Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study

Lancet. 2021 Nov 13; 398(10313): 1825–1835.  doi: 10.1016/S0140-6736(21)02276-5 PMCID: PMC855091

Authors: Raphael Sonabend, PhD,a,† Lilith K Whittles, PhD,a,b,c,† Natsuko Imai, PhD,a,† Pablo N Perez-Guzman, MD,a,† Edward S Knock, PhD,a,b,† Thomas Rawson, DPhil,a Katy A M Gaythorpe, PhD,a Bimandra A Djaafara, MRes,a Wes Hinsley, PhD,a Richard G FitzJohn, PhD,a John A Lees, PhD,a Divya Thekke Kanapram, PhD,a Erik M Volz, PhD,a Azra C Ghani, Prof, PhD,a Neil M Ferguson, Prof, DPhil,a,b,** Marc Baguelin, PhD,a,b,d and Anne Cori, PhDa,

Summary

Background

England’s COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories.

Methods

This mathematical modelling study was done to assess the UK Government’s four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions.

Findings

The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69–83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500–5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700–1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness.

Interpretation

Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures.

Funding

National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.

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Introduction

Despite the UK being the first country to start nationwide vaccination campaigns,1 the emergence of the alpha (B.1.1.7) variant of concern drove the severe second wave over the 2020–21 winter leading to a third lockdown in England from Jan 5, 2021.2 Informed by mathematical modelling, the UK Government published a roadmap out of lockdown policy for England, setting out the conditions for and expected timeline of a stepwise lifting of non-pharmaceutical interventions (NPIs).3 Between March 8 and July 19, 2021, NPIs were incrementally lifted as vaccination coverage increased. By July 19, 87·5% of the adult population in England had received at least one dose of vaccine, and 68·2% had received two doses.1 The impact of each roadmap step was assessed in real time before further interventions were lifted, against the Government’s four tests as follows: continued success of the vaccine programme; evidence of the effectiveness of vaccines against hospitalisation; no risk of overwhelming the National Health Service (NHS); and new variants of concern do not change the risk assessment.3

Research in context

Evidence before this study

We searched PubMed up to July 23, 2021, with no language restrictions using the following search terms: (COVID-19 or SARS-CoV-2 or 2019-nCoV or “novel coronavirus”) AND (vaccine or vaccination) AND (“non pharmaceutical interventions” OR “non-pharmaceutical interventions”) AND (model*). We found nine studies that analysed the relaxation of controls with vaccination roll-out. However, none explicitly analysed real-world evidence, balancing lifting of interventions, vaccination, and emergence of the delta variant.

Added value of this study

Our data synthesis approach combines real-world evidence from multiple data sources to retrospectively assess how relaxation of COVID-19 measures have been balanced with vaccination roll-out. We explicitly capture the emergence of the delta variant, its transmissibility over alpha, and quantify its impact on the roadmap. We show the benefits of maintaining non-pharmaceutical interventions while vaccine coverage continues to increase and capture key uncertainties in the epidemic trajectory after NPIs are lifted.

Implications of all the available evidence

Our study shows that lifting interventions must be balanced carefully and cautiously with vaccine roll-out. In the presence of a new, highly transmissible variant, vaccination alone might not be enough to control COVID-19. Careful monitoring of vaccine uptake, effectiveness, variants, and changes in contact patterns as restrictions are lifted will be crucial in any exit strategy.

The emergence of variants of concern, notably the lineages alpha, beta (B.1.351), gamma (P.1), and delta (B.1.617.2) first detected in the UK,4 South Africa,5 Brazil,6 and India,7 respectively, has posed recurring challenges for pandemic control efforts globally. The delta variant was designated a variant of concern on May 6, 2021, by Public Health England (PHE) and quickly became the dominant variant in the UK.8 Delta is substantially more transmissible than alpha,89 a variant already 50–80% more transmissible than previously circulating variants,410 and it is estimated to have a 1·85–2·6-fold increase in the risk of hospital admission.1112 It is also associated with partial immune escape and consequent reductions in vaccine effectiveness and cross-protection from previous non-delta infections.1314

Delta’s emergence in the UK in mid-April, 2021,15 drove a rapid increase in cases and hospital admissions across all areas of England,8 prompting the final roadmap step (step four) to be delayed by a month to July 19.16 Daily case numbers in England started increasing from mid-May, 2021, and grew exponentially from mid-June to mid-July, reaching a peak of 56 282 on July 15. Numbers then unexpectedly and synchronously fell to half that value over the following 2 weeks, before plateauing and beginning to rise slowly again in August.17

In this study, we quantify the impact of each of the four steps of the roadmap—school reopening; outdoor hospitality and non-essential retail reopening; indoor hospitality reopening; and lifting of all remaining restrictions.3 Our model framework was developed and adapted throughout the COVID-19 epidemic to provide quantitative evidence and epidemiological insights to the UK Government. As an update to this work, we show what the impact of the full roadmap would have been in the absence of delta, and summarise the real-time modelling of policy options that informed the delaying of the final step four.181920 We also assess the potential epidemic magnitude, timing, and main sources of uncertainty after step four under different vaccine effectiveness and immune escape assumptions of delta and the level of transmissibility after NPIs are lifted,21 taking into account recent trends in case incidence and hospital admissions.

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Methods

Study design

This epidemiological mathematical modelling study was done to assess the UK Government’s four-step process to easing lockdown restrictions in England, UK.

Ethics permission was sought for the study via Imperial College London’s (London, UK) standard ethical review processes and was approved by the College’s Research Governance and Integrity Team (ICREC reference 21IC6945).

Epidemiological model and fitting

We extended a previously described stochastic SARS-CoV-2 transmission model to include vaccination and to capture multiple variants.22 We used Bayesian methods to fit a single strain transmission model (capturing alpha and pre-alpha variants, referred to as alpha hereafter) to multiple data sources, including daily deaths, hospital admissions and bed occupancy, serological data, and population-level PCR tests, from each English NHS region up to March 8, 2021. By fitting a piecewise linear time-varying transmission rate with change points aligned to policy change dates, the model reliably captures the age-specific scale and timing of the first two waves in England.22 To model vaccine roll-out, we assumed an 11-week interval between first and second vaccine doses with the distribution of doses by vaccine type and uptake by age informed by detailed NHS data on vaccine administration. To explicitly model the emergence of the delta variant, we then fitted a two-strain version of the same model to data from March 8 to July 31, 2021, using information propagated from the first inference step and additionally fitting to the PHE variant and mutation dataset. The variant and mutation dataset lists all genotyped or sequenced SARS-CoV-2 cases in England by region and date of specimen. By fitting to the frequency of delta among alpha and delta cases over time, we were able to estimate the transmission advantage of delta over alpha.

We then used the fitted two-strain model to project the epidemic trajectory after July 19 under different scenarios. For these forward projections, we accounted for the effect of school holidays on transmission (except July 23–Sept 1, which overlaps with the period directly after step four where we assume a gradual increase in contacts) and seasonality in SARS-CoV-2 transmission (for full model description and data sources see appendix pp 3–6).

Characteristics of the delta variant

In our model fitting and forward projections (note that in our forward projections, our central projected trend is the median across all daily projections and is not a single trajectory), we explored plausible ranges of key epidemiological characteristics of delta. First, we used estimates of the transmission advantage of delta over alpha, which were informed by fitting the two-strain model to variant and mutation data. Second, we allowed for imperfect cross-immunity between alpha and delta: infection with a pre-delta variant (eg, alpha) provides only 75–100% protection against infection with delta (appendix p 7). This cross-protection was modelled independently from vaccine effectiveness. Third, we explored central, optimistic, and pessimistic assumptions for vaccine effectiveness against delta, including protection against death, severe disease, mild disease or infection, and onward infectiousness, informed by recent studies (table).122834 Fourth, we assumed that vaccine-induced and infection-induced immunity were independent, with vaccines inducing long-lasting immunity. We examined varying assumptions about the duration of infection-induced immunity: lifelong or average of 6-year or 3-year duration (appendix pp 38, 42).353637 Furthermore, we accounted for the increased severity of delta relative to alpha by assuming a 1·85-fold increased risk of hospital admission.12

Table

Vaccine effectiveness assumptions for three two-dose vaccines licensed for use in England

AlphaDelta (central)Delta (optimistic)Delta (pessimistic)
Effectiveness against death
AZ (one dose)232480%80%80%75%
AZ (two doses)23242595%95%95%95%
PF (one dose)232485%85%85%80%
PF (two doses)232495%95%95%95%
Effectiveness against severe disease
AZ (one dose)262780%80%80%75%
AZ (two doses)262728*90%90%90%85%
PF (one dose)2985%85%85%80%
PF (two doses)2830*95%95%95%90%
Effectiveness against mild disease or infection
AZ (one dose)12283150%33%45%20%
AZ (two doses)1213313274%58%70%45%
PF (one dose)1213273050%33%45%20%
PF (two doses)1213303393%85%90%78%
Effectiveness against infectiousness if infected
All vaccines (one and two doses)2345%40%45%35%

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We assumed that MD had the same vaccine effectiveness as PF for first and second doses. AZ=Oxford–AstraZeneca ChadOx1 nCov-19 AZD1222. MD=Moderna mRNA-1273. PF=Pfizer–BioNTech COVID-19 vaccine BNT162b2.

Vaccine effectiveness against infection was assumed equal to vaccine effectiveness against mild disease.

*Assumed greater than mild disease.

Assessing the impact of delta on the roadmap

We explored counterfactual scenarios of the impact of the roadmap on the epidemic trajectory in the presence and absence of delta. We compared the projected number of infections, hospital admissions, and deaths for scenarios with and without delta, varying cross-protection, vaccine effectiveness, and waning immunity as described above. To capture the easing of restrictions at step four, we sampled from a range of values for the reproduction number R, the average number of secondary infections generated by one case, in the absence of naturally induced and vaccine-induced immunity (Rexcl_immunity) that could occur at that stage. We also estimated, for each scenario, the resulting effective reproduction number (Rteff), which accounts for naturally induced and vaccine-induced immunity (appendix p 36). Our baseline scenario assumes contacts increase gradually over an 11-week period after step four to a maximum of 40% (low mixing), 70% (moderate mixing), or 100% (high mixing) greater than contact rates estimated for the period that step three was in place (average Rexcl_immunityappendix p 43),38 central vaccine effectiveness and cross-immunity (appendix pp 9, 42), and a 3-year average duration of infection-induced immunity. We also assessed the impact of delaying step four, planned initially for not before June 21, but delayed to July 19.

Sensitivity analysis

To understand the main drivers of uncertainty of the magnitude of the third wave, in the forward projections we systematically varied four factors as follows: vaccine effectiveness against delta; cross-protection against delta from previous infection with non-delta variants; the duration of natural immunity; and the level of transmissibility after step four (appendix pp 47–48). Shapley values are used to quantify the relative importance of each parameter (appendix p 71). We explored the impact of waning vaccine-induced protection in a separate sensitivity analysis (appendix pp 12, 76). We also assessed a further counterfactual scenario in which contacts increased to maximum levels immediately after the step four date, rather than gradually as in the baseline scenario.

Role of the funding source

The funders of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

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Results

The model effectively reproduces national (figure 1) and regional (appendix p 49) trends in the SARS-CoV-2 epidemic in England between Dec 1, 2020, and July 19, 2021, including the July dip in case numbers, hospital admissions (figure 1A, B), and hospital deaths (figure 1C, D), and the emergence and eventual dominance of the delta variant (figure 1E, F). In our model validation (figure A–D), we show that our original model projections match the subsequently observed hospital admissions and deaths between July 19 and Sept 16, 2021.

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Figure 1

Trajectory of the COVID-19 epidemic in England and the emergence of the delta variant

Observed (grey points) daily hospital admissions (A), log hospital admissions (B), hospital deaths (C), and log hospital deaths (D). The blue line shows the model fit up to July 19, 2021, and the red and purple lines show the projected admissions and deaths assuming a central vaccine effectiveness, cross-immunity, and immunity duration and a gradual increase in contacts and a return to high (dark red), moderate (light red), or low (purple) transmissibility (contact rates) after non-pharmaceutical interventions are lifted. The shaded area is the 95% credible interval. Note that the central projected trend is the median across all daily projections and is not a single trajectory. The yellow points in panels A–D show the most recent data (July 19–Sept 16, 2021), which were not fitted to. (E) Model fit to the daily proportion of cases due to the delta variant (variant and mutation data) over time from March 8–July 19, 2021, in London as an example (appendix p 54 shows other regions). Points show the data, bars are 95% CIs, the blue line is the model fit, and the shaded area is the 95% credible interval. (F) Estimated delta seeding date by UK National Health Service region. Points show the median estimate and horizontal bars show the 95% credible interval.

Figure 2 shows estimates of how the reproduction number and population immunity, under our central immunity and moderate transmissibility scenario, changed over time since December, 2020. Overall, while Rexcl_immunity increased markedly from March to July, 2021, as a result of the relaxation of lockdown and then the emergence of the delta variant, the rapid roll-out of vaccines progressively increases the gap between Rexcl_immunity and Rteff. The Christmas school holidays and accompanying near-lockdown physical distancing in December, 2020, followed by the third national lockdown in January, 2021, successfully brought Rteff below the critical threshold of one. This period coincided with a rapid expansion of the national vaccination programme. By March 8 (step one of the roadmap) when educational institutions reopened, 43% of eligible adults (>18 years) had received their first vaccine dose and 2% had their second dose.1 Cases, hospital admissions, and deaths continued to decrease and remained at low levels even after schools reopened (figure 1A, C). Although we estimated a slight increase in Rteff after step one, Easter holidays (from April 1, 2021) and the roll-out of vaccination maintained Rteff below one (figure 2A) when non-essential retail opened (61% first dose and 15% second dose coverage by April 12, step two).1 The delta variant, detected in early April, 2021, predominantly in London and the North West NHS regions (figure 1F), was designated as a variant under investigation on April 15, after increasing numbers of locally acquired infections with that variant were detected (figure 1E).15

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Figure 2

Prevalence-weighted effective R(t) and R(t) excluding infection-induced or vaccine-induced immunity (A) and proportion of the population in England protected after infection or vaccination against infection, severe disease, or death (B), over time

(A) Estimated values from the end of the second national lockdown up to July 19, 2021, and assumed values thereafter. The solid line shows the median R(t) and the shaded area shows the 95% credible interval. Shaded area shows school holidays—note we do not explicitly model the impact of school closures for the period July 23–Aug 31 in order to capture the overall gradual increase in contacts from July 19 to Oct 1 (appendix p 44). The forward projection section of the figure corresponds to the central immunity and gradual return to moderate mixing scenario. Note that our central projected trend is the median across all daily projections and is not a single trajectory. (B) Proportion of the English population, from Jan 1, 2021, protected after infection or vaccination over time against infection, severe disease, or death. The vertical dashed line shows the separation between the observed vaccination schedule up to July 31, followed by the simulated schedule assuming central immunity and a gradual return to moderate transmissibility after non-pharmaceutical restrictions are lifted. The white space in the plot corresponds to individuals who have neither been vaccinated against nor infected with SARS-CoV-2. R(t)=reproduction number. *Euro 2020 football tournament.

We estimated that the Rteff for alpha remained less than one (appendix p 54) after step two because of increasing population immunity from vaccination, with uptake increasing to 69% for first dose and 39% for second dose by May 17 (step three, resumption of indoor hospitality and inter-household mixing1). However, after initial seeding between late March and early April (figure 1F) the proportion of delta variant cases increased rapidly across all regions in this period (figure 1Eappendix p 54). This rapidly drove Rteff to greater than one by mid-May, reflecting the 76% (95% credible interval [CrI] 69–83) transmission advantage of delta compared with alpha that we estimate (figure 2A).

The increase in contacts after step three continued to be offset by the increasing vaccine-induced population immunity (figure 2); 82% had a first dose and 60% had a second dose by June 21).1 However, the net impact of infection-induced and vaccine-induced immunity differed qualitatively by variant. We estimated that Rteff for alpha remained less than one through to mid-July, whereas Rteff for delta remained greater than one (appendix p 54).

