Usefulness of vaccine boosters for Covid-19 in Italy and in UK and comparison between in intensive care admissions and deaths of vaccinated and unvaccinated patients. Surprises and implications

Marco Alessandria; Claudio Simion; Alberto Donzelli

doi:10.12688/f1000research.111023.2

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Research Article

Revised

Usefulness of vaccine boosters for Covid-19 in Italy and in UK and comparison between in intensive care admissions and deaths of vaccinated and unvaccinated patients. Surprises and implications

[version 2; peer review: 1 not approved]

Marco Alessandria ¹, Claudio Simion², Alberto Donzelli³

PUBLISHED 08 Aug 2022

Author details Author details

¹ Studio di Scienze Motorie e Terapie Manuali, Turin, Italy
² Engineering, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy
³ Scientific Committee of the Foundation Allineare Sanità e Salute, Milan, Italy

Marco Alessandria
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Claudio Simion
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization

Alberto Donzelli
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Coronavirus collection.

Abstract

Background: There is insufficient clarity about the different outcomes between unvaccinated and vaccinated people hospitalized with Covid-19, with reference to the variables “Intensive Care Unit” and “Deaths”. Moreover, it is unclear the real effectiveness of the vaccine boosters on the risks of infection and Covid-19 deaths, beyond the first few months after the booster. To verify the hypotheses that repeated vaccinations might expose to a progressively greater risk of severe Covid-19, and of a growing weakening of the immune response, primarily against infection, as the distance from the booster dose increases.
Methods: Through an analysis of the official Italian data we calculated significant differences, percentage variations and trends in the variables “Intensive Care Units” and “Deaths” in hospitalized patients among four groups with different vaccination status, and between the Unvaccinated and Vaccinated groups.
Through analyses of the UK Security Agency data in the weekly COVID-19 vaccine surveillance reports we explored the vaccine effectiveness against SARS-CoV-2 infections and against COVID-19 deaths in relation to the time elapsed from the booster doses.
Results: Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19, and fewer vaccinations might be enough to protect persons at greater risk.
The vaccine effectiveness against infection vanished and reversed in the medium term, and vaccinated persons with three doses become increasingly more infected versus unvaccinated persons.
Conclusions: The starting hypotheses have been supported, together with the need to combine carefully rethought vaccination campaigns with the implementation of other strategies, with the achievement of a healthy living and working environment, healthy lifestyles, and effective, safe and sustainable care.

Keywords

Covid-19 vaccinations, first second dose of Covid-19 vaccination, Covid-19 booster vaccination, Covid-19 vaccination and Intensive Care Unit admission, Covid-19 vaccination and deaths in hospitalized patients, vaccinations and SARS-CoV-2 infections, repeated vaccinations and long-term immune response

Corresponding author: Marco Alessandria

Competing interests: Marco Alessandria declares no conflicts of interest. Alberto Donzelli declares no conflicts of interest. Claudio Simion is the president of Comilva nonprofit organization (NPO).

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2022 Alessandria M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Alessandria M, Simion C and Donzelli A. Usefulness of vaccine boosters for Covid-19 in Italy and in UK and comparison between in intensive care admissions and deaths of vaccinated and unvaccinated patients. Surprises and implications [version 2; peer review: 1 not approved]. F1000Research 2022, 11:579 (https://doi.org/10.12688/f1000research.111023.2) First published: 26 May 2022, 11:579 (https://doi.org/10.12688/f1000research.111023.1) Latest published: 08 Aug 2022, 11:579 (https://doi.org/10.12688/f1000research.111023.2)

Revised Amendments from Version 1

No changes have been made in the title, authors and abstracts. We have added some clarification sentences in the Introduction section and the p - value of the percentage variations as requested by the reviewer. Furthermore, we explained how we calculated these significance levels in the Statistical Analysis section.
The longitudinal subfigures have been modified by adding descriptions of the axes and we have expanded the statistical model already described to all age groups (see Supplementary Material), inasmuch, the ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis as requested by the reviewer.
The p - value values have been added to the tables of the percentage variations and we have added table 6 which describes the relative risk of reported COVID-19 cases, hospitalization, ICU admission and death per 100,000 by vaccination status in Italy.
In the Results section we have added the paragraph describing the results of the statistical analysis for all age groups.
4 references have been added to justify increase in hazard ratio for infections within the 13 days from first injection.
Finally, we have now implemented also a standardization based on the age structure of the English population, as request by the reviewer.

See the authors' detailed response to the review by Gokhan Tut

Introduction

Most Italian mainstream media emphasize the success of the Covid-19 vaccination campaign, following the information provided by the Istituto Superiore di Sanità (ISS). However, there is insufficient clarity about the different outcomes between unvaccinated and vaccinated people hospitalized with Covid-19, with reference to the variables “Intensive Care Unit (ICU)” and “Deaths”. Moreover, it is unclear the real effectiveness of the vaccine boosters on the risks of infection and of Covid-19 death in a period of time not limited to the first months after the booster.

The effects of repeated vaccinations on lowering the risk of disease have been shown for other infectious diseases.

In the Canadian influenza season 2014 – 2015 repeated vaccinations increased the risk of medically attended A(H3N2) disease up to 50% versus unvaccinated people.¹ Similar results were also shown in Italy, where most participants had received repeated doses,² and in Michigan, where the influenza vaccine effectiveness was lower in subjects vaccinated in both the current and prior season in all age groups. On the contrary, in subjects with no evidence of prior vaccination, the vaccine effectiveness was higher for all age groups.³ Moreover, the dose-response relationship, calculated with adjusted geometric mean fold rise (MFR) after influenza vaccination, seems to be inversely related to the number of prior vaccinations, with higher protection without prior vaccination and lower protection in subjects up to four prior vaccination.⁴

Some authors provide two hypotheses as potential explanation of these phenomena. The first is the Antigenic Distance hypothesis, meaning that variation in repeated vaccine effectiveness (VE) is due to differences in antigenic distances among vaccine strains and between the vaccine strains and the epidemic strain in each outbreak; if antibody titer is high for more than a year, it has the potential to negatively interfere with a subsequent revaccination if the antigenic similarity between the prior season vaccine strain and the epidemic strain is high.⁵ The second hypothesis is the Original Antigenic Sin (OAS), meaning that the immunity response to the previous met viral strain permanently shapes itself in order to boost the antibody production to related strain.⁶

Moreover, it seems that the hypothesis based entirely on the antibody response to a unique viral antigen is not enough, but it is likely that the adaptive immune response to other virus components might contribute.⁷ In fact, a recent study suggests that the presence of pre-existing non-spike cross-reactive memory T cells protects the SARS-CoV-2-naïve contacts from infection.⁸

Although many important questions related to the mechanism of OAS remain unanswered,⁹ it seems that the effectiveness of the current vaccine will be higher in people not previously vaccinated, or if the previous vaccine strain cross-reacts minimally with the current vaccine strain, even with no close match to the circulating strain.⁶

Moreover, it has been hypothesized that anti-COVID-19 vaccinations, too frequently or too close repeated, could weaken the immune response or damage the immune system itself.

The aim of this study is to verify significant differences, percentage variations and trends in the ICU and Deaths variables, in the hospitalized population among four groups: an Incomplete Cycle (IC – defined as all reported cases with a confirmed diagnosis of SARS-CoV-2 virus infection, occurring at least 14 days after the first dose – in subjects who have received a two-dose vaccine course – or, within 14 days days after administration of the second dose¹⁰) group, a double dose since less than 4 months (2D<4) group, a double dose more than 4 months ago (2D>4) group, and a third dose (3D) group.

Furthermore, we aim to verify significant differences, percentage variations and trends in the same variables between the Unvaccinated (UV) and Vaccinated (V) groups.

We hypothesize that repeated vaccinations might expose the recipients to a progressively greater risk of Covid-19, included severe diseases, and that fewer vaccinations might be enough to protect the subjects at greater risk.

Finally, we have verified the hypothesis of a progressive weakening of the immune response as the time elapsed from the booster dose increased, analyzing the data published weekly by the UK Security Agency in the COVID-19 vaccine surveillance reports.

Methods

The official data relating to Covid-19 Hospitalizations, ICU and Deaths between Unvaccinated and Vaccinated (and their subgroups: IC, 2D<4, 2D>4, 3D) were collected from the ISS bulletins starting by January 14, 2022 until February 18, 2022. For the variable Deaths data were collected starting from January 21, 2022 because the division between <4 and >4 months started from this date.

The reference populations among the bulletins were never aligned with the observation period of all 3 variables taken into consideration. Therefore, it was necessary to proceed with this alignment considering the “median date” of the observation period of each variable and using the populations of the previous bulletins whose reference date coincided with the median date.

The observation period of one month allowed us to carry out analyzes on populations stable enough, despite the changes of parts of the population among the groups (</> 5 months, </> 4 months, and third doses).

Rates/1,000 were calculated for both total events and for each group on the “Intensive Care Unit (ICU)” and “Deaths” variables.

