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. 2023 Sep 26;20(9):e1004289. doi: 10.1371/journal.pmed.1004289

Evaluating socioeconomic inequalities in influenza vaccine uptake during the COVID-19 pandemic: A cohort study in Greater Manchester, England

Ruth Elizabeth Watkinson 1,*, Richard Williams 2, Stephanie Gillibrand 1, Luke Munford 1, Matt Sutton 1
Editor: Rebecca F Grais3
PMCID: PMC10522043  PMID: 37751419

Abstract

Background

There are known socioeconomic inequalities in annual seasonal influenza (flu) vaccine uptake. The Coronavirus Disease 2019 (COVID-19) pandemic was associated with multiple factors that may have affected flu vaccine uptake, including widespread disruption to healthcare services, changes to flu vaccination eligibility and delivery, and increased public awareness and debate about vaccination due to high-profile COVID-19 vaccination campaigns. However, to the best of our knowledge, no existing studies have investigated the consequences for inequalities in flu vaccine uptake, so we aimed to investigate whether socioeconomic inequalities in flu vaccine uptake have widened since the onset of the COVID-19 pandemic.

Methods and findings

We used deidentified data from electronic health records for a large city region (Greater Manchester, population 2.8 million), focusing on 3 age groups eligible for National Health Service (NHS) flu vaccination: preschool children (age 2 to 3 years), primary school children (age 4 to 9 years), and older adults (age 65 years plus). The sample population varied between 418,790 (2015/16) and 758,483 (2021/22) across each vaccination season. We estimated age-adjusted neighbourhood-level income deprivation-related inequalities in flu vaccine uptake using Cox proportional hazards models and the slope index of inequality (SII), comparing 7 flu vaccination seasons (2015/16 to 2021/22). Among older adults, the SII (i.e., the gap in uptake between the least and most income-deprived areas) doubled over the 7 seasons from 8.48 (95% CI [7.91,9.04]) percentage points to 16.91 (95% CI [16.46,17.36]) percentage points, with approximately 80% of this increase occurring during the pandemic. Before the pandemic, income-related uptake gaps were wider among children, ranging from 15.59 (95% CI [14.52,16.67]) percentage points to 20.07 (95% CI [18.94,21.20]) percentage points across age groups and vaccination seasons. Among preschool children, the uptake gap increased in 2020/21 to 25.25 (95% CI [24.04,26.45]) percentage points, before decreasing to 20.86 (95% CI [19.65,22.05]) percentage points in 2021/22. Among primary school children, inequalities increased in both pandemic years to reach 30.27 (95% CI [29.58,30.95]) percentage points in 2021/22. Although vaccine uptake increased during the pandemic, disproportionately larger increases in uptake in less deprived areas created wider inequalities in all age groups. The main limitation of our approach is the use of a local dataset, which may limit generalisability to other geographical settings.

Conclusions

The COVID-19 pandemic led to increased inequalities in flu vaccine uptake, likely due to changes in demand for vaccination, new delivery models, and disruptions to healthcare and schooling. It will be important to investigate the causes of these increased inequalities and to examine whether these increased inequalities also occurred in the uptake of other routine vaccinations. These new wider inequalities in flu vaccine uptake may exacerbate inequalities in flu-related morbidity and mortality.

Ruth Elizabeth Watkinson and colleagues investigate whether socioeconomic inequalities in influenza vaccine uptake have widened since the onset of the COVID-19 pandemic.

Author summary

Why was this study done?

  • There are known socioeconomic inequalities in flu vaccine uptake, with uptake lower in more deprived neighbourhoods compared to less deprived areas.

  • During the Coronavirus Disease 2019 (COVID-19) pandemic, there were many changes that may have affected flu vaccine uptake, including changes in flu vaccine eligibility and delivery, extensive vaccine messaging and debate linked to the COVID-19 mass vaccination campaign, and widespread disruption to healthcare services.

  • However, to the best of our knowledge, no existing studies had analysed whether the COVID-19 pandemic was linked to changes in socioeconomic inequalities in flu vaccine uptake.

What did the authors do and find?

  • We used health records for the population of Greater Manchester, a large metropolitan region in the North West of England, to look at changes in socioeconomic inequalities in flu vaccine uptake before and during the COVID-19 pandemic.

  • We focused on young children and older adults, as these age groups are at higher risk of severe outcomes from flu infection and are eligible for annual flu vaccination provided free at the point of service.

  • We found that the difference in uptake between the least and most deprived areas increased markedly during the pandemic years (2020/21 and 2021/22), with the greatest increase in inequalities among older adults.

What do these findings mean?

  • The COVID-19 pandemic led to increased socioeconomic inequalities in flu vaccine uptake, likely due to changes in demand for and access to flu vaccination.

  • It will be important to better understand the causes of these increased inequalities and to investigate whether inequalities have also increased across other sociodemographic factors and for uptake of other routine vaccinations.

  • A limitation of this study is the use of a local dataset for Greater Manchester, so although this is a relatively diverse region, our results may not be generalisable to other areas.

Introduction

Seasonal influenza (flu) infection is associated with high demand for primary care consultations, tens of thousands of hospital admissions, and excess mortality each winter [1–3]. Flu vaccination provides protection against severe clinical flu presentation [2,3], so vaccine uptake is an important determinant of outcomes from flu infection.

In England, the National Health Service (NHS) provides healthcare free at the point of service, and this includes annual flu vaccination for groups at higher risk of severe outcomes from flu infection (young children and adults aged 65 years plus, groups with certain preexisting health conditions, or pregnant people [3]). Despite this free vaccination offer, previous studies show substantial socioeconomic inequalities in flu vaccine uptake prior to the Coronavirus Disease 2019 (COVID-19) pandemic [4,5]. Vaccine uptake was lower among those living in more deprived neighbourhoods, despite higher rates of hospitalisation due to flu infection in deprived areas [4,5]. Possible explanations for these observed inequalities include differential health knowledge and information, with understanding of the risks of flu infection, perceived vulnerability, and concerns about flu vaccine side effects each potentially differing across socioeconomic groups [5].

Pre-COVID-19 pandemic, flu vaccine uptake among older adults was stable, varying between 71% and 75% for over a decade [6]. However, uptake among adults increased to over 80% in each of the 2 winter seasons following the onset of the COVID-19 pandemic, suggesting a link between the pandemic period and flu vaccine uptake [6]. Flu vaccine uptake among preschool and school aged children has consistently been lower than among older adults, ranging from approximately 30% to 60% prepandemic [7,8].

The COVID-19 pandemic was associated with several factors that may impact overall flu vaccine uptake and uptake inequalities. Firstly, there was widespread but unequal disruption to NHS services and to schools (a key site where vaccinations are delivered to children), which may have affected access to flu vaccination [9,10]. There were also changes to flu vaccine delivery in some areas during 2021/22, with optional coadministration of flu and COVID-19 vaccines for eligible adults [11]. Alongside differences in access, attitudes towards flu vaccination may have changed. For example, concerns about COVID-19, particularly the risks of coinfection with flu [12], may have increased people’s perceived need for flu vaccination. The high-profile COVID-19 mass vaccination programme, accompanied by extensive public health messaging and substantial misinformation and disinformation, may also have affected attitudes [13–15]. Concerns about the risk of side effects from vaccination may also have increased, after rollouts of several COVID-19 vaccines were restricted or suspended in some countries following reported links to blood clots and myocarditis [16–19].

To the best of our knowledge, there are no published studies or grey literature that investigate whether inequalities in flu vaccine uptake have changed since the COVID-19 pandemic. We use population-wide data from a large metropolitan area (Greater Manchester, population 2.8 million) to analyse socioeconomic inequalities in flu vaccine uptake before and during the COVID-19 pandemic. Although Greater Manchester is mostly a major urban conurbation, it also encompasses some smaller urban towns as well as some rural towns and villages [20]. Greater Manchester is also a socioeconomically diverse area, including some of the least deprived neighbourhoods nationally, despite overall higher deprivation levels than the national average [21]. For these reasons, Greater Manchester is a useful setting to study changes in socioeconomic inequalities in vaccine uptake.

Methods

Ethics statement

The GMCR Research Governance Group approved this project (Ref: IDCR-RQ-25, approval date: 04 February 2021) based on the control of patient information (COPI) notice, which required NHS organisations to share data for the COVID-19 response. The patient data were deidentified, so informed consent was not required. We used the REporting of studies Conducted using Observational Routinely collected Data (RECORD) guidelines to write up this study (S1 RECORD).

NHS England institutional context

Annual NHS flu vaccination is available to at-risk adults and preschool children (aged 2 to 3 years) free at the point of service at their general practice (GP) surgery [22]. Alternatively, eligible adults can access flu vaccination at community pharmacies [22]. NHS flu vaccination is also available to children aged between 4 and 11 years, but this is delivered through primary schools [23]. Adults are usually given an injected inactivated flu vaccine, while children generally receive a live attenuated nasal spray vaccine [22].

