Ethnic inequalities in COVID-19 vaccine uptake and comparison to seasonal influenza vaccine uptake in Greater Manchester, UK: A cohort study.

BACKGROUND: COVID-19 vaccine uptake is lower amongst most minority ethnic groups compared to the White British group in England, despite higher COVID-19 mortality rates. Here, we add to existing evidence by estimating inequalities for 16 minority ethnic groups, examining ethnic inequalities within population subgroups, and comparing the magnitudes of ethnic inequalities in COVID-19 vaccine uptake to those for routine seasonal influenza vaccine uptake. METHODS AND
FINDINGS: We conducted a retrospective cohort study using the Greater Manchester Care Record, which contains de-identified electronic health record data for the population of Greater Manchester, England. We used Cox proportional hazards models to estimate ethnic inequalities in time to COVID-19 vaccination amongst people eligible for vaccination on health or age (50+ years) criteria between 1 December 2020 and 18 April 2021 (138 days of follow-up). We included vaccination with any approved COVID-19 vaccine, and analysed first-dose vaccination only. We compared inequalities between COVID-19 and influenza vaccine uptake adjusting by age group and clinical risk, and used subgroup analysis to identify populations where inequalities were widest. The majority of individuals (871,231; 79.24%) were White British. The largest minority ethnic groups were Pakistani (50,268; 4.75%), 'other White background' (43,195; 3.93%), 'other ethnic group' (34,568; 3.14%), and Black African (18,802; 1.71%). In total, 83.64% (919,636/1,099,503) of eligible individuals received a COVID-19 vaccine. Uptake was lower compared to the White British group for 15 of 16 minority ethnic groups, with particularly wide inequalities amongst the groups 'other Black background' (hazard ratio [HR] 0.42, 95% CI 0.40 to 0.44), Black African (HR 0.43, 95% CI 0.42 to 0.44), Arab (HR 0.43, 95% CI 0.40 to 0.48), and Black Caribbean (HR 0.43, 95% CI 0.42 to 0.45). In total, 55.71% (419,314/752,715) of eligible individuals took up influenza vaccination. Compared to the White British group, inequalities in influenza vaccine uptake were widest amongst the groups 'White and Black Caribbean' (HR 0.63, 95% CI 0.58 to 0.68) and 'White and Black African' (HR 0.67, 95% CI 0.63 to 0.72). In contrast, uptake was slightly higher than the White British group amongst the groups 'other ethnic group' (HR 1.11, 95% CI 1.09 to 1.12) and Bangladeshi (HR 1.08, 95% CI 1.05 to 1.11). Overall, ethnic inequalities in vaccine uptake were wider for COVID-19 than influenza vaccination for 15 of 16 minority ethnic groups. COVID-19 vaccine uptake inequalities also existed amongst individuals who previously took up influenza vaccination. Ethnic inequalities in COVID-19 vaccine uptake were concentrated amongst older and extremely clinically vulnerable adults, and the most income-deprived. A limitation of this study is the focus on uptake of the first dose of COVID-19 vaccination, rather than full COVID-19 vaccination.
CONCLUSIONS: Ethnic inequalities in COVID-19 vaccine uptake exceeded those for influenza vaccine uptake, existed amongst those recently vaccinated against influenza, and were widest amongst those with greatest COVID-19 risk. This suggests the COVID-19 vaccination programme has created additional and different inequalities beyond pre-existing health inequalities. We suggest that further research and policy action is needed to understand and remove barriers to vaccine uptake, and to build trust and confidence amongst minority ethnic communities.

Introduction

The COVID-19 pandemic has disproportionately affected people with pre-existing socioeconomic and health disadvantages, with wide inequalities in COVID-19 mortality driven by socially patterned risk factors for both SARS-CoV-2 infection and subsequent severe COVID-19 disease [1]. Older adults from minority ethnic groups have been particularly at risk, with COVID-19 mortality rates in England more than 3-fold higher amongst people belonging to Black ethnic groups, and more than 2-fold higher amongst those from Asian ethnic groups, compared to rates amongst those from White ethnic groups [2]. These disproportionate impacts are likely due in part to socioeconomic disadvantages and wide existing health inequalities for those belonging to minority ethnic groups compared to White British adults in England [2,3].

The development and rollout under emergency use legislation of several safe and effective vaccines against COVID-19 has offered the potential to protect individuals and communities from severe COVID-19 [4]. COVID-19 vaccination has been offered free at the point of use through the English National Health Service (NHS) to priority groups, based on age and clinical vulnerability to COVID-19 [5]. However, even for healthcare services provided free at the point of use, disadvantaged populations with the greatest ‘need’ for care tend to have lower uptake of services, formalised as the ‘inverse care law’ [6]. Potential barriers to vaccine uptake include logistical and practical barriers, such as the need to book and travel to vaccination appointments, as well as informational barriers [7]. Personal, family, and cultural views may also influence individuals’ intention to take up vaccination, as may concerns about vaccine safety.

Access to COVID-19 vaccination initially required an appointment booked in advance via the NHS national booking service, a predominantly online booking service available only in English and that requires users to be registered with a general practice (GP) surgery [8]. Many people booked appointments following public announcements indicating that their priority group was eligible to book, and some individuals received letters in the post and/or text messages or telephone calls from their GP inviting them to book an appointment through the national booking service. During the first few months of the COVID-19 vaccine rollout, the majority of vaccination appointments available were at out-of-town mass vaccination centres or hospital hubs. However, as the rollout has progressed, vaccinations have increasingly been provided at local and pop-up sites and without the need for pre-booked appointments, and information is increasingly (though not consistently) available in additional non-English languages.

Existing evidence from England indicates early COVID-19 vaccine uptake has been lower amongst minority ethnic groups in older adults and healthcare workers [9-14]. However, these studies focused on specific population groups, lacked granularity in ethnic group definitions, and did not compare inequalities in uptake to those of other vaccination programmes. Here, we estimate ethnic inequalities in COVID-19 vaccine uptake using a novel electronic health record dataset across a large, ethnically diverse city region in England (Greater Manchester) [15]. We analyse vaccine uptake amongst adults aged 50+ years or with clinical vulnerability to severe COVID-19, and undertake subgroup analysis to identify populations where inequalities are concentrated. To contextualise inequalities in COVID-19 vaccine uptake, we also estimate inequalities in uptake of influenza vaccine in winter 2019/2020, the season immediately prior to the major impacts of the COVID-19 pandemic. We use this comparison because similar groups of adults to those eligible for priority COVID-19 vaccination are routinely encouraged to take up seasonal influenza vaccination [16], and so this comparison allows us to see if the inequalities in vaccination uptake existed before.

