Omicron’s Mutations Impaired Vaccine Effectiveness, CDC Says

Authors: Madison Muller  August 25, 2022 Bloomberg

Almost 40% of people hospitalized in the US with the Covid subvariant that circulated this spring were vaccinated and boosted, highlighting how new strains have mutated to more readily escape the immunity offered by current shots.

The findings from scientists at the US Centers for Disease Control and Prevention underscore the importance of having Covid shots that are better at targeting omicron subvariants. 

From the end of March through May, when the omicron BA.2 and BA.2.12.1 subvariants were dominant in the US, weekly hospitalization rates increased for all adults — with those over 65 hit the hardest. Even so, the total number of hospitalizations remained much lower than when the delta variant was rampant last fall. 

The overall number of hospitalizations is an important point, said Abraar Karan, an infectious disease doctor at Stanford University.

“When you look at who’s hospitalized, it’s much more likely that they will have been vaccinated because so many people are vaccinated now,” Karan said. “The real comparison is how many hospitalizations do we have now versus in the past when people were not vaccinated or not up-to-date with boosters.”

CDC scientists found that vaccines and boosters did a better job of keeping people with delta infections out of the hospital than those with later variants. Effectiveness decreased slightly with the BA.1 variant, then changed significantly with BA.2 — with a much greater share of hospitalized adults who had been vaccinated with at least one booster. 

Read more: Retiring Fauci expected Covid to be ‘behind us’

Immunity from vaccines starts to wane within six months, so staying up-to-date with shots is key to being fully protected. Fewer than half of Americans have gotten a booster shot.

Adults with at least two booster shots fared better than other people when BA.2 was dominant. The majority of those admitted to the hospital also had at least one underlying condition. Unvaccinated adults were more than three times as likely to be hospitalized, but breakthrough infections still represented a significant number of the severe Covid cases, the data show.

US regulators have pushed Moderna Inc., Pfizer Inc. and BioNTech SE to expedite development of omicron-specific boosters for a September rollout. The drugmakers this week submitted early data to the US Food and Drug Administration seeking emergency clearance for updated shots that target the BA.4 and BA.5 virus strains. Scientists and vaccinemakers are already beginning to look toward next-generation shots that may provide longer-lasting protection against more variants. 

The new report’s findings also indicate that along with vaccination, other pharmaceutical and non-pharmaceutical measures should be used by those at highest risk of getting Covid. That includes easy access to therapeutics such as Pfizer’s antiviral drug Paxlovid and Gilead Sciences’ remdesivir, as well as AstraZeneca’s Evusheld for immunocompromised people. Scientists also note that wearing a mask can help guard the wearer from getting sick.  

Though the number of Covid deaths is the lowest it has been since last July, the US continues to see hundreds of deaths each day from Covid, CDC data show.

Self-reported outcomes, choices and discrimination among a global COVID-19 unvaccinated cohort

Authors: Robert Verkerk PhD1, Christof Plothe DO2, Naseeba Kathrada MBChB3 and
Katarina Lindley DO4, Science Unit, Alliance for Natural Health International, 78 Dorking Road, Chilworth, Surrey, GU4 8NS, United Kingdom, Praxis für Biophysikalische Osteopathie, Am Wegweiser Alzey, Germany, Dr Kats, 86 Jan Hofmeyer Road, Dawncrest, Westville, 3629, South Africa, Lindley Medical, 2100 FM 1189, Brock, Texas 76087, USA June 8, 2022

INTRODUCTION

Since COVID-19 was declared a pandemic by the World Health Organization (WHO) in March 2020, there have been conflicting views among health authorities and in the published literature about the risks posed by SARS-CoV-2 to healthy populations that have not been COVID-19 injected. Additionally, health authorities and the media have frequently suggested that such unvaccinated populations pose a significant risk of infection to the COVID-19 vaccinated and vulnerable. For example, a study published in the preprint server MedRxiv found less severe outcomes among fully vaccinated COVID-19 patients requiring hospitalization, compared with those not vaccinated, yet the risk of in-hospital death was greater among the vaccinated than unvaccinated (Mielke et al 2022). A global study (68 countries) by Subramanian & Krishna (2021) found a strong tendency for countries classified as “low transmission” countries to have low rates of COVID-19 ‘vaccine’ coverage (<20%), the reverse being the case for “high transmission” countries. The UK REACT study (DHSC 2021) reported that of 98,000 volunteers studied those who were double vaccinated COVID-19 were three times less likely to test positive by PCR than those who were unvaccinated (1.21% vs 0.4%, respectively). However, the data on which such findings are based cannot demonstrate a causative relationship with vaccination owing to numerous behavioural and other confounding factors between the two groups. Furthermore, data on cases and deaths relied upon by UK authorities have been shown to be spurious owing to mis-categorisation of vaccination status (Fenton et al, 2021). There have been very few studies that either assess the health outcomes of unvaccinated populations, or compare matched unvaccinated and vaccinated populations. One such study, by Lyons-Weiler and Thomas (2020), of a paediatric patient population at an integrative clinic in Portland, Oregon, found that the health status of unvaccinated children exceeded that of those subject to the routine childhood vaccination program in the USA. However, the journal that published the study, the International Journal of Environmental

Research and Public Health, was forced to retract the study 8 months following publication
given the implications of its findings. There is a significant population of individuals and communities around the world that have not been persuaded that COVID-19 ‘genetic vaccines’ (notably the widely used mRNA or adenoviral vector based injections, sometimes referred to simply as vaccines in this paper for simplicity) are either sufficiently safe or effective to justify mass roll-out into healthy populations. This is represented by the fact that over one-third of the world’s population has yet to be COVID-19 vaccinated, the majority of these being in low-income countries (Our World In Data, 2022). In response to such concerns, a UK citizen-led cooperative, the Control Group Cooperative (CGC) (vaxcontrolgroup.com), was formed in July 2021 to represent the interests of individuals and families around the world who have chosen to not receive COVID-19 ‘vaccines.’ Among the aims of the CGC is to evaluate long-term health outcomes among the COVID-19 vaccine-free, as well as linking its members to country support networks and online community groups. Participants who join the ‘control group’ may obtain an ID card (Fig. 1), in the relevant language. The card includes the statement that the individual is part of a SARS-CoV-2 Control Group and “must not be vaccinated”. Many members have reported that these ID cards have been successful in allowing travel, preventing forced vaccination (vaccination without informed consent) or avoiding the loss of liberties, such as access to venues otherwise limited to COVID-19-vaccinated individuals.

When joining or becoming a member of the CGC, subscribers are asked to participate in a survey (see Methods). It is the survey findings over the first five months of operation from a specific cohort of subscribers to the CGC that forms the primary subject of this paper.

We, the authors of the present work, are entirely independent of the CGC and have received no funding to undertake it. Since mid-2021, we have collaborated on a diverse range of scientific and medical issues as part of our work with the Health & Humanities Committee (co-chaired by two of the authors: Dr Naseeba Kathrada and Robert Verkerk PhD) of the non-profit World Council for Health (worldcouncilforhealth.org).

METHODS
This survey is based on self-reported data among self-selected individuals from around the world who have subscribed to the CGC ‘control group’ project (vaxcontrolgroup.com). All respondents on which the present analysis is based completed an online survey (see Supplementary Information) on the CGC website on a monthly basis over 5 consecutive months (October 2021 to February 2022 inclusive). This period included the time during which, in most parts of the world, omicron replaced the delta variant as the dominant, circulating variant of SARS-CoV-2. The cohort (n = 18,497) that is the subject of this analysis is a sub-group comprising 6.2% of the 297,618 people who had registered on the website by the end of February 2022 and provided data on a monthly basis over the first 5 consecutive months of the survey. Comparison of findings from this cohort with selected responses from the less complete but entire survey data set of CGC (that includes some 305,000 respondents from around the
world at the time of writing), suggests that this smaller data set is representative of the full
dataset. The online survey includes some initial profile questions (Supplementary Information;
Annex 1) that were answered on registration followed by a further series of questions (Supplementary Information; Annex 2) answered by respondents on a monthly basis thereafter. Recruitment of respondents was entirely organic and relied on respondents being made aware of the CGC project through largely alternative media outlets, given censorship on mainstream media and social media channels. It is important to recognize that because the cohort represents a self-selected, as opposed
to randomly selected, sample, the findings cannot be directly compared with other observational studies based on self-reported data based on randomly selected subjects. However, what the survey aimed to do is gather insights about health outcomes, choices and discrimination experienced by the marginalized sub-population of people from diverse socio-economic backgrounds, ethnicities and cultures who have elected to exercise their right of refusal of COVID-19 injections. As a self-reported survey, the interpretation of results in this paper has focused primarily on providing perspectives on the responses of an unvaccinated population to a variety of factors. Accordingly, central to this ‘look and see’ approach are the proportion of respondents who have given particular responses to the questions provided. ©2022 Alliance for Natural Health International 4 Given not all questions have been answered by all respondents, the denominators for the proportional analyses vary considerably according to how many relevant answers are provided and where these are unexpected, explanation is given in the tables or figures.Some analyses involve just a subset of the respondents (e.g. menstruating, menopausal and post-menopausal women aged 20 to 69) and, again, the denominator is stated.

SURVEY FINDINGS
Characterizing the cohort a. Geographic location The vast majority (98.8%) of non-COVID-19 injected participants were from 6 major continents or regions (Table 1), most being from Europe (40%), with the next larges constituents from Oceania (principally Australia and New Zealand) and North America (USA
and Canada), 27% and 25%, respectively. Table 1. Continental distribution of respondents in cohort.
The geographical distribution of respondents in the self-selected cohort is shown in Fig. 2

Region n %, Africa 171 0.9%; Asia 555 3.0%; Europe 7442 40.2%; North America 4657 25.2%; Oceania 4982 26.9%; South America 576 3.1%; Unknown 114 0.6%; TOTAL (n ) 18383 100.0%

b. Reported age groups and biological sex
The age distribution of the cohort is shown in Figure 3. Overall, of the respondents who disclosed their biological sex (96.3%), 57% of respondents were female and 43% male. The age groups with the greatest numbers of respondents were middle-aged and accordingly would generally be regarded by health authorities as highly susceptible to COVID-19 disease. ©2022 Alliance for Natural Health International 6Figure 3. Age and biological sex distribution of cohort. C. Blood group The blood groups and rhesus factors were reported by 51% of respondents, with expected variations between regions and almost twice as many females rather than males disclosing data (Table 2). Given prevalence of Caucasian ethnicities, the relative order of blood groups (most common to least common) was as expected, as follows: O+ > A+ > O- > B+ > A- > AB+ >B- >AB-

Table 2. Blood group by biological sex of cohort.
Blood group; Female (%); Male (%); Undisclosed biological sex (%) Total
A- 436 (7.0) 182 (5.8) 6 (8.6) 624
A+ 1,778 (28.7) 901 (28.5) 24 (34.3) 2,703
AB- 71 (1.1) 33 (1.0) 0 (0.0) 104
AB+ 265 (4.3) 141 (4.5) 2 (2.9) 408
B- 145 (2.3) 56 (1.8) 0 (0.0) 201
B+ 598 (9.7) 294 (9.3) 5 (7.1) 897
O- 665 (10.7) 359 (11.4) 5 (7.1) 1,029
O+ 2,235 (36.1) 1,196 (37.8) 28 (40.0) 3,459
Total with known blood groups 6193 3162 70 9,425 Rather not Disclose 1,383 1,289 538 3,210
Unknown 2,946 2,842 74 5,862d. Primary reason for not electing to receive COVID-19 ‘vaccine’ Table 3 lists, in descending order of frequency, the most important reasons given by cohort respondents for deciding against COVID-19 injection. Respondents were able to select multiple reasons if they felt them to be of equal importance, hence n = 54,152. Table 3. Frequency among cohort where each reason was reported to be the single most important reason for declining COVID-19 ‘vaccination’.
Reasons for not being covid vaccinated Number of respondents who considered each reason the most
important % Prefer natural medicine interventions 9,084 16.8 Distrust of pharmaceutical interventions 8,896 16.4 Distrust of government information 8,888 16.4 Poor/limited trial study data 8,841 16.3
Fear of long-term adverse reactions 8,348 15.4 Fear of short-term adverse reactions 6,216 11.5
Medical complications 2,376 4.4 Previous vaccine injuries 1,503 2.8 Total 54,152 100.0 ©2022 Alliance for Natural Health International 8 The survey results suggest that five reasons were of almost equal significance (with only 1.4% variance), namely preference for natural medicine interventions, distrust of pharmaceutical companies, distrust of government information, insufficient trial data and concerns over long-term adverse reactions. Only 7% of respondents gave either medical complications or concerns stemming from previous vaccine injuries as the primary reasons for COVID-19 ‘vaccine’ avoidance. e. History of past vaccination Approximately one-third of the cohort reported having been vaccinated as a child, while another one-third reported having not received any vaccine within the last 5 years(Table 4). Table 4. Reported vaccination history for cohort Reported vaccination history N %
As a child 5,405 29.2 In Last 12 months 912 4.9 Less than 5 years ago 2,837 15.3 More than 5 years ago 6,246 33.8Never Vaccinated 782 4.2Rather not Disclose 2,315 12.5 Total 18,497 100.0 The age groups from 20 years through to 84 years had the smallest proportions (2.0- 2.9%) reporting that they had never been vaccinated. Conversely, the youngest age group (ages 0-19 years) reported by far the highest rate of not having received any vaccine (15.9%) (Table 5). Table 5. Reported vaccination history by age group Age group (% in each group) Reported vaccination history 0-19 % 20-49 % 50-64 % 65-84 % 85+ % As a child 494 20.0 1,957 33.5 2,131 30.0 810 26.8 11 17.5 More than 5 years ago 313 12.7 1,956 33.4 2,755 38.8 1,200 39.8 19 30.2 Less than 5 years ago 567 23.0 858 14.7 967 13.6 436 14.4 9 14.3
Rather not Disclose 492 20.0 671 11.5 811 11.4 327 10.8 12 19.0 In Last 12 months 206 8.4 238 4.1 273 3.8 185 6.1 9 4.8Never Vaccinated 392 15.9 170 2.9 157 2.2 60 2.0 3 4.8 Total 2,464 5,850 7,094 3,018 63
Preprint draft, uploaded to ResearchGate June 8, 2022©2022 Alliance for Natural Health International 9. Future vaccination choices Nearly two-thirds of the cohort (64.2%) reported that they would refuse all future vaccines of any type, with about one-fifth (22.5%) choosing to not disclose their choices
(Fig. 4). Only 1.3% reported an interest in receiving flu vaccinations and less than 5% reported that they would receive ‘holiday vaccinations’. The choices were generally similar regardless of age group. Figure 4. Responses to future vaccination choices for all age groups in cohort. g. Willingness to donate blood Around 60% of non-COVID-19 vaccinated respondents, regardless of their blood group indicated their willingness in donating blood, these numbers being approximately three times greater than those unwilling to do so or not disclosing a clear preference one way or another (Fig. 5).
0, 10, 20, 30, 40, 50, 60, 70, 80, 90 ,100
No to All Rather not Disclose Any Non-Trial Vaccinations Holiday Vaccinations Flu Vaccinations
Percentage of respondents ©2022 Alliance for Natural Health International 10 Fig. 5. Percentage of respondents reporting willingness or otherwise to donate blood. Reported outcomes, choices and attitudes a. Respondents who reported COVID-19 during survey period Respondents between the ages of 20 and 49 years reported the greatest incidence of COVID-19 disease (~10-12%), with females consistently reporting slightly more often than males regardless of age group, this likely reflecting the female bias of the cohort. Those aged 70 and over reported the lowest incidence of COVID-19 disease (4.0% females, 3.7% males) (Fig. 6).

