Vaccination against the virus that causes COVID-19 triggers the body to produce antibodies that help fight future infections. But some people generate more antibodies after vaccination than others. People with lower levels of antibodies are more likely to get COVID-19 in the future. Identifying people with low antibody levels after COVID-19 vaccination is important. It could help decide who receives priority for future vaccination.

Previous studies show that people with certain health conditions produce fewer antibodies after one or two doses of a COVID-19 vaccine. For example, people with weakened immune systems. Now that third booster doses are available, it is vital to determine if they increase antibody levels for those most at risk of severe COVID-19.

Cheetham et al. show that a third booster dose of a COVID-19 vaccine boosts antibodies to high levels in 90% of individuals, including those at increased risk. In the experiments, Cheetham et al. measured antibodies against the virus that causes COVID-19 in 9,361 individuals participating in two large long-term health studies in the United Kingdom. The experiments found that UK individuals advised to shield from the virus because they were at increased risk of complications had lower levels of antibodies after one or two vaccine doses than individuals without such risk factors. This difference was also seen after a third booster dose, but overall antibody levels had large increases. People who received the Oxford/AstraZeneca vaccine as their first dose also had lower antibody levels after one or two doses than those who received the Pfizer/BioNTech vaccine first. Positively, this difference in antibody levels was no longer seen after a third booster dose. Individuals with lower antibody levels after their first dose were also more likely to have a case of COVID-19 in the following months.

Antibody levels were high in most individuals after the third dose. The results may help governments and public health officials identify individuals who may need extra protection after the first two vaccine doses. They also support current policies promoting booster doses of the vaccine and may support prioritizing booster doses for those at the highest risk from COVID-19 in future vaccination campaigns.

Immunological responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and SARS-CoV-2 vaccination vary between individuals and over time (Shrotri et al., 2021a; Shrotri et al., 2021b; Wei et al., 2021b). Within 2–4 weeks of infection, most individuals generate detectable levels of several antibody subtypes (immunoglobulin A, M, G) directed against different domains of the virus (Nucleocapsid protein, Spike protein, receptor-binding domain of Spike), which gradually decline over time (Seow et al., 2020; Brochot et al., 2020; Wei et al., 2021a; Long et al., 2020; Gaebler et al., 2021). Anti-Nucleocapsid antibodies are generated following infection but not by any current SARS-CoV-2 vaccines, while anti-Spike antibodies are generated following infection or vaccination. Levels of anti-Spike antibodies correlate with SARS-CoV-2-neutralising anti-receptor-binding domain antibody titre (Perkmann et al., 2021). A similar profile of antibody induction with subsequent waning is observed after vaccination against SARS-CoV-2 (Shrotri et al., 2021a; Shrotri et al., 2021b; Matsunaga et al., 2022; Pegu et al., 2021). Waning of antibody levels are likely correlated with observed reductions in vaccine effectiveness over time (Pouwels et al., 2021; UKHSA, 2021; Lopez Bernal et al., 2021). Reduced antibody neutralising activity and vaccine effectiveness have been observed for variants of concern in comparison to ancestral SARS-CoV-2 (UKHSA, 2021; Collie et al., 2022; Yu et al., 2022; Nemet et al., 2022; Cheng et al., 2022).

Anti-Spike antibody levels have been found to be inversely proportional to risk of future infection, and so identified as a correlate of protection (Goldblatt et al., 2022a; Perry et al., 2022; Gilbert et al., 2022; Goldblatt et al., 2022b; Feng et al., 2021; Dimeglio et al., 2022a; Dimeglio et al., 2022b; Khoury et al., 2021). Goldblatt et al. estimated protective thresholds of 154 (95% CI: 42, 559) and 171 (95% CI: 57, 519) BAU/mL for wild-type and alpha variant SARS-CoV-2 respectively and an initial estimate range of 36–490 BAU/ml for delta variant (Goldblatt et al., 2022b), while Feng et al., 2021 estimated 80% vaccine effectiveness against alpha variant for levels above 264 (95% CI: 108, 806) BAU/mL. Dimeglio et al. estimated much higher levels of more than 6000 BAU/mL were needed for protection against omicron variant BA.1, while no relationship was found between infection and antibody level for BA.2 (Dimeglio et al., 2022b).

Several clinical variables contribute to variation in antibody response following vaccination. Lower antibody levels following both first and second vaccinations have been observed in individuals with particular comorbidities (including cancer, renal disease, and hepatic disease; Parry et al., 2021; Monin et al., 2021; Kearns et al., 2021), individuals using immunosuppressant medications (Shrotri et al., 2021a; Shrotri et al., 2021b; Parry et al., 2021; Monin et al., 2021), and individuals identified from electronic health records data as of moderate or high risk of COVID-19 complications (according to UK government prior ‘Shielded Patient List’ criteria of conditions, ongoing treatments, and medications) (Shrotri et al., 2021a; Shrotri et al., 2021b; GOV.UK, 2022b). Studies testing for associations between antibody response and non-clinical factors such as socio-demographics have been more limited. Here, the use of longitudinal studies, with broader catalogues of bio-social data, are well suited to such investigations.

Here, we aimed to examine variables associated with variation in post-vaccination antibody levels, and the subsequent likelihood of post-vaccination infection. We measured the antibody levels of participants from two population-based longitudinal cohorts during the time of the UK vaccination roll-out: TwinsUK (in April-May 2021 and November 2021-January 2022) (Verdi et al., 2019) and Avon Longitudinal Study of Parents and Children (ALSPAC) (Fraser et al., 2013; Boyd et al., 2013) (April-May 2021 only). We aimed firstly to assess the relationship between anti-Spike antibody levels (identified as a correlate of protection against infection), measured after first or second vaccination in April-May 2021, and an outcome of subsequent post-vaccination infection over the following 6–9 months (identified through further serological evidence and/or self-reported COVID-19 from data collected in TwinsUK between November 2021 and January 2022). Secondly, we used peri-pandemic and historical data to investigate associations with an outcome of having relatively low antibody levels following first, second (ALSPAC and TwinsUK), or third (TwinsUK only) vaccination, for multiple socio-demographic, physical and mental health characteristics, prior SARS-CoV-2 infection, and vaccination history. Finally, we used twin-pair analysis within TwinsUK to probe genetic and environmental contributions to antibody level variation.

In this study we used SARS-CoV-2 anti-Nucleocapsid and anti-Spike antibody testing, and questionnaire data collected at multiple timepoints during and before the COVID-19 pandemic, to investigate associations with antibody response to vaccination in TwinsUK and ALSPAC longitudinal population-based cohorts.

Firstly, we observed large non-linear increases in antibody levels between first, second, and third vaccination, both at the median and 10th percentile levels where risk of infection is heightened, with a relatively narrowed antibody level distribution after third vaccination producing a more even response across the sampled population. Secondly, individuals with lower levels of anti-Spike antibodies following first vaccination were at higher risk of future SARS-CoV-2 infection at any subsequent time, including after further vaccinations, providing further indication of anti-Spike antibody levels as a correlate of protection. Thirdly, the following groups all had higher odds of having lower antibody levels following vaccination: those on the UK ‘Shielded Patient List’; those with lower self-rated health; those who received AZD1222 (Oxford/AstraZeneca) vaccine for first and second vaccination; those sampled at longer time since second vaccination and third vaccination; those prescribed immunosuppressant medication (in TwinsUK) or with self-reported immunosuppression (in ALSPAC). These findings were consistent across multiple rounds of vaccination and/or in both cohorts. Individuals with evidence of SARS-CoV-2 infection prior to sampling were less likely to have lower antibody levels, consistent with previous studies that postulating that the quantity and quality of antibody response were linked to the total number of exposures to SARS-CoV-2 (Walls et al., 2022; Bates et al., 2022). Finally, in analyses exploiting the twin-pair design of the TwinsUK cohort, we found that genetic factors influenced antibody level variation (considered only after third vaccination), with smaller differences in antibody levels within genetically identical MZ pairs compared with DZ pairs. Twin-pair regression models showed that association between antibody levels and ‘Shielded Patient List’ status was independent of genetic and other shared factors, after explicit adjustment for key vaccination and infection variables.

Longitudinal antibody testing within TwinsUK at Q4 highlighted the effectiveness of third vaccination at both increasing absolute levels of antibodies and reducing variability in post-vaccination antibody levels evident after earlier doses. Even among sub-groups associated with having the lowest antibody levels and/or higher risk of severe COVID-19, such as shielding, frail, and/or immunosuppressed individuals, over 75% of individuals had levels above 6000 BAU/mL (Supplementary file 4), the minimum level estimated to give partial protection against omicron BA.1 variant (Dimeglio et al., 2022b). Moreover, although individuals receiving AZD1222 vaccine (versus BNT162b2 [Pfizer BioNTech]) were more likely to have lower antibody levels after first and second vaccination, this disparity was no longer evident after third vaccination, consistent with lower vaccine effectiveness and increased post-vaccination infection after first or second vaccination following AZD1222 versus BNT162b2 (UKHSA, 2021; Katikireddi et al., 2022; Stouten et al., 2022), but only minor differences after third vaccination (UKHSA, 2021; Menni et al., 2022).

Notably, health-related variables associated with lower antibody levels were more general (self-rated poor health, immunosuppression indicators) and/or collective measures with wide-ranging criteria (e.g., ‘Shielded Patient List’, very frail, multimorbidity), rather than specific factors such as individual comorbidities (e.g., rheumatoid arthritis). These more general and collective measures may contain more specific risk factors for which we did not have data or sufficient sample size to detect, or could suggest that variation in post-vaccination antibody levels between individuals may originate from a wide range of variables in combination. Of the several variables associated with antibody levels, only serology-based evidence of prior SARS-CoV-2 infection was directly associated (here, negatively associated) with subsequent post-vaccination infection between April-May 2021 and November 2021-January 2022 (majority sampled before the January 2022 UK omicron variant peak). We found no consistent associations of lower antibody levels with age or employment status, but a very strong age gradient (lower incidence with older age) and lower likelihood among retired (versus employed) individuals of post-vaccination infection. These results are consistent with risk of infection being a complex combination of SARS-CoV-2 case prevalence, individual immune response to vaccination, and individual level of exposure. Given the relaxation of measures across many countries, groups previously less exposed, for example, due to shielding guidance, may become more at risk.