A further sharp increase in Rteff was then seen in the first half of July. At the time, we and other modelling groups advising the UK Government concluded that the increase was a belated result of step three, but the rapid drop after July 11 (and other data indicating a sex imbalance in case incidence),39 suggest that the increase in transmission rates seen in that period resulted from a transient increase in population contact rates, particularly in young men, probably associated with the Euro 2020 football tournament. At the time of writing, that decrease has reversed, with a gradual increase in case incidence in August.17

Despite high vaccine coverage in adults, sufficient population susceptibility remains for a third wave to occur as contact rates rise (figure 2). The proportion protected against a delta infection after vaccination is substantially lower than for alpha. Additionally, most individuals younger than 18 years have neither been vaccinated nor infected. In this context, delta’s high transmissibility means that population immunity, whether vaccine induced or infection induced, is insufficient to keep Rteff below one.

With the emergence of delta, our projections show that had step four occurred on June 21, as initially planned, it might have caused a substantial third wave of hospital admissions and deaths, but with wide uncertainty regarding the magnitude and trajectory of that wave (figure 3). Projected total deaths between June 21, 2021, and June 1, 2022, ranged from 13 400 (95% CrI 8300–22 700) in the most optimistic scenario (high vaccine effectiveness, high cross-protection, slower waning of natural immunity, and 40% increase in contacts) to 40 600 (33 000–49 500) in the most pessimistic scenario (low vaccine effectiveness, low cross-protection, faster waning of natural immunity, and 100% increase in contacts; appendix pp 9, 38, 43).

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Figure 3

England COVID-19 daily infections (A), hospital admissions (B), deaths (C), and total additional deaths between June 21, 2021, and Jan 1, 2022 (D)

Assumptions were that all remaining NPIs were lifted on June 21 (blue) or July 19 (red and purple) with a gradual increase in contacts over 11 weeks thereafter, and a return to low (light blue or purple), moderate (medium blue or red), or high (dark blue or red) transmissibility (contact rates). The grey points show the fitted data and yellow the most recent trends (July 19–Sept 16, not fitted). Each column shows projections assuming delta variant with optimistic (left column); central (middle column); and pessimistic (right column) vaccine effectiveness, cross-protection, and waning immunity assumptions (appendix pp 9, 38). The plots are truncated on Jan 1, 2022, but model results in the main text are based on simulations up to June 1, 2022. The central projected trend is the median across all daily projections and is not a single trajectory. Shading shows 95% credible intervals.

We found that delaying step four until July 19, was beneficial for all scenarios considered, with a much smaller projected wave, although still highly uncertain and sensitive to assumptions around vaccine effectiveness against delta and mixing after NPIs are lifted (figure 3). The delay allowed the distribution of an additional 2·8 million first doses and 3·8 million second doses between June 21 and July 19,1 reducing the projected future peak of daily hospital admissions by three fold from 3900 (95% CrI 1500–5700) to 1400 (700–1700) in the baseline scenario of central immunity and moderate mixing (figure 3B). It also reduced the total predicted deaths between June 21, 2021, and June 1, 2022, by about 20% (appendix pp 62–70). Had contact rates increased more abruptly after step four, the reductions in peak hospital admissions and deaths caused by delaying until July 19, would have been substantially greater (see appendix p 60). However, trends in cases, hospital admissions, and death data in the months up to mid-August, 2021, suggest that contacts have increased gradually since step four, in line with the assumptions of our baseline scenario (figure 1B, D).

In sensitivity analyses, we found that the level of mixing of people after NPIs are lifted was the main driver of uncertainty of the magnitude of the third wave, followed by vaccine effectiveness, the level of cross-immunity, and waning immunity. The rate at which previous infection-induced immunity waned had a greater impact on expected total infections but only minimally impacted expected hospital admissions or deaths (appendix pp 72–73). As expected, allowing for waning of vaccine-induced immunity resulted in a higher number of hospital admissions and deaths. However, this sensitivity analysis did not include the potential mitigating impact of planned booster doses (appendix pp 12, 76).

We project future deaths predominantly in fully vaccinated individuals aged 75 years or older because of the high vaccine uptake of a highly effective, but not 100% effective, vaccine in the most vulnerable age groups; it does not imply a poor vaccine effectiveness against death. We also anticipate a substantial number of deaths among fully vaccinated adults aged 50–74 years (appendix p 61).

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Discussion

Mathematical models are valuable tools to inform the design, monitoring, and evaluation of vaccination programmes.40 In this study, we retrospectively assessed steps one to three of England’s roadmap out of lockdown policy, and prospectively explored the impact of step four. By extending our evidence synthesis framework to account for vaccinations and multiple variants, we reliably captured the past epidemic and quantified the impact of each phase of lifting interventions and the emergence of the delta variant on transmission.22

We show that the roadmap was successful in mitigating the increase in mixing due to lifting NPIs, by increasing population-level immunity through the mass vaccination programme. Our projections show that in the absence of the delta variant, lifting NPIs at the planned earliest date of the final step (June 21) would not have resulted in a substantial third wave. This emphasises the importance of carefully aligning the lifting of interventions with immunity levels in the population. Israel rapidly vaccinated a high proportion of their population throughout their reopening plan, and then implemented vaccination passes for entry to high-risk settings.4142 Conversely, case incidence and hospital admissions are currently surging in several US states, due to restrictions being relaxed when vaccination coverage was too low to give sufficient population immunity.43

Our analyses emphasise the significant impact that the emergence of delta had on the planned roadmap. Similar to previous studies, we estimated that delta has an average 76% (95% CrI 69–83) transmissibility advantage over alpha.9 Together with reductions in vaccine effectiveness for delta and waning of natural immunity, this drove a rapid increase in cases and hospital admissions from mid-May that was not offset by the vaccination programme.2834 A similar displacement of alpha and a surge in cases was observed in India where delta was first detected.44 The dominance of delta has now led to a tightening of restrictions in many countries, including in Israel, the country with the fastest vaccination roll-out.45

Across all the scenarios we examined, delaying step four until July 19 was beneficial because it allowed more individuals to be vaccinated. In the baseline scenario, this delayed and reduced the peak of hospital admissions by three fold and reduced total deaths between June, 2021 and June, 2022 by about 20%.

We projected that a substantial autumn wave of transmission is possible, at least in the absence of substantial additional vaccination (eg, booster doses and full vaccination of teenagers), but with large uncertainty around the resulting peak number of hospital admissions and total deaths. This uncertainty is driven by uncertainty around levels of mixing after NPIs are lifted and by imperfect knowledge of vaccine effectiveness against delta. Transmission intensity (Rteff) in the coming months will depend on how high and how quickly population contact rates will increase, the continued use of face coverings,46 physical distancing, and adherence to case isolation.4748

The rapid exponential growth in case incidence in the first half of July, 2021, illustrates the high transmission levels that could be reached if contact rates approach prepandemic levels in the coming months. Fortunately, that increase in contact rates, probably caused by the Euro 2020 football tournament,39 proved transient, and was then followed by the synchronous self-isolation of contacts alerted through NHS test and trace49 and the start of school holidays a week later, driving transmission down for the last 2 weeks of July. Polls showed that 57% of UK adults were worried about the removal of legal restrictions and 66% would continue to wear a face covering after July 19.50 The average number of contacts in early September, 2021, was still much lower than prepandemic levels,51 and our baseline scenario, which assumes a gradual increase in contact rates, most closely reflects current trends. We estimate that this slow increase in contact rates after July 19 will reduce the peak number of hospital admissions and total deaths compared with an abrupt increase.

Uncertainty around vaccine effectiveness means it is difficult to accurately estimate the overall level of population immunity accounting for waning and imperfect cross-protection. At high vaccine coverage, even a difference of 98% versus 95% in vaccine effectiveness against mortality translates to a doubling of projected deaths.52 The duration of infection-induced and vaccine-induced immunity for all SARS-CoV-2 lineages remains another key unknown that will determine long-term transmission dynamics. We explored the impact of waning vaccine-induced protection in a simple sensitivity analysis. Including waning of vaccine protection had no impact on our retrospective assessment of steps one to three of the roadmap, but resulted in a substantially higher number of projected hospital admissions and deaths. Better characterising the duration of natural and vaccine-induced protection against infection and severe disease will be important for informing booster vaccination programmes. We have focused on deaths and hospital admissions as primary outcomes in our analysis, given the impact of hospital admissions on NHS capacity. However, an estimated 1·5% of individuals in the UK had symptoms of so-called long COVID (symptoms lasting >4 weeks) on July 4, 2021.53 Although our estimates of infections and cases over time might capture this wider burden of disease,54 we did not explicitly quantify this. Overall infection levels also determine the risks of new variants of concern emerging within the UK with the risk increasing with transmission levels.

Our analysis has a number of limitations. We did not consider reintroduction of NPIs, vaccination of people younger than 18 years, nor booster doses in our projections. These measures might at least partially mitigate the third wave. A first dose of vaccine has now been advised for all eligible 12–17-year-olds and booster doses for individuals aged 50 years or older is being rolled out.5556 However, given the age profile of projected hospital admissions and deaths, we anticipate expansion of vaccine eligibility to children might only have a moderate effect. Although we modelled heterogeneity by age in mixing patterns and capture changes in the overall level of mixing over time, we assumed that age-related mixing patterns remained constant over time. We also modelled age-dependent vaccine uptake, which we assumed was independent of mixing patterns or viral transmission, but did not explicitly model other types of heterogeneity (eg, by occupation, sociodemographic, and ethnic groups5758) which might affect both the risk of infection and vaccine uptake. Groups of individuals who are both at high risk of infection and less likely to take the vaccine might lead to continued outbreaks among vulnerable populations and reduce the overall impact of vaccination. Furthermore, our analysis focused only on outcomes directly related to COVID-19: we did not consider the impact on health services, other diseases, mental health, or the economic impact of measures.

In summary, our study shows how the phased lifting of NPIs in England, coordinated with vaccine roll-out, has been largely successful at keeping hospital admissions and deaths at low levels since March, 2021. However, our projections show that the high transmissibility of delta, imperfect vaccine effectiveness, and future increases in contact rates are likely to lead to a substantial wave of transmission in the autumn, albeit of highly uncertain magnitude. Overall, our analysis highlights the clear benefit of early and accessible national vaccination programmes that allow population immunity to increase to high levels before NPIs are lifted. Furthermore, we have shown that vaccination alone in the absence of NPIs might not be sufficient to control delta, even with high vaccination coverage. We quantified how the emergence of delta affected the progress of the roadmap and the benefit of delaying step four of that roadmap by 1 month. The experience of delta highlights the threat posed by any future variants of concern and underscores the need for global collaborative efforts to control transmission and mitigate the risk of emergence of new variants of concern through equitable global access to vaccines.

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Data sharing

All data files and source code required to reproduce this analysis are publicly available at GitHub.

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Declaration of interests

AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases. KAMG has received honoraria from Wellcome Genome Campus for lectures and salary support from the Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance, through Imperial College London for work outside this study. All other authors declare no competing interests.

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Acknowledgments

We thank all colleagues at PHE and front-line health professionals who have not only driven and continue to drive the daily response to the COVID-19 epidemic in England but also provided the necessary data to inform this study. This work would not have been possible without the dedication and expertise of said colleagues and professionals. The use of pillar-2 PCR testing data, vaccination data, and the variant and mutation data was made possible thanks to PHE colleagues, and we extend our thanks to N Gent and A Charlett for facilitation and insights into these data. The use of serological data was made possible by colleagues at PHE Porton Down, Colindale, and the NHS Blood Transfusion Service. We are particularly grateful to G Amirthalingam and N Andrews for helpful discussions around these data. We thank all the REal-time Assessment of Community Transmission (REACT) Study investigators for sharing PCR prevalence data. We also thank the entire Imperial College London COVID-19 response team for support and feedback throughout. The views expressed are those of the authors and not necessarily those of the UK Department of Health and Social Care, the NHS, the National Institute for Health Research (NIHR), PHE, UK Medical Research Council (MRC), UK Research and Innovation, or the EU. This work was supported jointly by the Wellcome Trust and the Department for International Development (DFID; 221350]. We acknowledge joint Centre funding from the UK MRC and DFID (MR/R015600/1). This work was also supported by the NIHR Health Protection Research Unit in Modelling Methodology, a partnership between PHE, Imperial College London, and London School of Hygiene & Tropical Medicine (NIHR200908), the Abdul Latif Jameel Foundation, and the EDCTP2 programme supported by the EU.

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Contributors

RS, PNP-G, ESK, LKW, NI, EMV, ACG, NMF, MB, and AC were involved in conceptualisation of the study, including formulation or evolution of overarching research goals and aims. KAMG, WH, BAD, NI, RGF, PNP-G, and ESK curated the data, including management activities to annotate (produce metadata), scrub data, and maintain research data. RS, LKW, PNP-G, ESK, KAMG, MB, and AC did formal analysis, including application of statistical, mathematical, computational, or other formal techniques to analyse or synthesise study data. MB, NMF, and AC acquired funding. RS, LKW, PNP-G, ESK, TR, MB, and AC did the investigation, including conducting a research and investigation process, specifically performing the experiments, or data or evidence collection. RS, LKW, PNP-G, ESK, MB, NMF, and AC developed or designed the methodology, including creation of models. NI, RS, NMF, MB, and AC were involved in project administration, including management and coordination responsibility for the research activity planning and execution. JAL and RGF were responsible for provision of computing resources or other analysis tools. RS, LKW, PNP-G, ESK, TR, WH, JAL, RGF, MB, and AC were responsible for software, including programming and software development; designing computer programs; implementation of the computer code and supporting algorithms; and testing of existing code components. NMF, MB, and AC supervised the study, including oversight and leadership responsibility for the research activity planning and execution, and mentorship external to the core team. RS, LKW, NI, PNP-G, ESK, TR, NMF, MB, and AC were responsible for verification, whether as a part of the activity or separate, of the overall replication or reproducibility of results or experiments and other research outputs. RS, LKW, PNP-G, TR, and WH prepared, created, or presented the published work, specifically regarding visualisation and data presentation. NI, TR, RS, ESK, LKW, MB, and AC wrote the original draft, including preparation, creation, or presentation of the published work. NI, RS, LKW, TR, ESK, PNP-G, DTK, KAMG, JAL, ACG, MB, AC, and NMF reviewed and edited the manuscript, including preparation, creation, or presentation of the published work by those from the original research group, and gave critical review, commentary, or revision, including stages before and after publication. All authors had access to the underlying data and KAMG, WH, BAD, and NI accessed and verified the data.

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Supplementary Material

Supplementary appendix:

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Less Than 1 In 100 Million Chance That COVID-19 Has Natural Origin

Authors: Hans Mahncke via The Epoch Times October 24, 2022

A new study on the origins of the pandemic, “Endonuclease fingerprint indicates a synthetic origin of SARS-CoV2,” published on the preprint server bioRxiv, concludes that it is highly likely that the SARS-CoV-2 virus that causes COVID-19 originated in a laboratory. The odds of a natural origin, according to the study, are placed at less than 1 in 100 million.

Unlike previous studies that analyzed qualitative aspects such as virus features, the new study for the first time assesses the likelihood of a laboratory origin on a quantitative basis. This breakthrough methodology allowed the authors to present objective findings that appear to exceed any previous studies. 

Significantly, the new study does not rely on any of the known evidence pointing toward a lab origin of the SARS-CoV-2 virus. For instance, it does not take into consideration the highly unusual Furin Cleavage Site that makes the virus particularly virulent and which it is widely thought to have been inserted into the virus at the Wuhan Institute of Virology. Nor does it factor in the huge coincidence that the pandemic started on the door steps of the world’s premier coronavirus laboratory

Instead, the authors—Valentin Bruttel, a molecular immunologist at the University of Würzburg in Germany; Alex Washburne, a mathematical biologist at Selva Science; and Antonius VanDongen, a pharmacologist at Duke University—took a novel approach that assesses the genesis of the SARS-CoV-2 virus from an entirely new angle. The authors examined tiny fingerprints left behind in the process in which viruses are assembled in laboratories. While use of seamless genetic engineering techniques in creating viruses in laboratories typically conceals evidence of manipulation, the new study developed a statistical process for uncovering such hidden evidence by comparing the distribution of certain strands of genetic code in wild viruses and lab-made viruses. 

