Efficacy and effectiveness of covid-19 vaccine - absolute vs. relative risk reduction

Over the last few months, there has been a passionate debate within the scientific community on the risks and the benefits of the vaccines against COVID-19 disease. Reporting relative risk reduction (RRR), as usually done in phase 3 studies, does not consider the background risk of being infected and becoming ill with COVID-19, which varies between population and over time. For this reason, several researchers argued that the absolute risk reduction (ARR), namely the difference between attack rates with and without the vaccine, should also be reported [1,2]. The two indices, being conceptually different, are of different order of magnitude (Table 1) [3–10]; for example, the 95% RRR for BNT162b2 (Pfizer-BioNTech) vaccine [3] corresponds to 0.85% ARR [1]. Using one of the two measures to estimate the risk/ benefit ratio would lead to different conclusions. We need to examine some points to use these indicators in an optimal way. Comparison with vaccines against other respiratory-borne viruses is likely to be of little use in interpreting these data. As an example, efficacy and effectiveness of influenza vaccines are estimated on studies lasting the whole influenza season (5– 6 months), considerably longer than COVID-19 vaccine studies [11], and are tested in a completely different background situation, with no or limited use of mitigation procedures at population level (social distancing, use of face-masks). Both these elements, as we will see shortly, can substantially influence the effectiveness of vaccines as estimated by phase 3 trials. Actually, ARR and its derivative number needed to vaccinate to prevent a disease (NNV) are time-dependent parameters, affected by follow-up duration (Figure 1) [12]. The above mentioned fall of risk reduction indices of BNT162b2 vaccine could therefore be, at least in part, correlated to the very short duration of the study [3] (median value 2 months). A pivotal question is: how effective is a vaccine with 95% RRR in preventing COVID-19 in next years in a large population exposed to a long-lasting risk of infection? It may be useful to compare data of COVID-19 vaccines with those of other primary prevention studies in different fields of preventive medicine. Polypill (fixed doses of aspirin, statin and antihypertensive drugs given to large populations) is a strategy used to reduce cardiovascular disease burden in primary prevention, appointed of a number needed to treat (NNT) of 36– 57 to prevent an acute cardiovascular event in 5 years [13], a period 30 times longer than phase 3 studies on COVID-19 vaccines. Since survival event-free curves showed an approximately constant slope, we could therefore estimate that polypill had, in the first months of treatment, a NNT roughly around 1000, considerably higher than the NNV calculated for BNT162b2 vaccine during the phase 3 study (119) [3]. Needless to say that this is an oversimplified model and that many factors (in the case of vaccines, progressive reduction in the number of vulnerable subjects after vaccination or natural infection, immunity decline and emerging of new variants able to escape the vaccine) can modify the effectiveness of vaccine over time. Nevertheless, very recent papers reporting five to six-months update on BNT162b2 and mRNA1273 (ModernaNIH) vaccines [14,15], actually gives the measure of the dependence of ARR on the time of observation, showing an increase of ARR up to 3.7% and 4.9%, respectively (corresponding to NNVs of 27 and 21, respectively). Another relevant point is that ARR, unlike RRR, is not an intrinsic property of a drug or a vaccine, but it’s rather a predictor of the effectiveness of an intervention during the translation in the ‘real world,’ being the result of the interaction between that drug or vaccine (with its own efficacy expressed by RRR) and the baseline population risk. The relationship between the effectiveness of a preventive action and the baseline risk is well-known. For example, in the early 2000s the Seven Countries Study [16] showed that the relationship between blood pressure values and mortality for coronary heart disease had different slopes in different populations. In other words, the relationship between risk factor (hypertension) and the relative risk of death for ischemic heart disease was similar while the relationship with absolute risk was different across the populations studied. For that reason, the same intervention (blood pressure reduction of a given value) will give a different number of prevented events in different population, being the effectiveness higher the higher the slope of the relationship and, hence, baseline cardiovascular risk. The same is expected for vaccination against an infectious disease: the higher the viral circulation in the population (i.e. the infection rate), the higher the effectiveness (ARR) (Figure 1). In this regard, we have to take into account that the effect of COVID19 vaccines has been evaluated over a background of strict preventive measures at population level (social distancing, hand hygiene, mask wearing) that reduced significantly the