We also acknowledge limitations of this work. Both TwinsUK and ALSPAC (Generation 0) participants are disproportionately older, female, and more likely of white ethnicity, in comparison to the UK population. Geographically, TwinsUK (based in London) is skewed towards lower deprivation areas in south east England and ALSPAC (based in Bristol) towards south west England. Consequently, the generalisability of our findings to non-white UK and international populations, in addition to our ability to detect associations with smaller effect sizes, is limited. Our analyses are subject to selection biases due to use of multiple and varying data collections that rely on voluntary participation. This may cause collider bias and affect findings as outlined elsewhere (Griffith et al., 2020; Munafò et al., 2018). For example, indicators of poorer health have been associated with lower response to COVID-19 questionnaires in ALSPAC (Fernández-Sanlés et al., 2021), which may bias the observed results. Acknowledging the potential effects of biases, the replication of multiple associations with lower antibody levels across compositionally varied TwinsUK and ALSPAC cohorts and across multiple rounds of vaccination support the robustness of our findings. It is these replicated findings that we chose to discuss primarily.

In conclusion, our results highlight the large boost across the antibody level distribution produced by third vaccination, and suggest that measurement of anti-Spike antibodies after first SARS-CoV-2 vaccination may have potential use as an early indicator to identify individuals at higher risk of a future SARS-CoV-2 infection, particularly in the many countries where vaccination roll-out is at an earlier stage. Individuals who previously met UK ‘Shielded Patient List’ criteria had consistently lower antibody responses to vaccination than other participants, highlighting the importance of continuing to inform such individuals of their personal risk of SARS-CoV-2 infection, despite the UK government decision to end shielding guidance in April 2021 (GOV.UK, 2021). This result should inform prioritisation of vaccination towards these individuals in any future immunisation campaigns.

Data from all analyses presented in figures and tables herein are tabulated and available as a supplementary spreadsheet file. Original antibody test data are available within the UK Longitudinal Linkage Collaboration upon application (see https://ukllc.ac.uk/apply/). UK LLC houses COVID-19 related datasets from over 20 UK longitudinal population studies (see https://ukllc.ac.uk/datasets/). Original TwinsUK data are available to researchers on application. Access to original TwinsUK data is managed by the TwinsUK Resource Executive Committee (see https://twinsuk.ac.uk/resources-for-researchers/access-our-data/) and access to original ALSPAC data via an online proposal system (see http://www.bristol.ac.uk/media-library/sites/alspac/documents/researchers/data-access/ALSPAC_Access_Policy.pdf). This is to ensure privacy and protect against misuse. ALSPAC study data were collected and managed using REDCap electronic data capture tools hosted at the University of Bristol. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies (doi:https://doi.org/10.1016/J.JBI.2008.08.010). The study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool on the study website (http://www.bristol.ac.uk/alspac/researchers/our-data/). Analysis code is openly available via GitHub at: https://github.com/nathan-cheetham/NCS_SARSCOV2_Antibody_Study (copy archived at swh:1:rev:7103349369b7a4f00d6cf73f780283ba86fe8570).

1. 1. Bates TA
   2. McBride SK
   3. Leier HC
   4. Guzman G
   5. Lyski ZL
   6. Schoen D
   7. Winders B
   8. Lee J-Y
   9. Lee DX
   10. Messer WB
   11. Curlin ME
   12. Tafesse FG

   (2022) Vaccination before or after SARS-cov-2 infection leads to robust humoral response and antibodies that effectively neutralize variants

   Science Immunology 7:eabn8014.

   https://doi.org/10.1126/sciimmunol.abn8014

   • PubMed
   • Google Scholar
2. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
3. 1. Boyd A
   2. Golding J
   3. Macleod J
   4. Lawlor DA
   5. Fraser A
   6. Henderson J
   7. Molloy L
   8. Ness A
   9. Ring S
   10. Davey Smith G

   (2013) Cohort profile: the '’Children of the 90s’’-- the index offspring of the avon longitudinal study of parents and children

   International Journal of Epidemiology 42:111–127.

   https://doi.org/10.1093/ije/dys064

   • PubMed
   • Google Scholar
4. 1. Brochot E
   2. Demey B
   3. Touzé A
   4. Belouzard S
   5. Dubuisson J
   6. Schmit JL
   7. Duverlie G
   8. Francois C
   9. Castelain S
   10. Helle F

   (2020) Anti-spike, anti-nucleocapsid and neutralizing antibodies in SARS-cov-2 inpatients and asymptomatic individuals

   Frontiers in Microbiology 11:584251.

   https://doi.org/10.3389/fmicb.2020.584251

   • PubMed
   • Google Scholar
5. 1. Carlin JB
   2. Gurrin LC
   3. Sterne JA
   4. Morley R
   5. Dwyer T

   (2005) Regression models for twin studies: a critical review

   International Journal of Epidemiology 34:1089–1099.

   https://doi.org/10.1093/ije/dyi153

   • PubMed
   • Google Scholar
6. Website

   1. CDC

   (2021) Centers for Disease Control and Prevention. Interim Guidelines for COVID-19 Antibody Testing

   Accessed July 6, 2021.

   https://www.cdc.gov/coronavirus/2019-ncov/lab/resources/antibody-tests-guidelines.html
7. Software

   1. Cheetham NJ

   (2023) Antibody levels following vaccination against SARS-cov-2: twinsuk code, version swh:1:rev:7103349369b7a4f00d6cf73f780283ba86fe8570

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:6a8a9368a4d03a4fee639e9f42479c03a8fa3d3c;origin=https://github.com/nathan-cheetham/NCS_SARSCOV2_Antibody_Study;visit=swh:1:snp:62d158efbe828bafa1828bf9c527eb37a3402dd1;anchor=swh:1:rev:7103349369b7a4f00d6cf73f780283ba86fe8570
8. 1. Cheng SMS
   2. Mok CKP
   3. Leung YWY
   4. Ng SS
   5. Chan KCK
   6. Ko FW
   7. Chen C
   8. Yiu K
   9. Lam BHS
   10. Lau EHY
   11. Chan KKP
   12. Luk LLH
   13. Li JKC
   14. Tsang LCH
   15. Poon LLM
   16. Hui DSC
   17. Peiris M

   (2022) Neutralizing antibodies against the SARS-cov-2 omicron variant BA.1 following homologous and heterologous coronavac or bnt162b2 vaccination

   Nature Medicine 28:486–489.

   https://doi.org/10.1038/s41591-022-01704-7

   • PubMed
   • Google Scholar
9. 1. Collie S
   2. Champion J
   3. Moultrie H
   4. Bekker L-G
   5. Gray G

   (2022) Effectiveness of bnt162b2 vaccine against omicron variant in South Africa

   The New England Journal of Medicine 386:494–496.

   https://doi.org/10.1056/NEJMc2119270

   • PubMed
   • Google Scholar
10. 1. Dimeglio C
    2. Herin F
    3. Martin-Blondel G
    4. Miedougé M
    5. Izopet J

    (2022a) Antibody titers and protection against a SARS-cov-2 infection

    The Journal of Infection 84:248–288.

    https://doi.org/10.1016/j.jinf.2021.09.013

    • PubMed
    • Google Scholar
11. 1. Dimeglio C
    2. Migueres M
    3. Bouzid N
    4. Chapuy-Regaud S
    5. Gernigon C
    6. Da-Silva I
    7. Porcheron M
    8. Martin-Blondel G
    9. Herin F
    10. Izopet J

    (2022b) Antibody titers and protection against omicron (BA.1 and BA.2) SARS-cov-2 infection

    Vaccines 10:1548.

    https://doi.org/10.3390/vaccines10091548

    • PubMed
    • Google Scholar
12. 1. Farbmacher H
    2. Kögel H

    (2017) Testing under a special form of heteroscedasticity

    Applied Economics Letters 24:264–268.

    https://doi.org/10.1080/13504851.2016.1181827

    • Google Scholar
13. 1. Feng S
    2. Phillips DJ
    3. White T
    4. Sayal H
    5. Aley PK
    6. Bibi S
    7. Dold C
    8. Fuskova M
    9. Gilbert SC
    10. Hirsch I
    11. Humphries HE
    12. Jepson B
    13. Kelly EJ
    14. Plested E
    15. Shoemaker K
    16. Thomas KM
    17. Vekemans J
    18. Villafana TL
    19. Lambe T
    20. Pollard AJ
    21. Voysey M
    22. Oxford COVID Vaccine Trial Group

    (2021) Correlates of protection against symptomatic and asymptomatic SARS-cov-2 infection

    Nature Medicine 27:2032–2040.

    https://doi.org/10.1038/s41591-021-01540-1

    • PubMed
    • Google Scholar
14. 1. Fernández-Sanlés A
    2. Smith D
    3. Clayton GL
    4. Northstone K
    5. Carter AR
    6. Millard LA
    7. Borges MC
    8. Timpson NJ
    9. Tilling K
    10. Griffith GJ
    11. Lawlor DA

    (2021) Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC

    Wellcome Open Research 6:184.

    https://doi.org/10.12688/wellcomeopenres.17041.2

    • PubMed
    • Google Scholar
15. 1. Fraser A
    2. Macdonald-Wallis C
    3. Tilling K
    4. Boyd A
    5. Golding J
    6. Davey Smith G
    7. Henderson J
    8. Macleod J
    9. Molloy L
    10. Ness A
    11. Ring S
    12. Nelson SM
    13. Lawlor DA

    (2013) Cohort profile: the Avon longitudinal study of parents and children: ALSPAC mothers cohort

    International Journal of Epidemiology 42:97–110.

    https://doi.org/10.1093/ije/dys066

    • PubMed
    • Google Scholar
16. 1. Gaebler C
    2. Wang Z
    3. Lorenzi JCC
    4. Muecksch F
    5. Finkin S
    6. Tokuyama M
    7. Cho A
    8. Jankovic M
    9. Schaefer-Babajew D
    10. Oliveira TY
    11. Cipolla M
    12. Viant C
    13. Barnes CO
    14. Bram Y
    15. Breton G
    16. Hägglöf T
    17. Mendoza P
    18. Hurley A
    19. Turroja M
    20. Gordon K
    21. Millard KG
    22. Ramos V
    23. Schmidt F
    24. Weisblum Y
    25. Jha D
    26. Tankelevich M
    27. Martinez-Delgado G
    28. Yee J
    29. Patel R
    30. Dizon J
    31. Unson-O’Brien C
    32. Shimeliovich I
    33. Robbiani DF
    34. Zhao Z
    35. Gazumyan A
    36. Schwartz RE
    37. Hatziioannou T
    38. Bjorkman PJ
    39. Mehandru S
    40. Bieniasz PD
    41. Caskey M
    42. Nussenzweig MC