Data collection

We extracted data from the Greater Manchester Care Record (GMCR) on 6 October 2022. The GMCR has almost population-level coverage within Greater Manchester (GM), holding data on approximately 3 million patients registered with GP surgeries [24]. The GMCR contains all patients in GM who were alive on 1 February 2020. There is some survival bias, as some localities in GM do not provide data on patients who died prior to this date.

Populations

We restricted analysis to people resident within GM (3,021,322 individuals) to avoid using potentially out-of-date records from individuals who have left the region and may have re-registered with a non-GM GP surgery. There were no known missing data on flu vaccine uptake, clinical vaccination eligibility, or age. We excluded 2,107,742 people who were not eligible for NHS flu vaccination on age criteria at any point during the study period, leaving 913,580 individuals for the main analysis. The sample varied between vaccination seasons based on eligibility (age) and deaths (S1 Fig).

Trends in flu vaccine uptake among school-age children are complex, as there has been a piloted and phased rollout [23]. We therefore focused on a subset of ages and seasons as indicated in Fig 1. In the most recent season (2021/22), NHS flu vaccination was also available to older children (aged 11 to 16 years) through schools [23]. However, since uptake data are available for only one season, we excluded this group from the main analysis. We were unable to reliably identify people eligible for NHS flu vaccination due to pregnancy or preexisting health conditions over time, so the main analysis focused on people eligible for vaccination due to age.

Fig 1.

Fig 1

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Percent flu vaccine uptake by age and flu vaccination season among (A) children and (B) older adults. The main study populations and seasons are highlighted with coloured boxes as indicated in the legend. The vertical dashed grey line indicates the onset of the pandemic.

Variables

Outcomes

The primary outcome was flu vaccination for the 2015/16 to 2021/22 vaccination seasons (1 September to 31 March the following year). We included date of death (all causes) to identify censoring events. Outcomes appear in GM residents’ GMCR record for NHS vaccination across any setting (GP, pharmacy, or school) and whether they occur within GM or elsewhere. Awareness of the COVID-19 pandemic in the United Kingdom (UK) emerged towards the end of the 2019/20 vaccination season, so we consider the 2020/21 and 2021/22 vaccination seasons to have taken place since the start of the pandemic.

Exposures

The primary exposure was income deprivation, using the 2019 income deprivation affecting older people index (IDAOPI) rank and income deprivation affecting children index (IDACI) rank from the Ministry of Housing, Communities, and Local Government website at the lower layer super output area (LSOA) level [21]. IDAOPI and IDACI are age-specific income domain components of the index of multiple deprivation (IMD) [21]. Specifically, these measures are derived from the proportion of children aged 0 to 15 years (IDACI) or older adults aged 60 years plus (IDAOPI) in each LSOA who are living in income-deprived households, which is defined by receipt of income-assessed benefits or tax credits with an equivalised income of less than 60% of the national median (before housing costs) [25]. These statistics are based on administrative records held by the Department for Work and Pensions and His Majesty’s Revenue and Customs [25].

Covariates

The main covariate was age, recorded as age in years at the index date (1 February 2020). Children are eligible for vaccination based on their age before the start of each vaccination season (1 September), whereas adults are eligible based on their age at the end of the season (31 March), much closer to the index date. Age was adjusted for each vaccination season, using the lower bound of possible age during the vaccination season for children and the upper bound for adults to minimise misspecification of vaccine eligibility. Practically, this means that children aged “2” may have been aged 2 or 3 years, during the vaccination season, while adults aged “65” may have been aged 64 or 65 years. In regression analysis, age in years was used for children, and age grouped into 5-year age bands up to 80 years plus was used for adults. We included clinical eligibility only in sensitivity analysis, as it was only available for 2021/22 [26].

Statistical analysis

Analysis plan and changes

The hypothesis for this study was that changes associated with the onset of the COVID-19 pandemic may have widened socioeconomic inequalities in flu vaccine uptake. There was no formal prospective protocol, but in advance of any analysis, we planned to test this by using area-level IMD decile as the socioeconomic measure and investigating both relative inequalities using age-adjusted Cox proportional hazards models for each flu vaccination season, and absolute inequalities using age-standardised uptake. We later added the slope index of inequality (SII) analysis following feedback from colleagues. At the suggestion of one of the reviewers, we added supplementary analysis stratified by sex and added results unadjusted by age at the request of the editor. We initially planned to focus only on older adults, as we were not aware data for people aged under 16 years was available in this dataset. However, after discussion with data engineers, we also included children who were eligible for flu vaccination. There were no data-driven changes in our approach to the analysis.

Main analysis

We estimated associations between income deprivation decile and time-to-vaccination (from 1 September each season) using the least deprived decile as the reference group and adjusting by age, using separate Cox proportional hazards models for each season. Death was included as a censoring event. We used the Breslow method to handle ties, with standard errors corrected for heteroscedasticity. We reported results as hazard ratios (HRs) with 95% confidence intervals (CIs). For clarity, we did not report p-values as these refer to within-season comparisons while most comparisons made in the text are between vaccination seasons. We confirmed that the proportional hazards assumption was reasonable by visual inspection of log(−log[survival]) versus log(time) plots (S2 Fig). We used a time-to-event approach, rather than the binary outcome of vaccination received, because delayed vaccination is also a component of the overall vaccine uptake inequality, with earlier vaccination able to offer protection throughout the peak of the winter flu season.

We estimated age-standardised vaccine uptake for each deprivation decile at the end of each vaccination season, excluding those who died before or during each season and using direct standardisation to the total sample population. We estimated absolute inequalities using SII [27] adjusted by age group, using the fractional rank of each LSOA (as opposed to deciles) with LSOAs ranked in descending order of deprivation.

Sensitivity analysis

We first repeated the main analysis (Cox regression) without adjustment by age. We then restricted the main analysis sample to those who remained alive throughout all 7 vaccination seasons. We also reestimated results using overall IMD as an alternative measure of deprivation [21]. We then reestimated results excluding people at the limit of age eligibility in each group (i.e., aged 3, 9, or 65 years) to assess potential bias due to misspecification of eligibility due to using age in years (rather than date of birth). We also reestimated 2021/22 results adding in individuals who were clinically eligible for flu vaccination, and those aged 9 to 16 years and 50 to 64 years to match the expanded age-based eligibility during 2021/22 [26]. In addition, we reestimated the main results stratifying by sex (male or female).

All statistical analysis used Stata 16.1.

Public and community involvement and engagement

This study was part of a larger flu and COVID-19 Vaccine Equity Project. We held several public discussion groups with diverse members of the GM community in partnership with the National Institute of Health and Care Research (NIHR) Applied Research Collaboration GM (ARC-GM) panel and Health Innovation Manchester (HInM) forum as described previously and worked collaboratively with a Public Advisory Group throughout the project [24].

Results

Among those eligible for flu vaccination based on age during the 2021/22 vaccination season, there were 70,419 children aged 2 to 3 years (referred to as preschool children); 233,277 children aged 4 to 9 years (referred to as primary school children); and 454,787 adults aged 65 years plus (referred to as older adults) (Table 1). Income deprivation was higher in GM than the England average, with approximately 24% of preschool (16,612) and primary school (55,478) children living in the 10% most deprived neighbourhoods nationally. Older adults in GM also disproportionately lived in income-deprived areas (66,568, 14.6%) (Table 1). Flu vaccine uptake was higher in older age groups, with 72.8% of older adults (331,092), 51.1% of primary school children (119,223), and 34.9% of preschool children (24,575) receiving flu vaccination.

Table 1. Baseline study population statistics.

Population for 2021/22 season is shown as an illustrative example.