Methods

Data collection

We extracted data on individuals aged 18+ years from the Greater Manchester Care Record (GMCR) on 18 April 2021. The GMCR is supported by Graphnet, and holds de-identified primary care and COVID-19-related electronic health record data on approximately 2.8 million patients registered with GP surgeries in Greater Manchester (GM), independent of residence or citizenship status [15]. This gives almost population-level coverage, with data from 433 of 435 (99.5%) GP surgeries in the region. The 2 GP surgeries that do not contribute data have chosen to opt out of data sharing into the GMCR. For reference, one is located in Tameside and the other is in Bolton.

Variables

Outcomes

We included data on date of first-dose COVID-19 vaccination (all approved COVID-19 vaccine types) between 1 December 2020 and 18 April 2021, and date of seasonal influenza vaccination between 1 September 2019 and 31 March 2020 (the seasonal influenza vaccine is a single dose only). Vaccinations appear in GM residents’ GMCR record whether they received vaccination within or outside of GM. We used date of death (all causes) to identify censoring events during the analysis period.

Vaccine eligibility groups

The Joint Committee on Vaccination and Immunisation (JCVI) created eligibility groups for COVID-19 vaccination in the UK based on clinical risk and age. There were 2 clinical risk groups. We defined the ‘high clinical risk’ group as patients on the Shielded Patient List who were eligible for COVID-19 vaccination in JCVI priority group 4 [5]. The clinical risk criteria for the Shielded Patient List were published by NHS Digital and included patients with severe respiratory conditions such as cystic fibrosis and severe chronic obstructive pulmonary disease, those with immunosuppression due to health conditions or treatments, and patients undergoing kidney dialysis [17]. We defined the ‘moderate clinical risk’ group as patients eligible for free NHS seasonal influenza vaccination as a result of pre-existing health conditions, to approximate those eligible for COVID-19 vaccination in JCVI priority group 6 [5,16]. We defined age groups in 5-year age bands across ages 50–79 years, plus the age group 80+ years, derived from age in years at the index date (1 February 2020). We assigned COVID-19 vaccine eligibility group (by health status or age group) by determining the earliest possible eligibility for each individual in line with JCVI criteria [5].

COVID-19 vaccination eligibility groups are relatively concordant with 2019/2020 seasonal influenza vaccine eligibility criteria, with those aged 65+ years or in clinical risk groups eligible [16]. We used data from the previous season to capture ‘routine’ vaccine uptake amongst older or clinically vulnerable adults. We chose not to use the 2020/2021 influenza vaccination season (concurrent with COVID-19 vaccination) for 4 reasons. First, vaccine eligibility was extended to additional age groups (50+ years) [18] because of pandemic-related concerns about hospital capacity, so for many individuals this was a non-routine invitation for vaccination. Second, the widened eligibility criteria led to initial shortages in influenza vaccine supply [18], with unknown local impacts on vaccine availability. Third, there were high-profile awareness campaigns about both influenza and COVID-19 vaccine uptake that may have altered people’s intentions to get vaccinated. Finally, the pandemic caused widespread disruption to in-person healthcare services and may have limited some people’s access, or perception of access, to vaccination. Since influenza vaccine uptake analysis was for the previous season, we restricted the sample to individuals in one of the clinical risk groups or aged 66+ years at the index date (65+ years in 2019/2020). In contrast to the COVID-19 vaccination rollout, there were no formal criteria determining when subgroups within those eligible for 2019/2020 seasonal influenza vaccination were able to access vaccination.

Exposures

Ethnic group was assigned by Graphnet prior to data extraction, using an algorithm drawing on multiple electronic health record sources for each individual. NHS ethnic group categories were recoded to the 18 UK Census 2011 ethnic groups, in line with standard practice. We subsequently recoded the Gypsy or Irish Traveller group to ‘other White background’ due to very low numbers.

We obtained 2019 Index of Multiple Deprivation (IMD) income domain data from the Ministry of Housing, Communities and Local Government website [19] and linked these to individual-level data by residential Lower Layer Super Output Area (LSOA).

Statistical analyses

We followed a prospective analysis plan that was submitted with the application to use the GMCR data. This plan is provided in S3 Analysis Plan, along with an explanation of the changes we made. The rationale for the main changes is outlined in the public and community involvement and engagement (PCIE) section below.

We restricted analysis to individuals resident in GM to avoid inclusion of individuals who may have subsequently registered with a non-GM GP, in which case records may be out of date. We further restricted analysis to individuals eligible for COVID-19 vaccination in priority cohorts (no missing data on age) and with complete data on LSOA and ethnicity (Fig A in S1 Appendix). Exploration of missing ethnicity data is provided in S2 Missing Data for Ethnic Group. We estimated the cumulative probability of vaccination over time stratified by COVID-19 vaccination eligibility group for both COVID-19 vaccination (first dose) and seasonal influenza vaccination, and present the results as Kaplan–Meier failure curves with 95% confidence intervals. We estimated percentage vaccine uptake at the end of the analysis period with logit-transformed confidence intervals, excluding those who died during follow-up.

To estimate inequalities in vaccine uptake, we estimated associations between ethnic group and time to vaccination (first dose only for COVID-19 vaccination) using Cox proportional hazards models for each vaccination eligibility group, treating death as a censoring event. No other form of censoring was included. It is possible a small proportion of individuals moved away from the area during the 138-day analysis period, but this is not reliably recorded in the GMCR. We used standard errors corrected for heteroscedasticity, and the Breslow method for handling ties. We used the Breslow method for pragmatic reasons as this method is much less computationally intensive than other methods, particularly for a large dataset. We confirmed that the parallel trends assumption was reasonable by inspection of log(−log[survival]) versus log(time) plots (Fig B in S1 Appendix). We also estimated associations between ethnicity and time to vaccine uptake adjusted by vaccine eligibility group (age and clinical risk groups) amongst those eligible for both COVID-19 vaccination and 2019/2020 seasonal influenza vaccination. We repeated the adjusted analysis for both COVID-19 and influenza vaccine uptake amongst this population stratified by gender. We also re-estimated results first restricting analysis to individuals resident in the least income-deprived LSOAs (IMD income domain quintile 1), then restricting the population to those resident in the most income-deprived LSOAs (quintile 5). Finally, we estimated adjusted associations between ethnic group and time to COVID-19 vaccination stratified by whether individuals previously received the 2019/2020 seasonal influenza vaccine. All results from Cox regression models are presented as hazard ratios (HRs) with 95% confidence intervals.

We estimated vaccination coverage at the end of the analysis period standardised by eligibility group using direct standardisation to the total eligible population for each vaccine, excluding individuals who died during follow-up. Results are reported by ethnic group with 95% confidence intervals.

We conducted all statistical analysis using Stata 16.1.