    (2021) Evolution of antibody immunity to SARS-cov-2

    Nature 591:639–644.

    https://doi.org/10.1038/s41586-021-03207-w

    • PubMed
    • Google Scholar
17. 1. Gilbert PB
    2. Montefiori DC
    3. McDermott AB
    4. Fong Y
    5. Benkeser D
    6. Deng W
    7. Zhou H
    8. Houchens CR
    9. Martins K
    10. Jayashankar L
    11. Castellino F
    12. Flach B
    13. Lin BC
    14. O’Connell S
    15. McDanal C
    16. Eaton A
    17. Sarzotti-Kelsoe M
    18. Lu Y
    19. Yu C
    20. Borate B
    21. van der Laan LWP
    22. Hejazi NS
    23. Huynh C
    24. Miller J
    25. El Sahly HM
    26. Baden LR
    27. Baron M
    28. De La Cruz L
    29. Gay C
    30. Kalams S
    31. Kelley CF
    32. Andrasik MP
    33. Kublin JG
    34. Corey L
    35. Neuzil KM
    36. Carpp LN
    37. Pajon R
    38. Follmann D
    39. Donis RO
    40. Koup RA
    41. Immune Assays Team§
    42. Moderna, Inc. Team§
    43. Coronavirus Vaccine Prevention Network /Coronavirus Efficacy Team
    44. United States Government /CoVPN Biostatistics Team

    (2022) Immune correlates analysis of the mrna-1273 COVID-19 vaccine efficacy clinical trial

    Science 375:43–50.

    https://doi.org/10.1126/science.abm3425

    • PubMed
    • Google Scholar
18. 1. Goldblatt D
    2. Alter G
    3. Crotty S
    4. Plotkin SA

    (2022a) Correlates of protection against SARS-cov-2 infection and COVID-19 disease

    Immunological Reviews 310:6–26.

    https://doi.org/10.1111/imr.13091

    • PubMed
    • Google Scholar
19. 1. Goldblatt D
    2. Fiore-Gartland A
    3. Johnson M
    4. Hunt A
    5. Bengt C
    6. Zavadska D
    7. Snipe HD
    8. Brown JS
    9. Workman L
    10. Zar HJ
    11. Montefiori D
    12. Shen X
    13. Dull P
    14. Plotkin S
    15. Siber G
    16. Ambrosino D

    (2022b) Towards a population-based threshold of protection for COVID-19 vaccines

    Vaccine 40:306–315.

    https://doi.org/10.1016/j.vaccine.2021.12.006

    • PubMed
    • Google Scholar
20. Website

    1. Gov.scot

    (2020) Scottish Index of Multiple Deprivation 2020 - gov.scot

    Accessed November 29, 2021.

    https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/
21. Website

    1. GOV.UK

    (2019) English indices of deprivation 2019 - GOV.UK

    Accessed November 29, 2021.

    https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019
22. Website

    1. GOV.UK

    (2021) Shielding advice for the clinically extremely vulnerable to stop from April

    Accessed February 3, 2023.

    https://www.gov.uk/government/news/shielding-advice-for-the-clinically-extremely-vulnerable-to-stop-from-april
23. Website

    1. GOV.UK

    (2022a) Rural Urban Classification - GOV.UK

    Accessed March 15, 2022.

    https://www.gov.uk/government/collections/rural-urban-classification
24. Website

    1. GOV.UK

    (2022b) COVID-19: guidance on protecting people defined on medical grounds as extremely vulnerable - GOV.UK

    Accessed March 18, 2022.

    https://www.gov.uk/government/publications/guidance-on-shielding-and-protecting-extremely-vulnerable-persons-from-covid-19
25. Website

    1. GOV.WALES

    (2021) Welsh Index of Multiple Deprivation (full Index update with ranks): 2019

    Accessed November 29, 2021.

    https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019
26. 1. Griffith GJ
    2. Morris TT
    3. Tudball MJ
    4. Herbert A
    5. Mancano G
    6. Pike L
    7. Sharp GC
    8. Sterne J
    9. Palmer TM
    10. Davey Smith G
    11. Tilling K
    12. Zuccolo L
    13. Davies NM
    14. Hemani G

    (2020) Collider bias undermines our understanding of COVID-19 disease risk and severity

    Nature Communications 11:5749.

    https://doi.org/10.1038/s41467-020-19478-2

    • PubMed
    • Google Scholar
27. 1. Harris PA
    2. Taylor R
    3. Thielke R
    4. Payne J
    5. Gonzalez N
    6. Conde JG

    (2009) Research electronic data capture (redcap) -- a metadata-driven methodology and workflow process for providing translational research informatics support

    Journal of Biomedical Informatics 42:377–381.

    https://doi.org/10.1016/j.jbi.2008.08.010

    • PubMed
    • Google Scholar
28. 1. Hayes AF
    2. Cai L

    (2007) Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation

    Behavior Research Methods 39:709–722.

    https://doi.org/10.3758/bf03192961

    • PubMed
    • Google Scholar
29. 1. Katikireddi SV
    2. Cerqueira-Silva T
    3. Vasileiou E
    4. Robertson C
    5. Amele S
    6. Pan J
    7. Taylor B
    8. Boaventura V
    9. Werneck GL
    10. Flores-Ortiz R
    11. Agrawal U
    12. Docherty AB
    13. McCowan C
    14. McMenamin J
    15. Moore E
    16. Ritchie LD
    17. Rudan I
    18. Shah SA
    19. Shi T
    20. Simpson CR
    21. Barreto ML
    22. Oliveira V
    23. Barral-Netto M
    24. Sheikh A

    (2022) Two-dose chadox1 ncov-19 vaccine protection against COVID-19 hospital admissions and deaths over time: a retrospective, population-based cohort study in scotland and brazil

    Lancet 399:25–35.

    https://doi.org/10.1016/S0140-6736(21)02754-9

    • PubMed
    • Google Scholar
30. 1. Kearns P
    2. Siebert S
    3. willicombe m
    4. Gaskell C
    5. Kirkham A
    6. Pirrie S
    7. Bowden S
    8. Magwaro S
    9. Hughes A
    10. Lim Z
    11. Dimitriadis S
    12. Murray SM
    13. Marjot T
    14. Win Z
    15. Irwin SL
    16. Meacham G
    17. Richter AG
    18. Kelleher P
    19. Satsangi J
    20. Miller P
    21. Rea D
    22. Cook G
    23. Turtle L
    24. Klenerman P
    25. Dunachie S
    26. Basu N
    27. de Silva TI
    28. Thomas D
    29. Barnes E
    30. Goodyear CS
    31. McInnes I

    (2021) Examining the immunological effects of COVID-19 vaccination in patients with conditions potentially leading to diminished immune response capacity – the OCTAVE trial

    SSRN Electronic Journal 1:3910058.

    https://doi.org/10.2139/ssrn.3910058

    • Google Scholar
31. Book

    1. Kendall M

    (1975)

    Rank Correlation Measures

    Charles Griffin.

    • Google Scholar
32. 1. Khoury DS
    2. Cromer D
    3. Reynaldi A
    4. Schlub TE
    5. Wheatley AK
    6. Juno JA
    7. Subbarao K
    8. Kent SJ
    9. Triccas JA
    10. Davenport MP

    (2021) Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-cov-2 infection

    Nature Medicine 27:1205–1211.

    https://doi.org/10.1038/s41591-021-01377-8

    • PubMed
    • Google Scholar
33. 1. Lee PH
    2. Guyatt AL
    3. John C
    4. Ali A
    5. Wang X
    6. Williams AT
    7. Zhao B
    8. Batini C
    9. Bee C
    10. Adams E
    11. Melbourne CA
    12. Brightling CE
    13. Hsu R
    14. Bethea J
    15. Reeve N
    16. Ntalla I
    17. Terry S
    18. Pareek M
    19. Brunskill NJ
    20. Barwell J
    21. Hollox EJ
    22. Miola J
    23. Wallace SE
    24. Shepherd DJ
    25. Packer R
    26. Venn L
    27. Wain LV
    28. Free RC
    29. Tobin MD

    (2021) Extended cohort for e-health, environment and DNA (exceed) COVID-19 focus

    Wellcome Open Research 6:349.

    https://doi.org/10.12688/wellcomeopenres.17437.1

    • Google Scholar
34. 1. Long Q-X
    2. Liu B-Z
    3. Deng H-J
    4. Wu G-C
    5. Deng K
    6. Chen Y-K
    7. Liao P
    8. Qiu J-F
    9. Lin Y
    10. Cai X-F
    11. Wang D-Q
    12. Hu Y
    13. Ren J-H
    14. Tang N
    15. Xu Y-Y
    16. Yu L-H
    17. Mo Z
    18. Gong F
    19. Zhang X-L
    20. Tian W-G
    21. Hu L
    22. Zhang X-X
    23. Xiang J-L
    24. Du H-X
    25. Liu H-W
    26. Lang C-H
    27. Luo X-H
    28. Wu S-B
    29. Cui X-P
    30. Zhou Z
    31. Zhu M-M
    32. Wang J
    33. Xue C-J
    34. Li X-F
    35. Wang L
    36. Li Z-J
    37. Wang K
    38. Niu C-C
    39. Yang Q-J
    40. Tang X-J
    41. Zhang Y
    42. Liu X-M
    43. Li J-J
    44. Zhang D-C
    45. Zhang F
    46. Liu P
    47. Yuan J
    48. Li Q
    49. Hu J-L
    50. Chen J
    51. Huang A-L

    (2020) Antibody responses to SARS-cov-2 in patients with COVID-19

    Nature Medicine 26:845–848.