Age 2 to 3 years
(Preschool children)		 Age 4 to 9 years
(Primary school children)		 Age 65 years plus
(Older adults)
Total population N 70,419 233,277 454,787
Age (years) median (IQR1) 3 (2,3) 7 (5,8) 73 (68,79)
Sex N (%)
 Male 36,171 (51.4%) 119,400 (51.2%) 214,009 (47.1%)
 Female 34,244 (48.6%) 113,872 (48.8%) 240,775 (52.9%)
Income deprivation decile2 N (%)
 D1 (Most deprived) 16,612 (23.6%) 55,478 (23.8%) 66,568 (14.6%)
 D2 13,180 (18.7%) 43,501 (18.6%) 60,309 (13.3%)
 D3 8,281 (11.8%) 27,104 (11.6%) 45,252 (10.0%)
 D4 6,539 (9.3%) 20,850 (8.9%) 40,744 (9.0%)
 D5 4,732 (6.7%) 15,019 (6.4%) 41,541 (9.1%)
 D6 3,696 (5.2%) 12,178 (5.2%) 40,447 (8.9%)
 D7 3,933 (5.6%) 12,917 (5.5%) 46,625 (10.3%)
 D8 3,936 (5.6%) 13,253 (5.7%) 44,664 (9.8%)
 D9 4,146 (5.9%) 14,412 (6.2%) 40,415 (8.9%)
 D10 (Least deprived) 5,364 (7.6%) 18,565 (8.0%) 28,222 (6.2%)
Received flu vaccine (2021/22 season) N (%)
 No 45,844 (65.1%) 114,054 (48.9%) 123,695 (27.2%)
 Yes 24,575 (34.9%) 119,223 (51.1%) 331,092 (72.8%)
Died during follow-up N (%)
 No 70,408 (100.0%) 233,261 (100.0%) 440,778 (96.9%)
 Yes 11 (<0.1%) 16 (<0.1%) 14,009 (3.1%)
Greater Manchester locality N (%)
 Manchester 14,574 (20.7%) 49,917 (21.4%) 59,746 (13.1%)
 Oldham 6,455 (9.2%) 20,413 (8.8%) 34,478 (7.6%)
 Bolton 7,496 (10.6%) 25,116 (10.8%) 50,574 (11.1%)
 Salford 7,330 (10.4%) 22,022 (9.4%) 34,172 (7.5%)
 Rochdale 5,909 (8.4%) 19,540 (8.4%) 37,729 (8.3%)
 Tameside 5,011 (7.1%) 16,061 (6.9%) 34,692 (7.6%)
 Wigan 6,732 (9.6%) 21,424 (9.2%) 60,712 (13.3%)
 Bury 4,583 (6.5%) 15,157 (6.5%) 36,297 (8.0%)
 Trafford 5,605 (8.0%) 20,078 (8.6%) 42,544 (9.4%)
 Stockport 6,724 (9.5%) 23,549 (10.1%) 63,843 (14.0%)
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1IQR, interquartile range.

2IDACI (income deprivation affecting children index) used as income deprivation measure for age 2–3 years and age 4–9 years; IDAOPI (income deprivation affecting older people index) used as income deprivation measure for age 65 plus years.

Fig 1 shows flu vaccine uptake by age group and vaccination season. The staggered rollout of flu vaccination across children aged 4 to 11 years is visible in the pattern of gradually increasing uptake (Fig 1A). Similarly, uptake among those aged 50 to 64 years increased markedly after eligibility was expanded to this age group in the 2020/21 season (Fig 1B). To compare trends in inequalities over time, we applied vaccine eligibility criteria that were consistent over the study period as indicated by the highlighted boxes in Fig 1. We included preschool children (aged 2 to 3 years) and older adults aged 65 years plus. Given the staggered vaccination rollout in primary schools, we used a subset of primary school ages (aged 4 to 9 years) over a shorter range of seasons (2018/19 to 2021/22).

Relative inequalities

There were income-related inequalities in flu vaccine uptake across all age groups and seasons, with higher income deprivation associated with lower vaccine uptake (Fig 2, S1, S3, and S5 Tables). Across prepandemic vaccination seasons, uptake inequalities between the most and least income-deprived deciles were greatest among preschool children (HR range 0.52 (95% CI [0.50,0.54]) to 0.54 (95% CI [0.52,0.57])), followed by primary school children (HR range 0.59 (95% CI [0.58,0.61]) to 0.61 (95% CI [0.60,0.63])), with more moderate inequalities among older adults (HR range 0.77 (95% CI [0.76,0.79]) to 0.82 (95% CI [0.81,0.84])) (Fig 2, S1, S3, and S5 Tables). For comparison, estimates unadjusted by age are shown in S2, S4, and S6 Tables.

Fig 2. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake. Results from Cox proportional hazards models adjusted by age are plotted as HRs with 95% CIs.

Fig 2

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The reference group is D10 (least deprived areas) for each season. The vertical dashed grey line indicates the onset of the pandemic. (A) Inequalities among preschool children (age 2–3 years), estimated using the IDACI. (B) Inequalities among primary school children (age 4–9 years), estimated using IDACI (C) Inequalities among older adults (age 65 years plus), estimated using IDAOPI. CI, confidence interval; HR, hazard ratio; IDACI, income deprivation affecting children index; IDAOPI, income deprivation affecting older people index.

There was no clear prepandemic trend in the extent of uptake inequalities over time among preschool children. However, inequalities increased moderately after the onset of the pandemic to HR 0.47 (95% CI [0.45,0.50]) in 2020/21 and HR 0.49 (95% CI [0.47,0.51]) in 2021/22 (Fig 2, S1 Table).

Among primary school children, although flu vaccine uptake inequalities between the most and least income-deprived were relatively stable in the 2 prepandemic seasons, across most deciles inequalities tended to increase (Fig 2, S3 Table). Following the onset of the pandemic, inequalities between the most and least income-deprived increased substantially, now equivalent to the inequalities among preschool children (HR 0.47 (95% CI [0.45,0.48]) in 2020/21 and HR 0.48 (95% CI [0.47,0.49]) in 2021/22).

Relative income-related inequalities in flu vaccine uptake among older adults increased slightly over the prepandemic flu vaccination seasons, with uptake in the most deprived compared to the least deprived decile ranging from HR 0.81 (95% CI [0.80,0.83]) in 2015/16 to HR 0.78 (95% CI [0.77,0.70]) in 2019/20. This was followed by sharper increases in inequality after the onset of the COVID-19 pandemic, with uptake in the most deprived compared to the least deprived decile reaching HR 0.69 (95% CI [0.68,0.71]) in 2020/21 and HR 0.63 (95% CI [0.62,0.64]) in 2021/22 (Fig 2, S5 Table).

Results were robust to excluding individuals who died during the study period (S7, S8, and S9 Tables) and were generally robust to using total IMD as an alternative deprivation measure (S10, S11, and S12 Tables), though estimated changes in inequalities were slightly smaller among older adults when using total IMD (S12 Table). Results for primary school children and older adults were robust to excluding those on the border of age eligibility (S14 and S15 Tables). However, among preschool children, excluding those who may have attended primary school resulted in estimates of inequality that were no longer statistically significantly different across vaccination seasons (S13 Table). Estimated inequalities in 2021/22 were slightly wider following inclusion of those eligible due to expanded 2021/22 age criteria (HR most deprived 0.54 (95% CI [0.54,0.55])) and people clinically eligible for flu vaccination (HR 0.56 most deprived (95% CI [0.55,0.56])) compared to the restricted main study population (HR most deprived 0.57 (95% CI [0.56,0.58])) (S16 Table). Stratification by sex (male or female) indicated that socioeconomic inequalities in flu vaccine uptake did not vary substantially by sex across any age group or vaccination season (S17–S22 Tables).

Absolute inequalities

During prepandemic flu vaccination seasons, age-standardised flu vaccine uptake among each age group tended to gradually increase each season among people living in the least income-deprived neighbourhoods as well as among those in the most income-deprived areas (Fig 3 and S23 Table).

Fig 3. Absolute age-adjusted income deprivation-related inequalities in flu vaccine uptake over time.

Fig 3

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Age-standardised vaccine uptake (%) with 95% CIs. The vertical dashed grey line indicates the onset of the pandemic. (A) Inequalities among preschool children (age 2–3 years), estimated using the IDACI. (B) Inequalities among primary school children (age 4–9 years), estimated using IDACI. (C) Inequalities among older adults (age 65 years plus), estimated using IDAOPI. CI, confidence interval; IDACI, income deprivation affecting children index; IDAOPI, income deprivation affecting older people index.

Among preschool children, the magnitude of increases in uptake tended to be slightly higher in the least deprived areas, such that the large uptake gap between the most and least deprived (estimated as the SII) increased from 15.59 percentage points (95% CI [14.52,16.67]) in 2015/16 to 19.82 (95% CI [18.66,20.98]) percentage points in 2019/20. Following the onset of the pandemic, the uptake gap increased more sharply to 25.25 (95% CI [24.04,26.45]) percentage points in 2020/21, driven by disproportionately higher uptake among people in the least deprived areas. The following season (2021/22), flu vaccine uptake among preschool children returned to approximately prepandemic levels, with the uptake gap also reducing to 20.86 (95% CI [19.65,22.05]) percentage points (Figs 3 and 4 and S23 and S24 Tables).

Fig 4. SII by income deprivation for age-adjusted flu vaccine uptake inequalities over time.