When viruses are constructed in a lab, they are typically assembled by piecing together various virus parts. According to a blog post from Washburne that accompanied the release of the study, it is like taking Mr. Potato Head from the movie Toy Story and replacing his arms with the arms of GI Joe to help “us study things like whether GI Joe arms provide any clear benefit for an important task in the virus life cycle like lifting weights.”

In other words, one of the main purposes of manipulating viruses is to better understand which parts of viruses make them particularly infectious, lethal, or transmissible. A related purpose is to develop bioweapons but the authors of the new study reject the idea that that is why SARS-CoV-2 was made. They believe that the virus “was assembled in a lab via common methods used to assemble infectious clones pre-COVID.”

recent experiment at Boston University is an example of piecing together virus parts. Researchers created a COVID-19 variant that killed 80 percent of exposed mice using the backbone of the ancestral SARS-CoV-2 virus and replacing its spike gene with that from the Omicron variant. Put another way, the Boston lab created a COVID-19 version of Frankenstein’s monster by piecing together different parts from different variants of the SARS-CoV-2 virus.  

Piecing together viruses in labs is subject to limitations. The genetic information for SARS-CoV-2 is contained in 30,000 base pairs of RNA nucleotides. However, the 30,000 base pairs are not pieced together all at once. Instead, laboratory viruses are assembled from a collection of smaller strands of base pairs that are later “glued” back together as chimeras, or compounds. Enzymes are used to cut viruses apart at certain points along the DNA strand (laboratories use DNA instead of RNA as it is more stable; the assembled DNA is then added to bacteria that create RNA viruses). 

Enzymes are proteins that cut through DNA strands at specific recognition sites. These recognition sites, or cutting sites, are the genetic sequences within DNA strands that are sought out by the enzymes. Enzymes are like biological scissors that cut only at particular cutting sites marked by sequences that are recognized by particular enzymes. 

Since cutting sites look like normal sequences of nucleotides, they can be found on RNA strands of naturally occurring viruses as well as on lab-made viruses. This is why this form of genetic engineering leaves no seams or obvious fingerprints. However, there is an important difference between cutting sites on wild-type and lab-made viruses that the authors exploited. Naturally occurring cutting sites are not necessarily located where scientists want them to be. Laboratories therefore routinely insert cutting sites in favorable locations and remove them from unfavorable locations.

While naturally occurring cutting sites and cutting sites added in a lab are biologically indistinguishable, Bruttel, Washburne and VanDongen hypothesized that they could detect a “very subtle but identifiable fingerprint” by plotting the distribution of the cutting sites on the SARS-CoV-2 virus. They would then compare this to the distribution of such sites on wild-type SARS viruses, as well as on other, pre-pandemic lab-made SARS viruses. They carried out their analyses for the most commonly used enzymes (biological “scissors”) which, according to a series of pre-pandemic publications from the Wuhan Institute of Virology, were also used for experiments in the Wuhan lab.

The results of the new study are stark. While cutting sites on wild-type SARS viruses are randomly distributed, they tend to be regularly spaced on pre-pandemic lab-made viruses, as well as on SARS-CoV-2. So the authors found that regular spacing suggests that the location of the cutting sites was manipulated in a lab.

The new study also compared the length of the longest segments seen in wild-type viruses and lab-made viruses. The longest segments in wild-type viruses are far longer than any found in lab-made viruses, including in SARS-CoV-2. The findings again pointed to a lab origin for COVID-19.

The longest segments in lab-made viruses were found to be unusually short. As previously noted, the process of genetically engineering viruses requires scientists to use several shorter segments, which are then pieced together. Natural viruses are not pieced together and thus the length of segments is randomly determined and includes very short and very long segments. 

Bruttel, Washburne, and VanDongen estimate that the odds that the SARS-CoV-2 virus arose naturally lie between 1 in 100 and 1 in 1,400. However, this estimate only factors in the distribution of cutting sites. The authors also observed a concentration of mutations within the cutting sites that was “extremely unlikely in wild coronaviruses and nearly universal in synthetic viruses.” The estimate drops to a 1 in 100 million chance that SARS-CoV-2 was a naturally-occurring virus if these mutations are factored in. When considering additional criteria, such as the fact that the “sticky ends” where the viruses are “glued” back together all happen to fit perfectly, the authors estimate the odds of a natural origin to be even lower. 

The authors conclude that SARS-CoV-2 was assembled in a lab using common methods for assembling viruses. The authors do not speculate on which lab the virus escaped from.In response to the new study, Kristian Andersen, the leading author of the Proximal Origin paper—the Dr. Anthony Fauci-led effort to dispel the lab leak theory—went on Twitter to slam the new study as “kindergarten molecular biology.” Andersen’s criticism is that cutting sites are common in naturally occurring SARS viruses. However, this criticism does not explain the very unusual placement of cutting sites in SARS-CoV-2.

Ophthalmology Overview: COVID-19 Infection in Eye Cells, Vision Loss and Blindness Prevalence, and More

Authors: AJMC Staff

Highlighting the latest ophthalmology-related news reported across MJH Life Sciences™.Highlighting the latest ophthalmology-related news reported across MJH Life Sciences.

Mount Sinai Study Finds COVID-19 Can Infect Eye Cells

Although aerosol transmission is considered the primary cause of COVID-19 infection, findings from a study published this week by researchers at Mount Sinai indicates that the virus may also be transmitted through the eye, with the limbus especially susceptible.

As reported by Ophthalmology Times®, implications of COVID-19 on ocular manifestations have been seen with a previous study suggesting that the virus can cause conjunctivitis, or pink eye, along with epiphora and chemosis. The researchers of the present study sought to delve further into quantifying the entry factor of the virus and antigen expression in postmortem patients who had COVID-19.

Examining adult human eye donor cells obtained via autopsy of patients who had COVID-19. in an in vitro stem cell model, analysis via RNA sequencing confirmed that the virus infected the ocular surface cells, with the protein associated with infection, ACE2, and an enzyme that facilitates viral entry, TMPRSS2, also identified.

Assessing US Prevalence of Visual Acuity Loss, Blindness

Reported by Ophthalmology Times®, a study published last week in JAMA Ophthalmology indicates that more than 7 million people in the United States are living with uncorrectable vision loss, including more than 1 million cases of blindness.

Identifying cases across all age groups, people younger than 40 years accounted for nearly 1 in 4 cases of vision loss or blindness, with 1.6 million cases overall and 141,000 cases of blindness—13% of all people with blindness in the United States.

Notably, the estimated number of cases is a 68% increase over the previous estimate created by the 2012 Vision Problems in the US study. In delineating at-risk populations, a higher risk of vision loss was found in Hispanic/Latino and Black individuals than among White individuals, with more females than males experiencing permanent vision loss or blindness.

Inherited Retinal Disease Awareness, Benefits of Genetic Testing

This week, Prevent Blindness is holding its second annual Inherited Retinal Disease (IRD) Genetic Testing Week, in which the nonprofit is posting educational content related to IRD, a unit of diseases that can lead to severe vision loss or blindness.

Affecting patients of all ages, an article by Modern Retina highlights that because many IRD conditions are degenerative, genetic testing may help identify treatment options early in the process. Moreover, due to the retina’s physical makeup, patients with IRD are strong candidates for gene therapy treatments that can help control disease progression, particularly in at-risk children and infants.

Providing a free fact sheet on IRD, the Prevent Blindness website also lists causes, risk factors, research and therapy options, financial assistance services, and more.

Long COVID: which symptoms can be attributed to SARS-CoV-2 infection?

Authors: Christopher E Brightling Rachael A Evans Published:August 06, 2022DOI:https://doi.org/10.1016/S0140-6736(22)01385-X The Lancet

Mortality rates following SARS-CoV-2 infection have decreased as a consequence of public health policies, vaccination, and acute antiviral and anti-inflammatory therapies.1 However, in the wake of the pandemic, post-acute sequelae of COVID-19, or long COVID, has emerged: a chronic illness in people who have ongoing multidimensional symptomatology and disability weeks to years after the initial infection.2 Early reports of long COVID prevalence, summarized in a systematic review examining the frequency and variety of persistent symptoms after COVID-19, found that the median proportion of people who had at least one persistent symptom 60 days or more after diagnosis or at least 30 days after recovery from COVID-19 infection was 73%. 3 However, the estimated prevalence depends on the duration, population, and symptoms used to define long COVID. More recently, community-based studies have suggested a lower prevalence of persistent symptoms; 4 whereas among people who were hospitalised following COVID-19 infection, a high proportion do not fully recover (50–70%).56

The number of COVID-19 cases continues to rise and now exceeds 500 million worldwide.1 Consequently, the number of people with long COVID is similarly increasing. Indeed, the UK Office for National Statistics (ONS) survey up to May, 2022 estimated that 2 million people in the UK had self-reported long COVID. 8 Of these people, 72% reported having long COVID for at least 12 weeks, 42% for at least 1 year, and 19% for at least 2 years. Consistent with other studies, fatigue was the most common symptom in the ONS survey, followed by breathlessness, cough, and muscle ache.45678 Risk factors for long COVID are female sex, obesity, middle age (35–65 years), living in areas of greater socioeconomic deprivation, and the presence of another activity-limiting health condition.156 Importantly, health-care use is increased in those with long COVID, with increased general practitioner consultation rates.

How many of the symptoms currently attributed to long COVID actually represent pre-existent disease or are unrelated to COVID-19 is uncertain. Symptoms that were present before SARS-CoV-2 infection are often not recorded or assessed by recall. In The Lancet, Aranka V Ballering and colleagues 10  report the findings of a longitudinal cohort study conducted in the north of the Netherlands between April, 2020, and August, 2021, where 23 somatic symptoms were assessed using 24 repeated measurements in digital COVID-19 questionnaires. The study was embedded within the large, population-based Lifelines COVID-19 cohort. The main strengths of this study were that participants were their own control, with the pattern and severity of symptoms assessed before and 3–5 months after SARS-CoV-2 infection, and were also compared with a matched control group of COVID-19-negative participants. Of the 76 422 participants, 4231 (5·5%) had COVID-19 and were compared with 8462 matched controls. Participants had a mean age of 53·7 years (SD 12·9), 46 329 (60·8%) were female, and nearly all were of White ethnicity. The proportion of participants who had at least one core symptom of substantially increased severity to at least moderate was 21·4% (381 of 1782) in COVID-19-positive participants versus 8·7% (361 of 4130) in COVID-19-negative controls. Thus, this study found that core symptoms were attributed to COVID-19 in 12·7% of participants, or approximately one in eight. This is a major advance on previous long COVID prevalence estimates, as it includes a matched control group without SARS-CoV-2 infection and accounts for symptoms that were present before infection.

The pattern of symptomatology observed by Ballering and colleagues 0  was similar to previous reports, with fatigue and breathlessness among the most common symptoms, but other symptoms such as chest pain were more common in people who had COVID-19 than in COVID-19-negative controls. Ballering and colleagues10  propose a core symptom set to be considered as part of the case definition for long COVID. Although an agreed diagnostic core symptom set would inform clinical pathways and research, the study by Ballering and colleagues 10 did not fully consider the impact on mental health, it was conducted in one region in the Netherlands, and it did not include an ethnically diverse population; thus the concept of a core symptom set will require further validation. Importantly, the study by Ballering and colleagues 10  does not provide new mechanistic insights, which are key to uncovering new therapeutic targets. In other studies, clustering of patient-reported outcomes has identified different severity groups of long COVID and identified increased systemic inflammation in people with very severe long COVID.5, 6

 How patient-centred outcomes, together with biomarkers, can further refine long COVID diagnosis and inform precision medicine approaches warrants further consideration. Encouragingly, emerging data from other studies suggest that the proportion of newly infected people developing long COVID is reduced in people who have received vaccination before SARS-CoV-2 infection,11  and might be lower in people infected with the omicron variant than those infected with earlier variants.2

 Findings from the ONS survey suggested that vaccination following infection might reduce the symptom burden of long COVID after the first dose, with sustained improvement after a second dose13  Whether acute treatments for COVID-19 affect the likelihood of developing long COVID or its severity is unknown.

Current evidence supports the view that long COVID is common and can persist for at least 2 years after SARS-CoV-2 infection, although severe debilitating disease is present in a minority. The long COVID case definition needs to be further improved, potentially to describe different types of long COVID, of which better mechanistic understanding is crucial. This will lead to personalised multimodality treatments that can be implemented to manage the increasingly high number of people with long COVID.

CEB has received consultancy and or grants paid to his institution from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Novartis, Chiesi, Genentech, Roche, Sanofi, Regeneron, Mologic, and 4DPharma for asthma and chronic obstructive pulmonary disease research. RAE has received consultancy fees from AstraZeneca on the topic of long COVID and from GlaxoSmithKline on digital health, and speaker’s fees from Boehringer Ingelheim on long COVID. RAE holds a National Institute for Health and Care Research (NIHR) clinician scientist award CS-2016-16-020.

References

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    Coronavirus Resource Centre.https://coronavirus.jhu.edu/Date: 2022Date accessed: June 27, 2022View in Article 
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    COVID-19 rapid guideline: managing the long-term effects of COVID-19.https://www.nice.org.uk/guidance/ng188Date: 2021Date accessed: June 27, 2022View in Article 
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    Assessment of the frequency and variety of 511 persistent symptoms among patients with COVID-19: a systematic review.JAMA Netw Open. 2021; 4e2111417View in Article 
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    Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study.Lancet Respir Med. 2021; 9: 1275-1287View in Article 
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    Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study.Lancet Respir Med. 2022; (published online April 23.)https://doi.org/10.1016/S2213-2600(22)00127-8View in Article 
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    Health outcomes in people 2 years after surviving hospitalisation with COVID-19: a longitudinal cohort study.Lancet Respir Med. 2022; (published online May 11.)https://doi.org/10.1016/S2213-2600(22)00126-6View in Article 
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    • Office for National Statistics
    Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK.https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/prevalenceofongoingsymptomsfollowing coronaviruscovid19infectionintheuk/1june2022Date: June 1, 2022Date accessed: June 27, 2022View in Article 
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    • Whittaker HR 
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    GP consultation rates for sequelae after acute COVID-19 in patients managed in the community or hospital in the UK: population based study.BMJ. 2021; 375e065834View in Article 
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    • Ballering AV 
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    Persistence of somatic symptoms after COVID-19 in the Netherlands: an observational cohort study.Lancet. 2022; 400: 452-461View in Article 
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    Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study.Lancet Infect Dis. 2022; 22: 43-55View in Article 
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9

WHO report: 17 million in EU may have suffered long COVID-19

SEPTEMBER 13, 2022 Journal information:Nature Medicine

New research suggests at least 17 million people in the European Union may have experienced long COVID-19 symptoms during the first two years of the coronavirus pandemic, with women more likely than men to suffer from the condition, the World Health Organization said Tuesday.

The research, conducted for the WHO/Europe, was unclear on whether the symptoms that linger, recur or first appear at least one month after a coronavirus infection were more common in vaccinated or unvaccinated people. At least 17 million people met the WHO’s criteria of long COVID-19—with symptoms lasting at least three months in 2020 and 2021, the report said.

“Millions of people in our region, straddling Europe and Central Asia, are suffering debilitating symptoms many months after their initial COVID-19 infection,” said Hans Henri P. Kluge, WHO Regional Director for Europe, during a conference in Tel Aviv.

The modeling also suggests that women are twice as likely as men to experience long COVID-19, and the risk increases dramatically among severe infections needing hospitalization, the report said. One-in-three women and one-in-five men are likely to develop long COVID-19, according to the report.