Sensitivity analyses

The GMCR dataset only includes individuals who were alive when active data feeds to the GMCR from each Clinical Commissioning Group (CCG) were established. This means some individuals who died prior to the index date (1 February 2020)—and therefore potentially during the influenza vaccine analysis period—were excluded from the dataset. To assess the sensitivity of the COVID-19 and influenza vaccination comparisons to this, inequalities in COVID-19 vaccine uptake were re-estimated excluding individuals who died during follow-up. We also repeated the main analysis adjusting by income deprivation quintile and then GM locality to assess sensitivity to geographical variation. Sensitivity to missing ethnicity data is explored in S2 Missing Data for Ethnic Group.

Public and community involvement and engagement

We held 3 online public discussion groups with diverse members of the GM community, in partnership with the NIHR Applied Research Collaboration for Greater Manchester [20] and Health Innovation Manchester [21], to inform study design. Themes and priorities raised led to our focus on inequalities in vaccine uptake (as opposed to inequalities in vaccine eligibility) and prompted comparison with seasonal influenza vaccine uptake. Key themes raised included mistrust (rooted in experiences of racism and historical context, but compounded by the pandemic); safety concerns; the complexities of personal, family, and cultural attitudes towards vaccination; and logistical, practical, and informational barriers to vaccine access. These discussions also shaped our use of language, for example our decision not to refer to vaccine ‘hesitancy’, as this term could be perceived as dismissing genuine concerns or delegitimising critical dialogue. We held follow-up meetings with an advisory group formed from discussion group attendees, consisting of 4 public contributors from 4 ethnic and faith communities. We discussed preliminary results and framing of findings with the advisory group and local stakeholders including vaccination leads, with these meetings providing a means to reflect on findings and enable critical dialogue to inform subsequent work.

Ethical approval

The GMCR Research Governance Group approved the protocol (Ref: IDCR-RQ-25, approval date: 4 February 2021) on the basis of the national control of patient information notice, which requires NHS organisations to share data to aid the COVID-19 response. All patient data were de-identified; therefore, informed consent was not required.

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S4 Checklist).

Results

Descriptive statistics

There were 2,543,124 individuals aged 18+ years, resident and registered with a GP surgery in GM, and living in the community included in the GMCR dataset; 1,255,117 individuals were eligible for COVID-19 vaccination in priority cohorts (aged 50+ years or with specified health conditions). Within this group we restricted the sample to 1,099,503 individuals (87.60%) with complete data (Fig A in S1 Appendix).

Most individuals were White British (871,231; 79.24%). The largest minority ethnic groups were Pakistani (50,268; 4.57%), ‘other White background’ (43,195; 3.93%), ‘other ethnic group’ (34,568; 3.14%), and Black African (18,802; 1.71%). The population was more income-deprived than the national average, with 37.59% (413,325) living in the 20% most income-deprived neighbourhoods (Table 1).

Table 1

Baseline characteristics and vaccine uptake.

Characteristic	Total analysis population1	Influenza vaccine eligible population2
N	1,099,503	752,715
Ethnic group
White
White British	871,231 (79.24%)	591,695 (78.61%)
White Irish	11,877 (1.08%)	9,287 (1.23%)
Other White background	43,195 (3.93%)	27,224 (3.62%)
Mixed or multiple ethnic groups
White and Black Caribbean	2,760 (0.25%)	1,893 (0.25%)
White and Black African	2,752 (0.25%)	1,830 (0.24%)
White and Asian	1,848 (0.17%)	1,266 (0.17%)
Other mixed or multiple background	3,729 (0.34%)	2,421 (0.32%)
Asian or Asian British
Indian	17,562 (1.60%)	12,629 (1.68%)
Pakistani	50,268 (4.57%)	39,175 (5.20%)
Bangladeshi	9,560 (0.87%)	7,975 (1.06%)
Chinese	6,396 (0.58%)	3,417 (0.45%)
Other Asian background	12,527 (1.14%)	8,047 (1.07%)
Black, African, Caribbean, or Black British
Black African	18,802 (1.71%)	12,472 (1.66%)
Black Caribbean	7,409 (0.67%)	5,023 (0.67%)
Other Black background	3,605 (0.33%)	2,288 (0.30%)
Other ethnic group
Arab	1,414 (0.13%)	925 (0.12%)
Other ethnic group	34,568 (3.14%)	25,148 (3.34%)
Vaccine eligibility group 3
High clinical risk	109,267 (9.94%)	109,267 (14.52%)
Moderate clinical risk	253,158 (23.02%)	253,158 (33.63%)
Age (years)
80+	102,530 (9.33%)	102,530 (13.62%)
75–79	81,890 (7.45%)	81,890 (10.88%)
70–74	116,748 (10.62%)	116,748 (15.51%)
65–69	102,756 (9.35%)	89,122 (11.84%)
60–64	85,233 (7.75%)	—
55–59	114,777 (10.44%)	—
50–54	133,144 (12.11%)	—
Received first-dose COVID-19 vaccine	919,636 (83.64%)	635,670 (84.45%)
Received 2019/2020 seasonal influenza vaccine	447,782 (40.73%)	419,314 (55.71%)
Died during follow-up period	26,400 (2.40%)	24,667 (3.28%)
IMD income deprivation quintile
Least deprived	140,374 (12.77%)	102,078 (13.56%)
Q2	172,752 (15.71%)	110,311 (14.66%)
Q3	155,911 (14.18%)	117,326 (15.59%)
Q4	217,141 (19.75%)	137,629 (18.28%)
Most deprived	413,325 (37.59%)	285,371 (37.91%)

1Adults aged 50+ years or in high or moderate COVID-19 clinical risk groups.

2Adults aged 65+ years or in high or moderate COVID-19 clinical risk groups.

3Individuals were assigned to the highest priority vaccine eligibility group for which they were eligible. Priority proceeded from those aged 80+ years down the age brackets, with the high clinical risk group eligible at the same time as the age group 70–74 years, and the moderate clinical risk group eligible between the age groups 65–69 and 60–64 years. Vaccine eligibility groups are mutually exclusive, such that, for example, an individual aged 80 with high clinical vulnerability was categorized in the age 80+ eligibility group, whereas an individual aged 65 with high clinical vulnerability was categorized in the high clinical risk eligibility group.

Rapid rollout of vaccination to priority groups occurred over the 138-day analysis period (1 December 2020–18 April 2021) (Fig C in S1 Appendix), with high uptake (>76%) by the end of the period amongst all priority groups (Table A in S1 Appendix). Uptake was highest amongst those aged 75–79 years (89.34%, 95% CI 89.14% to 89.55%), with uptake decreasing monotonically across younger age groups to 76.20% (95% CI 75.99% to 76.41%) in those aged 50–54 years. Amongst those eligible due to health conditions, uptake was higher for those at high clinical risk (87.21%, 95% CI 87.02% to 87.40%) than for those at moderate clinical risk (80.02%, 95% CI 79.87% to 80.17%). COVID-19 vaccine uptake was slightly higher amongst women (83.24%, 95% CI 83.15% to 83.33%) than men (81.70%, 95% CI 81.60% to 81.80%).