The rates of the variable “ICU” were calculated on the hospitalized population, while the rates of the variable Covid-19 “Deaths” were calculated on the hospitalized population added to the ICU population. The reason for this choice is based on the fact that the number of deaths was higher than the number of accesses to ICU.

The calculation of the variation in the rates rate variations was carried out according to the formula:

(\frac{rate latest bulletin}{rate first bulletin} - 1) \times 100 .

To compare the Unvaccinated group and Vaccinated group, data of IC, 2D<4, 2D>4 and 3D were aggregated and was calculated the total rate of Vaccinated group. The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error).¹⁰

The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people. This data, albeit non-significant, represent a tendency confirmed by data from Qatar in the two weeks following the first dose, mostly for asymptomatic infections.¹²^,¹³ This phenomenon might be related to a post-vaccination fall in neutrophil and lymphocyte.¹⁴^,¹⁵

Examples of some increase in hazard ratio for infections within the 13 days from first injection are are highlighted by several authors.¹⁶^–¹⁹

The same proceeding above described was used for analysis of the trend of the rates for the all age groups (see Extended data).

Data published weekly by the UK Health Security Agency in the COVID-19 vaccine surveillance reports were analyzed from the time the majority of the population (>30 million people) received the third dose, in order to verify the immune response as the time elapsed from the booster dose increased, calculating the ratio of deaths of unvaccinated versus vaccinated persons with at least 3 doses.

Statistical analysis

A non-parametric statistic was used after performing the Shapiro – Wilk normality test, inasmuch the distribution of the rates of the variables considered (“Intensive Care Unit” and “Deaths”) do not show a normal distribution.

The Kruskal-Wallis test with Dunn’s post-hoc test was used to compare Incomplete Cycle (IC), double dose <4 months (2D<4), double dose >4 months (2D>4) and third doses (3D) groups, while the Mann-Whitney test was used to compare Unvaccinated (UV) and Vaccinated (V) groups.

The level of significance of the rates variation was calculated using the linear regression between rates and date of the bulletins considering the rates as dependent variable.

For the descriptive statistic, data were processed using Excel, while for the inferential statistic was used GraphPad Prism 5 software (GraphPad Software, Inc., USA); the “p” significance level was fixed at <0.05.

Results

Comparison between Incomplete Cycle (IC), double dose <4 months (<4), double dose> 4 months (>4) and third doses (3D) groups.

a. Intensive Care Unit (ICU) variable

Statistical significant difference (p=0.0001) was observed among groups. Pairwise comparisons using Dunn’s test indicated that IC group was significantly different from the 3D group and 2D>4m. The 2D<4m group was significantly different by the 2D>4m.

The trend of 3D group is growing with an increase of the 253.3% (p=0.0001), while the trend of 2D>4m is degrowing with a decrease of the 39.7% (p=0.0001). The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% (p=0.001) and 13.9% (p=0.28) (Figure 1 and Table 1).

b. Deaths variable

Figure 1. Significant difference (p<.05) and trend between IC, <4, >4, 3D for the ICU variable.

Table 1.

Rate/1,000 ICU variable.

		Rate/1,000
Date	IC	2D<4m	2D>4m	3D
1-14/01/2022	2,2	3,6	27,2	3
2-21/01/2022	2	3,1	25,2	3,9
3-28/01/2022	1,7	2,7	22,1	5,7
4-04/02/2022	1,6	2,8	19,6	7,1
5-11/02/2022	1,5	2,8	18,2	8,6
6-18/02/2022	1,2	3,1	16,4	10,6
Variations (%)	-45,5	-13,9	-39,7	253,3
P-value	0.001*	0.28	0.0001*	0.0001*

* p value is significant.

Statistical significant difference (p=0.001) was observed among groups. Pairwise comparisons using Dunn’s test indicated that IC group was significantly different from the 2D>4m. The 2D<4m group was significantly different by the 2D>4m group.

The trend of 3D group and 2D>4m are growing with an increase respectively of the 416 % (p=0.003) and 3.5%. The trend of IC group and 2D<4m are overlapping and show an increase respectively of 39.9% (p=0.04) and 49.8% (p=0.017) (Figure 2 and Table 2).

Figure 2. Significant difference (p<.05) and trend between IC, <4, >4, 3D for the deaths variable.

Table 2.

Rate/1,000 deaths variable.

		Rate/1,000
Date	IC	2D<4m	2D>4m	3D
1-21/01/2022	3,8	3,9	55,9	7,8
2-28/01/2022	3,4	3,8	50,0	12,0
3-04/02/2022	4,0	4,3	51,1	19,2
4-11/02/2022	4,7	4,9	53,1	28,2
5-18/02/2022	5,3	5,8	57,8	40,2
Variations (%)	39,9	49,8	3,5	416,0
P-value	0.04*	0.017*	0.58	0.003

* p value is significant.

Comparison between Unvaccinated (UV) and Vaccinated (V) groups

a. ICU variable

Statistical significant difference (p=0.0087) was observed between groups. The trend of UV group and V are both degrowing with an decrease of 51.1% (p=0.0001) and 12.8% (p=0.02) respectively (Figure 3 and Table 3).

b. Deaths variable

Figure 3. Significant difference (p<.05) and trend between UV versus V for the ICU variable.

Table 3.

Rate/1,000 ICU variable

	Rate/1,000
Date	UV	V
1-14/01/2022	69,6	36
2-21/01/2022	61,9	34,2
3-28/01/2022	52,7	32,1
4-04/02/2022	45,4	31,1
5-11/02/2022	38,8	31,1
6-18/02/2022	34	31,4
Variations (%)	-51,1	-12,8
P-value	0.0001*	0.02*

* p value is significant.

Statistical significant difference (p=0.0159) was observed between groups. The trend of UV and V groups are both growing with an increase of 9.5% (p=0.178) and 53.2% (p=0.021) respectively (Figure 4 and Table 4).

Figure 4. Significant difference (p<.05) and trend between UV versus V for the deaths variable.

Table 4.

Rate/1,000 deaths variable.

	Rate/1,000
Date	UV	V
1-21/01/2022	64,3	71,3
2-28/01/2022	58,7	69,2
3-04/01/2022	61,9	78,6
4-11/02/2022	66,5	90,8
5-18/02/2022	70,4	109,2
Variations (%)	9,5	53,2
P-value	0.178	0.021*

* p value is significant.

Comparison amongst Incomplete Cycle (IC), double dose <4 months (<4), double dose> 4 months (>4) and third doses (3D) groups, Unvaccinated (UV) and Vaccinated (V) groups for all age groups (see Extended data)

The IC group showed rates significant lower for all age groups compared to 2D>4m or 2D<4m doses, both in the ICU variable and Death variable. For the all age groups, the 2D<4m did not show a significant difference compared to IC group both in the ICU variable and Death variable. Only in the 12–39 age group we observed a significant increase of the 2D<4m group compared to IC group in the ICU variable (S1). Furthemore, the trend of IC group shows a significant decrease for the age group 60–79 and 80+ both in the ICU variable and Death variable.

The 2D>4m group showed a significant decrease of the trend in the 60–79 and 80+ age group both in the ICU variable and Death variable while we observed a significant decrease of the trend in the 2D<4m in the 12–39 age group in the ICU variable.

The third doses show a significant increase of the trend in all age groups both in the ICU variable and Death variable. In particular, in the 80+ group we observed a trend reversal between 2D>4m group and 3D group both in the ICU variable and Death variable (S13–S14).

The comparison between UV and V groups shows: 1. no significant difference in the 12–39 age group but a significant decrease in the trend of UV group in the ICU variable. Furthermore we observed a trend reversal near the fifth bulletin in the ICU variable (S3); 2. significant difference in the 40–59 age group both in the ICU variable and Death variable to the disadvantage of the UV group, but a significant decrease in the trend of UV group accompanied by a significant increase on V group in the ICU variable, while in the Death variable we observed a significant increase of the trend of V group; 3. significant difference in the 60–79 age group in the ICU variable to the disadvantage of the UV group but no significant difference in the Death variable. The trend in the ICU variable decrease in both group but to a greater extent in the UV group (S11), while in the Death variable we observed a trend reversal between second and third bulletin (S12); 4. significant difference both in the ICU variable and Death variable to the disadvantage of the V group in the 80+ age group and a decrease trend both UV group and V group only in the ICU variable.

The reports of COVID-19 vaccine surveillance, weekly published by the UK Health Security Agency, show a clear trend towards an increase in infections in vaccinated persons versus not vaccinated persons per 100,000, compared in each of eight age classes: under 18, 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 or over. Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (−13%) the column of not vaccinated.

In the following weeks the COVID-19 cases among the vaccinated persons continued to increase more than proportionally, until in Week 39 the ratio was reversed, with more COVID-19 cases among the vaccinated persons (+7% overall). Thereafter, the relative increases among the vaccinated persons were always higher than those in their counterpart, up to Week 2 of 2022, when the excess reached +117%. From Week 3 of 2022 the tables published in the reports show the comparison only between unvaccinated versus vaccinated persons “with at least 3 doses”, with an attenuated excess of cases temporarily in vaccinated (+34%). However, in the following weeks the excess of cases has continued incessantly to increase, to reach +275% in Week 13, when the UK Health Security Agency announced the decision to stop the publication of these data and of the related table.

