Protective effect conferred by prior infection and vaccination on COVID-19 in a healthcare worker cohort in South India.

BACKGROUND: The emergence of newer variants with the immune escape potential raises concerns about breakthroughs and re-infections resulting in future waves of infection. We examined the protective effect of prior COVID-19 disease and vaccination on infection rates among a cohort of healthcare workers (HCW) in South India during the second wave driven mainly by the delta variant. METHODS AND
FINDINGS: Symptomatic HCWs were routinely tested by RT-PCR as per institutional policy. Vaccination was offered to all HCWs in late January, and the details were documented. We set up a non-concurrent cohort to document infection rates and estimated protective efficacy of prior infection and vaccination between 16th Apr to 31st May 2021, using a Cox proportional hazards model with time-varying covariates adjusting for daily incidence. Between June 2020 and May 2021, 2735 (23.9%) of 11,405 HCWs were infected, with 1412, including 32 re-infections, reported during the second wave. 6863 HCWs received two doses of vaccine and 1905 one dose. The protective efficacy of prior infection against symptomatic infection was 86.0% (95% CI 76.7%-91.6%). Vaccination combined with prior infection provided 91.1% (95% CI 84.1%-94.9%) efficacy. In the absence of prior infection, vaccine efficacy against symptomatic infection during the second wave was 31.8% (95% CI 23.5%- 39.1%).
CONCLUSIONS: Prior infection provided substantial protection against symptomatic re-infection and severe disease during a delta variant driven second wave in a cohort of health care workers.

Introduction

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infected 27 Million people and caused 0.3 Million deaths till May 2021 in India [1]. The dramatic increase in the number of cases during the second wave of the COVID-19 pandemic was attributed to multiple factors like a large susceptible unvaccinated population, lack of durability of an immune response, variants and mutants with their enhanced disease transmission and virulence capabilities [2]. SARS-CoV2 infection produces variable levels of antibodies in humans based on the nature of infection i.e. asymptomatic or symptomatic, the severity of illness, and the host’s immune response [3, 4]. Most studies published on humoral antibody responses to SARS-CoV2 infection have shown that the persistence of spike protein antibodies in COVID-19 infected persons range from 3 to 10 months [5, 6]. In a study conducted among health care workers (HCW) in Oxford university hospitals, United Kingdom, there were no symptomatic infections reported among HCW who had detectable levels of anti-spike antibodies [7]. As the duration of antibody persistence differs across the population, the risk of re-infection remains a concern. In addition, it is unclear whether the presence of anti-spike antibodies can be correlated with the presence of protective neutralising antibodies. COVID-19 re-infection in a previously infected individual can occur either due to decay in the antibody response or due to emerging new variants or mutants. The emerging mutants have raised concerns globally with respect to their transmission rates, the severity of infection and escape from immunity [8]. SARS-CoV2 variants of concern identified so far, have shown either slightly reduced or a potential reduction in neutralisation by post-vaccination sera [8-10]. The newer variants circulating in India, namely B.1.617, B.1.617.1, B.1.617.2, B.1.617.3, and now B.1.529, have been of great concern as they can evade neutralising antibodies [11], thereby causing re-infections among previously infected persons causing breakthrough infections in vaccinated individuals. Evidence on the durability of the protective immune response after COVID-19 vaccination and prior infection is still evolving. In addition, studying the protective effect of vaccination in a population already exposed or infected will not reflect the actual contribution of protective effect that a previous infection confers or adds to vaccination. Hence, we conducted this study to look at the rate of COVID-19 re-infections and the protective effect of a previous COVID-19 infection among a cohort of HCW in a tertiary care institution located in South India.

Materials and methods

This study was approved by the institutional ethics committee and research board (IRB Min no. 13980). This non-concurrent cohort study was conducted among the staff of Christian Medical College, Vellore, a 2600 bedded tertiary care institution located in South India, and examined the incidence and predictors of infection during the second wave. Students and temporary workers were excluded from the study as they were not on the payroll database and were not required to be at the institution during the study period. Participant data has been anonymized and administrative approval for sharing the deidentified HCW data has been obtained. Hence our institution`s ethical committee has waived consent requirements. As per institutional protocol, employees were required to monitor symptoms and report to the staff health services if they developed symptoms of COVID-19, where a nasopharyngeal specimen was obtained and tested for SARS-COV2 using an RT-PCR assay (RealStar® SARS-CoV2 RT-PCR kit 1.0, Altona Diagnostics). Since staff are offered comprehensive healthcare free of cost, all HCW with COVID-19 infections were treated in our institution. All staff who undergo COVID testing are registered through a single portal and positive results are captured from the laboratory registry. Health care worker found to have Influenza Like Illness (ILI) symptoms i.e., fever and cough ± ILI symptoms) or Severe Acute Respiratory Infection (SARI) symptoms i.e., (fever, cough and breathlessness) with laboratory confirmed COVID-19 infection were defined as having previous infection. HCW who developed a laboratory confirmed COVID-19 infection 12 weeks from the date of first positive infection were considered to have a re-infection. The demographic, clinical and exposure variables and vaccination history were prospectively documented in an electronic database from all those presenting for COVID testing. The severity of COVID-19 infection was assessed by the WHO severity scale (May 2020) [12]. All patient related data were abstracted from medical records.

