Assessing healthy vaccinee effect in COVID-19 vaccine effectiveness studies: a national cohort study in Qatar

Hiam Chemaitelly; Houssein H Ayoub; Peter Coyle; Patrick Tang; Mohammad R Hasan; Hadi M Yassine; Asmaa A Al Thani; Zaina Al-Kanaani; Einas Al-Kuwari; Andrew Jeremijenko; Anvar Hassan Kaleeckal; Ali Nizar Latif; Riyazuddin Mohammad Shaik; Hanan F Abdul-Rahim; Gheyath K Nasrallah; Mohamed Ghaith Al-Kuwari; Hamad Eid Al-Romaihi; Mohamed H Al-Thani; Abdullatif Al-Khal; Roberto Bertollini; Adeel A Butt; Laith J Abu-Raddad

doi:10.7554/eLife.103690

. 2025 Jun 9;14:e103690. doi: 10.7554/eLife.103690

Assessing healthy vaccinee effect in COVID-19 vaccine effectiveness studies: a national cohort study in Qatar

Hiam Chemaitelly ^1,^2,^3,^✉, Houssein H Ayoub ⁴, Peter Coyle ^5,^6,⁷, Patrick Tang ⁸, Mohammad R Hasan ⁹, Hadi M Yassine ^5,¹⁰, Asmaa A Al Thani ^5,¹⁰, Zaina Al-Kanaani ⁶, Einas Al-Kuwari ⁶, Andrew Jeremijenko ⁶, Anvar Hassan Kaleeckal ⁶, Ali Nizar Latif ⁶, Riyazuddin Mohammad Shaik ⁶, Hanan F Abdul-Rahim ¹¹, Gheyath K Nasrallah ^5,¹⁰, Mohamed Ghaith Al-Kuwari ¹², Hamad Eid Al-Romaihi ¹³, Mohamed H Al-Thani ¹³, Abdullatif Al-Khal ⁶, Roberto Bertollini ¹², Adeel A Butt ^3,^6,¹⁴, Laith J Abu-Raddad ^2,^3,^11,^15,^✉

Editors: Joshua T Schiffer¹⁶, Joshua T Schiffer¹⁷

PMCID: PMC12148324 PMID: 40488740

Abstract

Background:

This study investigated the presence of the healthy vaccinee effect—the imbalance in health status between vaccinated and unvaccinated individuals—in two rigorously conducted COVID-19 vaccine effectiveness studies involving primary series and booster vaccinations. It also examined the temporal patterns and variability of this effect across different subpopulations by analyzing the association between COVID-19 vaccination and non-COVID-19 mortality in Qatar.

Methods:

Two matched, retrospective cohort studies assessed the incidence of non-COVID-19 death in national cohorts of individuals with a primary series vaccination versus no vaccination (two-dose analysis), and individuals with three-dose (booster) vaccination versus primary series vaccination (three-dose analysis), from January 5, 2021, to April 9, 2024.

Results:

The adjusted hazard ratio (aHR) for non-COVID-19 death was 0.76 (95% CI: 0.64–0.90) in the two-dose analysis and 0.85 (95% CI: 0.67–1.07) in the three-dose analysis. In the first 6 months of follow-up in the two-dose analysis, the aHR was 0.35 (95% CI: 0.27–0.46); however, the combined analysis of all subsequent periods showed an aHR of 1.52 (95% CI: 1.19–1.94). In the first 6 months of follow-up in the three-dose analysis, the aHR was 0.31 (95% CI: 0.20–0.50); however, the combined analysis of all subsequent periods showed an aHR of 1.37 (95% CI: 1.02–1.85). The overall effectiveness of the primary series and third-dose vaccinations against severe, critical, or fatal COVID-19 was 95.9% (95% CI: 94.0–97.1) and 34.1% (95% CI: –46.4–76.7), respectively. Subgroup analyses showed that the healthy vaccinee effect is pronounced among those aged 50 years and older and among those more clinically vulnerable to severe COVID-19.

Conclusions:

A pronounced healthy vaccinee effect was observed during the first 6 months following vaccination, despite meticulous cohort matching. This effect may have stemmed from a lower likelihood of vaccination among seriously ill, end-of-life individuals, and less mobile elderly populations.

Funding:

Biomedical Research Program and the Biostatistics, Epidemiology, and Biomathematics Research Core, and Junior Faculty Transition to Independence Program, all at Weill Cornell Medicine-Qatar, Qatar University, Ministry of Public Health, Hamad Medical Corporation, Sidra Medicine, Qatar Genome Programme, Qatar University Biomedical Research Center, and L’Oréal-UNESCO For Women In Science Middle East Regional Young Talents Program.

Research organism: Viruses

eLife digest

Before new vaccines are made widely available, their efficacy and safety are tested in laboratories and clinical trials. Once approved, researchers can continue to monitor how these vaccines perform in the ‘real world’ by analysing healthcare data. This can provide further insights over a longer timeframe and across a broader range of people.

However, these real-world analyses can be skewed by a range of factors. For example, if healthier people are more likely to receive the vaccine, researchers may overestimate its effectiveness due to fewer deaths or severe illnesses amongst that group. This ‘healthy vaccinee effect’ has been observed in influenza vaccines among older people, for example.

To determine whether the healthy vaccinee effect influenced COVID-19 vaccine studies, Chemaitelly et al. analysed national health records in Qatar. Groups of vaccinated and unvaccinated people were selected to have matching demographics, such as age, sex and number of preexisting conditions. Overall, the study confirmed strong protection from vaccination against severe forms of COVID-19.

However, the results showed that, compared to their unvaccinated peers with similar characteristics, vaccinated people were 65 per cent less likely to die due to reasons unrelated to COVID-19 in the next six months after having received the vaccination. This effect was even stronger among individuals aged 50 years or older, as well as those with clinical vulnerabilities. Chemaitelly et al. suggest that this likely reflects lower vaccine uptake among seriously ill, end-of-life individuals and less mobile older populations with short life expectancy. Future work may be needed to understand if this can be generalised to countries that differ from Qatar, whose population mainly consists of healthy adult migrant workers.

Introduction

While randomized controlled trials (RCTs) remain the gold standard for determining vaccine efficacy, they often have short follow-up durations, primarily involve healthy participants, and may cover only a limited range of clinical outcomes (Janiaud et al., 2021; Nelson et al., 2009; Simonsen et al., 2007). Real-world observational studies are frequently utilized to evaluate vaccine effectiveness beyond the controlled trial environment (Chambers, 2021; de Waure et al., 2024; Nelson et al., 2009). In these settings, diverse health statuses, variable health behaviors, and structural determinants can influence vaccine uptake, potentially biasing estimates of effectiveness (Chambers, 2021; de Waure et al., 2024; Nelson et al., 2009).

The accuracy of vaccine effectiveness estimates from observational studies can be affected by bias arising from two opposing effects: the indication effect and the healthy vaccinee effect (Remschmidt et al., 2015; Nelson et al., 2009). The indication effect occurs when individuals with underlying health conditions are more likely to receive vaccination, potentially leading to an underestimation of vaccine effectiveness (Remschmidt et al., 2015). Conversely, the healthy vaccinee effect occurs when healthier or health-conscious individuals are more likely to receive vaccination, leading to an overestimation of vaccine effectiveness (Remschmidt et al., 2015). Both effects can bias and skew effectiveness results by conflating health status with the protective effects of the vaccine (Remschmidt et al., 2015; Nelson et al., 2009).

Such effects have been documented in observational studies of influenza vaccine effectiveness (Remschmidt et al., 2015; Simonsen et al., 2005; Nelson et al., 2009; Jackson et al., 2006a), coronavirus disease 2019 (COVID-19) vaccine effectiveness (Fürst et al., 2024; Høeg et al., 2023; Xu et al., 2023), and prescriptive medication effectiveness (Nelson et al., 2009). In particular, studies have documented a strong healthy vaccinee effect in estimations of influenza vaccine effectiveness among the elderly (Jackson et al., 2006a; Nelson et al., 2009). While it is common for studies to control for such effect by adjusting for coexisting conditions based on administrative healthcare utilization databases, this approach may not sufficiently or properly adjust for this effect, as it may not capture the illness severity, recency, duration, or the functional status of individuals—factors that can confound the association between vaccination and health outcomes, particularly in the elderly (Jackson et al., 2006a; Remschmidt et al., 2015; Nelson et al., 2009). Assessing coexisting conditions based on database variables can also be affected by differential misclassification, as these variables not only reflect chronic diseases but also inherently measure utilization of health services (Nelson et al., 2009).

A notable feature of the healthy vaccinee effect is its potentially strong time dependence, most pronounced immediately after vaccination but gradually diminishing (Jackson et al., 2006a; Nelson et al., 2009). This trend is observed because seriously ill individuals, those with deteriorating health, and frail, less mobile elderly persons are less likely to be vaccinated, resulting in a higher short-term mortality risk among the unvaccinated (Jackson et al., 2006a; Nelson et al., 2009). Studies show that elderly individuals who are more mobile or have fewer functional limitations are more likely to be vaccinated (Nelson et al., 2009; Jackson et al., 2006b). For instance, one study demonstrated that the inability to bathe independently was associated with a 13-fold increase in mortality risk and a 52% reduced likelihood of receiving a vaccination (Jackson et al., 2006b). Over time, as the less functional and seriously ill individuals in the unvaccinated group die, the disparities between the vaccinated and unvaccinated groups diminish (Jackson et al., 2006a; Nelson et al., 2009). Additional changes in health status over time among members of both groups also contribute to this equilibration (Jackson et al., 2006a; Nelson et al., 2009).

In this national retrospective cohort study, the presence of the indication or healthy vaccinee effects was investigated within a conventionally designed and well-controlled COVID-19 vaccine effectiveness study, covering both primary series and booster mRNA vaccinations. Three aspects of these effects were explored: their existence, their temporal pattern, and their variability across different subpopulations. This was achieved by assessing the association between COVID-19 vaccination and non-COVID-19 mortality, which serves as a suitable control outcome to gauge the degree of potential residual effect in well-controlled vaccine effectiveness estimates against SARS-CoV-2 infection or severe forms of COVID-19 (Nelson et al., 2009).

Methods

Study population and data sources

This study was conducted among the resident population of Qatar from January 5, 2021, which marks the earliest record of a completed COVID-19 primary series vaccination, to April 9, 2024, the study’s end date. Data on COVID-19 laboratory testing, vaccination, hospitalization, and death were retrieved from the integrated, nationwide digital health information platform (Section S1 in Supplementary Appendix). Deaths not related to COVID-19 were sourced from the national federated mortality database, which captures all deaths in the country, occurring in healthcare facilities and elsewhere, including forensic deaths investigated by Qatar’s Ministry of Interior.

The national digital health information platform includes all SARS-CoV-2-related records, encompassing COVID-19 vaccinations, hospitalizations, and polymerase chain reaction (PCR) tests, irrespective of location or facility, and, from January 5, 2022, medically supervised rapid antigen tests (Section S2). Until October 31, 2022, Qatar maintained an extensive testing approach, testing 5% of the population weekly, primarily for routine purposes such as screening or travel-related requirements (Chemaitelly et al., 2021b; Altarawneh et al., 2022b). From November 1, 2022, onward, testing was reduced to below 1% of the population weekly (Chemaitelly et al., 2023a). Most COVID-19 infections in Qatar were identified through routine testing rather than symptomatic presentation (Section S1) (Altarawneh et al., 2022b; Chemaitelly et al., 2021b). The national platform further contains data on coexisting conditions for individuals who have accessed care through the universal public healthcare system since the establishment of the digital health platform in 2013 (Section S3).

Demographic information was obtained from the national health registry. Qatar’s demographic composition is distinct, with only 9% of the population aged 50 years or older and 89% being resident expatriates from over 150 countries (Abu-Raddad et al., 2021a). Further details on Qatar’s population and COVID-19 databases have been previously published (Chemaitelly et al., 2021b; Altarawneh et al., 2022b; Chemaitelly et al., 2023e; Abu-Raddad et al., 2021a; AlNuaimi et al., 2023; Chemaitelly et al., 2021a).

COVID-19 vaccination

COVID-19 vaccination in Qatar was predominantly conducted using mRNA vaccines and adhered to United States Food and Drug Administration-approved protocols throughout the pandemic (Chemaitelly et al., 2021b; Altarawneh et al., 2022b). Vaccines were provided free of charge to all individuals, regardless of citizenship status, exclusively through the public healthcare system (Chemaitelly et al., 2021b; Altarawneh et al., 2022b). The immunization campaign was launched on December 21, 2020, with the BNT162b2 vaccine (Polack et al., 2020), and 3 months later, the mRNA-1273 vaccine (Baden et al., 2021) was added. Most primary series vaccinations were administered in 2021 due to the rapid scale-up of mass vaccination efforts (Figure 1—figure supplement 1A).

The vaccine rollout was implemented in phases, prioritizing frontline healthcare workers, individuals with severe or multiple chronic conditions, and those aged ≥70 years (Chemaitelly et al., 2021b). Vaccination was subsequently extended to select professional groups, such as teachers, and then to the general population, beginning with individuals aged 50 years or older (Chemaitelly et al., 2021b). Age served as the primary eligibility criterion throughout the campaign (Chemaitelly et al., 2021b). Booster vaccinations were introduced in the fall of 2021, following a similar prioritization plan (Figure 1—figure supplement 1B; Abu-Raddad et al., 2022a). However, with the increased availability of vaccine doses, eligibility for boosters was rapidly expanded to include all adults.

Study design

Two national, matched, retrospective cohort studies were conducted to investigate the potential for indication effect or healthy vaccinee effect influencing estimated effectiveness of COVID-19 primary series (two-dose) and booster (three-dose) vaccinations in Qatar’s population. Given the objective of exploring these effects, the studies were designed as vaccine effectiveness studies, adhering to cohort designs developed and implemented in Qatar’s population since the pandemic’s onset (Abu-Raddad et al., 2022a; Abu-Raddad et al., 2021b; Chemaitelly et al., 2022b; Chemaitelly et al., 2023a; Chemaitelly et al., 2023d; Chemaitelly et al., 2023c; Mahmoud et al., 2023).

The healthy vaccinee effect was defined as a bias in vaccine effectiveness studies where healthier individuals are more likely to receive vaccination, even after controlling for differences in health status based on available data on coexisting conditions. Meanwhile, the indication effect was defined as a bias in vaccine effectiveness studies where individuals with underlying health conditions are more likely to be vaccinated, even after controlling for differences in health status based on available data on coexisting conditions.

In the first study (two-dose analysis), the incidence of non-COVID-19 death in the national cohort of individuals who received the primary series vaccination (designated as the two-dose cohort) was compared with that in the national cohort of unvaccinated individuals (designated as the unvaccinated cohort). In the second study (three-dose analysis), the incidence of non-COVID-19 death in the national cohort of individuals who received a third (booster) dose of vaccination (designated as the three-dose cohort) was compared with that in the two-dose cohort. For both studies, vaccine effectiveness was also estimated by comparing the incidence of SARS-CoV-2 infection and of severe forms of COVID-19 between the study cohorts.

Severe, critical, and fatal COVID-19

Severe forms of COVID-19 were classified by trained medical personnel independent of the study investigators (AlNuaimi et al., 2023; Chemaitelly et al., 2023e; Chemaitelly et al., 2023b). The classifications were based on individual chart reviews, adhering to the World Health Organization (WHO) guidelines for defining COVID-19 case severity (acute care hospitalization) (World Health Organization, 2023b), criticality (intensive care unit hospitalization) (World Health Organization, 2023b), and fatality (World Health Organization, 2023a) (Section S4) (AlNuaimi et al., 2023; Chemaitelly et al., 2023e; Chemaitelly et al., 2023b).

These evaluations were implemented throughout the pandemic as part of a national protocol, under which every individual with a SARS-CoV-2-positive test and a concurrent COVID-19 hospital admission was assessed for infection severity at regular intervals until discharge or death, regardless of the hospital length of stay (AlNuaimi et al., 2023; Chemaitelly et al., 2023e; Chemaitelly et al., 2023b).

All COVID-19 deaths in Qatar were systematically identified through this protocol, supplemented by a similar protocol applied to all deaths, irrespective of the cause, to determine whether the death met the criteria for classification as a COVID-19 death (AlNuaimi et al., 2023; Chemaitelly et al., 2023e; Chemaitelly et al., 2023b). Deaths in the population that were not classified as COVID-19 were assumed to be non-COVID-19 deaths.

COVID-19 death was defined per WHO classification as “a death resulting from a clinically compatible illness, in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease (e.g. trauma). There should be no period of complete recovery from COVID-19 between illness and death. A death due to COVID-19 may not be attributed to another disease (e.g. cancer) and should be counted independently of preexisting conditions that are suspected of triggering a severe course of COVID-19” (World Health Organization, 2023a).