Figure 6. Percentage of respondents reporting COVID-19 disease, by age group and biological sex during study period. b. Respondents who reported not experiencing, or at least not being sure of experiencing, COVID-19 disease Over 80% of respondents over the age of 70 and almost 80% between 1 and 19 years were either sure they had not experienced symptomatic COVID-19 disease or were not sure if they had or had not (implying any symptoms were likely to be have been mild and transient). Around three quarters of the age bands between 20 and 49 and 50 to 69 similarly reported no COVID-19 disease (Fig. 7). 0 2 4 6 8 10 12 1-19 20-49 50-69 70+ Percentage of respondents Age category Male Female ©2022 Alliance for Natural Health International 12 Figure 7. Respondents reporting that they had not had COVID-19 disease or were not sure if they had experienced the disease. Additionally, 11.6% of the respondents aged 50 to 69 chose not to disclose their past or current COVID-19 disease status, this choice to not disclose status being considerably lower in other age groups (2.0 – 3.5%). c. Reported COVID-19 antigen testing outcomes Nearly 20% of respondents aged 50 to 69 reported having received one or more positive tests while also experiencing symptoms, with only 1.9% in this same age range reporting positivity in the absence of symptoms (Fig. 8). Those over 70 reported the lowest rate of positive tests, with all age groups reporting much greater rates of positivity with, rather than without, symptoms (Fig. 8). 66 68 70 72 74 76 78 80 82 84 1 to 19 20 to 49 50 to 69 70+ Percentage of respondents % Female % Male Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 13 Figure 8. Percentage of respondents reporting positive antigen tests both with and without COVID-19 symptoms. d. SARS-CoV-2 neutralising antibody outcomes Over 1 in 5 (23.5%) respondents between the ages of 50 and 69 reported having been being positive for SARS-CoV-2 (neutralising) antibodies during the survey period, although only 8.3% of these were confirmed with positive serology tests. Figure 9. Reported positive serology (SARS-CoV-2 neutralising antibodies) by age group. 0 5 10 15 20 25 1-19 20-49 50-69 70+ Percentage of respondents % +ve with symptoms % +ve without symptoms 0 5 10 15 20 25 1-19 20-49 50-69 70+ Percentage of respondents % Confirmed % Unverified ©2022 Alliance for Natural Health International 14 Confirmed or unverified presence of SARS-CoV-2 antibodies were reported least often by the oldest age band, the over 70s, which also had the lowest reported incidence of COVID-19 disease (Fig. 6). e. Reported COVID-19 disease by age group and month The greatest incidence of reported COVID-19 disease was in January 2022, with a clear escalation which mirrors the generalised, global displacement of the dominant circulating SARS-CoV-2 variant from delta to omicron, especially during the European winter (where respondent numbers were greatest) (Fig. 10). Figure 10. Reported COVID-19 disease over 5 months of survey showing proportion in each of four age bands. In terms of age bands, the 50 to 69 years age range reported the highest incidence of COVID-19 disease (12.3% of respondents), followed by the 20 to 49 year group (10.7%), with considerably lower reporting (1.3 -3.8%) of suspected or confirmed COVID-19 disease among both the youngest and oldest age bands (Fig. 11). 0 1 2 3 4 5 6 7 8 9 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 Perecentage of subjects reporting COVID-19 1-19 20-49 50-69 70+ Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 15 Figure 11. Reported COVID-19 disease by age band during the 5 months of survey. f. Severity of COVID-19 symptoms One quarter (25.1%) of the survey cohort reported some symptomatic disease (n = 4636) at some stage during the survey period, most (~14%) being mild, around 8% reportedly moderate and just 2% with severe disease (Fig. 12). Some 3% reported asymptomatic disease. The 50 to 69 age band reported the highest incidences of disease of all severities (Fig. 12) Figure 12. Reported severity of COVID-19 disease among those with known or suspected SARS-CoV-2 infection as a proportion of the survey cohort. ©2022 Alliance for Natural Health International 16 When patients reporting COVID-19 symptoms were asked for how long they were sick or unwell, of those who answered (n= 4496), 54% indicated they were sick for less than a week, 20% between 1 and 2 weeks and 11% for over 3 weeks (Table 6). Table 6. Reported duration of sickness following suspected or known SARS-CoV-2 infection. Health status n % Generally Well 649 14.4 Sick < 1 Week 2440 54.3 Sick 1-2 Weeks 902 20.1 Sick 3 Weeks+ 505 11.2 Total 4496 100.0 g. Symptoms in relation to age ‘Fatigue’ was the most commonly reported symptom of COVID-19 disease, closely followed by ‘cough’ and ‘muscle or body pain’. Symptom ranking by frequency of reports is shown in Table 7. Table 7. Ranking symptoms by reporting frequency during survey period Symptom Qty Reports Ranking Fatigue 4786 1 Cough 4305 2 Muscle or Body Aches 4296 3 Fever 3613 4 Loss Of Taste 1846 5 Loss Of Smell 1791 6 Difficulty Breathing 1346 7 Diarrhoea 915 8 Most symptoms were reported among the 50 to 69 year age band, with between one and 3 symptoms being most commonly reported in all age classes. In the youngest age class (1-19 years), there were proportionately fewer respondents reporting 4 to 6 symptoms compared with the other three age classes (Fig. 13). Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 17 Figure 13. Number of COVID-19 symptoms reported by age band among those with suspected or known COVID-19 disease. There was relatively little variation in the frequency of reporting of the 8 different symptoms, as shown in Figure 14. 0 200 400 600 800 1000 1200 1-19 20-49 50-69 70+ Number of respondents reporting symptoms Age class No Symptoms Reported 1-3 symptoms 4-6 symptoms 7+ symptoms ©2022 Alliance for Natural Health International 18 Figure 14. Symptoms reported by respondents in 4 age bands with known or suspected COVID-19 disease. h. Reported within-household transmission Over twice (2.2-fold) the number of respondents with suspected or known SARS-CoV-2 infection indicated that other family members within the same household had also suffered COVID-19 disease, compared with those who did not report disease. However, of these, nearly one-third (31%, n = 1435) indicated no other family members in the same household had become ill. i. Hospitalisations Only 74 respondents out of the 5196 (1.4%) who reported suspected or known SARSCoV-2 infection also reported that they were hospitalised following infection. Therefore, outpatient or inpatient hospitalisation was reported in just 0.4% of the full survey cohort. Of these, 15 were outpatient only, another 15 were hospitalised for less than 3 days, 26 were hospitalised between 3 and 7 days, 11 for between 7 and 14 days and only 10 for more than 14 days. 0 10 20 30 40 50 60 Cough Diarrhoea Difficulty Breathing Fatigue Fever Loss Of Smell Loss Of Taste Muscle Or Body Aches Percentage of respondents with reported COVID-19 1-19 20-49 50-69 70+ Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 19 These figures represent an overestimate as in some cases, a single individual made more than one visit to hospital. j. Self-administered treatments among COVID-19 patients The majority of respondents with suspected or confirmed COVID-19 engaged in selfadministered treatments using vitamins (C, D), minerals (zinc) and off-label medications (ivermectin [IVM] and hydroxychloroquine [HCQ]) during the 5-month survey period. Vitamins C, D and zinc were the most common self-administered treatments reported, with some 71% of the survey cohort (n = 3701 out of 5196) reporting regular usage. Selfadministration of these treatments or supportive nutrients was much lower in a hospital setting than at home and declined in frequency as symptom severity increased (Fig. 15). Figure 15. Respondents reporting COVID-19 disease who self-administered vitamins C and D and zinc (=Vit/min), off-label medications (ivermectin [IVM] or hydroxychloroquine [HCQ]) (=IVM/HCQ), or other products or medications (=Other) during the survey period. 0 500 1000 1500 2000 2500 Not hospitalised Hospitalised Not hospitalised Hospitalised Not hospitalised Hospitalised Not hospitalised Hospitalised Generally Well Mild Moderate Severe Number of respondents reporting COVID-19 disease Vit/min IVM / HCQ Other ©2022 Alliance for Natural Health International 20 k. Dietary supplement use among cohort Sixty four percent of all respondents reported taking vitamin C, vitamin D, zinc or quercetin, or any combination of these, routinely during the survey period for preventative purposes (Fig. 16). Among those taking supplements, vitamin D was most commonly consumed (53.3% of respondents), closely followed by vitamin C (51.7%), in turn followed by zinc (42.4%), with quercetin being the least used (15.5%) of the four. Supplement use in North America (USA and Canada) exceeded other parts of the world (Fig. 16). Figure 16. Distribution of CGC respondents routinely taking specific dietary supplements (vitamin C, vitamin D, zinc or quercetin) for prevention. l. Mental health Around 4 in 10 respondents in the survey cohort, regardless of age, reported sustained mild or moderate mental health issues through the duration of the survey. Half this number reported severe mental health issues (Fig. 17). Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 21 Figure 17. Percentage of cohort reporting mild, moderate or severe mental issues during each month of the survey. Over the 5-month survey period, around half the respondents reported sustained mild mental health issues throughout the survey’s duration, the reports being highest for the oldest and youngest age bands. Reports of moderate mental health issues dropped to around 3 to 4 in 10, with reports then being higher among the intermediate age bands. About 2 in 10 in each age band reported severe, sustained mental health issues (Fig. 18). Figure 18. Proportion of respondents reporting mental health issues by age band. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 Percentage of respondents (n = 18,497) Mild Moderate Severe 0 10 20 30 40 50 60 1 – Mild 2 – Moderate 3 – Severe Percentage of respondents ( n = 18,497) 1-19 20-49 50-69 70+ ©2022 Alliance for Natural Health International 22 m. Bleeding abnormalities There were significant numbers of reports of unusual bleeding among the non-COVID-19 ‘vaccinated’ women in the cohort, particularly those in the age band, representing the highest proportion of menstruating women, ages 20 to 49 (Fig. 19). The most commonly reported named menstrual abnormality was irregular periods (1,210 reports) among the 20 to 49 year age band, this representing 36% of women in the age band. Figure 19. Number of female respondents reporting menstrual or other bleeding abnormalities Additionally, 12.0% of female respondents reported unusual nosebleeds during the course of the survey, compared with 4.7% of men. This difference between females and males was even more pronounced for reports of unspecified unusual bruising, which was reported by 12.7% of females, but just 1.7% of males (all age groups). n. Mask wearing In October and November 2021 (before the omicron variant surge around the world became dominant) there were only slight variations associated with different durations of mask wearing, despite those who never wore masks having the lowest rates of COVID-19 symptom reports. In December 2021 through to February 2022 inclusive, however, there was an apparent 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Heavier BleedingIrregular Periods Longer Bleeding Missed Period Unusual Clotting Other Number of female respondents 1-19 20-49 50-69 70+ Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 23 and clear association between those reporting never wearing a mask and those experiencing the lowest rates of suspected or known COVID-19 disease. These data provide no information on any causal association between mask wearing and COVID-19 disease incidence given the wide range of uncontrolled behavioural and confounding factors likely to be involved. Figure 20. Percentage of respondents with known or suspected COVID-19 disease according to their mask wearing habit over the 5 months of the survey. o. Job losses Job losses among the survey cohort were determined as a proportion of the respondents reporting loss of employment during the survey period, using as the denominator the numbers in the cohort between the ages of 20 and 69 inclusive (the primary working age range) (Fig. 21). The greatest reported job losses in relation to the numbers of respondents in each region were reported in Australia and New Zealand (n = 1,097; 29% of respondents). This rate was over double that reported in North America (n = 467; 13%), and much greater than that from the areas with the next highest losses, namely Southern Europe (n = 73; 13%) and South East Asia (n = 39; 12%). 0 5 10 15 20 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 Percentage of respondents reporting COVID-19 symptoms % of people with covid disease wearing/not wearing masks > 8 hours Most Days > 4 hours Most Days > 2 hours Most Days Rarely Never ©2022 Alliance for Natural Health International 24 Figure 21. Job losses in different regions among the COVID-19 unvaccinated survey cohort as a proportion of respondents of working age (20 to 69 years). Among the occupations affecting job losses, teachers were the most common, followed by nurses, those declared as self-employed, support workers and social workers. p. Discrimination The survey requested information about whether respondents had faced discrimination personally by members of society, or by their state (country). Between 20% and nearly 50% of respondents, depending on region, reported being personal targets of hate, implying victimisation, owing to their COVID-19 vaccination status (Fig. 22). Proportionately, rates of such victimisation were highest in Southern Europe and South America and lowest in Western Asia and Southern Africa (although the number of respondents in these latter regions were substantially lower). Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 25 Figure 22. Percentage of respondents by region reporting hate or victimisation during the 5-month survey period. Respondents reported feeling even more victimised by their respective states, with rates among respondents being greatest in Southern Europe (61%), Western Europe (59%), Australia and New Zealand (57%) and South America (57%) (Fig. 23). Figure 23. Reported state victimisation of ‘unvaccinated’ respondents. ©2022 Alliance for Natural Health International 26 DISCUSSION As soon as COVID-19 intra-muscular genetic vaccines were issued with emergency use authorisation by national regulatory authorities towards the end of 2020, coercive pressure was placed on populations to receive the injections, starting with the oldest age groups and those deemed most vulnerable to severe COVID-19 disease. These genetic vaccines all utilised either the mRNA (Pfizer, Moderna) or adenoviral vector (e.g. AstraZeneca, Johnson & Johnson, Serum Institute of India, Gamaleya Institute) platform (Heinz and Stiasny, 2021). Large numbers of people in different parts of the world have chosen to avoid the injections. Such dissenters have been widely stigmatised and marginalised by mainstream society, being referred to variously as “anti-vaxxers” or “conspiracy theorists”. At the time of writing, Our World in Data (2022), which consolidates data from official country sources, suggests that 35% of the world population has yet to receive any COVID-19 injections, this number rising to 84% in low-income countries. The same database suggests 77% of the population of the African continent, equating to over 1 billion people, and nearly 31% of Europeans, equating to some 232 million people, have yet to receive any COVID-19 vaccines. Some 22% of Americans (73 million), 14% of Canadians (5.3 million) and 13% of Australians (3.3 million) have reportedly not yet received COVID-19 vaccines (Our World in Data, 2022). The CGC is a grassroots, UK-based, internationally active organisation that came into being in mid-2021 to help support this substantial group of COVID-19 unvaccinated people who had already been subject to victimisation, stigmatisation, discrimination or marginalisation by mainstream society, especially in industrialised countries. By contrast, mainstream society immediately backed, in the absence of robust scientific evidence, global mass vaccination with what were initially experimental products reliant on novel platforms that had never before been tested at scale. CGC respondents in the survey gave various reasons for declining COVID-19 injection, including distrust of health authorities, governments or the pharmaceutical industry, insufficient evidence of safety or effectiveness, or concerns over injuries or potential adverse reactions, for which the manufacturers typically have indemnity in the event of compensation for injuries resulting from vaccination. Since the mass roll-out of experimental products was initiated in late 2020, the products have been found to deliver very little protection against transmission of the current, dominant, circulating, omicron variant (Amanatidou et al, 2022). This means the products do not fulfil the widely accepted purpose of a vaccine, which is to induce herd immunity by triggering an immune response that fully neutralises or sterilises the pathogen so preventing transmission. The World Health Organization (WHO) updated its description of ‘herd immunity’ on 31 December 2020, stating: “WHO supports achieving ‘herd immunity’ through vaccination, not by allowing a disease to spread through any segment of the population, as this would result in unnecessary cases and deaths” (WHO, 2020). Additionally, immunologic effectiveness even against disease was found to wane rapidly, within a few months (Israel et al, 2021; Ferdinands et al, 2022) implying that those relying on COVID-19 injections would Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 27 need to consent to regular, e.g. 6-monthly, exposure to the injections, a regimen that had yet to be subject to any safety trials. There is a growing body of evidence that suggests that individuals reliant on naturallyacquired immunity develop broader-based and more robust immunity to SARS-CoV-2 than those reliant on vaccine-induced immunity (Gazit et al, 2021; Turner et al, 2021; Cohen et al, 2021). Such naturally-acquired immunity is likely to play a key role in dampening the hostpathogen population dynamics of the virus that appears to have been new to humanity prior to 2019, as well as reducing the risk of developing more virulent and transmissible variants (Koyama et al, 2022). Jonathan Pugh and colleagues from the Faculty of Philosophy at the University of Oxford, argued in the Journal of Medical Ethics that “[w]ithout compelling evidence for the superiority of vaccine-induced immunity, it cannot be deemed necessary to require vaccination for those with natural immunity.” (Pugh et al, 2022). It follows that discrimination against individuals who have elected to invoke natural immunity, in place of vaccine-induced immunity, would be unjust. The data from the first 5 months of the CGC survey suggest that unvaccinated populations have not placed any significant additional burden on healthcare systems in their respective countries, as compared with those who consented to COVID-19 injections. In the UK, official data reveals that 33% of the population tested positive via either PCR or lateral flow tests during the whole pandemic, with the highest case rates occurring in late 2021 and early 2022 during the period of the CGC survey (GOV.UK, 2022). While some 25% of CGC survey respondents reported symptomatic COVID-19 disease during the 5 months of the survey, the incidence of disease does not itself indicate the burden on healthcare systems or society; this is better assessed by hospitalization rates and mortality (there were no CGC data available for the latter). The COVID-19 disease burden for the USA was estimated by the US Centers for Disease Control and Prevention (CDC) for the period February 2020 to September 2021 (CDC, 2022). The estimate included 124 million cases of symptomatic illness, 7.5 million hospitalisations and 921,000 deaths. This equates, following a pro rata adjustment to include mean data over a 5-month period to match the survey period of CGC, an average of 10.4% of the US population had symptomatic disease, 0.6% of the US population was hospitalised, and 0.3% died with COVID-19 on their death certificate. By comparison, the self-selected, self-reported, CGC population sample reported 25% symptomatic disease (suspected or confirmed), with just 0.4% of the cohort (one-third less than the adjusted CDC estimate) being hospitalised. The CGC survey did not report on mortality given the self-reporting nature of data collection. While the number in the CGC cohort reported to have experienced symptomatic disease is substantially greater than the CDC figures (25% versus 10.4%), this may be in part because the majority were suspected, rather than confirmed, cases, and so were more likely to have been reported. Cases manifesting as symptomatic disease were greatest among middleaged people in the age band 50 to 69 years, which likely reflects age-dependent ©2022 Alliance for Natural Health International 28 manifestation of disease (Omori et al, 2020), and shielding among the oldest, most vulnerable age group. The adjusted CDC estimates and the CGC survey data should be compared with caution as they originate from different regions of the world, they have been derived from different time periods, the CDC includes different proportions of vaccinated and unvaccinated over the 19 months of its collection, and both datasets relied on different reporting systems. However, it is of interest that the CGC cohort included a period (October 2021 to February 2022 inclusive) with the highest rates of SARS-CoV-2 infection in many parts of the world, including North America and Europe, during the first omicron wave. Overall, the survey findings suggest there is no adequate basis on which to suggest the CGC cohort and, by extension, other health-aware populations who have elected to avoid COVID19 injections, have represented a disproportionate burden on health systems compared with those who have received one or more injections. To the contrary, almost 3 out of 4 respondents who had COVID-19 engaged in self-care using vitamins (D and C), minerals (notably zinc) and/or quercetin. Reported selfadministration of these micronutrients, as well as ivermectin and hydroxychloroquine, dropped off dramatically for those who were hospitalised, presumably at least in part because of lack of support for use of natural products in hospital settings (a phenomenon that has been widely reported to the authors anecdotally). The percentage of populations engaging in preventative self-care using dietary supplements containing vitamins C, D, zinc or quercetin was highest in the USA at 71% of respondents, and somewhat lower, but still high (60-65%), in Europe, Australia and New Zealand. These data compare favourably with the 47% of UK users of the Zoe app in the COVID-19 Symptom Study (n = 372,720) who reported using dietary supplements (Louca et al, 2021). This latter study found modest reductions in risk of infection (9-14%) among those routinely using vitamin D, multivitamins, omega-3 fatty acids or probiotics. Among the most surprising findings in this COVID-19 unvaccinated cohort were the commonly reported instances of menstrual disturbances and bleeding abnormalities in women. Such disturbances have been reported in the literature in association with COVID19 disease (e.g. Sharp et al, 2021), lifestyle changes associated with the pandemic (Bruinvels et al, 2021), and particularly following COVID-19 vaccination (e.g. Alvergne et al, 2021; Trogstad, 2022). The disturbances reported in the survey are likely to be related to COVID19 disease, but other factors such as shedding exposure, chronic stress and changes to lifestyles caused by restrictions and related measures, as well as chronic spike protein exposure (‘spikopathy’) in domestic and occupational settings, could also have been involved. There was a high proportion (around 40%) of respondents who reported mental health issues during the reporting period. This was in line with the effects of ongoing chronic, psychological stress associated with the pandemic, as found in other studies, 66 of which have been pooled as part of a comprehensive, global, systematic review and meta-analysis carried out by a group of Chinese researchers (Wu et al, 2021). Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 29 In this specific cohort that has placed more trust in the human immune system than in novel ‘genetic vaccines’, the mental health burden may be associated more to the human response to the pandemic, rather than psychological, fear-based reactions to any threat posed by the SARS-CoV-2 virus itself. This includes discrimination in the workplace, by peers or by family members, as well as victimisation by states (governments/health authorities) owing to ‘unvaccinated’ status. Much of this disproportionate and discriminatory treatment is likely the result of widespread misunderstandings about, and over-stated benefits of, COVID-19 ‘vaccines’, false claims over societal risks posed by the unvaccinated, media and state propaganda and coercion to ensure high rates of COVID-19 vaccination, institutional mandates, and the desire for in-group identity as explained by social identity theory (Scheepers and Derks, 2016). In line with the scapegoating of those who have not consented to COVID-19 injection, it was also relevant that those respondents in the CGC survey who reported never wearing facial coverings or masks also experienced the lowest incidence of suspected or confirmed COVID19 disease. The scientific basis for the continued pressure on populations to receive COVID-19 ‘vaccines’ and boosters remains elusive. There is still inadequate governmental and health authority recognition of the breadth and depth of injuries which are underreported to the Vaccine Adverse Event Reporting System (VAERS) in the USA (refer to OpenVAERS [www.openvaers.com] for summaries), the Medicines and Healthcare products Regulatory Agency (MHRA) Yellow Card system in the UK, EudraVigilance in Europe, and similar national reporting systems elsewhere. Research by a German insurance company, BKK ProVita, suggested in February 2022 following its own analysis of available data, that there is already a “violent alarm signal” in Germany which implies substantial underreporting of injection injuries by the responsible health authority, the Paul Ehrlich Institute. The findings allude that between 4 and 5% of those to whom COVID-19 injections have been administered are engaging, or have engaged, with treatments to deal with COVID-19 injection injuries (Deutsche Wirtschaft Nachricten, 2022), amounting to 2.5 to 3 million people in Germany (Phillips, 2022). Unfortunately, given the desire to uphold the mainstream narrative that wrongly insinuates mass roll-out of COVID-19 vaccines is the only means of resolving the pandemic, the executive responsible for disclosing these findings, Andreas Schöfbeck, was sacked by BKK following public release of the findings (Deutsche Wirtschaft Nachricten, 2022). This is another stark reminder of the discriminatory consequences of speaking out against the mainstream narrative even where ample supporting data are available and in the public interest Similar findings from Israel suggest the scale of COVID-19 injection injuries, and the need for medical support for those affected, is much greater than widely reported (Guetzkow, 2022). ©2022 Alliance for Natural Health International 30 Thus, when comparing health system burdens between COVID-19 vaccinated and ever more constrained unvaccinated (‘control’) populations, the short- and long-term impacts of injection-related injuries needs to be accounted for. There has been a seemingly deliberate effort by vaccine manufacturers and associated Phase 3 clinical trial study teams to remove data that allows comparison of outcomes between COVID-19 injected and un-injected (control) populations. The release of Pfizer data (322 documents at the time of writing) following the successful legal action in the USA by Public Health and Medical Professionals for Transparency (phmpt.org), with which the authors are associated, will likely in time confirm the misleading nature of the safety and effectiveness claims made by health authorities and vaccine manufacturers for the current crop of COVID-19 injections. The findings from the present survey have five main limitations; 1) the respondents are selfselected and therefore not randomly selected; 2) the data are self-reported and therefore have not been verified independently; 3) the ~18,500 participant cohort may have been biased towards the most diligent, health-conscious participants given they all completed monthly surveys (although a number of cross-checks with the main ~300,000 cohort suggests this bias is likely low); 4) there is no available comparative ‘control’ population that includes individuals who have consented to one or more COVID-19 vaccines of different types; and; 5) the questionnaire design is limited and does not account for multiple variables that affect health status, such as socioeconomic status, urban, peri-urban or rural residence, diet, or lifestyle. That being said, the survey data do offer indicative or suggestive evidence that the CGC COVID-19 unvaccinated cohort prioritizes self-care and has placed very little burden on healthcare systems in respective countries. It follows, then, that the marginalization, stigmatization, coercion of and discrimination against this population group, one that has not consented to COVID-19 injections, is neither valid nor ethical. Such discrimination and restriction of liberties based on vaccination status may fall foul of relevant national antidiscrimination laws and international treaties, such as the UN’s International Covenant on Economic, Social and Cultural Rights (ICESCR, 1966), which includes fundamental rights to liberty and security of person, freedom of movement, privacy, religion and belief, freedom of expression, and peaceful assembly. The findings also amplify the great need to ensure that well conducted observational studies are carried out in order to compare outcomes, choices and potential discrimination in COVID-19 vaccinated and unvaccinated populations. ACKNOWLEDGEMENTS Derren Fielder, Diny Fielder-Van Kleef and Rachael Tubbs were responsible for the CGC questionnaire and data collection from respondents. The full CGC dataset was made available to RV who then worked with Derren Fielder to extract selected data for this work. The authors thank Melissa Smith of the Alliance for Natural Health International for helping collate and analyse the data in Excel. Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 31 DECLARATION OF INTERESTS None of the authors have any competing interests. FUNDING STATEMENT CGC is a membership organization and accordingly receives subscription fees, as well as donations, to help conduct research and provide support for vaccine-free communities as well as COVID-19 vaccinated individuals who have decided to opt out of ongoing vaccination programs.