Someone might observe that these are raw data, without multivariable adjustment. However, there are three considerations in this regard.

First, we lack too many informations to implement a multivariable correction.

Second, why UK Health Security Agency itself has not implemented these corrections, owning all the needed informations? A possible answer is that the results are not so striking, and that the substance would not change much.

Third, in the face of an impressive trend such as the one highlighted, we think it is more important to bring it to the attention of the scientific community, to open a debate on the subject, also soliciting the necessary investigations/appropriate adjustments. Suppressing the reporting of the phenomenon because the methodological rules have not been previously and meticulously observed risks further delaying awareness of a trend with profound possible public health implications.

In the weeks when more than half of the English people have received the vaccine booster (from Week 3 of 2022 onwards),²³ the Week 3 shows a death rate of unvaccinated 9.4 times than the one of the vaccinated persons, while Week 11 shows a rate unvaccinated/vaccinated persons only about 1.6 (Table 5). We have now the first consistent Italian data, beginning from the age classes that anticipated a turnaround also in England (Table 6).

Table 5.

Covid Vaccine Surveillance Report – UK Security Agency – Unadjusted rates of COVID-19 deaths (per 100,000) in vaccinated (≥3 doses) and unvaccinated persons by week 3 to 11 of 2022.

Week/2022	Death within 28 days of positive Covid-19 test		Not vaccinated/Vaccinated	Death within 60 days of positive Covid-19 test		Not vaccinated/Vaccinated
Week/2022	Unadjusted rates among persons vaccinated ≥3 doses (per 100,000)	Unadjusted rates among persons not vaccinated ≥3 doses (per 100,000)	Not vaccinated/Vaccinated	Unadjusted rates among persons vaccinated ≥3 doses (per 100,000)	Unadjusted rates among persons not vaccinated ≥3 doses (per 100,000)	Not vaccinated/Vaccinated
3	42.0	438.4	10.438	50.3	473.1	9.405
4	62.2	453.1	7.284	71.3	487.4	6.835
5	86.9	459.6	5.288	98.8	501.9	5.079
6	107.8	431.3	4.000	128.9	482.5	3.743
7	111.6	353.8	3.170	142.6	411.8	2.887
8	105.5	276.2	2.618	148.4	349.5	2.355
9	91.5	197.6	2.159	148.6	272.7	1.835
10	71.0	142.4	2.005	136.3	192.9	1.415
11	59.4	118.8	2.000	124.9	198.1	1.586

Table 6.

Relative risk of reported COVID-19 cases, hospitalization, ICU admission and death per 100,000 by vaccination status (Epidemia COVID-19 (iss.it)).

Persons 40-59 years Date	DIAGNOSIS Not vacc. vs vacc. complete cycle ≤120 days	DIAGNOSIS Not vacc. vs vacc. complete cycle + booster	HOSPITALIZATION Not vaccinated vs vacc. complete cycle ≤120 days	HOSPITALIZATION Not vaccinated vs vacc. complete cycle + booster	Intensive Care Unit Not vaccinated vs vacc. Complete cycle ≤120 days	Intensive Care Unit Not vacc. vs vacc. complete cycle + booster	DECEASED Not vacc. vs vacc. complete cycle ≤120 days	DECEASED Not vacc. vs vacc. complete cycle + booster
19/01/2022	2.1	2.6	13.8	9.4	27.6	32.2	27.0	7.7
26/01/2022	2.0	2.8	9.1	7.7	21.3	21.3	16.2	9.3
02/02/2022	1.9	3.1	6.8	7.4	21.3	17.8	18.0	18.0
09/02/2022	1.9	3.4	5.1	6.9	15.1	16.3	15.1	17.7
16/02/2022	1.9	3.5	3.8	5.8	8.8	12.2	11.9	13.6
23/02/2022	1.8	3.5	3.0	5.4	5.4	10.3	8.0	11.0
02/03/2022	1.7	3.2	2.6	5.3	3.6	10.7	5.0	7.8
09/03/2022	1.7	2.8	2.3	4.9	2.5	8.9	3.1	7.6
16/03/2022	1.6	2.2	2.2	4.6	2.0	6.8	2.4	8.2
23/03/2022	1.4	1.7	2.0	4.0	2.0	5.3	2.0	6.0
30/03/2022	1,2	1.4	1.8	3.3	2.0	4.4	1.8	8.0
06/04/2022	1.2	1.3	1.6	2.5	1.7	3.2	2.6	6.0
13/04/2022	1.1	1.2	1.6	2.3	1.8	3.7	4.2	7.0
20/04/2022	1.0	1.1	1.4	2.0	2.4	3.4	5.2	7.0
27/04/2022	1.0	1.1	1.4	2.0	3.7	3.1	4.2	5.2
04/05/2022	0.9	0.9	1.2	1.8	3.1	3.1	5.2	5.2
11/05/2022	0.9	0.9	1.2	1.7	3.3	2.9	3.3	5.0
18/05/2022	0.9	0.9	1.2	1.5	2.4	2.0	2.6	4.5
25/05/2022	0.9	0.9	1.2	1.5	2.4	2.0	2.6	4.5
31/05/2022	0.8	0.8	1.1	1.3	2.2	2.2	1.4	2.8
08/06/2022	0.8	0.9	1.0	1.4	1.5	2.2	1.3	3.0
15/06/2022	0.8	0.9	0.9	1.5	2.0	2.5	2.7	2.7
22/06/2022	0.9	0.8	0.9	1.6	2.0	3.3	1.0	2.5
28/06/2022	1.0	0.8	0.8	1.6	0.7	2.0	2.3	3.5
06/07/2022	1.0	0.8	0.9	1.6	0.7	1.7	inf	6.0

Discussion

The aim of this study was to understand the impact of the Covid-19 vaccine boosters in the ICU and Deaths variables and to compare the weight of the Unvaccinated and Vaccinated groups in the same variables.

The results support our hypothesis.

In fact, the Kruskal-Wallis test used to compare IC, 2D<4, 2D>4, 3D groups have shown that, from the comparison between the rates, a single dose is already associated with reduced access to ICU and Deaths, and that the third doses are associated with increased accesses in ICU and Deaths, unlike the second doses and incomplete cycles (IC).

Moreover, the 2D>4m are associated with significantly greater values than the IC and 2D<4m both in the ICU and Deaths variables. However, on the whole, the 2D>4m show a progressive and continuous decrease over time up to levels almost comparable for the ICU variable to the 3D values (which instead seem to worsen constantly); instead, in Deaths variable the 2D>4m show a slight rise during the considered period.

Overall, the 2D <4m do not seem to improve ICU accesses and deaths, if compared to the single doses (IC) and, moreover, they show a trend over time similar to the IC group.

The Mann-Whitney test was used to compare Unvaccinated (UV) and Vaccinated (V) groups shows that, overall, the UV group has significantly higher ICU accesses, but it shows a sharp declining trend over the whole period considered (-51%), while the V group shows only a 13% decrease.

There is an overlap of values between UV and V groups in the last bulletin of the period considered for the ICU variable; instead, the Deaths variable shows a progressive divergence of values throughout the considered period.

In contrast to the ICU variable, the deaths in the UV group are significantly lower than those in V group, with an increase of 9.5% over the considered period, while the increase in the V group is 53.2%.

The analysis by age groups shows an increase in rates both in the ICU variable and in the Death variable in all age groups in the 3D group. Furthermore, the weaker age groups seem to show greater sensitivity to this phenomenon inasmuch a trend reversal is observed between the 2D group> 4m and the 3D group. This observation seems to be congruent with what has already been observed in Canada, Italy and Michigan,¹^–⁴ where it has been shown that repeated vaccinations increase the risk of disease.

The hypothesis of a progressive weakening of the immune response with the increase of the time from the booster dose is supported by the data in the weekly publications of the UK Health Security Agency - COVID-19 vaccine surveillance reports.

In fact, such a clear trend not only argues for the waning of the 3rd dose effectiveness in preventing SARS-CoV-2 infection, but also for a possible, progressive worsening of the immune response.

Note that the Authors of the UK Health Security Agency Report always repeat that: “Comparing case rates among vaccinated and unvaccinated populations should not be used to estimate vaccine effectiveness against COVID-19 infection. Vaccine effectiveness (VE) has been formally estimated from a number of different sources and is summarised on pages 4 to 15 in this report.” However, the four cohort studies mentioned in the Report in the paragraph “Effectiveness against infection”¹¹^,²⁰^–²² have a short follow-up, ranging on average from 45 to 80 days, during the so-called honeymoon between the vaccine and the vaccinated persons. In this period the protection is at its maximum, also against the infection, and it is before the beginning of its rapid decline. Moreover, these inconsistent arguments of the Report authors do not stand up to comparison with the very strong and linear trend of increasing infections in vaccinated individuals shown by the weekly data, albeit unadjusted.