Overall, 2019/2020 seasonal influenza vaccine uptake was lower than for COVID-19 vaccination, though patterns across COVID-19 vaccination eligibility groups were similar to those for COVID-19 vaccine uptake (Table A and Fig C in S1 Appendix). Uptake was highest amongst those aged 80+ years (70.18%, 95% CI 69.90% to 70.46%), and lowest amongst those eligible due to moderate clinical risk (36.82%, 95% CI 36.64% to 37.00%). Influenza vaccine uptake was slightly lower amongst women (52.93%, 95% CI 52.78% to 53.08%) compared to men (53.78%, 95% CI 53.62% to 53.94%).

Ethnic inequalities in COVID-19 vaccine uptake

Inequalities in COVID-19 vaccine uptake between the White British group and minority ethnic groups were large across all eligibility groups (Fig 1A; Table B in S1 Appendix). Compared to the White British group, uptake differences tended to be largest for the ethnic groups Black African (HR range: 0.28 [95% CI 0.24 to 0.32] to 0.47 [95% CI 0.45 to 0.49]), Black Caribbean (HR range: 0.36 [95% CI 0.33 to 0.38] to 0.57 [95% CI 0.51 to 0.64]), ‘other Black background’ (HR range: 0.38 [95% CI 0.29 to 0.50] to 0.48 [95% CI 0.40 to 0.57]), and Arab (HR range: 0.38 [95% CI 0.30 to 0.49] to 0.54 [95% CI 0.40 to 0.74]). Patterns of inequalities in uptake across eligibility groups varied between ethnic groups, decreasing with decreasing age for some (6 of 16) groups (e.g., Black African and Indian) but increasing amongst younger age groups for others (e.g., ‘other White background’ and ‘other ethnic group’). For 5 of 16 minority ethnic groups, the widest inequality compared to the White British group occurred within the high clinical risk eligibility group. In line with these results, there were substantial differences in standardised total vaccine uptake at the end of the analysis period, with eligibility-group-standardised uptake at least 20 percentage points lower than that of the White British group for 6 of 16 minority ethnic groups (Table C in S1 Appendix).

Fig 1

Associations between ethnic group and vaccine uptake.

Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups stratified by vaccine eligibility group for (A) COVID-19 vaccine and (B) 2019/2020 seasonal influenza vaccine. (C) Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups adjusted by vaccine eligibility group amongst individuals eligible for both the COVID-19 and 2019/2020 seasonal influenza vaccines. High clinical = high clinical risk. Mod. clinical = moderate clinical risk.

Ethnic inequalities in influenza vaccine uptake

There were some moderate inequalities in influenza vaccine uptake by ethnic group (Fig 1B; Table D in S1 Appendix). Considering each vaccine eligibility group, inequalities compared to the White British group were widest for the groups White and Black African (HR range: 0.45 [95% CI 0.28 to 0.75] to 0.76 [95% CI 0.67 to 0.85]) and Black African (HR range: 0.53 [95% CI 0.46 to 0.62] to 0.77 [95% CI 0.47 to 0.80]). Following adjustment by eligibility group, overall inequalities compared to the White British group in influenza vaccine uptake were widest for the groups White and Black Caribbean (HR 0.63, 95% CI 0.58 to 0.68) and White and Black African (HR 0.67, 95% CI 0.63 to 0.72) (Fig 1C; Table E in S1 Appendix). In contrast, relative time to vaccination was shorter in at least 1 eligibility group for the ethnic groups Indian, Pakistani, Bangladeshi, and ‘other ethnic group’ compared to the White British group (Fig 1B; Table D in S1 Appendix). Similarly, influenza vaccine uptake at the end of the analysis period (standardised by vaccine eligibility group) was over 3 percentage points higher for the ethnic groups Bangladeshi and ‘other ethnic group’ compared to the White British group (Table C in S1 Appendix).

Comparison of vaccine uptake inequalities

Amongst the population eligible for both vaccinations, inequalities in COVID-19 vaccine uptake adjusted by eligibility group exceeded inequalities in influenza vaccine uptake for almost all (15 of 16) minority ethnic groups (Fig 1C; Table E in S1 Appendix). The magnitudes of the differences were substantial, with inequalities in COVID-19 vaccine uptake at least double those for influenza vaccine uptake for 7 of 16 minority ethnic groups. Inequalities in vaccine uptake between ethnic groups followed very consistent patterns amongst men and women, though the magnitudes of inequalities were moderately greater for women than men for many minority ethnic groups (10 of 16 for COVID-19 vaccination; 7 of 16 for influenza vaccination) (Fig D and Table F in S1 Appendix). There were no minority ethnic groups for whom inequalities in vaccine uptake were statistically significantly greater amongst men than women.

Results were not sensitive to excluding those who died during the COVID-19 vaccination follow-up period, to parallel exclusion in influenza vaccine analysis (Table E in S1 Appendix), and were robust to different handling of missing ethnicity data (S2 Missing Data for Ethnic Group). Results were also not sensitive to adjustment by neighbourhood-level income deprivation or geographical locality (Fig E and Table G in S1 Appendix).

Subgroup analysis by income deprivation

Uptake of COVID-19 and influenza vaccines followed the income deprivation gradient, with uptake of each vaccine approximately 9 percentage points lower in the most, compared to the least, income-deprived areas (Table A in S1 Appendix). Restricting the population eligible for both vaccinations to those living in the least income-deprived areas attenuated the differences in ethnic inequalities in COVID-19 vaccine uptake as compared to those for influenza vaccine uptake, with inequalities greater for COVID-19 vaccine uptake in only 4 of 16 minority ethnic groups (Fig 2A; Table H in S1 Appendix). In contrast, wide ethnic inequalities in COVID-19 vaccine uptake persisted within the most income-deprived areas, exceeding the magnitude of influenza vaccine uptake inequalities for almost all (15 of 16) minority ethnic groups (Fig 2B; Table H in S1 Appendix).

Fig 2

Associations between ethnic group and vaccine uptake by income deprivation.

Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups adjusted by vaccine eligibility group amongst individuals eligible for both the COVID-19 and 2019/2020 seasonal influenza vaccines living in (A) the 20% least income-deprived neighbourhoods and (B) the 20% most income-deprived neighbourhoods.

