Abstract
Objective
We sought to examine the extent to which populations experiencing inequities were considered in studies of COVID‐19 vaccination in individuals with autoimmune inflammatory rheumatic diseases (AIRDs).
Methods
We included all studies (n = 19) from an ongoing Cochrane living systematic review on COVID‐19 vaccination in patients with AIRDs. We used the PROGRESS‐Plus framework (place of residence, race/ethnicity, occupation, gender/sex, religion, education, socioeconomic status, and social capital, plus: age, multimorbidity, and health literacy) to identify factors that stratify health outcomes. We assessed equity considerations in relation to differences in COVID‐19 baseline risk, eligibility criteria, description of participant characteristics and attrition, controlling for confounding factors, subgroup analyses, and applicability of findings.
Results
All 19 studies were cohort studies that followed individuals with AIRDs after vaccination. Three studies (16%) described differences in baseline risk for COVID‐19 across age. Two studies (11%) defined eligibility criteria based on occupation and age. All 19 studies described participant age and sex. Twelve studies (67%) controlled for age and/or sex as confounders. Eight studies (47%) conducted subgroup analyses across at least 1 PROGRESS‐Plus factor, most commonly age. Ten studies (53%) interpreted applicability in relation to at least 1 PROGRESS‐Plus factor, most commonly age (47%), then ethnicity (16%), sex (16%), and multimorbidity (11%).
Conclusion
Sex and age were the most frequently considered PROGRESS‐Plus factors in studies of COVID‐19 vaccination in individuals with AIRDs. The generalizability of evidence to populations experiencing inequities is uncertain. Future COVID‐19 vaccine studies should report participant characteristics in more detail to inform guideline recommendations.
INTRODUCTION
Individuals with autoimmune inflammatory rheumatic diseases (AIRDs) have been disproportionately affected by the COVID‐19 pandemic. Such individuals have a higher risk of testing positive for COVID‐19, COVID‐19‐related hospitalization, intensive care unit admission, and death (1, 2). This can be attributed to a variety of factors, including the presence of comorbidities, underlying disease activity, and the use of targeted immune‐modulating therapies (3). The disproportionate burden of COVID‐19 on individuals with AIRDs increases their risk of experiencing health inequities, differences in health that are considered avoidable and unfair.
SIGNIFICANCE & INNOVATIONS.
Age and sex were the only dimensions of inequities consistently reported in the participant characteristics.
Almost one‐half of included studies considered age when evaluating the applicability of study findings.
COVID‐19 vaccine studies of patients with autoimmune inflammatory rheumatic diseases lack the consideration of dimensions of inequities beyond age and sex, limiting the use of the study data and potential external validity.
Even if a social factor does not have a direct impact on vaccine response, it is necessary to prioritize equity in studies to lend insight into the applicability of results to diverse populations, especially by describing participant characteristics.
The development of vaccines has dramatically shifted the pandemic landscape, with COVID‐19–related cases, hospitalizations, and deaths substantially lower in fully vaccinated populations compared to those who are not fully vaccinated (4). In the early stages of vaccine trials, individuals with AIRDs are often excluded due to uncertainties regarding clinical equipoise and differences in risk–benefit profiles from the general population. Given the severity of the COVID‐19 pandemic, policy mandates necessitate that these populations, among other high‐risk populations, are included in vaccination target populations. Thus, guidelines regarding the response to vaccination have been mainly assessed using observational data.
Several factors could stratify health opportunities and outcomes. For example, racial and ethnic minorities with AIRDs are at higher risk for worse COVID‐19 outcomes (5). This has also been documented extensively with outcomes after COVID‐19 vaccination. Immune response to vaccines has been demonstrated to be age, sex, and ethnicity dependent (6, 7). Vaccine uptake is lower in populations with increased risk of COVID‐19 infection, including those of minority race and ethnicity (Black, Asian, and Hispanic individuals), low socioeconomic status, and low health literacy (8, 9, 10). Women often have greater caregiving responsibilities and lower autonomy, decreasing their access to vaccination (11). These inequity factors intersect with the underlying chronic disease of individuals with AIRDs, which further magnifies inequities (12). Thus, populations experiencing inequities should be considered in COVID‐19 vaccination studies of individuals with AIRDs to avoid the exacerbation of existing inequities.
There is limited information on the extent to which the evidence that supports the development of guidelines adequately represents the entire AIRD patient population. Thus, we assessed how populations experiencing inequities are considered in studies of COVID‐19 vaccination of individuals with AIRDs.
