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

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.

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).

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.

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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Author details

1. Hiam Chemaitelly

   1. Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar
   2. World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation – Education City, Doha, Qatar
   3. Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, United States

   Contribution

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

   For correspondence

   hsc2001@qatar-med.cornell.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8756-6968

2. Houssein H Ayoub

   Mathematics Program, Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Peter Coyle

   1. Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
   2. Hamad Medical Corporation, Doha, Qatar
   3. Wellcome-Wolfson Institute for Experimental Medicine, Queens University, Belfast, United Kingdom

   Contribution

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

   Competing interests

   No competing interests declared

4. Patrick Tang

   Department of Pathology, Sidra Medicine, Doha, Qatar

   Contribution

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

   Competing interests

   No competing interests declared

5. Mohammad R Hasan

   Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada

   Contribution

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

   Competing interests

   No competing interests declared

6. Hadi M Yassine

   1. Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
   2. Biomedical Research Center, QU Health, Qatar University, Doha, Qatar

   Contribution

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

   Competing interests

   No competing interests declared

7. Asmaa A Al Thani

   1. Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
   2. Biomedical Research Center, QU Health, Qatar University, Doha, Qatar

   Contribution

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

   Competing interests

   No competing interests declared

8. Zaina Al-Kanaani

   Hamad Medical Corporation, Doha, Qatar

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Einas Al-Kuwari

   Hamad Medical Corporation, Doha, Qatar

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Andrew Jeremijenko

    Hamad Medical Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Anvar Hassan Kaleeckal

    Hamad Medical Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Ali Nizar Latif

    Hamad Medical Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

13. Riyazuddin Mohammad Shaik

    Hamad Medical Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

14. Hanan F Abdul-Rahim

    Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Gheyath K Nasrallah

    1. Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
    2. Biomedical Research Center, QU Health, Qatar University, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9252-1038

16. Mohamed Ghaith Al-Kuwari

    Primary Health Care Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Hamad Eid Al-Romaihi

    Ministry of Public Health, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

18. Mohamed H Al-Thani

    Ministry of Public Health, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

19. Abdullatif Al-Khal

    Hamad Medical Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

20. Roberto Bertollini

    Primary Health Care Corporation, Doha, Qatar

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

21. Adeel A Butt

    1. Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, United States
    2. Hamad Medical Corporation, Doha, Qatar
    3. Department of Medicine, Weill Cornell Medicine, Cornell University, New York, United States

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

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

22. Laith J Abu-Raddad

    1. World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation – Education City, Doha, Qatar
    2. Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, United States
    3. Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
    4. College of Health and Life Sciences, Hamad bin Khalifa University, Doha, Qatar

    Contribution

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

    For correspondence

    lja2002@qatar-med.cornell.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0790-0506

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