In late January 2021, the institution organised a systematic effort to vaccinate all staff against SARS-COV2. The vaccines given were ChAdOx1 nCoV-19 Corona Virus Vaccine COVISHIELD™ and the whole virion inactivated BBV152 vaccine COVAXIN™. All immunisation was documented along with the date of vaccination, type of vaccine and any adverse events. Linking the SARS-COV2 testing data set with the vaccination and administrative payroll information, we established a non-concurrent cohort that included all current employees. Every employee has an unique employment ID which was used to match across the datasets. Two investigators independently assessed the datasets to verify the accuracy of the data and linkages between the datasets. Cases of SARS-COV2 that were detected before 1st April 2021 were considered as the cases that occurred prior to the second wave. Cases emerging between 1st April 2021, and 31st May 2021, were part of the large second wave that coincided with the emergence of the Delta variant. Participants were considered fully immunised 14 days after the second dose of the vaccine and partially immune 21 days after the first dose of the vaccine. Since most employees received their vaccination just before the second wave’s onset and were considered immune by 16th April 2021, we used this date for entry into a survival analysis and to establish the baseline risk status.

Statistical analysis

Those who were either unvaccinated or had not completed 14 days after the second dose by entry, were considered unvaccinated. Anyone who reported a RT-PCR confirmed SARS-COV2 infection previously were considered to have been previously infected. Participants were categorised into four risk groups based on their prior infection and vaccination status, namely, the unvaccinated and previously uninfected; vaccinated and previously uninfected; unvaccinated and previously infected; and vaccinated and previously infected. A sensitivity analysis that excluded participants who had received a single dose was not significantly different from the one that included those received one dose as unvaccinated. Hence the binary classification of vaccinated and unvaccinated was based on the completion of two doses of vaccination 2 weeks after the second dose.

Kaplan Meier Survival analysis was done with failure defined as the acquisition of infection during the analysis period. A Log-rank test was performed to compare the survival curves across the four risk groups. We developed a Cox-proportional hazards (PH) model with time-varying covariates adjusting for smoothed daily incidence of COVID-19 and potential confounders (S1 Table). The model included participant age, type of work, sex, history of prior infection and vaccination, as epidemiologically relevant factors. The model was tested for the proportional-hazards assumption on Schoenfeld’s residuals and the PH assumption was not violated (p value—0.134). Efficacy of prior infection and vaccines to prevent symptomatic infection in the study period were calculated as VE = 1- hazard ratio from the Cox proportional hazard model. All data analysis was performed using Stata 15.1 (Statacorp LLC, College Station, TX).

Results

Between 1st June 2020 to 31st May 2021, 11405 health care workers were on the payroll. 41% of the study population are males. The median [IQR] for age in the study cohort is 33.9 years [27.8–42.5 years] (S1 Fig). Among the professional categories, 33.64% were nurses, 18.59% support staff, 17.53% doctors, 14.86% hospital attendants, 9.34% technicians, 3.06% pharmacists and 2.97% clerical staff (Table 1). By 31st May 2021, 2735 (23.9%) developed COVID-19 infection (Fig 1). 1355 HCW were infected with COVID-19 infection prior to second wave and 1380 in the second wave. Assessing the severity of COVID-19 infections, 98.0%, 0.7% and 1.3% belonged to mild, moderate and severe/critical categories in the first wave and 99.44%, 0.2% and 0.4% in the second wave, respectively.

Table 1

Demographic details of the health care workers cohorted based on COVID results.