“Knowing how many people are affected and for how long is important for health systems and government agencies to develop rehabilitative and support services,” said Christopher Murray, director of the Institute for Health Metrics and Evaluation, which conducted the research for the WHO.

The research, which represents estimates and not actual numbers of affected people, tracks with some other recent studies on the constellation of longer-term symptoms after coronavirus infections.

A U.S. study of veterans published in Nature Medicine in May provided fresh evidence that long COVID-19 can happen even after breakthrough infections in vaccinated people, and that older adults face higher risks for the long-term effects. The study showed that about one-third who had breakthrough infections exhibited signs of long COVID.

A separate report from the Centers for Disease Control and Prevention found that up to a year after an initial coronavirus infection, 1 in 4 adults aged 65 and older had at least one potential long COVID-19 health problem, compared with 1 in 5 younger adults.

Most people who have COVID-19 fully recover. But the WHO in Europe report on Tuesday estimated that 10% to 20% develop mid- and long-term symptoms such as fatigue, breathlessness and cognitive dysfunction.

Risk of Myocarditis After Sequential Doses of COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex

Authors: Martina Patone, PhD; Xue W. Mei, PhD; Lahiru Handunnetthi, PhD; Sharon Dixon, MD; Francesco Zaccardi, PhD; Manu Shankar-Hari, PhD; Peter Watkinson, MD; Kamlesh Khunti, PhD; Anthony Harnden, PhD; Carol A.C. Coupland, PhD; Keith M. Channon, MD; Nicholas L. Mills, PhD; Aziz Sheikh, MD; Julia Hippisley-Cox, MD August 28, 2022 ORIGINAL RESEARCHARTICLECirculation. 2022;146:00–00. DOI: 10.1161/CIRCULATIONAHA.122.059970 xxx xxx, 20223Patone et al

BACKGROUND: Myocarditis is more common after severe acute respiratory syndrome coronavirus 2 infection than after COVID-19 vaccination, but the risks in younger people and after sequential vaccine doses are less certain.

METHODS:

A self-controlled case series study of people ages 13 years or older vaccinated for COVID-19 in England between December 1, 2020, and December 15, 2021, evaluated the association between vaccination and myocarditis, stratified by age and sex. The incidence rate ratio and excess number of hospital admissions or deaths from myocarditis per million people were estimated for the 1 to 28 days after sequential doses of adenovirus (ChAdOx1) or mRNA-based (BNT162b2, mRNA-1273) vaccines, or after a positive SARS-CoV-2 test.RESULTS: In 42842345 people receiving at least 1 dose of vaccine, 21242629 received 3 doses, and 5934153 had SARS-CoV-2 infection before or after vaccination. Myocarditis occurred in 2861 (0.007%) people, with 617 events 1 to 28 days after vaccination. Risk of myocarditis was increased in the 1 to 28 days after a first dose of ChAdOx1 (incidence rate ratio, 1.33 [95% CI, 1.09–1.62]) and a first, second, and booster dose of BNT162b2 (1.52 [95% CI, 1.24–1.85]; 1.57 [95% CI, 1.28–1.92], and 1.72 [95% CI, 1.33–2.22], respectively) but was lower than the risks after a positive SARS-CoV-2 test before or after vaccination (11.14 [95% CI, 8.64–14.36] and 5.97 [95% CI, 4.54–7.87], respectively). The risk of myocarditis was higher 1 to 28 days after a second dose of mRNA-1273 (11.76 [95% CI, 7.25–19.08]) and persisted after a booster dose (2.64 [95% CI, 1.25–5.58]). Associations were stronger in men younger than 40 years for all vaccines. In men younger than 40 years old, the number of excess myocarditis events per million people was higher after a second dose of mRNA-1273 than after a positive SARS-CoV-2 test (97 [95% CI, 91–99] versus 16 [95% CI, 12–18]). In women younger than 40 years, the number of excess events per million was similar after a second dose of mRNA-1273 and a positive test (7 [95% CI, 1–9] versus 8 [95% CI, 6–8]).CONCLUSIONS: Overall, the risk of myocarditis is greater after SARS-CoV-2 infection than after COVID-19 vaccination and remains modest after sequential doses including a booster dose of BNT162b2 mRNA vaccine. However, the risk of myocarditis after vaccination is higher in younger men, particularly after a second dose of the mRNA-1273 vaccine.

We recently reported an association between the first and second dose of COVID-19 vaccination and myocarditis, which generated considerable scientific, policy, and public interest.1 It added to evidence emerging from multiple countries that has linked exposure to BNT162b2 mRNA vaccine with acute myocarditis.2–8In the largest and most comprehensive analysis to date, we reported an increased risk of hospital admission or death from myocarditis after both adenoviral (ChAdOx1) vaccines and mRNA (BNT162b2 or mRNA-1273) vac-cines. It is important that we also demonstrated across the entire vaccinated population in England that the risk of myocarditis after vaccination was small compared with the risk after a positive SARS-CoV-2 test.1However, myocarditis is more common in younger people younger than the age of 40 years and in men in particular.9,10 Additional analyses stratified by age and sex are important because vaccine campaigns are rap-idly being extended to include children and young adults. Furthermore, given the consistent observation that the risk of myocarditis is higher after the second dose of vac-cine compared with the first dose,1,11 there is an urgent need to evaluate the risk associated with a booster dose because booster programs are accelerated internation-ally to combat the omicron variant.12Because new data were available, we have extended our analysis to include people ages 13 years or older and those receiving a booster dose to further evaluate the association between COVID-19 vaccination or infection and risk of myocarditis, stratified by age and sex.

METHODS

Transparency and Openness Promotion This analysis makes use of multiple routinely collected health care data sources that were linked, deidentified, and held in a trusted research environment that was accessible to approved individuals who had undertaken the necessary governance training. Because of the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality proto-cols may be sent to National Health Service Digital and the United Kingdom Health Security Agency. Simulated data and the analysis code are available publicly at https://github.com/qresearchcode/COVID-19-vaccine-safety. National Health Service Research Ethics Committee approval was obtained from the East Midlands–Derby Research Ethics Committee (Reference 18/EM/0400]. Anonymized data are analyzed, so there is no requirement for written informed consent. Data Sources We used the National Immunisation Database of COVID-19 vaccination to identify vaccine exposure. This includes vaccine type, date, and doses for all people vaccinated in England. We linked National Immunisation Database vaccination data, at the individual level, to national data for mortality (Office for National Statistics), hospital admissions (Hospital Episode Statistics and Secondary User’s service data), and SARS-CoV-2 infection data (Second Generation Surveillance System).Study Design and Oversight We undertook a self-controlled case series design, originally developed to examine vaccine safety.12 The analyses are conditional on each case, so any fixed characteristics during the study period, such as sex, ethnicity, or chronic conditions, are inherently controlled for. Age was considered as a fixed variable because the study period was short. Any time-varying factors, such as seasonal variation, need to be adjusted for in the analy-ses. Hospital admissions were likely to be influenced by the pressure on the health systems because of COVID-19, which was not uniform during the pandemic study period. To allow for these underlying seasonal effects, we split the study observation period into weeks and adjusted for week as a factor vari-able in the statistical models.Study Period and Population We included all people ages 13 years or older who had received at least 1 dose of ChAdOx1 (AstraZeneca), BNT162b2 (Pfizer), and mRNA-1273 (Moderna) vaccine and were admit-ted to hospital or died from myocarditis between December 1, 2020, and December 15, 2021.OutcomeThe primary outcome of interest was the first hospital admis-sion caused by the myocarditis, or death recorded on the death Clinical PerspectiveWhat Is New?•We performed an evaluation of the risk of myocar-ditis after COVID-19 vaccine in >42 million vacci-nated people 13 years or older, including 21 million people receiving a booster dose, stratified by age and sex.•We extend our previous findings demonstrating that the risk of hospitalization or death from myo-carditis after SARS-CoV-2 infection is substantially higher than the risk associated with a first dose of ChAdOx1, and a first, second, or booster dose of BNT162b2 mRNA vaccine.

•Associations were stronger in younger men <40 years for all vaccines and after a second dose of mRNA-1273 vaccine, where the risk of myocarditis was higher after vaccination than SARS-CoV-2 infection. What Are the Clinical Implications?•Our findings will inform recommendations on the type of vaccine offered to younger people and will help to shape public health policy on booster pro-grams enabling an informed discussion of the risk of vaccine associated myocarditis when considering the net benefit of vaccination.

Myocarditis After COVID-19 Vaccine and Infection certificate with the International Classification of Diseases, Tenth Revision code (Table S1) related to myocarditis within the study period (December 1, 2020, to December 15, 2022). We used the earliest date of hospitalization or date of death as the event date.ExposuresThe exposure variables were a first, second, or booster dose of the ChAdOx1, BNT162b2, or mRNA-1273 vaccines, and SARS-CoV-2 infection, defined as the first SARS-CoV-2–positive test in the study period. All exposures were included in the same model. We defined the exposure risk intervals as the following prespecified time periods: 0, 1 to 7, 8 to 14, 15 to 21, and 22 to 28 days after each exposure date, under the assumption that the adverse events under consideration are unlikely to be related to exposure later than 28 days after expo-sure. A pre-risk interval of 1 to 28 days before each exposure date was included to account for potential bias that might arise if the occurrence of the outcome temporarily influenced the likelihood of exposure. The baseline period for the vaccination exposures was the remaining time from December 1, 2020, until 29 days before the first dose date and from 29 days after the first or second dose until 29 days before the second or booster dose (if applicable), and from 29 days after the booster dose until December 15, 2021, or the censored date if earlier. We assumed that the risks might be different after each vac-cine dose, and hence we allowed for a dose effect, by defining a separate risk interval after each dose: 0, 1 to 7, 8 to 14, 15 to 21, or 22 to 28 days after the first, second, or booster dose. To avoid overlapping risk periods, we assumed that later expo-sures take precedence over earlier ones, except for the 1- to 28-day pre-risk period for the second or booster dose. A posi-tive SARS-CoV-2 test was considered as a separate exposure in the models, which allowed overlapping risk windows with vaccination exposure.Statistical AnalysisWe described the characteristics of the whole study population by vaccine dose and type, and in those with myocarditis strati-fied by age and sex.In vaccinated people with myocarditis, the self-controlled case series models were fitted using a conditional Poisson regression model with an offset for the length of the expo-sure risk period. Incidence rate ratios (IRR), the relative rate of hospital admissions or deaths caused by myocarditis in expo-sure risk periods relative to baseline periods, and their 95% CIs were estimated by the self-controlled case series model adjusted for calendar time. We investigated if associations between vaccine exposure and the myocarditis outcome were sex- or age-dependent by performing subgroup analyses strati-fied by sex and age (men age <40 years, men age≥ 40 years, women age <40 years, and women age ≥40 years). We also conducted analyses stratified by vaccination history, restricted to those who had the same type of vaccine in the first and sec-ond dose and by lag in days between the first and second dose (≤65, 66 to 79, and ≥80 days).We conducted sensitivity analyses to assess the robustness of results to assumptions, such as that the occurrence of an outcome event did not influence the probability of subsequent exposures by (1) excluding those who died from the outcome and (2) restricting analysis to the period after the first dose and (3) after the second dose, without censoring at death; and to assess potential reporting delays in the data by (4) restricting the study to the period up to December 1, 2021.We also performed sensitivity analyses (5) removing patients who had outcomes in the 28 days after a first dose, but before a second dose, and (6) removing patients who had outcomes in the 28 days after a second dose, but before a booster dose, because they are less likely to have a second dose if they experienced an adverse event after the first. Last, we conducted a sensitivity analysis (7) restricted to those with-out a positive SARS-CoV-2 test during the observation period.We used Stata (version 17) for these analyses.

RESULTS

Between December 1, 2020, and December 15, 2021, there were 42 842 345 people vaccinated with at least 1 dose of ChAdOx1 (n=20 650 685), BNT162b2 (n=20 979 704), or mRNA-1273 (n=1 211 956) (Table 1). Of these, 39 118 282 received a sec-ond dose of ChAdOx1 (n=20 080 976), BNT162b2 (n=17 950 086), or mRNA-1273 (n=1 087 220), and 21 242 629 people received a third vaccine dose: ChAdOx1 (n=53 606), BNT162b2 (n=17 517 692), and mRNA-1273 (n=3 671 331).Among people receiving at least 1 vaccine dose, 5 934 153 (13.9%) tested positive for SARS-CoV-2, including 2 958 026 (49.8%) before their first vac-cination.Of the 42 842 345 people in the study population, 2861 (0.007%) were hospitalized or died from myocar-ditis during the study period; 345 (<0.001%) patients died within 28 days from a hospital admission with myo-carditis or with myocarditis as cause of death recorded in the death certificate. A total of 617 (0.001%) of these events occurred 1 to 28 days after any dose of vaccine (Table 2). Of the 524 patients admitted to the hospital with myocarditis in the 1 to 28 days after any first or sec-ond vaccine dose, 151 (28.8%) had received a booster dose: 34.4% (79/230) of those who had ChAdOx1 in the first or second dose and 29.7% (72/243) of those who had BNT162b2 in the first or second dose (Table 2). Of the 5 934 153 patients with a SARS-CoV-2 infection, 195 (0.003%) were hospitalized or died with myocarditis in the 1 to 28 days after the positive test; 114 (58.5%) of these events occurred before vaccination (Table S2).Vaccine-Associated MyocarditisIn the study period, we observed 140 and 90 patients who were admitted to the hospital or died of myocardi-tis after a first and second dose of ChAdOx1 vaccine, respectively. Of these, 40 (28.6%) and 11 (12.2%)‚ re-spectively, died with myocarditis or within 28 days from hospital admission. Similarly, there were 124, 119, and 85 patients who were admitted to the hospital or died