Incidence of SARS-CoV-2 infection

Incidence of SARS-CoV-2 infection was defined as any PCR-positive or rapid-antigen-positive test after the start of follow-up, irrespective of symptomatic presentation. Individuals whose infection progressed to severe, critical, or fatal COVID-19 were classified based on their worst outcome, starting with COVID-19 death (World Health Organization, 2023a), followed by critical disease (World Health Organization, 2023b), and then severe disease (World Health Organization, 2023b) (Section S4). Incidence of outcomes of severe forms of COVID-19 was recorded on the date of the SARS-CoV-2-positive test confirming the infection.

Cohorts’ eligibility and matching

Individuals qualified for inclusion in the two-dose cohort if they received two doses of an mRNA vaccine and in the three-dose cohort if they received three doses of an mRNA vaccine. Those who were administered the ChAdOx1 nCoV-19 (AZD1222) vaccine, a small proportion of the population, or the pediatric 10 µg BNT162b2 vaccine were excluded. Individuals qualified for inclusion in the unvaccinated cohort if they had no vaccination record at the start of follow-up.

Cohorts were matched exactly one-to-one by sex, 10-year age group, nationality, exact coexisting conditions (Section S3), and prior documented SARS-CoV-2 infection status (no prior infection, prior pre-omicron infection, prior omicron infection, or prior pre-omicron and omicron infections). Since prior infection can affect the health status of an individual—potentially leading to conditions such as Long COVID (Al-Aly et al., 2022), affecting vaccination uptake (Nguyen et al., 2021), or altering protection against subsequent infection and severe COVID-19 (Abu-Raddad et al., 2021c; Chemaitelly et al., 2022c)—matching by prior infection status was implemented to balance this confounder across cohorts. Prior infections were classified as pre-omicron if they occurred before December 19, 2021, the onset of the omicron wave in Qatar (Altarawneh et al., 2022b) and as omicron thereafter.

For the two-dose analysis, individuals who received their second vaccine dose in a specific calendar week in the two-dose cohort were additionally matched to individuals who had a record of a SARS-CoV-2-negative test in that same calendar week in the unvaccinated cohort. This matching approach ensured that matched pairs were present in Qatar during the same time period and were subject to the same vaccination policies and practices at the time of study recruitment. Individuals who were tested after death or who had an unascertained or discrepant death date were excluded.

Similarly, for the three-dose analysis, individuals who received their third vaccine dose in a specific calendar week in the three-dose cohort were matched to individuals who had a record of a SARS-CoV-2-negative test in that same calendar week in the two-dose cohort. Additionally, individuals in the three-dose cohort were matched to individuals in the two-dose cohort by the calendar week of the second vaccine dose. These matching criteria ensured that the paired individuals received their primary series vaccinations at the same time and were present in Qatar during the same period.

Iterative matching was implemented so that, at the start of follow-up, individuals were alive, had maintained their vaccination status, had the same prior infection status as their match, and had no documented SARS-CoV-2 infection within the previous 90 days. The 90-day threshold was used to avoid misclassification of a previous (prolonged) SARS-CoV-2 infection as an incident infection (Pilz et al., 2022; Kojima et al., 2021; Chemaitelly et al., 2024). Consequently, a prior infection was defined as a SARS-CoV-2-positive test that occurred ≥90 days before the start of follow-up.

The above-detailed matching approach aimed to balance observed confounders that could potentially affect the risk of non-COVID-19 death or the risk of infection across the exposure groups (Abu-Raddad et al., 2021a; Coyle et al., 2021; Jeremijenko et al., 2021; Al Thani et al., 2021; AlNuaimi et al., 2023). The matching factors were selected based on findings from earlier studies on Qatar’s population (Chemaitelly et al., 2021b; Abu-Raddad et al., 2022b).

The matching algorithm was implemented using ccmatch command in Stata 18.0 supplemented with conditions to retain only controls that fulfilled the eligibility criteria and was iterated using loops with as many replications as needed until exhaustion (i.e. no more matched pairs could be identified).

According to this study design and matching approach, individuals in the matched unvaccinated cohort in the two-dose analysis may have contributed follow-up time before receiving the primary series vaccination and subsequently contributed follow-up time as part of the two-dose cohort after receiving the primary series vaccination. Similarly, in the three-dose analysis, individuals in the matched two-dose cohort may have contributed follow-up time before receiving the third (booster) dose, as part of the two-dose cohort, and subsequently contributed follow-up time as part of the three-dose cohort after receiving the third dose.

Cohorts’ follow-up

Follow-up started from the calendar date of the second dose in the two-dose analysis and from the calendar date of the third dose in the three-dose analysis. To ensure exchangeability (Barda et al., 2021; Abu-Raddad et al., 2022a), both members of each matched pair were censored at the earliest occurrence of receiving an additional vaccine dose.

Accordingly, individuals were followed until the first of any of the following events: a documented SARS-CoV-2 infection (irrespective of symptoms), first-dose vaccination for individuals in the unvaccinated cohort (with matched-pair censoring), third-dose vaccination for individuals in the two-dose cohort (with matched-pair censoring), fourth-dose vaccination for individuals in the three-dose cohort (with matched-pair censoring), death, or the administrative end of follow-up at the end of the study.

Oversight

The institutional review boards at Hamad Medical Corporation and Weill Cornell Medicine-Qatar approved this retrospective study with a waiver of informed consent. The study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE; Reporting Standards document).

Statistical analysis

Eligible and matched cohorts were described using frequency distributions and measures of central tendency, and were compared using standardized mean differences (SMDs). An SMD of ≤0.1 indicated adequate matching (Austin, 2009). The cumulative incidence of non-COVID-19 death, defined as proportion of individuals at risk whose primary endpoint during follow-up was a non-COVID-19 death, was estimated using the Kaplan-Meier estimator method (Kaplan and Meier, 1958).

Incidence rate of non-COVID-19 death in each cohort, defined as number of non-COVID-19 deaths divided by number of person-weeks contributed by all individuals in the cohort, was estimated along with the corresponding 95% confidence interval (CI), using a Poisson log-likelihood regression model with the Stata 18.0 stptime command.

Overall adjusted hazard ratio (aHR), comparing incidence of non-COVID-19 death between the cohorts, and corresponding 95% CI, were calculated using Cox regression models with adjustment for the matching factors, via the Stata 18.0 stcox command. This adjustment was implemented to ensure precise and unbiased estimation of the standard variance (Sjölander and Greenland, 2013). CIs were not adjusted for multiplicity. Schoenfeld residuals and log-log plots for survival curves were used to examine the proportional hazards assumption. An aHR less than 1 indicated evidence of a healthy vaccinee effect. An aHR greater than 1 indicated evidence of an indication effect.

The overall aHR provides a weighted average of the time-varying hazard ratio (Stensrud and Hernán, 2020). To explore differences in the risk of non-COVID-19 death over time, the aHR was also estimated by 6-month intervals from the start of follow-up, using separate Cox regressions, with ‘failure’ restricted to specific time intervals.

Subgroup analyses estimating the overall aHR stratified by age group (<50 years versus ≥50 years), clinical vulnerability status, and prior infection status were also conducted. Individuals were classified as less clinically vulnerable to severe COVID-19 if they were <50 years of age and had one or no coexisting conditions, and as more clinically vulnerable to severe COVID-19 if they were either ≥50 years of age or <50 years of age but with ≥2 coexisting conditions (Chemaitelly et al., 2023d; Chemaitelly et al., 2023e).

The study analyzed non-COVID-19 mortality in the population of Qatar. However, some deaths may have occurred outside Qatar when expatriates were traveling abroad or had permanently left the country after the start of follow-up. The matching strategy aimed to mitigate any differential effects of these out-of-country deaths on the matched groups, for instance, by matching on a SARS-CoV-2-negative test among controls to ensure their presence in Qatar during the same period.

To assess whether our results could have been affected by bias due to out-of-country deaths or the matching requirement of a SARS-CoV-2-negative test, two sensitivity analyses were conducted: first, by restricting the cohorts to only Qataris, where out-of-country deaths are unlikely, and second, by eliminating the requirement for matching by a SARS-CoV-2-negative test.

Analogous methods were used to compare incidence of SARS-CoV-2 infection and of severe forms of COVID-19 between study cohorts. The overall aHR, comparing incidence of SARS-CoV-2 infection (or severe forms of COVID-19) between study cohorts, was calculated, including an additional adjustment for the testing rate. Vaccine effectiveness against infection and against severe forms of COVID-19, along with the associated 95% CIs, were derived from the aHR as 1-aHR if the aHR was <1, and as 1/aHR-1 if the aHR was ≥1 (Tseng et al., 2022; Chemaitelly et al., 2023d). This approach ensured a symmetric scale for both negative and positive effectiveness, spanning from −100% to 100%, resulting in a meaningful interpretation of effectiveness, regardless of the value being positive or negative.

Statistical analyses were performed using Stata/SE version 18.0 (Stata Corporation, College Station, TX, USA).

Results

Two-dose analysis

Figure 1—figure supplement 2 illustrates the process of selecting the study cohorts. Table 1 outlines the cohorts’ baseline characteristics. Each matched cohort comprised 812,583 individuals. Median date of the second vaccine dose was June 21, 2021, for the two-dose cohort. Median duration of follow-up was 206 days (interquartile range [IQR], 41–925 days) in the two-dose cohort and 199 days (IQR, 36–933 days) in the unvaccinated cohort (Figure 1A).

Table 1. Baseline characteristics of the full and matched cohorts for investigating an indication effect or a healthy vaccinee effect among recipients of primary series or booster (third dose) vaccination in Qatar.

	Two-dose analysis						Three-dose analysis
Characteristics	Full eligible cohorts			Matched cohorts^*			Full eligible cohorts			Matched cohorts^†
	Two-dose	Unvaccinated	SMD^‡	Two-dose	Unvaccinated	SMD^‡	Three-dose	Two-dose	SMD^‡	Three-dose	Two-dose	SMD^‡
	N=2,168,050	N=3,811,694	SMD^‡	N=812,583	N=812,583	SMD^‡	N=714,893	N=2,231,443	SMD^‡	N=330,568	N=330,568	SMD^‡
Median age (IQR)—years	38 (31–45)	32 (24–41)	0.50^§	34 (28–41)	33 (27–40)	0.07^§	40 (33–49)	38 (31–45)	0.21^§	38 (32–45)	39 (34–47)	0.01^§
Age group—no. (%)
0–19 years	106,156 (4.9)	622,215 (16.3)	0.58	69,673 (8.6)	69,673 (8.6)	0.00	33,216 (4.6)	107,885 (4.8)	0.23	9,221 (2.8)	9,221 (2.8)	0.00
20–29 years	326,484 (15.1)	909,809 (23.9)		191,420 (23.6)	191,420 (23.6)		72,966 (10.2)	334,458 (15.0)		40,015 (12.1)	40,015 (12.1)
30–39 years	809,250 (37.3)	1,228,030 (32.2)		326,985 (40.2)	326,985 (40.2)		239,713 (33.5)	834,373 (37.4)		139,067 (42.1)	139,067 (42.1)
40–49 years	576,564 (26.6)	660,453 (17.3)		158,847 (19.5)	158,847 (19.5)		204,224 (28.6)	595,300 (26.7)		98,080 (29.7)	98,080 (29.7)
50–59 years	244,963 (11.3)	268,839 (7.1)		51,661 (6.4)	51,661 (6.4)		107,990 (15.1)	252,382 (11.3)		36,284 (11.0)	36,284 (11.0)
60–69 years	80,555 (3.7)	92,395 (2.4)		12,014 (1.5)	12,014 (1.5)		43,815 (6.1)	82,558 (3.7)		7,355 (2.2)	7,355 (2.2)
70+ years	24,078 (1.1)	29,953 (0.8)		1,983 (0.2)	1,983 (0.2)		12,969 (1.8)	24,487 (1.1)		546 (0.2)	546 (0.2)
Sex
Male	1,599,920 (73.8)	2,682,394 (70.4)	0.08	593,856 (73.1)	593,856 (73.1)	0.00	467,443 (65.4)	1,645,973 (73.8)	0.18	245,116 (74.1)	245,116 (74.1)	0.00
Female	568,130 (26.2)	1,129,300 (29.6)	0.08	218,727 (26.9)	218,727 (26.9)	0.00	247,450 (34.6)	585,470 (26.2)	0.18	85,452 (25.9)	85,452 (25.9)	0.00
Nationality ^¶
Bangladeshi	306,251 (14.1)	269,021 (7.1)	0.30	68,102 (8.4)	68,102 (8.4)	0.00	66,000 (9.2)	312,475 (14.0)	0.39	37,670 (11.4)	37,670 (11.4)	0.00
Egyptian	106,392 (4.9)	184,152 (4.8)		40,791 (5.0)	40,791 (5.0)		59,691 (8.3)	109,910 (4.9)		18,103 (5.5)	18,103 (5.5)
Filipino	201,002 (9.3)	277,459 (7.3)		76,146 (9.4)	76,146 (9.4)		99,405 (13.9)	209,620 (9.4)		40,680 (12.3)	40,680 (12.3)
Indian	531,366 (24.5)	1,074,425 (28.2)		268,830 (33.1)	268,830 (33.1)		222,135 (31.1)	549,694 (24.6)		121,774 (36.8)	121,774 (36.8)
Nepalese	233,558 (10.8)	347,108 (9.1)		68,279 (8.4)	68,279 (8.4)		28,584 (4.0)	239,262 (10.7)		20,694 (6.3)	20,694 (6.3)
Pakistani	103,600 (4.8)	223,498 (5.9)		46,416 (5.7)	46,416 (5.7)		34,161 (4.8)	106,177 (4.8)		14,548 (4.4)	14,548 (4.4)
Qatari	195,030 (9.0)	319,209 (8.4)		64,135 (7.9)	64,135 (7.9)		40,519 (5.7)	199,550 (8.9)		23,062 (7.0)	23,062 (7.0)
Sri Lankan	75,586 (3.5)	127,750 (3.4)		21,827 (2.7)	21,827 (2.7)		20,759 (2.9)	77,913 (3.5)		10,988 (3.3)	10,988 (3.3)
Sudanese	45,213 (2.1)	78,528 (2.1)		17,594 (2.2)	17,594 (2.2)		12,920 (1.8)	46,586 (2.1)		4,140 (1.3)	4,140 (1.3)
Other nationalities^**	370,052 (17.1)	910,544 (23.9)		140,463 (17.3)	140,463 (17.3)		130,719 (18.3)	380,256 (17.0)		38,909 (11.8)	38,909 (11.8)
Coexisting conditions
0	1,809,569 (83.5)	3,352,859 (88.0)	0.14	746,840 (91.9)	746,840 (91.9)	0.00	540,392 (75.6)	1,860,263 (83.4)	0.20	311,376 (94.2)	311,376 (94.2)	0.00
1	183,168 (8.4)	261,898 (6.9)		45,414 (5.6)	45,414 (5.6)		78,872 (11.0)	189,770 (8.5)		12,288 (3.7)	12,288 (3.7)
2	86,673 (4.0)	102,968 (2.7)		13,988 (1.7)	13,988 (1.7)		44,676 (6.2)	89,926 (4.0)		5,049 (1.5)	5,049 (1.5)
3	39,989 (1.8)	42,960 (1.1)		3,842 (0.5)	3,842 (0.5)		22,684 (3.2)	41,422 (1.9)		1,149 (0.3)	1,149 (0.3)
4	22,810 (1.1)	23,715 (0.6)		1,602 (0.2)	1,602 (0.2)		13,504 (1.9)	23,539 (1.1)		558 (0.2)	558 (0.2)
5	13,035 (0.6)	13,575 (0.4)		657 (0.1)	657 (0.1)		7,590 (1.1)	13,415 (0.6)		122 (<0.01)	122 (<0.01)
≥6	12,806 (0.6)	13,719 (0.4)		240 (<0.01)	240 (<0.01)		7,175 (1.0)	13,108 (0.6)		26 (<0.01)	26 (<0.01)
Prior infection status ^††
No prior infection	1,957,313 (90.3)	–	–	764,366 (94.1)	764,366 (94.1)	0.00	591,083 (82.7)	–	–	287,773 (87.1)	287,773 (87.1)	0.00
Prior pre-omicron infection	208,058 (9.6)	–		46,631 (5.7)	46,631 (5.7)		96,567 (13.5)	–		33,864 (10.2)	33,864 (10.2)
Prior omicron infection	2,463 (0.1)	–		1,548 (0.2)	1,548 (0.2)		24,690 (3.5)	–		8,624 (2.6)	8,624 (2.6)
Prior pre-omicron and omicron infections	216 (<0.01)	–		38 (<0.01)	38 (<0.01)		2,553 (0.4)	–		307 (0.1)	307 (0.1)

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IQR, interquartile range; SMD, standardized mean difference.

Cohorts were matched exactly one-to-one by sex, 10-year age group, nationality, type of coexisting conditions, and prior infection status. Persons who received their second vaccine dose in a specific calendar week in the two-dose cohort were additionally matched to persons who had a record for a SARS-CoV-2-negative test in that same calendar week in the unvaccinated cohort, to ensure that matched pairs had presence in Qatar over the same time period.

