.

COVID-19 vaccination and governance in the case of low, middle and high-income countries

“…Therefore, guiding the distribution of COVID-19 vaccines based on public statistical data should be of particular interest for governments, all the more so as there were countries that purchased a much larger number of vaccine doses (Australia, Canada, Great Britain) and later, they had to redistribute or donate them to other countries in need [ 78 ]; there were also situations where vials were wasted for various reasons [ 79 , 80 ]. Several studies clearly emphasize that the access to COVID-19 vaccines is facilitated by favorable socio-economic conditions [ 81 ], and the priority target group in obtaining them should be the population at major risks [ 82 , 83 ]. Thus, low-income countries should be generously supported to reach a significant immunization threshold and to have an equitable vaccination so that marginalized groups are not extremely affected [ 84 , 85 ].…”

Number Needed to Vaccinate to prevent one death: a propensity score matching approach to COVID-19 vaccine evaluation in the center of Iran (short communication)

“…The ndings of another prospective cohort study using international data revealed that 70year-old patients and older ones requiring cancer and non-cancer surgery had more desirable (i.e., lower) NNVs to prevent mortality than the general population. Since the NNV statistic is highly associated with the incidence and prevalence of the disease of interest, this suggests the more favorable NNVs among subpopulations at higher risk of morbidity and mortality from the COVID-19 disease (7,16). These were consistent with the recommendations by the US Centers for Disease Control and Prevention and Public Health England in which they suggested that vulnerable individuals for morbidity and mortality from the COVID-19 disease should be in the frontline of receiving the vaccine (17).…”

“…In a commentary, the NNVs of 78, 81, 84, 108, and 119 were reported respective to the types of vaccines, namely AstraZeneca-Oxford, Moderna-NIH, Johnson & Johnson, Gamaleya, P zer-BioNTech. Moreover, NNV variations could be attributable to the differences in location, time, target groups, SARS-CoV-2 variant, and et cetera (7,8).…”