MATERIALS AND METHODS
Identification of studies
This is a study within a review conducted alongside an ongoing Cochrane living systematic review on the evidence for benefits and harms of COVID‐19 vaccination for patients with AIRDs receiving immunomodulatory therapies (13). Details of the search strategy are described in the protocol of the living systematic review (13). Monthly searches are conducted in the Cochrane COVID‐19 study register and the Epistemonikos COVID‐19 Living Overview of Evidence (LOVE) platform. These include the following searches: 1) daily searches of PubMed/Medline; 2) daily searches of ClinicalTrials.gov; 3) daily searches of the WHO International Clinical Trials Registry Platform (ICTRP); 4) daily searches of preprint archives, including medRxiv, bioRxiv, and the Social Science Research Network (SSRN); and 5) weekly searches of Embase. Search results were not limited by language, date, or study design. For the purposes of this study, we assessed the studies identified in the living systematic review by June 16, 2021, as this was the most updated search as of June 2022.
Eligibility criteria
The living systematic review included adults age ≥18 years treated with any immunomodulatory medication for AIRDs. The list of medications is detailed in the published protocol of the systematic review (13). We excluded studies that did not report their findings for adults with AIRDs separately from the general population. We included randomized controlled trials (RCTs) that compared any vaccine directed against SARS–CoV‐2 virus with placebo injection, no intervention, or different COVID‐19 vaccine regimes. In the absence of RCTs, observational studies of COVID‐19 vaccines in patients with AIRDs receiving immunomodulatory therapies were included.
Screening and data extraction
Two review authors independently screened studies at the title and abstract and full‐text stages for relevant studies. Discrepancies were resolved by consensus. One reviewer extracted data using a pretested form while a second reviewer validated the extracted data (14). We collected the following descriptive characteristics of included studies: country, year of publication, study design, and number of participants. We used the PROGRESS‐Plus framework to identify dimensions where inequities could exist. This acronym refers to Place of residence (P), Race or ethnicity (R), Occupation (O), Gender (G), Religion (R), Education (E), Socioeconomic status (S), Social capital (S) (15), Age (Plus), Multimorbidity (Plus), and Health literacy (Plus) (16). We defined multimorbidity as the coexistence of ≥2 chronic conditions (17) and health literacy as the capacity to seek, understand, and use health information (18). Conflicts were resolved by discussion until consensus was reached between the 2 reviewers. We assessed the consideration of equity in the following 6 sections: 1) description of differences in COVID‐19 baseline risk; 2) eligibility criteria; 3) description of participant characteristics and attrition; 4) controlling for confounding factors; 5) subgroup analyses; and 6) applicability of results. Approaches for controlling for confounding factors included matching individuals with AIRDs with healthy controls in the study design, covariate adjustment in multivariable models, and restricting analysis to 1 category of a selected confounder.
Statistical analysis
We analyzed results using summary statistics and described findings using descriptive tables and figures. We also planned to calculate participation‐to‐prevalence ratio (PPR) for sex comparing the representation of women in studies relative to their proportion in the overall disease population. We aimed to calculate PPRs for the most common AIRDs, including rheumatoid arthritis and psoriasis. A PPR between 0.8 and 1.2 reflects bias‐free enrollment, while values below or above suggest underrepresentation and overrepresentation, respectively (19).
RESULTS
The systematic review team screened 2,566 studies at title and abstract stage (see Figure 1 for a flow chart of the characteristics of the included studies). Of those, 46 studies were assessed for eligibility at the full‐text stage. At full‐text review, 27 documents were excluded: 16 registered clinical trials without any results, 8 guidelines, and 3 studies that did not assess the effects of COVID‐19 vaccination. The remaining 19 studies were included in our methodologic assessment (see Supplementary Table 1, available on the Arthritis Care & Research website at http://onlinelibrary.wiley.com/doi/10.1002/acr.25034).
Figure 1.
Flow chart of study screening.
All 19 included studies were observational studies, as no RCTs were identified. Eleven (58%) were full‐length articles, and 8 (42%) were brief research reports. Over two‐thirds of the studies (68%) were prospective in design. Eight (42%) compared outcomes of vaccination in patients with AIRDs with healthy controls, while 5 (26%) were single‐armed studies (only vaccinated individuals with AIRDs). Eighty‐four percent of the studies were conducted in high‐income countries. Anti–SARS–CoV‐2 antibody level was the most commonly measured outcome (74%) in the included studies. Details are described in Table 1. Figure 2 provides an assessment of equity considerations.