After COVID-19 Vaccine and Infection Table 1.Baseline Demographic Characteristics of People Receiving ChAdOx1, BNT162b2, or mRNA-1273 Vaccines or Testing Positive for SARS-CoV-2 Virus (in Those Vaccinated) in England Between December 1, 2020, and December 15, 2021 ChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273SARS- CoV-2 positive*One dose (n=42 842 345)Two doses (n=39 118 282)Booster doses (n=21 242 629)(n= 5 934 153)% (n)% (n) % (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)Total no. of people20 650 68520 979 7041 211 95620 080 97617 950 0861 087 22053 60617 517 6923 671 3315 934 153SexWomen49.5(10 215 079)49.1(10 295 561)38.7(469 114)49.5(9 945 533)50.1(9 000 748)39.5(429 705)61.2(32 792)54.2(9 489 364)48.4(1 778 317)52.3(3 103 168)Men43.3(8 933 572)40.4(8 476 032)42.0(508 416)43.3(8 697 560)39.8(7 148 539)42.1(457 629)34.8(18 674)41.4(7 244 858)44.2(1 623 230)40.5(2 405 336)Not recorded7. 3(1 502 034)10.5(2 208 110)19.3(234 426)7. 2(1 437 882)10.0(1 800 799)18.4(199 886)4.0(2140)4.5(783 471)7. 3(269 784)7. 2(425 649)Age, yMean age (SD)54.9 (14.8)43.0 (22.4)32.3 (9.7)55.0 (14.7)46.5 (21.7)32.7 (9.8)63.1 (17.0)61.8 (15.9)53.7 (12.4)41.4 (18.0)13–17 <0.1(10 214)10.6(2 219 006)0.1(838)<0.1(9105)2.6(468 569)0.1 (623)0.1 (31)0.1(23 826)0.1(2961)8.3(493 728)18–29 5.2(1 081 177)24.4(5 127 151)43.1(521 916)5.1(1 022 847)24.9(4 472 159)41.3(449 436)3.7(1964)3.6(624 465)4.0(146 688)21.6(1 279 933)30–39 7. 9(1 634 841)21.5(4 517 781)35.6(431 515)7. 8(1 556 785)23.1(4 146 117)36.1(392 581)5.8(3102)6.1(1 067 916)8.6(315 936)18.3(1 084 406)40–49 22.1(4 564 393)8.5(1 784 664)18.4(222 849)22.0(4 414 864)9.3(1 665 983)19.5(212 187)11.5 (6171)11.1(1 949 092)19.2(706 004)19.4(1 152 196)50–59 2 7. 5(5 673 878)8.0(1 684 013)1.8(22 320)2 7. 6(5 549 187)9.1(1 636 430)1.9(20 463)19.9(10 644)20.8(3 635 337)35.3(1 295 168)16.7(989 499)60–69 19.8(4 083 887)8.5(1 777 370)0.7(8330)20.0(4 013 588)9.8(1 753 552)0.7(8145)19.3(10 371)22.5(3 938 515)24.8(910 586)8.5(505 389)70–79 13.4(2 763 041)9.4(1 979 901)0.3(3241)13.5(2 717 638)10.9(1 959 318)0.3(2789)22.6(12 090)23.1(4 049 042)6.5(237 287)4.2(248 415)80–89 3.1(630 457)7. 7(1 621 129)0.1(842)3.0(604 788)8.9(1 591 216)0.1(837)12.5 (6710)10.8(1 888 973)1.3(47 228)2.2(132 459)90+ 1.0(208 753)1.3(268 563)<0.1(103)1.0(192 162)1.4(256 698)<0.1(158)4.7(2523)1.9(340 498)0.3(9473)0.8(48 117)Not recorded<0.1 (44)<0.1 (125)<0.1 (2)<0.1 (11)<0.1 (44)<0.0 (1)0<0.1 (29)0<0.1 (11)Women age groups, y <40 14.8(1 510 119)51.7(5 325 910)7 7. 9(365 443)14.4(1 437 517)45.9(4 131 123)76.4(328 311)9.2(3020)10.9(1 032 366)14.2(252 054)4 7. 6(1 477 776)≥40 85.2(8 704 960)48.3(4 969 651)22.1(103 671)85.5(8 508 009)54.1(4 869 604)23.6(101 394)90.8(29 772)89.1(8 456 981)85.8(1 526 263)52.4(1 625 385) Not recorded<0.1 (16)<0.1 (59)0<0.1 (7)<0.1 (21)0000<0.1 (7)Men age groups, y<40 11.2(998 025)56.2(4 762 038)78.2(397 521)10.9(949 865)49.4(3 533 806)76.7(35 074)8.8(1650)7. 5(541 432)10.5(171 132)46.2(1 110 723)≥40 88.8(7 935 546)43.8(3 712 994)21.8(110 895)89.1(7 747 692)50.8(3 614 721)23.3(106 834)91.2(17 024)92.5(6 703 416)89.5(1 452 098)53.8(1 294 609) Not recorded<0.1 (21)<0.1 (42)<0.1 (2)<0.1 (3)<0.1 (12)<0.1 (1)000<0.1 (4)EthnicityWhite6 7. 9(14 012 353)63.6(13 344 722)53.0(642 168)68.0(13 656 716)64.2(11 530 182)54.0(587 123)74.3(39 827)73.6(12 891 303)69.6(2 553 453)66.9(3 971 366)Indian2.0(406 066)2.2(469 302)1.1(13 385)2.0(395 171)2.2(394 274)1.1(11 902)2.1(1141)2.0(354 433)1.4(51 193)2.6(153 403)Pakistani1.2(253 523)1.6(335 100)1.0(12 213)1.2(239 511)1.4(249 446)0.9(9732)0.9(477)0.6(109 038)0.5(19 186)2.0(118 522)Bangladeshi0.5 (96 392)0.5 (111 314)0.5 (5966)0.5 (92 835)0.5 (83 524)0.5 (4902)0.4 (217)0.2 (43 360)0.3 (10 775)0.7 (40 093)Other Asian0.9 (177 629)1.1 (238 245)1.0 (11 859)0.9 (171 863)1.1 (191 996)1.0 (10 365)0.8 (436)0.7 (128 434)0.6 (23 284)1.1 (67 392)Caribbean0.6 (117 507)0.5 (96 994)0.4 (4265)0.6 (110 470)0.4 (80 146)0.3 (3296)1.3 (706)0.4 (77 095)0.3 (11 820)0.5 (28 327)(Continued)ORIGINAL RESEARCHARTICLECirculation. 2022;146:00–00. DOI: 10.1161/CIRCULATIONAHA.122.059970xxx xxx, 20225Patone et alMyocarditis After COVID-19 Vaccine and Infectionof myocarditis after a first, second, and third dose of BNT162b2 vaccine, respectively. Of these, 22 (17.7%), 14 (11.8%), and 13 (15.3%) patients died with myo-carditis or within 28 days from hospital admission. Last, there were 11, 40, and 8 patients who were admitted to the hospital for myocarditis after, respectively, a first, second, and third dose of mRNA-1273 vaccine. None of these patients died with myocarditis or within 28 days from hospital admission with myocarditis (Table2).In the overall population, we confirmed our previous findings that the risk of hospitalization or death from myocarditis was higher after SARS-CoV-2 infection than vaccination and was greater after the first 2 doses of mRNA vaccine than after adenovirus vaccine (Table3; Table S3; Figure). There was an increased risk of myo-carditis at 1 to 28 days after the first dose of ChAdOx1 (IRR, 1.33 [95% CI, 1.09–1.62]) and BNT162b2 (IRR, 1.52 [95% CI, 1.24–1.85]).There was an increased risk of myocarditis at 1 to 28 days after a second dose of BNT162b2 (IRR, 1.57 [95% CI, 1.28–1.92]) and mRNA-1273 (IRR, 11.76 [95% CI, 7.25–19.08]); and after a booster dose of BNT162b2 (IRR, 1.72 [95% CI, 1.33–2.22]) and mRNA-1273 (IRR, 2.64 [95% CI, 1.25–5.58]).Vaccine-Associated Myocarditis in MenOf the 17918020 men vaccinated in England in the study period, 6158584 (34.4%) were younger than 40 years, and 11759 436 (65.6%) were 40 years or older (Table1). Analysis restricted to younger men age younger than 40 years showed an increased risk of myocarditis Black African0.9 (185 852)1.0 (218 158)1.0 (12 121)0.9 (176 094)0.9 (164 260)0.9 (9258)1.1 (588)0.6 (98 216)0.5 (16 997)1.0 (57 157)Chinese0.3 (63 180)0.3 (70 206)0.4 (5176)0.3 (61 902)0.3 (58 438)0.5 (4902)0.3 (149)0.3 (47 390)0.3 (11 899)0.2 (11 732)Other1.8 (378 719)2.4 (502 815)2.6 (31 811)1.8 (363 257)2.2 (388 674)2.5 (27 107)1.7 (902)1.4 (245 301)1.4 (50 501)2.3 (138 024)Not recorded24.0(4 959 464)26.7(5 592 847)39.0(472 992)24.0(4 813 156)26.8(4 809 146)38.5(418 633)1 7. 1(9163)20.1(3 523 123)25.1(922 223)22.7(1 348 137)History of myocarditis Previous myo-carditis<0.1 (1837)<0.1 (1632)<0.1 (69)<0.1 (1778)<0.1 (1511)<0.1 (56)<0.1 (18)<0.1 (1885)<0.1 (272)<0.1 (687)COVID-19 status†No COVID-1986.3(17 815 732)86.0(18 052 842)85.8(1 039 833)86.3(17 334 448)8 7. 3(15 674 125)86.2(937 147)88.4(47 367)90.5(15 846 583)88.0(3 230 055)…COVID-19 previous vac-cination5.9(1 227 131)7. 8(1 629 334)8.4(101 484)5.9(1 183 882)6.5(1 170 434)7. 8(85 166)6.3(3398)4.7 (815 805)5.3(194 056)49.8(2 958 026)COVID-19 after first dose0.7(143 526)2.8(594 914)3.2(38 200)0.5(99 981)2.2(401 516)3.0(32 222)0.9(456)0.6 (108 097)0.4(15 316)13.1(776 725)COVID-19 after second dose6.7(1 383 490)3.0(638 578)2.7(32 215)6.9(1 381 868)3.6(639 976)3.0(32 452)1.8(969)3.5 (621 836)5.8(213 627)34.6(2 054 331)COVID-19 after booster dose0.4(80 807)0.3(64 035)<0.1(224)0.4(80 796)0.4(64 035)<0.1(233)2.6(1416)0.7(125 372)0.5(18 277)2.4(145 071)No. of dosesOne dose only2.3(467 328)14.8(3 114 034)11.9(144 026)………………12.8(761 515)Two doses only36.0(7 430 747)45.1(9 464 269)80.8(979 495)36.5(7 328 422)53.2(9 550 989)91.7(996 599)………51.5(3 054 000)Two doses + booster61.8(12 752 610)40.0(8 401 400)7. 3(88 435)63.5(12 752 553)46.8(8 399 097)8.3 (90 621)100.0(53 606)100.0(17 517 692)100.0(3 671 331)35.7(2 118 638)Type of vaccinesTwo doses of ChAdOx19 7. 0(20 040 458)……99.8(20 040 458)……83.0(44 472)55.8(9 780 549)79.1(2 903 545)46.2(2 741 419)Two doses of BNT162b2…84.9(17 815 058)……99.2(17 815 058)…5.1(2760)43.7(7 653 274)19.6(720 535)38.0(2 256 069)Two doses of mRNA-1273……8 7. 5(1 060 277)……9 7. 5(1 060 277)<0.1(8)0.3(45 269)1.2(42 783)2.5(146 385)*Among vaccinated individuals. †Determined by a SARS-CoV-2 test. Table 1.ContinuedChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273ChAdOx1BNT162b2mRNA-1273SARS- CoV-2 positive*One dose (n=42 842 345)Two doses (n=39 118 282)Booster doses (n=21 242 629)(n= 5 934 153)% (n)% (n) % (n)% (n)% (n)% (n)% (n)% (n)% (n)% (n)

After COVID-19 Vaccine and Infectionafter a first dose of BNT162b2 (IRR, 1.85 [95% CI, 1.30–2.62]) and mRNA-1273 (IRR, 3.06 [95% CI, 1.33–7.03]); and a second dose of ChAdOx1 (IRR, 2.73 [95% CI, 1.62–4.60]), BNT162b2 (IRR, 3.08 [95% CI, 2.24–4.24]), and mRNA-1273 (IRR, 16.83 [95% CI, 9.11–31.11]). The risk of myocarditis for older men 40 years or more was associated with a booster dose of both mRNA vaccines, BNT162b2 (IRR, 2.15 [95% CI, 1.46–3.17]) and mRNA-1273 (IRR, 3.76 [95% CI, 1.41–10.02]) (Table 3).Vaccine-Associated Myocarditis in WomenOf the 20 979 754 women vaccinated in England in the study period, 7 201 472 (34.3%) were younger than 40 Table 2. Demographic and Clinical Characteristics of Patients Who Were Admitted to the Hospital for Myocarditis in the 1 to 28 Days After a COVID-19 Vaccine First Dose, Second Dose, and Booster Dose or SARS-CoV-2 Infection Among the Vaccinated Population in England from December 1, 2020, Until December 15, 2021VariableBaselineRisk set (1–28 days after exposure)ChAdOx1BNT162b2mRNA-1273 First dose Second dose Booster dose First dose Second dose Booster dose First dose Second dose Booster dose Total no. of people22441409001241198511408Sex Women40.4 (907)40.7 (57)26.7 (24)…41.1 (51)28.6 (34)45.9 (39)*** Men59.4 (1333)59.3 (83)73.3 (66)…58.1 (72)70.6 (84)54.1 (46)>5>5>5 Not recorded0.2 (4)00…0.8 (1)0.8 (1)0000Age Mean age (SD)53.8 (19.7)57.5 (17.5)54.2 (18.0)…48.7 (24.3)45.0 (24.8)67.2 (15.8)27.0 (9.5)24.9 (6.3)61.8 (14.8) <40 y26.3 (590)14.3 (20)25.6 (23)…46.8 (58)58.8 (70)7.1 (6)>5>5≥40 y73.7 (1654)85.7 (120)74.4 (67)…53.2 (66)41.2 (49)92.9 (79)>5Deaths with myocarditis or within 28 days of hospital admission with myocarditis No. of deaths10.9 (245)28.6 (40)12.2 (11)…17.7 (22)11.8 (14)15.3 (13)……… Mean age of death (SD), y68.7 (14.3)62.1 (17.4)65.2 (10.4)…67.8 (20.4)69.2 (21.6)78 (8.7)……… No. of deaths Women38.2 (92)35.0 (14)…57.1 (12)46.1 (6)……… Men61.8 (149)65.0 (26)>5…42.9 (9)53.9 (7)> 5……… Not recorded0.2 (4)000.8 (1)0.8 (1)0COVID-19 status (positive SARS-CoV-2 test) No COVID-19…72.9 (102)82.2 (74)…71.8 (89)88.2 (105)81.2 (69)54.5 (6)90.0 (36)100.0 (8) COVID-19 previous vac-cination…12.9 (18)11.1 (10)…10.5 (13)8.2 (7)… COVID-19 after first dose…11.4 (16)…15.3 (19)… COVID-19 after second dose…5.6 (5)…5.0 (6)… COVID-19 after booster dose…7.1 (6)…No. of doses One …45.7 (64)…53.2 (66)90.9 (10)* Two …23.6 (33)60.0 (54)…16.9 (21)70.6 (84)97.5 (39)* Two + booster…30.7 (43)40.0 (36)…29.8 (37)29.4 (35)100.0 (85)100.0 (8)Type of first 2 doses received ChAdOx1…50.7 (71)98.9 (89)………49.4 (42)……62.5 (5) BNT162b2…………43.5 (54)99.2 (118)50.6 (43)……* mRNA-1273………………100.0 (40)Lag between first and second doses (days)≤655.7 (8)16.7 (15)…8.1 (10)47.9 (57)24.7 (21)55.0 (22)* 6 6–7931.4 (44)55.6 (50)…25.8 (32)32.8 (39)54.1 (46)…22.5 (9)*≥8017.1 (24)27.8 (25)…12.9 (16)19.3 (23)21.2 (18)…22.5 (9)Cells with counts <5 are suppressed. ORIGINAL RESEARCH