^†

Cohorts were matched exactly one-to-one by sex, 10-year age group, nationality, type of coexisting conditions, prior infection status, and calendar week of the second vaccine dose. Persons who received their third vaccine dose in a specific calendar week in the three-dose cohort were additionally matched to persons who had a record for a SARS-CoV-2-negative test in that same calendar week in the two-dose cohort, to ensure that matched pairs had presence in Qatar over the same time period.

^‡

SMD is the difference in the mean of a covariate between groups divided by the pooled standard deviation. An SMD≤0.1 indicates adequate matching.

^§

SMD is for the mean difference between groups divided by the pooled standard deviation.

^¶

Nationalities were chosen to represent the most populous groups in Qatar.

^**

These comprise up to 183 other nationalities in the unmatched and 148 other nationalities in the matched two-dose analyses, and up to 169 other nationalities in the unmatched and 111 other nationalities in the matched three-dose analyses.

^††

Ascertained at the start of follow-up. Accordingly, distribution is not available for the unmatched unvaccinated cohort in the two-dose analysis and unmatched two-dose cohort in the three-dose analysis, as the start of follow-up for each person in these reference/control cohorts is determined by that of their match after the matching process is completed.

Figure 1—figure supplement 1. — Figure 1—source data 1. Data used to generate Figure 1A and B.

elife-103690-fig1-data1.xlsx^{(188.1KB, xlsx)}

During follow-up, 237 non-COVID-19 deaths occurred in the two-dose cohort compared to 306 in the unvaccinated cohort (Table 2A and Figure 1—figure supplement 2). There were 54,427 SARS-CoV-2 infections recorded in the two-dose cohort, of which 23 progressed to severe, 6 to critical, and none to fatal COVID-19. Meanwhile, 57,974 SARS-CoV-2 infections were recorded in the unvaccinated cohort, of which 539 progressed to severe, 66 to critical, and 25 to fatal COVID-19.

Table 2. Hazard ratios for incidence of non-COVID-19 death, SARS-CoV-2 infection, and severe, critical, or fatal COVID-19 in the (A) two-dose analysis and (B) three-dose analysis.

(A) Two-dose analysis	Two-dose cohort^*	Unvaccinated cohort^*
Sample size	812,583	812,583
Number of non-COVID-19 death	237	306
Number of incident infections	54,427	57,974
Number of severe, critical, or fatal COVID-19 disease	29	630
Total follow-up time (person-weeks)	46,028,318	46,275,391
Non-COVID-19 death
Incidence rate of non-COVID-19 death (per 10,000 person-weeks; 95% CI)	0.05 (0.05–0.06)	0.07 (0.06–0.07)
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.77 (0.65–0.91)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	0.76 (0.64–0.90)
SARS-CoV-2 infection
Unadjusted hazard ratio for SARS-CoV-2 infection (95% CI)	0.93 (0.92–0.94)
Adjusted hazard ratio for SARS-CoV-2 infection (95% CI)^‡	0.89 (0.88–0.90)
Effectiveness against SARS-CoV-2 infection (95% CI)^‡	10.7 (9.6–11.7)
Severe, critical, or fatal COVID-19 disease
Unadjusted hazard ratio for severe, critical, or fatal COVID-19 disease (95% CI)	0.05 (0.03–0.07)
Adjusted hazard ratio for severe, critical, or fatal COVID-19 disease (95% CI)^‡	0.04 (0.03–0.06)
Effectiveness against severe, critical, or fatal COVID-19 disease (95% CI)^‡	95.9 (94.0–97.1)
(B) Three-dose analysis	Three-dose cohort^§	Two-dose cohort^§
Sample size	330,568	330,568
Number of non-COVID-19 death	132	147
Number of incident infections	26,842	35,411
Number of severe, critical, or fatal COVID-19 disease	6	9
Total follow-up time (person-weeks)	24,015,307	23,088,912
Non-COVID-19 death
Incidence rate of non-COVID-19 death (per 10,000 person-weeks; 95% CI)	0.05 (0.05–0.07)	0.06 (0.05–0.07)
Unadjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.87 (0.68–1.10)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.85 (0.67–1.07)
SARS-CoV-2 infection
Unadjusted hazard ratio for SARS-CoV-2 infection (95% CI)	0.74 (0.72–0.75)
Adjusted hazard ratio for SARS-CoV-2 infection (95% CI)^**	0.74 (0.72–0.75)
Effectiveness against SARS-CoV-2 infection (95% CI)^**	26.3 (25.2–27.5)
Severe, critical, or fatal COVID-19 disease
Unadjusted hazard ratio for severe, critical, or fatal COVID-19 disease (95% CI)	0.64 (0.23–1.81)
Adjusted hazard ratio for severe, critical, or fatal COVID-19 disease (95% CI)^**	0.66 (0.23–1.86)
Effectiveness against severe, critical, or fatal COVID-19 disease (95% CI)^**	34.1 (−46.4–76.7)

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CI, confidence interval; COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

^†

Adjusted for sex, 10-year age group, nationality, number of coexisting conditions, prior infection status, and calendar week of the second vaccine dose for the two-dose cohort or SARS-CoV-2-negative test for the unvaccinated cohort.

^‡

Adjusted for sex, 10-year age group, nationality, number of coexisting conditions, prior infection status, calendar week of the second vaccine dose for the two-dose cohort or SARS-CoV-2-negative test for the unvaccinated cohort, and testing rate.

^§

^¶

Adjusted for sex, 10-year age group, nationality, number of coexisting conditions, prior infection status, and calendar week of the second vaccine dose.

^**

Adjusted for sex, 10-year age group, nationality, number of coexisting conditions, prior infection status, calendar week of the second vaccine dose, and testing rate.

The cumulative incidence of non-COVID-19 death was 0.070% (95% CI: 0.061–0.081%) for the two-dose cohort and 0.071% (95% CI: 0.062–0.080%) for the unvaccinated cohort after 990 days of follow-up (Figure 1A). The overall aHR comparing the incidence of non-COVID-19 death in the two-dose cohort to that in the unvaccinated cohort was 0.76 (95% CI: 0.64–0.90), indicating evidence of a healthy vaccinee effect (Table 2A).

In the first 6 months of follow-up, the aHR was 0.35 (95% CI: 0.27–0.46), indicating strong evidence of a healthy vaccinee effect (Figure 2A). However, the combined analysis of all periods after the first 6 months showed an aHR of 1.52 (95% CI: 1.19–1.94).

Figure 2—figure supplement 1. — Error bars indicate the corresponding 95% confidence intervals.

Figure 2—source data 1. Data used to generate Figure 2A and B.

elife-103690-fig2-data1.xlsx^{(29.7KB, xlsx)}

The subgroup analyses estimated the aHR at 0.89 (95% CI: 0.72–1.11) among individuals under 50 years of age and at 0.56 (95% CI: 0.42–0.75) among those 50 years of age and older (Table 3A). The aHR was 0.98 (95% CI: 0.79–1.22) for those less clinically vulnerable to severe COVID-19 and 0.51 (95% CI: 0.39–0.68) for the more clinically vulnerable group. The aHR by prior infection status was 0.74 (95% CI: 0.63–0.89) for no prior infection and 1.00 (95% CI: 0.45–2.20) for prior pre-omicron infection.

Table 3. Subgroup analyses.

Hazard ratios for incidence of non-COVID-19 death stratified by age group, clinical vulnerability status, and prior infection status in the (A) two-dose analysis and (B) three-dose analysis.

(A) Two-dose analysis	Two-dose cohort^*	Unvaccinated cohort^*
Age
<50 years of age
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.91 (0.73–1.12)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	0.89 (0.72–1.11)
≥50 years of age
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.59 (0.44–0.78)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	0.56 (0.42–0.75)
Clinical vulnerability status
Less clinically vulnerable to severe COVID-19
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.99 (0.80–1.23)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	0.98 (0.79–1.22)
More clinically vulnerable to severe COVID-19
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.53 (0.41–0.70)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	0.51 (0.39–0.68)
Prior infection status
No prior infection
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.76 (0.64–0.90)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	0.74 (0.63–0.89)
Prior pre-omicron infection
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	1.05 (0.48–2.30)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	1.00 (0.45–2.20)
Prior omicron infection
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	-- ^‡
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	-- ^‡
Prior pre-omicron & omicron infections
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	-- ^‡
Adjusted hazard ratio for non-COVID-19 death (95% CI)^†	-- ^‡
(B) Three-dose analysis	Three-dose cohort ^§	Two-dose cohort ^§
Age
<50 years of age
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.90 (0.67–1.21)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.90 (0.67–1.20)
≥50 years of age
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.80 (0.54–1.18)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.76 (0.51–1.13)
Clinical vulnerability status
Less clinically vulnerable to severe COVID-19
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.91 (0.68–1.23)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.91 (0.67–1.22)
More clinically vulnerable to severe COVID-19
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.78 (0.54–1.15)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.76 (0.52–1.12)
Prior infection status
No prior infection
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	0.80 (0.63–1.03)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	0.79 (0.61–1.01)
Prior pre-omicron infection
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	1.63 (0.71–3.73)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	1.63 (0.71–3.72)
Prior omicron infection
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	1.32 (0.30–5.90)
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	1.32 (0.30–5.91)
Prior pre-omicron & omicron infections
Unadjusted hazard ratio for non-COVID-19 death (95% CI)	-- ^‡
Adjusted hazard ratio for non-COVID-19 death (95% CI)^¶	-- ^‡

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CI, confidence interval; COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

^†

Adjusted for sex, 10-year age group, nationality, number of coexisting conditions, prior infection status (where applicable), and calendar week of the second vaccine dose for the two-dose cohort or SARS-CoV-2-negative test for the unvaccinated cohort.

^‡

Could not be estimated because of no or small number of events.

^§

^¶

Adjusted for sex, 10-year age group, nationality, number of coexisting conditions, prior infection status (where applicable), and calendar week of the second vaccine dose.

In the two sensitivity analyses—one including only Qataris and the other also including only Qataris but without matching on a SARS-CoV-2-negative test among controls—the aHRs for non-COVID-19 death were 0.29 (95% CI: 0.19–0.43) and 0.38 (95% CI: 0.30–0.50), respectively (Table 4A). Both analyses are consistent with each other and with the main analysis results (Table 4). However, the healthy vaccinee effect is more pronounced among Qataris, as the proportion of individuals above 50 years of age or those with serious coexisting conditions is substantially higher among Qataris compared to the rest of the population, which primarily comprises working-age male craft and manual workers (Abu-Raddad et al., 2021a; Al Thani et al., 2021; AlNuaimi et al., 2023).

Table 4. Sensitivity analyses.

Hazard ratios for incidence of non-COVID-19 death among Qataris with and without matching on a SARS-CoV-2-negative test among controls in the (A) two-dose analysis and (B) three-dose analysis.

(A) Two-dose analysis	Two-dose cohort	Unvaccinated cohort
Sensitivity analysis I-Restricting analysis to Qataris ^*
Unadjusted hazard ratiofor non-COVID-19 death (95% CI)	0.29 (0.19–0.43)
Adjusted hazard ratiofor non-COVID-19 death (95% CI)^†	0.29 (0.19–0.43)
Sensitivity analysis II-Restricting analysis to Qataris and not matching by a SARS-CoV-2-negative test among controls ^‡
Unadjusted hazard ratiofor non-COVID-19 death (95% CI)	0.40 (0.31–0.51)
Adjusted hazard ratiofor non-COVID-19 death (95% CI)^§	0.38 (0.30–0.50)
(B) Three-dose analysis	Three-dose cohort	Two-dose cohort
Sensitivity analysis I-Restricting analysis to Qataris ^¶
Unadjusted hazard ratiofor non-COVID-19 death (95% CI)	0.77 (0.44–1.33)
Adjusted hazard ratiofor non-COVID-19 death (95% CI)^**	0.76 (0.43–1.32)
Sensitivity analysis II-Restricting analysis to Qataris and not matching by a SARS-CoV-2-negative test among controls ^††
Unadjusted hazard ratiofor non-COVID-19 death (95% CI)	0.77 (0.52–1.12)
Adjusted hazard ratiofor non-COVID-19 death (95% CI)^**	0.77 (0.53–1.13)

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CI, confidence interval; COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Cohorts were matched exactly one-to-one by sex, 10-year age group, type of coexisting conditions, and prior infection status. Persons who received their second vaccine dose in a specific calendar week in the two-dose cohort were additionally matched to persons who had a record for a SARS-CoV-2-negative test in that same calendar week in the unvaccinated cohort, to ensure that matched pairs had presence in Qatar over the same time period.

^†

Adjusted for sex, 10-year age group, number of coexisting conditions, prior infection status, and calendar week of the second vaccine dose for the two-dose cohort or SARS-CoV-2-negative test for the unvaccinated cohort.

^‡

Cohorts were matched exactly one-to-one by sex, 10-year age group, type of coexisting conditions, and prior infection status.

^§

Adjusted for sex, 10-year age group, number of coexisting conditions, and prior infection status.

^¶

Cohorts were matched exactly one-to-one by sex, 10-year age group, type of coexisting conditions, prior infection status, and calendar week of the second vaccine dose. Persons who received their third vaccine dose in a specific calendar week in the three-dose cohort were additionally matched to persons who had a record for a SARS-CoV-2-negative test in that same calendar week in the two-dose cohort, to ensure that matched pairs had presence in Qatar over the same time period.

^**

Adjusted for sex, 10-year age group, number of coexisting conditions, prior infection status, and calendar week of the second vaccine dose.

^††

Cohorts were matched exactly one-to-one by sex, 10-year age group, type of coexisting conditions, prior infection status, and calendar week of the second vaccine dose.

The overall effectiveness of primary series vaccination compared to no vaccination was 10.7% (95% CI: 9.6–11.7) against infection and 95.9% (95% CI: 94.0–97.1) against severe, critical, or fatal COVID-19 (Table 2A). Figure 2—figure supplement 1 further illustrates the effectiveness of primary series vaccination against severe, critical, or fatal COVID-19, stratified by time since vaccination, both overall and within the subgroups of individuals aged <50 years and those aged ≥50 years.

Three-dose analysis

Figure 1—figure supplement 3 illustrates the process of selecting the study cohorts. Table 1 outlines the cohorts’ baseline characteristics. Each matched cohort comprised 330,568 individuals. The median date of the second vaccine dose was May 15, 2021, for both the two-dose and three-dose cohorts. The median date of the third vaccine dose in the three-dose cohort was January 24, 2022. The median duration of follow-up was 695 days (IQR, 66–802 days) in the three-dose cohort and 685 days (IQR, 49–798 days) in the two-dose cohort (Figure 1B).

During follow-up, 132 non-COVID-19 deaths occurred in the three-dose cohort compared to 147 in the two-dose cohort (Table 2B and Figure 1—figure supplement 3). There were 26,842 SARS-CoV-2 infections recorded in the three-dose cohort, of which 3 progressed to severe, 2 to critical, and 1 to fatal COVID-19. Meanwhile, 35,411 SARS-CoV-2 infections were recorded in the two-dose cohort, of which 8 progressed to severe, 1 to critical, and none to fatal COVID-19.

The cumulative incidence of non-COVID-19 death was 0.064% (95% CI: 0.054–0.076%) for the three-dose cohort and 0.070% (95% CI: 0.059–0.083%) for the two-dose cohort, after 840 days of follow-up (Figure 1B). The overall aHR comparing the incidence of non-COVID-19 death in the three-dose cohort to that in the two-dose cohort was 0.85 (95% CI: 0.67–1.07), indicating no overall evidence of a healthy vaccinee effect (Table 2B).

In the first 6 months of follow-up, the aHR was 0.31 (95% CI: 0.20–0.50), indicating strong evidence of a healthy vaccinee effect (Figure 2B). However, the combined analysis of all subsequent periods showed an aHR of 1.37 (95% CI: 1.02–1.85).

The subgroup analyses estimated the aHR at 0.90 (95% CI: 0.67–1.20) among individuals under 50 years of age and at 0.76 (95% CI: 0.51–1.13) among those 50 years of age and older (Table 3B). The aHR was 0.91 (95% CI: 0.67–1.22) for those less clinically vulnerable to severe COVID-19 and 0.76 (95% CI: 0.52–1.12) for the more clinically vulnerable group. The aHR by prior infection status was 0.79 (95% CI: 0.61–1.01) for no prior infection, 1.63 (95% CI: 0.71–3.72) for prior pre-omicron infection, and 1.32 (95% CI: 0.30–5.91) for prior omicron infection.

The overall effectiveness of the third-dose (booster) vaccination compared to the primary series vaccination against infection was 26.3% (95% CI: 25.2–27.5) (Table 2B). However, no significant effect of the third dose was observed against severe, critical, or fatal COVID-19 (34.1%; 95% CI: –46.4 to 76.7). Figure 2—figure supplement 1 further illustrates the overall effectiveness of the third-dose vaccination against severe, critical, or fatal COVID-19, stratified by time since vaccination.

Discussion

The results confirm the presence of a healthy vaccinee effect in rigorously conducted vaccine effectiveness studies. Despite meticulous cohort matching, a particularly pronounced healthy vaccinee effect was evident during the first 6 months after vaccination. Notably, the same effect, with a similar magnitude, was observed in both primary series and booster vaccinations, suggesting a consistent underlying phenomenon.