Table 1.
Characteristics of included studies*
| Characteristic | Value (n = 19) |
|---|---|
| Article type | |
| Full‐length article | 11 (58) |
| Brief report | 8 (42) |
| Study design | |
| Post‐test only cohort study comparing individuals with AIRDs with healthy controls | 8 (42) |
| Post‐test only cohort study with single arm | 5 (26) |
| Retrospective chart review | 1 (5) |
| Retrospective cohort | 1 (5) |
| Cross‐sectional with controls | 1 (5) |
| Case series | 3 (16) |
| Income of country of study | |
| Lower or middle income | 3 (16) |
| High income | 16 (84) |
| Outcomes assessed | |
| Anti–SARS–CoV‐2 antibody levels | 14 (74) |
| T cell response | 3 (16) |
| Vaccine tolerability and safety | 4 (21) |
| Inflammatory disease activity | 2 (11) |
| Other | 2 (11) |
| Sample size, median (IQR), minimum–maximum | 78 (121), 4–910 |
Values are the number (%) unless indicated otherwise. AIRD = autoimmune inflammatory rheumatic disease; IQR = interquartile range.
Figure 2.
Distribution of PROGRESS‐Plus factors across various study components in COVID‐19 vaccine studies of individuals with autoimmune inflammatory rheumatic diseases.
Description of differences in baseline risk and eligibility criteria
Three articles (16%) described differences in baseline risk for COVID‐19 infection and adverse outcomes across age (20, 21, 22). Seyahi et al and Deepak et al were the 2 studies (11%) that defined inclusion/exclusion criteria based on PROGRESS‐Plus factors, both based on occupation (health care workers) and age (>65 years) (20, 21). Beyond the recruitment of health care workers in these 2 studies, there were no notable findings in the recruitment methods of studies that suggested the presence of selection bias disfavoring individuals experiencing inequities, such as including a higher proportion of wealthy, well‐educated individuals.
Description of participant characteristics and attrition
All 19 studies described participant age and sex. Five studies (26%) described the distribution of participant race or ethnicity (21, 23, 24, 25, 26), and 4 studies (21%) described multimorbidity (20, 27, 28, 29). Two studies (11%) focused on assessing response to vaccination in health care workers; thus occupation was described (20, 27).
In the abstract of the included studies, 5 studies (26%) described the age of the participants (20, 22, 30, 31, 32). Further, 2 (11%) described the distribution of sex (31, 32). Of these 2, one study reported a case series of women only (31). One of the studies on health care workers reported the occupation of the participants in the abstract as well (20). None of the included studies described the characteristics of individuals who dropped out during the course of the study; thus differential attrition according to PROGRESS‐Plus factors could not be assessed.
Controlling for confounding factors
Two studies presented a case series with findings that were narratively reported (27, 31). The remaining 17 studies quantitatively analyzed outcomes. Eight studies (47%) controlled for at least 1 PROGRESS‐Plus factor by 1 of the following methods: covariate adjustment (20, 21, 22, 24, 33), limiting analyses to 1 category of a selected confounder (34), or matching individuals with AIRDs with healthy controls (20, 28, 33, 35).
Three studies (16%) adjusted for age and sex (20, 21, 24), while 2 studies (11%) adjusted for age only (22, 33). One study accounted for the difference in age of individuals with AIRDs compared to healthy controls by restricting analysis to participants age ≤55 years (34). Of the 9 studies with a comparison between participants with AIRDs and healthy controls (20, 21, 22, 23, 28, 29, 33, 34, 35), 4 (44%) matched by age and sex (20, 28, 33, 35).
Subgroup analyses
Excluding the 2 studies that reported findings narratively (27, 31), 8 of 17 studies (47%) analyzed outcomes across at least 1 PROGRESS‐Plus factor (20, 24, 28, 30, 33, 36, 37, 38). Subgroup analysis across age was conducted in all 8, followed by sex (6 studies, 35%) (24, 28, 33, 36, 37, 38), race or ethnicity (4 studies, 24%) (28, 36, 37, 38), and occupation (1 study, 6%) (20).