After COVID-19 Vaccine and Infection Table 3. Incidence Rate Ratios (IRR [95% CI]) for Main Analysis and by Age Group (Age 40 Years or Older, Younger Than 40 Years) and Sex (Female and Male) for Myocarditis in Predefined Risk Periods Immediately Before and After Exposure to Vacci-nation and Before and After a Positive SARS-CoV-2 Test Result, Adjusted for Calendar Time From December 1, 2020, to December 15, 2021 (if 1 or no events, IRR has not been estimated and reported as n/a).Time periodChAdOx1 nCoV-19 vaccineBNT162b2 mRNA vaccinemRNA-1273 vaccine Positive SARS-CoV-2 test (before vaccine)Positive SARS-CoV-2 test (vaccinated) Events I RR (95% CI) Events I RR (95% CI)Events IRR (95% CI)Events IRR (95% CI)Events IRR (95% CI)Main analysis 1–28 days: first dose/positive test before any vaccination1401.33 (1.09–1.62)1241.52 (1.24–1.85)111.85 (0.93–3.66)11411.14 (8.64–14.36)815.97 (4.54–7.87) 1–28 days: second dose900.93 (0.74–1.17)1191.57 (1.28–1.92)4011.76 (7.25–19.08) 1–28 days: booster dose*n/a851.72 (1.33–2.22)82.64 (1.25–5.58)Women 1–28 days: first dose/positive test before any vaccination571.32 (0.97–1.81)511.59 (1.16–2.20)*1.07 (0.23–4.90)4714.23 (9.34–21.68)326.87 (4.38–10.78) 1–28 days: second dose240.54 (0.35–0.83)341.04 (0.72–1.50)*3.95 (1.20–13.04) 1–28 days: booster dose*n/a391.55 (1.06–2.27)*1.51 (0.35–6.47)Men 1–28 days: first dose/positive test before any vaccination831.33 (1.03–1.72)721.47 (1.14–1.90)92.35 (1.09–5.08)679.71 (7.03–13.40)495.55 (3.91–7.88) 1–28 days: second dose661.26 (0.96–1.65)841.93 (1.51–2.45)3614.98 (8.61–26.07) 1–28 days: booster dose*n/a461.89 (1.34–2.67)63.57 (1.48–8.64)Age <40 y 1–28 days: first dose/positive test before any vaccination201.31 (0.79–2.16)581.79 (1.33–2.41)102.76 (1.32–5.75)205.25 (3.11–8.86)81.18 (0.56–2.48) 1–28 days: second dose231.69 (1.06–2.71)702.59 (1.96–3.44)3913.97 (8.07–24.19) 1–28 days: booster dose*n/a61.53 (0.64–3.64)*n/aAge ≥40 y 1–28 days: first dose/positive test before any vaccination1201.21 (0.97–1.51)661.28 (0.97–1.71)*n/a9414.87 (10.98–20.14)7310.52 (7.61–14.54) 1–28 days: second dose670.72 (0.55–0.93)490.85 (0.62–1.16)*n/a 1–28 days: booster dose*n/a791.96 (1.48–2.59)72.97 (1.32–6.69)Women age <40 y 1–28 days: first dose/positive test before any vaccination71.20 (0.51–2.84)141.65 (0.91–2.97)*2.68 (0.54–13.25)79.80 (3.70–25.97)63.98 (1.52–10.42) 1–28 days: second dose/posi-tive test after any vaccination*0.32 (0.08–1.37)91.16 (0.57–2.34)*4.75 (1.11–20.40) 1–28 days: booster dose*n/a*0.83 (0.19–3.64)*n/aMen age <40 y 1–28 days: first dose/positive test before any vaccination131.34 (0.72–2.48)431.85 (1.30–2.62)83.06 (1.33–7.03)134.35 (2.31–8.21)*0.39 (0.09–1.60) 1–28 days: second dose212.73 (1.62–4.60)603.08 (2.24–4.24)3616.83 (9.11–31.11) 1–28 days: booster dose*n/a*2.28 (0.77–6.80)*n/a(Continued )ORIGINAL

After COVID-19 Vaccine and Infection years, and 13 778 282 (65.7%) were 40 years or older (Table 1). Analysis restricted to women younger than 40 years showed an increased risk of myocarditis after a second dose of mRNA-1273 (IRR, 4.75 [95% CI, 1.11–20.40]). For women 40 years or older, there was an in-creased risk of myocarditis associated with a first (IRR, 1.57 [95% CI, 1.05–2.33]) and third (IRR, 1.76 [95% CI, 1.17–2.65]) dose of BNT162b2 vaccine. It is important that for all subgroups, the higher risk of myocarditis was found in the 1 to 7 days or 8 to 14 days after vaccination (Table S4).Vaccine-Associated Myocarditis by Vaccination History Analyses restricted to people who had the same type of vaccine for the first and second doses (Table S5) showed that for patients having a first and second dose of ChAdOx1, there was an increased risk of myocarditis associated with a booster dose of BNT162b2 (IRR, 1.78 [95% CI, 1.22–2.60]) and mRNA-1273 (IRR, 2.97 [95% CI, 1.13–7.82]). For patients who had a first and second dose of BNT162b2 vaccine, there was an increased risk of myocarditis after the second dose of BNT162b2 (IRR, 1.53 [95% CI, 1.24–1.88]). Last, for patients who had a first and second dose of mRNA-1273 vaccine, there was an increased risk of myocarditis after a second dose of mRNA-1273 (IRR, 8.63 [95% CI, 3.98–18.75]).The risk after a second dose of BNT162b2 was higher for people who received the first 2 doses within 65 days of each other (IRR, 2.16 [95% CI, 1.60–2.91]) compared with people who received the first 2 doses with a longer lag: between 66 and 79 days (IRR, 1.01 [95% CI, 0.71–1.44]) and 80 days or more (IRR, 1.40 [95% CI, 0.88–2.21]). The risk after a second dose of mRNA-1273 was higher when the lag was of 80 or more days (IRR, 22.80 [95% CI, 7.48–69.48]) compared with when the lag was 65 days or less (IRR, 7.41 [95% CI, 3.98–13.77) (Table S6).SARS-CoV-2 Infection–Associated Myocarditis There was an increased risk of myocarditis in the 1 to 28 days after a SARS-CoV-2–positive test, which was higher if infection occurred before vaccination (IRR, 11.14 [95% CI, 8.64–14.36]) than in vaccinated individuals (IRR, 5.97 [95% CI, 4.54–7.87]). The risk of myocarditis associated with a SARS-CoV-2–positive test before vaccination was higher in people 40 years or older (IRR, 14.87 [95% CI, 10.98–20.14]) than in-dividuals younger than 40 years (IRR, 5.25 [95% CI, 3.11–8.86]), but no significant difference was observed between risks in women (IRR, 14.23 [95% CI, 9.34–21.68]) and men (IRR, 9.71 [95% CI, 7.03–13.40), al-though the point estimate for women was higher than the equivalent for men. A similar pattern of risk of myo-carditis was associated with a SARS-CoV-2–positive test occurring in vaccinated individuals; however, in this case, the increased risk was substantially lower and in particular was not observed for individuals younger than 40 years (IRR, 1.18 [95% CI, 0.56–2.48]) (Table 3).Absolute and Excess Risks After the first dose of the ChAdOx1 and BNT162b2 vaccines, an additional 2 (95% CI, 1–3) and 2 (95% CI, 1–3) myocarditis events per million people vaccinated would be anticipated, respectively. After the second dose of BNT162b2 and mRNA-1273, an additional 2 (95% CI, 2–3) and 34 (95% CI, 32–35) myocar-ditis events per million people would be anticipated, Women age ≥40 y 1–28 days: first dose/positive test before any vaccination501.30 (0.92–1.84)371.57 (1.05–2.33)*n/a4017.29 (10.70–27.96)268.65 (5.13–14.59) 1–28 days: second dose220.55 (0.35–0.86)250.98 (0.63–1.52)*n/a 1–28 days: booster dose*n/a371.76 (1.17–2.65)*2.00 (0.46–8.72)Men age ≥40 y 1–28 days: 1st dose/positive test before any vaccination701.16 (0.87–1.54)291.05 (0.69–1.59)*n/a5413.40 (9.04–19.88)4711.77 (7.77–17.85) 1–28 days: second dose450.85 (0.61–1.19)240.77 (0.49–1.18)*n/a 1–28 days: booster dose*n/a422.15 (1.46–3.17)53.76 (1.41–10.02)Day 0 of each exposure has been removed because of small numbers.*Cells with counts <5 are suppressed. Table 3. Continued Time periodChAdOx1 nCoV-19 vaccineBNT162b2 mRNA vaccinemRNA-1273 vaccine Positive SARS-CoV-2 test (before vaccine)Positive SARS-CoV-2 test (vaccinated) Events IRR (95% CI)Events IRR (95% CI)Events IRR (95% CI)Events IRR (95% CI) EventsIRR (95%

After COVID-19 Vaccine and Infectionres pectively. After a booster dose of BNT162b2 and mRNA-1273, an additional 2 (95% CI, 1–3) and 1 (95% CI, 0–2) myocarditis events per million people would be anticipated, respectively. These estimates compare with an additional 35 (95% CI, 34–36) and 23 (95% CI, 21–24) myocarditis events per million people in the 1 to 28 days after a SARS-CoV-2–posi-tive test before vaccination and in vaccinated individu-als, respectively (Table 4; Figure).In men younger than 40 years, we estimate an additional 4 (95% CI, 2–6) and 14 (95% CI, 5–17) myocarditis events per million in the 1 to 28 days after a first dose of BNT162b2 and mRNA-1273, respectively; and an additional 14 (95% CI, 8–17), 11 (95% CI, 9–13) and 97 (95% CI, 91–99) myocarditis events after a second dose of ChAdOx1, BNT162b2, and mRNA-1273, respectively. These estimates compare with an additional 16 (95% CI, 12–18) myocarditis events per million men younger than 40 years in the 1 to 28 days after a SARS-CoV-2–positive test before vaccination (Table 4; Figure).Robustness of the ResultsOverall, our main findings were not sensitive to censoring because of death (Table S7, sensitivity analyses 1 through 3), and IRRs for the second dose of vaccination agreed with main results when we removed those who had the outcome after the first dose of any vaccine, but before the second dose (Table S7, sensitivity analysis 5). Similarly, IRRs for the booster dose of vaccination agreed with main results when we removed those who had the outcome af-ter the second dose of any vaccine, but before the booster dose (Table S7, sensitivity analysis 6). There was no bias caused by possibly not complete data near the end of the study period (Table S7, sensitivity analysis 4). Estimates for vaccines exposures agreed with the main analysis when restricted to patients who never tested positive to SARS-CoV-2 (Table S8, sensitivity analysis 7).

DISCUSSIONIn

a population of >42 million vaccinated individuals, we re-port several new findings that could influence public health Figure. Risk of myocarditis in the 1 to 28 days after COVID-19 vaccines or SARS-CoV-2.(Left) Incidence rate ratios with 95% CIs and (right) number of excess myocarditis events for million people with 95% CIs in the 1 to 28 day risk periods after the first, second, and booster doses of ChAdOx1, BNT162b2,and mRNA-1273 vaccine or a positive SARS-CoV-2 test in (top) a population of 42 842 345 vaccinated individuals and (bottom) younger men (age <40 years), older men (age ≥40 years), younger women (age <40 years), and older women (aged ≥40 years).ORIGINAL

First, the risk of myocar-ditis is substantially higher after SARS-CoV-2 infection in unvaccinated individuals than the increase in risk observed after a first dose of ChAdOx1nCoV-19 vaccine, and a first, second, or booster dose of BNT162b2 vaccine. Second, although the risk of myocarditis with SARS-CoV-2 infec-tion remains after vaccination, it was substantially reduced, suggesting vaccination provides some protection from the cardiovascular consequences of SARS-CoV-2. Third, in contrast with other vaccines, the risk of myocarditis ob-served 1 to 28 days after a second dose of mRNA-1273 vaccine was higher and similar to the risk after infection. Last, vaccine-associated myocarditis was largely restrict-ed to men younger than 40 years with 1 exception; both younger men and women were at increased risk of myo-carditis after a second dose of mRNA-1273.Vaccination against COVID-19 has both major public health and economic benefits. Although the net benefit of vaccination for the individual or on a population level should not be framed exclusively around the risks of myocarditis, quantifying this risk is important, particularly in young people who are less likely to have a severe ill-ness with SARS-CoV-2 infection. Multiple studies have identified an increase in myocarditis after exposure to the BNT162b2 mRNA vaccine.1–8,13 Some of our find-ings are confirmatory, but we also demonstrate that the risk of myocarditis is not restricted to this vaccine but is observed after vaccination with adenovirus and other mRNA vaccines and after a booster dose.It is important to place our findings into context. One of the strengths of our analysis is that we quantify the risk of myocarditis associated with both vaccination and SARS-CoV-2 infection in the same population. Myocarditis is an uncommon condition. The risk of vaccine-associated myocarditis is small, with up to an additional 2 events per million people in the 28-day period after exposure to all vaccine doses other than mRNA-1273. This is substan-tially lower than the 35 additional myocarditis events observed with SARS-CoV-2 infection before vaccination. Furthermore, vaccination reduced the risk of infection associated myocarditis by approximately half, suggest-ing that the prevention of infection associated myocarditis may be an additional longer-term benefit of vaccination.The risk of vaccine-associated myocarditis is con-sistently higher in younger men, particularly after a second dose of mRNA-1273, where the number of additional events during 28 days was estimated to be 97 per million people exposed. An important consid-eration for this group is that the risk of myocarditis after a second dose of mRNA-1273 was higher than the risk after infection. Indeed, in younger women, although the relative risks of myocarditis were lower than in younger men, the number of additional events per million after a second dose of mRNA-1273 was similar to the number after infection. These findings may justify some reconsideration of the selection of vaccine type, the timing of vaccine doses, and the net benefit of booster doses in young people, particularly in young men. However, there are some important caveats that need to be considered. First, the num-ber of people vaccinated with mRNA-1273 was small compared with those receiving other types of vaccine, Table 4. Measures of the Effect of Vaccinations and SARS-CoV-2 Infections Presented as Excess Events Per 1 Million Exposed Excess myocarditis events per 1 000 000 exposed (95% CI)Main analysis Age <40 yAge ≥40 y Women Men Age <40 yAge ≥40 y Women Men Women Men ChAdOx1 First dose2 (1–3)………2 (0–4)………… Second dose…4 (0–6)…………14 (8–17)…… Booster dose………………………BNT162b2 First dose2 (1–3)2 (1–3)…2 (1–3)3 (1–4)…4 (2–6)3 (0–4)… Second dose2 (1–3)5 (4–5)……6 (4–7)…11 (9–13)…… Booster dose2 (1–3)…2 (2–3)1 (0–2)3 (2–4)……2 (1–3)3 (2–4)mRNA-1273 First dose…7 (3–9)……10 (1–14)…14 (5–17)…… Second dose34 (32–35)43 (41–44)…7 (2–9)73 (70–76)7 (1–9)97 (91–99)…… Booster dose1 (0–2)…1 (1–2)…3 (1–3)………3 (1–3)SARS-CoV-2 Positive test (before vaccine)35 (34–36)10 (9–11)63 (62–64)28 (27–29)50 (48–51)8 (6–8)16 (12–18)51 (49–52)85 (82–87) Positive test (vaccinated)23 (21–24)…39 (38–40)17 (16–19)34 (30–36)7 (3–8)…26 (24–27)61 (58–63)Only significant increased risks were reported during the 1 to 28 days after exposure. When incidence rate ratios were not significant during the 1 to 28 days after vaccine, absolute measures are not given.

Second, the average age of those receiving this vaccine was younger at 32 years compared with other vaccines where recipients were in their mid-40s and 50s. The observed excess risk related to mRNA-1273 may in part be a result of the higher probability of myocarditis in this younger age group. Our findings are consistent with 2 recent studies from the United States and Denmark in which the risks of myocarditis after mRNA-1273 and BNT162b2 were compared.7,14 In the Vaccine Adverse Event Reporting System, 1991 cases of myocarditis were reported to August 31, 2021, with a median age of 21 years and 82% male.14 Although our findings are not directly com-parable because the Vaccine Adverse Event Reporting System dataset relies on clinician reporting, the risks of myocarditis were higher after a second dose of both BNT162b2 and mRNA-1273 and were greater for mRNA-1273 in most younger age groups. In Denmark, a population-based study that applied both case-control and self-controlled case series study methods observed a greater increase in the risk of myocarditis or myopericarditis 1 to 28 days after mRNA-1273 (adjusted hazard ratio, 3.92 [95% CI, 2.30–6.68]) than after BNT162b2 (adjusted hazard ratio, 1.34 [95% CI, 0.90–2.00]).7 They also observed the risk was largely confined to those younger than 40 years and was present for both younger men and women for mRNA-1273. The reasons for male predominance in myocarditis is not known but may relate to sex hormone differences in both the immune response and myocarditis, or to the underdiagnosis of cardiac dis-ease in women.15,16This study has several strengths. First, the United Kingdom offered an ideal place to carry out this study given that 3 types of COVID-19 vaccination have been rolled out at the same speed and scale as each other. Second, this was a population-based study of data recorded prospectively and avoided recall and selection biases linked to case reports. Third, the large sample size provided sufficient power to investigate these rare outcomes, which could not be assessed through clini-cal trials. Fourth, the self-controlled case series study design removes potential confounding from fixed char-acteristics, and the breakdown of our study period into weekly blocks accounted for temporal confounding. Of note, the estimated IRRs were consistently <1 in the pre-exposure period before vaccination and >1 in the pre-risk period before a SARS-CoV-2–positive test. This was expected because events are unlikely to happen shortly before vaccination (relatively healthy people are receiving the vaccine) and more likely to happen before a SARS-CoV-2–positive test (as a standard procedure, patients admitted to the hospital are tested for SARS-CoV-2). We also assessed the robustness of our results through several sensitivity analyses.There are some limitations to consider. First, the number of people receiving a booster dose of ChAdOx1 or mRNA-1273 vaccine was too small to evaluate the risk of myocar-ditis. Second, we relied on hospital admission codes and death certification to define myocarditis, and it is possible that we might have over- or underestimated risk because of misclassification. Third, although we were able to include 2 230 058 children age 13 to 17 years in this analysis, the number of myocarditis events was small (56 events in all periods and 16 events in the 1 to 28 days after vac-cination) in this subpopulation and precluded a separate evaluation of risk. It should also be noted that only the first occurrence of myocarditis in the study period is used in this analysis. Therefore, the results found for the risk of myo-carditis after a third dose do not include repeated instances of myocarditis in the same individual. A comparison of rates of death with myocarditis between those infected with SARS-CoV-2 or vaccinated was not possible, given that for this analysis, we have included only people who had been vaccinated. Therefore, a patient with COVID-19 who died after myocarditis before receiving a vaccination will not be included, and rates of myocarditis death after SARS-CoV-2 will be under estimated.In summary, the risk of hospital admission or death from myocarditis is greater after SARS- CoV2 infection than COVID-19 vaccination and remains modest after sequential doses including a booster dose of BNT162b2 mRNA vaccine. However, the risk of myocarditis after vaccination is higher in younger men, particularly after a second dose of the mRNA-1273 vaccine.