It is not enough. Looking at the weeks when more than half of the English people have received the vaccine booster (from Week 3 of 2022 onwards),²³ it is detectable a clear trend to an attenuation of the VE even towards deaths, because in Week 3 the death rate of unvaccinated was 9.4 times the vaccinated rate, while in the Week 10 the rate unvaccinated/vaccinated was only about 1.4 (Table 5). [Note that this trend seems in place in Italy as well, starting from the age group 40-59 (Table 6)]. The rate in England week 11, nearly 1.6, might be an interruption of this trend, or an effect of chance, or an interference with the (temporary?) effects of the fourth dose, reported only from 9 March 2022.²³

The usual explanation is to acknowledge a progressive attenuation of the VE against the infection (although for now, despite mounting evidence,¹²^,¹³^,²⁴^–²⁶ most main stream researchers are far from admitting a VE negativization in the medium period, in comparison to the unvaccinated persons). But, together, the typical narrative states definitely that the VE remains very good against a severe or critical COVID-19. Unfortunately, the data in Tables 5 and 6 show a clear trend towards an attenuation of the VE even for deaths, and the data should call into question the strategy of repeated, continuous vaccine boosters. Other strategies as well should be debated and studied in depht and implemented, from allowing natural infections (as Icelandic Public Healthcare is proposing,²⁷ in the setting of a mild dominant variant such as Omicron), to implementation of safer environments,²⁸^–³⁰ healthy lifestyles,³¹^–³⁶ and reasonably effective, safe and sustainable early care.³⁷^–⁴¹

Conclusion

The results do not provide support to vaccination campaigns with multiple and repeated doses (and related obligations), both because of the doubtful net advantage of subsequent doses, and because of the suspicion that the repeated stimulation of the immune system with this type of vaccine may expose the vaccinated people to an increased risk of serious disease.

The negative effect of this uninterrupted vaccination campaign seems to affect particularly the age groups that the campaign aims to protect; moreover, the net benefits declared for the younger are not evident.

In addition to continuing researching for better vaccines, it would be time to implement different strategies as well, to tackle this and other pandemics: from the achievement of a healthy living and working environment, to healthy lifestyles, to accessible early, safe and sustainable care.

Data availability

Underlying data

As the data are not owned by the authors, it is not possible to upload the data to a repository. Data analysed will be available in an accessible form by contacting the corresponding author.

ISS bulletins

15/1/2022: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_12-gennaio-2022.pdf

22/1/2022: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_19-gennaio-2022.pdf

29/1/2022: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_26-gennaio-2022.pdf

5/2/2022: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_2-febbraio-2022.pdf

12/2/2022: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_9-febbraio-2022.pdf

19/2/2022: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_16-febbraio-2022.pdf

Extended data

Figshare: Supplementary material.pdf, https://doi.org/10.6084/m9.figshare.20473503.v1.⁴¹

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

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Version 2

VERSION 2 PUBLISHED 26 May 2022

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Claudio Simion
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization

Competing interests

Marco Alessandria declares no conflicts of interest. Alberto Donzelli declares no conflicts of interest. Claudio Simion is the president of Comilva nonprofit organization (NPO).

Grant information

The author(s) declared that no grants were involved in supporting this work.

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Alessandria M, Simion C and Donzelli A. Usefulness of vaccine boosters for Covid-19 in Italy and in UK and comparison between in intensive care admissions and deaths of vaccinated and unvaccinated patients. Surprises and implications [version 2; peer review: 1 not approved] F1000Research 2022, 11:579 (https://doi.org/10.12688/f1000research.111023.2)

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Reviewer Report 01 Sep 2022

Gokhan Tut, Immunology and immunotherapy, University of Birmingham, Birmingham, UK

Not Approved

https://doi.org/10.5256/f1000research.136607.r146800

Editorial Note from F1000Research – 11^th October 2022
This report was edited to clarify an abbreviation in the initial report. The edited sentence is as follows: "As far as I can tell no Kaplan Meier analysis was performed in this in ... Continue reading

The authors are yet to address my main comment
'The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least'
Just looking at generalised data without stratifying, even at the simplest level by Delta era (pre-December 2021) and Omicron era post-December 2021 would show that vaccination results are varied. Across England, the rollout of the booster vaccines coincided with Omicron becoming the dominant variant and vaccine breakthrough events with Omicron are much more common. So the conclusion drawn by the authors with the statement 'Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19' is unconfirmed and can not be confirmed with the current analysis plan implemented. Minimally a survival analysis will need to be implemented - such as a Kaplan Meier or a Cox regression model with hazard ratios. The Omicron era and Delta era would need to be analysed separately and the effectiveness of vaccines reported separately.
The authors have improved upon the figures but still need a lot of work, particularly the figure legends.
Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.'
If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.
To address this part of the manuscript:
'In fact, the Kruskal-Wallis test used to compare IC, 2D<4, 2D>4, 3D groups have shown that, from the comparison between the rates, a single dose is already associated with reduced access to ICU and Deaths, and that the third doses are associated with increased accesses in ICU and Deaths, unlike the second doses and incomplete cycles (IC).'
I can not find this analysis in your paper. As far as I can tell no Kaplan Meier analysis was performed in this in this manuscript.
Just to address the 2nd to the last paragraph in the results section:
January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: COVID-19 immunology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Report a concern

Author Response 14 Oct 2022

Marco Alessandria, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy

14 Oct 2022

Author Response
In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either ... Continue reading
In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either the answers we had provided or the corrections made following his objections to the first version.

Below we list our main concerns for each point written by the reviewer:

The reviewer asks again, and in the same words, what had already asked in his previous review, without mentioning or taking into account the answer given previously. Our original answer was as follows: “The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor did the ISS itself carry out any such multivariate analysis. To comply with your suggestion, we asked the ISS all the data needed to perform this analysis, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.

So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we deem that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the necessary information.

Anyway, we believe that putting forward a few remarks and opening a debate about the ISS data as they are made available, highlighting the emerging trends in those data, might be better than giving it up, in absence of the information needed to carry out a more appropriate analysis.

For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" which can be found under Extended Data in the article.

In this comment it is clear that the reviewer is confusing the English data with the Italian ones. In fact, the sentence that is contested to us ('Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19') refers to the Italian data rather than to the English ones. In any case, even admitting that we had not been clear enough, a reviewer might be expected to ask the authors for a more detailed explanation, before bluntly declare that that statement cannot be confirmed.

The figure legends are not necessary, as the acronyms used are extensively described in the text, but we can accept the reviewer’s request.

The reviewer writes: “Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.' If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.”

We believe the statement “…drawing conclusions based on incomplete analysis pathways should be discouraged” to be somewhat curious, especially in the final part. As already explained, the request to carry out a multivariate analysis is acceptable, but our institutions do not provide the data necessary to perform it (see ISS response above). Therefore, we have carried out statistics with the available data, highlighting a problem of the vaccination campaign that will have to be confirmed or disconfirmed by more precise analyses, if it will eventually be possible to access the complete data held by our institutions. Furthermore, as already written in the response to the first review “…, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving it up, in absence of information to carry out a more appropriate analysis.”

Regarding the statistical analysis we have implemented, the reviewer writes: “I can not find this analysis in your paper. As far as I can tell no Kaplan-Meier analysis was performed in this in this manuscript.” We wonder how it is possible to make such a strong statement (questioning our professionalism), when our statistical analysis has been well described in the "Statistical analysis" section, making explicit that the Kruskal-Wallis test was carried out and the reasons why we have chosen that statistical model. For this reason, we wonder where the reviewer read that we used the statistical model of Kaplan-Meier. We hope the reviewer is not getting confused between the two statistical models.

The reviewer states: “Just to address the 2nd to the last paragraph in the results section: January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.” Indeed, we did not observe “vaccines making outcome worse” in comparison with the Delta era.
Instead, we highlighted the clear trend towards an increase in infections in vaccinated persons versus not vaccinated persons per 100,000, compared in each of eight age classes: under 18, 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 or over. The new Omicron variant did not create, but has only enhanced this trend, despite the fact that in Week 3 of 2022 the UK Health Security Agency decided to compare with not vaccinated persons no more persons vaccinated with 2 doses, but only persons vaccinated with at least 3 doses. Despite this change in the comparator group, the gap between infection cases in not vaccinated versus vaccinated persons has widened dramatically, until Week 13, when the UK Health Security Agency decided to stop publishing these embarrassing data.

We are aware that “correlations do not mean causation”. However, we wonder why whoever might have the information (not provided to us) required to carry out “a proper analysis” does not carry it out.

In our view, the UK Health Security Agency would have the burden to explain the incredible association shown by the unadjusted data, which we exhibit in the following graph: Fig 1
In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either the answers we had provided or the corrections made following his objections to the first version.