    https://doi.org/10.1038/s41591-020-0897-1

    • PubMed
    • Google Scholar
35. 1. Lopez Bernal J
    2. Andrews N
    3. Gower C
    4. Robertson C
    5. Stowe J
    6. Tessier E
    7. Simmons R
    8. Cottrell S
    9. Roberts R
    10. O’Doherty M
    11. Brown K
    12. Cameron C
    13. Stockton D
    14. McMenamin J
    15. Ramsay M

    (2021) Effectiveness of the pfizer-biontech and oxford-astrazeneca vaccines on covid-19 related symptoms, hospital admissions, and mortality in older adults in england: test negative case-control study

    BMJ 373:n1088.

    https://doi.org/10.1136/bmj.n1088

    • PubMed
    • Google Scholar
36. 1. Major-Smith D
    2. Matthews S
    3. Breeze T
    4. Crawford M
    5. Woodward H
    6. Wells N
    7. Mitchell R
    8. Molloy L
    9. Northstone K
    10. Timpson NJ

    (2021) The avon longitudinal study of parents and children-a resource for COVID-19 research: antibody testing results, april-june 2021

    Wellcome Open Research 6:283.

    https://doi.org/10.12688/wellcomeopenres.17294.2

    • PubMed
    • Google Scholar
37. 1. Mann HB

    (1945) Nonparametric tests against trend

    Econometrica 13:245.

    https://doi.org/10.2307/1907187

    • Google Scholar
38. 1. Mann HB
    2. Whitney DR

    (1947) On a test of whether one of two random variables is stochastically larger than the other

    The Annals of Mathematical Statistics 18:50–60.

    https://doi.org/10.1214/aoms/1177730491

    • Google Scholar
39. 1. Matsunaga H
    2. Takeuchi H
    3. Oba Y
    4. Fujimi S
    5. Honda T
    6. Tomonaga K

    (2022) Waning of anti-SARS-cov-2 spike antibody levels 100 to 200 days after the second dose of the bnt162b2 vaccine

    Vaccines 10:177.

    https://doi.org/10.3390/vaccines10020177

    • PubMed
    • Google Scholar
40. 1. Menni C
    2. May A
    3. Polidori L
    4. Louca P
    5. Wolf J
    6. Capdevila J
    7. Hu C
    8. Ourselin S
    9. Steves CJ
    10. Valdes AM
    11. Spector TD

    (2022) COVID-19 vaccine waning and effectiveness and side-effects of boosters: a prospective community study from the ZOE COVID study

    The Lancet. Infectious Diseases 22:1002–1010.

    https://doi.org/10.1016/S1473-3099(22)00146-3

    • PubMed
    • Google Scholar
41. 1. Monin L
    2. Laing AG
    3. Muñoz-Ruiz M
    4. McKenzie DR
    5. Del Molino Del Barrio I
    6. Alaguthurai T
    7. Domingo-Vila C
    8. Hayday TS
    9. Graham C
    10. Seow J
    11. Abdul-Jawad S
    12. Kamdar S
    13. Harvey-Jones E
    14. Graham R
    15. Cooper J
    16. Khan M
    17. Vidler J
    18. Kakkassery H
    19. Sinha S
    20. Davis R
    21. Dupont L
    22. Francos Quijorna I
    23. O’Brien-Gore C
    24. Lee PL
    25. Eum J
    26. Conde Poole M
    27. Joseph M
    28. Davies D
    29. Wu Y
    30. Swampillai A
    31. North BV
    32. Montes A
    33. Harries M
    34. Rigg A
    35. Spicer J
    36. Malim MH
    37. Fields P
    38. Patten P
    39. Di Rosa F
    40. Papa S
    41. Tree T
    42. Doores KJ
    43. Hayday AC
    44. Irshad S

    (2021) Safety and immunogenicity of one versus two doses of the COVID-19 vaccine bnt162b2 for patients with cancer: interim analysis of a prospective observational study

    The Lancet. Oncology 22:765–778.

    https://doi.org/10.1016/S1470-2045(21)00213-8

    • PubMed
    • Google Scholar
42. 1. Munafò MR
    2. Tilling K
    3. Taylor AE
    4. Evans DM
    5. Davey Smith G

    (2018) Collider scope: when selection bias can substantially influence observed associations

    International Journal of Epidemiology 47:226–235.

    https://doi.org/10.1093/ije/dyx206

    • PubMed
    • Google Scholar
43. 1. Nemet I
    2. Kliker L
    3. Lustig Y
    4. Zuckerman N
    5. Erster O
    6. Cohen C
    7. Kreiss Y
    8. Alroy-Preis S
    9. Regev-Yochay G
    10. Mendelson E
    11. Mandelboim M

    (2022) Third bnt162b2 vaccination neutralization of SARS-cov-2 omicron infection

    The New England Journal of Medicine 386:492–494.

    https://doi.org/10.1056/NEJMc2119358

    • PubMed
    • Google Scholar
44. Website

    1. NHS Digital

    (2021) Shielded Patient List - NHS Digital

    Accessed November 29, 2021.

    https://digital.nhs.uk/coronavirus/shielded-patient-list
45. Website

    1. NHS Digital

    (2022) COVID-19 – high risk shielded patient list identification methodology - NHS Digital

    Accessed February 7, 2022.

    https://digital.nhs.uk/coronavirus/shielded-patient-list/methodology
46. Website

    1. NIBSC

    (2020) WHO International Standard: First WHO International Standard for anti-SARS-CoV-2 immunoglobulin (human) NIBSC code: 20/136 Instructions for use

    Accessed November 29, 2021.

    http://www.nibsc.org/standardisation/international_standards.aspx
47. Website

    1. Northern Ireland Statistics and Research Agency

    (2017) Northern Ireland Multiple Deprivation Measure 2017 (NIMDM2017)

    Accessed November 29, 2021.

    https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017
48. 1. Northstone K
    2. Howarth S
    3. Smith D
    4. Bowring C
    5. Wells N
    6. Timpson NJ

    (2020a) The avon longitudinal study of parents and children-a resource for COVID-19 research: questionnaire data capture april-may 2020

    Wellcome Open Research 5:127.

    https://doi.org/10.12688/wellcomeopenres.16020.2

    • PubMed
    • Google Scholar
49. 1. Northstone K
    2. Smith D
    3. Bowring C
    4. Wells N
    5. Crawford M
    6. Haworth S
    7. Timpson NJ

    (2020b) The avon longitudinal study of parents and children - a resource for COVID-19 research: questionnaire data capture may-july 2020

    Wellcome Open Research 5:210.

    https://doi.org/10.12688/wellcomeopenres.16225.2

    • PubMed
    • Google Scholar
50. 1. Northstone K
    2. Mummé M
    3. Mitchell R
    4. Timpson N

    (2021) The avon longitudinal study of parents and children-a resource for COVID-19 research: approaches to the identification of cases november 2020

    Wellcome Open Research 6:122.

    https://doi.org/10.12688/wellcomeopenres.16808.1

    • Google Scholar
51. 1. Parry H
    2. McIlroy G
    3. Bruton R
    4. Ali M
    5. Stephens C
    6. Damery S
    7. Otter A
    8. McSkeane T
    9. Rolfe H
    10. Faustini S
    11. Wall N
    12. Hillmen P
    13. Pratt G
    14. Paneesha S
    15. Zuo J
    16. Richter A
    17. Moss P

    (2021) Antibody responses after first and second covid-19 vaccination in patients with chronic lymphocytic leukaemia

    Blood Cancer Journal 11:136.

    https://doi.org/10.1038/s41408-021-00528-x

    • PubMed
    • Google Scholar
52. 1. Pegu A
    2. O’Connell SE
    3. Schmidt SD
    4. O’Dell S
    5. Talana CA
    6. Lai L
    7. Albert J
    8. Anderson E
    9. Bennett H
    10. Corbett KS
    11. Flach B
    12. Jackson L
    13. Leav B
    14. Ledgerwood JE
    15. Luke CJ
    16. Makowski M
    17. Nason MC
    18. Roberts PC
    19. Roederer M
    20. Rebolledo PA
    21. Rostad CA
    22. Rouphael NG
    23. Shi W
    24. Wang L
    25. Widge AT
    26. Yang ES
    27. Beigel JH
    28. Graham BS
    29. Mascola JR
    30. Suthar MS
    31. McDermott AB
    32. Doria-Rose NA
    33. Arega J
    34. Beigel JH
    35. Buchanan W
    36. Elsafy M
    37. Hoang B
    38. Lampley R
    39. Kolhekar A
    40. Koo H
    41. Luke C
    42. Makhene M
    43. Nayak S
    44. Pikaart-Tautges R
    45. Roberts PC
    46. Russell J
    47. Sindall E
    48. Albert J
    49. Kunwar P
    50. Makowski M
    51. Anderson EJ
    52. Bechnak A
    53. Bower M
    54. Camacho-Gonzalez AF
    55. Collins M
    56. Drobeniuc A
    57. Edara VV
    58. Edupuganti S
    59. Floyd K
    60. Gibson T
    61. Ackerley CMG
    62. Johnson B
    63. Kamidani S
    64. Kao C
    65. Kelley C
    66. Lai L
    67. Macenczak H
    68. McCullough MP
    69. Peters E
    70. Phadke VK
    71. Rebolledo PA
    72. Rostad CA
    73. Rouphael N
    74. Scherer E
    75. Sherman A
    76. Stephens K
    77. Suthar MS
    78. Teherani M
    79. Traenkner J
    80. Winston J
    81. Yildirim I
    82. Barr L
    83. Benoit J
    84. Carste B
    85. Choe J
    86. Dunstan M
    87. Erolin R
    88. Ffitch J
    89. Fields C
    90. Jackson LA
    91. Kiniry E
    92. Lasicka S
    93. Lee S
    94. Nguyen M
    95. Pimienta S
    96. Suyehira J
    97. Witte M
    98. Bennett H
    99. Altaras NE
    100. Carfi A
    101. Hurley M
    102. Leav B
    103. Pajon R
    104. Sun W
    105. Zaks T
    106. Coler RN
    107. Larsen SE
    108. Neuzil KM
    109. Lindesmith LC
    110. Martinez DR
    111. Munt J
    112. Mallory M
    113. Edwards C
    114. Baric RS
    115. Berkowitz NM
    116. Boritz EA
    117. Carlton K
    118. Corbett KS
    119. Costner P
    120. Creanga A
    121. Doria-Rose NA
    122. Douek DC
    123. Flach B
    124. Gaudinski M
    125. Gordon I
    126. Graham BS
    127. Holman L
    128. Ledgerwood JE
    129. Leung K
    130. Lin BC
    131. Louder MK
    132. Mascola JR
    133. McDermott AB
    134. Morabito KM
    135. Novik L
    136. O’Connell S
    137. O’Dell S
    138. Padilla M
    139. Pegu A
    140. Schmidt SD
    141. Shi W
    142. Swanson PA
    143. Talana CA
    144. Wang L
    145. Widge AT
    146. Yang ES
    147. Zhang Y
    148. Chappell JD
    149. Denison MR
    150. Hughes T
    151. Lu X
    152. Pruijssers AJ
    153. Stevens LJ
    154. Posavad CM
    155. Gale M
    156. Menachery V
    157. Shi P-Y
    158. mRNA-1273 Study Group§