The authors are entirely independent of CGC and received no funding to undertake the present work.

REFERENCES

1. Alvergne A, Kountourides G, Austin Argentieri M, et al. COVID-19 vaccination and menstrual cycle changes: A United Kingdom (UK) retrospective case-control study. medRxiv 2021.11.23.21266709; doi: 10.1101/2021.11.23.21266709.

2. Amanatidou AG, Pella E, Serafidi M, et al. Breakthrough infections after COVID-19 vaccination: Insights, perspectives and challenges. Metabolism Open 2022; 14: 100180.

3. Bruinvels G, Goldsmith E, Blagrove RC, et al. How lifestyle changes within the COVID19 global pandemic have affected the pattern and symptoms of the menstrual cycle. medRxiv 2021.02.01.21250919; doi: 10.1101/2021.02.01.21250919.

4. CDC, 2022. Estimated COVID-19 Burden; updated Nov. 16, 2021: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/burden.html [last accessed 7 June 2021].

5. Cohen KW, Linderman SL, Moodie Z, et al. Longitudinal analysis shows durable and broad immune memory after SARS-CoV-2 infection with persisting antibody responses and memory B and T cells. medRxiv 2021.04.19.21255739; doi: 10.1101/2021.04.19.21255739.

6. Deutsche Wirtschaft Nachricten, 24 Feb 2022: Health insurance BKK raises the alarm: number of vaccination side effects much higher than known. https://deutsche-wirtschafts-nachrichten.de/517708/Krankenkasse-BKK-schlaegtAlarm-Zahl-der-Impfnebenwirkungen-viel-hoeher-als-bekannt [last accessed 7 June 2022].

7. Gazit S, Shlezinger R, Perez G, et al. Comparing SARS-CoV-2 natural immunity to vaccine-induced immunity: reinfections versus breakthrough infections. medRxiv 2021.08.24.21262415; doi: 10.1101/2021.08.24.21262415. ©2022 Alliance for Natural Health International 32

8. Guetzkow J. The Israeli Ministry of Health Actually Did a Survey of Adverse Events After the Booster Dose. Substack article: https://jackanapes.substack.com/p/theisraeli-ministry-of-health-actually-db7?s=r, 18 Feb 2022 [last accessed 7 June 2022].

9. Heinz FX, Stiasny K. Distinguishing features of current COVID-19 vaccines: knowns and unknowns of antigen presentation and modes of action. npj Vaccines 6; 104 (2021). doi: 10.1038/s41541-021-00369-6. 10. Fenton N, Martin N, McLachlan S. Paradoxes in the reporting of Covid19 vaccine effectiveness: Why current studies (for or against vaccination) cannot be trusted and what we can do about it. ResearchGate. 2021. DOI: 10.13140/RG.2.2.32655.30886.

11. Ferdinands JM, Rao S, Dixon BE, et al. Waning 2-Dose and 3-Dose Effectiveness of mRNA Vaccines Against COVID-19-Associated Emergency Department and Urgent Care Encounters and Hospitalizations Among Adults During Periods of Delta and Omicron Variant Predominance – VISION Network, 10 States, August 2021-January 2022. MMWR Morb Mortal Wkly Rep. 2022; 71(7): 255-263.

12. GOV.UK. Cases in United Kingdom: https://coronavirus.data.gov.uk/details/cases; 33,151.9 cases per 100,000 population [last accessed 7 June 2022].

13. ICESCR, 1966. The International Covenant on Economic, Social and Cultural Rights. Adopted December 1966 by General Assembly resolution 2200A (XXI): https://www.ohchr.org/en/instruments-mechanisms/instruments/internationalcovenant-economic-social-and-cultural-rights [last accessed 7 June 2022].

14. Israel A, Shenhar Y, Green I, et al. Large-scale study of antibody titer decay following BNT162b2 mRNA vaccine or SARS-CoV-2 infection. medRxiv 2021. 08.19.21262111; doi: 10.1101/2021.08.19.21262111. 15. Koyama T, Miyakawa K, Tokumasu R, et al. Evasion of vaccine-induced humoral immunity by emerging sub-variants of SARS-CoV-2. Future Microbiology 2022; 17(6): 417-424.

16. Mielke N, Johnson S, Bahl A. Fully Vaccinated and Boosted Patients Requiring Hospitalization for COVID-19: an Observational Cohort Analysis. MedRxiv 2022; doi: 10.1101/2022.01.05.22268626.

17. DHSC (Department of Health & Social Care, UK). Press release: REACT study shows fully vaccinated are three times less likely to be infected. GOV.UK. 4 August 2021. https://www.gov.uk/government/news/react-study-shows-fully-vaccinated-arethree-times-less-likely-to-be-infected 1

18. Louca P, Murray B, Klaser K, et al. Modest effects of dietary supplements during the COVID-19 pandemic: insights from 445 850 users of the COVID-19 Symptom Study app. BMJ Nutr Prev Health. 2021; 4(1): 149-157. Preprint draft, uploaded to ResearchGate June 8, 2022 ©2022 Alliance for Natural Health International 33

19. Lyons-Weiler J, Thomas P. Relative Incidence of Office Visits and Cumulative Rates of Billed Diagnoses Along the Axis of Vaccination. Int J Environ Res Public Health 2020; 17: 8674. https://www.mdpi.com/1660-4601/17/22/8674.

20. Omori R, Matsuyama R, Nakata Y. The age distribution of mortality from novel coronavirus disease (COVID-19) suggests no large difference of susceptibility by age. Sci Rep 2020; 10: 16642.

21. Our World in Data, 2022: https://ourworldindata.org/covid-vaccinations [last accessed 7 June 2022).

22. Phillips J. ‘Data Show ‘Significant Underreporting’ of Vaccine Side Effects: German Health Insurer’. Epoch Times, 23 February 2022: https://www.theepochtimes.com/data-show-significant-underreporting-of-vaccineside-effects-german-health-insurer_4297632.html [last accessed 7 June 2022]

23. Pugh J, Savulescu J, Brown RCH, et al. The unnaturalistic fallacy: COVID-19 vaccine mandates should not discriminate against natural immunity. J Med Ethics 2022; 48: 371-377.

24. Scheepers D, Derks B. Revisiting social identity theory from a neuroscience perspective. Curr Opin Psychol. 2016; 11: 74-78.

25. Sharp GC, Fraser A, Sawyer G, et al. The COVID-19 pandemic and the menstrual cycle: research gaps and opportunities. Int J Epidemiol. 2021: dyab239.

26. Subramanian SV, Kumar A. Increases in COVID-19 are unrelated to levels of vaccination across 68 countries and 2947 counties in the United States. Eur J Epidemiol. 2021; 36(12): 1237-1240. https://pubmed.ncbi.nlm.nih.gov/34591202/

27. Trogstad, Lill, Increased Occurrence of Menstrual Disturbances in 18- to 30-Year-Old Women after COVID-19 Vaccination (January 1, 2022). Available at SSRN: https://ssrn.com/abstract=3998180 or http://dx.doi.org/10.2139/ssrn.3998180. [last accessed 7 June 2022].

28. Turner JS, Kim W, Kalaidina E. et al. SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Nature 2021; 595: 421–425.

29. WHO, 2020. “Coronavirus disease (COVID-19): Herd immunity, lockdowns and COVID-19”: https://www.who.int/news-room/questions-and-answers/item/herdimmunity-lockdowns-and-covid-19; updated 31 December 2020 [last accessed 7 June 2022].

30. Wu T, Jia X, Shi H, et al. Prevalence of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis. J Affec Disorders 2021; 81: 91-98.

Severe COVID ‘Rare’ in People Who Didn’t Get Vaccine, Survey Reveals

A survey of 300,000 people who didn’t get the COVID-19 vaccine revealed the unvaccinated didn’t place a disproportionate burden on health systems — in fact, they experienced very low rates of hospitalization and severe COVID-19.

Authors: Alliance for Natural Health International 06/09/22

There have been very few studies looking at how those who’ve elected to rely on natural immunity and natural products, as compared with those who’ve consented to COVID-19 genetic vaccines, the latter who may or may not have, also tried to optimize their immune systems, fare when it comes to COVID-19.

The few that have been done often mix vaccinated with unvaccinated, as shown in the case of U.K. data by professor Norman Fenton and his group at Queen Mary, University of London.

That changes with the first release of the analysis of survey data from the international Control Group project — also known as the Vax Control Group.

The citizen-led project was initiated by an Eastbourne (U.K.) cooperative, the Control Group Cooperative — and it’s had more than 300,000 subscribers.

Rob Verkerk Ph.D. of Alliance for Natural Health has led a team, including Dr. Naseeba Kathrada (general practitioner, South Africa, Caring Healthcare Workers Coalition), Christof Plothe D.O. (integrative and osteopathic practitioner, Germany) and Dr. Kat Lindley (family physician, USA), that has collated, analyzed and interpreted the first five months of survey data from “control group” participants.

The survey data offer important revelations, including:

  • The unvaccinated “control group” participants don’t place a disproportionate burden on health systems — in fact, quite the opposite, they have experienced very low hospitalization rates and severe COVID-19 disease is rare.
  • They are more likely to self-care, using natural products like vitamin D, vitamin C, zinc and quercetin.
  • Many have used ivermectin and hydroxychloroquine.
  • Women have suffered menstrual and bleeding abnormalities despite being unvaccinated, possibly owing to spike protein exposure and shedding.
  • Their mental health burden has been considerable, possibly aggravated by their stigmatization by the mainstream, “vaccinated” society.
  • They have been heavily discriminated against because of their decision to exercise their right to informed consent and refuse the administration of “genetic vaccines.”

No jab, lower hospitalizations — finds international survey

An international survey of a health-aware, “Control Group” that includes over 300,000 people who have chosen to avoid COVID-19 vaccination, shows participants place minimal burden on health systems through their strong reliance on natural immunity, self-care and the use of natural health supplements to help prevent or even treat COVID-19.

Yet this group faces unfounded discrimination, job losses and mental health issues intensified by its marginalization by mainstream society.

The survey of participants in the “Control Group” includes a sub-group from the over 305,000 participants from more than 175 countries who have joined the citizen-led project and opted to not receive COVID-19 vaccines.

The findings just uploaded to the preprint server ResearchGate, show that during the 5-month survey period (Sept. 2021 through to Feb. 2022 inclusive), participants suffered low rates of severe COVID-19 disease, were infrequently hospitalized, and used natural health products extensively both for prevention and for treatment of mild to moderate COVID-19.

Data from these first five months of the Control Group survey were analyzed and interpreted by an independent, international team led by Robert Verkerk Ph.D., a multi-disciplinary scientist and the founder, executive and scientific director of the non-profit Alliance for Natural Health International.

Co-authors included three practicing clinicians, Dr. Naseeba Kathrada from South Africa, Christof Plothe D.O. from Germany and Dr. Katarina Lindley from the USA.

The authors came together to assess the survey data through their collaboration in recent months with the World Council for Health, a non-profit, global coalition of health-focused organizations and civil society groups.

The survey findings were based on a sub-cohort of approximately 18,500 Control Group participants who had completed questionnaires on a monthly basis over the first five months of the survey.

Among the wide-ranging data collected, the survey captured reasons why participants avoided vaccines, with distrust of governments and pharmaceutical companies as well as concerns over adverse reactions from insufficiently tested vaccines being high on the list.

Participants reported extensive mental health problems that may have been compounded by the stigmatization and discrimination facing those who shunned COVID-19 vaccines.

It also found that women, despite being unvaccinated for COVID-19, suffered menstrual and bleeding abnormalities that may have been associated with viral exposure, shedding, spike protein exposure or pandemic-related behavioral changes. Those who never wore masks reported the lowest levels of COVID-19 disease.

Given the participants are self-selected and have self-reported, the survey findings need to be interpreted with care when comparing them with national statistics or studies based on randomly selected populations.

The U.K.-based Control Group project was established in mid-2021 as a citizen-led cooperative that aims to evaluate long-term health outcomes among the COVID-19 vaccine-free as well as linking its members to country support networks and online community groups.

COVID-19: Understanding long COVID

Authors: Emory News Center

For some individuals, the road to recovery from COVID-19 is long. While most people recover from mild COVID-19 symptoms over the course of one to two weeks, “long-haul” patients can suffer from lingering symptoms for months on end. This syndrome, called post-acute COVID-19 or “long COVID,” can have devastating effects on the daily lives of millions of patients.

To discuss what we know about long COVID, Jodie Guest, PhD, professor and vice chair of the department of epidemiology at Emory’s Rollins School of Public Health, teamed up with Alex Truong, MD, co-director of the post-COVID clinic at Emory’s Executive Park. Truong is also an assistant professor in the Division of Pulmonary and Critical Care Medicine at Emory University School of Medicine.

Their conversation follows up on a previous discussion conducted in 2021 and is part of an online video series hosted by Guest, who leads the Emory COVID-19 Outbreak Response Team, answering questions related to the pandemic.

Q: What symptoms are associated with long COVID?

A: “A lot of our patients come in with very, very similar symptoms of brain fog, fatigue and shortness of breath,” says Truong. “It’s almost as if I can cut and paste one patient’s story to the next patient.”

“It’s very rare that someone comes in with a singular issue,” he continues. “It’s always a host of issues. Most of the time, patients are complaining that their brain fog and fatigue are the biggest limiters of their activities of daily living — their ability to get back to work, the ability to go back to school or take care of their kids. There’s a smaller population of patients who have had chronic pain syndromes and chronic shortness of breath syndromes that are often very challenging to figure out.”

Q: How many people who have COVID-19 will develop long COVID?

A: “That’s a really hard question to answer, because I think we are lacking the data,” Truong says. “I think, unfortunately, we aren’t at a place yet where we really know who’s at risk, and so we don’t know what the proportions are.”

Guest notes that current estimates for the risk of long-term symptoms range broadly from 15% to 80% of COVID-19 patients.

“That span of statistics, being all over the board, includes patients who are severely sick in the hospital and those who are not severely sick,” Truong adds. “If I had to guess, I would have to say it’s probably closer to the 15 to 20% range, depending on the population you’re looking at.”

Q: Is hospitalization due to severe COVID-19 associated with an increased risk of long COVID?

A: “A lot happens in the hospital and in the ICU that puts patients at risk for long-term outcomes, regardless of whether they have COVID or not,” Truong says, noting that survivors of any critical illness are prone to develop symptoms such as cognitive dysfunction, chronic fatigue and chronic pain.

“We have a syndrome called post-ICU syndrome that actually captures this collection of conditions that patients suffer from,” he says. “I think it’s difficult to separate what is post-COVID syndrome versus what is post-ICU syndrome. As we move forward in this diagnosis, this category of post-COVID folks will have to be better defined so that we can better separate it from the post-ICU folks.”