Below we list our main concerns for each point written by the reviewer:

The reviewer asks again, and in the same words, what had already asked in his previous review, without mentioning or taking into account the answer given previously. Our original answer was as follows: “The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor did the ISS itself carry out any such multivariate analysis. To comply with your suggestion, we asked the ISS all the data needed to perform this analysis, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.

So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we deem that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the necessary information.

Anyway, we believe that putting forward a few remarks and opening a debate about the ISS data as they are made available, highlighting the emerging trends in those data, might be better than giving it up, in absence of the information needed to carry out a more appropriate analysis.

For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" which can be found under Extended Data in the article.

In this comment it is clear that the reviewer is confusing the English data with the Italian ones. In fact, the sentence that is contested to us ('Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19') refers to the Italian data rather than to the English ones. In any case, even admitting that we had not been clear enough, a reviewer might be expected to ask the authors for a more detailed explanation, before bluntly declare that that statement cannot be confirmed.

The figure legends are not necessary, as the acronyms used are extensively described in the text, but we can accept the reviewer’s request.

The reviewer writes: “Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.' If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.”

We believe the statement “…drawing conclusions based on incomplete analysis pathways should be discouraged” to be somewhat curious, especially in the final part. As already explained, the request to carry out a multivariate analysis is acceptable, but our institutions do not provide the data necessary to perform it (see ISS response above). Therefore, we have carried out statistics with the available data, highlighting a problem of the vaccination campaign that will have to be confirmed or disconfirmed by more precise analyses, if it will eventually be possible to access the complete data held by our institutions. Furthermore, as already written in the response to the first review “…, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving it up, in absence of information to carry out a more appropriate analysis.”

Regarding the statistical analysis we have implemented, the reviewer writes: “I can not find this analysis in your paper. As far as I can tell no Kaplan-Meier analysis was performed in this in this manuscript.” We wonder how it is possible to make such a strong statement (questioning our professionalism), when our statistical analysis has been well described in the "Statistical analysis" section, making explicit that the Kruskal-Wallis test was carried out and the reasons why we have chosen that statistical model. For this reason, we wonder where the reviewer read that we used the statistical model of Kaplan-Meier. We hope the reviewer is not getting confused between the two statistical models.

The reviewer states: “Just to address the 2nd to the last paragraph in the results section: January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.” Indeed, we did not observe “vaccines making outcome worse” in comparison with the Delta era.
Instead, we highlighted the clear trend towards an increase in infections in vaccinated persons versus not vaccinated persons per 100,000, compared in each of eight age classes: under 18, 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 or over. The new Omicron variant did not create, but has only enhanced this trend, despite the fact that in Week 3 of 2022 the UK Health Security Agency decided to compare with not vaccinated persons no more persons vaccinated with 2 doses, but only persons vaccinated with at least 3 doses. Despite this change in the comparator group, the gap between infection cases in not vaccinated versus vaccinated persons has widened dramatically, until Week 13, when the UK Health Security Agency decided to stop publishing these embarrassing data.

We are aware that “correlations do not mean causation”. However, we wonder why whoever might have the information (not provided to us) required to carry out “a proper analysis” does not carry it out.

In our view, the UK Health Security Agency would have the burden to explain the incredible association shown by the unadjusted data, which we exhibit in the following graph: Fig 1
Competing Interests: No competing interests were disclosed. Close
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Author Response 14 Oct 2022

Marco Alessandria, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy

14 Oct 2022

Author Response
In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either ... Continue reading
In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either the answers we had provided or the corrections made following his objections to the first version.

Below we list our main concerns for each point written by the reviewer:

The reviewer asks again, and in the same words, what had already asked in his previous review, without mentioning or taking into account the answer given previously. Our original answer was as follows: “The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor did the ISS itself carry out any such multivariate analysis. To comply with your suggestion, we asked the ISS all the data needed to perform this analysis, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.

So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we deem that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the necessary information.

Anyway, we believe that putting forward a few remarks and opening a debate about the ISS data as they are made available, highlighting the emerging trends in those data, might be better than giving it up, in absence of the information needed to carry out a more appropriate analysis.

For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" which can be found under Extended Data in the article.

In this comment it is clear that the reviewer is confusing the English data with the Italian ones. In fact, the sentence that is contested to us ('Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19') refers to the Italian data rather than to the English ones. In any case, even admitting that we had not been clear enough, a reviewer might be expected to ask the authors for a more detailed explanation, before bluntly declare that that statement cannot be confirmed.

The figure legends are not necessary, as the acronyms used are extensively described in the text, but we can accept the reviewer’s request.

The reviewer writes: “Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.' If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.”

We believe the statement “…drawing conclusions based on incomplete analysis pathways should be discouraged” to be somewhat curious, especially in the final part. As already explained, the request to carry out a multivariate analysis is acceptable, but our institutions do not provide the data necessary to perform it (see ISS response above). Therefore, we have carried out statistics with the available data, highlighting a problem of the vaccination campaign that will have to be confirmed or disconfirmed by more precise analyses, if it will eventually be possible to access the complete data held by our institutions. Furthermore, as already written in the response to the first review “…, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving it up, in absence of information to carry out a more appropriate analysis.”

Regarding the statistical analysis we have implemented, the reviewer writes: “I can not find this analysis in your paper. As far as I can tell no Kaplan-Meier analysis was performed in this in this manuscript.” We wonder how it is possible to make such a strong statement (questioning our professionalism), when our statistical analysis has been well described in the "Statistical analysis" section, making explicit that the Kruskal-Wallis test was carried out and the reasons why we have chosen that statistical model. For this reason, we wonder where the reviewer read that we used the statistical model of Kaplan-Meier. We hope the reviewer is not getting confused between the two statistical models.

The reviewer states: “Just to address the 2nd to the last paragraph in the results section: January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.” Indeed, we did not observe “vaccines making outcome worse” in comparison with the Delta era.
Instead, we highlighted the clear trend towards an increase in infections in vaccinated persons versus not vaccinated persons per 100,000, compared in each of eight age classes: under 18, 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 or over. The new Omicron variant did not create, but has only enhanced this trend, despite the fact that in Week 3 of 2022 the UK Health Security Agency decided to compare with not vaccinated persons no more persons vaccinated with 2 doses, but only persons vaccinated with at least 3 doses. Despite this change in the comparator group, the gap between infection cases in not vaccinated versus vaccinated persons has widened dramatically, until Week 13, when the UK Health Security Agency decided to stop publishing these embarrassing data.

We are aware that “correlations do not mean causation”. However, we wonder why whoever might have the information (not provided to us) required to carry out “a proper analysis” does not carry it out.

In our view, the UK Health Security Agency would have the burden to explain the incredible association shown by the unadjusted data, which we exhibit in the following graph: Fig 1
In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either the answers we had provided or the corrections made following his objections to the first version.

Below we list our main concerns for each point written by the reviewer:

The reviewer asks again, and in the same words, what had already asked in his previous review, without mentioning or taking into account the answer given previously. Our original answer was as follows: “The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor did the ISS itself carry out any such multivariate analysis. To comply with your suggestion, we asked the ISS all the data needed to perform this analysis, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.

So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we deem that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the necessary information.

Anyway, we believe that putting forward a few remarks and opening a debate about the ISS data as they are made available, highlighting the emerging trends in those data, might be better than giving it up, in absence of the information needed to carry out a more appropriate analysis.

For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" which can be found under Extended Data in the article.

In this comment it is clear that the reviewer is confusing the English data with the Italian ones. In fact, the sentence that is contested to us ('Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19') refers to the Italian data rather than to the English ones. In any case, even admitting that we had not been clear enough, a reviewer might be expected to ask the authors for a more detailed explanation, before bluntly declare that that statement cannot be confirmed.

The figure legends are not necessary, as the acronyms used are extensively described in the text, but we can accept the reviewer’s request.

The reviewer writes: “Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.' If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.”

We believe the statement “…drawing conclusions based on incomplete analysis pathways should be discouraged” to be somewhat curious, especially in the final part. As already explained, the request to carry out a multivariate analysis is acceptable, but our institutions do not provide the data necessary to perform it (see ISS response above). Therefore, we have carried out statistics with the available data, highlighting a problem of the vaccination campaign that will have to be confirmed or disconfirmed by more precise analyses, if it will eventually be possible to access the complete data held by our institutions. Furthermore, as already written in the response to the first review “…, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving it up, in absence of information to carry out a more appropriate analysis.”

Regarding the statistical analysis we have implemented, the reviewer writes: “I can not find this analysis in your paper. As far as I can tell no Kaplan-Meier analysis was performed in this in this manuscript.” We wonder how it is possible to make such a strong statement (questioning our professionalism), when our statistical analysis has been well described in the "Statistical analysis" section, making explicit that the Kruskal-Wallis test was carried out and the reasons why we have chosen that statistical model. For this reason, we wonder where the reviewer read that we used the statistical model of Kaplan-Meier. We hope the reviewer is not getting confused between the two statistical models.