Characteristics	Positive cohort (n = 2735)	Negative cohort (n = 8670)	All health care workers (n = 11405)
Gender
Male	1094 (40.0%)	3580 (41.3%)	4674 (41.0%)
Female	1641(60.0%)	5090 (58.7%)	6731 (59.0%)
Age
Less than 30 years	3078 (35.5%)	868 (31.7%)	3946 (34.6%)
30 to 39 years	2955 (34.1%)	967 (35.4%)	3922 (34.4%)
40 to 49 years	1785 (20.6%)	621 (22.7%)	2406 (21.1%)
50 to 59 years	807 (9.3%)	277 (10.1%)	1084 (9.5%)
60 years or older	45 (0.5%)	2 (0.1%)	47 (0.4%)
Median (IQR)	33.7 (27.7–42.4)	34.6 (28.5–42.9)	33.9 (27.8–42.5)
Professional category
Consultant doctor	146 (5.34%)	664 (7.66%)	810 (7.10%)
Trainee doctor	243 (8.88%)	947 (10.92%)	1190 (10.43%)
Nursing	1031 (37.70%)	2806 (32.36%)	3837 (33.64%)
Technician	232 (8.48%)	833 (9.61%)	1065 (9.34%)
Pharmacist	87 (3.18%)	262 (3.02%	349 (3.06%)
Attendant	424 (15.50%)	1271 (14.66%)	1695 (14.86%)
Clerical staff	93 (3.40%)	246 (2.84%)	339 (2.97%)
Support staff/others	479 (17.51%)	1641 (18.93%)	2120 (18.59%)

Fig 1

Flow diagram.

* received two doses of vaccine by 2nd April 2021.

During the second wave, 32 out of 1355 (2.36%) previously infected HCW were re-infected, including 28 between 16th April and 31st May 2021. The median duration from the first infection to the second infection was 258.5 days. All 32 cases of re-infection were mild. One patient with re-infection was hospitalised for an unrelated indication. Fourteen of the 32 re-infected cases had received two doses of vaccination at least two weeks before being detected to have COVID-19.

The vaccination campaign from January 2021 successfully vaccinated 8768 HCW (76.9%) by 31st May 2021. The vaccines used were ChAdOx1 nCoV-19 Corona Virus Vaccine COVISHIELD™ (94.23%) and the whole virion inactivated BBV152 vaccine COVAXIN™ (5.77%). Among the vaccinated individuals, 1905 received one dose while 6863 had completed two doses of vaccination. The temporal trends of COVID-19 infection and vaccination are presented in Fig 2. Between 16th April 2021 to 31st May 2021, the cumulative incidence risk of COVID-19 infection among those previously uninfected was 14.9% if unvaccinated and 11.1% when vaccinated, respectively. Among those previously infected, the cumulative incidence in the unvaccinated was 2.1% and 1.4% in the vaccinated (S2 Fig). The Kaplan Meier survival curve in Fig 3 compares the unadjusted incidence rates across vaccination and prior infection status. All those included in the analysis were present from the beginning of the study to the end. There was no mortality during the study period.

Fig 2

Temporal trends of COVID-19 infection and vaccination in our study cohort.

Fig 3

Incidence rates of COVID-19 based on vaccination and prior infection.

The protective efficacy of prior infection against symptomatic infection was 86.0% (95%CI 76.7%–91.6%). Vaccination combined with prior infection provided 91.1% (95%CI 84.1%–94.9%) efficacy. In the absence of prior infection, vaccine efficacy against symptomatic infection during the second wave was 31.8% (95%CI 23.5%– 39.1%). The minimum interval between two doses of vaccine was one month but varied among different individuals. Sample size was inadequate to look at VE for varied intervals.

Of the 2760 episodes of COVID-19, 35 (1.3%) were moderate to severe episodes. The episodes during the second wave were milder with only 8 of 1412 episodes (0.57%) being classified as moderate or severe as compared to 27 of 1355 episodes (2.0%) prior to second wave.

Discussion

In this large single-centre cohort study of HCW, 32 were re-infected (2.36%) in the second wave. A systematic review conducted by SeyedAlinaghi et al., 2020 evaluated 31 studies, of which only 8 reported re-infections in a previously infected and recovered group of patients [13]. Another large scale multicentric study on health care workers (SIREN study) published recently by Hall et al., 2021 in the United Kingdom that showed the incidence risk ratio for all re-infections was 0.159 compared with PCR-confirmed primary infections [14]. The re-infection rates in our study population is slightly higher than reported by SIREN study, probably due to the B.1.617.2 delta variant predominance in India during the second wave. An invitro neutralisation study performed with sera from the convalescent and vaccinated individuals, revealed that the delta variant B.1.617.2 has increased resistance to neutralisation in sera from convalescent individuals who were unvaccinated vs those who were vaccinated [11, 15].