This effect, similar to that found in influenza vaccine effectiveness studies (Jackson et al., 2006a; Nelson et al., 2009), may stem from seriously ill and end-of-life individuals, such as terminal cancer patients, as well as frail and less mobile elderly persons, being less likely to be vaccinated (Jackson et al., 2006a; Nelson et al., 2009). This leads to a higher short-term mortality risk among the unvaccinated (Jackson et al., 2006a; Nelson et al., 2009). This is supported by this effect being only evident in the first 6 months, and specifically among those aged 50 years and older and those more clinically vulnerable to severe COVID-19. Given the strength of this effect, it seems unlikely that it can be attributed to an effect of vaccination-induced nonspecific immune activation or trained/bystander immunity that protects against a range of infectious and noninfectious outcomes (Benn et al., 2013; Netea et al., 2020; Xu et al., 2023; Tayar et al., 2023).

These findings raise a concern, as vaccine effectiveness is typically estimated for the first few months after vaccination for seasonal infections, or for infections with repeated waves, such as influenza and SARS-CoV-2 (Jackson et al., 2006a; Nelson et al., 2009; Remschmidt et al., 2015; Feikin et al., 2022). The findings support a rationale for excluding seriously ill, immunosuppressed, or functionally impaired individuals in studies of vaccine effectiveness in the general population (Jackson et al., 2006a; Nelson et al., 2009).

While the mortality risk was higher among the unvaccinated group during the first 6 months after vaccination, it subsequently reversed, becoming higher among the vaccinated group. This reversal may be attributed to the depletion of seriously ill individuals in the unvaccinated group during the initial 6 months, leaving behind a cohort enriched with relatively healthier individuals.

While the healthy vaccinee effect was evident, it is plausible that both a healthy vaccinee effect and an indication effect could coexist, manifesting at different strengths and time points. The healthy vaccinee effect is likely driven by seriously ill or end-of-life individuals who are less likely to seek vaccination. In contrast, the indication effect could be driven by individuals with serious but less immediately life-threatening conditions, such as heart disease or diabetes, who pursue vaccination to mitigate their heightened risk of severe infection or complications related to these conditions. Such health conditions elevate mortality risk over a longer time horizon rather than immediately. Consequently, the higher mortality risk observed beyond 6 months is not inconsistent with a potential presence of an indication effect.

Although a healthy vaccinee effect was observed in this study, the extent to which this effect may have biased and skewed the estimated vaccine effectiveness remains uncertain. This effect is presumably more likely to bias vaccine effectiveness against severe forms of COVID-19 than against infection alone (Høeg et al., 2023). The impact of this effect might also have been mitigated somewhat by using specific infection outcomes—such as severe, critical, or fatal COVID-19—and by confirming infections through laboratory methods, rather than relying on broad nonspecific outcomes like all-cause mortality, commonly used in influenza vaccine effectiveness studies (Jackson et al., 2006a; Nelson et al., 2009; Remschmidt et al., 2015). Ironically, the overall healthy vaccinee effect over the entire duration of follow-up may have been partially mitigated by an indication effect.

The results indicated strong protection from vaccination against severe forms of COVID-19, with an observed effectiveness of 96% for the primary series. However, vaccine effectiveness against infection was modest, which is expected given that this type of protection rapidly diminishes within the first few months after vaccination (Feikin et al., 2022; Abu-Raddad et al., 2022c; Chemaitelly et al., 2021b), and effectiveness was estimated over 3 years of follow-up.

This study has limitations, which were assessed within the context of potential risk of bias in nonrandomized studies of interventions (Sterne et al., 2016), drawing on prior literature related to the investigated effects and our previous work using similar study designs on these national databases.

While all COVID-19-related deaths in Qatar were systematically identified through national protocols, as described in the Methods, and made available to the study investigators, the specific causes of non-COVID-19 deaths were not accessible. This limitation constrained the scope of additional analyses that could have been conducted. As a result, while this study provides evidence of the healthy vaccinee effect in rigorously conducted vaccine effectiveness studies, characterizing both its effect size and temporal profile, it does not identify the specific cause of this bias.

Further research is needed to investigate this bias by collecting primary data on the relationship between comorbidity and frailty and vaccination behavior. For example, while older, independent, and active community members may demonstrate a greater preference for vaccination, this may not hold true for frail residents in long-term care facilities. However, such long-term care facilities may enforce a policy of vaccinating all residents. It is also important to examine whether this relationship varies across different vaccine types, such as COVID-19 and influenza.

A number of non-COVID-19 deaths had unascertained or discrepant death date; therefore, these individuals were excluded from the study from the onset. However, this exclusion is not likely to materially affect the analyses, as there were only 23 deaths with unascertained or discrepant death dates in the entire population of Qatar over the 3 years of this study.

Documented COVID-19 deaths may not include all deaths that occurred because of COVID-19 (Islam et al., 2021; Kontis et al., 2020), and thus there could be some misclassification bias affecting the distinction between COVID-19 and non-COVID-19 deaths. However, the number of COVID-19 deaths was small (Figure 1—figure supplement 2 and Figure 1—figure supplement 3), and the COVID-19 death rate in the young and working-age population of Qatar has been one of the lowest worldwide, with less than 0.1% of documented infections resulting in death (AlNuaimi et al., 2023; Chemaitelly et al., 2023e; Chemaitelly et al., 2023b; Johns Hopkins Coronavirus Resource, 2022). Earlier studies suggest that the number of undocumented COVID-19 deaths in Qatar is too small to appreciably affect the analyses of this study (AlNuaimi et al., 2023; Chemaitelly et al., 2023e; Chemaitelly et al., 2023b).

The study analyzed all deaths occurring within Qatar; however, some deaths might have occurred outside the country. Data on deaths outside the country were not available for our analysis. Nevertheless, the matching process was designed to ensure that participants were present in Qatar during the same period and to balance the risk of out-of-country deaths across cohorts. Consequently, these out-of-country deaths are not likely to have influenced the comparative outcomes of the matched cohorts. Further supporting our results, the sensitivity analysis, which was restricted to only Qataris—a group very unlikely to experience out-of-Qatar deaths—corroborated the main study results.

The national testing database served as a sampling frame for unvaccinated individuals in Qatar. However, this database does not capture individuals who have never had a SARS-CoV-2 test since the onset of the pandemic. Nevertheless, testing has been extensive in Qatar, with the vast majority conducted for routine reasons (Altarawneh et al., 2022b; Chemaitelly et al., 2021b). Given the widespread testing mandates and the large volume of tests conducted, it is not likely that any citizen or resident in Qatar has not had at least one SARS-CoV-2 test since the onset of the pandemic (Altarawneh et al., 2022b; Chemaitelly et al., 2021b).

Matched unvaccinated individuals were required to have tested negative for SARS-CoV-2 in the week their matched vaccinated counterparts received their vaccine, ensuring that both groups were present in Qatar during the same time period. Different eligibility criteria between the two arms could bias the study if there was a correlation between testing and non-COVID-19 death. However, the sensitivity analysis for Qataris, which eliminated the requirement for matching by a SARS-CoV-2-negative test, confirmed similar results, suggesting that this matching requirement may not have biased the results.

A consequence of the rigorous matching employed in this study is that the matched cohorts are not fully representative of the population of Qatar. However, the study was specifically designed to address a focused research question: the existence of the healthy vaccinee effect in rigorously conducted vaccine effectiveness studies. Therefore, the emphasis is on the internal validity of the study—ensuring that the relationship between the exposure and outcome is measured accurately and is free from bias and confounding—rather than on external validity or generating estimates specific to the population of Qatar.

This approach parallels the design of vaccine efficacy RCTs, which are typically conducted in select populations that may not fully represent the broader population (e.g. excluding pregnant women, children, or individuals with certain coexisting conditions). The objective of such RCTs and similarly this study—through the use of rigorous matching to control for selection bias, akin to the role of randomization in an RCT—is not to generate population-wide estimates but rather to provide a precise and unbiased measure of the exposure-outcome relationship under investigation.

The study was conducted in a specific national population consisting mainly of healthy working-age adults, thus the generalizability of the findings to other populations remains uncertain. As an observational study, the investigated cohorts were neither blinded nor randomized, so unmeasured or uncontrolled confounding factors cannot be excluded. Although matching accounted for key factors affecting risks of death and infection (Abu-Raddad et al., 2021a; Coyle et al., 2021; Al Thani et al., 2021; Jeremijenko et al., 2021), it was not possible for other factors such as geography or occupation, for which data were unavailable. However, Qatar is essentially a city-state where infection incidence was broadly distributed across neighborhoods. Nearly 90% of Qatar’s population are expatriates from over 150 countries, primarily coming for employment (Abu-Raddad et al., 2021a). In this context, nationality, age, and sex serve as powerful proxies for socioeconomic status (Abu-Raddad et al., 2021a; Coyle et al., 2021; Al Thani et al., 2021; Jeremijenko et al., 2021). Nationality is also strongly associated with occupation (Abu-Raddad et al., 2021a; Coyle et al., 2021; Al Thani et al., 2021; Jeremijenko et al., 2021).

The matching procedure used in this study has been evaluated in previous studies with different epidemiologic designs and using control groups to test for null effects (Abu-Raddad et al., 2021d; Chemaitelly et al., 2021b; Chemaitelly et al., 2021c; Abu-Raddad et al., 2022c; Abu-Raddad et al., 2022b). These prior studies demonstrated that this procedure balances differences in infection exposure to estimate vaccine effectiveness (Abu-Raddad et al., 2021d; Chemaitelly et al., 2021b; Chemaitelly et al., 2021c; Abu-Raddad et al., 2022c; Abu-Raddad et al., 2022b), suggesting that the matching strategy may also have mitigated differences in mortality risk. Lastly, the aHRs were estimated both overall and by 6-month intervals from the start of follow-up. However, the interval-based analysis can be susceptible to changes in the composition of the study population over time.

The study has strengths. It was implemented on Qatar’s entire population and sizable cohorts, representing a diverse range of national backgrounds. Extensive, validated databases from numerous prior COVID-19 studies were utilized in this study. The availability of an integrated digital health information platform provided data on various confounding factors, facilitating rigorous matching based on specific coexisting conditions and prior infection statuses. The ascertainment of COVID-19 deaths was meticulously conducted by trained personnel, adhering to a national protocol and WHO guidelines for classifying COVID-19 case fatalities (World Health Organization, 2023a).

In conclusion, a healthy vaccinee effect was observed, but only in the first 6 months following COVID-19 vaccination and specifically among those aged 50 years and older and those more clinically vulnerable to severe COVID-19. The same effect, with similar magnitude, was observed for both primary series and booster vaccinations, suggesting a consistent underlying phenomenon, perhaps a lower likelihood of vaccination among seriously ill, end-of-life individuals, and less mobile elderly populations. COVID-19 booster vaccine policies should account for this effect when interpreting effectiveness estimates and formulating vaccine guidelines. Despite this effect, the results confirm strong protection from vaccination against severe forms of COVID-19.

Contributors

HC co-designed the study, performed the statistical analyses, and co-wrote the first draft of the article. LJA conceived and co-designed the study, led the statistical analyses, and co-wrote the first draft of the article. HC and LJA accessed and verified all the data. PVC designed mass PCR testing to allow routine capture of variants and conducted viral genome sequencing. PT and MRH designed and conducted multiplex, RT-qPCR variant screening and viral genome sequencing. HMY and AAAT conducted viral genome sequencing. All authors contributed to data collection and acquisition, database development, discussion and interpretation of the results, and to the writing of the article. All authors have read and approved the final manuscript.

Code availability

Standard epidemiological analyses were conducted using standard commands in Stata/SE 18.0.

Acknowledgements

We acknowledge the many dedicated individuals at Hamad Medical Corporation, the Ministry of Public Health, the Primary Health Care Corporation, Qatar Biobank, Sidra Medicine, and Weill Cornell Medicine-Qatar for their diligent efforts and contributions to make this study possible. The authors are grateful for institutional salary support from the Biomedical Research Program and the Biostatistics, Epidemiology, and Biomathematics Research Core, both at Weill Cornell Medicine-Qatar, as well as for institutional salary support provided by the Ministry of Public Health, Hamad Medical Corporation, and Sidra Medicine. HC gratefully acknowledges salary support from the Junior Faculty Transition to Independence Program at Weill Cornell Medicine-Qatar and L’Oréal-UNESCO For Women In Science Middle East Regional Young Talents Program. The authors are also grateful for the Qatar Genome Programme and Qatar University Biomedical Research Center for institutional support for the reagents needed for the viral genome sequencing. Statements made herein are solely the responsibility of the authors.

Appendix 1

Section S1: Study population and data sources

Qatar's national and universal public healthcare system uses the Cerner Millenium electronic medical record (EMR) system to track all the public healthcare encounters of each individual in the country, including all citizens and residents registered in the national and universal public healthcare system. Registration in the public healthcare system is mandatory for citizens and residents.

The databases analyzed in this study are data-extract downloads from the national EMR that have been implemented on a regular weekly schedule since the onset of pandemic by the Business Intelligence Unit at Hamad Medical Corporation (HMC). HMC is the national public healthcare provider in Qatar. At every download, all severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests, coronavirus disease 2019 (COVID-19) vaccinations, hospitalizations related to COVID-19, and all death records regardless of cause are provided to the authors through .csv files. These databases have been analyzed throughout the pandemic not only for study-related purposes, but also to provide policymakers with summary data and analytics to inform the national response.

Every health encounter in the national EMR is linked to an individual through the HMC Number, which serves as a unique identifier that links all records for this individual at the national level. Databases were merged and analyzed using the HMC Number to link all records pertaining to testing, vaccinations, hospitalizations, and deaths. All deaths in Qatar are recorded by the public healthcare system. COVID-19-related healthcare was provided exclusively in the public healthcare system. COVID-19 vaccination was also provided only through the public healthcare system. These health records were tracked throughout the COVID-19 pandemic using the national EMR system. This pre-established system ensured that we had access to comprehensive health records related to this study for both citizens and residents throughout the entire pandemic, allowing us to follow each person over time.

Demographic details for every HMC Number (individual) such as sex, age, and nationality are collected upon issuing of the universal health card, based on the Qatar Identity Card, which is a mandatory requirement by the Ministry of Interior to every citizen and resident in the country. Data extraction from the Qatar Identity Card to the digital health platform is performed electronically through scanning techniques.

All SARS-CoV-2 testing in any facility in Qatar is tracked nationally in one database, the national testing database. This database covers all testing throughout the country, whether in public or private facilities. Every polymerase chain reaction (PCR) test and a proportion of the facility-based rapid antigen tests conducted in Qatar, regardless of location or setting, are classified on the basis of symptoms and the reason for testing, such as the presence of clinical symptoms, contact tracing, participation in surveys or random testing campaigns, individual requests for testing, routine healthcare testing, pre-travel requirements, at the point of entry into the country, or any other relevant reasons for testing.

Before November 1, 2022, SARS-CoV-2 testing in Qatar was performed extensively with about 5% of the population tested every week (Altarawneh et al., 2022b). Based on the distribution of the reason for testing up to November 1, 2022, most of the tests in Qatar were conducted for routine reasons, such as travel-related purposes, and about 75% of infections were diagnosed not because of presence of symptoms (Chemaitelly et al., 2021c, Altarawneh et al., 2022b). Starting from November 1, 2022, testing for SARS-CoV-2 was substantially reduced, but still close to 1% of the population are being tested every week (Chemaitelly et al., 2021c). This study factored all SARS-CoV-2-related testing included in the national testing database over the duration of follow-up.

The first omicron wave that reached its peak in January of 2022 was massive and strained the testing capacity in the country (Chemaitelly et al., 2023a;, Altarawneh et al., 2022b, Altarawneh et al., 2022c, Chemaitelly et al., 2022b; Chemaitelly et al., 2023b). To alleviate the burden on PCR testing, rapid antigen testing was rapidly introduced. The swift change in testing policy precluded incorporating reason for testing for a number of rapid antigen tests. While the reason for testing is documented for all PCR tests, it is not uniformly available for all rapid antigen tests.

Rapid antigen test kits are accessible for purchase at pharmacies in Qatar, but results of home-based testing are neither reported nor documented in the national databases. Since SARS-CoV-2-test outcomes were linked to specific public health measures, restrictions, and privileges, testing policy and guidelines stress facility-based testing as the core testing mechanism in the population. While facility-based testing is provided free of charge or at low subsidized costs, depending on the reason for testing, home-based rapid antigen testing is de-emphasized and not supported as part of national policy.

Qatar launched its COVID-19 vaccination program in December 2020, employing mRNA vaccines and prioritizing individuals based on coexisting conditions and age criteria (Chemaitelly et al., 2021b, Abu-Raddad et al., 2022b). COVID-19 vaccination was provided free of charge, regardless of citizenship or residency status, and was nationally tracked (Chemaitelly et al., 2021b, Abu-Raddad et al., 2022b).