Fig 4

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Age-adjusted estimated difference in vaccine uptake (percentage points) between the least and most income-deprived areas. Estimates shown with 95% CIs. The vertical dashed grey line indicates the onset of the pandemic. Results for preschool (age 2–3 years) and primary school (age 4–9 years) children estimated using IDACI; results for older adults (age 65 years plus) estimated using IDAOPI. CI, confidence interval; IDACI, income deprivation affecting children index; IDAOPI, income deprivation affecting older people index; SII, slope index of inequality.

Among primary school children, the prepandemic vaccine uptake gap was similarly large (19.12 (95% CI [19.65,22.05]) to 19.23 (95% CI [18.54,19.91]) percentage points). Following the onset of the pandemic, there were disproportionately higher increases in vaccine uptake among those in the least deprived areas, leading to a widening of the uptake gap to 28.09 (95% CI [27.41,28.77]) percentage points in 2020/21 and further to 30.27 (95% CI [29.58,30.95]) percentage points in 2021/22 (Figs 3 and 4 and S23 and S24 Tables).

Absolute inequalities in flu vaccine uptake were smaller among older adults in 2015/16, with an uptake gap of 8.48 (95% CI [7.91,9.04]) percentage points between the least and most income deprived. This uptake gap doubled over the study period to 16.91 (95% CI [16.46,17.36]) percentage points in 2021/22, with most of this increase (approximately 80%) occurring since the onset of the COVID-19 pandemic (Fig 4 and S24 Table). Again, widening inequalities were driven primarily by increases in uptake among those in the least deprived areas, with uptake in the most deprived areas increasing or stable in most years (Fig 3 and S23 Table).

Discussion

Income deprivation was associated with lower flu vaccine uptake among children and older adults in GM prior to the pandemic. Following the onset of the COVID-19 pandemic, flu vaccine uptake increased across all age groups, but uptake increased disproportionately in the least deprived areas, widening preexisting inequalities. Both overall uptake and uptake inequalities increased further among primary school children and older adults in 2021/22, while among preschool children both overall uptake and uptake inequalities reverted to close to prepandemic levels. Although vaccine uptake in the most deprived areas increased for each age group across the study period, 2021/22 age-standardised uptake in deprived areas remained below, or similar to, 2015/16 uptake levels in the least deprived areas. Meanwhile, in the least deprived areas, age-standardised uptake climbed 12 to 22 percentage points over the study period.

The moderate to large prepandemic socioeconomic inequalities in flu vaccine uptake are consistent with previous reports [4,5]. Existing research has suggested that these inequalities may be partly explained by differences across socioeconomic groups in health knowledge about the potential consequences of flu infection and about vaccine effectiveness and safety [5]. Previous studies also found that although flu vaccine uptake is usually highly correlated with previous flu vaccine uptake [28], the COVID-19 pandemic was associated with increases in people intending to take up vaccination for the first time [14]. In a large survey, the most frequently reported motivations for first-time flu vaccine uptake were to protect oneself—both from flu directly and from coinfection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)—as well as uptake being viewed as “sensible” and a way to protect healthcare capacity during pandemic pressures [14]. Given that older adults are more vulnerable than children to severe COVID-19 [29], these changes in vaccine uptake intention may apply more to the older adult population, potentially explaining the wider increases in inequalities among this group. Conversely, the lower risk of severe COVID-19 in children led to substantial debate about the relative risks and benefits of vaccinating children and adolescents against COVID-19 [30,31], with the UK being slower than some other high-income countries to offer vaccination to those under 18 years [32]. These policy debates may have highlighted the potential risks of vaccination to parents, potentially also increasing parental uncertainty about other vaccines during childhood.

Our estimates of overall flu vaccine uptake within GM are consistently lower than national uptake estimates [6,33], likely in part due to above-average income deprivation within the region. Estimated uptake was particularly low among preschool children (34.9% in 2021/22), which was also substantially lower than estimated national uptake (50.1%) [33]. However, our estimates of uptake among this age group are consistent with published data from Merseyside, a similarly deprived area in the North West of England, where almost 80% of neighbourhoods had less than 35% flu vaccine uptake among preschool children in 2015/16 [5]. The authors suggest that this may be in part due to lack of parental awareness of the risks of flu infection to young children, despite relatively high rates of hospitalisations among this age group [5]. However, low estimated uptake among children may also be partly driven by issues with assigning eligibility based on age at the index date discussed in more detail below, and we cannot exclude the possibility of some missing vaccination records in our dataset. Although this could have affected exact estimates of inequalities, given the consistent differences in uptake estimates over time, it seems unlikely that this could have driven our finding that inequalities increased.

A strength of this study is the use of a dataset with almost population-level coverage for a large city region [24], along with robust measures of income deprivation that specifically capture the proportion of children or older adults living in poverty at a neighbourhood level [21]. The availability of data covering 7 vaccination seasons also allows us to robustly identify changes during the pandemic. A limitation of this analysis is the focus on a single region (GM), which may limit generalisability to other geographical contexts. However, GM is a large ethnically and socioeconomically diverse metropolitan region, which also includes smaller towns and some rural areas [20,21,24]. We therefore think the observed widening of inequalities is likely to apply more broadly to the UK population.

Another limitation of the dataset is that age is included only as age in years at the index date (1 February 2020), to minimise any potentially identifiable patient information. While this allowed us to calculate age in years on 1 February, for each vaccination season, it will have introduced some errors in assigned flu vaccine eligibility. Sensitivity analysis indicated that this is unlikely to have affected the results for primary school children or older adults. However, the widening inequalities among preschool children may have been partially driven by the inclusion of some school-age children in this group.

There is some survival bias in this dataset, as some people who died before the index date (1 February 2020) are excluded. While this could introduce bias, sensitivity analysis excluding those who died at any point in the study period indicated that this is unlikely to have substantially affected estimates of inequality. Another data limitation is that we were unable to reliably identify people who were eligible for flu vaccination due to health conditions over time. Sensitivity analysis including current clinical eligibility and expanded age eligibility for the 2021/22 season suggested that our results likely slightly underestimate the full extent of uptake inequalities across the total population.

Although we did not find evidence that socioeconomic inequalities in flu vaccine uptake varied by sex across any age group, there are likely other intersecting axes of health inequality that may contribute to overall inequalities in flu vaccine uptake. For example, a limitation of our analysis is that we did not explore inequalities by ethnic group, which is a known determinant of both flu vaccine and COVID-19 vaccine uptake in this study population [24]. Given that structural racism results in people from ethnic minority backgrounds being overrepresented in more deprived areas [34], our results raise the possibility that ethnic inequalities in flu vaccine uptake may also have widened, which will be an important area for future research.

This study, together with previous evidence, indicates that the COVID-19 pandemic was associated with increases in flu vaccine uptake across all eligible age group and all levels of deprivation. However, uptake increased disproportionately in less income-deprived areas, potentially due in part to inequalities in access to vaccination. The COVID-19 pandemic was associated with increased demand for primary care, alongside reduced service capacity and additional primary care workload due to COVID-19 vaccine delivery [9]. While these factors likely impacted all areas, there are fewer GPs per population in more deprived areas [35], so deprived areas likely experienced increased disruptions in access to routine care.

Similarly, there was widespread disruption to in-person schooling, but children in deprived areas experienced greater loss of in-school time due to longer periods of time subject to government-mandated school closures as well as increased staff and pupil absences resulting from COVID-19 sickness or requirements to self-isolate [10]. This may have contributed to increased inequalities in flu vaccine uptake among school-aged children.

Another factor that may have affected inequalities in access to flu vaccination is the differential impact of long-term ill health due to long COVID [36] and pandemic-related delays in elective healthcare [37]. While increased morbidity may have increased some people’s perceived need for flu vaccination, worsening health might also have increased the difficulty of accessing vaccination. Given the higher burden of both long COVID [36] and long-term health conditions [38] among people living in more deprived areas, these factors could have contributed to widening inequalities.

Finally, the COVID-19 pandemic may also have differentially affected demand for flu vaccination between more and less deprived areas. Overall increases in vaccine uptake suggest that public health messaging and increased awareness of the personal and societal importance of flu vaccination [6,14,28] may have increased demand for flu vaccination. However, misinformation and disinformation shared on social media is also linked to vaccine uptake intentions, and there were large volumes of anti-vaccine disinformation associated with the COVID-19 vaccines [13–15]. Similarly, the pandemic was associated with extensive sharing of false conspiracy theories related to the COVID-19 vaccines and the pandemic more broadly, and endorsement of such ideas has been shown to be associated with lower intention to take up vaccination [39]. Trust in public health messaging over misinformation and disinformation is therefore important for vaccine uptake, and there may have been social gradients in exposure to, or responses to, recent pro- and anti-vaccine messaging.