In our study, the estimates for overall protection after COVID-19 infection against a repeat infection was 86.0% which is supported by other studies conducted in UK, Denmark, and the USA [14, 16, 17]. The protective efficacy of COVID-19 vaccination in various studies is reported to be between 60 to 90% based on the type of vaccine and the doses received. However, this efficacy is likely to be the result of a robust immune response contributed to both by the previous infection and vaccination and not the vaccine alone. Our study looked at a captive population of HCW of age group 18 to 85 years who were closely monitored for development of COVID-19 infections both before and after vaccination. With an adequate testing facility and appropriate documentation of COVID-19 infection, we were able to explore the protective effect of a previous COVID-19 infection. Hence the high efficacy of vaccination of 91.1% is likely to be due to the combined protective effect of previous infection and vaccination together rather than vaccination alone.

We found a lower severity of COVID-19 infection during the second wave 0.4% as compared to 1.3% in the first wave. Age did not seem to influence the risk of acquiring infection in our study as the median age of health care workers in our cohort was 33.9 years. This younger age was in variance with other large health care cohort study (SIREN) reported from developed countries which reported a median of 45.7 years [IQR 35·4–53·5] but in keeping with that reported in general population and in HCW in India [18, 19].

A recently published study from England has shown that the adjusted vaccine efficacy of ChAdOx1 nCoV-19 two doses against alpha variant was 74.5% (95% CI 68.4 to 79.4) but drops against delta variant with one dose and two doses offering 30.0% (24.3–35.3) and 67.0% protection (61.3–71.8) respectively [20]. Even though, the protective efficacy of vaccination against the delta variant differs based on the type of vaccine and study population, the impact of vaccination in bringing down the hospitalisation rates and severity of illness is of utmost importance.

This study supports the premise that a robust immune response is produced by a natural COVID-19 infection and an additional protective effect is contributed to by vaccination. This maybe important to inform public health policies for optimisation of vaccination campaigns targeting the HCW and then unaffected epidemiological areas first followed by the recently affected hotspots during the second wave. As the immunity lasts for 3–10 months [5, 6], mass vaccination should be targeted at districts/states with a lower rate of seroconversion on a priority basis before newer variants emerge.

Limitations

We noted several limitations. Firstly, we were unable to use the standard definition of re-infections, as we do not do routine genomic sequencing for all infections, vaccine breakthroughs or even re infections. Hence all these cases were considered as probable re-infections if infections occurred greater than 12 weeks from a previous infection with a Ct value <33 in all those considered re infected. Secondly, it is unclear whether these figures could be extrapolated to the community as HCW as a population are at a constantly higher risk of exposure and in our hospital belonging to a younger age group. Since our hospital policy mandated compulsory testing, if symptomatic, it is likely that we detected a higher rate of infection [21, 22]. Thirdly, though HCW with symptoms or a close contact with a COVID-19 case were tested,we probably missed asymptomatic infections. Hence assessment of vaccine efficacy may have been confounded by prior asymptomatic infection that was undetected. This could have impacted the vaccine efficacy in either direction. It could have decreased the incidence of a second infection, in those labelled as uninfected or it could have potentiated the protection of vaccinated individuals who may have had undetected asymptomatic previous infection.

Conclusion

Natural COVID-19 infection likely produces a robust immune response with 86% protection against re infection. However, when previously infected individuals are vaccinated, the protective efficacy goes up to 91%. Vaccination should be encouraged irrespective of a prior COVID-19 infection.

Age histogram of the study cohort.

(TIF)

Click here for additional data file.

Unadjusted incidence of symptomatic COVID-19 infection by vaccination and previous infection status.

(TIF)

Click here for additional data file.

Cox-proportional hazards (PH) model.

(DOCX)

Click here for additional data file.

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31 Jan 2022

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Reviewer #1: The paper addresses an important question and the data is relevant, but several questions still need to be addressed before considering publication.

1/ More detailed informations is needed on the patient caracteristics with respect to target population, e.g. is the cohort younger/older, has it more co-morbidities, etc; unless I missed a table, this data is simply absent. Please give mean/median, min/max/quartiles,std (where applicable) etc. information. This is even more a requirement given the Cox analysis latter that

includes e.g. age as confounder.

2/ More data would be welcome to quantify the severity of the infection: do you have any viral load data in order to quantify it ? Or maybe the number of PCR cycles ...