One key difference Truong does see between the two syndromes is that post-acute COVID patients generally report issues with attention, concentration and brain fog, while individuals with post-ICU syndrome often experience more memory loss.

Q: Is long COVID associated with damage to cells in the body caused by COVID-19?

A: “Initially, we thought of COVID as a lung infection virus. We do see a lot of patients in our post-COVID populations who have persistent lung inflammation, and some patients progress to scarring, representing that there is direct lung parenchyma damage,” Truong says.

“We’re also realizing that COVID is possibly a vascular problem,” he continues. “It will affect the blood vessels and can cause a whole cohort of symptoms that may be explained by decreased blood flow, such as brain fog and some of the cardiomyopathies that we may be having.”

Some of these symptoms, such as lung and heart inflammation, may be reversed with medication. Unfortunately, symptoms like brain fog are more difficult to resolve.

“We’re only at the very beginning steps of understanding how COVID affects the brain,” Truong says. “There are some data that suggest there are proinflammatory changes within the brain and movements of proinflammatory cells past the blood-brain barrier that may affect the limbic system, or the core systems of your brain that are responsible for things like mood, attention and memory.”

“We’re still in the process of trying to figure out what the importance of these different pathologies are,” he adds, noting that further research is needed to better understand these symptoms.

Q: Can people who fully recover from COVID-19 develop long COVID symptoms later?

A: “It was originally assumed that long COVID was associated with severe symptoms during your original infection, but now there seems to be some data that people are developing new issues after fully recovering from COVID-19,” Guest says. “Even people who didn’t have symptoms can experience some of these post-COVID-related health issues, which can present as a lot of different types and combinations of health problems and seem to range for a long period of time.”

Truong says the causes of post-COVID syndrome among this category of seemingly recovered patients are particularly frustrating to trace.

“We do get a population of patients who have very mild symptoms or in some cases don’t have any symptoms and just incidentally were found to have COVID, and then months later developed a syndrome of brain fog, fatigue and shortness of breath that isn’t explained by some pathology and the lung,” he says.

“I think that initially, there was a lot of conversation as to whether inflammation was playing a role in why patients are having post-COVID syndrome,” he continues. “The thought was the more severely sick you initially were, the more likely you are to have post-COVID. That has totally been blown out of the water. We do not see that signal whatsoever.”

Truong adds that post-COVID symptoms among these patients may potentially be explained by preliminary research regarding COVID-19 and autoimmunity.

“Patients who get infected with COVID may recover from the initial illness easily, but subsequently develop some sort of auto-antibody or auto-protein that the body does react to, and then manifests a lot of these other symptoms,” he says.

Q: How do you know whether a patient has long COVID or another condition?

A: “That’s been the big challenge in our clinic,” Truong says. “When patients come in with what sounds like post-COVID syndrome, the first step is always to make sure that there were confirmed COVID tests.” Patients can’t be diagnosed with post-COVID syndrome without having had a confirmed positive COVID-19 test.

Next, the clinic conducts several tests to rule out other diagnoses for patients who struggle with symptoms such as brain fog and fatigue.

“We do a whole slew of lab work that checks for thyroid levels, vitamin D deficiency, vitamin B12 deficiency, anemia and a bunch of other abnormalities,” he explains.

Easily treatable conditions like hypothyroidism and significant anemia are rarely diagnosed at the post-COVID clinic, and while vitamin deficiencies are common, Truong says patients often continue to experience symptoms even after those vitamins have been repleted.

“I think, unfortunately, we’re at the stage where if you have a COVID diagnosis and I can’t figure out other causes for your symptoms, then I’m blaming it on post-COVID syndrome,” he says. “That’s not very sexy or scientific, but at this point in the evolution of this syndrome, this is where we’re at.”

Q: When should people seek care for long-term symptoms after having COVID-19?

A: “I think that what patients should expect is that after their acute illness, in the next two to four weeks after they’ve been infected, they’re not going to feel well,” Truong says. “They’re going feel like they have brain fog, they’re probably going to have fevers and chills, body aches and fatigue.”

“If those symptoms last beyond four to six weeks from their initial infection and it’s affecting their lives and the ways that they do their job, or affecting how they’re functioning and activities of daily living, then I think that’s when it’s really important for them to come see us,” he continues.

“There is a population of patients who do have lingering symptoms past those four to six weeks after infection, and it’s just getting better on its own, but slowly,” Truong adds. “To those patients, I would say, wait and watch. But if it’s really affecting you, and it’s not improving at all, then please don’t wait too long to come see us.”

Q: Is the post-COVID clinic at Emory’s Executive Park still busy?

A: “Yes, we are very busy,” Truong says. “Interestingly, I was expecting a much bigger wave of new patients with the Omicron wave. I think that we’re still seeing patients who were of the previous waves right now.”

“I feel like we’re so much better at taking care of patients,” he adds, highlighting the progress the clinic has made since last year. “I think we have a little bit more data on our side, we have a lot more experience on our side, and I think we’re able to approach patients in a way that is much more systematic.”

Q: Where else can long COVID patients turn for support?

A: Many individuals with long COVID find support through social media. Online communities for people suffering from post-COVID symptoms have formed over platforms such as Facebook, Reddit and Twitter.

“There are several communities that actually have been really helpful,” Truong says. “Patients are passing information around, both for the good and bad. But I think right now, patients are finding the Internet is a really good resource for finding information on how to take care of themselves, as well as finding professionals who may be able to help them along with their journey.”

“That support system can be so good if the information is accurate,” Guest adds.

Q: Can children experience long COVID?

A: “Children can definitely get the long-haul or post-COVID syndrome. I think they tend to be less likely to have symptoms of shortness of breath and respiratory issues, and more likely to have a lot of the brain fog and fatigue issues that we’re seeing,” Truong says.

While Truong only treats adult patients at the post-COVID clinic, he says he hears about teenagers who have experienced long-haul symptoms for more than a year after initial infection. “They can’t get back to school and do the things that they need to do — most of which, again, centers around the brain fog and fatigue.”

Q: Does long COVID impact women more than men?

A: At the Emory post-COVID clinic, Truong sees about twice as many female patients as he does male patients.

“I think the data we have is that more women are showing up to the clinic than men are,” he says. “I have no idea whether that is because post-COVID syndrome is affecting women more than men, or if it is that they’re just seeking care and men are being stubborn and not seeking care.”

“I think in part it has to do with a selection bias, but I wouldn’t be surprised if the rates are truly a little bit higher in women than men,” he adds. “In similar syndromes such as chronic fatigue syndrome, we do see that those rates are slightly higher in women than men.”

Q: Does vaccination impact the risk of developing long COVID?

A: “The data we have so far have suggested that yes, indeed, it is helping protect you from having post-COVID syndrome if you get a breakthrough infection after you’ve had all three of your shots, which includes the booster,” Truong says. “I do have a small population of patients who have had post-COVID syndrome after they’ve been vaccinated, but it seems like those folks tend to have rather mild disease and tend to resolve a lot faster than my folks who have not been vaccinated and got COVID, and thus post-COVID syndrome, after.”“The safest way to keep from getting long COVID, or post-COVID syndrome, is certainly to make sure you keep from getting COVID-19,” Guest says. “The best way to do that is by getting vaccinated, getting a booster, making sure you protect those people who are around you, and when cases are still high out in the community, please wear a mask when you’re indoors in public spaces.”

Long COVID symptoms lasted a median of 15 months, Northwestern study finds

Authors: Lisa Schencker, Chicago Tribune May 24, 2022

People with long COVID-19 who visited a Northwestern Medicine clinic were still experiencing symptoms such as headaches, dizziness, fatigue and brain fog for a median of 15 months after first falling ill, despite never needing hospitalization, according to a new Northwestern study.

The study looked at 52 patients who were seen at Northwestern’s Neuro COVID-19 clinic between May 2020 and November 2020, who initially had mild COVID-19 symptoms. Study senior author Dr. Igor Koralnik said the study is the first to look, over such a long time period, at neurological symptoms in people who didn’t need to be hospitalized for COVID-19.

The study was published Tuesday in peer-reviewed journal Annals of Clinical and Translational Neurology.

“It’s important because … long COVID is not going to be going away,” said Koralnik, who is chief of Neuro-infectious Diseases and Global Neurology at Northwestern Medicine and oversees the Neuro COVID-19 Clinic.

Researchers believe long COVID may affect up to 30% of people who get COVID-19, which means an estimated 24 million people in the U.S. may be experiencing lingering symptoms, according to the American Academy of Physical Medicine and Rehabilitation, though some studies have found that being vaccinated may reduce a person’s risk of developing long COVID if they catch COVID-19.

“This is something people need to know about because it impacts a very large population in the U.S.,” Koralnik said.

In the study, there was no significant change in the frequency of patients experiencing symptoms including brain fog, numbness/tingling, headache, dizziness, blurred vision, tinnitus and fatigue, between their first appointments and when they completed questionnaires six to nine months later.

Loss of taste and smell decreased over time, but heart rate and blood pressure variation and gastrointestinal symptoms increased at follow-up.

The average age of the participants was 43, and nearly two-thirds were women. More than two-thirds were vaccinated, but they were vaccinated after they began experiencing COVID-19 symptoms because the vaccines were not yet available when they first got sick. The vaccines did not seem to worsen or improve their cognitive function or fatigue, according to the study.

For the study, researchers reached out to the first 100 non-hospitalized patients who visited the Northwestern clinic, and the 52 studied were those who completed follow-up questionnaires. Those patients had varying experiences with long COVID, with some mainly experiencing loss of taste and/or smell, while others, like Emily Caffee, struggled with a debilitating litany of symptoms.

Caffee said she likely got COVID-19 at the very beginning of the pandemic while traveling for a rowing competition. She had body aches, fatigue, shortness of breath, chest pain and foggy thinking. Though she felt ill, she wasn’t so sick that she had to be hospitalized.

After her initial bout with COVID-19, she returned to her then-job as a physical therapist for Northwestern, but her symptoms soon worsened, to the point that she took a three-month-long medical leave from work, starting in May 2020.

She was plagued by crushing fatigue, brain fog, heart palpitations, vision issues, pain in her legs and neck and unrelenting anxiety — what she called her “buffet of misery.”

“I couldn’t stand for five minutes without getting so dizzy and nauseous I needed to lay down for an hour,” said Caffee, 36, of Wheaton. “If I knew I had to take a shower or follow a recipe or walk downstairs to get the mail, that was it, that was all I could do all day.”

In August 2020, she returned to work, slowly ramping up her hours. By September 2020, when she saw Koralnik, she was feeling about 50% better, she said. Koralnik told her to continue doing what she was doing, slowly resuming her activities, she said.

The feedback was validating, considering that she had previously been told her problems were related to anxiety. She never tested positive for COVID-19, as testing wasn’t yet widespread when she got sick.

“Just hearing from them, from a physician … that what I was going through was real and not just anxiety was key for me,” she said.

Caffee, who now works at PT Solutions in Bloomingdale, said she now feels about 95% back to normal. “This took over two years of my life that I feel was just suffering in so many ways, but I consider myself lucky I’ve come through it without any major complications.”

Like Caffee, about half of the patients in the study never tested positive for COVID-19. But Koralnik said it was important to include them because, like Caffee, many likely had COVID-19, based on their symptoms, before testing was readily available.

“Those patients have experienced a lot of rejection and stigma, and those people are often women in their 40s,” Koralnik said. “There are millions of those people who couldn’t get tested in 2020 yet they continued to have long COVID symptoms.”

Koralnik acknowledges that the study has its limitations. The study is based on just 52 patients, but Koralnik said researchers felt it was important to share what they’ve learned so far, rather than wait longer to study a larger group of people. Also, because the study consists of people who visited the Northwestern clinic and chose to participate in the study, it’s not representative of all people with long COVID who didn’t require hospitalization. About 90% of the study participants were white.

Still, Koralnik said Northwestern has had an open-door policy for its clinic, meaning patients did not need to be referred by other doctors or show proof of insurance. The clinic also has seen people from across the country, by performing both in-person appointments and telehealth visits.

The findings in the new study follow up on a Northwestern study published in March 2021 that found that 85% of people with long COVID-19, who did not require hospitalization, experienced four or more neurological symptoms that impacted their quality of life, and, in some cases, their cognitive abilities.

“This study is the first of its kind that was started in a difficult circumstance, in the lockdown in Chicago, and provides very unique and important data on this population of patients,” Koralnik said. “We hope it’s going to help clinicians take care of those patients and further studies are going to be done.”

More of the vaccinated and boosted landing in hospital with COVID-19

Authors: By Ariel Hart Zachary Hansen May 19. 2022 – The Atlanta Journal-Constitution

Doctors say it’s caused by a combination of a variant that can escape the vaccine’s effects and the most vulnerable also being the most vaccinated

As summer once again brings signs of a coming COVID-19 wave, an unusual trend has emerged: The Georgians who are fully vaccinated and boosted are increasingly winding up in the hospital with serious COVID-19 symptoms.

The phenomenon points to two changes in the unpredictable pandemic battleground more than two years in. The circulating omicron variant has become better at evading the vaccine, which was designed on the first version of coronavirus to appear in China. And the people most likely to get boosted are those who were most vulnerable to begin with: the elderly, or patients with pre-existing conditions. Despite the extra vaccine protection, those people remain the most vulnerable.

Even in light of the unexpected hospitalizations of those vaccinated and boosted, doctors say it’s still true that boosted groups are the least likely to die.

“I’ve had several older patients who have been boosted and had the vaccine,” said Dr. William Cleveland, a nephrologist in southwest Atlanta. “They get hospitalized, and they had to have some significant medical attention, but they get discharged. And I know that just because of their frailty, without having had the vaccine they would not have survived.”

The rate of hospitalizations for boosted Georgians fell again this week, but still remains higher than the rate of hospitalizations for those with only the primary vaccine series (2 shots). The fact that boosted patients’ hospitalizations nearly outstripped all others even for one week was an unprecedent moment in the pandemic. In the past, hospitalization rates for unvaccinated groups have drastically outnumbered those who have taken the vaccine — sometimes tenfold.

The trend emerged at the tail-end of the omicron variant outbreak and has accelerated over the past two months, setting off alarm bells for state public health experts already expecting a surge in cases this summer.

Dr. Eva Lee, director of the Center for Operations Research in Medicine and Healthcare at Georgia Tech, agreed that the rate of hospitalizations among boosted people was on track to outpace other populations. However, she said it’s not a sign of vaccines losing all effectiveness — it has to do with who is choosing to get boosted.

“A big part of the people that are boosted are also the ones that are really at high risk already to begin with, right?” Lee said. “But what has remained and hasn’t changed is the following: The people that are at risk remain at risk. That means the people that are immune-compromised and the people that are like the elderly people, and people who have coexisting conditions, their risk is still higher.”

Growing number of breakthroughs

Overall, the number of people hospitalized with COVID remains at or near the lowest rate since the beginning of the pandemic. But state data shows that the most protected and least protected groups are starting to find themselves fighting for their lives in Georgia hospitals at nearly the same levels.

According to Georgia Department of Public Health data, unvaccinated groups were being hospitalized due to COVID at twice the rate of other populations at the beginning of March. By the end of April, there were 1.3 hospitalizations per 100,000 vaccinated and boosted Georgians compared to 1.6 hospitalizations for every 100,000 unvaccinated Georgians.https://datawrapper.dwcdn.net/qtaSR/1/

In addition to at-risk groups being more likely to get every shot available to them, omicron and its subvariants have presented a challenge for the U.S.’s current vaccines. Breakthrough cases of less serious illness are now common, and health experts warn they are a sign of the vaccines’ waning immunity.

“Prior to Omicron we could, with a booster, assume there was well over 90-95% vaccine effectiveness vs severe disease,” Eric Topol, founder and director of the Scripps Research Translational Institute in New York, wrote in a recent column sounding the alarm for a summer surge in COVID-19 infections. “It is clear, however, from multiple reports … that this level of protection has declined to approximately 80%, particularly taking account the more rapid waning than previously seen. That represents a substantial drop-off.”

The growing number of breakthrough cases has prompted national health officials to discuss reformulating the current vaccines to specifically target omicron and its subvariants. The U.S. Food and Drug Administration has a meeting scheduled for June 28 to evaluate vaccine efficiency and composition.

Georgia hasn’t seen any noticeable uptick in COVID-19 deaths, but death reports often lag behind increasing hospitalization rates by several weeks.

While health experts are troubled by the rising hospitalization rates, they emphasize that COVID’s death toll would already be on the rise if the most at-risk Georgians weren’t vaccinated and boosted.

Surprised to still be alive

Raymond Fain knew he couldn’t risk getting COVID-19. Given he has kidney disease, the 58-year-old made sure to not only get fully vaccinated but he took a Pfizer booster shot to boot.

Just two months later, during the onslaught of the omicron variant this winter, he was shocked to be told that in spite of his vaccinations he caught COVID. What followed was a bad sickness and two rounds of hospitalization that totaled nearly a month. But at the end of it, came another surprise: He lived.

“I was sort of shocked that that disease that I caught didn’t overcome me with, the failed kidneys. You know what I’m saying?” Fain said.

Cleveland works with Fain’s doctor, both of whom have pleaded with their kidney patients to get vaccinated. Cleveland is all too familiar with kidney patients who get COVID and don’t make it. He’s heard all the excuses, and he’s ready to counter them.

“I’ve seen so much of that (kidney patients succumbing to COVID) that I do not hesitate to try to explain to my patients that I’ve just seen this too many times to to be comfortable with them saying that they are afraid,” Cleveland said.

The percentage of Georgia residents who’ve been vaccinated is among the lowest in the country — the peach state currently ranks 45th. The state’s booster adoption rate is even worse, with less than half of all fully vaccinated people choosing to get one booster dose.

There’s also a large age disparity among those getting boosted. Nearly 60% of all Georgia seniors, people 65 and older, have gotten a booster dose, but there’s a stark drop-off for younger populations. Only about 15% of 25- to 34-year-old Georgians are boosted.

The low booster adoption rate for younger people, who are less likely to be at a high risk of life-threatening infections, is an explanation for why boosted groups seem to be hospitalized at higher rates, health experts said.https://datawrapper.dwcdn.net/KYHdI/1/

“All such people need to have vaccination and booster coverage but our (Centers for Disease Control and Prevention) has failed to convey their life-saving impact from the get go…” Topol wrote in his column. “That’s why we have 31% of Americans who had had 1 booster shot whereas most peer countries are double that proportion.”

For Fain, he’s surprised he was able to pull through his severe bout with COVID and get back on his feet, but his friends and loved ones haven’t let him forget how close he was to death.

“Everybody’s going to talk to me now, they say, ‘Boy when you started, we thought you was going to get gone. You sounded so bad,’” Fain said. “Yeah, but everything is okay now. I’m strong.”