Applicability of findings
Ten studies (53%) discussed at least 1 PROGRESS‐Plus factor in the applicability of the results (20, 21, 22, 25, 26, 28, 29, 32, 34, 37). Age was most commonly discussed (9 studies, 47%) (20, 22, 25, 26, 28, 29, 32, 34, 37), followed by race or ethnicity (3 studies, 16%) (21, 25, 37), sex (3 studies, 16%) (25, 26, 37), multimorbidity (2 studies, 11%) (20, 22), and occupation (1 study, 5%) (20). As described in Table 2, 4 studies (21%) described limited applicability to older adults (22, 25, 26, 37), 1 study (5%) described limited applicability to children (32), 2 studies (11%) described limited applicability to men (25, 37), 4 (21%) described limited applicability to non‐White patients (11%) (21, 25, 26, 37), and 2 described limited applicability to individuals with multimorbidities (20, 22).
Table 2.
Applicability of findings from vaccination studies of individuals with autoimmune inflammatory rheumatic diseases to populations experiencing inequities*
| Author, year (ref.) | Findings | Limited applicability (according to authors) | ||||
|---|---|---|---|---|---|---|
| Older adults | Children | Men | Non‐White | Individuals with multimorbidity | ||
| Connolly et al, 2021 (26) | Patients without detectable antibodies used a B lymphocyte–depleting agent or medication that affects lymphocytes. | ✓ | ✓ | |||
| Seyahi et al, 2021 (20) | Majority of patients with IMD and healthy controls developed a significant humoral response, lowered by increased age and rituximab use. | ✓ | ||||
| Geisen et al, 2021 (22) | Anti–SARS–CoV‐2 antibodies and neutralizing activity detected in all study participants. | ✓ | ✓ | |||
| Deepak et al, 2021 (21) | Compared to healthy controls, a 3‐fold reduction in anti‐S IgG titers and SARS–CoV‐2 neutralization (P < 0.0001) were observed in patients with chronic inflammatory disease. | ✓ | ||||
| Ruddy et al, 2021 (37) | The vast majority of participants developed anti–SARS–CoV‐2 S receptor binding domain antibodies. | ✓ | ✓ | ✓ | ||
| Connolly et al, 2021 (25) | Local and systemic adverse events were consistent with expected vaccine reactogenicity, mainly mild and similar in frequency to those reported in the vaccine trials. Systemic reactions were common. | ✓ | ✓ | ✓ | ||
| Watad et al, 2021 (32) | Twenty‐seven cases of either immune‐mediated disease flares or new‐onset disease following vaccination included 17 flares and 10 new‐onset IMDs. | ✓ | ||||
IMD = immune‐mediated disease.
PPR
While most studies reported the number of participants with each specific AIRD, the distribution of sex within each condition was only reported in 6 studies. As the majority of these studies were case series with low sample sizes, a meaningful PPR could not be calculated.
DISCUSSION
COVID‐19 vaccine studies of individuals with AIRDs describe the age and sex of participants adequately, but there is limited reporting of other sociodemographic characteristics that are associated with vaccination outcomes (6). While matching, covariate adjustment, and analysis might not be expected across all factors, the description of participants across PROGRESS‐Plus factors underpins the assessment of applicability to settings and populations beyond those included in the studies. As such, the applicability of the findings remains vaguely understood. As therapeutic studies are conducted to evaluate the safety and response to approved interventions, participants involved in the studies should be representative of the applicable patient population. Inadequate reporting of participant characteristics hinders the reader's ability to assess the applicability of the evidence to populations experiencing inequities. Further, the majority of studies were conducted in high‐income countries, thus applicability to low‐ and middle‐income countries might be limited. Our findings accord with previous evidence of poor reporting for dimensions of inequities in observational studies (39).
Transparent reporting of equity characteristics allows individuals to judge whether evidence applies to themselves, clinicians to assess whether evidence applies to their population of care, and policymakers to make informed decisions that take into account the entire population that they serve. If studies do not adequately report the social characteristics of participants, such judgments cannot be made in an equitable manner. There have been calls worldwide by journals, funding bodies, and research institutes to develop mandates to prioritize equity, diversity, and inclusion in research studies, a key example being the mandate on inclusion of women and minorities in clinical research by the National Institutes of Health (40). Underrepresentation of minorities in health research and vaccine trials has been described as a key factor that contributes to vaccine hesitancy (41), which is a crucial issue faced around the world currently, especially among ethnic minorities (42). Thus, even if a social factor does not have a direct impact on vaccine response, it is necessary to prioritize equity in studies, especially in describing participants.