Qatar has unusually young, diverse demographics, in that only 9% of its residents are ≥50 years of age, and 89% are expatriates from over 150 countries (Planning and Statistics Authority-State of Qatar, 2020 , Abu-Raddad et al., 2021a). Further descriptions of the study population and these national databases were reported previously (Abu-Raddad et al., 2021a, Chemaitelly et al., 2021c, Chemaitelly et al., 2021b, Chemaitelly et al., 2022a, Chemaitelly et al., 2022b, Chemaitelly et al., 2023b, Chemaitelly et al., 2023c, AlNuaimi et al., 2023; Abu-Raddad et al., 2022a; Altarawneh et al., 2022c).

Section S2: Laboratory methods and variant ascertainment

Real-time reverse-transcription polymerase chain reaction testing

Nasopharyngeal and/or oropharyngeal swabs were collected for PCR testing and placed in Universal Transport Medium (UTM). Aliquots of UTM were: (1) extracted on KingFisher Flex (Thermo Fisher Scientific, USA), MGISP-960 (MGI, China), or ExiPrep 96 Lite (Bioneer, South Korea) followed by testing with real-time reverse-transcription PCR (RT-qPCR) using TaqPath COVID-19 Combo Kits (Thermo Fisher Scientific, USA) on an ABI 7500 FAST (Thermo Fisher Scientific, USA); (2) tested directly on the Cepheid GeneXpert system using the Xpert Xpress SARS-CoV-2 (Cepheid, USA); or (3) loaded directly into a Roche cobas 6800 system and assayed with the cobas SARS-CoV-2 Test (Roche, Switzerland). The first assay targets the viral S, N, and ORF1ab gene regions. The second targets the viral N and E-gene regions, and the third targets the ORF1ab and E-gene regions.

All PCR testing was conducted at the Hamad Medical Corporation Central Laboratory or Sidra Medicine Laboratory, following standardized protocols.

Rapid antigen testing

SARS-CoV-2 antigen tests were performed on nasopharyngeal swabs using one of the following lateral flow antigen tests: Panbio COVID-19 Ag Rapid Test Device (Abbott, USA); SARS-CoV-2 Rapid Antigen Test (Roche, Switzerland); Standard Q COVID-19 Antigen Test (SD Biosensor, Korea); or CareStart COVID-19 Antigen Test (Access Bio, USA). All antigen tests were performed point-of-care according to each manufacturer's instructions at public or private hospitals and clinics throughout Qatar with prior authorization and training by the Ministry of Public Health (MOPH). Antigen test results were electronically reported to the MOPH in real time using the Antigen Test Management System which is integrated with the national Coronavirus Disease 2019 (COVID-19) database.

Classification of infections by variant type

Surveillance for SARS-CoV-2 variants in Qatar is based on viral genome sequencing and multiplex RT-qPCR variant screening (Vogels et al., 2021) of weekly collected random positive clinical samples (Abu-Raddad et al., 2021b , Chemaitelly et al., 2021c, National project of surveillance for variants of concern and viral genome sequencing, 2021, Benslimane et al., 2021, Hasan et al., 2021, Chemaitelly et al., 2021b), complemented by deep sequencing of wastewater samples (National project of surveillance for variants of concern and viral genome sequencing, 2021, Saththasivam et al., 2021, El-Malah et al., 2022). Further details on the viral genome sequencing and multiplex RT-qPCR variant screening throughout the SARS-CoV-2 waves in Qatar can be found in previous publications (National project of surveillance for variants of concern and viral genome sequencing, 2021, Abu-Raddad et al., 2021b, Chemaitelly et al., 2021c, Benslimane et al., 2021, Hasan et al., 2021, Chemaitelly et al., 2021b, Abu-Raddad et al., 2022a, Tang et al., 2021, Altarawneh et al., 2022b, Altarawneh et al., 2022c, Chemaitelly et al., 2022a, Qassim et al., 2022, Altarawneh et al., 2022a, Chemaitelly et al., 2023d; Chemaitelly et al., 2024).

Section S3: Classification of coexisting conditions

Coexisting conditions were ascertained and classified based on the ICD-10 codes for the conditions as recorded in the electronic health record encounters of each individual in the national EMR database that includes all citizens and residents registered in the national and universal public healthcare system. The public healthcare system provides healthcare to the entire resident population of Qatar free of charge or at heavily subsidized costs, including prescription drugs. With the mass expansion of this sector in recent years, facilities have been built to cater to specific needs of subpopulations. For example, tens of facilities have been built, including clinics and hospitals, in localities with high density of craft and manual workers (Al Thani et al., 2021).

All encounters for each individual were analyzed to determine the coexisting-condition classification for that individual. The national EMR database includes encounters starting from 2013, after this system was launched in Qatar. As long as each individual had at least one encounter with a specific coexisting-condition diagnosis since 2013, this person was classified with this coexisting condition. Individuals who may have coexisting conditions but never sought care in the public healthcare system were classified as individuals with no coexisting condition due to absence of recorded encounters for them.

The classification of coexisting conditions spanned the following conditions: (1) Behchet's disease, (2) cancer, (3) cardiovascular diseases, (4) infectious and parasitic diseases, (5) Chron's disease, (6) chronic kidney disease (CKD), (7) chronic liver disease (CLD), (8) chronic lung disease, (9) congenital malformations, deformations and chromosomal abnormalities, (10) diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism, (11) diseases of the ear and mastoid process, (12) deep vein thrombosis (DVT), (13) dermatitis, (14) diabetes mellitus, (15) diseases of the circulatory system, (16) diseases of the digestive system, (17) diseases of the eye and adnex, (18) diseases of the genitourinary system, (19) diseases of the musculoskeletal system and connective tissue, (20) diseases of the nervous system, (21) diseases of the respiratory system, (22) diseases of the skin and subcutaneous tissue, (23) endocrine, nutritional and metabolic diseases, (24) gingivitis, (25) hypertension, (26) injury, poisoning and certain other consequences of external causes, (27) mental and behavioral disorders, (28) neoplasms, (29) periodontitis, (30) pregnancy, childbirth and the puerperium, (31) pulmonary tuberculosis, (32) rheumatoid arthritis, (33) Sjogren's syndrome, (34) stroke or neural conditions, (35) symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified, (36) systemic lupus erythematosus, (37) systemic sclerosis, (38) organ transplant, and (39) other unspecified factors influencing health status and contact with health services.

Section S4: COVID-19 severity, criticality, and fatality classification

Classification of COVID-19 case severity (acute-care hospitalizations) (World Health Organization, 2023b), criticality (intensive-care-unit hospitalizations) (World Health Organization, 2023b), and fatality (World Health Organization, 2023a) followed World Health Organization (WHO) guidelines. Assessments were made by trained medical personnel independent of study investigators and using individual chart reviews, as part of a national protocol applied to every hospitalized COVID-19 patient. Each hospitalized COVID-19 patient underwent an infection severity assessment every three days until discharge or death. We classified individuals who progressed to severe, critical, or fatal COVID-19 between the time of the documented infection and the end of the study based on their worst outcome, starting with death (World Health Organization, 2023a), followed by critical disease (World Health Organization, 2023b), and then severe disease (World Health Organization, 2023b).

Severe COVID-19

Severe COVID-19 disease was defined per WHO classification as a SARS-CoV-2 infected person with “oxygen saturation of <90% on room air, and/or respiratory rate of >30 breaths/minute in adults and children >5 years old (or ≥60 breaths/minute in children <2 months old or ≥50 breaths/minute in children 2-11 months old or ≥40 breaths/minute in children 1–5 years old), and/or signs of severe respiratory distress (accessory muscle use and inability to complete full sentences, and, in children, very severe chest wall indrawing, grunting, central cyanosis, or presence of any other general danger signs)” (World Health Organization, 2023b). Detailed WHO criteria for classifying Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection severity can be found in the WHO technical report (World Health Organization, 2023b).

Critical COVID-19

Critical COVID-19 disease was defined per WHO classification as a SARS-CoV-2 infected person with “acute respiratory distress syndrome, sepsis, septic shock, or other conditions that would normally require the provision of life sustaining therapies such as mechanical ventilation (invasive or non-invasive) or vasopressor therapy” (World Health Organization, 2023b). Detailed WHO criteria for classifying SARS-CoV-2 infection criticality can be found in the WHO technical report (World Health Organization, 2023b).

Fatal COVID-19

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Hiam Chemaitelly, Email: hsc2001@qatar-med.cornell.edu.

Laith J Abu-Raddad, Email: lja2002@qatar-med.cornell.edu.

Joshua T Schiffer, Fred Hutchinson Cancer Research Center, United States.

Funding Information

This paper was supported by the following grants:

Weill Cornell Medicine-Qatar Biomedical Research Program and the Biostatistics, Epidemiology, and Biomathematics Research Core to Hiam Chemaitelly.
Weill Cornell Medicine-Qatar Junior Faculty Transition to Independence Program to Hiam Chemaitelly.
Ministry of Public Health to Houssein H Ayoub, Peter Coyle, Patrick Tang, Mohammad R Hasan, Hadi M Yassine, Asmaa A Al Thani, Zaina Al Kanaani, Einas Al Kuwari, Andrew Jeremijenko, Anvar Hassan Kaleeckal, Ali Nizar Latif, Riyazuddin Mohammad Shaik, Hanan F Abdul Rahim, Gheyath K Nasrallah, Mohamed Ghaith Al Kuwari, Hamad Eid Al Romaihi, Mohamed H Al Thani, Abdullatif Al Khal, Roberto Bertollini, Adeel A Butt.
Sidra Medicine to Houssein H Ayoub, Peter Coyle, Patrick Tang, Mohammad R Hasan, Hadi M Yassine, Asmaa A Al Thani, Zaina Al Kanaani, Einas Al Kuwari, Andrew Jeremijenko, Anvar Hassan Kaleeckal, Ali Nizar Latif, Riyazuddin Mohammad Shaik, Hanan F Abdul Rahim, Gheyath K Nasrallah, Mohamed Ghaith Al Kuwari, Hamad Eid Al Romaihi, Mohamed H Al Thani, Abdullatif Al Khal, Roberto Bertollini, Adeel A Butt.
Hamad Medical Corporation to Houssein H Ayoub, Peter Coyle, Patrick Tang, Mohammad R Hasan, Hadi M Yassine, Asmaa A Al Thani, Zaina Al Kanaani, Einas Al Kuwari, Andrew Jeremijenko, Anvar Hassan Kaleeckal, Ali Nizar Latif, Riyazuddin Mohammad Shaik, Hanan F Abdul Rahim, Gheyath K Nasrallah, Mohamed Ghaith Al Kuwari, Hamad Eid Al Romaihi, Mohamed H Al Thani, Abdullatif Al Khal, Roberto Bertollini, Adeel A Butt.
Qatar Genome Programme to Houssein H Ayoub, Peter Coyle, Patrick Tang, Mohammad R Hasan, Hadi M Yassine, Asmaa A Al Thani, Zaina Al Kanaani, Einas Al Kuwari, Andrew Jeremijenko, Anvar Hassan Kaleeckal, Ali Nizar Latif, Riyazuddin Mohammad Shaik, Hanan F Abdul Rahim, Gheyath K Nasrallah, Mohamed Ghaith Al Kuwari, Hamad Eid Al Romaihi, Mohamed H Al Thani, Abdullatif Al Khal, Roberto Bertollini, Adeel A Butt.
Qatar University Biomedical Research Center to Houssein H Ayoub, Peter Coyle, Patrick Tang, Mohammad R Hasan, Hadi M Yassine, Asmaa A Al Thani, Zaina Al Kanaani, Einas Al Kuwari, Andrew Jeremijenko, Anvar Hassan Kaleeckal, Ali Nizar Latif, Riyazuddin Mohammad Shaik, Hanan F Abdul Rahim, Gheyath K Nasrallah, Mohamed Ghaith Al Kuwari, Hamad Eid Al Romaihi, Mohamed H Al Thani, Abdullatif Al Khal, Roberto Bertollini, Adeel A Butt.
L'Oréal For Women In Science Middle East Regional Young Talents Program to Hiam Chemaitelly.
United Nations Educational, Scientific and Cultural Organization For Women In Science Middle East Regional Young Talents Program to Hiam Chemaitelly.
Qatar University to Houssein H Ayoub, Peter Coyle, Hadi M Yassine, Asmaa A Al Thani, Gheyath K Nasrallah, Hanan F Abdul-Rahim, Laith J Abu-Raddad.

Additional information

Competing interests

No competing interests declared.

has received institutional grant funding from Gilead Sciences unrelated to the work presented in this paper.

Author contributions

Resources, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing.

Resources, Data curation, Writing – review and editing.

Resources, Data curation, Investigation, Writing – review and editing.

Resources, Data curation, Writing – review and editing.

Conceptualization, Resources, Data curation, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing.

Ethics

The institutional review boards at Hamad Medical Corporation and Weill Cornell Medicine-Qatar approved this retrospective study with a waiver of informed consent.

Additional files

MDAR checklist

elife-103690-mdarchecklist1.docx^{(88.4KB, docx)}

Data availability

The dataset of this study is the property of the Qatar Ministry of Public Health and was provided to the researchers through a restricted-access agreement that prohibits sharing the dataset with third parties or making it publicly available. Access to the data is restricted to preserve the confidentiality of patient information and was granted to researchers for research purposes only. Individuals or entities interested in accessing the data may contact Dr. Hamad Al-Romaihi, Director of the Health Protection and Communicable Diseases Control Department at the Ministry of Public Health in Qatar, via email at halromaihi@MOPH.GOV.QA. All proposed research must obtain the necessary ethical approvals. Commercial use of the data is strictly prohibited. Requests for access are assessed by the Ministry of Public Health in Qatar, and approval is granted at its discretion. In compliance with data privacy laws and the data-sharing agreement with the Ministry of Public Health in Qatar, no datasets, whether raw or de-identified, can be publicly released by the researchers. However, aggregate data that do not compromise individual privacy are included within the manuscript and supplementary materials. This ensures transparency of the research findings and supports the reproducibility of results while maintaining compliance with legal requirements.

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eLife. doi: 10.7554/eLife.103690.sa0

Editor's evaluation

Joshua T Schiffer ¹

This fundamental work devoted to the effectiveness of COVID-19 vaccination is the first to calculate within a single paper the COVID vaccine effectiveness as well as a crucial confounder – the so-called healthy vaccinee effect/bias that influences results of observational vaccine effectiveness studies. Using rigorous methods and providing compelling evidence, the authors found a 65 % decrease in the likelihood of dying from non-COVID causes in the vaccinated individuals in the first six months after vaccination compared to the meticulously matched unvaccinated individuals. This indicates that observational studies on COVID-19 vaccines may inflate vaccine effectiveness, even if it is evaluated using the best available industry-standard methods. The work will be of broad interest not only to epidemiologists and vaccinologists but virtually to any scientist investigating the role of vaccines.

eLife. doi: 10.7554/eLife.103690.sa1

Decision letter

Editor: Joshua T Schiffer¹

Reviewed by: David Henry

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Assessing Healthy Vaccinee Effect in COVID-19 Vaccine Effectiveness Studies: A National Cohort Study in Qatar" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Joshua Schiffer as the Senior Editor.

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

Essential Revisions:

1) Provide more details about the vaccine rollout strategy that was employed in Qatar and who was initially targeted for vaccination.

2) Provide sensitivity analysis removing criteria of negative test for controls for matching.

3) Provide sub-analysis of vaccine effectiveness against severe, critical, and fatal COVID in the first six months for comparison with the temporality of putative healthy vaccinee effect.

4) Provide further discussion of potential biases present in the study, and temper claims where warranted due to the high possibility of bias.

Reviewer #1 (Recommendations for the authors):

This study examines whether there is evidence of a healthy vaccinee effect, where those who opt for vaccination are on average healthier than those who remain unvaccinated, in Qatar in the context of COVID-19 vaccination. This question is interesting to assess because a healthy vaccinee effect would tend to bias estimates of eventual vaccine effectiveness, leading to potential underestimation of the vaccine's effectiveness since healthier individuals are more likely to become vaccinated. The study finds that the risk of non-COVID-19 related deaths was higher in vaccinated individuals, especially during the first 6 months after vaccination.

Particular strengths of this study include the high population coverage of the datasets, which include the entire population of Qatar and allow the linkage of all vaccinations and death records in the population, including non-permanent residents, as well as the very large sample size providing high statistical power for analyses.

I think perhaps the largest weakness of this study is the potential for selection bias. In particular, the decision to match vaccinated persons with unvaccinated persons who have a negative SARS-CoV-2 test could have led to selection bias because this means that different selection criteria were applied to vaccinated and unvaccinated individuals. Many of the reasons that would lead persons to get tested for SARS-CoV-2 would be related to health conditions and so might very plausibly be associated with their mortality risk, meaning that the selection of controls may not be independent from death risk. While the authors argue there is not much difference between a sensitivity analysis where they remove these matching criteria, I find there is a non-negligible difference in the hazard ratio estimates when these matching criteria are not applied, with the hazard ratios being somewhat attenuated. This suggests that while there is likely to be a healthy vaccinee effect, this effect may be slightly overestimated in the main analysis due to selection bias. I would have liked to see an analysis where these matching criteria were not applied in the full study population, not just in the subpopulation of Qatari citizens, who represent only some 9% of the total sample.