The Impact of Initial COVID-19 Episode Inflammation Among Adults on Mortality Within 12 Months Post-hospital Discharge

Authors: Arch G. Mainous III1,2*Benjamin J. Rooks1 and Frank A. Orlando1 May 12, 2022 Frontiers in Medicine

Background: Inflammation in the initial COVID-19 episode may be associated with post-recovery mortality. The goal of this study was to determine the relationship between systemic inflammation in COVID-19 hospitalized adults and mortality after recovery from COVID-19.

Methods: An analysis of electronic health records (EHR) for patients from 1 January, 2020 through 31 December, 2021 was performed for a cohort of COVID-19 positive hospitalized adult patients. 1,207 patients were followed for 12 months post COVID-19 episode at one health system. 12-month risk of mortality associated with inflammation, C-reactive protein (CRP), was assessed in Cox regressions adjusted for age, sex, race and comorbidities. Analyses evaluated whether steroids prescribed upon discharge were associated with later mortality.

Results: Elevated CRP was associated other indicators of severity of the COVID-19 hospitalization including, supplemental oxygen and intravenous dexamethasone. Elevated CRP was associated with an increased mortality risk after recovery from COVID-19. This effect was present for both unadjusted (HR = 1.60; 95% CI 1.18, 2.17) and adjusted analyses (HR = 1.61; 95% CI 1.19, 2.20) when CRP was split into high and low groups at the median. Oral steroid prescriptions at discharge were found to be associated with a lower risk of death post-discharge (adjusted HR = 0.49; 95% CI 0.33, 0.74).

Discussion: Hyperinflammation present with severe COVID-19 is associated with an increased mortality risk after hospital discharge. Although suggestive, treatment with anti-inflammatory medications like steroids upon hospital discharge is associated with a decreased post-acute COVID-19 mortality risk.

Introduction

The impact of coronavirus disease 2019 (COVID-19) has been immense. In terms of directly measured outcomes, as of February, 2022, worldwide more than 5.9 million people have died from directly linked COVID-19 episodes. More than 950,000 direct deaths from COVID-19 have been documented in the United States (1). Some evidence has suggested that some patients with COVID-19 may be at risk for developing health problems after the patient has recovered from the initial episode (24). Common sequelae that have been noted are fatigue, shortness-of-breath, and brain fog. Perhaps more concerningly, in addition to these symptoms, several studies have shown that following recovery from the initial COVID-19 episode, some patients are at risk for severe morbidity and mortality (58). Patients who have recovered from COVID-19 are at increased risk for hospitalization and death within 6–12 months after the initial episode. This morbidity and mortality is typically not listed or considered as a COVID-19 linked hospitalization or death in the medical records and thus are underreported as a post-acute COVID-19 sequelae.

The reason for this phenomenon of severe outcomes as post-acute sequelae of COVID-19 is not well understood. Early in COVID-19 episode, the disease is primarily driven by the replication of SARS-CoV-2. COVID-19 also exhibits a dysregulated immune/inflammatory response to SARS-CoV-2 that leads to tissue damage. The downstream impact of the initial COVID-19 episode is consistently higher in people with more severe acute infection (569). Cytokine storm, hyperinflammation, and multi-organ failure have also been indicated in patients with a severe COVID-19 episode (10). Cerebrospinal fluid samples indicate neuroinflammation during acute COVID-19 episodes (11). Moreover, even 40–60 days post-acute COVID-19 infection there is evidence of a significant remaining inflammatory response in patients (12). Thus, it could be hypothesized that the hyperinflammation that some COVID-19 patients have during the initial COVID-19 episode creates a systemic damage to multiple organ systems (1314). Consequently, that hyperinflammation and the corresponding systemic damage to multiple organ systems may lead to severe post-acute COVID-19 sequelae.

Following from this hyperinflammation, the use of steroids as anti-inflammatory treatments among patients with high inflammation during the initial COVID-19 episode may do more than just help in the initial episode but may act as a buffer to the downstream morbidity and mortality from the initial COVID-19 episode (1415).

The purpose of this study was to examine the relationship between substantial systemic inflammation, as measured by C-reactive protein (CRP), with post-acute COVID-19 sequelae among patients hospitalized with COVID-19. This 12-month mortality risk was examined in a longitudinal cohort of patients who tested positive for COVID-19 as determined by Polymerase Chain Reaction (PCR) testing within a large healthcare system.

Methods

The data for this project comes from a de-identified research databank containing electronic health records (EHR) of patients tested for or diagnosed with COVID-19 in any setting in the University of Florida (UF) Health system. Usage of the databank for research is not considered human subjects research, and IRB review was not required to conduct this study.

Definition of Cohort

The cohort for this study consisted of all adult patients aged 18 and older who were tested for COVID-19 between January 01, 2020 and December 31, 2021 within the UF Health system, in any encounter type (ambulatory, Emergency Department, inpatient, etc.). Although a patient in the cohort could have had a positive test administered in any of these settings, a patient was only included into the cohort if that patient experienced a hospitalization for COVID-19. Since this study included data from the early stages of the pandemic before consistent coding standards for documenting COVID-19 in the EHR had been established, a patient was considered to have been hospitalized for COVID-19 if they experienced any hospitalization within 30 days of a positive test for COVID-19. The databank contained EHR data for all patients in the cohort current through December 31, 2021. COVID-19 diagnosis was validated by PCR. Baseline dates for COVID-19 positive patients were established at the date of their earliest recorded PCR-confirmed positive COVID-19 test. Each patient was only included once in the analysis. For patients with multiple COVID-19 tests, if at least one test gave a positive result, the patient was classified as COVID-19 positive, and the date of their earliest positive COVID-19 test result was used as their baseline date. Patients who did not have a positive COVID-19 test were not included in the analysis. Patients were tested in the context of seeking care for COVID-19; the tests were not part of general screening and surveillance.

Only patients with at least 365 days of follow-up time after their baseline date were retained in the cohort. Patients with more than 365 days of follow-up were censored at 365 days. The cohort was also left censored at the 30-day mark post-hospital discharge to ensure that health care utilization was post-acute and not part of the initial COVID-19 episode of care (e.g., not a readmission).

Inflammation

C-reactive protein (CRP) was used as the measure of inflammation in this study. The UF Health laboratory measured CRP in serum using latex immunoturbidimetry assay. CRP measures were sourced from patient EHR data. The cohort was restricted to only include patients with at least one CRP measurement within their initial COVID-19 episode of care (between the date of their initial positive COVID-19 test and the left-hand censoring date). For patients with multiple measurements of CRP, the maximum value available was used.

Steroids

Intravenous dexamethasone during their initial COVID-19 hospitalization was assessed. Prescriptions for oral steroids (tablets of dexamethasone) that were prescribed either at or post-hospital discharge for their initial COVID-19 episode of care were included into the analysis. Prescriptions were identified using RxNorm codes available in each patient’s EHR.

Severity of Initial COVID-19 Hospitalization

We also measured the severity of the initial episode of COVID-19 hospitalization. This severity should track with the level of inflammation in the initial COVID-19 episode. We used the National Institutes of Health’s “Therapeutic Management of Hospitalized Adults With COVID-19” disease severity levels and definitions (16). The recommendations are based on four ascending levels: hospitalized but does not require supplemental oxygen, hospitalized and requires supplemental oxygen, hospitalized and requires supplemental oxygen through a high-flow device or noninvasive ventilation, hospitalized and requires mechanical ventilation or extracorporeal membrane oxygenation. For this study, because of the general conceptual model of severity moving from no supplemental oxygen to supplemental oxygen to mechanical ventilation, we collapsed the two supplemental non-mechanical ventilation oxygen into one intermediate category of severity.

Outcome Variables

The primary outcome investigated in this study was the 365-day all-cause mortality. Mortality data was sourced both from EHR data and the Social Security Death Index (SSDI), allowing for the assessment of deaths which occurred outside of UF’s healthcare system. When conflicting dates of death were observed between the EHR and SSDI, the date recorded in the patient’s medical record was used. Patients who died within their 365-day follow-up window were censored at the date of their recorded death. The cause of death was not available in the EHR based database and was not routinely and reliably reported in either the SSDI or EHR. We were unable to estimate the cause of death.

Comorbidities

Comorbidities and demographic variables which could potentially confound the association between inflammation represented by CRP and mortality post-acute COVID-19 were collected at baseline for each member of the cohort. Demographic variables included patient age, race, ethnicity, and sex. The Charlson Comorbidity Index was also calculated, accounting for the conditions present for each patient at their baseline. The Charlson Comorbidity Index was designed to be used to predict 1-year mortality and is a widely used measure to account for comorbidities (17).

Analysis

CRP was evaluated using descriptive statistics. We performed a median split of the CRP levels and defined elevated inflammation as a CRP level at or above the median and levels below the median as low inflammation. Additionally, as a way to examine greater separation between high and low inflammation, we segmented CRP levels into tertiles and categorized elevated inflammation as the top tertile and compared it to the first tertile by chi-square tests.

CRP level was also cross classified by severity of COVID-19 hospitalization and associations between the two variables were assessed using one-way ANOVA tests.

Kaplan-Meier curves comparing the survival probabilities of the high and low inflammation groups were created and compared using a log-rank test. Hazard ratios for the risk of death for post-acute COVID-19 complications by COVID-19 status were determined using Cox proportional hazard models. We obtained hazard ratios for mortality based on tertile and median splits of CRP. These analyses were then modified to control for age, sex, race, ethnicity, and the Charlson Comorbidity Index.

Additional analyses stratified by use of steroids were performed to compare the strength of the association between inflammation and death. The proportional hazards assumption was confirmed by inspection of the Schoenfeld residual plots for each variable included in the models and testing of the time-dependent beta coefficients. Analyses were conducted using the survival package in R v4.0.5.

Results

A total of 1,207 patients were included in the final cohort (Table 1). The characteristics of the patients are featured in Table 1. The mean CRP rises with the severity of illness in these COVID-19 inpatients. The mean CRP in the lowest severity (no supplemental oxygen) is 59.4 mg/L (SD = 61.8 mg/L), while the mean CRP in the intermediate severity group (supplemental oxygen) is 126.9 mg/L (SD = 98.6 mg/L), and the mean CRP in the highest severity group (ventilator or ECMO) is 201.2 mg/L (SD = 117.0 mg/L) (p < 0.001). Similarly, since dexamethasone is only recommended for the most severe patients with COVID-19, patients with dexamethasone had higher CRP (158.8 mg/L; SD = 114.9 mg/L) than those not on Dexamethasone (102.8 mg/L; SD = 90/8 mg/L) (p < 0.001).TABLE 1

Table 1. Characteristics of the patients in the cohort.

Figure 1 presents the Kaplan-Meier curves comparing the risk of mortality by inflammation over time. A log-rank test indicated there was a statistically significant difference in survival probabilities between the two groups (p = 0.002).FIGURE 1

Figure 1. All-cause mortality Kaplan-Meier curve comparing individuals with median or greater vs. below median C-reactive protein levels. Log rank test = p.002.

Table 2 shows the relationship between levels of inflammation and mortality post-recovery from COVID-19. In both unadjusted and adjusted analyses, elevated inflammation has a significantly increased risk compared to those with low inflammation in the initial COVID-19 episode. This finding of higher inflammation during the initial COVID-19 hospitalization and increased mortality risk after recovery was similar when CRP was split at the median and when the third tertile of CRP was compared to the first tertile of CRP. The proportional hazards assumption was met when the Schoenfeld plots.TABLE 2

Table 2. All-cause mortality hazard ratios by inflammation and steroid use.

We examined the hypothesized relationship that potentially decreasing inflammation in COVID-19 patients with an initial severe episode may have beneficial downstream effects on post-acute COVID-19 sequelae. Oral steroid prescriptions at discharge among these hospitalized COVID-19 patients were found to be associated with a lower risk of death post-discharge (Table 2).

Discussion

The results of this study reaffirm the importance of post-acute COVID-19 sequelae. This study is the first to show the impact of inflammation in the initial COVID-19 hospitalization episode on downstream mortality after the patient has recovered. This expands our understanding of post-acute COVID-19 sequelae by providing a better concept of why certain patients have post-acute COVID-19 mortality risk.

Previous studies have shown that patients who are hospitalized with COVID-19 have an increased risk of mortality 12 months after recovery (5). Those findings suggest that prevention of COVID-19 hospitalizations is of paramount importance. However, some patients will be hospitalized. The finding that elevated inflammation during the initial hospitalization episode is associated with mortality risk after recovery suggests that it may be worthwhile treating the viral episode but also consider treating the hyperinflammation. The NIH recommendations for care of COVID-19 hospitalized patients recommend steroids only for patients who need supplemental oxygen (16). The finding that the use of steroids prescribed upon discharge from the hospital and the corresponding reduced risk of mortality indicate that treating inflammation after the acute COVID-19 episode may act as a buffer to the downstream mortality risk from the initial COVID-19 episode (1415). Perhaps this requires a reconceptualization of COVID-19 as both an acute disease and potentially a chronic disease because of the lingering risks. Future research is needed to see if ongoing treatment for inflammation in a clinical trial has positive benefits.

There are several strengths and limitations to this study. The strengths of this study include the PCR validated COVID-19 tests at baseline for the cohort. Further, the linked electronic health record allows us to look not only at health care utilization like hospitalizations and both inpatient and outpatient medication but also laboratory tests like CRP levels. The cohort also allows us to have a substantial follow-up time.

In terms of limitations, the first that needs to be considered is that the analysis was based on hospitalized patients seen in one health system with a regional catchment area. Although more than 1200 hospitalized patients with PCR validated COVID-19 diagnoses were included in the analysis, and the cohort was followed for 12 months, the primary independent variable was systemic inflammation which should not be substantially affected by region of the country. Second, the data are observational. Thus, the analyses related to steroids and downstream mortality require a clinical trial to confirm these suggestive findings. Third, we did not have death certificates available to us to compute cause of death. The Social Security Death Index in partnership with the EHR allows us to be confident that the patient died and so we have a strong measure of all-cause mortality but we were unable to determine specific causes of death within this database. Fourth, although there are a variety of other markers of inflammation (e.g., D dimer, IL 6), CRP is one of the most robust measures of systemic inflammation. Moreover, it is much more widely used and was the most prevalent marker among the patients in the study.

In conclusion, hyperinflammation present with severe COVID-19 is associated with an increased mortality risk after hospital discharge. Although suggestive, treatment with anti-inflammatory medications like steroids upon hospital discharge is associated with a decreased post-acute COVID-19 mortality risk. This suggests that treating inflammation may also benefit other post-acute sequelae like long COVID. A reconceptualization of COVID-19 as both an acute and chronic condition may be useful.

References

1. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available online at: https://coronavirus.jhu.edu/map.html (accessed February 28, 2022).

Google Scholar

2. Bell ML, Catalfamo CJ, Farland LV, Ernst KC, Jacobs ET, Klimentidis YC, et al. Post-acute sequelae of COVID-19 in a non-hospitalized cohort: results from the Arizona CoVHORT. PLoS ONE. (2021) 16:e0254347. doi: 10.1371/journal.pone.0254347

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Garrigues E, Janvier P, Kherabi Y, Le Bot A, Hamon A, Gouze H, et al. Post-discharge persistent symptoms and health-related quality of life after hospitalization for COVID-19. J Infect. (2020) 81:e4–6. doi: 10.1016/j.jinf.2020.08.029

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Alkodaymi MS, Omrani OA, Fawzy NA, Shaar BA, Almamlouk R, Riaz M, et al. Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clin Microbiol Infect. (2022). doi: 10.1016/j.cmi.2022.01.014. [Epub ahead of print].

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Mainous AG 3rd, Rooks BJ, Wu V, Orlando FA. COVID-19 post-acute sequelae among adults: 12 month mortality risk. Front Med. (2021) 8:778434. doi: 10.3389/fmed.2021.778434

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Mainous AG 3rd, Rooks BJ, Orlando FA. Risk of New Hospitalization Post-COVID-19 Infection for Non-COVID-19 Conditions. J Am Board Fam Med. (2021) 34:907–13. doi: 10.3122/jabfm.2021.05.210170

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Bhaskaran K, Rentsch CT, Hickman G, Hulme WJ, Schultze A, Curtis HJ, et al. Overall and cause-specific hospitalisation and death after COVID-19 hospitalisation in England: a cohort study using linked primary care, secondary care, and death registration data in the OpenSAFELY platform. PLoS Med. (2022) 19:e1003871. doi: 10.1371/journal.pmed.1003871

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature. (2021) 594:259–64. doi: 10.1038/s41586-021-03553-9

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Xie Y, Bowe B, Al-Aly Z. Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status. Nat Commun. (2021) 12:6571. doi: 10.1038/s41467-021-26513-3

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Wong RSY. Inflammation in COVID-19: from pathogenesis to treatment. Int J Clin Exp Pathol. (2021) 14:831–44.

Google Scholar

11. Spudich S, Nath A. Nervous system consequences of COVID-19. Science. (2022) 375:267–9. doi: 10.1126/science.abm2052

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Doykov I, Hällqvist J, Gilmour KC, Grandjean L, Mills K, Heywood WE. ‘The long tail of Covid-19’ – The detection of a prolonged inflammatory response after a SARS-CoV-2 infection in asymptomatic and mildly affected patients. F1000Res. (2020) 9:1349. doi: 10.12688/f1000research.27287.1

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Theoharides TC. Could SARS-CoV-2 spike protein be responsible for long-COVID syndrome? Mol Neurobiol. (2022) 59:1850–61. doi: 10.1007/s12035-021-02696-0

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Cron RQ, Caricchio R, Chatham WW. Calming the cytokine storm in COVID-19. Nat Med. (2021) 27:1674–5. doi: 10.1038/s41591-021-01500-9

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Keller MJ, Kitsis EA, Arora S, Chen JT, Agarwal S, Ross MJ, et al. Effect of systemic glucocorticoids on mortality or mechanical ventilation in patients with COVID-19. J Hosp Med. (2020) 15:489–93. doi: 10.12788/jhm.3497

PubMed Abstract | CrossRef Full Text | Google Scholar

16. National Institutes of Health. Therapeutic Management of Hospitalized Adults With COVID-19. (2021). https://www.covid19treatmentguidelines.nih.gov/management/clinical-management/hospitalized-adults–therapeutic-management/ (accessed February 28, 2022).

Google Scholar

17. Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom. (2022) 91:8–35. doi: 10.1159/000521288

PubMed Abstract | CrossRef Full Text | Google Scholar

Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England

Authors: T. BeaneyA. L. NevesA. AlboksmatyH. AshrafianK. FlottA. FowlerJ. R. Benger

P. AylinS. ElkinA. Darzi & J. Clarke  Nature Communications volume 13, Article number: 2356 (2022) 

Abstract

The Covid-19 mortality rate varies between countries and over time but the extent to which this is explained by the underlying risk in those infected is unclear. Using data on all adults in England with a positive Covid-19 test between 1st October 2020 and 30th April 2021 linked to clinical records, we examined trends and risk factors for hospital admission and mortality. Of 2,311,282 people included in the study, 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days of a positive Covid-19 test. We found significant variation in the case hospitalisation and mortality risk over time, which remained after accounting for the underlying risk of those infected. Older age groups, males, those resident in areas of greater socioeconomic deprivation, and those with obesity had higher odds of admission and death. People with severe mental illness and learning disability had the highest odds of admission and death. Our findings highlight both the role of external factors in Covid-19 admission and mortality risk and the need for more proactive care in the most vulnerable groups.