3/ In principle everybody is convinced that initial infection provides a protection against a challenge (secondary infection); in order to be useful the work needs to give more quantitative insights, for instance is there any data available to correlate, in a quantitative manner protection with antibody level following a previous infection ?

Any other similar data welcome.

3/ Technical question: I'm not sure I understood the Cox results in suppl. table 1: for instance, for age HR turns out to be 1.0, or I would expect the age to have a really visible impact. Please comment this into the discussion section.

4/ On the other hand, if (see below) the age in the cohort is really so centered in the 30-40years region, maybe age is not so important in this range. Please provide a FULL age distribution histogram.

5/ This should be listed more clearly as a limitation of the study. Just to be sure, can you positively confirm that age and the other confounders in suppl. table 1. were indeed considered as parts of a MULTIVARIATE Cox analysis ?

6/ Also, I see no p-values in the results ... Or is maybe "standard error" column in fact a "p-value" column ?

Small typo (in many places) and remarks:

- replace "cox" by "Cox".

- in the table 1 "Vaccinated Infected 0.90" I guess the value is not "0.90" but rather "0.10" or "0.09" ; please check very thoroughly

- second line in "Results": what does "median IQR for age" means ? is 33.9 the median and 27.8 Q1 and 42.5 the Q3 ? If so, say so, the actual formulation is incorrect, a minimalistic change is to write "median (IQR)"

- is it possible to provide data in a more detailed manner (e.g. as a supplementary online data file) ?

Reviewer #2: The authors present a well studied HCW cohort and looked at the protective effect of vaccination and prior infection. I think it is a very interesting study, and a unique cohort to untangle the effects of vaccination vs prior infection. It was very well written and very clearly explained.

The limitations of the study are all acknowledged, but could have been expanded further in the discussion as to how they effect the results. For example, you mention in the limitations you could only look at symptomatic infections, but what impact might this have on your results? i.e., your reference group is unvaccinated, no prior infection - but some of those may have in fact had asymptomatic infections, so how might that influence your results? The same with those who are vaccinated, no prior infection. You could even consider doing a sensitivity to see what effect it would have if XX percent of those who report as not infected were in fact previously infected. This is only a suggestion, and I do not think it is an absolute requirement, but I do think more discussion on this limitation would help readers better understand the context of these results. You also mention about the fact that HCW are likely to have repeated exposures - but how might that effect the results?

Furthermore, you report a much lower vaccine efficacy in this study compared to previous studies, and I think this could be expanded on a little in the discussion. Why do you believe that vaccine efficacy (no prior infection) was so much lower- was this because you looked at it in isolation? And is this different to how other studies have looked at vaccine efficacy? And what effect does the fact you've looked HCW (with repeated exposures) have (if any_?

Minor points:

I thought the methods were very well written and clear. I just found the description of the cohort study a little confusing at times understanding the start and end dates. From my understanding you had a cohort between 16th April to 30th/31st May (second wave) 2021 and included all HCW. You then looked at infection events during this wave. Might be useful to have a sentence summing this up at the end of your descriptions of the cohort. (Also, minor point you mention 30th May in abstract but 31st in methods).

I think in Supplementary table 1 it should maybe say "prior infection" rather than infection, and it could be a little confusing. I also think the reference group (which I believe is the unvaccinated, no prior infection) should be clearly stated - perhaps below the table.

I think there's a mistake in Supplementary table 1 - the 95% confidence intervals for "vaccinated infected" group are smaller than the hazard ratio?

There are some details missing from the methods (which are mentioned in the discussion) but I think it would help to have them in the methods:

- Type of testing done, and a clearer explanation of who was eligible (symptomatic and if you have a positive household contact?

- Vaccine type

Previous infection (what was classified as a previous infection)

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18 Feb 2022

RESPONSE TO REVIEWER COMMENTS & QUERIES

Manuscript ID

PONE-D-21-35055

Reviewer #1:

Major points:

The paper addresses an important question and the data is relevant, but several questions still need to be addressed before considering publication.

1. More detailed information’s is needed on the patient characteristics with respect to target population, e.g. is the cohort younger/older, has it more co-morbidities, etc; unless I missed a table, this data is simply absent. Please give mean/median, min/max/quartiles, std (where applicable) etc. information. This is even more a requirement given the Cox analysis latter that includes e.g. age as confounder.

Authors` response: We thank you for your suggestions. As mentioned by the reviewer, age of the target population was already present in the manuscript, however for clarity, we have added the patient characteristics (Age stratification, professional category and gender) in table 1.