Subgroup analysis by prior influenza vaccine uptake

Amongst those eligible for both vaccinations, COVID-19 vaccine uptake was substantially higher amongst those who did previously take the influenza vaccine (95.46%, 95% CI 95.40% to 95.52%) than those who did not (74.64%, 95% CI 74.55% to 74.74%) (Table A in S1 Appendix). Amongst those who did previously take the influenza vaccine, wide ethnic inequalities in COVID-19 vaccine uptake persisted (15 of 16 groups) and were similar in magnitude to the inequalities amongst those not previously vaccinated against influenza for half (8 of 16) of the minority ethnic groups (Fig 3; Table I in S1 Appendix). Where there were differences, these were relatively small in magnitude and varied in directionality.

Fig 3

Associations between ethnic group and vaccine uptake by prior influenza vaccine uptake.

Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to COVID-19 vaccination across ethnic groups adjusted by vaccine eligibility group amongst individuals eligible for both the COVID-19 and 2019/2020 seasonal influenza vaccines who (A) did take up 2019/2020 seasonal influenza vaccination and (B) did not take up 2019/2020 seasonal influenza vaccination.

Discussion

We found wide inequalities in time to COVID-19 vaccination for most minority ethnic groups compared to the White British group. Inequalities were particularly wide for all Black or Black British groups and the Arab group. Vaccine uptake inequalities were substantially wider for COVID-19 vaccination than for influenza vaccination. We also found wide ethnic inequalities in COVID-19 vaccine uptake amongst individuals who previously took up influenza vaccination. COVID-19 vaccine inequalities were concentrated amongst those most at risk of severe COVID-19 (older and clinically vulnerable adults) and those living in income-deprived neighbourhoods.

Our finding that COVID-19 vaccine uptake was lower amongst almost all minority ethnic groups, and particularly low amongst people belonging to Black or Black British ethnic groups, is broadly consistent with existing reports [9-14]. However, previous studies have not analysed uptake inequalities for Arab individuals, so these inequalities were not previously recognised [9-14].

To the best of our knowledge, there is no published information about ethnic inequalities in influenza vaccine uptake amongst older or clinically vulnerable adults in the UK. However, several studies assessing intention to receive vaccination against the 2009 pandemic H1N1 influenza (‘swine flu’) found that people from minority ethnic groups were more likely than the White British group to intend to take up vaccination [22]. It has been suggested this could be attributable to the higher incidence of severe influenza amongst the minority ethnic population [22]. However, this factor appears not to apply to COVID-19 vaccination. These findings reinforce the conclusion that the magnitude of ethnic inequality in COVID-19 vaccine uptake is unusual, and far exceeds the inequalities associated with uptake of other vaccines.

Recent prior influenza vaccine uptake suggests individuals have a degree of confidence in vaccination, and have chosen and successfully navigated access to vaccination, perhaps somewhat explaining the higher COVID-19 vaccine uptake amongst this group. However, even within the group with recent influenza vaccine uptake, we found lower uptake of COVID-19 vaccination amongst people from minority ethnic groups relative to the White British group. This suggests that new ethnic health inequalities have been generated, perhaps resulting from additional barriers to vaccine access, lower vaccine confidence, or both.

There were many differences in how individuals were able to access COVID-19 vaccination compared to 2019/2020 influenza vaccination, which may have introduced additional barriers that disproportionately affected minority ethnic groups. For example, there was a strict requirement for booked COVID-19 vaccination appointments, and much delivery was through mass vaccination centres or hospital hubs. As discussed by Razai et al. [7], residential segregation driven by systemic racism may therefore have resulted in additional barriers—in terms of journey time and cost—to accessing centralised vaccination sites for some communities. In contrast, influenza vaccines are available through GPs and community pharmacies, often via drop-in services [16]. Community pharmacies have been shown to be more accessible in areas of high deprivation than in less deprived areas [23]. Participants in PCIE discussion groups also raised concerns that vaccine information was rarely presented in languages other than English, and that public health messaging sometimes failed to address the concerns of target populations. It is unclear whether any type of health equity impact assessment or community engagement was undertaken during the very rapid centralised design of the COVID-19 vaccine rollout, and this may have contributed to the observed exacerbation of inequalities. Further interdisciplinary research is needed to understand the potential impacts of each of these factors on equity of access to COVID-19 vaccination.

Several studies have investigated attitudes towards COVID-19 vaccination across sociodemographic groups, with relatively consistent reports of lower vaccine confidence amongst minority ethnic groups, which likely contributes to observed uptake disparities [24-29]. Concerns about the novelty and potential unknown side effects or long-term effects of COVID-19 vaccines were frequently reported [24,26-28]. These concerns may have been heightened amongst people from minority ethnic groups due to their underrepresentation in COVID-19 vaccine clinical trials [7,26,29]. Erosion of trust in government, mainstream media, and medical authorities during the COVID-19 pandemic has also been reported [7,25-31] and was a key theme raised in PCIE discussions. A combination of factors—such as the disproportionate burden of COVID-19 mortality amongst minority ethnic groups [2], apparent government suppression of a report into COVID-19 disparities [30], and disregard for the impact of restrictions on non-Christian religious festivals—may have exacerbated loss of trust amongst minority ethnic groups [7]. Moreover, these factors likely compounded existing mistrust amongst some minority ethnic communities stemming from racism, experiences of culturally insensitive healthcare, and awareness of previous unethical healthcare research [7,24]. It will be particularly important to understand whether these COVID-19 vaccination findings represent a shift in attitudes towards vaccination per se, as this may lead to wider inequalities in future routine influenza and other vaccination campaigns.

A strength of this study is the comparison of inequalities in uptake of COVID-19 and influenza vaccines. This brings crucial context to observed inequalities, and demonstrates that COVID-19 vaccine uptake reproduces previous health inequalities, but also harbours new inequalities that are particularly concentrated in income-deprived neighbourhoods. Stratification by previous influenza vaccine uptake demonstrated that the COVID-19 vaccine rollout has generated inequalities even amongst individuals previously accessing influenza vaccination.

Another strength is the population-level coverage of electronic health record data across a large, ethnically diverse metropolitan area, which allowed estimation of inequalities for 16 minority ethnic groups. However even with this large sample, we were unable to estimate inequalities in vaccine uptake for the Gypsy or Irish Traveller group due to very low numbers. There are also limitations in the recording of sociodemographic information in electronic health records. For example, there is incomplete granularity in the recording of ethnicity, which leads to relatively large populations in each of the ‘other’ ethnic groupings, a problem highlighted by our community advisory group. Analysis stratified by gender indicated that ethnic inequalities in vaccine uptake were moderately wider for women than for men for many minority ethnic groups. This highlights the interactions between sociodemographic factors that may exacerbate health inequalities amongst some groups. However, we were unable to explore the role of other factors that may intersect with ethnicity and affect vaccination uptake, such as faith, country of birth, or preferred spoken and written languages, as these are rarely recorded in electronic health records.