Another source of potential bias is that over half of all potentially eligible persons from the full cohort were not included in analyses because they could not be matched. Table 1 suggests that the excluded participants were older and had more comorbidities than those who were included in the matched cohort. It is therefore unclear whether the results of the analysis can be generalized to the entire population of Qatar given that over half were excluded.

While the analysis included the number of coexisting comorbidities in the matching process, I think it could be argued that comorbidities/pre-existing conditions are part of the healthy vaccinee effect that the authors are trying to capture, and so it might not be appropriate to match on this factor as comorbidities are part of the effect. Table 1 suggests that unvaccinated individuals are in fact less likely to have co-morbidities than vaccinated individuals. The healthy vaccinee effect might therefore have been overestimated by matching on this factor, as there is evidence that less healthy individuals were more likely to get vaccinated prior to matching.

While the above issues potentially could have led to bias of the results, I think the effect would still likely be present in analyses, so I would agree with the authors' main conclusion that there likely exists a healthy vaccinee effect. I also agree that it is however unclear to what extent this might have biased the estimates of vaccine effectiveness against COVID-19 related outcomes.

I thought the analysis of hazard ratios stratified by time since vaccination was particularly interesting and valuable, showing that the healthy vaccinee effect is mostly found in the first 6 months after vaccination. However, I would dispute the interpretation that the higher hazard ratios after 6 months represent confounding by indication (which the authors call the indication effect). It is likely that this is a bias caused by the depletion of susceptibles, where the unvaccinated individuals most likely to die are depleted at the start so the remaining unvaccinated survivors have a lower death rate, and is not because the vaccinated individuals were at higher risk of death (confounding by indication). I think the authors interpret this correctly in the discussion as they mention that this observation is potentially due to the depletion of seriously ill individuals in the unvaccinated, but I think they use the wrong terminology to refer to this effect, which would a bias due to depletion of susceptibles rather than confounding by indication.

– In general there could be more details about the vaccine rollout strategy that was employed in Qatar and who was initially targeted for vaccination, as this can help us understand who potentially may be overrepresented among the vaccinees. Most countries did not vaccinate at random, so if there were particular demographic criteria that were used to target vaccination this should be made clearer.

– I think that the criteria of a negative test for controls for matching could have led to selection bias as this criteria was not applied to vaccinated cases. I think there is enough of a difference in the sensitivity analyses when removing these criteria to suggest that this may have affected the estimates. I would have liked to see a sensitivity analysis for the full cohort, not just in Qataris, where either this criteria is dropped for the unvaccinated to assess the difference between hazard ratio estimates.

– It is not clear whether the cohort members who were excluded due to inability to match are systematically different from those who were included. I would have liked to see an estimate of the death probability in those excluded vs. included to assess the potential for selection bias.

– The authors interpret their hazard ratios as being evidence of a healthy vaccinee effect and indication effect in the results. In general it is better to reserve interpretation of the hazard ratios for the discussion, I would recommend simply reporting the hazard ratios in the results and reserving the interpretation of these as healthy vaccinee effect for the discussion.

– It is not clear based on the methods how the cause of death was assessed, as the authors indicate in the discussion they could not ascertain causes of death, so it is unclear how they know whether a death is due to COVID-19 or not.

– There is some discrepancy in the authors' interpretation of the hazard ratios >1, as they say, it is confounding by indication, but their more detailed explanation in the discussion discusses an effect that is due to depletion of susceptibles in the unvaccinated, not confounding due to indication for vaccination among healthy individuals. I think that the explanation of bias due to depletion of susceptibles in the unvaccinated is more plausible, as I would expect confounding by indication to be more present closer to vaccination (as it is health status at the time of vaccination which leads to confounding by indication, and this effect would be expected to diminish with time since vaccination). The authors should distinguish between these two hypotheses, as they are different.

– The authors may also want to consider a sensitivity analysis where they do not include comorbidities in the matching process because comorbidities are part of the healthy vaccinee effect that the authors are trying to measure; a person with less comorbidities is by definition healthier than a person with more comorbidities.

Reviewer #2 (Recommendations for the authors):

First of all, I would like to applaud the authors of this paper. Although several papers on HVE based on data from the Czech Republic were published in regard to COVID vaccines, this is by far the most thorough paper on healthy vaccinee effect in COVID-19 vaccines so far and the methods are meticulous. I particularly like the fact that the patients were matched also for prior COVID-19 infection – besides the reasons mentioned by the authors, another reason is that prior COVID-19 infection provides also excellent protection from a subsequent severe or critical COVID-19, which could otherwise skew their results. The paper shows also another important fact concerning HVE – this effect remains huge in the first six months despite a methodologically accurate and correct approach involving matching for a wide range of variables.

Still, I have several concerns that I feel need to be addressed, especially with statements that are, in my opinion, not entirely justified:

1) I disagree with the statement (Lines 71 and 395) that the effectiveness of the booster dose against severe, critical, or fatal COVID was 34.1% (95% CI: -46.4-76.7). In view of the wide and zero-containing confidence interval, this phrasing leads to misinterpretation. I would rather suggest rephrasing it as “No significant effect of the third dose of vaccine against severe, critical, or fatal COVID was demonstrated (34.1%; 95% CI: -46.4-76.7)”. After all, calculating any vaccine effectiveness from 6 vs 9 severe, critical, or fatal cases among approx. 70 thousand infections is doubtful. Rather, this result highlights a very good resistance to severe/critical/fatal COVID-19 in both these groups.

2) As rightly discussed by the Authors from Line 411 onwards, the healthy vaccinee bias is present at the beginning but after a couple of months (during which most of the frailest individuals die), HVE disappears, being replaced by indication bias. Hence, I am surprised that the authors did not perform a separate analysis of vaccine effectiveness against severe, critical, and fatal COVID in the first six months. In my opinion, such an analysis needs to be added to the paper, especially considering the fact that the vast majority of COVID deaths in Qatar within the study period occurred from March to June 2021, i.e., in the first 6 months after the majority of the elderly and frail individuals took their second vaccine dose. It is the same period in which the biggest healthy vaccinee effect (HVE) of 65 % (i.e., aHR for non-COVID deaths of 0.35) was observed – and comparison of the same period is necessary to correctly interpret the findings.

3) From the perspective of the approx. 65 % healthy vaccinee bias (i.e., aHR=0.35) in the first 6 months when most COVID-related deaths occurred, I think that the statement "Despite the presence of a healthy vaccinee effect, the results still confirm strong protection from vaccination against severe forms of COVID-19, as the observed effectiveness for the primary series was extremely high, at 96%" is too strong.

4) As mentioned in the summary of my review, it is, in my opinion, necessary to highlight the fact that HVE was present despite meticulous matching. This is actually the most important finding of this study – the failure to remove HVE through matching is crucial information for the whole bulk of observational studies on COVID-19 vaccination effectiveness. I suggest adding something along the lines that "Despite meticulous matching, large healthy vaccinee effect (aHR for non-COVID death in vaccinated vs unvaccinated of 0.35) was observed in the first six months of follow-up", probably to the Discussion as well as to the Abstract.

5) Seeing the virtually zero effect of booster vaccination, I would be very much interested in seeing the comparison of two doses vs the unvaccinated for the "after-booster" period (not saying that this is a must, I understand it is a lot of work; it could come as a new paper).

6) Calculating and reporting HVE over a period of several years is not optimal. The authors discuss this very well but still analyze the combined HVE+indication bias over the whole period and report it as one of the most prominent findings. I think that the individual periods (in which these two biases can be to a large degree separated) are much more interesting and important. It would be very helpful to calculate and compare vaccine effectiveness against severe, critical, or fatal COVID for each of the periods shown in Figure 2 and present it along with the results shown in Figure 2 now. This should be ideally also done separately for the most vulnerable group of 50+ years of age.

7) Was there any period with testing bias in Qatar? In other words, was there any period when the vaccinated did not have to test for COVID-19 while the unvaccinated had to test? If so, could it have influenced the results of your study? I think the paper would benefit from this discussion.

8) I think that the peculiar composition of Qatar's population is leading to problems with low numbers, especially in the elderly population most vulnerable to COVID-19, is basically the only limitation of this study. However, the reported pattern is in line with what was observed elsewhere and the authors did, as mentioned above, an excellent job in making up for this limitation by meticulous matching so I consider their work highly relevant and well performed.

Reviewer #3 (Recommendations for the authors):

The authors have conducted cohort studies using routinely collected data from Qatar. These datasets have been the basis of a series of high-quality and influential studies that were conducted in Qatar into COVID-19 vaccine effectiveness (VE) during the pandemic.

In this paper the authors are less focused on VE and more on defining the degree and temporal profile of a possible early 'healthy vaccinee effect' followed by an 'indication bias'. The category of 'non-COVID' death is a potentially useful negative outcome control. Deviation from the null indicates bias if the vaccines truly have no effect on the study outcomes. However, its value here is reduced by the lack of information on cause of death. In this study it may be a reasonable directional bias indicator but does not specify the cause of bias. I believe their methods and analyses are robust and they are observing real effects. However, the authors do not present direct data on causes of death or variables that would help define healthy vaccinees or underpin the claim of indication bias. I think they need to provide a more comprehensive defence of their conclusions.

1). It would be helpful if the authors defined the boundaries of the 'healthy vaccinee effect' they are investigating with their data. The closest to a definition I could find was in the Introduction where the authors state 'the healthy vaccinee effect occurs when healthier or health-conscious individuals are more likely to receive vaccination'. In this setting 'health-conscious individuals' are making a deliberate choice to be vaccinated, and this is consistent with other choices they make (exercise, avoiding smoking and excess alcohol, eating a healthy diet, undergoing screening). At times in the paper the authors appear to equate the healthy vaccinee effect with the absence of underlying disease and, at its most extreme, the absence of disease that would lead to avoidance of vaccination because of clinical futility. There is no doubt that omission of vaccination is practiced in patients with terminal disease, but I think it should be regarded as a separate bias and its absence does not denote a 'healthy vaccinee effect'. I think this needs clarification.

2) I think the overall relationship between comorbidity and frailty and vaccination choice needs deeper discussion. The authors cite community studies of influenza vaccination, which generally have lower population coverage than COVD-19 vaccination, allowing more room for selection biases. While older independent community members who are active may demonstrate a greater preference for influenza vaccination, this will not be the case for those in long-term care facilities, where a common policy is to vaccinate all residents.

3) The authors also mention confounding by indication, which with COVID-19 has been influenced by government policies to provide early vaccination to high-risk groups and healthcare workers. As they say, this runs counter to the presumed healthy vaccinee effect. These policies change quickly and can introduce time-varying exposures in certain groups. I am uncertain if vaccination policies were considered as potential sources of bias – but they may be controlled by the decision to match on calendar week. The same comment applies to 'environmental risk' – variation in community attack rates over time and region.

4) The authors seem somewhat dismissive of misclassification of deaths as contributing to their findings. As noted, they don't have comprehensive cause of death data. I agree that a non-specific immunostimulant benefit of the vaccines is unlikely. However, some COVID-related deaths may be missed during coding. For instance, someone who dies of a heart attack or stroke 4 weeks after a serious COVID illness. The coding decision could also be influenced by knowledge of vaccination status as it appears that coders were unblinded.

5) But misclassification of outcomes is not the only potential source of bias. Because the authors don't have direct evidence to support healthy user bias and confounding by indication, I think they should use a bias framework (e.g., ROBINS-I) to discuss and (as appropriate) reject the other potential causes of bias

6) I am not arguing their assertions are wrong. But I think their language is over-confident and they need to make a more comprehensive case to back their conclusions.

7) This is an accomplished group performing sophisticated studies. However, the level of self-citation seems excessive. In some cases, it is justified to anchor the current work. But there is a large literature on the effects of COVID-19 on COVID mortality with a significant number of studies that also reported all causes of non-COVID mortality. This is not reflected in the authors' choice of references.

eLife. 2025 Jun 9;14:e103690. doi: 10.7554/eLife.103690.sa2

Author response

Essential Revisions:

1) Provide more details about the vaccine rollout strategy that was employed in Qatar and who was initially targeted for vaccination.

A subsection has been added to describe the rollout strategy employed in Qatar, including the initial target groups prioritized for vaccination (Methods, Page 8, Paragraph 3 and Page 9, Paragraphs 1-2). Additionally, a figure has been included to illustrate the rollout of both the primary vaccination series and booster doses (Figure 1—figure supplement 1).

In this context, we would also like to highlight a key strength of this study. While vaccination policies evolve over time, potentially introducing time-varying exposures across different groups, we addressed this by matching participants by calendar week and initiating follow-up from this time point. This approach ensured that each matched pair was recruited into the cohorts during a period with similar vaccination policies and practices. This point has now been clarified in the revised manuscript (Methods, Page 12, Paragraph 3).

2) Provide sensitivity analysis removing criteria of negative test for controls for matching.

This sensitivity analysis was already included in the original manuscript but was restricted to the cohort of Qatari nationals. This subgroup represents the segment of the population that can be confidently assumed to have continuous presence in Qatar. Conducting this analysis for the entire population, as suggested by the first reviewer, may introduce bias, as it removes the requirement for evidence of presence in Qatar.

The expatriate population in Qatar primarily consists of craft and manual workers, and this demographic has experienced turnover due to infrastructure projects associated with the 2022 FIFA World Cup. Consequently, comparing vaccinated individuals, whose presence in Qatar is verified through their vaccination records, with unvaccinated individuals, for whom there is uncertainty about presence and timing, risks underestimating death and infection incidence among the unvaccinated group. Including a sensitivity analysis under such conditions, where there is a reasonable basis to suspect bias, could compromise the validity of the results, and we were therefore hesitant to include such an analysis.

To address this request and the reviewer's concern, we conducted the suggested sensitivity analysis, and the results are presented in Author response image 1. The analysis reaffirmed, with statistical significance, the same observed healthy vaccinee effect and its characteristic temporal evolution as in the main analysis. However, as expected, the effect size was smaller due to bias introduced by the under ascertainment of deaths in the unvaccinated group.

Author response image 1. — Adjusted hazard ratios for incidence of non-COVID-19 death in the entire population without matching on a SARS-CoV-2-negative test among controls. Results are shown for the (A) two-dose analysis and the (B) three-dose analysis, in the first 6 months of follow-up and the period thereafter.

Given these considerations, we are inclined to retain the original sensitivity analysis restricted to Qataris and exclude the new analysis for the entire population due to concerns about bias affecting the accuracy of the effect size estimation, even though the new analysis qualitatively demonstrated similar results.

3) Provide sub-analysis of vaccine effectiveness against severe, critical, and fatal COVID in the first six months for comparison with the temporality of putative healthy vaccinee effect.

Excellent idea, thank you. This sub analysis has now been incorporated, examining overall effectiveness as well as effectiveness within the subgroups of individuals aged <50 years and those aged ≥50 years, as also requested by Reviewer 2 (Figure 2—figure supplement 1). The findings were consistent with the main study results. This addition has been also described in the Results section (Results, Page 19, Paragraph 1 and Page 20, Paragraph 4).

4) Provide further discussion of potential biases present in the study, and temper claims where warranted due to the high possibility of bias.

The manuscript has been substantially revised and enhanced with a more detailed discussion of potential biases, as well as additional analyses, all of which support the study findings (changes throughout the manuscript). Claims have been tempered where appropriate to ensure a balanced interpretation of the results. For a comprehensive overview of these changes, please note our detailed responses to the reviewers' comments, as outlined below.

Reviewer #1 (Recommendations for the authors):

This study examines whether there is evidence of a healthy vaccinee effect, where those who opt for vaccination are on average healthier than those who remain unvaccinated, in Qatar in the context of COVID-19 vaccination. This question is interesting to assess because a healthy vaccinee effect would tend to bias estimates of eventual vaccine effectiveness, leading to potential underestimation of the vaccine's effectiveness since healthier individuals are more likely to become vaccinated. The study finds that the risk of non-COVID-19 related deaths was higher in vaccinated individuals, especially during the first 6 months after vaccination.

Particular strengths of this study include the high population coverage of the datasets, which include the entire population of Qatar and allow the linkage of all vaccinations and death records in the population, including non-permanent residents, as well as the very large sample size providing high statistical power for analyses.

I think perhaps the largest weakness of this study is the potential for selection bias. In particular, the decision to match vaccinated persons with unvaccinated persons who have a negative SARS-CoV-2 test could have led to selection bias because this means that different selection criteria were applied to vaccinated and unvaccinated individuals. Many of the reasons that would lead persons to get tested for SARS-CoV-2 would be related to health conditions and so might very plausibly be associated with their mortality risk, meaning that the selection of controls may not be independent from death risk. While the authors argue there is not much difference between a sensitivity analysis where they remove these matching criteria, I find there is a non-negligible difference in the hazard ratio estimates when these matching criteria are not applied, with the hazard ratios being somewhat attenuated. This suggests that while there is likely to be a healthy vaccinee effect, this effect may be slightly overestimated in the main analysis due to selection bias. I would have liked to see an analysis where these matching criteria were not applied in the full study population, not just in the subpopulation of Qatari citizens, who represent only some 9% of the total sample.