2. More data would be welcome to quantify the severity of the infection: do you have any viral load data in order to quantify it ? Or maybe the number of PCR cycles.

Authors` response: The Indian Council of Medical Research, did not recommend cycle threshold (ct) values be used as an indicator of clinical severity as many sampling or interpretation (pre-analytical and analytical) factors can influence the ct values. Therefore, viral loads are not a suitable correlate of severity of infection. Hence, we have provided data based on the WHO clinical severity scale which is a clinically robust assessment of baseline clinical status and likely to be far more accurate.

Reference:https://www.icmr.gov.in/pdf/covid/techdoc/Advisory_on_correlation_of_COVID_severity_with_Ct_values.pdf

3. In principle everybody is convinced that initial infection provides a protection against a challenge (secondary infection); in order to be useful the work needs to give more quantitative insights, for instance is there any data available to correlate, in a quantitative manner protection with antibody level following a previous infection ?

Any other similar data welcome.

Authors` response: At the point when this data was collected, there was inadequate recognition of the impact of prior infection on subsequent infection (study was submitted in November 2021). This study was uniquely able to establish based on RT-PCR confirmed infections, that previously infected individuals were protected from delta infection independent of the vaccination status. We believe that this is mediated in a large part by the cellular immunity in addition to the humoral response to an exposure. Quantitative antibody assays were not performed in this cohort and are not recommended as per CDC.

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

4. Technical question: I'm not sure I understood the Cox results in suppl. table 1: for instance, for age HR turns out to be 1.0, or I would expect the age to have a really visible impact. Please comment this into the discussion section.

Authors` response: Age does not seem to influence the risk of acquiring infection in our study as the median age of health care workers in our cohort was 33.9 years which is a relatively younger age group. However, we agree with the reviewer that it has been shown in peer reviewed literature that older age does correlate with an increased risk of acquisition of infection and progression to severe disease.

We have included this in the discussion section.

5. On the other hand, if (see below) the age in the cohort is really so centered in the 30-40years region, maybe age is not so important in this range. Please provide a FULL age distribution histogram.

Authors` response: Age distribution has been provided in the table 1. A histogram has been provided as a supplementary material (figure 2).

6. This should be listed more clearly as a limitation of the study. Just to be sure, can you positively confirm that age and the other confounders in suppl. table 1. were indeed considered as parts of a MULTIVARIATE Cox analysis ?

Authors` response: We do not think the fact that age was not correlating with the risk of acquisition of infection is a limitation, we feel that this is so because of the predominantly younger cohort in our study. The raw data has been shared already and we are happy to share the codes for statistical analysis.

7. Also, I see no p-values in the results ... Or is maybe "standard error" column in fact a "p-value" column ?

Authors` response: The SE column is not a p value column. We did not add the p value as it is a non-randomized comparison and it would be statistically inappropriate to provide p values here. However, we can remove the SE column and replace with the p value column if reviewer feels that is necessary.

Minor points:

8. Small typo (in many places) and remarks - replace "cox" by "Cox".

Authors` response: We have corrected the typo.

9. in the table 1 "Vaccinated Infected 0.90" I guess the value is not "0.90" but rather "0.10" or "0.09" ; please check very thoroughly

Authors` response: We thank you for pointing out the error. The hazard ratio is 0.09 (95 % CI 0.05-0.159). We have corrected it in the manuscript.

10. second line in "Results": what does "median IQR for age" means ? is 33.9 the median and 27.8 Q1 and 42.5 the Q3 ? If so, say so, the actual formulation is incorrect, a minimalistic change is to write "median (IQR)"

Authors` response: The changes has been made as median (IQR).

11. is it possible to provide data in a more detailed manner (e.g. as a supplementary online data file) ?

Authors` response: Yes we can provide the dataset. We have uploaded the data and codes in data repository (Dryad - https://doi.org/10.5061/dryad.n8pk0p2x8) and its under private view.

Reviewer #2:

Major points:

1. The authors present a well-studied HCW cohort and looked at the protective effect of vaccination and prior infection. I think it is a very interesting study, and a unique cohort to untangle the effects of vaccination vs prior infection. It was very well written and very clearly explained. The limitations of the study are all acknowledged, but could have been expanded further in the discussion as to how they effect the results. For example, you mention in the limitations you could only look at symptomatic infections, but what impact might this have on your results? i.e., your reference group is unvaccinated, no prior infection - but some of those may have in fact had asymptomatic infections, so how might that influence your results? The same with those who are vaccinated, no prior infection. You could even consider doing a sensitivity to see what effect it would have if XX percent of those who report as not infected were in fact previously infected. This is only a suggestion, and I do not think it is an absolute requirement, but I do think more discussion on this limitation would help readers better understand the context of these results. You also mention about the fact that HCW are likely to have repeated exposures - but how might that effect the results?