ARTICLE INFORMATIONReceived March 10, 2022; accepted June 7, 2022.AffiliationsNuffield Department of Primary Health Care Sciences (M.P., X.W.M., S.D., A.H., C.A.C.C., J.H.-C.), Wellcome Centre for Human Genetics (L.H.), British Heart Foundation Centre of Research Excellence, National Institute for Health Research, Oxford Biomedical Research Centre, Radcliffe Department of Medicine, John Rad-cliffe Hospital (K.M.C.): National Institute for Health Research Biomedical Research Centre, Oxford University Hospitals National Health Service Trust (P.W.); University of Oxford. School of Immunology and Microbial Sciences, King’s College London, Centre for Inflammation Research (M.S.-H.). Leicester Real World Evidence Unit, Diabetes Research Centre (F.Z., K.K.), University of Leicester. Usher Institute (M.S.-H., N.L.M., A.S.), British Heart Foundation University Centre for Cardiovascular Sci-ence (N.L.M.), University of Edinburgh. Centre for Academic Primary Care, School of Medicine, University of Nottingham (C.A.C.C.)

What’s to blame for the surge in excess deaths?

Authors: Ross Clark 19 August 2022, The Spectator

From the beginning, the debate over lockdowns was skewed by the fact that Covid deaths were imminent – and any other effects from lockdown would become apparent over a longer period. But are we beginning to see that now? Over the past few months the Office for National Statistics has been recording ‘excess’ non-covid deaths of around 1,000 a week in England and Wales – that is to say deaths above and beyond the level which would be expected at this time of year. Deaths over the summer months have been more in line with the number of deaths which might be expected in a normal winter.

Many of the excess deaths appear to be from heart and circulatory diseases. Recent heatwaves may have contributed negatively to this – warmer weather has long been associated with excess deaths. But the current bulge in excess deaths can be traced back to April, long before the heatwave. There have been suggestions that Covid could have weakened people’s health and that we are seeing a delayed reaction to being infected with the virus. Others point to delays in NHS treatment, with long waits in A&E.

https://datawrapper.dwcdn.net/aorwZ/1/

But the possibility remains that we are seeing the result of lockdowns – in particular, the failure of people to seek treatment or the difficulty of obtaining a consultation when we were all ordered to stay at home. The first lockdown, for example, resulted in a 33 per cent fall in diagnosis of early-stage cancers. The government was forced to change its messaging when it became clear that telling people to ‘stay at home’ and ‘protect the NHS’ was dissuading many from seeking treatment, even when they had ominous symptoms.

That lockdowns could themselves cause significant excess deaths was suspected by the government. In July 2020, the Department for Health quietly published a study which concluded that the number of Quality Adjusted Life Years (QALYS) from the indirect effects of the pandemic could eventually outstrip the number of QALYS which had been lost to Covid at that point. Covid, it estimated, would cost 530,000 QALYS. But 41,000 would be lost to reduced access to A&E, 73,000 lost to early discharge from hospital and reduced access to primary care services and 45,000 would be lost to delays in elective surgery. An additional 157,000 QALYS would eventually be lost to the effects of recession – and 294,000 to deprivation as a result of lower economic growth in the long term.

This is just modelling, of course – the limitations of which became plain during the pandemic. Moreover, not all these effects can be laid at the door of the government’s decision to order a lockdown. Had the NHS become overwhelmed by Covid cases, there would have been all manner of delays to treatment for other conditions. Lockdown or not, the economy would have taken a hit – although Sweden, which decided against the measure, suffered a lot less, in economic terms, than Britain and other European countries which did call lockdowns.

Nevertheless, the debate on the wisdom of ordering a lockdown in respect to an outbreak of infectious disease is far from over. Studies on the long-term effects are likely to rumble on for years. But the possibility that a lockdown could itself cause excess deaths was certainly known to the government in July 2020 – well before it decided to repeatedly resort to the measure.

New study suggests covid increases risks of brain disorders

Authors: Frances Stead Sellers Fri, August 19, 2022  Washington Post

A study published this week in the Lancet Psychiatry showed increased risks of some brain disorders two years after infection with the coronavirus, shedding new light on the long-term neurological and psychiatric aspects of the virus.

The analysis, conducted by researchers at the University of Oxford and drawing on health records data from more than 1 million people around the world, found that while the risks of many common psychiatric disorders returned to normal within a couple of months, people remained at increased risk for dementia, epilepsy, psychosis and cognitive deficit (or brain fog) two years after contracting covid. Adults appeared to be at particular risk of lasting brain fog, a common complaint among coronavirus survivors.

The study was a mix of good and bad news findings, said Paul Harrison, a professor of psychiatry at the University of Oxford and the senior author of the study. Among the reassuring aspects was the quick resolution of symptoms such as depression and anxiety.

“I was surprised and relieved by how quickly the psychiatric sequelae subsided,” Harrison said.

David Putrino, director of rehabilitation innovation at Mount Sinai Health System in New York, who has been studying the lasting impacts of the coronavirus since early in the pandemic, said the study revealed some very troubling outcomes.

“It allows us to see without a doubt the emergence of significant neuropsychiatric sequelae in individuals that had covid and far more frequently than those who did not,” he said.

Because it focused only on the neurological and psychiatric effects of the coronavirus, the study authors and others emphasized that it is not strictly long-covid research.

“It would be overstepping and unscientific to make the immediate assumption that everybody in the [study] cohort had long covid,” Putrino said. But the study, he said, “does inform long-covid research.”

Between 7 million and 23 million people in the United States have long covid, according to recent government estimates – a catchall term for a wide range of symptoms including fatigue, breathlessness and anxiety that persist weeks and months after the acute infection has subsided. Those numbers are expected to rise as the coronavirus settles in as an endemic disease.

The study was led by Maxime Taquet, a senior research fellow at the University of Oxford who specializes in using big data to shed light on psychiatric disorders.

The researchers matched almost 1.3 million patients with a diagnosis of covid-19 between Jan. 20, 2020, and April 13, 2022, with an equal number of patients who had other respiratory diseases during the pandemic. The data, provided by electronic health records network TriNetX, came largely from the United States but also included data from Australia, Britain, Spain, Bulgaria, India, Malaysia and Taiwan.

The study group, which included 185,000 children and 242,000 older adults, revealed that risks differed according to age groups, with people age 65 and older at greatest risk of lasting neuropsychiatric affects.

For people between the ages of 18 and 64, a particularly significant increased risk was of persistent brain fog, affecting 6.4 percent of people who had had covid compared with 5.5 percent in the control group.

Six months after infection, children were not found to be at increased risk of mood disorders, although they remained at increased risk of brain fog, insomnia, stroke and epilepsy. None of those affects were permanent for children. With epilepsy, which is extremely rare, the increased risk was larger.

The study found that 4.5 percent of older people developed dementia in the two years after infection, compared with 3.3 percent of the control group. That 1.2-point increase in a diagnosis as damaging as dementia is particularly worrisome, the researchers said.

The study’s reliance on a trove of de-identified electronic health data raised some cautions, particularly during the tumultuous time of the pandemic. Tracking long-term outcomes may be hard when patients may have sought care through many different health systems, including some outside the TriNetX network.

“I personally find it impossible to judge the validity of the data or the conclusions when the data source is shrouded in mystery and the sources of the data are kept secret by legal agreement,” said Harlan Krumholz, a Yale scientist who has developed an online platform where patients can enter their own health data.

Taquet said the researchers used several means of assessing the data, including making sure it reflected what is already known about the pandemic, such as the drop in death rates during the omicron wave.

Also, Taquet said, “the validity of data is not going to be better than validity of diagnosis. If clinicians make mistakes, we will make the same mistakes.”

The study follows earlier research from the same group, which reported last year that a third of covid patients experienced mood disorders, strokes or dementia six months after infection with the coronavirus.

While cautioning that it is impossible to make full comparisons among the effects of recent variants, including omicron and its subvariants, which are currently driving infections, and those that were prevalent a year or more ago, the researchers outlined some initial findings: Even though omicron caused less severe immediate symptoms, the longer-term neurological and psychiatric outcomes appeared similar to the delta waves, indicating that the burden on the world’s health-care systems might continue even with less-severe variants.

Hannah Davis, a co-founder of the Patient-Led Research Collaborative, which studies long covid, said that finding was meaningful. “It goes against the narrative that omicron is more mild for long covid, which is not based on science,” Davis said.

“We see this all the time,” Putrino said. “The general conversation keeps leaving out long covid. The severity of initial infection doesn’t matter when we talk about long-term sequalae.

Estimates of long Covid are startlingly high. Here’s how to understand them

Authors:  Elizabeth Cooney July 2022 STAT

Think about the adults you know who have had Covid: Does 1 out of every 5 have long Covid, as the CDC estimates?

Asking that question should in no way diminish the suffering of people who thought they were done with their infections, only to find their return to well-being still beyond reach. But knowing how many people are living with that bitter legacy of Covid-19, and who among working-age adults can’t work or care for their families, is critical to their care and to the health of our society.

It’s important to remember that long Covid is an evolving umbrella term for an array of symptoms that vary in both number and degree. Some housebound people are assailed by brain fog that completely robs them of concentration, while others find memory aids help them get through their workdays. Some former athletes can’t complete a 6-minute walk test, while others can gradually return to activity if they monitor their heart rate. Long Covid clinics that adapt techniques from rehabilitation medicine see people eventually get better. In a world transitioning away from bustling downtowns to hybrid work-from-home status, we may not see who’s missing.

Whatever long Covid’s toll turns out to be, it will be too many people. However you gather or analyze the data, experts told STAT, the proportion of people whose troublesome, sometimes disabling symptoms linger after their acute Covid-19 infections clear is sizable and worrying. It’s the cruelty of large numbers: Even if the actual prevalence of long Covid is much smaller than recent estimates, a small percentage of a large number is a large number.

And yet, the U.S. has for months been operating in a nearly normal fashion. What could explain this discrepancy between estimates and common experience? It’s eerily similar to the pandemic’s early days, when people asked one another if they knew anyone who had caught the coronavirus, followed more than two years later by the flip side: knowing few people who haven’t been infected and no one who hasn’t been exposed.

Here are some factors that make the current range of estimates easier to understand.

First, what are the numbers?

That 20% figure, from a recent CDC analysis of millions of health records, implies that tens of millions of Americans — a fifth of people infected with Covid — have at least one lingering post-infection symptom that is seriously affecting their daily life. Compared to other estimates, like an April meta-analysis that puts global long Covid at closer to 50% or a June household survey from CDC saying 1 in 3, it’s even on the low side.

Nathan Praschan, a psychiatry researcher at Massachusetts General Hospital, trusts it, calling the more rigorous CDC study’s epidemiology among the best he’s seen because for over a year it used a control group to tease out Covid effects. Still, he thinks it might have missed some people who don’t show up in medical records. Long Covid is defined by symptoms — psychiatric disorders and cognitive problems, to name two — that could make finding care more difficult, as would the same social determinants of health that mean Covid infection is more likely in some populations in the first place. “So, 1 in 5 may be an underestimate.”

What about different definitions?

CDC’s vs. WHO’s, for instance. The CDC defines long Covid, which it calls Post-Covid Conditions, as symptoms lasting four weeks after first infection. The World Health Organization starts the clock ticking after three months. Praschan said it makes sense to be inclusive, as in on the earlier side, while data are still being collected to avoid missing important information from these patients.

There may be differences in the data.

While some U.K. studies relied on records a national health system provides, others culled responses from a smartphone app asking people about their post-Covid symptoms. That limits the respondents to people who have smartphones and are also motivated to report how they are feeling.

The CDC report’s large numbers give power to the analysis, senior epidemiologist Lara Bull-Otterson told STAT. “While all studies have limitations, we believe the strengths of the data and the analysis are solid and are also supported by prior research,” she said. “Future research is always needed to support and expand on the findings of this study.”

Bruce Levy, chief of pulmonary and critical care medicine at Brigham and Women’s Hospital, doesn’t think the 20% estimate is rock solid, noting how studies have varied widely in the U.S. and in other parts of the world. “Even if it’s in single digits at the end of the day, once a formal case definition and a true prevalence study can be accomplished, it’s still a lot of people. But it’s very hard to pinpoint a solid number.”

If the size of the CDC study is impressive, the source of the data has limits, epidemiologist Priya Duggal of Johns Hopkins Bloomberg School of Public Health said. Patient records reflect only the people who sought care and whose symptoms were coded in their charts. Such data don’t include people who didn’t have access to health care, didn’t seek it, or gave up, thinking there was no help for their crazy quilt of symptoms.

“It doesn’t mean the data’s not right. It doesn’t mean that what we’re looking at isn’t important,” she told STAT. “It just means that that’s a different group of people that you might be looking at.”

Even with caveats, she finds the data pretty consistent for a range of 20% to 30% of people experiencing long Covid symptoms “It’s still a substantial number of people. To me, that’s the take-home point,” she said. “The second point is that it’s real.”

Long Covid is a constellation of diseases that manifest differently.

Symptoms linked to long Covid hit bodies from head to toe: brain fog, fatigue, shortness of breath, digestive problems, muscle weakness. The symptoms vary in severity and number, depending on the study. But most patients don’t necessarily have all of them. Some patients don’t have debilitating fatigue, but might report persistent digestive problems they didn’t have before getting Covid.

Some long Covid may be something else.

With long Covid so disparate and common, it’s possible that some doctors are misattributing symptoms to long Covid and missing the diagnosis of a different disease. Or, because lifesaving measures in intensive care units can be like a train wreck for the body, it’s hard to tease out the treatment from the disease.

Some long Covid is hidden to bystanders.

“Some of it is going to be visible like, oh, they’re weak, they’re sickly, they can’t walk, they can’t go upstairs,” Duggal said. “Then there’s also long Covid where you have kidney damage now, and the average person walking down the street doesn’t know that.”

She’s heard people say they don’t know anyone who has long Covid. “I’m like, you probably do.”

Long Covid isn’t all debilitating.

The CDC definitions capture thousands who fit the worst-case image of long Covid: formerly healthy people who can no longer function. But its prevalence estimate also includes anyone reporting at least one symptom, Bruce Walker, director of the Ragon Institute of Massachusetts General Hospital, MIT and Harvard, reminded reporters on a recent call. Estimates may also capture a worsening pre-Covid condition like asthma, an important consideration for the many people with underlying conditions before they caught Covid.

What’s next?