We appreciate the reviewer's perspective, but we respectfully disagree with the assertion that there is a non-negligible difference in the hazard ratio estimates in the cited sensitivity analyses when the matching criteria are not applied. The hazard ratios were 0.29 (95% CI: 0.19–0.43) and 0.38 (95% CI: 0.30–0.50), both with overlapping 95% confidence intervals.

It is important to note that these two hazard ratios were derived from analyses conducted on two distinct cohort studies, each involving different cohorts, as the entire matching process had to be repeated for the analysis in which the matching to a negative SARS-CoV-2 test was removed. Consequently, due to sampling variation, the hazard ratios were not identical; however, they remain consistent within overlapping 95% confidence intervals. Even if the exact same analysis were repeated under identical conditions, the hazard ratios would, as the reviewer would appreciate, still vary due to sampling variation, but the estimates would fall within overlapping 95% confidence intervals.

We also appreciate the reviewer's point that, under normal circumstances, many of the factors prompting individuals to undergo SARS-CoV-2 testing might be related to underlying health conditions and could plausibly be associated with mortality risk. However, this association is much less likely in the context of the COVID-19 pandemic due to the extensive testing conducted primarily for routine reasons unrelated to health conditions. Until October 31, 2022, Qatar implemented a widespread testing strategy, testing 5% of the population weekly, primarily for routine purposes such as screening or travel-related requirements [1, 2].

We conducted the sensitivity analysis removing the matching requirement for a SARS-CoV-2-negative test exclusively for Qataris, as this subgroup represents the portion of the population that can be confidently assumed to be continuously present in Qatar. Conducting this analysis for the entire population, as suggested by the reviewer, may introduce bias, as it removes the requirement for evidence of presence in Qatar.

To address the reviewer's concern, we have now conducted the suggested sensitivity analysis, and the results are presented in Author response image 1. The analysis reaffirmed, with statistical significance, the same observed healthy vaccinee effect and its characteristic temporal evolution as in the main analysis. However, as expected, the effect size was smaller due to bias introduced by the under ascertainment of deaths in the unvaccinated group.

In this context, we would also like to indicate a key strength of our matching approach. As vaccination policies evolved over time, potentially introducing time-varying exposures across different groups, we addressed this issue by matching participants by calendar week (using the matching by negative SARS-CoV-2 test) and initiating follow-up from this time point. This approach ensured that each matched pair was recruited into the cohorts during a period with similar vaccination policies and practices. This point has now been clarified in the revised manuscript (Methods, Page 12, Paragraph 3).

Another source of potential bias is that over half of all potentially eligible persons from the full cohort were not included in analyses because they could not be matched. Table 1 suggests that the excluded participants were older and had more comorbidities than those who were included in the matched cohort. It is therefore unclear whether the results of the analysis can be generalized to the entire population of Qatar given that over half were excluded.

The study was designed to address a specific research question: the existence of the healthy vaccinee effect in rigorously conducted vaccine effectiveness studies. As such, the focus is on the internal validity of the study, that is ensuring that the relationship between the exposure and outcome is measured accurately and free from bias and confounding. This approach is analogous to the design of vaccine efficacy RCTs, which are typically conducted in select populations that may not fully represent the wider population (e.g., excluding pregnant women, children, or individuals with specific coexisting conditions). The purpose of such RCTs, and similarly this study, is not to provide population-wide estimates but to generate a precise and unbiased measure of the exposure-outcome relationship that is being investigated.

To achieve this goal, the study employed rigorous matching across multiple factors to control for selection bias, similar to the role of randomization in an RCT. While this matching process resulted in study cohorts that are not fully representative of Qatar's population, this was intentional and aligns with the study's objective. The aim was not to estimate the healthy vaccinee effect for Qatar's broader population but to produce a valid and accurate measure of this specific effect under investigation and its existence.

This point has now been discussed and clarified in the revised manuscript (Discussion, Page 25, Paragraphs 3-4).

While the analysis included the number of coexisting comorbidities in the matching process, I think it could be argued that comorbidities/pre-existing conditions are part of the healthy vaccinee effect that the authors are trying to capture, and so it might not be appropriate to match on this factor as comorbidities are part of the effect. Table 1 suggests that unvaccinated individuals are in fact less likely to have co-morbidities than vaccinated individuals. The healthy vaccinee effect might therefore have been overestimated by matching on this factor, as there is evidence that less healthy individuals were more likely to get vaccinated prior to matching.

We appreciate the reviewer's point but respectfully disagree. The objective of this study is not to describe differences between individuals who receive the vaccine and those who do not but to assess the healthy vaccinee effect in specifically rigorously conducted vaccine effectiveness studies. In such studies, it is critical to control for observable differences in health status to ensure an unbiased measure of vaccine effectiveness. Rigorous vaccine effectiveness studies have conventionally addressed this by controlling for coexisting conditions using administrative healthcare utilization databases.

Please note our detailed discussion of this point in the Introduction (Introduction, Page 5, Paragraph 3 and Page 6, Paragraphs 1-2), as well as the expanded Methods (Methods Page 9, Paragraph 4 and Page 10, Paragraph 1). To further clarify the study's objective and avoid potential confusion, we have also emphasized this point in the Abstract and at the opening of the Discussion section (Abstract and Discussion, Page 20, Paragraph 5).

While the above issues potentially could have led to bias of the results, I think the effect would still likely be present in analyses, so I would agree with the authors' main conclusion that there likely exists a healthy vaccinee effect. I also agree that it is however unclear to what extent this might have biased the estimates of vaccine effectiveness against COVID-19 related outcomes.

The manuscript has been substantially revised and enhanced to include a more detailed discussion of potential biases, as well as additional analyses, all of which support the study findings (changes incorporated throughout the manuscript). We hope these revisions address the reviewers' concerns.

I thought the analysis of hazard ratios stratified by time since vaccination was particularly interesting and valuable, showing that the healthy vaccinee effect is mostly found in the first 6 months after vaccination. However, I would dispute the interpretation that the higher hazard ratios after 6 months represent confounding by indication (which the authors call the indication effect). It is likely that this is a bias caused by the depletion of susceptibles, where the unvaccinated individuals most likely to die are depleted at the start so the remaining unvaccinated survivors have a lower death rate, and is not because the vaccinated individuals were at higher risk of death (confounding by indication). I think the authors interpret this correctly in the discussion as they mention that this observation is potentially due to the depletion of seriously ill individuals in the unvaccinated, but I think they use the wrong terminology to refer to this effect, which would a bias due to depletion of susceptibles rather than confounding by indication.

We appreciate the reviewer's feedback on this analysis and for raising this point. We also apologize for the confusion caused by these statements. This part of the discussion aimed to address a salient point in the findings. It is possible that both a healthy vaccinee effect and an indication effect could coexist, though they likely operate at different strengths and timeframes. The healthy vaccinee effect is primarily driven by seriously ill and end-of-life individuals, such as terminal cancer patients, as well as frail and less mobile elderly persons. In contrast, the indication effect would be driven by individuals with less immediately life-threatening but still serious health conditions who would pursue vaccination to reduce the risk of complicating their health conditions with infection, such as heart disease or diabetes, which may increase mortality risk over a longer time horizon rather than immediately.

Our results indicate that the healthy vaccinee effect was pronounced during the first six months after vaccination. However, over the longer term, there was an increased risk of mortality among those vaccinated. While this is primarily attributed to the depletion of seriously ill individuals in the unvaccinated group during the initial six months, leaving a relatively healthier cohort, it is also not inconsistent with a potential presence of some indication effect. This effect would be driven by individuals with conditions such as heart disease or diabetes, who sought vaccination to reduce the risk of infection exacerbating their health conditions but who inherently have a higher mortality risk over time.

To address this comment, we have now substantially revised this part of the discussion to address the reviewer's point (Discussion, Page 21, Paragraph 4 and Page 22, Paragraph 1). Additionally, we have removed references to indication bias in contexts where it might cause confusion (Results, Page 18, Paragraph 2 and Page 20, Paragraph 1).

– In general there could be more details about the vaccine rollout strategy that was employed in Qatar and who was initially targeted for vaccination, as this can help us understand who potentially may be overrepresented among the vaccinees. Most countries did not vaccinate at random, so if there were particular demographic criteria that were used to target vaccination this should be made clearer.

Thank you for the useful suggestion. A subsection has been added to describe the rollout strategy employed in Qatar, including the initial target groups prioritized for vaccination (Methods, Page 8, Paragraph 3 and Page 9, Paragraphs 1-2). Additionally, a figure has been included to illustrate the rollout of both the primary vaccination series and booster doses (Figure 1—figure supplement 1).

– I think that the criteria of a negative test for controls for matching could have led to selection bias as this criteria was not applied to vaccinated cases. I think there is enough of a difference in the sensitivity analyses when removing these criteria to suggest that this may have affected the estimates. I would have liked to see a sensitivity analysis for the full cohort, not just in Qataris, where either this criteria is dropped for the unvaccinated to assess the difference between hazard ratio estimates.

It is important to note that these two hazard ratios were derived from analyses conducted on two distinct cohort studies, each involving different cohorts, as the entire matching process had to be repeated for the analysis in which the matching to a negative SARS-CoV-2 test was removed. Consequently, due to sampling variation, the hazard ratios were not identical; however, they remain consistent within overlapping 95% confidence intervals. Even if the exact same analysis were repeated under identical conditions, the hazard ratios would still vary due to sampling variation, but the estimates would fall within overlapping 95% confidence intervals.

– It is not clear whether the cohort members who were excluded due to inability to match are systematically different from those who were included. I would have liked to see an estimate of the death probability in those excluded vs. included to assess the potential for selection bias.

To address the specific research question of this study, the study employed rigorous matching across multiple factors to control for selection bias, similar to the role of randomization in an RCT. While this matching process resulted in study cohorts that are not fully representative of Qatar's population, this was intentional and aligns with the study's objective. The aim was not to estimate the healthy vaccinee effect for Qatar's broader population but to produce a valid and accurate measure of the exposure-outcome relationship, specifically the effect under investigation and its existence.

This point has now been discussed and clarified in the revised manuscript (Discussion, Page 25, Paragraphs 3-4).

– The authors interpret their hazard ratios as being evidence of a healthy vaccinee effect and indication effect in the results. In general it is better to reserve interpretation of the hazard ratios for the discussion, I would recommend simply reporting the hazard ratios in the results and reserving the interpretation of these as healthy vaccinee effect for the discussion.

We fully appreciate the reviewer's point, and during the original drafting of this manuscript, the reviewer's suggested approach was initially considered as our plan. However, we found that it disrupted the flow of the narrative and arguments, leading to potential for confusion. Given the multiple subtle and interconnected concepts in this study, we concluded that the most effective approach was to provide only the immediate interpretation (but not the implications) of the hazard ratios at the point of presentation. This approach ensures greater clarity and coherence in conveying the findings. Additionally, we have now removed all references to indication bias in the Results section (Results, Page 18, Paragraph 2 and Page 20, Paragraph 1).

– It is not clear based on the methods how the cause of death was assessed, as the authors indicate in the discussion they could not ascertain causes of death, so it is unclear how they know whether a death is due to COVID-19 or not.

COVID-19 deaths in Qatar were systematically identified through a national protocol applied to every COVID-19 case. However, this protocol was designed to determine whether a death was classified as a COVID-19 death or not, and did not investigate the specific detailed causes of non-COVID-19 deaths (Discussion, Page 23, Paragraph 3).

To clarify this matter, we have now expanded the description of this aspect of the methods into a subsection, detailing the classification process for COVID-19 deaths (Methods, Page 10, Paragraphs 3-4 and Page 11, Paragraphs 1-3).

– There is some discrepancy in the authors' interpretation of the hazard ratios >1, as they say, it is confounding by indication, but their more detailed explanation in the discussion discusses an effect that is due to depletion of susceptibles in the unvaccinated, not confounding due to indication for vaccination among healthy individuals. I think that the explanation of bias due to depletion of susceptibles in the unvaccinated is more plausible, as I would expect confounding by indication to be more present closer to vaccination (as it is health status at the time of vaccination which leads to confounding by indication, and this effect would be expected to diminish with time since vaccination). The authors should distinguish between these two hypotheses, as they are different.

We agree with the reviewer that it was necessary to clearly distinguish between these two hypotheses. This part of the discussion aimed to address a salient point in the findings. It is possible that both a healthy vaccinee effect and an indication effect could coexist, though they likely operate at different strengths and timeframes. The healthy vaccinee effect is primarily driven by seriously ill and end-of-life individuals, such as terminal cancer patients, as well as frail and less mobile elderly persons. In contrast, the indication effect would be driven by individuals with less immediately life-threatening but still serious health conditions who would pursue vaccination to reduce the risk of complicating their health conditions with infection, such as heart disease or diabetes, which may increase mortality risk over a longer time horizon rather than immediately.

To clarify these nuances and distinguish between these two hypotheses, we have now substantially revised this part of the discussion to address the reviewer's point (Discussion, Page 21, Paragraph 4 and Page 22, Paragraph 1). Additionally, we have removed references to indication bias in contexts where it might cause confusion (Results, Page 18, Paragraph 2 and Page 20, Paragraph 1).

– The authors may also want to consider a sensitivity analysis where they do not include comorbidities in the matching process because comorbidities are part of the healthy vaccinee effect that the authors are trying to measure; a person with less comorbidities is by definition healthier than a person with more comorbidities.

We appreciate the reviewer's comment but respectfully do not agree with this point. The objective of this study is not to describe differences between individuals who receive the vaccine and those who do not but to assess the healthy vaccinee effect in specifically rigorously conducted vaccine effectiveness studies. In such studies, it is critical to control for observable differences in health status to ensure an unbiased measure of vaccine effectiveness. Rigorous vaccine effectiveness studies have conventionally addressed this by controlling for coexisting conditions using administrative healthcare utilization databases.

Reviewer #2 (Recommendations for the authors):

First of all, I would like to applaud the authors of this paper. Although several papers on HVE based on data from the Czech Republic were published in regard to COVID vaccines, this is by far the most thorough paper on healthy vaccinee effect in COVID-19 vaccines so far and the methods are meticulous. I particularly like the fact that the patients were matched also for prior COVID-19 infection – besides the reasons mentioned by the authors, another reason is that prior COVID-19 infection provides also excellent protection from a subsequent severe or critical COVID-19, which could otherwise skew their results. The paper shows also another important fact concerning HVE – this effect remains huge in the first six months despite a methodologically accurate and correct approach involving matching for a wide range of variables.

We thank the reviewer for the time and effort put into this review, the assessment of our work, and the constructive feedback on our manuscript that enriched it and improved its readability. Please find below a point-by-point reply addressing each of the reviewer's comments.

We thank the reviewer for raising the useful point regarding the matching by prior infection status, which has now been indicated in the revised manuscript (Methods, Page 12, Paragraph 2).

Still, I have several concerns that I feel need to be addressed, especially with statements that are, in my opinion, not entirely justified:

1) I disagree with the statement (Lines 71 and 395) that the effectiveness of the booster dose against severe, critical, or fatal COVID was 34.1% (95% CI: -46.4-76.7). In view of the wide and zero-containing confidence interval, this phrasing leads to misinterpretation. I would rather suggest rephrasing it as „No significant effect of the third dose of vaccine against severe, critical, or fatal COVID was demonstrated (34.1%; 95% CI: -46.4-76.7). After all, calculating any vaccine effectiveness from 6 vs 9 severe, critical, or fatal cases among approx. 70 thousand infections is doubtful. Rather, this result highlights a very good resistance to severe/critical/fatal COVID-19 in both these groups.

We agree with the reviewer. This statement has now been edited as suggested (Results, Page 20, Paragraph 4).

2) As rightly discussed by the Authors from Line 411 onwards, the healthy vaccinee bias is present at the beginning but after a couple of months (during which most of the frailest individuals die), HVE disappears, being replaced by indication bias. Hence, I am surprised that the authors did not perform a separate analysis of vaccine effectiveness against severe, critical, and fatal COVID in the first six months. In my opinion, such an analysis needs to be added to the paper, especially considering the fact that the vast majority of COVID deaths in Qatar within the study period occurred from March to June 2021, i.e., in the first 6 months after the majority of the elderly and frail individuals took their second vaccine dose. It is the same period in which the biggest healthy vaccinee effect (HVE) of 65 % (i.e., aHR for non-COVID deaths of 0.35) was observed – and comparison of the same period is necessary to correctly interpret the findings.

Excellent idea, thank you. This sub analysis has now been incorporated, examining overall effectiveness as well as effectiveness within the subgroups of individuals aged <50 years and those aged ≥50 years (Figure 2—figure supplement 1). This addition has been also described in the Results section (Results, Page 19, Paragraph 1 and Page 20, Paragraph 4).

3) From the perspective of the approx. 65 % healthy vaccinee bias (i.e., aHR=0.35) in the first 6 months when most COVID-related deaths occurred, I think that the statement "Despite the presence of a healthy vaccinee effect, the results still confirm strong protection from vaccination against severe forms of COVID-19, as the observed effectiveness for the primary series was extremely high, at 96%" is too strong.