Some of the greater increases in demand for flu vaccination among older adults may also be linked to the option for coadministration with COVID-19 vaccines, perhaps due to convenience, or to greater perception of risk from COVID-19. However, given the steep socioeconomic gradients in uptake of COVID-19 vaccination [40], this may also have inadvertently contributed to wider flu vaccine uptake inequalities.

Comparing between age groups, inequalities were widest among primary school children by the end of the study period. This is interesting, as delivery within schools could reduce barriers to vaccination compared to the preschool group, for whom a GP appointment is required. The overall higher rates of uptake among school children compared to preschool children suggest that the school setting may indeed facilitate uptake, perhaps especially in the second pandemic season where uptake among preschool children fell across all deprivation levels. However, more research is required to understand the much wider inequalities within the school context since the onset of the COVID-19 pandemic.

Although flu circulation and disease were very low during 2020/21 and 2021/22, this was likely driven by COVID-19 control measures [41]. However, most COVID-19 control measures have since been suspended, and winter 2022/23 has seen high levels of SARS-CoV-2 and flu virus cocirculation and increased flu-related hospitalisations [42]. Alongside lower flu vaccine uptake, people in more deprived areas are also less likely to have taken up COVID-19 vaccination and are more likely to have long-term health conditions that increase the risk of severe outcomes if infected [38,40]. Wider inequalities in flu vaccine uptake are therefore likely to translate into increased morbidity and mortality inequalities over time.

Here, we report widening income-related inequalities in routine flu vaccination among children and older adults in the context of the COVID-19 pandemic. Further research is needed to understand whether inequalities along other sociodemographic axes of inequality and for other routine vaccinations have also widened. Finally, it is crucial to improve our understanding of what has driven the increase in vaccine uptake inequalities and to address and reverse these growing inequalities.

Supporting information

S1 Fig. Study population flow chart.

(DOCX)

Click here for additional data file. (205.2KB, docx)
S2 Fig. Log(−log[survival]) versus log(time) plots by deprivation decile for Cox proportional hazards models by age group and vaccination season.

(DOCX)

Click here for additional data file. (1.9MB, docx)
S1 RECORD. Checklist of items, extended from the STROBE statement, which should be reported in observational studies using routinely collected health data.

(DOCX)

Click here for additional data file. (21.5KB, docx)
S1 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years).

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic. Results also shown in Fig 2 in the main text.

(DOCX)

Click here for additional data file. (17.3KB, docx)
S2 Table. Relative unadjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years) (for comparison with S1 Table).

Results from Cox proportional hazards models are reported as hazard ratios with 95% confidence intervals. The reference group is D10 (least deprived areas) for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (17.5KB, docx)
S3 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years).

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic. Results also shown in Fig 2 in the main text.

(DOCX)

Click here for additional data file. (16.8KB, docx)
S4 Table. Relative unadjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years) (for comparison with S3 Table).

Results from Cox proportional hazards models are reported as hazard ratios with 95% confidence intervals. The reference group is D10 (least deprived areas). The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (16.1KB, docx)
S5 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus).

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic. Results also shown in Fig 2 in the main text.

(DOCX)

Click here for additional data file. (18KB, docx)
S6 Table. Relative unadjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus) (for comparison with S5 Table).

Results from Cox proportional hazards models are reported as hazard ratios with 95% confidence intervals. The reference group is D10 (least deprived areas) for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (17.7KB, docx)
S7 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years)—Sensitivity analysis excluding individuals who died during the study period.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (17.3KB, docx)
S8 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years)—Sensitivity analysis excluding individuals who died during the study period.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (16.9KB, docx)
S9 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus)−Sensitivity analysis excluding individuals who died during the study period.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (18KB, docx)
S10 Table. Relative age-adjusted multiple deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years)—Sensitivity analysis using the index of multiple deprivation (IMD) as an alternative measure of deprivation.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (17.2KB, docx)
S11 Table. Relative age-adjusted multiple deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years)—Sensitivity analysis using the index of multiple deprivation (IMD) as an alternative measure of deprivation.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (16.9KB, docx)
S12 Table. Relative age-adjusted multiple deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus)—Sensitivity analysis using the index of multiple deprivation (IMD) as an alternative measure of deprivation.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (17.9KB, docx)
S13 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years)—Sensitivity analysis excluding children on the border of age-based vaccine eligibility (i.e., excluding age 3/4 years).

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (17.1KB, docx)
S14 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years)—Sensitivity analysis excluding children on the border of age-based vaccine eligibility (i.e., excluding age 9/10 years).

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (16.7KB, docx)
S15 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus)—Sensitivity analysis excluding adults on the border of age-based vaccine eligibility (i.e., excluding age 64/65 years).

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 66–69 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (18KB, docx)
S16 Table. Relative age-adjusted deprivation-related inequalities in flu vaccine uptake for 2021/22 vaccination season—Sensitivity analysis comparing all-age inequalities across (1) main sample (2) expanded age eligibility for 2021/22 and (3) expanded age eligibility and clinical eligibility.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas), age 0–4 years, and no clinical eligibility for flu vaccination for each season. Deprivation measure is the index of multiple deprivation (IMD).

(DOCX)

Click here for additional data file. (19.6KB, docx)
S17 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years) stratified by sex—Male results.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (18.7KB, docx)
S18 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years) stratified by sex—Female results.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (18.5KB, docx)
S19 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years) stratified by sex—Male results.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (18KB, docx)
S20 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years) stratified by sex—Female results.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (18.1KB, docx)
S21 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus) stratified by sex—Male results.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (19.5KB, docx)
S22 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus) stratified by sex—Female results.

Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

(DOCX)

Click here for additional data file. (19.9KB, docx)
S23 Table. Absolute age-adjusted income deprivation-related inequalities in flu vaccine uptake over time.

Age-standardised vaccine uptake (%) with 95% confidence intervals. The vertical line indicates the onset of the pandemic. Results also shown in Fig 3 in the main text.

(DOCX)

Click here for additional data file. (16.1KB, docx)
S24 Table. Slope index of inequality (SII) by income deprivation for age-adjusted flu vaccine uptake inequalities over time.

Age-adjusted estimated difference in vaccine uptake (percentage points) between the least and most income-deprived areas. Estimates shown with 95% confidence intervals. The vertical line indicates the onset of the pandemic. Results also shown in Fig 4 in the main text.

(DOCX)

Click here for additional data file. (15KB, docx)

Acknowledgments

We are grateful to Nicky Timmis, Aneela McAvoy, Joanna Ferguson, and Sue Wood for their support in organising and hosting PCIE discussion groups. We would like to thank all members of the NIHR Applied Research Collaboration for Greater Manchester Public and Community Involvement and Engagement Panel and the Health Innovation Manchester Public Community Involvement and Engagement Forum. We would particularly like to thank Nasrine Akhtar, Basma Issa, Nicholas Filer, and Charles Kwaku-Odoi for all their input as part of the Vaccine Equity Project Public Advisory Group. We would also like to recognise the GMCR (a partnership of Greater Manchester Health and Social Care Partnership, Health Innovation Manchester, and Graphnet Health, on behalf of Greater Manchester localities) for the provision of data required to undertake this work. This work uses data provided by patients and collected by the NHS as part of patient care and support. Using patient data is vital to improving health and care for everyone. There is huge potential to make better use of information from people’s patient records, to understand more about disease, develop new treatments, monitor safety, and plan NHS services. Patient data should be kept safe and secure, to protect everyone’s privacy, and it is important that there are safeguards to make sure that data are stored and used responsibly. Everyone should be able to find out about how patient data are used.

Abbreviations

ARC-GM

Applied Research Collaboration GM

CI

confidence interval

COVID-19

Coronavirus Disease 2019

GM

Greater Manchester

GMCR

Greater Manchester Care Record

GP

general practice

HInM

Health Innovation Manchester

HR

hazard ratio

IDACI

income deprivation affecting children index\

IDAOPI

income deprivation affecting older people index

IMD

index of multiple deprivation

LSOA

lower layer super output area

NHS

National Health Service

NIHR

National Institute of Health and Care Research

SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2

SII

slope index of inequality

Data Availability

The patient data used in this study cannot be shared publicly. The legal basis for use of patient data in this study was defined in the national Control of Patient Information (COPI) notice, which gives National Health Service (NHS) organisations a legal requirement to share data for the purposes of the COVID-19 response and COVID-19-related research. A strict governance process involving stakeholders groups (data controllers, healthcare professionals, patients and members of the public, and researchers) exists for granting researchers access to Greater Manchester Care Record data. For further details please see https://gmwearebettertogether.com/gm-care-record/ or contact GMCR-ops@manchester.ac.uk. All codes, algorithms, and code set validation used to define the populations, outcomes, exposures, and covariates can be found here: https://github.com/rw251/gm-idcr/tree/master/projects/025%20-%20Watkinson.