The high clinical risk group was robustly identified using clinical codes for the ‘extremely clinically vulnerable’ population of individuals who were instructed to ‘shield’ during the first COVID-19 wave in England. However, the moderate clinical risk group was approximated using codes for clinical eligibility for seasonal influenza vaccination, potentially introducing some misclassification. Similarly, health and social care workers were not identified in our dataset but were eligible for priority vaccination. These misclassifications may have differed with respect to ethnicity, but Kaplan–Meier plots indicated reasonable temporal agreement between eligibility and uptake, suggesting this is unlikely to have substantially biased the estimates. Data were highly complete (≥99.9%) for most variables, but 12.31% of the population was missing data for ethnic group, which could have introduced bias. Detailed exploration of the demographic patterns of missing ethnicity data suggested that a large majority of those with missing data were likely to be White British. Sensitivity analysis showed this did not substantially bias our estimates of vaccine uptake inequalities. Another limitation of our analysis is that we used the Breslow method to handle ties in the Cox regression models, as opposed to the Efron method, which has been shown to generally be less biased [32]. Although using the Breslow method may have biased our results towards underestimation of inequalities in vaccine uptake, this method is more computationally feasible with a large dataset and is unlikely to have markedly biased estimates, given our large sample size [32].

Another limitation is that we considered only first-dose COVID-19 vaccine uptake. Reported differential uptake of second doses [14] may already have led to wider inequalities in terms of full COVID-19 vaccination than those reported here. It may be particularly important to understand whether inequalities widen for subsequent doses, given the planned rollout of ‘booster’ COVID-19 vaccine doses [33].

Finally, given the ethnically diverse urban study setting, our results are likely informative for similar regions, but may not be fully generalizable to England as a whole, or applicable across international contexts.

In summary, we identified wide ethnic inequalities in COVID-19 vaccine uptake, and demonstrated that inequalities were concentrated amongst groups at greatest risk of severe COVID-19—older adults, extremely clinically vulnerable individuals, and those living in the most income-deprived neighbourhoods. By contextualising inequalities in relation to previous influenza vaccine uptake, we found a marked exacerbation of ethnic health inequalities in vaccine uptake. Further research and policy action is needed to understand and remove barriers to vaccine uptake, and to build trust and confidence amongst minority ethnic communities.

Supplementary figures and tables.

Fig A: Flow chart showing inclusion criteria and missing data. GM, Greater Manchester; GP, general practice; LSOA, Lower Layer Super Output Area. LSOAs are neighbourhood-level administrative boundaries containing approximately 1,000 residents. Fig B: log(−log[survival]) versus log(time) plots stratified by ethnic group for COVID-19 and influenza vaccine uptake. Fig C: Kaplan–Meier failure curves indicating the cumulative probability of vaccination over time for (A) COVID-19 and (B) influenza vaccines, stratified by COVID-19 vaccination eligibility group. Individuals were assigned to the highest priority vaccine eligibility group for which they were eligible. Priority proceeded from those aged 80+ years down the age brackets, with the high clinical risk group eligible at the same time as the age group 70–74 years, and the moderate clinical risk group eligible between the age groups 65–69 and 60–64 years, as indicated in the legend group order. Vaccine eligibility groups are mutually exclusive, such that, for example, an individual aged 80 with high clinical vulnerability was categorized in the age 80+ eligibility group, whereas an individual aged 65 with high clinical vulnerability was categorized in the high clinical risk eligibility group. Fig D: Associations between ethnic group and vaccine uptake by gender (results also in Table F). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups, adjusted by vaccine eligibility group, stratified by gender. (A) Male; (B) female. Fig E: Associations between ethnic group and COVID-19 vaccine uptake—sensitivity analysis adjusting by locality or income deprivation (results also in Table G). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to COVID-19 vaccination across ethnic groups, adjusted by vaccine eligibility group, plus additional adjustment by GM locality (10 local authority areas) or income domain quintile. Table A: Vaccine uptake by population subgroup and vaccine type (percentage uptake and 95% CI). Table B: Associations between ethnic group and COVID-19 vaccine uptake (results also in Fig 1A). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to COVID-19 vaccination across ethnic groups, stratified by vaccine eligibility group. Table C: Vaccine uptake standardised to GM vaccine eligibility group structure. Estimated percentage uptake with 95% confidence intervals. Table D: Associations between ethnic group and influenza vaccine uptake (results also in Fig 1B). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to influenza vaccination across ethnic groups, stratified by vaccine eligibility group. Table E: Associations between ethnic group and vaccine uptake (results also in Fig 1C). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups, adjusted by vaccine eligibility group. Table F: Associations between ethnic group and vaccine uptake by gender (results also in Fig D). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups, adjusted by vaccine eligibility group, stratified by gender. Table G: Associations between ethnic group and COVID-19 vaccine uptake—sensitivity analysis adjusting by locality or income deprivation (results also in Fig E). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to COVID-19 vaccination across ethnic groups, adjusted by vaccine eligibility group, plus additional adjustment by GM locality or income domain quintile. Table H: Associations between ethnic group and vaccine uptake by income deprivation (results also in Fig 2). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups, adjusted by vaccine eligibility group, stratified by income deprivation. Table I: Associations between ethnic group and vaccine uptake by prior influenza vaccine uptake (results also in Fig 3). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to COVID-19 vaccination across ethnic groups, adjusted by vaccine eligibility group, stratified by prior influenza vaccine uptake.

(PDF)

Click here for additional data file.

Supplementary figures and tables regarding missing data for ethnic group.

Table A: Missing ethnicity data by population subgroup (percentage missing and 95% CI). Table B: Census estimates of the percentage of individuals from each ethnic group by GM locality. Table C: Percentage of individuals in sample population from each ethnic group by GM locality, including missing data as an additional ethnic group category. Table D: Difference between sample population and census estimates (sample population estimate minus census estimate) of percentage of individuals from each ethnic group by GM locality, including missing data as an additional ethnic group category. Table E: Associations between ethnic group and vaccine uptake following partial recoding of missing ethnicity data (results also shown in Fig B). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups, adjusted by vaccine eligibility group, following partial recoding of missing ethnicity data to White British. Fig A: Histogram showing the distribution of percentage missing ethnic group data across LSOAs (neighbourhoods). Fig B: Associations between ethnic group and vaccine uptake following partial recoding of missing ethnicity data (results also shown in Table E). Hazard ratios with 95% confidence intervals from Cox proportional hazards models estimating time to vaccination across ethnic groups, adjusted by vaccine eligibility group, following partial recoding of missing ethnicity data to White British, compared to original ethnicity coding used in the main analysis. (A) COVID-19 vaccination; (B) 2019/2020 seasonal influenza vaccination.

(PDF)

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Prospective analysis plan with information about changes made.

(PDF)

Click here for additional data file.

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist completed with section and paragraph numbers for each item.

