DOI: 10.5061/dryad.n8pk0p2x8
title: Protective effect conferred by prior infection and vaccination on COVID-19 in a healthcare worker cohort in South India

description: 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. 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%). 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.
description: This non-concurrent cohort
 study was conducted among the staff of a tertiary care teaching hospital
 in South India. The demographic, clinical and exposure
 variables and vaccination history were prospectively documented in an
 electronic database from all those presenting for COVID testing. All immunization 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 a 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. Participants were categorized 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 who 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.
description: 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).