    (2021) Durability of mrna-1273 vaccine-induced antibodies against SARS-cov-2 variants

    Science 373:1372–1377.

    https://doi.org/10.1126/science.abj4176

    • PubMed
    • Google Scholar
53. 1. Perkmann T
    2. Koller T
    3. Perkmann-Nagele N
    4. Klausberger M
    5. Duerkop M
    6. Holzer B
    7. Hartmann B
    8. Mucher P
    9. Radakovics A
    10. Ozsvar-Kozma M
    11. Wagner OF
    12. Binder CJ
    13. Haslacher H

    (2021) Spike protein antibodies mediate the apparent correlation between SARS-cov-2 nucleocapsid antibodies and neutralization test results

    Microbiology Spectrum 9:e0021821.

    https://doi.org/10.1128/Spectrum.00218-21

    • PubMed
    • Google Scholar
54. 1. Perry J
    2. Osman S
    3. Wright J
    4. Richard-Greenblatt M
    5. Buchan SA
    6. Sadarangani M
    7. Bolotin S

    (2022) Does a humoral correlate of protection exist for SARS-cov-2? A systematic review

    PLOS ONE 17:e0266852.

    https://doi.org/10.1371/journal.pone.0266852

    • PubMed
    • Google Scholar
55. 1. Pouwels KB
    2. Pritchard E
    3. Matthews PC
    4. Stoesser N
    5. Eyre DW
    6. Vihta K-D
    7. House T
    8. Hay J
    9. Bell JI
    10. Newton JN
    11. Farrar J
    12. Crook D
    13. Cook D
    14. Rourke E
    15. Studley R
    16. Peto TEA
    17. Diamond I
    18. Walker AS

    (2021) Effect of delta variant on viral burden and vaccine effectiveness against new SARS-cov-2 infections in the UK

    Nature Medicine 27:2127–2135.

    https://doi.org/10.1038/s41591-021-01548-7

    • PubMed
    • Google Scholar
56. 1. Raîche M
    2. Hébert R
    3. Dubois MF

    (2008) PRISMA-7: A case-finding tool to identify older adults with moderate to severe disabilities

    Archives of Gerontology and Geriatrics 47:9–18.

    https://doi.org/10.1016/j.archger.2007.06.004

    • PubMed
    • Google Scholar
57. Software

    1. R Development Core Team

    (2021) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.r-project.org/index.html
58. Website

    1. Roche

    (2021) Elecsys Anti-SARS-CoV-2

    Accessed November 29, 2021.

    https://diagnostics.roche.com/gb/en/products/params/elecsys-anti-sars-cov-2.html
59. 1. Searle SD
    2. Mitnitski A
    3. Gahbauer EA
    4. Gill TM
    5. Rockwood K

    (2008) A standard procedure for creating A frailty index

    BMC Geriatrics 8:1–10.

    https://doi.org/10.1186/1471-2318-8-24

    • PubMed
    • Google Scholar
60. 1. Seow J
    2. Graham C
    3. Merrick B
    4. Acors S
    5. Pickering S
    6. Steel KJA
    7. Hemmings O
    8. O’Byrne A
    9. Kouphou N
    10. Galao RP
    11. Betancor G
    12. Wilson HD
    13. Signell AW
    14. Winstone H
    15. Kerridge C
    16. Huettner I
    17. Jimenez-Guardeño JM
    18. Lista MJ
    19. Temperton N
    20. Snell LB
    21. Bisnauthsing K
    22. Moore A
    23. Green A
    24. Martinez L
    25. Stokes B
    26. Honey J
    27. Izquierdo-Barras A
    28. Arbane G
    29. Patel A
    30. Tan MKI
    31. O’Connell L
    32. O’Hara G
    33. MacMahon E
    34. Douthwaite S
    35. Nebbia G
    36. Batra R
    37. Martinez-Nunez R
    38. Shankar-Hari M
    39. Edgeworth JD
    40. Neil SJD
    41. Malim MH
    42. Doores KJ

    (2020) Longitudinal observation and decline of neutralizing antibody responses in the three months following SARS-cov-2 infection in humans

    Nature Microbiology 5:1598–1607.

    https://doi.org/10.1038/s41564-020-00813-8

    • PubMed
    • Google Scholar
61. Preprint

    1. Shrotri M
    2. Fragaszy E
    3. Geismar C

    (2021a) Spike-Antibody Responses Following First and Second Doses of ChAdOx1 and BNT162b2 Vaccines by Age, Gender, and Clinical Factors - a Prospective Community Cohort Study

    medRxiv.

    https://doi.org/10.1101/2021.05.12.21257102

    • Google Scholar
62. 1. Shrotri M
    2. Navaratnam AMD
    3. Nguyen V
    4. Byrne T
    5. Geismar C
    6. Fragaszy E
    7. Beale S
    8. Fong WLE
    9. Patel P
    10. Kovar J
    11. Hayward AC
    12. Aldridge RW
    13. Virus Watch Collaborative

    (2021b) Spike-antibody waning after second dose of bnt162b2 or chadox1

    Lancet 398:385–387.

    https://doi.org/10.1016/S0140-6736(21)01642-1

    • PubMed
    • Google Scholar
63. 1. Smith D
    2. Bowring C
    3. Wells N
    4. Crawford M
    5. Timpson NJ
    6. Northstone K

    (2021) The avon longitudinal study of parents and children-a resource for COVID-19 research: questionnaire data capture november 2020-march 2021

    Wellcome Open Research 6:155.

    https://doi.org/10.12688/wellcomeopenres.16950.2

    • PubMed
    • Google Scholar
64. 1. Spitzer RL
    2. Kroenke K
    3. Williams JBW
    4. Löwe B

    (2006) A brief measure for assessing generalized anxiety disorder: the GAD-7

    Archives of Internal Medicine 166:1092–1097.

    https://doi.org/10.1001/archinte.166.10.1092

    • PubMed
    • Google Scholar
65. Website

    1. statsmodels

    (2021) statsmodels.regression.linear_model.OLSResults.get_robustcov_results — statsmodels

    Accessed November 29, 2021.

    https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.get_robustcov_results.html
66. 1. Stouten V
    2. Hubin P
    3. Haarhuis F
    4. van Loenhout JAF
    5. Billuart M
    6. Brondeel R
    7. Braeye T
    8. Van Oyen H
    9. Wyndham-Thomas C
    10. Catteau L

    (2022) Incidence and risk factors of COVID-19 vaccine breakthrough infections: a prospective cohort study in Belgium

    Viruses 14:802.

    https://doi.org/10.3390/v14040802

    • PubMed
    • Google Scholar
67. 1. Suthahar A
    2. Sharma P
    3. Hart D
    4. García MP
    5. Horsfall R
    6. Bowyer RCE
    7. Thompson EJ
    8. Steves CJ

    (2021) Twinsuk COVID-19 personal experience questionnaire (cope): wave 1 data capture april-may 2020

    Wellcome Open Research 6:123.

    https://doi.org/10.12688/wellcomeopenres.16671.1

    • Google Scholar
68. 1. Turner N
    2. Joinson C
    3. Peters TJ
    4. Wiles N
    5. Lewis G

    (2014) Validity of the short mood and feelings questionnaire in late adolescence

    Psychological Assessment 26:752–762.

    https://doi.org/10.1037/a0036572

    • PubMed
    • Google Scholar
69. Website

    1. UKHSA

    (2021) SARS-CoV-2 variants of concern and variants under investigation in England. Technical briefing: Update on hospitalisation and vaccine effectiveness for Omicron

    Accessed January 5, 2022.

    https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1044481/Technical-Briefing-31-Dec-2021-Omicron_severity_update.pdf
70. Website

    1. University College London

    (2022) COVID-19 Longitudinal Health and Wellbeing National Core Study - UCL – University College London

    Accessed March 21, 2022.

    https://www.ucl.ac.uk/covid-19-longitudinal-health-wellbeing/
71. Website

    1. University of Bristol

    (2022) Explore data and samples-Avon Longitudinal Study of Parents and Children

    Accessed March 29, 2022.

    http://www.bristol.ac.uk/alspac/researchers/our-data/
72. Software

    1. Van Rossum G
    2. Drake FL

    (2009) Python 3 reference manual, version 3.11.1

    Python.

    https://docs.python.org/3/reference/
73. 1. Verdi S
    2. Abbasian G
    3. Bowyer RCE
    4. Lachance G
    5. Yarand D
    6. Christofidou P
    7. Mangino M
    8. Menni C
    9. Bell JT
    10. Falchi M
    11. Small KS
    12. Williams FMK
    13. Hammond CJ
    14. Hart DJ
    15. Spector TD
    16. Steves CJ

    (2019) Twinsuk: the UK adult twin registry update

    Twin Research and Human Genetics 22:523–529.

    https://doi.org/10.1017/thg.2019.65

    • PubMed
    • Google Scholar
74. 1. Walls AC
    2. Sprouse KR
    3. Bowen JE
    4. Joshi A
    5. Franko N
    6. Navarro MJ
    7. Stewart C
    8. Cameroni E
    9. McCallum M
    10. Goecker EA
    11. Degli-Angeli EJ
    12. Logue J
    13. Greninger A
    14. Corti D
    15. Chu HY
    16. Veesler D