Authors` response: We thank the reviewer for a positive feedback and suggestions. As mentioned by the reviewer, the assessment of vaccine efficacy may have been confounded by prior asymptomatic infection that was undetected. This could have impacted the vaccine efficacy in either direction. It could have decreased the incidence of a second infection, in those labelled as uninfected or it could have potentiated the protection of vaccinated individuals who may have had undetected asymptomatic previous infection. It is therefore difficult to speculate in which direction or the magnitude of impact undetected infections would have had on apparent VE.

We have added this in the discussion.

2. Furthermore, you report a much lower vaccine efficacy in this study compared to previous studies, and I think this could be expanded on a little in the discussion. Why do you believe that vaccine efficacy (no prior infection) was so much lower- was this because you looked at it in isolation? And is this different to how other studies have looked at vaccine efficacy? And what effect does the fact you've looked HCW (with repeated exposures) have (if any_? )

Authors` response: Our population is provided health care free of cost and hence we were able to obtain clear datasets of health care workers who were vaccinated vs unvaccinated and a documentation of every HCW infection in our system. Therefore, we were indeed able to tease out/isolate the effect that a previous infection could have on acquisition on subsequent infection in vaccinated vs unvaccinated individuals.

In addition, we have looked at the protective efficacy of vaccination during the epidemic caused by the delta variant which could have contributed to lower efficacy as well. Lower vaccine efficacy in infections due to the delta variant has been shown in other studies as well.

Minor points:

3. I thought the methods were very well written and clear. I just found the description of the cohort study a little confusing at times understanding the start and end dates. From my understanding you had a cohort between 16th April to 30th/31st May (second wave) 2021 and included all HCW. You then looked at infection events during this wave. Might be useful to have a sentence summing this up at the end of your descriptions of the cohort. (Also, minor point you mention 30th May in abstract but 31st in methods).

Authors` response: We thank you for pointing out the error. We have done the changes. We have attempted to clarify this in the manuscript.

4. I think in Supplementary table 1 it should maybe say "prior infection" rather than infection, and it could be a little confusing. I also think the reference group (which I believe is the unvaccinated, no prior infection) should be clearly stated - perhaps below the table.

Authors` response: We have done the changes.

5. I think there's a mistake in Supplementary table 1 - the 95% confidence intervals for "vaccinated infected" group are smaller than the hazard ratio?

Authors` response: We thank you for pointing out the error. The hazard ratio is 0.09 (95 % CI 0.05-0.159). We have corrected it in the manuscript.

6. There are some details missing from the methods (which are mentioned in the discussion) but I think it would help to have them in the methods:

• Type of testing done, and a clearer explanation of who was eligible (symptomatic and if you have a positive household contact?

• Vaccine type - To be added in the methodology section

• Previous infection (what was classified as a previous infection)

Authors` response: As suggested by the reviewer, we have added the above details in the methods section.

Submitted filename: Response to Reviewers .docx

Click here for additional data file.

24 Mar 2022

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #1: Following the initial review the authors corrected a good deal of criticism. However several points remain which are, for me, crucial to be addressed:

- the data was not made available, NOT EVEN TO THE REVIEWERS because the link the authors provide is not working: when trying to access it I get "DOI Not Found" the error; let me be very clear about this: if the next time the link is still not working I will be rejecting the paper for good. Here is the link provided by authors : Dryad - https://doi.org/10.5061/dryad.n8pk0p2x8

- there are severe limitations of the study because of the age homogeneity (everything concerns people with similar ages); these limitations should be VERY CLEARLY STATED : a) in the abstract, b) in the discussion section, c) when discussing the Cox regression, d) when discussing any generalization of the findings to another population.