The PROGRESS‐Plus framework provides a comprehensive set of inequity factors to assess the consideration of equity in research (15). Nevertheless, not all of its components apply to all research contexts, and the framework must be considered within the scope of the evidence being analyzed and the research questions at hand. There is work underway in expanding the well‐known Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines to assess health equity in studies, titled STROBE‐Equity (43). Interim guidelines have outlined key aspects of COVID‐19 studies to analyze for equity considerations, many of which have been incorporated into this analysis. In studies of COVID‐19 vaccination of individuals with AIRDs, inequity factors such as age, sex, and ethnicity might be considered immediately relevant due to their well‐described role in modulating immune response to vaccines (6, 7). However, beyond these factors, certain PROGRESS‐Plus factors are crucial to ensure proper representation in studies. Such factors include ethnicity, socioeconomic status, and health literacy, which have been demonstrated to influence COVID‐19 vaccine confidence and uptake (8, 9, 10).
The strengths of our analysis include a predefined plan for data extraction and analysis. This minimized risk of bias in the screening and data collection process. A limitation of our work is the relatively high proportion of brief research reports (44%). These reports are needed to accelerate the translation of research into practice in the context of a pandemic; however, they may have limited detail on health equity considerations due to their brevity. As a result, it is possible that certain equity characteristics will be reported in future publications. Furthermore, we were unable to assess whether some characteristics of studies were associated with greater analysis or focus on equity, such as country setting or type of vaccination.
Considering social determinants of health in research is needed to inform equity‐focused decisions. To our knowledge, this is the first study to assess the extent of reporting of inequity factors in COVID‐19 studies. The analyses conducted are based on studies from an ongoing living review that informs the development of guidelines and recommendations. Our assessment finds that there is a paucity of equity considerations in such studies, resulting in uncertainty on the generalizability of evidence to the overall population for which guidelines are being developed. Acknowledging the pressing need to develop evidence informing COVID‐19 vaccination amidst the ongoing pandemic, it might be argued that equity considerations were not a focal point in such studies. However, it is precisely in these situations where equity must be prioritized.
Our findings indicate paucity in equity considerations regarding the applicability of the findings to the overall AIRD patient population. Although all 19 studies are of adults, only 1 study discussed limited applicability to children. In fact, the available body of evidence for this population group does not include children in the analysis. Furthermore, the inadequate reporting of patient characteristics and consideration of populations experiencing inequities in assessing applicability of findings may impede guideline developers in judging the “impact on equity” criterion in the Evidence to Decision frameworks for the developed recommendations (44). In turn, these limitations might correspond to suboptimal patient care, and unknowingly exacerbate inequities. Given the magnitude of the inequities that have been exacerbated by the COVID‐19 pandemic for individuals with AIRDs, it is imperative to prioritize equity in vaccine studies for such populations. Future COVID‐19 vaccine studies should report social characteristics of participants consistently beyond age and sex, as well as information such as health literacy if relevant to the intervention under study (45). This will facilitate informed decisions about the applicability of study results to the population of interest.
The studies included in our analysis are from the first search of the living review earlier in the pandemic; thus are all observational in design. We are aware that there are now randomized trials that are underway that were started after the search. As more randomized trials are conducted, this analysis will be of greater value to inform clinical decision‐making. As a pilot study to inform STROBE‐Equity, we hope that our framework will help ensure that studies will be conducted in an increasingly equitable manner, closing the gap in health outcomes for populations that disproportionately face intersecting social barriers to health.
AUTHOR CONTRIBUTIONS
All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Mr. Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study conception and design
Wang, Dewidar, Whittle, Ghogomu, Hazlewood, Leder, Mbuagbaw, Pardo Pardo, Robinson, Buchbinder, Welch.
Acquisition of data
Wang, Dewidar, Welch.
Analysis and interpretation of data
Wang, Dewidar, Whittle, Ghogomu, Hazlewood, Leder, Mbuagbaw, Pardo Pardo, Robinson, Buchbinder, Welch.
Supporting information
Disclosure Form
Supplementary Table 1 Studies included in analysis
ACKNOWLEDGMENTS
We acknowledge Vanessa Glennon, Renea Johnston, Jodie Avery, Stephanie Perron, and Sandrine Soucy for their help in screening.
Supported by the University of Ottawa Faculty of Medicine Summer Studentship Award (award to Mr. Wang).
Drs. Buchbinder and Welch contributed equally to this work.
Author disclosures are available at https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Facr.25034&file=acr25034‐sup‐0001‐Disclosureform.pdf.
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Supplementary Table 1 Studies included in analysis