We agree with the reviewer. This statement has now been revised to adopt a more measured tone (Discussion, Page 22, Paragraph 3).

4) As mentioned in the summary of my review, it is, in my opinion, necessary to highlight the fact that HVE was present despite meticulous matching. This is actually the most important finding of this study – the failure to remove HVE through matching is crucial information for the whole bulk of observational studies on COVID-19 vaccination effectiveness. I suggest adding something along the lines that "Despite meticulous matching, large healthy vaccinee effect (aHR for non-COVID death in vaccinated vs unvaccinated of 0.35) was observed in the first six months of follow-up", probably to the Discussion as well as to the Abstract.

We fully agree with the reviewer, and this has now been indicated in both the Abstract and the Discussion as suggested (Abstract and Discussion, Page 20, Paragraph 5)

5) Seeing the virtually zero effect of booster vaccination, I would be very much interested in seeing the comparison of two doses vs the unvaccinated for the "after-booster" period (not saying that this is a must, I understand it is a lot of work; it could come as a new paper).

First of all, thank you for the thoughtful comment and insightful suggestion. We agree with the reviewer that this is a topic of great interest and forms part of our other ongoing work examining the long-term effects of COVID-19 vaccines. However, given the extensive length of this manuscript (about 6000 words) and the density of results and analyses already presented, we feel it is more appropriate to reserve this analysis for a separate study specifically focused on vaccine effectiveness, rather than the healthy vaccinee effect.

6) Calculating and reporting HVE over a period of several years is not optimal. The authors discuss this very well but still analyze the combined HVE+indication bias over the whole period and report it as one of the most prominent findings. I think that the individual periods (in which these two biases can be to a large degree separated) are much more interesting and important. It would be very helpful to calculate and compare vaccine effectiveness against severe, critical, or fatal COVID for each of the periods shown in Figure 2 and present it along with the results shown in Figure 2 now. This should be ideally also done separately for the most vulnerable group of 50+ years of age.

Excellent idea, thank you. This sub analysis has now been incorporated, examining overall effectiveness as well as effectiveness within the subgroups of individuals aged <50 years and those aged ≥50 years (Figure 2—figure supplement 1). This addition has been also described in the Results section (Results, Page 19, Paragraph 1 and Page 20, Paragraph 4).

We included the results of this analysis as a separate figure because it was not feasible to perform the analysis for all the time intervals of Figure 2. This limitation arose from the relative rarity of severe forms of COVID-19. Additionally, the figure addresses a different outcome—severe COVID-19 as opposed to non-COVID-19 death. However, the analysis was conducted for the most critical time periods: 1–6 months and >6 months post vaccination (Figure 2—figure supplement 1).

7) Was there any period with testing bias in Qatar? In other words, was there any period when the vaccinated did not have to test for COVID-19 while the unvaccinated had to test? If so, could it have influenced the results of your study? I think the paper would benefit from this discussion.

Yes, there were time periods when testing requirements, such as those for travel, varied between vaccinated and unvaccinated individuals. However, we do not believe this variability would have impacted the findings of this study. This is supported by the sensitivity analysis reported in the manuscript (Table 4), as well as an additional sensitivity analysis included in our response to Reviewer 1 (please see the response above and Figure 1 in this document), where the matching requirement for a SARS-CoV-2-negative test was removed. The results of these analyses are consistent with those of the main analysis, further reinforcing the robustness of our findings.

8) I think that the peculiar composition of Qatar's population is leading to problems with low numbers, especially in the elderly population most vulnerable to COVID-19, is basically the only limitation of this study. However, the reported pattern is in line with what was observed elsewhere and the authors did, as mentioned above, an excellent job in making up for this limitation by meticulous matching so I consider their work highly relevant and well performed.

We sincerely appreciate the reviewer's thoughtful feedback and useful suggestions, which have enriched this study and its contribution to the literature.

Reviewer #3 (Recommendations for the authors):

The authors have conducted cohort studies using routinely collected data from Qatar. These datasets have been the basis of a series of high-quality and influential studies that were conducted in Qatar into COVID-19 vaccine effectiveness (VE) during the pandemic.

In this paper the authors are less focused on VE and more on defining the degree and temporal profile of a possible early 'healthy vaccinee effect' followed by an 'indication bias'. The category of 'non-COVID' death is a potentially useful negative outcome control. Deviation from the null indicates bias if the vaccines truly have no effect on the study outcomes. However, its value here is reduced by the lack of information on cause of death. In this study it may be a reasonable directional bias indicator but does not specify the cause of bias. I believe their methods and analyses are robust and they are observing real effects. However, the authors do not present direct data on causes of death or variables that would help define healthy vaccinees or underpin the claim of indication bias. I think they need to provide a more comprehensive defence of their conclusions.

We agree with the reviewer that a limitation of this study is the lack of data on causes of death other than COVID-19. As a result, while the study provides an indicator of directional bias, it does not identify the specific cause of this bias. This limitation was acknowledged in the original manuscript. However, the study does provide clear evidence of the existence of the healthy vaccinee effect in rigorously conducted vaccine effectiveness studies, characterizing its effect size and temporal profile, which is the primary research question addressed in the study. This point has now been clarified and emphasized in the revised manuscript (multiple instances in the manuscript).

The manuscript has been also substantially revised and enhanced to include additional clarifications, a more detailed discussion of potential biases, and the inclusion of further analyses, all of which reinforce the study findings (changes incorporated throughout the manuscript).

1) It would be helpful if the authors defined the boundaries of the 'healthy vaccinee effect' they are investigating with their data. The closest to a definition I could find was in the Introduction where the authors state 'the healthy vaccinee effect occurs when healthier or health-conscious individuals are more likely to receive vaccination'. In this setting 'health-conscious individuals' are making a deliberate choice to be vaccinated, and this is consistent with other choices they make (exercise, avoiding smoking and excess alcohol, eating a healthy diet, undergoing screening). At times in the paper the authors appear to equate the healthy vaccinee effect with the absence of underlying disease and, at its most extreme, the absence of disease that would lead to avoidance of vaccination because of clinical futility. There is no doubt that omission of vaccination is practiced in patients with terminal disease, but I think it should be regarded as a separate bias and its absence does not denote a 'healthy vaccinee effect'. I think this needs clarification.

This study builds on research regarding this effect within the context of influenza vaccine effectiveness studies, as outlined in the Introduction (Introduction, Page 5, Paragraph 3 and Page 6, Paragraphs 1-2). To maintain consistency, we have adhered to similar definitions and conventions. While we appreciate the reviewer's suggestion, we believe it is important to align with established definitions and conventions to avoid causing confusion in this area of research. However, we have now, as suggested, explicitly defined both the healthy vaccinee effect and the indication effect in the manuscript (Methods, Page 9, Paragraph 4 and Page 10, Paragraph 1).

2) I think the overall relationship between comorbidity and frailty and vaccination choice needs deeper discussion. The authors cite community studies of influenza vaccination, which generally have lower population coverage than COVD-19 vaccination, allowing more room for selection biases. While older independent community members who are active may demonstrate a greater preference for influenza vaccination, this will not be the case for those in long-term care facilities, where a common policy is to vaccinate all residents.

We agree with the reviewer on the importance of further understanding the relationship between comorbidity and frailty and vaccination behavior. To address this, we have added a paragraph discussing this point and emphasizing the need for collecting direct primary data to better elucidate this relationship (Discussion, Page 23, Paragraph 4). Additionally, we highlighted the importance of examining how this relationship may differ across various vaccine types (Discussion, Page 23, Paragraph 4).

3) The authors also mention confounding by indication, which with COVID-19 has been influenced by government policies to provide early vaccination to high-risk groups and healthcare workers. As they say, this runs counter to the presumed healthy vaccinee effect. These policies change quickly and can introduce time-varying exposures in certain groups. I am uncertain if vaccination policies were considered as potential sources of bias – but they may be controlled by the decision to match on calendar week. The same comment applies to 'environmental risk' – variation in community attack rates over time and region.

Excellent point, thank you. As the reviewer has pointed out, vaccination policies evolve over time, potentially introducing time-varying exposures in different groups. However, as noted by the reviewer, we matched participants by calendar week and followed them starting from this time point, ensuring that each matched pair was recruited into the cohorts during a period with similar vaccination policies and practices. This approach also accounts for variations in community attack rates over time. This point has now been clarified in the revised manuscript (Methods, Page 12, Paragraph 3).

Moreover, vaccination policy in Qatar was primarily based on factors already matched in this study, such as age and coexisting conditions. To clarify this point and vaccination rollout, a subsection has been added to describe the rollout strategy employed in Qatar, including the initial target groups prioritized for vaccination (Methods, Page 8, Paragraph 3 and Page 9, Paragraphs 1-2). Additionally, a figure has been included to illustrate the rollout of both the primary vaccination series and booster doses (Figure 1—figure supplement 1).

4) The authors seem somewhat dismissive of misclassification of deaths as contributing to their findings. As noted, they don't have comprehensive cause of death data. I agree that a non-specific immunostimulant benefit of the vaccines is unlikely. However, some COVID-related deaths may be missed during coding. For instance, someone who dies of a heart attack or stroke 4 weeks after a serious COVID illness. The coding decision could also be influenced by knowledge of vaccination status as it appears that coders were unblinded.

We apologize for the confusion regarding this point. The classification of COVID-19 deaths followed a detailed and rigorous process, using strict criteria rather than a simplistic review of patient charts or reliance solely on ICD-10 codes. As a result, it is unlikely that misclassification occurred at a level that could meaningfully affect the study findings, particularly given that COVID-19 deaths in Qatar were much lower than overall mortality rates (Discussion, Page 24, Paragraph 2). Additionally, we refer the reviewer to our earlier detailed studies on COVID-19 mortality for further context [3-5].

For example, the specific scenario the reviewer highlights – a heart attack or stroke occurring four weeks after a severe COVID-19 illness – would not have been overlooked as long as the case met the defined criteria. According to the case definition, a death is classified as a COVID-19 death if there was no period of complete recovery from COVID-19 between the illness and death.

To address this matter, we have now expanded the description of this aspect of the methods into a subsection, detailing the classification process for COVID-19 deaths (Methods, Page 10, Paragraphs 3-4 and Page 11, Paragraphs 1-3).

5) But misclassification of outcomes is not the only potential source of bias. Because the authors don't have direct evidence to support healthy user bias and confounding by indication, I think they should use a bias framework (e.g., ROBINS-I) to discuss and (as appropriate) reject the other potential causes of bias

6) I am not arguing their assertions are wrong. But I think their language is over-confident and they need to make a more comprehensive case to back their conclusions.

We address these two comments together, as they are closely related. As the reviewer has noted, this study builds upon a substantial body of prior research that we have conducted on COVID-19 using similar study designs and the same national databases. Over the course of five years conducting these studies, we have engaged in an ongoing process to investigate a wide range of potential biases and limitations, factoring frameworks such as ROBINS-I and drawing on other relevant literature. This approach explains why the limitations sections in our publications, including the present study, are typically extensive—not due to lack of rigor in the studies themselves, but because of our comprehensive examination of various potential sources of bias and limitations.

Moreover, what is included in the final publication typically represents only a subset of the potential biases and limitations we assess during the study design and implementation process. In each publication, we focus on highlighting those aspects most relevant to the specific study.

Informed by ROBINS-I, other related literature, and prior literature on the investigated effects, as well as our previous work using similar study designs on these national databases, this manuscript has been substantially revised. The revisions include a more detailed discussion of potential biases and limitations, along with additional analyses, all of which reinforce the robustness of the study findings (changes incorporated throughout the manuscript). Notably, the limitations section now spans four pages, addressing various forms of potential bias and limitations and evaluating whether and how they might influence the study results (Discussion, Pages 23-26).

7) This is an accomplished group performing sophisticated studies. However, the level of self-citation seems excessive. In some cases, it is justified to anchor the current work. But there is a large literature on the effects of COVID-19 on COVID mortality with a significant number of studies that also reported all causes of non-COVID mortality. This is not reflected in the authors' choice of references.

We thank the reviewer for the positive assessment of our work and acknowledge the observation of substantial proportion of self-citations in the manuscript. This, however, reflects the complexity of the study and the necessity of thoroughly explaining the methods, particularly in a study investigating bias. The cited prior work was included to provide additional details on the methods, describe the databases in greater depth, avoid redundancy in explanations, offer the rationale for certain methodological choices, establish context for the study design, discuss potential biases and limitations, and support and justify specific arguments. It is worth noting that the vast majority of self-citations are in the Methods section or the limitations part of the Discussion. For example, the Introduction section does not include any self-citations.

We also recognize that there is a substantial body of literature on COVID-19 mortality and non-COVID mortality that is not cited in this manuscript. However, some relevant COVID-19 mortality literature was included in the original manuscript (such as [6-8]), and additional references were cited in our previous studies specifically focused on COVID-19 mortality [3-5]. This study, however, is not a general investigation of COVID-19 or all-cause mortality but a focused examination of the healthy vaccinee effect in vaccine effectiveness studies. Other COVID-19 mortality literature was cited when directly relevant, such as in discussion on documented versus undocumented COVID-19 deaths (Discussion, Page 24, Paragraph 2).

To address the reviewer's comment, we have carefully reviewed the manuscript to reassess the relevance of each citation and have added other pertinent references from the global literature where appropriate (throughout the manuscript).

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure 1—source data 1. Data used to generate Figure 1A and B.

elife-103690-fig1-data1.xlsx^{(188.1KB, xlsx)}

Figure 2—source data 1. Data used to generate Figure 2A and B.

elife-103690-fig2-data1.xlsx^{(29.7KB, xlsx)}

MDAR checklist

elife-103690-mdarchecklist1.docx^{(88.4KB, docx)}

Data Availability Statement

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PERMALINK

Assessing healthy vaccinee effect in COVID-19 vaccine effectiveness studies: a national cohort study in Qatar

Hiam Chemaitelly

Houssein H Ayoub

Peter Coyle

Patrick Tang

Mohammad R Hasan

Hadi M Yassine

Asmaa A Al Thani

Zaina Al-Kanaani

Einas Al-Kuwari

Andrew Jeremijenko

Anvar Hassan Kaleeckal

Ali Nizar Latif

Riyazuddin Mohammad Shaik

Hanan F Abdul-Rahim

Gheyath K Nasrallah

Mohamed Ghaith Al-Kuwari

Hamad Eid Al-Romaihi

Mohamed H Al-Thani

Abdullatif Al-Khal

Roberto Bertollini

Adeel A Butt

Laith J Abu-Raddad

Roles

Abstract

Background:

Methods:

Results:

Conclusions:

Funding:

eLife digest

Introduction

Methods

Study population and data sources

COVID-19 vaccination

Study design

Severe, critical, and fatal COVID-19

Incidence of SARS-CoV-2 infection

Cohorts’ eligibility and matching

Cohorts’ follow-up

Oversight

Statistical analysis

Results

Two-dose analysis

Table 1. Baseline characteristics of the full and matched cohorts for investigating an indication effect or a healthy vaccinee effect among recipients of primary series or booster (third dose) vaccination in Qatar.

Figure 1. Cumulative incidence of non-COVID-19 death in the matched (A) two-dose cohort compared to the unvaccinated cohort and (B) three-dose cohort compared to the two-dose cohort.

Figure 1—figure supplement 1. Distribution of vaccinations.

Figure 1—figure supplement 2. Flowchart describing the study population selection process for investigating an indication effect or a healthy vaccinee effect among recipients of primary series vaccination compared to those with no vaccination in Qatar.

Figure 1—figure supplement 3. Flowchart describing the study population selection process for investigating an indication effect or a healthy vaccinee effect among recipients of booster (third dose) vaccination compared to recipients of primary series vaccination in Qatar.

Table 2. Hazard ratios for incidence of non-COVID-19 death, SARS-CoV-2 infection, and severe, critical, or fatal COVID-19 in the (A) two-dose analysis and (B) three-dose analysis.

Figure 2. Adjusted hazard ratios for incidence of non-COVID-19 death in the (A) two-dose analysis and (B) three-dose analysis, by 6-month interval of follow-up.

Figure 2—figure supplement 1. Adjusted hazard ratios for incidence of severe, critical, or fatal COVID-19 in the two-dose and three-dose analyses, by 6-month interval of follow-up.

Table 3. Subgroup analyses.

Table 4. Sensitivity analyses.

Three-dose analysis

Discussion

Contributors

Code availability

Acknowledgements

Appendix 1

Section S1: Study population and data sources

Section S2: Laboratory methods and variant ascertainment

Real-time reverse-transcription polymerase chain reaction testing

Rapid antigen testing

Classification of infections by variant type

Section S3: Classification of coexisting conditions

Section S4: COVID-19 severity, criticality, and fatality classification

Severe COVID-19

Critical COVID-19

Fatal COVID-19

Funding Statement

Contributor Information

Funding Information

Additional information

Competing interests

Author contributions

Ethics

Additional files

Data availability