Funding Statement

This study was supported by National Institute for Health and Care Research (NIHR) (grant NIHR200174; to REW, RW, SG, LM & MS, grant PSTRC-2016-003; to RW). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

Decision Letter 0

Callam Davidson

17 Feb 2023

Dear Dr Watkinson,

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Decision Letter 1

Alexandra Schaefer

9 May 2023

Dear Dr. Watkinson,

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Suggest reporting statistical information as detailed above – see under ABSTRACT

Line 90 – Please define GP in the Methods.

Line 109 – Please change ‘focus’ to ‘focused’ and ensure to keep the tense consistent throughout the manuscript.

Line 159 – Please define NIHR in the Methods.

Please include in the figure caption of Figure 2 and 3 the age range of pre-school children, primary school children, and older adults.

In Figure 2 and 3, please show the axis beginning at zero. If this is not possible, please show a break in the axis.

In Figure 4, please define g IDACI and IDAOPI and include in the figure caption the age range of pre-school children, primary school children, and older adults.

DISCUSSION

Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

Lines 308-309: Please include a reference for the following statement “Given that older adults are more vulnerable than children to severe COVID-19, […]”.

FIGURES AND TABLES

Please ensure to define the abbreviations used in your figures and tables

(e.g. in Table 1 add “IQR = interquartile range” underneath the table)

Please provide titles and legends for all figures (including those in Supporting Information files).

Please indicate in figure captions whether analyses are adjusted or unadjusted and where adjusted please detail the factors adjusted for.

Where adjusted analyses are presented, to help facilitate transparent data reporting please also unadjusted analyses for comparison.

Where 95% CIs are reported PLOS Medicine also requires that p values are reported. Please include in the tables and figures. Please report p as <0.001 and where higher as p=0.002, for example

Please replace the use of hyphens with commas to separate upper and lower CI bounds as these can be confused with negative values.

Please consider the use of a color palate suitable for those with color blindness.

In the Supplementary Material, in the Table of contents, please change “STROBE Statemen” to “STROBE Statement”.

In the Supplementary Tables, please explain in the caption the values (IDACI decile D10 or age) used as reference points (in the table shown as ‘REF’).

REFERENCES

Please cite your Supporting Information as outlined here: https://journals.plos.org/plosmedicine/s/supporting-information

Please cite the reference numbers in square brackets (e.g., “We used the techniques developed by our colleagues [19] to analyze the data”). Citations should be preceding punctuation.

PLOS uses the numbered citation (citation-sequence) method and first six authors, et al.

Comments from the reviewers:

Reviewer #1: Alex McConnachie, Statistical Review

Watkinson et al present data from a General Practice database covering the Manchester area, looking at socioeconomic differences in flu vaccine uptake from 2015/16 to 2021/22. This review looks at the use of statistics in the paper.

These are generally very good. The data are clearly described and presented. Cox models are used to analyse time to vaccination, and age-standardised vaccination rates are analysed using the slope index of inequality. A number of sensitivity analyses are presented to assess the robustness of the findings. The results appear to be interpreted appropriately. My comments are relatively minor.

In the abstract, the statement "…vaccine uptake increased across all socioeconomic groups during the pandemic" is not supported by any of the data reported at that point.

The section on covariates makes no mention of gender. Looking at Table 1, this data is available, and looks virtually complete. Why was this not considered, at least in the adult cohort? The other obvious factor of interest would be ethnicity, though I assume the data here is less reliable. Should this at least be noted as a limitation?

As ever with Cox models, there is the question of the proportional hazards assumption. Was this checked? My guess, with a dataset of this size, there may be some evidence of non-PH, but as long as it is not too severe, then a PH model should be OK.

In Table 1, age is reported as median and quartiles, which is fine, but why report to one decimal place? I assume the data are integers. Also, on a stylistic note, perhaps the percentages for deaths in the child cohorts could be reported as "<0.1%" and ">99.9%"?

In the discussion, line 342 talks of "this winter". Someone reading this in a few years' time will not immediately know what that means.

Reviewer #2: See attachment

Reviewer #3: Thank you for asking me to review this paper. It is an interesting topic, may help inform future flu vaccination roll-outs and highlights important findings around widening social inequality.

The abstract the clearly written.

Introduction

Overall clearly written

Line 46 - could you expand here to give some potential cited reasons for lower vaccine uptake in more deprived neighbourhoods? Some general reasons here would be useful for context, before moving onto COVID-19 pandemic specific context.

Could something be mentioned in the introduction/later in the discussion about the effect of COVID-19 related illness (and long-COVID) and potential influence on flu vaccination uptake?

Methods

Line 124: I would like more information on how this data is recorded/collected.

Results

The results might be better structured with 3 separate subheadings to describe the three very different groups.

34.9% pre-school uptake seems very low? There are brief reasons described to explain this in the discussion, but this point requires further expansion in the discussion including correlation with previous studies.

Limitations

A suggested limitation in this study is lack of further information on other socio-demographic factors such as ethnicity. Ethnicity and cultural beliefs have been associated with lower vaccine uptake and higher morbidity/mortality from COVID-19 - it would have been informative to see this data and how it ties in with the widening inequalities by social deprivation, particularly in this urban area of GM

Comparison to literature

This section is very brief.

Line 303 - suggest expand here as to what previous reports have said about pre-pandemic inequalities.

As described above, need to expand more on previous study findings in relation to each of the three groups (particularly pre-school)

Overall, the conclusion considers a broad range of reasons for widening socio-economic inequalities with flu vaccine uptake during this period. There are 2 points I think should be covered that are missing:

- Those from more deprived backgrounds experienced much higher rates of COVID-19, morbidity and mortality (including long-Covid) - could this have affected uptake of flu vaccine afterwards?

- There is some discussion of misinformation, but not much around "vaccine hesitancy" and mistrust, which is prevalent amongst patients from more deprived backgrounds and minority ethnic groups - which was exacerbated during the COVID-19 pandemic; also vaccine hesitancy amongst parents around the time of the COVID-19 vaccine being introduced. Suggested reference for further context: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003826 - though there are others more relevant to this group - but this point is worth expanding on.

Overall this is a well executed study and clearly written paper with some important points on widening socioeconomic inequalities that would be valuable to those involved in future vaccination planning/ outreach to underserved communities.

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

[LINK]

Attachment

Submitted filename: Review PMED-D-23-00344_R1.docx

Click here for additional data file. (16.8KB, docx)

Decision Letter 2

Alexandra Schaefer

23 Jun 2023

Dear Dr. Watkinson,

Thank you very much for re-submitting your manuscript " Widening socioeconomic inequalities in flu vaccine uptake during the COVID-19 pandemic: a cohort study in Greater Manchester, England" (PMEDICINE-D-23-00344R2) 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]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

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. If you haven't already, we ask that you provide 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.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.  

We look forward to receiving the revised manuscript by Jun 30 2023 11:59PM.   

Sincerely,

Alexandra Schaefer, PhD

Associate Editor 

PLOS Medicine

plosmedicine.org

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

Requests from Editors:

GENERAL

Thank you for considered and detailed responses to editor and reviewer comments.

Please see below for further minor points that we request you respond to in full.

Please revise your manuscript using either British English or American English, e.g., colored/coloured (Description Figure 1) or age-standardised/age-standardized (Line 267).

Please choose a consistent spelling for the terms “income-deprived”/”income deprived” as well as “income deprivation”/”income-deprivation” and revise you manuscript accordingly.

Please ensure to use a consistent tense within a section of the manuscript.

Please add 'years’ to ages stated e.g. age two to three years, throughout your manuscript

ACADEMIC EDITOR COMMENTS

The manuscript has much improved and I am happy with the revised version.

TITLE

Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative. We suggest “Evaluating socioeconomic inequalities in influenza vaccine uptake during the COVID-19 pandemic: A cohort study in Greater Manchester, England” or similar

ABSTRACT

Line 33: Please temper assertions of primacy ("no existing studies have investigated…”) by adding ‘to the best of our knowledge’ or similar.

Line 38-9: Please add 'years’ to ages stated e.g., age two to three years.

Lines 43-51: Please add the according unit (e.g., percentage points) to numbers occurring in your abstract.

AUTHOR SUMMARY

The Author Summary should immediately follow the Abstract in your revised manuscript.

In the third bullet point under ‘Why was this study done?’, please temper assertions of primacy ("no existing studies had analysed…”) by adding ‘to the best of our knowledge’ or similar.

Please revise the second bullet point under “What did the authors do and find?”. Editorial suggestion: “We focused on young children and older adults, as these age groups are at higher risk of severe outcomes from flu infection and are eligible for annual flu vaccination provided free at the point of service.”

In the final bullet point of ‘What Do These Findings Mean?’, please describe the main limitations of the study in non-technical language.