Introduction

The Covid-19 case fatality ratio (CFR) varies widely between countries1 and definitions of mortality differ across the world, making comparisons challenging2. In England, the most widely reported measure is mortality within 28 days of a positive test3. Up to 21 September 2021, 539,921 hospital admissions and 118,846 deaths have occurred in England, out of a total of 6,398,633 cases, giving a crude case hospitalisation ratio (CHR) of 8.4% and a CFR of 1.9%4. Previous epidemiological studies have shown variation in the CFR over time1,5, but without individual level data, it is unclear the extent to which this variation is accounted for by differences in the risk of those infected.

Many risk factors for death from Covid-19 have been characterised, such as increased age, male gender, and obesity6. Several long-term conditions are strongly linked to a higher mortality risk; in England, this led to the early adoption of a ‘clinically extremely vulnerable’ (CEV) status for those deemed to be at highest risk, subsequently advised to isolate to reduce transmission7. Previous studies have focussed on the first wave of the pandemic in the first half of 2020, which may not be representative of subsequent pandemic waves, particularly given advances in the management of Covid-19 patients and the emergence of new variants8. Furthermore, to our knowledge, no study to date has used data with national coverage, including all laboratory-confirmed Covid-19 test results linked to electronic health record (EHR) data.

The main aim of this paper is to describe the changing trends in the Covid-19 case hospitalisation risk (CHR) and case fatality risk (CFR) in England, during the ‘second wave’ of the pandemic (i.e., from 1st October 2020 to 30th April 2021). The secondary aims are to identify patient characteristics associated with hospitalisation and mortality risk; and to evaluate whether residual unexplained variation in the CHR and CFR remains after accounting for differences in the underlying risk factors of those infected.

Results

From 1st October 2020 to 30th April 2021, data were available for 2,433,768 individuals with a positive Covid-19 test result in England. Data for 34,317 (1.4%) participants with a positive test result could not be linked to either primary or secondary care records and were excluded. Care home residents accounted for 3.7% of the total (n = 88,169) and were excluded from further analyses, resulting in a total population of 2,311,282.

Characteristics of the study population are provided in Table 1. The mean (SD) age of participants was 44.3 (17.1) years, with 43.6% under 40 years. The majority were female (54.3%) and of White ethnicity (72.8%). There were relatively higher proportions from more deprived deciles of IMD, with 56.7% in the bottom five deciles. Similar proportions of subjects with a healthy weight (28.4%), overweight (28.1%) or obese (26.1%) were observed, and only 3.4% were underweight. 16.3% were current smokers and 8.3% were designated as CEV. Chronic respiratory disease (21.2%), hypertension (15.0%) and diabetes (8.6%) were the three most prevalent chronic conditions in the population.Table 1 Characteristics of the study population with hospital admissions and deaths within 28 days (N = 2,311,282).Full size table

Case hospitalisation and fatality risk over time

Of the study population, 164,046 people were admitted to hospital at least once within 28 days of a positive test, giving a crude CHR of 7.1% over the seven-month period. 53,156 deaths occurred within 28 days of a positive test, giving a crude CFR of 2.3%. Of these, 49,172 (92.5%) had Covid-19 as a cause of death on the death certificate. There were significant differences over time in both the CHR and CFR (Supplementary Fig. 1). The age distribution of people with a positive test varied over time, with the highest proportions of all infection in people aged 60 years and above infected in November 2020 and January 2021 (Supplementary Table 1). Within all age groups, a similar pattern of change in the CHR and CFR over time was seen, with risk peaking in December 2020–February 2021 (Supplementary Tables 2 and 3, respectively, and Supplementary Fig. 2).

Factors associated with 28-day mortality and hospitalisation risk

Multiple imputation was used to impute missing data for 381,283 people. Multivariable logistic regression models were constructed for each outcome adjusting for all patient level covariates (model 2). Calibration plots indicated adequate calibration (Supplementary Figs. 3 and 4). Results for hospital admissions and mortality are presented in Figs. 1 and 2 (also Supplementary Tables 4 and 5). Males had 41% higher adjusted odds of admission (95% CI: 1.39–1.42) and 62% higher adjusted odds of mortality (95% CI: 1.58–1.65) compared to females. People of all four non-White ethnicities had higher odds of admission, and those of Asian and Black ethnicities also had higher odds of mortality compared to those of White ethnicity. People living in less deprived areas had lower odds of both admission and mortality compared to those in the most deprived areas. Compared to people of a healthy weight, those underweight had 10% higher odds of admission (95% CI: 1.05–1.14) and 99% higher odds of death (95% CI: 1.87–2.11). People who were overweight had a 24% increase in odds of admission (95% CI: 1.22–1.26) but 20% lower odds of death (95% CI: 0.77-0.82); those who were obese had 93% higher odds of admission (1.90–1.97) and 4% increased odds of death (95% CI: 1.01–1.07). Current smokers had lower odds of admission compared to non-smokers but an increase in the odds of death after adjustment.

figure 1
Fig. 1: Adjusted odds ratios for emergency hospital admission within 28 days of positive Covid-19 test.
figure 2
Fig. 2: Adjusted odds ratios for death within 28 days of positive Covid-19 test.

All chronic conditions included were strongly associated with an increase in odds of admission and death, except for dementia, which was associated with 6% lower odds of admission. People identified as CEV had 85% higher odds of being admitted to hospital (95% CI: 1.83–1.88) but 12% lower odds of death (95% CI: 0.86–0.90) after full adjustment. In a sub-analysis adjusting CEV status for age, time (and their interaction), sex, ethnicity, and deprivation only, odds of admission were significantly higher (aOR 2.62, 95% CI: 2.58–2.65) as were odds of death (aOR 1.52, 95% CI: 1.49–1.55).

A sensitivity analysis of the 1,929,999 complete cases showed similar estimates to the fully adjusted model (Supplementary Tables 6 and 7).

CHR and CFR over time

A significant association remained with time for both CHR and CFR models after adjusting for all patient covariates (p < 0.0001 in each model from likelihood ratio tests). The predicted CHR and CFR from the fully adjusted models are plotted for the whole population (Supplementary Fig. 5) and by age category in Fig. 3, showing that a significant time-varying relationship remained after adjustment. The relative change in predicted CHR and CFR from the baseline predicted risk in the first full week of October is shown in Fig. 4 (and Supplementary Figs. 6 and 7). The CFR increased across all age groups, peaking between late December 2020 to early February 2021in different age groups before declining towards April. A smaller relative increase in hospitalisation risk was seen across age groups. In most age groups, CHR peaked in January, except in the 18–39 age group, which continued to increase throughout the study period. After adjustment, the trends in absolute mortality and hospitalisation risk in each age group were similar to those in the unadjusted analyses (Fig. 4 and Supplementary Fig. 2) indicating that the distributions of risk factors of those infected within age groups did not change significantly over time.

figure 3
Fig. 3: 28-day case hospitalisation risk and fatality risk over time in people with Covid-19.
figure 4
Fig. 4: Relative change in 28-day case hospitalisation risk and fatality risk over time in people with Covid-19.

Discussion

In this retrospective cohort study including all adults in England with a positive Covid-19 test result, there was significant variation in the 28-day CHR and CFR by age group and over time, which remained after accounting for individual risk. Demographics and chronic conditions were strongly associated with hospitalisation and death.

Variation in CHR and CFR over time

Across the whole study population, CHR and CFR varied over time from 1st October 2020 to 30th April 2021. This was partially explained by the changing age distributions of those infected, but significant variation remained after adjustment. Within age groups, absolute differences in the CHR and CFR over time were greatest in older age groups, reflecting higher baseline risk, but the relative risk varied significantly across all groups. Historically, there is a strong seasonal component to mortality in England, with figures indicating 16.8% higher mortality in winter months compared to summer months9. An increased incidence of respiratory diseases, including influenza, are one of the main drivers of increased winter mortality, and the 28-day mortality metric used in this study includes deaths from non-Covid-19 causes. However, with influenza rates at lower levels than previous years, it is unlikely the variation in CFR over time can be explained by the incidence of other infectious diseases alone10.

Strain on the health system may also contribute to the patterns seen, with Covid-19 bed occupancy and critical care occupancy in England peaking in January 2021, associated with a lower proportion of patients seen in Accident & Emergency departments within 4 hours than in November 2020 and February 20214,11. Larger relative increases were seen in the CFR compared to the CHR, which may indicate a health system reaching full capacity and struggling to meet demand. A previous UK study of patients admitted to hospital with Covid-19 found a fall in mortality from March to July 2020, a time over which bed occupancy fell and evidence for new treatments, such as dexamethasone, became available, with similar findings from a US cohort between March and September 202012,13. Changes to care delivery at an organisational level may also have an impact, with triage models for Covid-19 patients on the national 111 urgent care service varying between services and over time14. The Alpha variant became the dominant Covid-19 strain in England in December 2020, and has been associated with a 64% increase in 28-day mortality compared to prior variants, which may explain part of the rise in the CHR and CFR15.

Declines in the CHR and CFR from January 2021 onwards are likely to be explained at least partially by the development of immunity, both through natural infection and by the vaccination programme, which was implemented from 8th December 2020 in England for the highest risk cohorts16. By February 2021, over 80% of over 80s had been vaccinated in most regions of the UK, with similar vaccine coverage in the 70–79 year age group by mid-February and in the 60–69 year age group by mid-March (Supplementary Figs. 810)17. However, our study population includes people with a positive Covid-19 test, who are more likely to be unvaccinated than the general population; population vaccine coverage is, therefore, unlikely to be representative of our study population and estimates could not be incorporated robustly into our modelling. Declines in CFR and CHR are most marked in older age groups, who were the first groups eligible for vaccination. However, declines in mortality are seen across all age groups, including the 18–39 year group, many of whom would not have been eligible for vaccination, suggesting vaccination does not fully account for the declines observed. Availability of new treatments may also explain the falls in mortality, with the RECOVERY trial demonstrating the benefit of tocilizumab published in February 2021, but is unlikely to explain the fall in admissions8,18.

Factors associated with hospitalisation and mortality

The findings of a higher risk of mortality in males, people of Asian and Black ethnic backgrounds, and those living in more deprived areas are consistent with a previous UK cohort and confirmed in our study, including an increased risk of admission6. People who were underweight were more likely to be admitted and had significantly higher risk of death, which might be partly accounted for by unmeasured associated conditions, such as frailty. People who were overweight and obese had higher risk of admission than those of a healthy weight, but mortality risk was lower in those overweight, which may indicate higher perceived risk amongst clinicians and a lower threshold for admission.

People identified as CEV were significantly more likely to be admitted but were found to have significantly lower mortality, after adjusting for other risk factors including co-morbidities. However, in partially adjusted models not including BMI, smoking, or clinical co-morbidities, those identified as CEV had significantly higher odds of death. Taken together, these findings indicate a lower threshold for clinical assessment and/or admission and escalation in CEV patients with a protective effect on mortality. All twelve included clinical co-morbidities were associated with significant increases in the odds of mortality and admission. Severe mental illness and learning disability had the strongest associations with mortality and admission, highlighting a need for more proactive care in these groups and more research into the reasons for mortality differences19. Those with dementia had significantly increased odds of mortality but were less likely to be admitted, suggesting they are more likely to receive care at home, although the cohort did not include those living in care homes and so will not represent the full population of those with dementia.

The emergence of the Delta and Omicron variants have shown the potential of Covid-19 to vary in both transmissibility and pathogenicity over time. In England, December 2021–January 2022 saw the highest case numbers but without the resulting number of hospitalisations and deaths associated with earlier variants and before widespread vaccination4. Despite the emergence of new variants, the findings of our study are relevant in highlighting that the risk of mortality was independent of an extensive panel of clinical and demographic factors in the winter of 2020/21, pointing to the role of wider strain on the health system as an important feature in outcomes in people with Covid-19. While the Omicron variant has contributed to an increase in hospitalisations and emergency department presentations in England and elsewhere, its impact on staff absence has been particularly marked. At the peak of the Omicron wave in early January 2022, almost 50,000 NHS staff were absent due to Covid-19, almost a five-fold increase from the end of November 202120,21,22. Ensuring health systems possess the resilience to weather the dual shocks of an increased demand for care and decreased capacity to provide it, without adversely affecting the quality and safety of healthcare, is an ongoing area of concern.

Strengths and limitations

A strength of this study is the inclusion of routine national laboratory data for positive Covid-19 test results in adults in England with only 1.5% unable to be linked to EHR data, and as a result, has lower risk of sampling bias23. To our knowledge, this is the largest such study including individual level data at a national level. Previous studies in England on predictors of mortality are reported on a smaller cohort of patients with 40% national coverage6. The use of multiple imputation assumes that data are missing at random, and we cannot rule out non-random missing patterns, particularly for data on ethnicity and deprivation, where more marginalised groups are less likely to be registered in the primary care record. However, sensitivity analyses showed inferences were similar between the complete case analysis and imputed results, suggesting limited impact of the missing data on model estimates. Associations with risk factors may also be confounded by differential uptake of vaccinations among risk groups; for example, if those with co-morbidities or defined as CEV were more likely to be vaccinated, the odds ratios for hospitalisation and death may be under-estimated.

Data represented here include only those who died within 28 days of a positive test result, in line with estimates reported by PHE. Deaths mentioning Covid-19 on a death certificate are an alternative metric used widely in many countries as recommended by the World Health Organisation24 and have tended to give a larger estimate of deaths in England, due to those attributable to Covid-19 after 28 days4. Over 90% of deaths within 28 days in our study also had Covid-19 as a cause of death on the death certificate, but we did not have corresponding data for those cases recorded on a death certificate without a positive Covid-19 test. The associations found in our study might be different if using deaths recorded on death certificates, rather than deaths within 28 days of a positive Covid-19 test, particularly if there were changes to death certification practices over time.

Through use of linked EHR data, we were able to incorporate detailed medical factors for the study cohort. However, we were unable to explore the relationship with external factors such as Covid-19 variants. Geographical and time-varying system factors, such as proximity to a hospital and hospital capacity are likely to impact on a person’s health-seeking behaviour. Our study included people living in the community and given patients in England may attend any hospital, and the size of hospital markets vary considerably across the country, we could not reliably model the impact of nearby hospital bed availability at an individual level. However, our modelling showed only minimal residual variation accounted for by CCG level clustering (intraclass correlation coefficient <1%), suggesting these additional factors would have minimal impact on the findings. Access to testing may also impact the probability of having a positive test. Positivity rates in England peaked on 31st December 2020 at 18.3% and fell to 1.7% by 1st April 20214, but the extent to which this reflects increased incidence or a lack of test availability is uncertain. It is possible that if testing were limited during the peak in cases in December 2020–January 2021, those with more symptomatic disease may have been more likely to receive a test, compared to those who were asymptomatic or with mild symptoms. This may in turn lead to an apparent increase in risk of mortality due to changes in the severity of illness of those testing positive, rather than the severity of disease within the community as a whole. Furthermore, access to testing may be driven by sociodemographic factors, and the finding of lower hospitalisation and mortality risk in less deprived areas could reflect better availability of testing. Exploring mortality risk in patients admitted to hospital or to intensive care units and whether this changed over time was outside the scope of the current study but is an area for further research.

The risk of hospitalisation and death from Covid-19 varied significantly over time from October 2020 to April 2021 in all age groups, independent of the underlying risk in those infected. Time-varying risks should be considered by researchers and policymakers in assessing the risks of hospitalisation and mortality from Covid-19. People with severe mental illness and learning disability were amongst those with the highest odds of both admission and mortality, indicating the need for proactive care in these groups.

Methods

The work was conducted as part of a wider service evaluation, approved by Imperial College Healthcare Trust on December 3rd 2020. Data access was approved by the Independent Group Advising on the Release of Data (IGARD; DARS-NIC-421524-R0Y3P) on April 15th 2021.

Study design and population

We conducted a retrospective cohort study including all adults (≥18 years) resident in England with a positive Covid-19 test result (polymerase chain reaction or lateral flow tests) from 1st October 2020 to 30th April 2021, excluding people resident in care homes. Study participants were followed-up for 28 days from the date of a first positive test. The two primary outcomes were (i) one or more emergency hospital admissions and (ii) death from any cause, each within 28 days from the date of the positive test.

Data sources and data processing

Several datasets were linked for this study and provided by NHS Digital as part of an evaluation of the NHS England Covid Oximetry @home programme25. Covid-19 testing data was sourced from the Public Health England (PHE) Second Generation Surveillance System26, the national laboratory reporting system for positive Covid-19 tests, covering the period from 1st October 2020 to 30th April 2021. Primary care data came from the General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)27. CEV status was linked to GDPPR from the Shielded Patient List (see Supplementary Methods)28. Data on hospital admissions came from Hospital Episode Statistics (HES) data set up to 31st May 2021, linked to Office for National Statistics (ONS) data on death registrations up to 5th July 2021. Datasets were linked using a de-identified NHS patient ID. Participants who could not be linked from testing data to at least one of GDPPR or HES were excluded.

Patient demographics were derived from GDPPR, or  where missing, from HES. Lower layer super output area (LSOA) of residence was linked to indices of relative deprivation using deciles of Index of Multiple Deprivation (IMD) 201929. Residence in a care home, CEV status, body mass index (BMI), and smoking status were derived from GDPPR only. BMI was categorised as underweight (<18.5 kg/m2), healthy weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese (≥30.0 kg/m2). Chronic conditions were extracted from GDPPR based on Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT) codes pertaining to relevant diagnosis code clusters. Only codes recorded prior to the date of a positive Covid-19 test were included, to exclude any diagnoses following Covid-19 infection. Where the latest code indicated resolution of a condition, the diagnosis was excluded for that individual. Further details on data curation are given in the Supplementary Methods.

Statistical analysis

Patients were followed from date of first positive Covid-19 test to emergency hospital admission or death within 28 days. Mixed effects logistic regression was conducted for each outcome, with a two-level hierarchical model incorporating Clinical Commissioning Group (CCG, of which there are 106 in England) of residence as a random intercept. Time, represented by the week of Covid-19 test, was modelled as a restricted cubic spline with five knots placed at equally spaced percentiles30. Two models were run for each outcome:

  1. 1.Model 1: incorporating age category and time splines along with their interaction.
  2. 2.Model 2: incorporating age category and time splines along with their interaction and including all additional patient level covariates: sex, ethnicity, IMD decile, BMI category, CEV status, smoking status, and presence of chronic conditions.