The reviewer states: “Just to address the 2nd to the last paragraph in the results section: January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.” Indeed, we did not observe “vaccines making outcome worse” in comparison with the Delta era.
Instead, we highlighted the clear trend towards an increase in infections in vaccinated persons versus not vaccinated persons per 100,000, compared in each of eight age classes: under 18, 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 or over. The new Omicron variant did not create, but has only enhanced this trend, despite the fact that in Week 3 of 2022 the UK Health Security Agency decided to compare with not vaccinated persons no more persons vaccinated with 2 doses, but only persons vaccinated with at least 3 doses. Despite this change in the comparator group, the gap between infection cases in not vaccinated versus vaccinated persons has widened dramatically, until Week 13, when the UK Health Security Agency decided to stop publishing these embarrassing data.

We are aware that “correlations do not mean causation”. However, we wonder why whoever might have the information (not provided to us) required to carry out “a proper analysis” does not carry it out.

In our view, the UK Health Security Agency would have the burden to explain the incredible association shown by the unadjusted data, which we exhibit in the following graph: Fig 1
Competing Interests: No competing interests were disclosed. Close
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Version 1

VERSION 1

PUBLISHED 26 May 2022

Views

Reviewer Report 07 Jun 2022

Gokhan Tut, Immunology and immunotherapy, University of Birmingham, Birmingham, UK

Not Approved

https://doi.org/10.5256/f1000research.122694.r139174

Major comments and concerns

The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least.

The quality of the figures is not up to standard and a Y-axis should be provided in the longitudinal subfigures. We should be able to understand the data by looking at the figures and legends alone.

"The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people."

In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.

"The trend of 3D group is growing with an increase of the 253.3%, while the trend of 2D>4m is degrowing with a decrease of the 39.7%. The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% and 13.9%"

Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.

"The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error)."

I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.

"Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (-17,2%) the column of not vaccinated."

The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.

The fundamental conclusions drawn from the analysis would need to be revisited after the multivariant analysis corrections have been implemented.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: COVID-19 immunology

CITE

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Author Response 12 Aug 2022

Marco Alessandria, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy

12 Aug 2022

Author Response

Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need ... Continue reading Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least.

Response
The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor the ISS itself has carried out a multivariate analysis. To comply to your suggestion, we asked the ISS all the needed data to perform this analisys, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.
So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we consider that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the information.
Anyway, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving up, in absence of information to carry out a more appropriate analysis.
For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" and sent to the editor for publication on the site.

Reviewer: The quality of the figures is not up to standard and a Y-axis should be provided in the longitudinal subfigures. We should be able to understand the data by looking at the figures and legends alone.
Response
The Figures are produced by the statistical software and meet accuracy standards. As you suggested, longitudinal sub-figures have been corrected by adding the Y axis and the description of the axes.

Reviewer: "The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people." In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.
Response
You are correct. We have added in the text that in the VIVALDI Study the difference is not statistically significant, but that it shows a tendency consistent with the results of other studies, with a mRNA vaccine or even with an inactivated COVID-19 vaccine (CoronaVac): see references.

- Craig C. Thinking beyond behavioural change as an explanation for increased COVID post vaccination. BMJ 26 March 2021. Rapid response to: Covid-19: Stronger warnings are needed to curb socialising after vaccination, say doctors and behavioural scientists
- BMJ 2021; 372 doi: https://doi.org/10.1136/bmj.n783 (Published 19 March 2021)
- U.S. Food and Drug Administration. Emergency Use Authorization (EUA) for an Unapproved Product Review Memorandum. https://www.fda.gov/media/144416/download

Hitchings MDT, Ranzani OT, Scaramuzzini Torres MS, et al. Effectiveness of CoronaVac among healthcare workers in the setting of high SARS-CoV-2 Gamma variant transmission in Manaus, Brazil: A test-negative case-control study. Lancet Reg Health Am 2021 Sep;1:100025. doi: 10.1016/j.lana.2021.100025. Epub 2021 Jul 25.

Reviewer: The trend of 3D group is growing with an increase of the 253.3%, while the trend of 2D>4m is degrowing with a decrease of the 39.7%. The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% and 13.9%" Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.
Response
All the significance of the percentage variation were calculated according to the methods described in the text.

Reviewer: "The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error)."
I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.
Response
We agree that the full potential of a vaccination can be seen after 14 days from the injection. This is a correct vaccine-oriented perspective.
We disagree with the way of setting up the groups by the ISS. In order to appropriately compare the groups, the subjects constituting them should have similar characteristics, as much as possible. In our opinion, it could be useful to separate the subjects vaccinated within 14 days, placing them in a separate group because, if they are aggregated in a single group together with the unvaccinated, they might alter the features of the entire group in an unpredictable way. Moreover, from a safety and person-oriented perspective, it could be useful to separate unvaccinated persons from the subjects who have received the first dose, even in the first 14 days from the inoculation.

Reviewer: "Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (-17,2%) the column of not vaccinated."
The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.
Response
Your point is correct. Indeed, the UK Health Security Agency compared eight (about) ten-year classes rates (per 100,000) among persons vaccinated with 2 doses and persons not vaccinated. In addition we have now implemented also a standardization based on the age structure of the English population. This has lead to minor changes in the numbers of the previous version, that we have corrected in the text.
Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least.

Response
The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor the ISS itself has carried out a multivariate analysis. To comply to your suggestion, we asked the ISS all the needed data to perform this analisys, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.
So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we consider that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the information.
Anyway, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving up, in absence of information to carry out a more appropriate analysis.
For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" and sent to the editor for publication on the site.

Reviewer: The quality of the figures is not up to standard and a Y-axis should be provided in the longitudinal subfigures. We should be able to understand the data by looking at the figures and legends alone.
Response
The Figures are produced by the statistical software and meet accuracy standards. As you suggested, longitudinal sub-figures have been corrected by adding the Y axis and the description of the axes.

Reviewer: "The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people." In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.
Response
You are correct. We have added in the text that in the VIVALDI Study the difference is not statistically significant, but that it shows a tendency consistent with the results of other studies, with a mRNA vaccine or even with an inactivated COVID-19 vaccine (CoronaVac): see references.

- Craig C. Thinking beyond behavioural change as an explanation for increased COVID post vaccination. BMJ 26 March 2021. Rapid response to: Covid-19: Stronger warnings are needed to curb socialising after vaccination, say doctors and behavioural scientists
- BMJ 2021; 372 doi: https://doi.org/10.1136/bmj.n783 (Published 19 March 2021)
- U.S. Food and Drug Administration. Emergency Use Authorization (EUA) for an Unapproved Product Review Memorandum. https://www.fda.gov/media/144416/download

Hitchings MDT, Ranzani OT, Scaramuzzini Torres MS, et al. Effectiveness of CoronaVac among healthcare workers in the setting of high SARS-CoV-2 Gamma variant transmission in Manaus, Brazil: A test-negative case-control study. Lancet Reg Health Am 2021 Sep;1:100025. doi: 10.1016/j.lana.2021.100025. Epub 2021 Jul 25.

Reviewer: The trend of 3D group is growing with an increase of the 253.3%, while the trend of 2D>4m is degrowing with a decrease of the 39.7%. The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% and 13.9%" Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.
Response
All the significance of the percentage variation were calculated according to the methods described in the text.

Reviewer: "The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error)."
I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.
Response
We agree that the full potential of a vaccination can be seen after 14 days from the injection. This is a correct vaccine-oriented perspective.
We disagree with the way of setting up the groups by the ISS. In order to appropriately compare the groups, the subjects constituting them should have similar characteristics, as much as possible. In our opinion, it could be useful to separate the subjects vaccinated within 14 days, placing them in a separate group because, if they are aggregated in a single group together with the unvaccinated, they might alter the features of the entire group in an unpredictable way. Moreover, from a safety and person-oriented perspective, it could be useful to separate unvaccinated persons from the subjects who have received the first dose, even in the first 14 days from the inoculation.

Reviewer: "Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (-17,2%) the column of not vaccinated."
The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.
Response
Your point is correct. Indeed, the UK Health Security Agency compared eight (about) ten-year classes rates (per 100,000) among persons vaccinated with 2 doses and persons not vaccinated. In addition we have now implemented also a standardization based on the age structure of the English population. This has lead to minor changes in the numbers of the previous version, that we have corrected in the text.
Competing Interests: No competing interests were disclosed. Close
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Respond or Comment

COMMENTS ON THIS REPORT

Author Response 12 Aug 2022

Marco Alessandria, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy

12 Aug 2022

Author Response

Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need ... Continue reading Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least.

Response
The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor the ISS itself has carried out a multivariate analysis. To comply to your suggestion, we asked the ISS all the needed data to perform this analisys, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.
So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we consider that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the information.
Anyway, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving up, in absence of information to carry out a more appropriate analysis.
For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" and sent to the editor for publication on the site.

Reviewer: The quality of the figures is not up to standard and a Y-axis should be provided in the longitudinal subfigures. We should be able to understand the data by looking at the figures and legends alone.
Response
The Figures are produced by the statistical software and meet accuracy standards. As you suggested, longitudinal sub-figures have been corrected by adding the Y axis and the description of the axes.