INTRODUCTION

Lines 65-66 suggest: “[…] (young children and adults who are aged over 65, groups with certain preexisting health conditions, or pregnant people [3]).

Lines 74-76 suggest: “Flu vaccine uptake amongst pre-school and school aged children has consistently been lower than amongst older adults, ranging from approximately 30% to 60% pre-pandemic.”

Line 90: Please write million instead of ‘M’.

Lines 93-94 suggest: “[…], despite overall higher levels of deprivation than the national average [21].”

Lines 94-95 suggest: “For these reasons, Greater Manchester is a useful setting to study changes in socioeconomic inequalities in vaccine uptake.”

METHODS

Line 110: Please change “roll-out” to “rollout” since you have used the latter spelling consistently throughout your manuscript.

Line 131: Please define the following abbreviation: UK.

Line 142: Please define the following abbreviation: HM (HM Revenue & Customs)

Please revise the following statement for improved clarity: “In regression analysis age in years was used for children, then age was grouped into five-year age bands up to 80 plus for adults”. Editorial suggestion: “In regression analysis, age in years was used for children and age grouped into five-year age bands up to 80 years plus was used for adults.”

Please ensure that the study is reported according to the RECORD guideline, and include the completed checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guideline (S1 Checklist)." The guideline can be found here: https://www.record-statement.org/checklist.php

When completing the checklist, please use section and paragraph numbers, rather than page numbers which will likely no longer correspond to the appropriate sections after copy-editing.

RESULTS

Table 1: We suggest changing the table title to “Table 1 – Baseline study population statistics.”

Line 214 suggest: “Figure 1 […]” instead of “Fig 1[…]”.

Figure 2: Please add a close bracket in the figure legend changing “(Most deprive” to “(Most deprived)”.

S2 Table/S4 Table/S6 Table: In your table description, you describe the results as adjusted by age (“Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals.”). However, the results presented are unadjusted by age as described in the main manuscript. Please revise accordingly.

Lines 258-258 suggest, “However, amongst pre-school children, excluding those who may have attended primary school resulted in estimates of inequality that were no longer statistically significantly different across vaccination seasons.”

Lines 262-264: Please cite the supplementary tables S17 Table – S22 Table at the end of the sentence.

S21 Table/S22 Table: Please remove “Results also shown in Figure 2 in the main text.” from your table description.

Line 269: Supplementary table S17 appears to be wrongfully cited here. Please check and revise throughout your entire manuscript (S23 should be cited instead).

Figure 3: In Figure 3C, please show the axis beginning at zero. If this is not possible, please show a break in the axis.

Line 270-272/Line 284: Please add units to all numbers occurring in your manuscript when applicable (instead of ‘19.82’ it should be ‘19.82 percentage points’). Please check throughout your entire manuscript.

Line 276/281/285/287: Supplementary tables S17 and/or S18 appear to be wrongfully cited here. Please check and revise throughout your entire manuscript (S23 and/or S24 should be cited instead).

DISCUSSION

Please remove the subheadings from your Discussion.

Lines 328-328 suggest: “Conversely, the lower risk of severe COVID-19 in children has led to considerable debate about the relative risks and benefits of vaccinating children and adolescents against COVID-19 [30,31], with the UK being slower than other high-income countries to offer vaccination to those under 18 years [32].”

Lines 330-331 suggest: “Our estimates of overall flu vaccine uptake within GM are consistently lower than national uptake estimates [6,33], likely in part due to above-average income deprivation within the region.”

Lines 344-346 suggest: “A strength of this study is the use of a dataset with almost population-level coverage for a large city region [24], along with robust measures of income deprivation that specifically capture the proportion of children or older adults living in poverty at a neighbourhood level [21].”

Lines 353-354 suggest: “Sensitivity analysis indicated that this is unlikely to have affected the results for primary school children or older adults.”

Lines 358-359 suggest: “While this could introduce bias, sensitivity analysis excluding those who died at any point in the study period indicated that this is unlikely to have substantially affected estimates of inequality.”

Line 367: Please change “over-represented” to “overrepresented”.

Line 383-384 suggest: “Another factor that may have affected inequalities in access to flu vaccination is the differential impact of long-term ill health due to long COVID [36] and pandemic-related delays in elective health care [37].”

REFERENCES

Please ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases (http://www.ncbi.nlm.nih.gov/nlmcatalog/journals), and are appropriately formatted and capitalised.

Where website addresses are cited, please specify the date of access.

SOCIAL MEDIA

To help us extend the reach of your research, please provide any Twitter handle(s) that would be appropriate to tag, including your own, your co-authors’, your institution, funder, or lab. Please detail any handles you wish to be included when we tweet this paper, in the manuscript submission form when you re-submit the manuscript.

Comments from Reviewers:

Reviewer #1: Alex McConnachie, Statistical Review

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

Reviewer #2: General comment

Thank you for inviting me to review this article.

The revised draft of the paper reads very well. The authors have addressed in detail the previous set of reviews.

Specific comments

L159 - The acronym SII is not defined.

L176 - This sentence of the main analysis mentions 'excluding those who died before or during each season' while L180-181 says 'We then restricted the main analysis sample to those who remained alive throughout each vaccination season.', which is part of the sensitivity analysis. What is the difference between these two analyses?

Line 203-210: The numerical data reported do not exactly match those in Table 1. Please could you check them again. For example, 70426 children aged 2-3 years are reported while Table 1 indicates a total population of 70419; another example, 16613 children aged 2-3 years in most deprived group vs 16612 in the table.

Line 264: Tables S17-22 are not cited.

Line 267-87: Tables 23 & 24 should be cited instead of tables 17 & 18.

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

[LINK]

Decision Letter 3

Alexandra Schaefer

4 Aug 2023

Dear Dr. Watkinson,

Thank you very much for re-submitting your manuscript "Evaluating socioeconomic inequalities in flu vaccine uptake during the COVID-19 pandemic: A cohort study in Greater Manchester, England" (PMEDICINE-D-23-00344R3) for review by PLOS Medicine.

As we are planning to accept the paper for publication in the journal, there are still a number of outstanding minor changes that need to be addressed.

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]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

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. If you haven't already, we ask that you provide 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.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.  

We look forward to receiving the revised manuscript by Aug 11 2023 11:59PM.   

Sincerely,

Alexandra Schaefer, PhD

Associate Editor 

PLOS Medicine

plosmedicine.org

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

Requests from Editors:

Please check your Competing Interest statement. In the Financial Disclosure statement, you have stated that one of the authors, Matt Sutton, is a Senior Investigator at the NIHR. As stated on our website, a “Relationship (paid or unpaid) with organizations and funding bodies including nongovernmental organizations, research institutions, or charities” is considered a non-financial competing interest.

All authors must declare their relevant competing interests per the PLOS policy, which can be seen here:

https://journals.plos.org/plosmedicine/s/competing-interests

For authors with ties to industry, please indicate whether any of the interests has a financial stake in the results of the current study.

Please check your manuscript carefully for grammar, spelling and punctuation. Please consider proofreading by a native English speaker.

Title: Please revise your title to replace 'flu' with 'influenza'. We suggest “Evaluating socioeconomic inequalities in influenza vaccine uptake during the COVID-19 pandemic: A cohort study in Greater Manchester, England”.

l.54: In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology or change “A limitation” to “The main limitation”.

l.95: Please define ‘NHS’.

ll.123-126 suggest: “Greater Manchester is also a socioeconomically diverse area, including some of the least deprived neighbourhoods nationally, despite overall higher deprivation levels than the national average [21].”

l.204: Please introduce the abbreviation “CI” for confidence intervals at first use.

l.307: Please, for add square brackets open for “increased from 15.59 percentage points (95% CI 14.52,16.67]) in 2015/16 to 19.82 (95% CI 18.66,20.98]) percentage”.

l.317: Please, add a square bracket open for “29.58,30.95])”. Please revise throughout your entire manuscript.

ll.443-444: Please provide a reference.

ll.477-478: Please remove the ‘Declaration of Interest’ statement. These details should only be provided in the according section of the online submission form.

Please check all figures and tables thoroughly.

Table 1: Define ‘GM’. The footnotes below the table do not correspond with the footnotes/asterisks in the table. Please revise and check throughout your entire manuscript.

Figure 1: Please add a unit for ‘uptake’ (%).

Figure 4/S24 Table: You describe the age-adjusted estimated difference in vaccine uptake between the least and most income-deprived areas as presented in percentage. However, the slope index of inequality is presented in percentage points. Please change the figure/table description and exchange “%” with “percentage points”.

Figure S2: Due to the size of the individual graphs, the visibility of the data presented in Figure S2 is poor. Please use a format in which the individual graphs are displayed larger.