(PDF)

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19 Oct 2021

Dear Dr Watkinson,

Thank you for submitting your manuscript entitled "Ethnic inequalities in Covid-19 vaccine uptake and comparison to seasonal Influenza vaccine uptake: retrospective cohort study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

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PLOS Medicine

9 Dec 2021

Dear Dr. Watkinson,

Thank you very much for submitting your manuscript "Ethnic inequalities in Covid-19 vaccine uptake and comparison to seasonal Influenza vaccine uptake: retrospective cohort study" (PMEDICINE-D-21-04381R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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Comments from the academic editor:

There should be some more information about how persons, who are eligible for a vaccination, get that information. Do they receive an invitation letter? And is this letter in different languages? Are there any additional efforts to reach and motivate people to make a vaccination appointment? Especially for non-UK readers, it is important to understand how the system works.

Requests from the editors:

Title: Please update title to ‘Ethnic inequalities in Covid-19 vaccine update and comparison to seasonal Influenza vaccine update in Greater Manchester: a cohort study’.

Financial disclosure: Please provide more information (see here for details of what is required): https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-financial-disclosure-statement

Abstract Methods and Findings:

* Please include the study design, time period during which the study took place, length of follow up, and main outcome measures.

* Please include basic demographic data (summarising that presented on lines 198 – 201).

* Please include the important dependent variables that are adjusted for in the analyses.

* In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

Line 27-8: Please update to ‘This suggests that the Covid-19 vaccination programme has created…’.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

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Please define the abbreviations in Figure S1.

Comments from the reviewers:

Reviewer #1: This study reveals the vaccine uptake inequalities by using a reliable public dataset. The results, including sensitivity analyses and public discussion, seem to justify the authors' conclusions. Please consider the following minor comments.

#1 L35

Adding more detail about the pandemic situation in England will help me understand. How much is the COVID-19 morbidity or mortality among the older adults from minority ethnic groups compared to White British adults in England?

#2 L43, L338

How are booking or travelling to vaccination appointments difficult for ethnic minority groups in England? Is it available through the internet, telephone, or hand-writing? How far are the vaccination centres are from their residence? Is there no language support? Are the workers available to access vaccination at night or their workplace?

#3 L329

I suppose fewer opportunities existed to reflect ethnic minority groups' voices in the new-introduced vaccination system. The rapid political decision-making process might play a significant role in such an unusual inequality.

#4 L351

I think the other sociodemographic factors might be the confounders in this study. The Covid-19 vaccine uptake may also differ across the participants' years of residence, occupation, or having family members.

Is there any possibility the participants had already got vaccinated in the other area? Does the database cover the population only who has permanent citizenship?

Reviewer #2: The authors present a detailed report of COVID-19 vaccination uptake among different racial/ethnic groups from a large multicultural city in the United Kingdom. Using survival analysis in an enormous data set, they give compelling evidence of disparities in vaccination uptake between groups. They additionally show how these disparities persist when stratified by deprivation, age category and vaccine eligibility group. Throughout, they contextualise COVID-19 vaccination uptake by comparing it to influenza vaccination uptake prior to the start of the pandemic. All in all, these results suggest that existing racial/ethnic disparities in vaccination uptake were exacerbated by the pandemic. This is vital evidence to encourage health policy makers to act to address these disparities and, additionally, for health researchers to investigate the specific causes of these disparities.

I thought this was a very well-written manuscript and I appreciated the great level of detail the authors went to to present their findings, including a rigorous exploration of bias due to missing data and sensitivity analyses. A few minor questions to address follow:

Introduction

-The final sentence of the penultimate and ultimate paragraphs (i.e. the sentences starting "However, these studies have focused..." and, "We use this comparison because similar groups...") could be rephrased to make it clearer that your study is using the influenza vaccine uptake comparison as an example of a pre-pandemic disparity in routine vaccine uptake. I suggest rephrasing the first sentence I referenced like so: "..., and have not been compared to inequalities in uptake of other vaccination programmes." The final sentence in the Introduction could be rephrased to: "...this allows us to see if the inequalities in vaccination uptake existed before." As it stands, these sections make me think you want to show that, if your study had revealed no disparity in influenza vaccination uptake by racial/ethnic group, that would be evidence that there were no such health disparities at all (i.e. in vaccination programme uptake or beyond).

Methods

-Data Collection: Please report why data were unavailable for one surgery, and where it was located

-Exposures: Although the number of participants in the Gypsy or Irish Traveller group may be too small to report meaningful statistics in this study, perhaps reporting them in the supplementary materials would be helpful for potential future researchers wishing to explore vaccination patterns in this ethnic group via meta-analysis.

-Statistical analyses: The Efron method for handling ties is generally less biased than the Breslow method (see Hertz-Picciotto and Rockhill 1997: Validity and Efficiency of Approximation Methods for Tied Survival Times in Cox Regression). Why did you choose the Breslow method instead?

Results

-Ethnic inequalities in Covid-19 vaccine uptake: The way the hazard ratio ranges are reported in the text is rather difficult to read; it can be hard to disentangle the 95% confidence interval values from the point estimates. I suggest either omitting the 95% confidence interval values from the text (because they are reported in Table S2 and quite narrow anyway) or changing the format to something like: "Hazard ratio (HR) range 0.276 (95% CI: 0.241, 0.317) to 0.471 (95% CI: 0.450, 0.494)."

-I am curious to see how these results differed by gender within racial/ethnic groups. Could you please report a subgroup analysis (e.g. in the same format as Figure 2) showing vaccine uptake patterns by racial/ethnic group stratified by gender?

Reviewer #3: Alex McConnachie, Statistical Review

The paper by Watkinson et al examines variations in COVID-19 vaccination uptake in 2020-21 between ethnic groups, and compares these with variations in flu vaccination uptake in 2019-20. This review considers the use of statistics in the paper.

Overall, the statistical methods and presentation is very good. The main analyses are supported by a range of adjusted, stratified, and sensitivity analyses, and the results are not over-interpreted. My comments are quite minor.

For my taste, there are too many decimal places reported. I think one less could be used throughout the paper.

Figure 3 might work better with the two panels overlaid on the same plot.

The Kaplan-Meier plots show survival probabilities, which is not incorrect, but may be confusing to a general readership, since the paper is not about mortality. The majority of the paper talks in terms of vaccine uptake. Would it therefore be better to flip the y-axis of the K-M plots, and show the cumulative probability of vaccination?

Looking at Figure S3, I assume the order of the legends is in terms of the final vaccination probability? Perhaps this would be clearer if the probabilities were shown (with 95% CIs?) as part of the legend. Also, I'm not sure if it is my eyesight, but the colours in the legend do not seem to match the colours of the curves.

Figure S3 is quite cramped, with 3 estimates and CIs per ethnic group. Could the y axis be stretched a little?

In Figure S6, a slight vertical offset to separate each pair of estimates and CIs, as in other similar figures, would help.