    (2022) SARS-cov-2 breakthrough infections elicit potent, broad, and durable neutralizing antibody responses

    Cell 185:872–880.

    https://doi.org/10.1016/j.cell.2022.01.011

    • Google Scholar
75. 1. Wei J
    2. Matthews PC
    3. Stoesser N
    4. Maddox T
    5. Lorenzi L
    6. Studley R
    7. Bell JI
    8. Newton JN
    9. Farrar J
    10. Diamond I
    11. Rourke E
    12. Howarth A
    13. Marsden BD
    14. Hoosdally S
    15. Jones EY
    16. Stuart DI
    17. Crook DW
    18. Peto TEA
    19. Pouwels KB
    20. Walker AS
    21. Eyre DW
    22. COVID-19 Infection Survey team

    (2021a) Anti-spike antibody response to natural SARS-cov-2 infection in the general population

    Nature Communications 12:6250.

    https://doi.org/10.1038/s41467-021-26479-2

    • PubMed
    • Google Scholar
76. 1. Wei J
    2. Stoesser N
    3. Matthews PC
    4. Ayoubkhani D
    5. Studley R
    6. Bell I
    7. Bell JI
    8. Newton JN
    9. Farrar J
    10. Diamond I
    11. Rourke E
    12. Howarth A
    13. Marsden BD
    14. Hoosdally S
    15. Jones EY
    16. Stuart DI
    17. Crook DW
    18. Peto TEA
    19. Pouwels KB
    20. Eyre DW
    21. Walker AS
    22. COVID-19 Infection Survey team

    (2021b) Antibody responses to SARS-cov-2 vaccines in 45,965 adults from the general population of the united kingdom

    Nature Microbiology 6:1140–1149.

    https://doi.org/10.1038/s41564-021-00947-3

    • PubMed
    • Google Scholar
77. 1. Yu X
    2. Wei D
    3. Xu W
    4. Li Y
    5. Li X
    6. Zhang X
    7. Qu J
    8. Yang Z
    9. Chen E

    (2022) Reduced sensitivity of SARS-cov-2 omicron variant to antibody neutralization elicited by booster vaccination

    Cell Discovery 8:4.

    https://doi.org/10.1038/s41421-022-00375-5

    • PubMed
    • Google Scholar
78. 1. Zigmond AS
    2. Snaith RP

    (1983) The hospital anxiety and depression scale

    Acta Psychiatrica Scandinavica 67:361–370.

    https://doi.org/10.1111/j.1600-0447.1983.tb09716.x

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Antibody levels following vaccination against SARS-CoV-2: associations with post-vaccination infection and risk factors in two UK longitudinal studies" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Miles Davenport as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Please shorten the paper and focus on its core scientific conclusions.

2) Please only include figures which most succinctly justify these conclusions which include multivariate analyses.

3) Please offer greater detail regarding the analyses as suggested by Reviewer 1.

4) Discuss the relevance of the paper to the current stage of the pandemic with novel VOCs.

Reviewer #1 (Recommendations for the authors):

1) The messaging is too diffuse. The Results section of the abstract provides a nice template for the focus of the paper's conclusions, but other sections (the first few paragraphs of the discussion for example) highlight other conclusions. I suggest focusing on how the 3rd vaccine minimized discrepancies between protection among subgroups and vaccine (AZ vs Pfzer) as the singular finding of the paper.

2) The paper needs to be rewritten with an earlier and more consistent focus on whether its findings have any relevance for the current pandemic situation in which omicron and its downstream VOCs predominate. In particular, are Q4 results related to omicron infections exclusively? Does the ALSPAC cohort have any relevance to current conditions? I sympathize that it is challenging to maintain scientific relevance with these types of analyses given the speed and frequency of novel VOC takeover. Nevertheless, it is no longer of high public health relevance to understand whether a single vaccine dose protects against the δ variant. As written, the paper provides no easy roadmap for public health officials to interpret and use its results for vaccine scheduling and prioritization. Moreover, the authors' claim that the paper's findings are relevant as mathematical model inputs is only true if they are relevant for currently dominant VOCs.

3) All conclusions that focus on univariate analyses should be downplayed and removed from the main body of the paper (Figure 3 and 4). I would suggest redoing these figures (with infection as the outcome) in a fashion like Figure 5 with a multivariate analysis. This would provide more compelling evidence that antibody levels protect against infection regardless of other potential exposure variables (age, sex, time since vaccine, etc….). The data for this more informative figure is partially listed in part in the 2nd paragraph on page 12 (OR=2.9, p=0.02) but should be in a figure.

4) Figure 3 would also be much more relevant and interesting if it included data after the 2nd shot and 3rd shot (either in aggregate or as separate panels). I might also consider a separate analysis with antibody levels (rather than quintiles) based on cut-off levels thought to be protective against omicron.

5) For Figure 5, I would choose one cutoff (10%) as the outcome variable to make this very key figure easier to read and less busy. Also, what is the baseline comparator for OR=1? It is not clear from the legend.

6) More detail is needed in the discussion on how much of a shortcoming the predominant white ethnicity is in terms of generalizing results to the UK or to other countries.

7) The conclusion that the variability of antibody levels after the 3rd vaccine decreased relative to the first vaccine requires statistical testing and verification rather than an eyeball assessment of the graph. I am not sure how this can be done based on the lack of a full quantitative range of anti-spike levels at Q2 (see point 9). In this case, I do not think the conclusions are currently justified by the current level of analysis.

8) Overall, the variability of antibody levels is not adequately demonstrated in the figures. First, the x-axes in Figure 1, and y-axes in Figures2 should be of equivalent scale (0-25000) to allow comparison across panels. Second, these axes should be log converted if this normalizes the data. Third, rather than just showing the 10% percentile, include standard boxplots which naturally show the IQR and values within the IQR so that the median, variability, and skew of the data can be visualized. Finally, please reveal what percentage of samples in each Q2 group fall at or above 250 to see if it is reasonable to not test for levels in these groups based on the "assay threshold (see point 9)".

9) Overall, the reasoning for using the assay threshold requires greater quantitative heft and justification.

10) Is there any data between 28 and 34 weeks after the 3rd vaccine to match after the second? This would solidify conclusions about the importance of the 3rd shot.

11) For context, references citing the percentage of the studied population with 1, 2, 3, or 4 vaccines should be included.

12) The "shielded patient list" is a valid exposure variable as it is convenient and apparently well known. However, it contains multiple more biologically relevant exposure variables that likely could better predict risk. This should be acknowledged and outlined in more detail in the discussion.

Other issues

1) 3rd intro paragraph: the list of risk factors should be expanded or contracted as it currently includes broad categories of diseases and very specific diseases (vasculitis).

2) Last paragraph intro: the word "origins" is too strong to explain the analysis of post-vaccine antibody level variability as it implies causality. A better phrase would be "variables associated with".

3) The comparison of median antibody levels among unvaccinated versus vaccinated persons on page 12 is not terribly interesting and the differences are slight (40 vs 57 BAU/ml median) despite a p-value that seems surprisingly very low (0.001) and should be double-checked.

4) Anti-spike antibody is referred to as a proxy for the immune response. This is not correct. The immune response is a broad term as immunity protects against infection but also modifies infection if there is no protection. Antibodies are thought to play somewhat more of a role in the former than the latter. It is better to say that antibodies have been identified as a correlate of protection and cite the appropriate articles.

Reviewer #2 (Recommendations for the authors):

(1) The manuscript is a bit too long, with many supplementary materials. I have not checked those SI materials in detail.

(2) Not sure if it is useful to stratify results by the vaccination type. For example, older individuals may be more likely to receive the AstraZeneca vaccine, whereas younger individuals may mainly receive mRNA vaccines. Could you examine if the antibody levels were independent of the type of vaccines?

(3) It is unclear if there were location-specific differences in the results. I understand that the authors analyzed rural-urban differences. But different cities may have some differential patterns.

(4) In the multivariable regression analysis, the authors examined many potential predictor variables. It might be useful to perform a collinearity test to first identify the most useful variables. For example, hierarchical clustering analysis using Ward's method [1] might be helpful.

Ref: [1] Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion? J Classif, 31 (2014), pp. 274-295

Essential revisions:

1) Please shorten the paper and focus on its core scientific conclusions.

Where at all possible we have removed text which is not immediately aligned to the main findings, figures and sections from both the main text (removing 1000 words) and supplementary information (removed 20 pages) to shorten the paper. We have revised introduction and Discussion sections in particular to focus more on the core conclusions. We hope the revised manuscript is therefore improved.

We have made substantive adjustments to the manuscript in efforts to address this point (which was also brought up by reviewers). We summarise key changes below (page numbers refer to when track changes set to ‘All Markup’):

– Introduction (page 6-7) has been revised to reduce length and update sections covering anti-Spike antibody levels as a correlate of protection against future infection and emerging variants, adding references to more recent work, as requested in Editor point (4) and Reviewer 1 point (2).

– Results:

– In “Cohort characteristics” subsection (page 8), paragraphs covering secondary results on infection prevalence across different subgroups and in other longitudinal cohorts have been condensed.

– In “Antibody levels after first, second, and third vaccination” section (page 8), text was revised based on Reviewer 1 point (7) – see below for full response to this point.

– In “Factors associated with post-vaccination infection in TwinsUK” section:

– Text relating to secondary results of sociodemographic associations with post-vaccination infection (page 10) has been condensed.

– Text referring to results testing association between post-vaccination infection and antibody levels (within double-vaccinated participants page 11) has been removed.

– In “Factors associated with lower antibody levels within TwinsUK and ALSPAC” section:

– Text relating to models testing association with the lowest 5% of antibody levels has been removed (page 11-12), to simplify message as suggested by reviewer 1 (point 5).

– Text added to justify choice of threshold antibody level outcome in regression models (page 10), in response to reviewer 1 (point 9) – see below for full response to this point.

– Discussion:

– Paragraph starting “Longitudinal antibody testing…” (page 15) has been moved to earlier in the discussion, as the text describes primary conclusions. This paragraph has been condensed, but also revised to refer to antibody levels in the context of correlates of protection for the omicron variant, as suggested by Editor (point 4) and Reviewer 1 (point 2).

– Paragraph starting “The cross-sectional nature of antibody testing…” (page 16) discussing certain strengths of the study has been removed to condense the text, as it does not add to the primary conclusions of the paper.

