RESUMEN
Vaccines against SARS-CoV-2, the virus that causes COVID-19, showed high efficacy against symptomatic illness caused by the ancestral strain. Yet recent variants such as Omicron and its sublineages substantially escape vaccine-induced neutralizing antibodies. In response, bivalent mRNA booster vaccines updated to better match the BA.4-5 lineages have been made available. Yet the reactogenicity of these vaccines might negatively impact willingness to receive the booster immunization. While serious side effects following vaccination are rare, mRNA vaccines frequently lead to mild adverse events such as injection site pain, lymphadenopathy, myalgia, and fever. Over-the-counter analgesics might mitigate some of these mild adverse events, but animal models of SARS-CoV-2 infection have shown that non-steroidal anti-inflammatory drugs (NSAIDs) substantially reduce antiviral antibody responses, which are the best correlates of protection against COVID-19. It remains unknown whether these same inhibitory effects are seen in humans after mRNA vaccination. We surveyed 6,010 individuals who received COVID-19 vaccines regarding analgesic use and correlated these results with Spike-specific antibody levels. We found no negative impact of analgesic use on antibody levels, and in fact observed slightly elevated concentrations of anti-Spike antibodies in individuals who used painkillers. Logistic regression analyses demonstrated a higher proportion of those experiencing fatigue and muscle aches between NSAID users and those not taking pain medication, suggesting that the elevated antibody levels were likely associated with inflammation and mild adverse events rather than analgesic use per se. Together, our results suggest no detriment to painkiller use to alleviate symptoms after vaccination against COVID-19.
RESUMEN
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.