Reviewer #2: (No Response)

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Reviewer #2: No

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30 Apr 2022

Response to the Reviewers` Comments

Manuscript ID: PONE-D-21-35055

Reviewer #1:

1. The data was not made available, NOT EVEN TO THE REVIEWERS because the link the authors provide is not working: when trying to access it I get "DOI Not Found" the error; let me be very clear about this: if the next time the link is still not working I will be rejecting the paper for good. Here is the link provided by authors : Dryad - https://doi.org/10.5061/dryad.n8pk0p2x8

Response:

The authors sincerely apologize for the trouble in the link. We have shared the private review link in the cover letter dated 18.02.2022. (https://datadryad.org/stash/share/fNoszj0lxymYnzlkSzW004Gu16B8UYYReQn8yqM6GSo) This dataset has been submitted to Dryad on 18.02.2022 and it was under the curation process. The DOI mentioned would have been activated only after acceptance of article, curation of the dataset and payment to the data repository. We have requested for an expedited data curation and we are happy to share that the data is now published on 17.04.2022. The variables which are considered as direct and indirect identifiers were removed as mandated by the data repository requirements. We are happy to share the entire dataset (excel) to the reviewer by email, if needed.

Link to access the dataset: https://doi.org/10.5061/dryad.n8pk0p2x8

2. There are severe limitations of the study because of the age homogeneity (everything concerns people with similar ages); these limitations should be VERY CLEARLY STATED : a) in the abstract, b) in the discussion section, c) when discussing the Cox regression, d) when discussing any generalization of the findings to another population.

Response:

The age distribution in our cohort varies mainly from 18-85 years of age which we feel is a fairly good distribution for HCW [Ref supplementary figure 1 which was provided on suggestion from the reviewer] and therefore we do not consider it as a severe limitation. The retirement age is 60 in our hospital and few employees contribute voluntary work after the retirement age (even upto 85 years). The age range is in keeping with other HCW studies in our country as we do have a younger work force when compared to western population (Eg SIREN study [1] which report a median age 45·7 years [IQR 35·4–53·5]).We do agree that median of 33.9 years of age does indicate a younger population but this is in keeping with the trend observed with COVID incidence in the general population in India (please note below)and hence we cannot categorize this as a severe limitation. Similar trends have been shown in studies published from India.

A study by Kushwaha et al., 2021 [2] showed that the mean age of all COVID-19 patients was 39.47 ± 17.59 years and the difference between the mean age of males (39.98 ± 17.19 years) and females (38.50 ± 18.29 years) in a cohort of 112,860 covid positive patients in India. Another study published by Chatterjee et al., 2020 [3] reported that the mean age of the cases was 34.73 yr [±standard deviation (SD): 9.64; median: 33.0; interquartile range (IQR): 27-40] in a health care worker cohort. Another study by Reddy et al., 2021 aimed at looking the age difference between the first and second wave of pandemic in India by analyzing a total of 2,19,832 and 2,34,815 samples respectively [4]. The mean age during the first and the second wave were 35.1 ± 15.9 years and 46.1 ± 16.8 years respectively.

As suggested by the reviewer, we have clearly mentioned this as a possible limitation for generalization to the community at large in the “Limitations” section (Ref: page no 11; line number 287-289) and compared it with other studies in the discussion section (Ref: page no 10; line number 261-264). We do not think it merits mention in the abstract.

References:

1. Hall VJ, Foulkes S, Charlett A, Atti A, Monk EJM, Simmons R, et al. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN). The Lancet. 2021;397: 1459–1469. doi:10.1016/S0140-6736(21)00675-9

2. Kushwaha S, Khanna P, Rajagopal V, Kiran T. Biological attributes of age and gender variations in Indian COVID-19 cases: A retrospective data analysis. Clin Epidemiol Glob Health. 2021;11: 100788. doi:10.1016/j.cegh.2021.100788

3. Chatterjee P, Anand T, Singh KhJ, Rasaily R, Singh R, Das S, et al. Healthcare workers & SARS-CoV-2 infection in India: A case-control investigation in the time of COVID-19. Indian J Med Res. 2020;151: 459–467. doi:10.4103/ijmr.IJMR_2234_20

4. Reddy MM, Zaman K, Mishra SK, Yadav P, Kant R. Differences in age distribution in first and second waves of COVID-19 in eastern Uttar Pradesh, India. Diabetes Metab Syndr. 2021;15: 102327. doi:10.1016/j.dsx.2021.102327

Submitted filename: Response to the Reviewers R2.docx

Click here for additional data file.

9 May 2022

Protective effect conferred by prior infection and vaccination on COVID-19 in a Healthcare Worker Cohort in South India

PONE-D-21-35055R2

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12 May 2022

PONE-D-21-35055R2

Protective effect conferred by prior infection and vaccination on COVID-19 in a healthcare worker cohort in South India

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