– We have removed some text on limitations in the paragraph starting “We also acknowledge limitations of this work” (page 16-17), some of which is now covered earlier in the discussion in revised text, and other text which is not relevant to the primary conclusions of the paper.

– Supplementary information:

– “Shielded patient list criteria” section (page 12-13) has been removed and replaced with a reference in the main text (reference 30) to a website which provides equivalent information

– “All cohort antibody testing summary” section (page 16-17) has been removed, as associated text in main text has been removed.

– In “Factors associated with post-vaccination infection” section (page 29), Table S 7 in our initial submission has been removed, as data included is covered sufficiently by main text Table 3.

– In “Factors associated with post-vaccination infection” section (page 34), Figure S 6 in our initial submission is removed as data is no longer referred to in main text.

– In “Factors associated with low antibody levels: logistic regression results” section (page 35-39), Table S 9 in our initial submission (Supplementary file 7 in revised submission) results columns for models testing association with the lowest 5% of antibody levels are removed, to simplify message as suggested by reviewer 1 (point 5).

– Sections “Factors associated with low antibody levels: TwinsUK figures” (page 47-53) and “Factors associated with low antibody levels: Age, sex and BMI (combined ALSPAC and TwinsUK)” (page 54) have been removed, as the figures duplicate results presented in Table S 9 in our initial submission (Supplementary file 7 in revised submission) and are removed to condense the manuscript.

2) Please only include figures which most succinctly justify these conclusions which include multivariate analyses.

We have made the following changes to figures:

a. Figure 1 (page 40) and Figure 2 (page 43) has been revised based on feedback given by Reviewer 1, making the following changes: (1) box plot layers have been added to visualise variability; (2) antibody level axis range is changed to logarithmic scale and is the same for all subplots; (3) the percentage of samples with values exceeding the assay limit is displayed in figure 1; (4) Lines illustrating the assay upper limit are added for clarity.

b. Former Figures 3 and 4 (page 45-46) which presented results of univariate analysis have been removed as part of condensing the paper, as we do not believe they add to key conclusions.

c. Figure 5 (now Figure 3, page 47) has been modified based on reviewer 1 (point 5) suggestion, removing results based on the lower 5% cut-off. Results and discussion have been changed accordingly. The titles of the sub-plots have been amended to show more clearly the reference used.

3) Please offer greater detail regarding the analyses as suggested by Reviewer 1.

We have revised the manuscript based on reviewer 1 feedback and responded individually to each point given by reviewer 1 below, giving specific references to changes in the manuscript.

4) Discuss the relevance of the paper to the current stage of the pandemic with novel VOCs.

We have made revisions to introduction and Discussion sections and give the specific references to changes in the manuscript in response to the relevant comments from Reviewer 1 below.

Reviewer #1 (Recommendations for the authors):

1) The messaging is too diffuse. The Results section of the abstract provides a nice template for the focus of the paper's conclusions, but other sections (the first few paragraphs of the discussion for example) highlight other conclusions. I suggest focusing on how the 3rd vaccine minimized discrepancies between protection among subgroups and vaccine (AZ vs Pfzer) as the singular finding of the paper.

Many thanks for this comment. We have made extensive changes to the manuscript which are highlighted above in our response to Editor (point 1) where we list changes made to address this point.

2) The paper needs to be rewritten with an earlier and more consistent focus on whether its findings have any relevance for the current pandemic situation in which omicron and its downstream VOCs predominate. In particular, are Q4 results related to omicron infections exclusively? Does the ALSPAC cohort have any relevance to current conditions? I sympathize that it is challenging to maintain scientific relevance with these types of analyses given the speed and frequency of novel VOC takeover. Nevertheless, it is no longer of high public health relevance to understand whether a single vaccine dose protects against the δ variant. As written, the paper provides no easy roadmap for public health officials to interpret and use its results for vaccine scheduling and prioritization. Moreover, the authors' claim that the paper's findings are relevant as mathematical model inputs is only true if they are relevant for currently dominant VOCs.

With respect to “focus on whether its findings have any relevance for the current pandemic situation in which omicron and its downstream VOCs predominate” and “it is no longer of high public health relevance to understand whether a single vaccine dose protects against the δ variant”, the primary focus of our results is on identifying groups more likely to have relatively lower levels of antibodies in response to vaccination. As we reference in the revised introduction (page 6, references 19-26), previous studies have repeatedly shown that antibody levels are inversely correlated with risk of future infection for all variants of concern throughout the COVID-19 pandemic. While we focus on relative antibody levels for these reasons, we have updated the introduction to include recent references which estimate protective threshold levels of anti-Spike antibodies, including for the omicron variant (reference 25, doi:10.3390/vaccines10091548). We then comment on our results in the context of these estimated thresholds for omicron, to make our results more relevant to the current dominant variant.

Similarly, with respect to “roadmap for public health officials to interpret and use its results”, we believe that the identification of certain groups as having consistently lower antibody levels across multiple rounds of vaccination is useful information for vaccine prioritisation and is independent of the dominant variant at a given time (given vaccine prioritisation itself is based on relative risk between groups). Knowledge of which groups of the population are more likely to have lower levels after vaccination is useful important indicator in public health of who is more likely to be at risk of infection, and so who could be prioritised in future vaccination rounds – a measure that could be a useful adjunct to antigen testing which retains use in the assessment of case status, but is less able to project the maintenance of circulating antibody response (albeit only one part of immunological response).

With respect to “Does the ALSPAC cohort have any relevance to current conditions?”, the primary purpose of the ALSPAC cohort results is to serve as an independent and parallel analysis of key findings within the primary cohort; namely that certain groups are more likely to have the lowest levels of antibodies following vaccination, therefore we believe it is important to keep these results in the manuscript.

The paragraph of the discussion where we stated that our serological results could be useful for mathematical modelling (page 16) has been removed as part of condensing the section as we decided it was not essential.

3) All conclusions that focus on univariate analyses should be downplayed and removed from the main body of the paper (Figure 3 and 4). I would suggest redoing these figures (with infection as the outcome) in a fashion like Figure 5 with a multivariate analysis. This would provide more compelling evidence that antibody levels protect against infection regardless of other potential exposure variables (age, sex, time since vaccine, etc….). The data for this more informative figure is partially listed in part in the 2nd paragraph on page 12 (OR=2.9, p=0.02) but should be in a figure.

Thank you for this suggestion. We see value in this shift of focus and have adjusted the paper to concentrate on multivariable model results – though not remove already reported univariable results. With respect to “The data for this more informative figure is partially listed in part in the 2nd paragraph on page 12 (OR=2.9, p=0.02) but should be in a figure”, the results of multivariable analyses in question are given in Table 3. Figures 3 and 4 (page 45-46) which presented results of univariate analysis have been removed as part of condensing the paper, as we do not believe they add to main conclusions.

4) Figure 3 would also be much more relevant and interesting if it included data after the 2nd shot and 3rd shot (either in aggregate or as separate panels). I might also consider a separate analysis with antibody levels (rather than quintiles) based on cut-off levels thought to be protective against omicron.

We agree that the proposed analyses would be interesting and useful. Unfortunately, our data does not allow these analyses to be undertaken. Our initial testing was done at a time when individuals had 1 or 2 vaccinations only. Furthermore, the assay limit of this testing round did not allow analysis of risk of infection after 2^nd vaccination, only after 1^st as presented.

5) For Figure 5, I would choose one cutoff (10%) as the outcome variable to make this very key figure easier to read and less busy. Also, what is the baseline comparator for OR=1? It is not clear from the legend.

Figure 5 has been changed as described in Editor revisions section (point 2) above.

6) More detail is needed in the discussion on how much of a shortcoming the predominant white ethnicity is in terms of generalizing results to the UK or to other countries.

We have revised relevant text in the discussion to explicitly highlight this as limiting the generalisability of our findings to non-white UK and international populations:

Revised text, Discussion, page 17, “Consequently, the generalisability of our findings to non-white UK and international populations, in addition to our ability to detect associations with smaller effect sizes, is limited.”

7) The conclusion that the variability of antibody levels after the 3rd vaccine decreased relative to the first vaccine requires statistical testing and verification rather than an eyeball assessment of the graph. I am not sure how this can be done based on the lack of a full quantitative range of anti-spike levels at Q2 (see point 9). In this case, I do not think the conclusions are currently justified by the current level of analysis.

With respect to “The conclusion that the variability of antibody levels after the 3rd vaccine decreased relative to the first vaccine requires statistical testing and verification rather than an eyeball assessment of the graph”, we have added additional text that compares the ratio of the median to interquartile range as an additional measure of the relative width of the antibody level distributions. We have also added box plots to Figures 1 and 2 to help visualise inter-quartile range as suggested in later reviewer comments.

Revised text, Results, Antibody levels after first, second, and third vaccination subsection, page 9, “The antibody level distribution after third vaccination was relatively narrower compared with earlier vaccination (median:IQR ratios of 0.54, 0.27, and 0.89 among Q2 single-vaccinated, Q4 double-vaccinated, and Q4 triple-vaccinated sub-samples respectively), with smaller scale-factor differences between those with median and lowest levels (median:10^th percentile ratios of 5.6, 11.8, and 2.7 among Q2 single-vaccinated, Q4 double-vaccinated, and Q4 triple-vaccinated sub-samples respectively)”

With respect to “I am not sure how this can be done based on the lack of a full quantitative range of anti-spike levels at Q2 (see point 9). In this case, I do not think the conclusions are currently justified by the current level of analysis.”, we do not agree that the assay limit affects our ability to make these calculations and comments – we limit the statement quoted above to comparison of variability between single-, double- and triple-vaccinated sub-samples where the assay limit affects the top 5-22% only, and exclude the Q2 double-vaccinated group from comment because the assay limit does not allow this calculation.

8) Overall, the variability of antibody levels is not adequately demonstrated in the figures. First, the x-axes in Figure 1, and y-axes in Figures2 should be of equivalent scale (0-25000) to allow comparison across panels. Second, these axes should be log converted if this normalizes the data. Third, rather than just showing the 10% percentile, include standard boxplots which naturally show the IQR and values within the IQR so that the median, variability, and skew of the data can be visualized. Finally, please reveal what percentage of samples in each Q2 group fall at or above 250 to see if it is reasonable to not test for levels in these groups based on the "assay threshold (see point 9)".