For model 2, multiple imputation using chained equations was used to impute missing values of covariates, under the assumption that values were missing at random. All variables included in the analysis model were included in the imputation model31. Fifteen imputations were created, with a burn-in of 10 iterations which gave adequate precision and convergence, respectively (Supplementary Methods). A sensitivity analysis was performed using complete cases only. Calibration was assessed using plots of predicted against observed probabilities for each decile of predicted probability.

For each outcome, the predicted probability of the outcome was computed within each age group and study week stratum to calculate age- and time-specific case hospitalisation risk (CHR) and case fatality risk (CFR). These were calculated using the fixed portion of the model (assuming zero random effects). The relative changes in the CHR and CFR over time were calculated as the predicted probability in each week relative to the week of 5th–11th October 2020 in each age group. In adjusted models (model 2), other model covariates were set to the population mean (or proportion for categorical variables) within each age group. For CEV status, an additional sub-analysis was conducted adjusting only for the age category and time splines (and their interaction), sex, ethnicity, and IMD decile. Further details of the statistical methods are given in Supplementary Methods.

Analyses were conducted in the Big Data and Analytics Unit Secure Environment, Imperial College, using Python version 3.9.5, Pandas version 1.2.3, and Stata version 17.0 (StataCorp).

Data availability

The patient level data used in this study are not publicly available but are available to applicants meeting certain criteria through application of a Data Access Request Service (DARS) and approval from the Independent Group Advising on the Release of Data. Further information is given below: https://digital.nhs.uk/about-nhs-digital/corporate-information-and-documents/independent-group-advising-on-the-release-of-data.

Code availability

The SNOMED terms used in defining chronic conditions are available in our GitHub repository: https://github.com/tbeaney/Imperial-COh-evaluation. Further analysis codes are available on request to the corresponding author.

References

  1. Sorci, G., Faivre, B. & Morand, S. Explaining among-country variation in COVID-19 case fatality rate. Sci. Rep. 10, 18909 (2020).ADS Article Google Scholar 
  2. Beaney, T. et al. Excess mortality: The gold standard in measuring the impact of COVID-19 worldwide? J. R. Soc. Med. 113, 329–334 (2020).Article Google Scholar 
  3. Public Health England. Technical summary: Public Health England data series on deaths in people with COVID-19. 1–15 https://www.gov.uk/government/publications/phe-data-series-on-deaths-in-people-with-covid-19-technical-summary (2020).
  4. UK Government. Coronavirus (COVID-19) in the UK. https://coronavirus.data.gov.uk/ (2021).
  5. Hasan, M. N. et al. The global case-fatality rate of COVID-19 has been declining since May 2020. Am. J. Trop. Med. Hyg. 104, 2176–2184 (2021).ADS Article Google Scholar 
  6. Williamson, E. J. et al. OpenSAFELY: Factors associated with COVID-19 death in 17 million patients. Nature https://doi.org/10.1038/s41586-020-2521-4 (2020).
  7. NHS Digital. Shielded patient list risk criteria. https://digital.nhs.uk/coronavirus/shielded-patient-list/risk-criteria (2021).
  8. Siemieniuk, R. A. C. et al. Drug treatments for covid-19: Living systematic review and network meta-analysis. BMJ 370, m2980 (2020).
  9. Office for National Statistics. Excess winter mortality in England and Wales: 2019 to 2020 (provisional) and 2018 to 2019 (final). https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/excesswintermortalityinenglandandwales/2019to2020provisionaland2018to2019final (2020).
  10. Public Health England. Surveillance of influenza and other seasonal respiratory viruses in the UK Winter 2020 to 2021. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/995284/Surveillance_of_influenza_and_other_seasonal_respiratory_viruses_in_the_UK_2020_to_2021-1.pdf (2021).
  11. NHS England. Urgent and Emergency Care Daily Situation Reports 2021–22https://www.england.nhs.uk/statistics/statistical-work-areas/uec-sitrep/urgent-and-emergency-care-daily-situation-reports-2021-22/ (2022).
  12. Bottle, A., Faitna, P. & Aylin, P. P. Patient-level and hospital-level variation and related time trends in COVID-19 case fatality rates during the first pandemic wave in England: Multilevel modelling analysis of routine data. BMJ Qual. Safhttps://doi.org/10.1136/bmjqs-2021-012990 (2021).
  13. Cai, M., Bowe, B., Xie, Y. & Al-Aly, Z. Temporal trends of COVID-19 mortality and hospitalisation rates: An observational cohort study from the US Department of Veterans Affairs. BMJ Open 11, e047369 (2021).
  14. Snooks, H. et al. Call volume, triage outcomes, and protocols during the first wave of the COVID-19 pandemic in the United Kingdom: Results of a national survey. J. Am. Coll. Emerg. Physicians Open 2, e12492 (2021).PubMed PubMed Central Google Scholar 
  15. Challen, R. et al. Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: Matched cohort study. BMJ 372, n579 (2021).
  16. Egan, C., Thorpe, M., Knight, S., Shaw, C. & Mclean, K. Hospital Admission for COVID-19 and impact of vaccination: Analysis of linked data from the National Immunisation Management Service (NIMS) and the Coronavirus Clinical Information Network (CO-CIN). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1018555/S1363_Hospital_Admission_for_COVID-19_and_impact_of_vaccination.pdf (2021).
  17. NHS England. COVID-19 vaccinations archive. https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-vaccinations/covid-19-vaccinations-archive/ (2022).
  18. Abani, O. et al. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): A randomised, controlled, open-label, platform trial. Lancet 397, 1637–1645 (2021).Article Google Scholar 
  19. Perera, B. et al. COVID-19 deaths in people with intellectual disability in the UK and Ireland: Descriptive study. BJPsych Open 6, e123 (2020).Article Google Scholar 
  20. McCay, L. Omicron: Urgent action needed on NHS staffing crisis. BMJ 376, o18 (2022).
  21. NHS England. Urgent and Emergency Care Daily Situation Reports 2020–21. https://www.england.nhs.uk/statistics/statistical-work-areas/uec-sitrep/urgent-and-emergency-care-daily-situation-reports-2020-21/ (2021).
  22. NHS England. Record number of NHS ambulance call outs for life-threatening conditions in December, despite jump in Omicron absences. https://www.england.nhs.uk/2022/01/record-number-of-nhs-ambulance-call-outs-for-life-threatening-conditions-in-december/ (2022).
  23. Griffith, G. J. et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat. Commun. 11, 5749 (2020).ADS CAS Article Google Scholar 
  24. World Health Organisation. International guidelines for certification and classification (coding) of COVID-19 as cause of death based on ICD International Statistical Classification of Diseases. https://www.who.int/classifications/icd/Guidelines_Cause_of_Death_COVID-19-20200420-EN.pdf (2020).
  25. NHS England. COVID Oximetry @home. https://www.england.nhs.uk/nhs-at-home/covid-oximetry-at-home/ (2021).
  26. Public Health England. Laboratory reporting to Public Health England: A guide for diagnostic laboratories. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/926838/PHE_Laboratory_reporting_guidelines_October-2020-v3.pdf (2020).
  27. NHS Digital. General Practice Extraction Service (GPES) Data for pandemic planning and research: A guide for analysts and users of the data. https://digital.nhs.uk/coronavirus/gpes-data-for-pandemic-planning-and-research/guide-for-analysts-and-users-of-the-data (2021).
  28. NHS Digital. Shielded patient list. https://digital.nhs.uk/coronavirus/shielded-patient-list (2021).
  29. Ministry of Housing & Communities & Local Government. English indices of deprivation 2019. https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 (2019).
  30. Harrell, F. E. Regression Modeling Strategies, With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer, 2001).
  31. White, I. R., Royston, P. & Wood, A. M. Multiple imputation using chained equations: Issues and guidance for practice. Stat. Med. 30, 377–399 (2011).MathSciNet Article Google Scholar 

COVID-19 Cases and Hospitalizations by COVID-19 Vaccination Status and Previous COVID-19 Diagnosis — California and New York, May–November 2021

Weekly / January 28, 2022 / 71(4);125–131 January 19, 2022, this report was posted online as an MMWR Early Release.

Authors: Tomás M. León, PhD1; Vajeera Dorabawila, PhD2; Lauren Nelson, MPH1; Emily Lutterloh, MD2,3; Ursula E. Bauer, PhD2; Bryon Backenson, MPH2,3; Mary T. Bassett, MD2; Hannah Henry, MPH1; Brooke Bregman, MPH1; Claire M. Midgley, PhD4; Jennifer F. Myers, MPH1; Ian D. Plumb, MBBS4; Heather E. Reese, PhD4; Rui Zhao, MPH1; Melissa Briggs-Hagen, MD4; Dina Hoefer, PhD2; James P. Watt, MD1; Benjamin J. Silk, PhD4; Seema Jain, MD1; Eli S. Rosenberg, PhD2,3

Summary

What is already known about this topic?

Data are limited regarding the risks for SARS-CoV-2 infection and hospitalization after COVID-19 vaccination and previous infection.

What is added by this report?

During May–November 2021, case and hospitalization rates were highest among persons who were unvaccinated without a previous diagnosis. Before Delta became the predominant variant in June, case rates were higher among persons who survived a previous infection than persons who were vaccinated alone. By early October, persons who survived a previous infection had lower case rates than persons who were vaccinated alone.

What are the implications for public health practice?

Although the epidemiology of COVID-19 might change as new variants emerge, vaccination remains the safest strategy for averting future SARS-CoV-2 infections, hospitalizations, long-term sequelae, and death. Primary vaccination, additional doses, and booster doses are recommended for all eligible persons. Additional future recommendations for vaccine doses might be warranted as the virus and immunity levels change.

By November 30, 2021, approximately 130,781 COVID-19–associated deaths, one in six of all U.S. deaths from COVID-19, had occurred in California and New York.* COVID-19 vaccination protects against infection with SARS-CoV-2 (the virus that causes COVID-19), associated severe illness, and death (1,2); among those who survive, previous SARS-CoV-2 infection also confers protection against severe outcomes in the event of reinfection (3,4). The relative magnitude and duration of infection- and vaccine-derived protection, alone and together, can guide public health planning and epidemic forecasting. To examine the impact of primary COVID-19 vaccination and previous SARS-CoV-2 infection on COVID-19 incidence and hospitalization rates, statewide testing, surveillance, and COVID-19 immunization data from California and New York (which account for 18% of the U.S. population) were analyzed. Four cohorts of adults aged ≥18 years were considered: persons who were 1) unvaccinated with no previous laboratory-confirmed COVID-19 diagnosis, 2) vaccinated (14 days after completion of a primary COVID-19 vaccination series) with no previous COVID-19 diagnosis, 3) unvaccinated with a previous COVID-19 diagnosis, and 4) vaccinated with a previous COVID-19 diagnosis. Age-adjusted hazard rates of incident laboratory-confirmed COVID-19 cases in both states were compared among cohorts, and in California, hospitalizations during May 30–November 20, 2021, were also compared. During the study period, COVID-19 incidence in both states was highest among unvaccinated persons without a previous COVID-19 diagnosis compared with that among the other three groups. During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; 7.2-fold (California) and 9.9-fold lower (New York) among unvaccinated persons with a previous COVID-19 diagnosis; and 9.6-fold (California) and 8.5-fold lower (New York) among vaccinated persons with a previous COVID-19 diagnosis. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These relationships changed after the SARS-CoV-2 Delta variant became predominant (i.e., accounted for >50% of sequenced isolates) in late June and July. By the week beginning October 3, compared with COVID-19 cases rates among unvaccinated persons without a previous COVID-19 diagnosis, case rates among vaccinated persons without a previous COVID-19 diagnosis were 6.2-fold (California) and 4.5-fold (New York) lower; rates were substantially lower among both groups with previous COVID-19 diagnoses, including 29.0-fold (California) and 14.7-fold lower (New York) among unvaccinated persons with a previous diagnosis, and 32.5-fold (California) and 19.8-fold lower (New York) among vaccinated persons with a previous diagnosis of COVID-19. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These results demonstrate that vaccination protects against COVID-19 and related hospitalization, and that surviving a previous infection protects against a reinfection and related hospitalization. Importantly, infection-derived protection was higher after the Delta variant became predominant, a time when vaccine-induced immunity for many persons declined because of immune evasion and immunologic waning (2,5,6). Similar cohort data accounting for booster doses needs to be assessed, as new variants, including Omicron, circulate. Although the epidemiology of COVID-19 might change with the emergence of new variants, vaccination remains the safest strategy to prevent SARS-CoV-2 infections and associated complications; all eligible persons should be up to date with COVID-19 vaccination. Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

Four cohorts of persons aged ≥18 years were assembled via linkages of records from electronic laboratory reporting databases and state-specific immunization information systems. Persons were classified based on whether they had had a laboratory-confirmed SARS-CoV-2 infection by March 1, 2021 (i.e., previous COVID-19 diagnosis)§; had received at least the primary COVID-19 vaccination series by May 16, 2021; had a previous COVID-19 diagnosis and were fully vaccinated**; or had neither received a previous COVID-19 diagnosis by March 1 nor received a first COVID-19 vaccine dose by the end of the analysis period. The size of the unvaccinated group without a previous diagnosis was derived by subtracting the observed groups from U.S. Census estimates.†† To maintain each defined cohort, persons who received a COVID-19 diagnosis during March 1–May 30, 2021, or who died before May 30, 2021, were excluded (to maintain eligibility for incident cases for all cohorts on May 30, 2021),§§ as were persons who received a first vaccine dose during May 30–November 20, 2021. During May 30–November 20, 2021, incident cases were defined using a positive nucleic acid amplification test (NAAT) result from the California COVID-19 Reporting System (CCRS) or a positive NAAT or antigen test result from the New York Electronic Clinical Laboratory Reporting System. In California, person-level hospitalization data from CCRS and supplementary hospitalization reports were used to identify COVID-19–associated hospitalizations. A lifetable method was used to calculate hazard rates (average daily cases during a 7-day interval or hospitalizations over a 14-day interval), hazard ratios, and 95% CIs for each cohort. Rates were age-adjusted to 2000 U.S. Census data using direct standardization.¶¶ Supplementary analyses stratified case rates by timing of previous diagnoses and primary series vaccine product. SAS (version 9.4; SAS Institute) and R (version 4.0.4; The R Foundation) were used to conduct all analyses. Institutional review boards (IRBs) in both states determined this surveillance activity to be necessary for public health work, and therefore, it did not require IRB review.

Approximately three quarters of adults from California (71.2%) and New York (72.2%) included in this analysis were vaccinated and did not have a previous COVID-19 diagnosis; however, 18.0% of California residents and 18.4% of New York residents were unvaccinated with no previous COVID-19 diagnosis (Table 1). In both states, 4.5% of persons were vaccinated and had a previous COVID-19 diagnosis; 6.3% in California and 4.9% in New York were unvaccinated with a previous diagnosis. Among 1,108,600 incident COVID-19 cases in these cohorts (752,781 in California and 355,819 in New York), the median intervals from vaccination or previous COVID-19 diagnosis to incident diagnosis were slightly shorter in California (138–150 days) than in New York (162–171 days).

Before the Delta variant became predominant in each state’s U.S. Department of Health and Human Services region (June 26 in Region 9 [California] and July 3 in Region 2 [New York]),*** the highest incidence was among unvaccinated persons without a previous COVID-19 diagnosis; during this time, case rates were relatively low among the three groups with either previous infection or vaccination and were lowest among vaccinated persons without a previous COVID-19 diagnosis (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/113253) (Supplementary Figure 2, https://stacks.cdc.gov/view/cdc/113253). During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; rates were 7.2-fold (California) and 9.9-fold (New York) lower among unvaccinated persons with a previous COVID-19 diagnosis and 9.6-fold (California) and 8.5-fold (New York) lower among vaccinated persons with a previous COVID-19 diagnosis (Table 2).

As the Delta variant prevalence increased to >95% (97% in Region 9 and 98% in Region 2 on August 1), rates increased more rapidly among the vaccinated group with no previous COVID-19 diagnosis than among both the vaccinated and unvaccinated groups with a previous COVID-19 diagnosis (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/113253) (Supplementary Figure 2, https://stacks.cdc.gov/view/cdc/113253). For example, during the week of October 3, compared with rates among unvaccinated persons without a previous COVID-19 diagnosis, rates among vaccinated persons without a previous diagnosis were 6.2-fold lower (95% CI = 6.0–6.4) in California and 4.5-fold lower (95% CI = 4.3–4.7) in New York (Table 2). Further, rates among unvaccinated persons with a previous COVID-19 diagnosis were 29-fold lower (95% CI = 25.0–33.1) than rates among unvaccinated persons without a previous COVID-19 diagnosis in California and 14.7-fold lower (95% CI = 12.6–16.9) in New York. Rates among vaccinated persons who had had COVID-19 were 32.5-fold lower (95% CI = 27.5–37.6) than rates among unvaccinated persons without a previous COVID-19 diagnosis in California and 19.8-fold lower (95% CI = 16.2–23.5) in New York. Rates among vaccinated persons without a previous COVID-19 diagnosis were consistently higher than rates among unvaccinated persons with a history of COVID-19 (3.1-fold higher [95% CI = 2.6–3.7] in California and 1.9-fold higher [95% CI = 1.5–2.3] in New York) and rates among vaccinated persons with a history of COVID-19 (3.6-fold higher [95% CI = 2.9–4.3] in California and 2.8-fold higher [95% CI = 2.1–3.4] in New York).

COVID-19 hospitalization rates in California were always highest among unvaccinated persons without a previous COVID-19 diagnosis (Table 2) (Figure). In the pre-Delta period during June 13–June 26, for example, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates were 27.7-fold lower (95% CI = 22.4–33.0) among vaccinated persons without a previous COVID-19 diagnosis, 6.0-fold lower (95% CI = 3.3–8.7) among unvaccinated persons with a previous COVID-19 diagnosis, and 7.1-fold lower (95% CI = 4.0–10.3) among vaccinated persons with a previous COVID-19 diagnosis. However, this pattern also shifted as the Delta variant became predominant. During October 3–16, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates were 19.8-fold lower (95% CI = 18.2–21.4) among vaccinated persons without a previous COVID-19 diagnosis, 55.3-fold lower (95% CI = 27.3–83.3) among unvaccinated persons with a previous COVID-19 diagnosis, and 57.5-fold lower (95% CI = 29.2–85.8) among vaccinated persons with a previous COVID-19 diagnosis.

Among the two cohorts with a previous COVID-19 diagnosis, no consistent incidence gradient by time since the previous diagnosis was observed (Supplementary Figure 3, https://stacks.cdc.gov/view/cdc/113253). When the vaccinated cohorts were stratified by the vaccine product received, among vaccinated persons without a previous COVID-19 diagnosis, the highest incidences were observed among persons receiving the Janssen (Johnson & Johnson), followed by Pfizer-BioNTech, then Moderna vaccines (Supplementary Figure 4, https://stacks.cdc.gov/view/cdc/113253). No pattern by product was observed among vaccinated persons with a previous COVID-19 diagnosis.