Bull-Otterson of the CDC urged routine screening for long Covid and better defining it so risk factors could be identified and treatments devised. The impact of vaccination and the wild card of variants also need to be understood.

Long Covid has the potential to widen existing gaps in health, Linda Sprague Martinez of the Boston University School of Social Work said on a video call with reporters, pointing to a map of counties with high case numbers but few long Covid clinics. “We don’t want to wait,” she said. “Getting ahead of it will be really important for us,” she said.

OK, what can we say now?

Estimates of long Covid will certainly evolve, and perhaps be refined into the systems they affect: cardiopulmonary, digestive, musculoskeletal, or neurological, including autonomic powers that control breathing, heart rate, and other unconscious functions. If, as experts say, there is an inevitability to catching Covid now, or catching it again, long Covid will likely follow in some proportion of cases, disabling some further fraction of those people. Recent studies suggest that Covid infections precede the risk of certain other chronic diseases like type 2 diabetes, but the mechanism isn’t clear. Even if the world wasn’t ready for one pandemic, it has to deal with its aftereffects somehow.

“We see people still two years out having long-term symptoms, so if that’s true and people can continue to get infected, this is going to be with us for quite a while,” Duggal said.

Americans Reflect on Nation’s COVID-19 Response

Priorities Are Misguided

Authors: BY CARY FUNKALEC TYSONGIANCARLO PASQUINI AND ALISON SPENCER July 7, 2022 Pew Research

As levels of public concern over the coronavirus outbreak recede, Americans offer a lackluster evaluation of how the country has balanced priorities during the outbreak. A majority of U.S. adults say the country has given too little priority to meeting the educational needs of K-12 students since the outbreak first took hold in February 2020. Assessments of the nation’s response across other domains are little better: Fewer than half of Americans say the country has done about the right amount to support quality of life and economic activity or to protect public health.

When asked to take stock of what measures have worked to limit the spread of the coronavirus, the public is conflicted. Vaccines and masks rank at the top of the list of effective steps; but even for these public health tools, sizable shares of Americans describe them as no more than somewhat effective at limiting the spread of the coronavirus.

A Pew Research Center survey of 10,282 U.S. adults conducted from May 2 to 8, 2022, finds 62% of Americans say the country has given too little priority to meeting the educational needs of K-12 students during its response to the coronavirus outbreak; far fewer (31%) say this has received about the right amount of priority since the outbreak first began in February of 2020 (just 6% say it’s received too much priority).

On balance, larger shares of Americans also say too little priority – rather than the right amount – has been given to supporting the public’s overall quality of life, supporting businesses and economic activity, and respecting individuals’ choices.

When it comes to the central goal of protecting public health, Americans have decidedly mixed views: 43% say the country has given about the right amount of priority to protecting public health, while 34% say this has received too little priority and 21% say it has received too much.

The overall findings reflect two competing critiques of the nation’s response. One, widely expressed among Republicans, is that the country has not focused enough on business concerns and respecting individual choices. The other, more widely held by Democrats, centers concern around efforts to protect public health and limit health risks for vulnerable populations.

In short, neither Republicans nor Democrats think the country has hit the mark in its response to the outbreak – one that has spanned the presidential administrations of both Donald Trump and Joe Biden.

Among Democrats and Democratic-leaning independents, larger shares say protecting public health has received too little priority than say it has received too much (46% vs. 7%), while 46% say it has gotten about the right amount of priority. Republicans and Republican leaners offer a very different assessment: More say public health has received too much priority (40%) than say it’s been given too little (20%), while 38% say it’s gotten about the right amount of priority.

Majorities of Republicans say the country has done too little during the outbreak when it comes to respecting individuals’ choices (69%) and supporting businesses and economic activity (62%). Relatively small shares of Democrats express these views. In fact, half of Democrats say there has been about the right amount of attention given to supporting businesses and economic activity. And Democrats are roughly as likely to say too much priority has been given to respecting individuals’ choices as to say too little (33% and 28%, respectively). See the Appendix for more details on this question.

Amid these contrasting views of the nation’s response to the coronavirus outbreak stands a notable point of general partisan agreement: Majorities of both Republicans (69%) and Democrats (57%) say the country has given too little priority to meeting the educational needs of K-12 students. A January survey by the Center found a majority of parents of K-12 students expressed concern about academic progress when it came to decisions about whether to keep schools open for in-person instruction.

Over the past two years, public health and elected officials have invested extensively in communicating ways to limit the spread of the coronavirus. For Americans, vaccines rank at the top of the list of what they believe has worked, followed by mask-wearing and limiting interactions with other people. Still, not all Americans see these measures as particularly effective.

For instance, a narrow majority (55%) says vaccination against COVID-19 has been extremely or very effective at limiting the spread of the coronavirus; 22% say this has been somewhat effective and 23% say it has been not too or not at all effective.

About half say wearing masks around people indoors (48%) and limiting activities and interactions with other people (47%) have been extremely or very effective at limiting the spread of the coronavirus. The remainder of Americans describe these two steps as no more than somewhat effective.

The partisan gaps over the effectiveness of these interventions are about as wide as any seen in the survey. For instance, 75% of Democrats say COVID-19 vaccines have been extremely or very effective at limiting the spread of the coronavirus; 16% say they have been somewhat effective and just 9% describe them as not too or not at all effective.

Republicans offer a much more skeptical view: A slightly larger share of Republicans say vaccines have been not too or not at all effective at limiting the spread of the coronavirus than say they have been extremely or very effective (39% vs. 32%); 29% fall between these two views and say vaccines have been somewhat effective.

Asked to assess where the country stands at this stage of the outbreak, about three-quarters of Americans (76%) say the worst of the country’s problems from the coronavirus are behind us. And declining shares express deep personal concern about getting the coronavirus themselves.

But while the intensity of public concern about the coronavirus outbreak has waned, cases in the U.S. remain stubbornly high and 86% of Americans say the outbreak remains at least a minor threat to the health of the U.S. population.

To date, over a million Americans have died from COVID-19. Firsthand connections to people who have experienced serious cases of COVID-19 are common among the public: 81% of U.S. adults – including 88% of Black and 86% of Hispanic adults – say they know someone personally who has been hospitalized or died from the coronavirus. See the Appendix for more details.

Ratings of Biden’s, public officials’ response to the coronavirus outbreak

Four months ahead of the November midterm elections, President Joe Biden’s standing on the issue of the coronavirus outbreak has diminished. A majority of adults (56%) say he is doing an only fair or poor job responding to the outbreak, compared with 43% who say he is doing an excellent or good job.

In October of 2020, Biden held a clear advantage over Donald Trump as the candidate voters saw as better able to handle the public health impact of the outbreak – among the issues voters identified as most important to the election. And at the start of Biden’s term, 65% of Americans said they were confident in his ability to deal with the outbreak.

Biden is not the only official, or set of officials, to see their ratings fall over the course of the outbreak. Ratings for state and local elected officials as well as for public health officials – such as those at the Centers for Disease Control and Prevention – are all lower today than at early stages of the outbreak, though they are about the same as they were in January of this year.

Ratings for the performance of local hospitals and medical centers stand well above those of other groups. Eight-in-ten Americans say hospitals and medical centers in their area are doing an excellent or good job responding to the coronavirus outbreak – far higher than ratings of all other groups and individuals included in the survey. The gap between ratings for local hospitals and medical centers and those for other groups, including public health and state and local officials, is much wider today than at early stages of the outbreak.

Ratings of public health officials are an example of intensifying partisan differences that have formed over the course of the outbreak. Democrats and those who lean to the Democratic Party are far more likely than Republicans and GOP leaners (72% vs. 29%) to say public health officials, such as those at the CDC, have done an excellent or good job responding to the coronavirus outbreak. In the early stages of the outbreak, majorities of both Republicans and Democrats gave public health officials positive ratings.

While the overall decline in ratings for public health officials has been driven by sharply lower assessments among Republicans, the declines in ratings for state and local elected officials have occurred among both Republicans and Democrats.

National preparedness for a future global health emergency

Asked to consider preparedness for a future global health emergency, 59% of Americans say they have either a great deal (15%) or some confidence (44%) in the U.S. health care system to handle a future global health emergency. Four-in-ten say they have not too much or no confidence at all in the U.S. health care system to handle a future global health emergency.

Overall views are similar to those measured in February of 2021, when 55% of Americans said they had at least some confidence in the health care system to handle a future global health emergency.

However, views among partisans have changed considerably over the last year. Democrats are now significantly more likely than Republicans to say they have a great deal of or some confidence in the health care system to handle a future emergency (67% vs. 51%). In February 2021, during the final days of the Trump administration, Republicans (57%) were about as likely as Democrats (54%) to express this level of confidence in the preparedness of the U.S. health care system.

Attitudes also differ on this question by vaccination status. A majority of adults (67%) who have received at least one dose of a COVID-19 vaccine say they have a great deal of or some confidence in the health care system to handle a future emergency, compared with just 34% of those who have not received a vaccine. Republicans and Democrats who have received a vaccine are each more likely to express confidence in the health care system than unvaccinated members of their respective parties.

Overall, 55% of Americans say vaccination against COVID-19 has been extremely (33%) or very (22%) effective at limiting the spread of the coronavirus; 22% say vaccines have been somewhat effective and 23% say they have been not too or not at all effective.

About half of Americans (48%) say wearing masks around other people indoors has been extremely or very effective at limiting the spread of the coronavirus. A similar share (47%) say limiting activities and interactions with other people has been extremely or very effective. Still, for both measures, roughly as many Americans describe these actions as no more than somewhat effective at limiting the spread of the coronavirus.

The wide availability of rapid COVID-19 tests is seen as very or extremely effective at limiting the spread of the coronavirus by 43% of the public. Relatively fewer (35%) say staying six feet apart from other people indoors has been extremely or very effective at limiting the spread of the coronavirus.

Democrats are much more likely than Republicans to view all five measures as extremely or very effective at limiting the spread of the coronavirus. For instance, 71% of Democrats say wearing masks around other people indoors is extremely or very effective at limiting the spread of the coronavirus; a considerably smaller share of Republicans (21%) say the same.

Across the five public health tools asked about in the survey, wide differences in views are also seen between adults that have received at least one dose of a COVID-19 vaccine and those that have not been vaccinated. Among respondents that have received at least one dose of a vaccine, a majority views several of these measures – vaccines, wearing masks and limiting social interactions – as extremely or very effective at limiting the spread of the coronavirus. Among the much smaller share of Americans who have not been vaccinated, no more than two-in-ten say any of these five measures are extremely or very effective.

A majority of Americans think treatments and drugs for those with the coronavirus have gotten a lot (46%) or a little (33%) better since the early stages of the outbreak. The share who say they have gotten a lot better is up 9 points from 37% in November of 2020, when this question was last asked.

Democrats and Democratic leaners are now more likely than Republicans and Republican leaners to say the effectiveness of treatments for the coronavirus has gotten a lot better (57% vs. 35%) since the early stages of the outbreak.

Democrats’ views about the improvement of medical treatments for COVID-19 have become more positive since November 2020, during the last months of the Trump administration. By contrast, Republicans are less likely today to say medical treatments have improved over the course of the outbreak than they were in November 2020.

Overall, 73% of U.S. adults say they are fully vaccinated for coronavirus as of May 2022. This share is the same as it was in a January 2022 Pew Research Center survey. According to the Centers for Disease Control and Prevention (CDC), “fully vaccinated” means having received two doses of Pfizer or Moderna vaccines or one dose of the Johnson & Johnson.

A relatively small share of U.S. adults say they have received one dose of a vaccine but need one more (5%); 21% say they have not received a vaccine for COVID-19. Both shares are virtually unchanged from January 2022.

Republicans and Republican-leaning independents (60%) continue to be less likely than Democrats and Democratic leaners (85%) to say they are fully vaccinated.

Older adults also continue to be more likely than younger adults to say they are fully vaccinated, a pattern that holds true within each party.

As in the past, those who live in urban or suburban communities (76% each) are more likely than those living in rural areas (64%) to say they are fully vaccinated.

When it comes to booster shots, about half (49%) of the public say they are fully vaccinated and have received a booster shot within the past six months. The share is about the same as it was in January 2022.

Differences by partisanship persist in both the shares who say they are fully vaccinated and in the shares who say they’ve received a booster shot among fully vaccinated adults. A narrow majority of fully vaccinated Republicans (56%) have received a booster shot. This group makes up 34% of all Republicans. Meanwhile, a larger majority (75%) of fully vaccinated Democrats – or 64% of all Democrats – say they have received a booster shot.

Among both partisan groups, younger adults who are fully vaccinated remain less likely than older adults who are fully vaccinated to say they have received a COVID-19 booster shot.

With vaccination rates among U.S. adults leveling off in recent months, differences across groups in the country have crystalized.

Looking across a wide range of characteristics associated with the decision to get a vaccine, some of those most likely to be fully vaccinated in the U.S. include those with a postgraduate degree, those in higher-income households with health insurance, and Americans ages 65 and older.

At the other end of the spectrum, those relatively less likely to be fully vaccinated include White evangelical Protestants, adults younger than 50 living in rural areas, and those without health insurance. See the Appendix for more details about vaccination rates across groups.

When it comes to personal experiences with the coronavirus, 46% of U.S. adults say they have tested positive for COVID-19 or been pretty sure they have had it.

The share of Americans who say they have had COVID-19 has risen since August 2021, when three-in-ten (30%) said this.

Across age groups, younger adults are more likely than older adults to say they have tested positive for COVID-19 or been pretty sure they had it. A majority (59%) of adults ages 18 to 29 say this, compared with 26% of adults 65 and older.

Those who are fully vaccinated (42%) are less likely to say they have had COVID-19 than those who are not vaccinated (61%). (The survey did not ask respondents whether they got COVID-19 before or after being vaccinated.)

Among those who are fully vaccinated, younger adults are more likely than older adults to say they have had COVID-19.

When vaccination status and exposure to COVID-19 are taken together, 90% of Americans report some level of immune response to COVID-19 (78% have received at least one dose of a vaccine and an additional 12% are not vaccinated but say they’ve had the coronavirus). Public health experts are continuing to evaluate how long immunity from vaccination or previous infection last as coronavirus variants evolve.

The CDC recommends the use of at-home coronavirus tests as one way for Americans to help reduce the spread of COVID-19.

About four-in-ten U.S. adults (39%) say they have taken an at-home COVID-19 test in the past six months.

Across age groups, younger adults are more likely to say that they have taken an at-home coronavirus test in the past six months. Around half (52%) of adults ages 18 to 29 say this, compared with 27% of those 65 and older.

Upper-income adults (48%) are more likely than middle-income (39%) or lower-income (36%) adults to say that they have taken an at-home COVID-19 test in the past six months.

When the 39% of Americans who have taken an at-home COVID-19 test in the past six months were asked about their reasons for doing so, a majority of this group (63%) say that a reason was that they were experiencing coronavirus symptoms.

Around four-in-ten or more say that a reason they had taken an at-home COVID-19 test was that they had contact with someone who tested positive (47%) or wanted to take one before attending a gathering with other people (41%).

About a quarter (24%) say that a reason they have taken an at-home COVID-19 test was that they were required to do so before an event.

For each of the possible reasons listed for taking a COVID-19 test, younger adults are generally more likely than older adults to say that each had been a factor. For example, 57% of adults ages 18 to 29 say that having contact with someone who tested positive for COVID-19 was a reason they had done an at-home test, compared with 37% of adults 65 and older.

About a third of Americans (34%) say they are at least somewhat concerned that they will get COVID-19 and require hospitalization, a much smaller share than said this at earlier stages of the outbreak.

Half of Americans say they are at least somewhat concerned that they might unknowingly spread COVID-19 to others. This share has declined steadily since November 2020, when about two-thirds (64%) of U.S. adults said this.

Consistent with these declines, Americans are also less likely to see the coronavirus outbreak as a major threat to their personal health than at earlier stages of the outbreak. About a quarter (23%) now say this, down from 30% in January 2022. See the Appendix for more details.