Figures 1 and 2 have been changed as described in Editor revisions section above.

With respect to “the variability of antibody levels is not adequately demonstrated in the figures”, we have kept the original dot plot layer showing individual values and lines showing the 10^th percentile value, as we believe it aids comparison of sample size and allows greater visualisation of values at the lower end of the antibody level distribution (which is a focus of our analysis). Examination of this variation is not the case solely using a box plot as suggested by the reviewer, which focuses on the central 50% of data.

9) Overall, the reasoning for using the assay threshold requires greater quantitative heft and justification.

Text providing additional explanation of the choice to use relative thresholds for antibody levels has been added:

Revised text, Results, Factors associated with lower antibody levels within TwinsUK and ALSPAC section, page 11,

“Lower antibody levels were defined as the lowest 10% within each sub-sample of cohort, testing round and vaccination status (< 250 BAU/mL threshold corresponding to lowest 8% in both TwinsUK and ALSPAC used for Q2 double-vaccinated sub-samples where assay limit did not allow lowest 10% to be identified). Relative thresholds were used rather than absolute values due to the variation in reported thresholds between studies and for different SARS-CoV-2 variants, while the more general principal that antibody levels are inversely correlated with risk of infection has remained consistent throughout the COVID-19 pandemic.”

10) Is there any data between 28 and 34 weeks after the 3rd vaccine to match after the second? This would solidify conclusions about the importance of the 3rd shot.

There is no data available at the requested time points since vaccination, beyond what is shown in the existing figure. This is due to the timing of the sample collection, where that length of time had not passed since 3^rd vaccination.

11) For context, references citing the percentage of the studied population with 1, 2, 3, or 4 vaccines should be included.

It is possible to calculate percentages from the sample sizes quoted in Table 1 in the main text and flowcharts Figure S1, S2, S3 in our initial submission (Supplementary files 1, 2, 3 in our revised submission) in the supplementary information. In all efforts to reduce the overall, non-essential, content of the paper we are keen not to add this in at this stage. If there is a persistent editorial need to include this detail, we are of course happy to add this.

12) The "shielded patient list" is a valid exposure variable as it is convenient and apparently well known. However, it contains multiple more biologically relevant exposure variables that likely could better predict risk. This should be acknowledged and outlined in more detail in the discussion.

We have revised an existing section of the discussion which comments on the fact that we saw associations with more general/collective exposures rather than more specific exposures, to include the possibility that the more general/collective exposures we identify as risk factors contain more biologically relevant exposures for which we didn’t have data available to identify directly as suggested by the reviewer.

Revised text, Discussion, page 15, “These more general and collective measures may contain more specific risk factors for which we did not have data for or sufficient sample size to detect, or could suggest that variation in post-vaccination antibody levels between individuals may originate from a wide range of variables in combination.”

Other issues

1) 3rd intro paragraph: the list of risk factors should be expanded or contracted as it currently includes broad categories of diseases and very specific diseases (vasculitis).

Vasculitis has been removed from list of comorbidities inside brackets have been deleted to avoid inconsistency in specificity of diseases.

Revised text, introduction, page 7, “Lower antibody levels following both first and second vaccinations have been observed in individuals with particular comorbidities (including cancer, renal disease, and hepatic disease [27–29]),…”

2) Last paragraph intro: the word "origins" is too strong to explain the analysis of post-vaccine antibody level variability as it implies causality. A better phrase would be "variables associated with".

The first sentence of the last paragraph of the introduction has been re-written using the reviewer suggestion, replacing ‘origins’ with ‘variables associated with’.

Revised text, introduction, page 7, “Here, we aimed to examine variables associated with variation in post-vaccination antibody levels…”

3) The comparison of median antibody levels among unvaccinated versus vaccinated persons on page 12 is not terribly interesting and the differences are slight (40 vs 57 BAU/ml median) despite a p-value that seems surprisingly very low (0.001) and should be double-checked.

Rather than unvaccinated vs vaccinated groups, the comparison referred to by the reviewer is a comparison of median levels among single-vaccinated individuals at time of testing, comparing individuals who subsequently did not vs did record a SARS-CoV-2 infection in the following 6-9 months. This text has been removed as per earlier reviewer suggestions to condense paper and focus on results of multivariable models.

4) Anti-spike antibody is referred to as a proxy for the immune response. This is not correct. The immune response is a broad term as immunity protects against infection but also modifies infection if there is no protection. Antibodies are thought to play somewhat more of a role in the former than the latter. It is better to say that antibodies have been identified as a correlate of protection and cite the appropriate articles.

The sentence has been revised, replacing previous text “used as a proxy for the humoral immune response” to state that anti-Spike antibody levels have been “identified as a correlate of protection against infection”.

Revised text, Introduction, page 7, “We aimed firstly to assess the relationship between anti-Spike antibody levels (identified as a correlate of protection against infection), measured after first or second vaccination in April-May 2021,…”

Reviewer #2 (Recommendations for the authors):

(1) The manuscript is a bit too long, with many supplementary materials. I have not checked those SI materials in detail.

Please refer to the response to Editor (point 1) and also to the comments of Reviewer #1. We have substantially revised the paper and list substantive changes at the top of this response.

(2) Not sure if it is useful to stratify results by the vaccination type. For example, older individuals may be more likely to receive the AstraZeneca vaccine, whereas younger individuals may mainly receive mRNA vaccines. Could you examine if the antibody levels were independent of the type of vaccines?

We agree it is important to note the relationships between personal characteristics and which vaccine was given at different points during the UK vaccination roll-out. We believe that by including age as an adjustment variable in our multivariable model testing the effect of vaccine type on the likelihood of having low antibody levels, we have controlled for the relationship between age and type of vaccine received.

(3) It is unclear if there were location-specific differences in the results. I understand that the authors analyzed rural-urban differences. But different cities may have some differential patterns.

We agree that different regions have different infection patterns, however we did not examine the potential association between geographic region on antibody levels. We anticipate that we would have insufficient numbers of participants from individual city locations to be able to robustly identify differences. We did test association between antibody levels and other sociodemographic variables that are correlated with city demographics and urban vs. rural, for example age, ethnicity and local area deprivation. These results are included in the supplementary information.

(4) In the multivariable regression analysis, the authors examined many potential predictor variables. It might be useful to perform a collinearity test to first identify the most useful variables. For example, hierarchical clustering analysis using Ward's method [1] might be helpful.

We thank reviewer for the suggestion. For this analysis, we decided to test a wide range of exposure variables, to produce a compendium of results that others can refer to when looking for associations with specific socio-demographic and health characteristics e.g. individual comorbidities. So we decided not to try to reduce our exposure variables as part of our analysis protocol.

Author details

1. Nathan J Cheetham

   Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   nathan.cheetham@kcl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2259-1556

2. Milla Kibble

   1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
   2. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
   3. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   received payment for attending the Health and Safety Executive (HSE) symposium. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0003-1130-4010

3. Andrew Wong

   MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

   Contribution

   Conceptualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

4. Richard J Silverwood

   Centre for Longitudinal Studies, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Anika Knuppel

   MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Dylan M Williams

   1. MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom
   2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Olivia KL Hamilton

   MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5874-0058

8. Paul H Lee

   Department of Health Sciences, University of Leicester, Leicester, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5729-6450

9. Charis Bridger Staatz

   Centre for Longitudinal Studies, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   received an ESRC and NIHR funded Grant, and MRC Funded Studentship. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0002-2872-6968

10. Giorgio Di Gessa

    Department of Epidemiology and Public Health, University College London, London, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6154-1845

11. Jingmin Zhu

    Department of Epidemiology and Public Health, University College London, London, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8325-7589

12. Srinivasa Vittal Katikireddi

    MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    participates on the Scottish Government Expert Reference Group on Ethnicity and COVID-19, and UK Scientific Advisory Group on Emergencies (SAGE) subgroup on Ethnicity. The author has no other competing interests to declare

13. George B Ploubidis

    Centre for Longitudinal Studies, University College London, London, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Ellen J Thompson

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Ruth CE Bowyer

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
    3. AI for Science and Government, The Alan Turing Institute, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Xinyuan Zhang

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Golboo Abbasian

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

18. Maria Paz Garcia

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Funding acquisition, Project administration, Writing – review and editing

    Competing interests

    is a member of the King's College London Health Faculties Research Ethics Subcommittee (Purple), and a Chair of the TwinsUK Volunteer Advisory Panel. The author has no other competing interests to declare

19. Deborah Hart

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

    Contribution

    Funding acquisition, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

20. Jeffrey Seow

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

21. Carl Graham

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

22. Neophytos Kouphou

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

23. Sam Acors

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6428-7707

24. Michael H Malim

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7699-2064

25. Ruth E Mitchell

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

26. Kate Northstone

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

27. Daniel Major-Smith

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6467-2023

28. Sarah Matthews

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

29. Thomas Breeze

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

30. Michael Crawford

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

31. Lynn Molloy

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

32. Alex SF Kwong

    1. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
    2. Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

33. Katie Doores

    Department of Infectious Diseases, King's College London, London, United Kingdom

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

34. Nishi Chaturvedi

    MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom

    Contribution

    Funding acquisition, Methodology, Project administration, Writing – review and editing

    Competing interests

    received payment for clinical trials of a diabetes drug from AstraZeneca. Nishi Chaturvedi is Chair of British Heart Foundation Fellowships Committee, a member of Diabetes UK research committee and a member of NWO Gravitational Awards Committee. The author has no other competing interests to declare

35. Emma L Duncan

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. Guy’s & St Thomas’s NHS Foundation Trust, London, United Kingdom

    Contribution

    Funding acquisition, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8143-4403

36. Nicholas J Timpson

    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    N.J.Timpson@bristol.ac.uk

    Competing interests

    No competing interests declared

37. Claire J Steves

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
    2. Guy’s & St Thomas’s NHS Foundation Trust, London, United Kingdom

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    claire.j.steves@kcl.ac.uk

    Competing interests

    received payment for consultancy work for Zoe Ltd. The author has no other competing interests to declare

    "This ORCID iD identifies the author of this article:" 0000-0002-4910-0489

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