Discussion

This analysis integrated laboratory testing, hospitalization surveillance, and immunization registry data in two large states during May–November 2021, before widespread circulation of the SARS-CoV-2 Omicron variant and before most persons had received additional or booster COVID-19 vaccine doses to protect against waning immunity. Rate estimates from the analysis describe different experiences stratified by COVID-19 vaccination status and previous COVID-19 diagnosis and during times when different SARS-CoV-2 variants predominated. Case rates were initially lowest among vaccinated persons without a previous COVID-19 diagnosis; however, after emergence of the Delta variant and over the course of time, incidence increased sharply in this group, but only slightly among both vaccinated and unvaccinated persons with previously diagnosed COVID-19 (6). Across the entire study period, persons with vaccine- and infection-derived immunity had much lower rates of hospitalization compared with those in unvaccinated persons. These results suggest that vaccination protects against COVID-19 and related hospitalization and that surviving a previous infection protects against a reinfection. Importantly, infection-derived protection was greater after the highly transmissible Delta variant became predominant, coinciding with early declining of vaccine-induced immunity in many persons (5). Similar data accounting for booster doses and as new variants, including Omicron, circulate will need to be assessed.

The understanding and epidemiology of COVID-19 has shifted substantially over time with the emergence and circulation of new SARS-CoV-2 variants, introduction of vaccines, and changing immunity as a result. Similar to the early period of this study, two previous U.S. studies found more protection from vaccination than from previous infection during periods before Delta predominance (3,7). As was observed in the present study after July, recent international studies have also demonstrated increased protection in persons with previous infection, with or without vaccination, relative to vaccination alone†††, §§§ (4). This might be due to differential stimulation of the immune response by either exposure type.¶¶¶ Whereas French and Israeli population-based studies noted waning protection from previous infection, this was not apparent in the results from this or other large U.K. and U.S. studies**** (4,8). Further studies are needed to establish duration of protection from previous infection by variant type, severity, and symptomatology, including for the Omicron variant.

The findings in this report are subject to at least seven limitations. First, analyses were not stratified by time since vaccine receipt, but only by time since previous diagnosis, although earlier studies have examined waning of vaccine-induced immunity (Supplementary Figure 3, https://stacks.cdc.gov/view/cdc/113253) (2). Second, persons with undiagnosed infection are misclassified as having no previous COVID-19 diagnosis; however, this misclassification likely results in a conservative bias (i.e., the magnitude of difference in rates would be even larger if misclassified persons were not included among unvaccinated persons without a previous COVID-19 diagnosis). California seroprevalence data during this period indicate that the ratio of actual (presumptive) infections to diagnosed cases among adults was 2.6 (95% CI = 2.2–2.9).†††† Further, California only included NAAT results, whereas New York included both NAAT and antigen test results. However, antigen testing made up a smaller percentage of overall testing volume reported in California (7% of cases) compared with New York (25% of cases) during the study period. Neither state included self-tests, which are not easily reportable to public health. State-specific hazard ratios were generally comparable, although differences in rates among unvaccinated persons with a previous COVID-19 diagnosis were noteworthy. Third, potential exists for bias related to unmeasured confounding (e.g., behavioral or geographic differences in exposure risk) and uncertainty in the population size of the unvaccinated group without a previous COVID-19 diagnosis. Persons might be more or less likely to receive testing based on previous diagnosis or vaccination status; however, different trajectories between vaccinated persons with and without a previous COVID-19 diagnosis, and similar findings for cases and hospitalizations, suggest that these biases were minimal. Fourth, this analysis did not include information on the severity of initial infection and does not account for the full range of morbidity and mortality represented by the groups with previous infections. Fifth, this analysis did not ascertain receipt of additional or booster COVID-19 vaccine doses and was conducted before many persons were eligible or had received additional or booster vaccine doses, which have been shown to confer additional protection.§§§§ Sixth, some estimates lacked precision because of sample size limitations. Finally, this analysis was conducted before the emergence of the Omicron variant, for which vaccine or infection-derived immunity might be diminished.¶¶¶¶ This study offers a surveillance data framework to help evaluate both infections in vaccinated persons and reinfections as new variants continue to emerge.

Vaccination protected against COVID-19 and related hospitalization, and surviving a previous infection protected against a reinfection and related hospitalization during periods of predominantly Alpha and Delta variant transmission, before the emergence of Omicron; evidence suggests decreased protection from both vaccine- and infection-induced immunity against Omicron infections, although additional protection with widespread receipt of booster COVID-19 vaccine doses is expected. Initial infection among unvaccinated persons increases risk for serious illness, hospitalization, long-term sequelae, and death; by November 30, 2021, approximately 130,781 residents of California and New York had died from COVID-19. Thus, vaccination remains the safest and primary strategy to prevent SARS-CoV-2 infections, associated complications, and onward transmission. Primary COVID-19 vaccination, additional doses, and booster doses are recommended by CDC’s Advisory Committee on Immunization Practices to ensure that all eligible persons are up to date with COVID-19 vaccination, which provides the most robust protection against initial infection, severe illness, hospitalization, long-term sequelae, and death.***** Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

Acknowledgments

Dana Jaffe, California Department of Public Health; Rebecca Hoen, Meng Wu, New York State Department of Health; Citywide Immunization Registry Program, New York City Department of Health and Mental Hygiene.

Corresponding author: Tomás M. León, tomas.leon@cdph.ca.gov.


1California Department of Public Health; 2New York State Department of Health; 3University at Albany School of Public Health, SUNY, Rensselaer, New York; 4CDC.

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

 https://covid.cdc.gov/covid-data-tracker/#cases_deathsper100klast7days

 Statewide immunization databases in California are the California Immunization Registry, Regional Immunization Data Exchange, and San Diego Immunization Registry; the laboratory system is the California COVID Reporting System (CCRS). In New York, immunization information systems include Citywide Immunization Registry and the New York State Immunization Information System; the laboratory system is the Electronic Clinical Laboratory Reporting System (ECLRS). California data were matched between the immunization and case registries using a probabilistic algorithm with exact match for zip code and date of birth and fuzzy match on first name and last name. New York data were matched to the ECLRS with the use of a deterministic algorithm based on first name, last name, and date of birth. In California, person-level hospitalization data from CCRS and supplementary hospitalization reports were used to identify COVID-19–associated hospitalizations.

§ For both classification into cohorts of persons with previous COVID-19 diagnoses and for measuring incident cases, laboratory-confirmed infection was defined as the receipt of a new positive SARS-CoV-2 nucleic acid amplification test (NAAT) or antigen test (both for New York and NAAT only for California) result, but not within 90 days of a previous positive result.

 Fully vaccinated with the primary vaccination series is defined as receipt of a second dose of an mRNA COVID-19 vaccine (Pfizer-BioNTech or Moderna) or 1 dose of the Janssen (Johnson & Johnson) vaccine ≥14 days before May 30, 2021.

** Because of the timing of full vaccination, the cohort definitions, and analysis timeframe, this cohort consisted nearly exclusively of persons who had previously received a laboratory-confirmed diagnosis of COVID-19 and later were fully vaccinated (California: 99.9%, New York: 99.7%), as opposed to the reverse order.

†† Whereas vaccinated cohorts were directly observed in the immunization information system databases, unvaccinated persons without a previous COVID-19 diagnosis were defined using U.S. Census population estimates minus the number of persons partially or fully vaccinated by December 11, 2021, and unvaccinated persons with a previous laboratory-confirmed infection before May 30, 2021. In California, the California Department of Finance population estimates were used for 2020, and the 2018 CDC National Center for Health Statistics Bridged Race file for U.S. Census population estimates were used in New York, consistent with other COVID-19 surveillance reporting.

§§ In California, a person-level match was performed to exclude deaths in each cohort before May 30, 2021. In New York, COVID-19 deaths were removed in aggregate from the starting number of unvaccinated persons with a previous COVID-19 diagnosis on May 30, 2021.

¶¶ https://www.cdc.gov/nchs/data/statnt/statnt20.pdfpdf icon

*** https://covid.cdc.gov/covid-data-tracker/#variant-proportions

††† https://www.medrxiv.org/content/10.1101/2021.09.12.21263461v1external icon

§§§ https://www.medrxiv.org/content/10.1101/2021.11.29.21267006v1external icon

¶¶¶ https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/vaccine-induced-immunity.html#anchor_1635540449320

**** https://www.medrxiv.org/content/10.1101/2021.12.04.21267114v1external icon

†††† https://www.medrxiv.org/content/10.1101/2021.12.09.21267565v1external icon

§§§§ https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status

¶¶¶¶ https://www.medrxiv.org/content/10.1101/2021.12.30.21268565v1external iconhttps://www.medrxiv.org/content/10.1101/2022.01.07.22268919v1external icon

***** https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html

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References

  1. Rosenberg ES, Holtgrave DR, Dorabawila V, et al. New COVID-19 cases and hospitalizations among adults, by vaccination status—New York, May 3–July 25, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1306–11. https://doi.org/10.15585/mmwr.mm7037a7external icon PMID:34529645external icon
  2. Rosenberg ES, Dorabawila V, Easton D, et al. Covid-19 vaccine effectiveness in New York State. N Engl J Med 2021. Epub December 1, 2021. https://doi.org/10.1056/NEJMoa2116063external icon PMID:34942067external icon
  3. Cavanaugh AM, Spicer KB, Thoroughman D, Glick C, Winter K. Reduced risk of reinfection with SARS-CoV-2 after COVID-19 vaccination—Kentucky, May–June 2021. MMWR Morb Mortal Wkly Rep 2021;70:1081–3. https://doi.org/10.15585/mmwr.mm7032e1external icon PMID:34383732external icon
  4. Grant R, Charmet T, Schaeffer L, et al. Impact of SARS-CoV-2 Delta variant on incubation, transmission settings and vaccine effectiveness: Results from a nationwide case-control study in France. Lancet Reg Health Eur 2021. Epub November 26, 2021.  https://doi.org/10.1016/j.lanepe.2021.100278external icon
  5. Self WH, Tenforde MW, Rhoads JP, et al.; IVY Network. Comparative effectiveness of Moderna, Pfizer-BioNTech, and Janssen (Johnson & Johnson) vaccines in preventing COVID-19 hospitalizations among adults without immunocompromising conditions—United States. MMWR Morb Mortal Wkly Rep 2021;70:1337–43. https://doi.org/10.15585/mmwr.mm7038e1external icon PMID:34555004external icon
  6. Lin D-Y, Gu Y, Wheeler B, et al. Effectiveness of Covid-19 vaccines in the United States over 9 months: surveillance data from the state of North Carolina. [Preprint posted online October 26, 2021.] https://www.medrxiv.org/content/10.1101/2021.10.25.21265304v1external icon
  7. Bozio CH, Grannis SJ, Naleway AL, et al. Laboratory-confirmed COVID-19 among adults hospitalized with COVID-19–like illness with infection-induced or mRNA vaccine-induced SARS-CoV-2 immunity—nine states, January–September 2021. MMWR Morb Mortal Wkly Rep 2021;70:1539–44. https://doi.org/10.15585/mmwr.mm7044e1external icon PMID:34735425external icon
  8. Kim P, Gordon SM, Sheehan MM, Rothberg MB. Duration of SARS-CoV-2 natural immunity and protection against the Delta variant: a retrospective cohort study. Clin Infect Dis 2021. Epub December 3, 2021. https://doi.org/10.1093/cid/ciab999external icon PMID:34864907external icon

Pandemic health consequences: Grasping the long COVID tail

Emerging evidence suggests that approximately 10% of people who survive Coronavirus Disease 2019 (COVID-19) will have lingering symptoms that negatively affect their quality of life, ability to work, and function [1,2]. This important group of people with the post-COVID-19 condition may seem small in comparison to the overall number of people with COVID-19 infection [3]. However, many patients who survive COVID-19 are likely to have considerable symptom burden, high resource utilization and health service needs, reduced economic productivity, and possibly a shortened life expectancy. The study by Bhaskaran and colleagues published in PLOS Medicine addresses an evolving, poorly studied, and important area of health policy and planning related to the care of patients who survive hospitalization for COVID-19 [4].

At face value, the scope of the COVID-19 pandemic is enormous. Within 2 years, nearly 300 million people have been infected with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, and more than 5 million people have died from it [5]. But, there is also a long tail to this statistical distribution of hardship. Studies report that numerous patients will continue to experience fatigue, shortness of breath, pain, sleep disturbances, anxiety, and depression [6]. More serious organ dysfunction such as pulmonary fibrosis, cognitive impairment, myocarditis, and renal failure may also develop [6]. Whether these translate into clinical diagnoses of chronic diseases like interstitial lung disease, dementia, heart failure, and chronic kidney disease remains to be seen. Collectively, the prospect for immense suffering among these individuals will undoubtedly have huge and enduring impacts on healthcare systems globally. As the world continues its largest vaccination effort in history and looks to eliminate the impacts of acute COVID-19, we must not forget that a meaningful minority who survive will transition from an acute to chronic disease state. In turn, management strategies and health resource planning must also appropriately transition. As a multisystem disease, the post-COVID-19 condition will require the involvement of multidisciplinary care teams [7]: Who will help to look after these patients?

Bhaskaran and colleagues studied over 164,000 hospitalized adults with COVID-19 matched to an “active control” group of adults hospitalized with influenza and to general population controls. They compared the medium- and long-term risks of hospital admission and death across the 3 study groups. The main findings were that people discharged following hospitalization for COVID-19 had a 2-fold higher associated risk for rehospitalization and death than the general population and similar risks compared to those hospitalized for influenza. These outcomes were most pronounced in the first 30 days following discharge yet remained substantially elevated over time. Further, those hospitalized with COVID-19 were more likely to be rehospitalized or die from mental health or cognitive-related causes, especially if they had preexisting dementia, compared to those hospitalized with influenza.

Initial hospitalization with COVID-19 represents a crucial touch point within the healthcare system. The study by Bhaskaran and colleagues sheds important light on the health service needs of patients who survive hospitalization for COVID-19. It further helps disentangle the effects of hospitalization from respiratory viral infection on important outcomes. The current work builds on similar findings from a recent study of 47,780 hospitalized adults with COVID-19 who survived to discharge with a mean follow-up of 140 days [8]. In that study, rates of hospital readmission and mortality were 3.5 and 7.7 times greater in the previously hospitalized group of COVID-19 patients, compared to general population controls, respectively. Other studies from the United States and China followed patients hospitalized for COVID-19 and reported lower 60-day and 1-year rehospitalization rates ranging from 13% to 19.9%. However, these studies did not account for the competing risk of death as was done in the current study [911].

There were also noteworthy limitations of Bhaskaran and colleagues’ study. First, cause-specific outcomes among adults with COVID-19 may be artificially higher than those with influenza due to availability bias. Put simply, patients and providers may be much more aware of COVID-19 and its complications, including those related to return to hospital, than might be the case for those with pneumonia or even confirmed influenza. Second, the study used administrative data from primary care. While 98% of the population in England are registered with a general practice (thereby minimizing selection biases due to health-seeking behaviors), there are some geographical differences in the use of the OpenSAFELY platform, which may introduce the potential for selection bias. Third, this study was conducted in a high-income nation with substantial resources to support patients following infection with COVID-19. The generalizability of these findings to middle- and low-income nations, or those with limited resources, is unknown.

The study by Bhaskaran and colleagues has clear applications to healthcare resource planning and policy in the care of individuals who survive COVID-19. This suggests a substantial added burden on global healthcare systems. It further builds on our evolving knowledge of the post-COVID-19 condition and its lingering impacts, including on many previously healthy adults in their prime years of productivity. Still, a wealth of research is required to develop prediction tools to proactively identify and support the healthcare needs of survivors, including end-of-life care, develop new strategies to prevent and treat the post-COVID-19 condition, and encourage interprofessional teams to provide longitudinal care through innovative health policy interventions.

Early pandemic public messaging strategies focused on flattening the peak of the acute COVID-19 infection curve to preserve healthcare system capacity and its ability to deliver high-quality care. These efforts were generally successful. To preserve ongoing system capacity and provide high-quality patient care, the long COVID tail does not require further flattening, but rather demands new clinical and health policy strategies to address its potential for long-term suffering. Here, we must recognize that the head of the pandemic often demands our immediate attention, but we must not ignore its long and deadly tail.

References

  1. 1.Pizarro-Pennarolli C, Sánchez-Rojas C, Torres-Castro R, Vera-Uribe R, Sanchez-Ramirez DC, Vasconcello-Castillo L, et al. Assessment of activities of daily living in patients post COVID-19: a systematic review. PeerJ. 2021;9:e11026. pmid:33868804
  2. 2.Groff D, Sun A, Ssentongo AE, Ba DM, Parsons N, Poudel GR, et al. Short-term and Long-term Rates of Postacute Sequelae of SARS-CoV-2 Infection. JAMA Netw Open. 2021;4:e2128568. pmid:34643720
  3. 3.Rubin R. As Their Numbers Grow, COVID-19 “Long Haulers” Stump Experts. JAMA. 2020;324:1381–3. pmid:32965460
  4. 4.Bhaskaran K, Rentsch CT, Hickman G, Hulme WJ, Schultze A, Curtis HJ, et al. Overall and cause-specific hospitalisation and death after COVID-19 hospitalisation in England: A cohort study using linked primary care, secondary care and death registration data in the OpenSAFELY platform. PLoS Med. 2022.
  5. 5.World Health Organization. WHO Coronavirus (COVID-19) Dashboard. [cited 2021 Nov 10]. Available from: https://covid19.who.int.
  6. 6.Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequalae of COVID-19. Nature. 2021:1–8. pmid:33887749
  7. 7.Greenhalgh T, Knight M, A’Court C, Buxton M, Husain L. Management of post-acute COVID-19 in primary care. BMJ. 2020;370:m3026. pmid:32784198
  8. 8.Ayoubkhani D, Khunti K, Nafilyan V, Maddox T, Humberstone B, Diamond I, et al. Post-COVID syndrome in individuals admitted to hospital with COVID-19: retrospective cohort study. BMJ. 2021;372:n693. pmid:33789877
  9. 9.Chopra V, Flanders SA, O’Malley M, Malani AN, Prescott HC. Sixty-Day Outcomes Among Patients Hospitalized With COVID-19. Ann Intern Med. 2020. pmid:33175566
  10. 10.Donnelly JP, Wang XQ, Iwashyna TJ, Prescott HC. Readmission and Death After Initial Hospital Discharge Among Patients With COVID-19 in a Large Multihospital System. JAMA. 2021;325:304–6. pmid:33315057
  11. 11.Huang L, Yao Q, Gu X, Wang Q, Ren L, Wang Y, et al. 1-year outcomes in hospital survivors with COVID-19: a longitudinal cohort study. Lancet. 2021;398:747–58. pmid:34454673