Reviewer: "The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people." In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.
Response
You are correct. We have added in the text that in the VIVALDI Study the difference is not statistically significant, but that it shows a tendency consistent with the results of other studies, with a mRNA vaccine or even with an inactivated COVID-19 vaccine (CoronaVac): see references.

- Craig C. Thinking beyond behavioural change as an explanation for increased COVID post vaccination. BMJ 26 March 2021. Rapid response to: Covid-19: Stronger warnings are needed to curb socialising after vaccination, say doctors and behavioural scientists
- BMJ 2021; 372 doi: https://doi.org/10.1136/bmj.n783 (Published 19 March 2021)
- U.S. Food and Drug Administration. Emergency Use Authorization (EUA) for an Unapproved Product Review Memorandum. https://www.fda.gov/media/144416/download

Hitchings MDT, Ranzani OT, Scaramuzzini Torres MS, et al. Effectiveness of CoronaVac among healthcare workers in the setting of high SARS-CoV-2 Gamma variant transmission in Manaus, Brazil: A test-negative case-control study. Lancet Reg Health Am 2021 Sep;1:100025. doi: 10.1016/j.lana.2021.100025. Epub 2021 Jul 25.

Reviewer: The trend of 3D group is growing with an increase of the 253.3%, while the trend of 2D>4m is degrowing with a decrease of the 39.7%. The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% and 13.9%" Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.
Response
All the significance of the percentage variation were calculated according to the methods described in the text.

Reviewer: "The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error)."
I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.
Response
We agree that the full potential of a vaccination can be seen after 14 days from the injection. This is a correct vaccine-oriented perspective.
We disagree with the way of setting up the groups by the ISS. In order to appropriately compare the groups, the subjects constituting them should have similar characteristics, as much as possible. In our opinion, it could be useful to separate the subjects vaccinated within 14 days, placing them in a separate group because, if they are aggregated in a single group together with the unvaccinated, they might alter the features of the entire group in an unpredictable way. Moreover, from a safety and person-oriented perspective, it could be useful to separate unvaccinated persons from the subjects who have received the first dose, even in the first 14 days from the inoculation.

Reviewer: "Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (-17,2%) the column of not vaccinated."
The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.
Response
Your point is correct. Indeed, the UK Health Security Agency compared eight (about) ten-year classes rates (per 100,000) among persons vaccinated with 2 doses and persons not vaccinated. In addition we have now implemented also a standardization based on the age structure of the English population. This has lead to minor changes in the numbers of the previous version, that we have corrected in the text.
Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least.

Response
The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor the ISS itself has carried out a multivariate analysis. To comply to your suggestion, we asked the ISS all the needed data to perform this analisys, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.
So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we consider that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the information.
Anyway, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving up, in absence of information to carry out a more appropriate analysis.
For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" and sent to the editor for publication on the site.

Reviewer: The quality of the figures is not up to standard and a Y-axis should be provided in the longitudinal subfigures. We should be able to understand the data by looking at the figures and legends alone.
Response
The Figures are produced by the statistical software and meet accuracy standards. As you suggested, longitudinal sub-figures have been corrected by adding the Y axis and the description of the axes.

Reviewer: "The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people." In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.
Response
You are correct. We have added in the text that in the VIVALDI Study the difference is not statistically significant, but that it shows a tendency consistent with the results of other studies, with a mRNA vaccine or even with an inactivated COVID-19 vaccine (CoronaVac): see references.

- Craig C. Thinking beyond behavioural change as an explanation for increased COVID post vaccination. BMJ 26 March 2021. Rapid response to: Covid-19: Stronger warnings are needed to curb socialising after vaccination, say doctors and behavioural scientists
- BMJ 2021; 372 doi: https://doi.org/10.1136/bmj.n783 (Published 19 March 2021)
- U.S. Food and Drug Administration. Emergency Use Authorization (EUA) for an Unapproved Product Review Memorandum. https://www.fda.gov/media/144416/download

Hitchings MDT, Ranzani OT, Scaramuzzini Torres MS, et al. Effectiveness of CoronaVac among healthcare workers in the setting of high SARS-CoV-2 Gamma variant transmission in Manaus, Brazil: A test-negative case-control study. Lancet Reg Health Am 2021 Sep;1:100025. doi: 10.1016/j.lana.2021.100025. Epub 2021 Jul 25.

Reviewer: The trend of 3D group is growing with an increase of the 253.3%, while the trend of 2D>4m is degrowing with a decrease of the 39.7%. The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% and 13.9%" Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.
Response
All the significance of the percentage variation were calculated according to the methods described in the text.

Reviewer: "The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error)."
I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.
Response
We agree that the full potential of a vaccination can be seen after 14 days from the injection. This is a correct vaccine-oriented perspective.
We disagree with the way of setting up the groups by the ISS. In order to appropriately compare the groups, the subjects constituting them should have similar characteristics, as much as possible. In our opinion, it could be useful to separate the subjects vaccinated within 14 days, placing them in a separate group because, if they are aggregated in a single group together with the unvaccinated, they might alter the features of the entire group in an unpredictable way. Moreover, from a safety and person-oriented perspective, it could be useful to separate unvaccinated persons from the subjects who have received the first dose, even in the first 14 days from the inoculation.

Reviewer: "Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (-17,2%) the column of not vaccinated."
The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.
Response
Your point is correct. Indeed, the UK Health Security Agency compared eight (about) ten-year classes rates (per 100,000) among persons vaccinated with 2 doses and persons not vaccinated. In addition we have now implemented also a standardization based on the age structure of the English population. This has lead to minor changes in the numbers of the previous version, that we have corrected in the text.
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Version 2

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Version 2 (revision) 08 Aug 22	read
Version 1 26 May 22	read

Gokhan Tut, University of Birmingham, Birmingham, UK

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50 Views

01 Sep 2022 | for Version 2

Gokhan Tut, Immunology and immunotherapy, University of Birmingham, Birmingham, UK

50 Views Cite this report Responses(1)

Not Approved

The authors are yet to address my main comment
'The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least'
Just looking at generalised data without stratifying, even at the simplest level by Delta era (pre-December 2021) and Omicron era post-December 2021 would show that vaccination results are varied. Across England, the rollout of the booster vaccines coincided with Omicron becoming the dominant variant and vaccine breakthrough events with Omicron are much more common. So the conclusion drawn by the authors with the statement 'Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19' is unconfirmed and can not be confirmed with the current analysis plan implemented. Minimally a survival analysis will need to be implemented - such as a Kaplan Meier or a Cox regression model with hazard ratios. The Omicron era and Delta era would need to be analysed separately and the effectiveness of vaccines reported separately.
The authors have improved upon the figures but still need a lot of work, particularly the figure legends.
Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.'
If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.
To address this part of the manuscript:
'In fact, the Kruskal-Wallis test used to compare IC, 2D<4, 2D>4, 3D groups have shown that, from the comparison between the rates, a single dose is already associated with reduced access to ICU and Deaths, and that the third doses are associated with increased accesses in ICU and Deaths, unlike the second doses and incomplete cycles (IC).'
I can not find this analysis in your paper. As far as I can tell no Kaplan Meier analysis was performed in this in this manuscript.
Just to address the 2nd to the last paragraph in the results section:
January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.

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COVID-19 immunology

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Author Response

14 Oct 2022

Marco Alessandria, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy

In general, the reviewer, focused only on the English data without making any comment on our analysis of the Italian data. Furthermore, he did not give any feedback to either the answers we had provided or the corrections made following his objections to the first version.

Below we list our main concerns for each point written by the reviewer:

The reviewer asks again, and in the same words, what had already asked in his previous review, without mentioning or taking into account the answer given previously. Our original answer was as follows: “The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor did the ISS itself carry out any such multivariate analysis. To comply with your suggestion, we asked the ISS all the data needed to perform this analysis, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.

So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we deem that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the necessary information.

Anyway, we believe that putting forward a few remarks and opening a debate about the ISS data as they are made available, highlighting the emerging trends in those data, might be better than giving it up, in absence of the information needed to carry out a more appropriate analysis.

For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" which can be found under Extended Data in the article.
In this comment it is clear that the reviewer is confusing the English data with the Italian ones. In fact, the sentence that is contested to us ('Repeated vaccinations seem to expose the recipients to a growing risk of severe Covid-19') refers to the Italian data rather than to the English ones. In any case, even admitting that we had not been clear enough, a reviewer might be expected to ask the authors for a more detailed explanation, before bluntly declare that that statement cannot be confirmed.
The figure legends are not necessary, as the acronyms used are extensively described in the text, but we can accept the reviewer’s request.
The reviewer writes: “Just to address this comment: 'First, we lack too many informations to implement a multivariable correction.' If you lack the information then it is not possible to confirm your current conclusions without proper analysis and drawing conclusions based on incomplete analysis pathways should be discouraged.”