The sensitivity analysis that includes geographic adjustment uses 10 LA areas, which is fine. Not for this paper, but would it be possible to do (and worth doing) an analysis by LSOA, adjusting for spatial correlation, and explore the associations between uptake and factors such as distance to nearest vaccination centre?

Reviewer #4: Many thanks for the opportunity to review this very interesting paper, which is of considerable public health relevance. I have some minor suggestions, which I think require clarification and which may improve the quality of this manuscript.

Abstract:

- It should be made clear that this paper is based on first/any dose of Covid-19 vaccination, and does not present information on full course of vaccination. This is clarified in the manuscript, but is relevant in the abstract too.

- It would be helpful to include any information on vaccine type(s)/make(s)

Methods:

- Was there any other form of censoring considered e.g. moving out of the region?

- Further information on full vaccination status would be helpful in the Methods, even if just to advise the reader that this was not possible to measure

- Some further elaboration on the "shielded patient list" would be helpful. This may be clear to a UK audience, but may be less clear for other international readers

- PPI section is excellent and a real strength of this paper

Results:

- The second paragraph of Descriptive Statistics section does not mention Pakistanis although they appear from the data to be the second largest ethnic group

- Table 1 under Vaccine eligibility group, are the clinical risk groups (high vs. moderate) and age groups mutually exclusive or is there overlap between some of these? Can this be clarified? If I understand correctly, you assigned individuals to whichever eligibility group they first fell in to, but what did this mean in practice for most older patients? Were they more likely to be in the moderate or high clinical risk groups (based on comorbidities), or more likely to be in their respective age groups? A footnote in this table may be helpful.

- Summary data are presented in Table 1 for death during follow-up. Is there any information on what proportion of deaths might have been attributed to Covid (either as primary or contributory cause)? Moreover, any summary data on Covid infection rates may be helpful for context if available.

- The findings relating to inequalities in vaccine uptake appear to be quite robust and are important to note. It would be helpful to contextualise this further by translating some of the key results (which are all presented in relative terms using HRs etc) in to absolute numbers if possible. For this population of approx. 1.1 million people, how do these results translate in to numbers of missed vaccines for certain ethnicities or certain risk groups? Translation in to absolute numbers may be particularly helpful for public health messaging.

- Did you have any information on length of time residing in the UK? These individuals are all registered with GPs but I do not get a sense whether they are all long-standing UK citizens who are otherwise integrated in to UK society, or if they might be relatively recent arrivals and still heavily influenced by cultural/health beliefs in their countries of origin. Or perhaps it is the case that different age groups have differentially integrated to UK society. Length of time residing in UK may be an important confounder, particularly considering the more marked inequalities observed among older age groups. Inequalities appear to be widest in those who are older in age or extremely clinically vulnerable (and as per above query - perhaps those who are more clinically vulnerable are also by default older in age because of increased risk of comorbidities?). One would also expect that the length of time individuals have been living in UK will affect/influence their acceptance of UK vaccine-related messaging, as well as likelihood of language barriers etc?

- Following on from this, did you have any information on whether some participants were asylum seekers or recently-arrived refugees? I'm sure this would only be a small minority (and likely to be very ethnically diverse, so may not allow the same level of detailed analysis), but they are a particularly important subgroup nonetheless.

- The results relating to the Bangladeshi population are interesting and warrant some further explanation or clarification. From Figure 1 C it appears that they had higher uptake of influenza vaccine vs. White British population in 2019/20. Figure 2B suggested that this was particularly the case in more deprived areas. This alone is an interesting finding. But Table S1 suggests that influenza vaccine uptake was lower in the Bangladeshi population vs. White British population in 2019/20. If I understand correctly, this is because of differences in eligibility groups between Bangladeshi and GM population (per Table S3). It would be very useful to acknowledge this finding in the manuscript as it is not entirely intuitive.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Submitted filename: Review Ethnic_Inequalities.docx

Click here for additional data file.

20 Dec 2021

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

24 Jan 2022

Dear Dr. Watkinson,

Thank you very much for re-submitting your manuscript "Ethnic inequalities in Covid-19 vaccine uptake and comparison to seasonal Influenza vaccine uptake in Greater Manchester: a cohort study" (PMEDICINE-D-21-04381R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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We look forward to receiving the revised manuscript by Jan 31 2022 11:59PM.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

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------------------------------------------------------------

Requests from Editors:

Please update your title to 'Ethnic inequalities in Covid-19 vaccine uptake and comparison to seasonal Influenza vaccine uptake in Greater Manchester, UK: a cohort study.

The Data Availability Statement (DAS) requires revision. I feel the URL included provides detailed information for those who have provided their data for research purposes but does not provide sufficient information for researchers wishing to access the data – please provide an additional URL/email address suitable for researcher enquiries.

Please consider quantifying key findings from the ethnic inequalities in influenza vaccine update in your abstract (e.g. those found on lines 301-2).

Please trim the limitations in your abstract (lines 30-33) down to one summarising sentence.

Lines 177 and 225: Please name and cite items located in your supplementary materials per our guidance (https://journals.plos.org/plosmedicine/s/supporting-information) e.g. (S1 Analysis Plan).

Please remove the data sharing agreement and data availability section from the end of the main text as this information is captured in your Data Availability Statement as part of the submission form and will be published as metadata.

Lines 203 and 420: The terms gender and sex are not interchangeable (as discussed in http://www.who.int/gender/whatisgender/en/ ); please ensure you are using the appropriate term in the main text and the relevant figures/tables in the supplementary materials.

Comments from Reviewers:

Reviewer #2: I thank the authors for addressing my comments and have nothing more to add.

Reviewer #3: Alex McConnachie, Statistical Review

I thank the authors for their consideration of my original comments, and I am happy with their responses. I have no further comments to make.

Reviewer #4: Thank you for the opportunity to review this revised manuscript. I believe that the authors have addressed my previous queries and recommend that the article should be accepted for publication.

Any attachments provided with reviews can be seen via the following link:

[LINK]

26 Jan 2022

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

27 Jan 2022

Dear Dr Watkinson,

On behalf of my colleagues and the Academic Editor, Dr Mirjam Kretzschmar, I am pleased to inform you that we have agreed to publish your manuscript "Ethnic inequalities in Covid-19 vaccine uptake and comparison to seasonal Influenza vaccine uptake in Greater Manchester, UK: a cohort study" (PMEDICINE-D-21-04381R3) in PLOS Medicine.

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When making the formatting changes, please also make the following update:

* For consistency with the rest of the manuscript and to avoid confusion, please update the bullet at line 56 to read: 'We used electronic health records for the population of Greater Manchester, England, to estimate inequalities in Covid-19 and seasonal Influenza vaccine uptake between the White British group and 16 minority ethnic groups'.

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