We believe the statement “…drawing conclusions based on incomplete analysis pathways should be discouraged” to be somewhat curious, especially in the final part. As already explained, the request to carry out a multivariate analysis is acceptable, but our institutions do not provide the data necessary to perform it (see ISS response above). Therefore, we have carried out statistics with the available data, highlighting a problem of the vaccination campaign that will have to be confirmed or disconfirmed by more precise analyses, if it will eventually be possible to access the complete data held by our institutions. Furthermore, as already written in the response to the first review “…, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving it up, in absence of information to carry out a more appropriate analysis.”
Regarding the statistical analysis we have implemented, the reviewer writes: “I can not find this analysis in your paper. As far as I can tell no Kaplan-Meier analysis was performed in this in this manuscript.” We wonder how it is possible to make such a strong statement (questioning our professionalism), when our statistical analysis has been well described in the "Statistical analysis" section, making explicit that the Kruskal-Wallis test was carried out and the reasons why we have chosen that statistical model. For this reason, we wonder where the reviewer read that we used the statistical model of Kaplan-Meier. We hope the reviewer is not getting confused between the two statistical models.
The reviewer states: “Just to address the 2nd to the last paragraph in the results section: January 2022 was when the Omicron variant was rampant in England. What you are observing is not vaccines making outcomes worse but the impact of a new variant emerging just after a busy holiday period with maximal social contact followed by vaccinations. Until proper correct analysis is done your correlations do not mean causation.” Indeed, we did not observe “vaccines making outcome worse” in comparison with the Delta era.
Instead, we highlighted the clear trend towards an increase in infections in vaccinated persons versus not vaccinated persons per 100,000, compared in each of eight age classes: under 18, 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, 80 or over. The new Omicron variant did not create, but has only enhanced this trend, despite the fact that in Week 3 of 2022 the UK Health Security Agency decided to compare with not vaccinated persons no more persons vaccinated with 2 doses, but only persons vaccinated with at least 3 doses. Despite this change in the comparator group, the gap between infection cases in not vaccinated versus vaccinated persons has widened dramatically, until Week 13, when the UK Health Security Agency decided to stop publishing these embarrassing data.

We are aware that “correlations do not mean causation”. However, we wonder why whoever might have the information (not provided to us) required to carry out “a proper analysis” does not carry it out.

In our view, the UK Health Security Agency would have the burden to explain the incredible association shown by the unadjusted data, which we exhibit in the following graph: Fig 1

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94 Views

07 Jun 2022 | for Version 1

Gokhan Tut, Immunology and immunotherapy, University of Birmingham, Birmingham, UK

94 Views Cite this report Responses(1)

Not Approved

In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.

Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.

I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.

The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.

The fundamental conclusions drawn from the analysis would need to be revisited after the multivariant analysis corrections have been implemented.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

COVID-19 immunology

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Responses (1)

Author Response

12 Aug 2022

Marco Alessandria, Studio di Scienze Motorie e Terapie Manuali, Turin, Italy

Reviewer:The authors have not stratified their data at all, simply looking at whole populations just based on how many vaccines participants have had is not enough. The analysis would need to be redone with multivariate analysis corrections for age, sex, time after vaccination and ethnicity at the very least.

Response
The ISS official bulletins do not provide all the data and variables needed to implement a multivariate logistic analysis, nor the ISS itself has carried out a multivariate analysis. To comply to your suggestion, we asked the ISS all the needed data to perform this analisys, but the reply was: “… for privacy reasons we are not authorized to provide individual or detailed data as you requested. In any case, if deemed useful, you may consult the page https://www.epicentro.iss.it/coronavirus/aggiornamenti, where the ISS weekly bulletins are stored…”.
So we have had to limit our analysis to a non-parametric comparison between groups. Anyhow, we consider that, if a multivariate analysis had brought relevant added value, the ISS itself would have probably implemented it, having all the information.
Anyway, we believe that proposing considerations and opening a debate about the ISS data as they are presented, highlighting the emerging trends in those data, might be better than giving up, in absence of information to carry out a more appropriate analysis.
For completeness, we performed the non-parametric statistical analysis for all age groups, inserting them in a file entitled "Supplementary Material" and sent to the editor for publication on the site.

Reviewer: The quality of the figures is not up to standard and a Y-axis should be provided in the longitudinal subfigures. We should be able to understand the data by looking at the figures and legends alone.
Response
The Figures are produced by the statistical software and meet accuracy standards. As you suggested, longitudinal sub-figures have been corrected by adding the Y axis and the description of the axes.

Reviewer: "The VIVALDI Study¹¹ showed a marginally greater hazard ratio for PCR-positive infection among vaccinated people after first dose of BNT162b2 vaccine within 7-13 days compared to unvaccinated people." In this study, this was not significantly different - authors should either remove this reference or state that the difference seen was not statistically significant in the VIVALDI study.
Response
You are correct. We have added in the text that in the VIVALDI Study the difference is not statistically significant, but that it shows a tendency consistent with the results of other studies, with a mRNA vaccine or even with an inactivated COVID-19 vaccine (CoronaVac): see references.

- Craig C. Thinking beyond behavioural change as an explanation for increased COVID post vaccination. BMJ 26 March 2021. Rapid response to: Covid-19: Stronger warnings are needed to curb socialising after vaccination, say doctors and behavioural scientists
- BMJ 2021; 372 doi: https://doi.org/10.1136/bmj.n783 (Published 19 March 2021)
- U.S. Food and Drug Administration. Emergency Use Authorization (EUA) for an Unapproved Product Review Memorandum. https://www.fda.gov/media/144416/download

Hitchings MDT, Ranzani OT, Scaramuzzini Torres MS, et al. Effectiveness of CoronaVac among healthcare workers in the setting of high SARS-CoV-2 Gamma variant transmission in Manaus, Brazil: A test-negative case-control study. Lancet Reg Health Am 2021 Sep;1:100025. doi: 10.1016/j.lana.2021.100025. Epub 2021 Jul 25.

Reviewer: The trend of 3D group is growing with an increase of the 253.3%, while the trend of 2D>4m is degrowing with a decrease of the 39.7%. The trend of IC group and 2D<4m are overlapping and show a decrease respectively of 45.5% and 13.9%" Is this statistically significant on the longitudinal graph? Again, this should be checked for every figure.
Response
All the significance of the percentage variation were calculated according to the methods described in the text.

Reviewer: "The reason for this choice is that the ISS considers Unvaccinated even people “… vaccinated with either the first dose or single-dose vaccine within the 14 days prior to diagnosis.” (although, in our opinion, this can introduce a bias and a systematic error)."
I do not understand this sentence. It takes at least 14 days for the immune response to mature, therefore only counting those after 14 days of vaccination will provide the true response to vaccination. Days 1-14 the immune response is not yet complete.
Response
We agree that the full potential of a vaccination can be seen after 14 days from the injection. This is a correct vaccine-oriented perspective.
We disagree with the way of setting up the groups by the ISS. In order to appropriately compare the groups, the subjects constituting them should have similar characteristics, as much as possible. In our opinion, it could be useful to separate the subjects vaccinated within 14 days, placing them in a separate group because, if they are aggregated in a single group together with the unvaccinated, they might alter the features of the entire group in an unpredictable way. Moreover, from a safety and person-oriented perspective, it could be useful to separate unvaccinated persons from the subjects who have received the first dose, even in the first 14 days from the inoculation.

Reviewer: "Indeed, the Week 36 of 2021 showed an unbalancement in age classes from 40 to 79 years, in which COVID-19 cases in vaccinated exceeded those in not vaccinated persons, although the entire column of vaccinated persons still outnumbered (-17,2%) the column of not vaccinated."
The authors would need to adjust the analysis to account for the larger number of vaccinated donors in this cohort.
Response
Your point is correct. Indeed, the UK Health Security Agency compared eight (about) ten-year classes rates (per 100,000) among persons vaccinated with 2 doses and persons not vaccinated. In addition we have now implemented also a standardization based on the age structure of the English population. This has lead to minor changes in the numbers of the previous version, that we have corrected in the text.

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Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Usefulness of vaccine boosters for Covid-19 in Italy and in UK and comparison between in intensive care admissions and deaths of vaccinated and unvaccinated patients. Surprises and implications

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Methods

Statistical analysis

Results

Comparison between Incomplete Cycle (IC), double dose <4 months (<4), double dose> 4 months (>4) and third doses (3D) groups.

Figure 1. Significant difference (p<.05) and trend between IC, <4, >4, 3D for the ICU variable.

Table 1.

Figure 2. Significant difference (p<.05) and trend between IC, <4, >4, 3D for the deaths variable.

Table 2.

Comparison between Unvaccinated (UV) and Vaccinated (V) groups

Figure 3. Significant difference (p<.05) and trend between UV versus V for the ICU variable.

Table 3.

Figure 4. Significant difference (p<.05) and trend between UV versus V for the deaths variable.

Table 4.

Comparison amongst Incomplete Cycle (IC), double dose <4 months (<4), double dose> 4 months (>4) and third doses (3D) groups, Unvaccinated (UV) and Vaccinated (V) groups for all age groups (see Extended data)

Table 5.

Table 6.

Discussion

Conclusion

Data availability

Underlying data

Extended data

References

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