S4 Table: Please remove “[…] and age 4 years for each season.” from the figure description as the results in this table are unadjusted for age (“The reference group is D10 (least deprived areas).”).

Table S1/S2/S7/S13/S17/S18: Please make sure to add an asterisk following IDACI in the tables.

S16 Table: A third reference group seems to be ‘people not clinically eligible for flu vaccination’. Please add in your table description.

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

[LINK]

Decision Letter 4

Alexandra Schaefer

1 Sep 2023

Dear Dr Watkinson, 

On behalf of my colleagues and the Academic Editor, Rebecca F. Grais, I am pleased to inform you that we have agreed to publish your manuscript "Evaluating socioeconomic inequalities in influenza vaccine uptake during the COVID-19 pandemic: A cohort study in Greater Manchester, England" (PMEDICINE-D-23-00344R4) in PLOS Medicine.

Prior to publication, we require that you make the following changes:

*Thank you for revising your Competing Interest/ Financial Disclosure statement. The paragraph starting with "MS is an NIHR Senior Investigator [...]" and ending with "[...] Member of five Study Steering Committees." only needs to be included in the Competing Interest section of the manuscript submission form. The statement “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” only needs to be included in the Financial Disclosure section of the manuscript submission form.

*Please define ‘NHS’ and 'NIHR' in the according manuscript submission form sections.

*Thank you for providing your RECORD checklist. Please replace the page numbers with paragraph numbers per section (e.g. "Methods, paragraph 1"), since the page numbers of the final published paper may be different from the page numbers in the current manuscript.

*Figure 1: Please add a unit for ‘age’ and ‘age group’ on the y-axis (years).

*Figure 2/3: Please define ‘D’ (decile).

*Figure 4: Please add a unit for ‘Slope index of inequality (SII)’ on the y-axis (percentage points).

*S Tables: In all supplementary tables, please ensure to define ‘D‘ along the acronym for the index.

*S1 Figure: Please define ‘NHS’.

*S2 Figure: Due to the space (first three graphs), please label each axis for the ‘older adults’ graphs on the right. In the figure description, please include the relevant indices (IDACI/IDAOPI) and define ‘D’ (decile).

*S16 Table: In the column header “Expanded age, plus age 17-49 who are clinically eligible”, please add ‘years’ (“Expanded age, plus age 17-49 years who are clinically eligible”).

*ll.189-190: Please additionally include the statement „All codes, algorithms, and code set validation used to define the populations, outcomes, exposures, and covariates can be found here: https://github.com/rw251/gm-idcr/tree/master/projects/025%20-%20Watkinson“ in the data availability section of the manuscript submission form.

*l.258: Please add ‘years’ following ‘across children aged four to eleven’.

We will carefully review whether these changes have been made prior to publication.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. 

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Sincerely, 

Alexandra Schaefer, PhD

Associate Editor 

PLOS Medicine

Associated Data

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

    Supplementary Materials

    S1 Fig. Study population flow chart.

    (DOCX)

    Click here for additional data file. (205.2KB, docx)
    S2 Fig. Log(−log[survival]) versus log(time) plots by deprivation decile for Cox proportional hazards models by age group and vaccination season.

    (DOCX)

    Click here for additional data file. (1.9MB, docx)
    S1 RECORD. Checklist of items, extended from the STROBE statement, which should be reported in observational studies using routinely collected health data.

    (DOCX)

    Click here for additional data file. (21.5KB, docx)
    S1 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years).

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic. Results also shown in Fig 2 in the main text.

    (DOCX)

    Click here for additional data file. (17.3KB, docx)
    S2 Table. Relative unadjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years) (for comparison with S1 Table).

    Results from Cox proportional hazards models are reported as hazard ratios with 95% confidence intervals. The reference group is D10 (least deprived areas) for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (17.5KB, docx)
    S3 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years).

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic. Results also shown in Fig 2 in the main text.

    (DOCX)

    Click here for additional data file. (16.8KB, docx)
    S4 Table. Relative unadjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years) (for comparison with S3 Table).

    Results from Cox proportional hazards models are reported as hazard ratios with 95% confidence intervals. The reference group is D10 (least deprived areas). The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (16.1KB, docx)
    S5 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus).

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic. Results also shown in Fig 2 in the main text.

    (DOCX)

    Click here for additional data file. (18KB, docx)
    S6 Table. Relative unadjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus) (for comparison with S5 Table).

    Results from Cox proportional hazards models are reported as hazard ratios with 95% confidence intervals. The reference group is D10 (least deprived areas) for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (17.7KB, docx)
    S7 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years)—Sensitivity analysis excluding individuals who died during the study period.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (17.3KB, docx)
    S8 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years)—Sensitivity analysis excluding individuals who died during the study period.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (16.9KB, docx)
    S9 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus)−Sensitivity analysis excluding individuals who died during the study period.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (18KB, docx)
    S10 Table. Relative age-adjusted multiple deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years)—Sensitivity analysis using the index of multiple deprivation (IMD) as an alternative measure of deprivation.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (17.2KB, docx)
    S11 Table. Relative age-adjusted multiple deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years)—Sensitivity analysis using the index of multiple deprivation (IMD) as an alternative measure of deprivation.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (16.9KB, docx)
    S12 Table. Relative age-adjusted multiple deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus)—Sensitivity analysis using the index of multiple deprivation (IMD) as an alternative measure of deprivation.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (17.9KB, docx)
    S13 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years)—Sensitivity analysis excluding children on the border of age-based vaccine eligibility (i.e., excluding age 3/4 years).

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (17.1KB, docx)
    S14 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years)—Sensitivity analysis excluding children on the border of age-based vaccine eligibility (i.e., excluding age 9/10 years).

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (16.7KB, docx)
    S15 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus)—Sensitivity analysis excluding adults on the border of age-based vaccine eligibility (i.e., excluding age 64/65 years).

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 66–69 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (18KB, docx)
    S16 Table. Relative age-adjusted deprivation-related inequalities in flu vaccine uptake for 2021/22 vaccination season—Sensitivity analysis comparing all-age inequalities across (1) main sample (2) expanded age eligibility for 2021/22 and (3) expanded age eligibility and clinical eligibility.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas), age 0–4 years, and no clinical eligibility for flu vaccination for each season. Deprivation measure is the index of multiple deprivation (IMD).

    (DOCX)

    Click here for additional data file. (19.6KB, docx)
    S17 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years) stratified by sex—Male results.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (18.7KB, docx)
    S18 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among preschool children (age 2–3 years) stratified by sex—Female results.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 2 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (18.5KB, docx)
    S19 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years) stratified by sex—Male results.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (18KB, docx)
    S20 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among primary school children (age 4–9 years) stratified by sex—Female results.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 4 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (18.1KB, docx)
    S21 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus) stratified by sex—Male results.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (19.5KB, docx)
    S22 Table. Relative age-adjusted income deprivation-related inequalities in flu vaccine uptake among older adults (age 65 years plus) stratified by sex—Female results.

    Results from Cox proportional hazards models adjusted by age are reported as hazard ratios with 95% confidence intervals. The reference groups are D10 (least deprived areas) and age 65–69 years for each season. The vertical line indicates the onset of the pandemic.

    (DOCX)

    Click here for additional data file. (19.9KB, docx)
    S23 Table. Absolute age-adjusted income deprivation-related inequalities in flu vaccine uptake over time.

    Age-standardised vaccine uptake (%) with 95% confidence intervals. The vertical line indicates the onset of the pandemic. Results also shown in Fig 3 in the main text.

    (DOCX)

    Click here for additional data file. (16.1KB, docx)
    S24 Table. Slope index of inequality (SII) by income deprivation for age-adjusted flu vaccine uptake inequalities over time.

    Age-adjusted estimated difference in vaccine uptake (percentage points) between the least and most income-deprived areas. Estimates shown with 95% confidence intervals. The vertical line indicates the onset of the pandemic. Results also shown in Fig 4 in the main text.

    (DOCX)

    Click here for additional data file. (15KB, docx)
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    Data Availability Statement

    The patient data used in this study cannot be shared publicly. The legal basis for use of patient data in this study was defined in the national Control of Patient Information (COPI) notice, which gives National Health Service (NHS) organisations a legal requirement to share data for the purposes of the COVID-19 response and COVID-19-related research. A strict governance process involving stakeholders groups (data controllers, healthcare professionals, patients and members of the public, and researchers) exists for granting researchers access to Greater Manchester Care Record data. For further details please see https://gmwearebettertogether.com/gm-care-record/ or contact GMCR-ops@manchester.ac.uk. All codes, algorithms, and code set validation used to define the populations, outcomes, exposures, and covariates can be found here: https://github.com/rw251/gm-idcr/tree/master/projects/025%20-%20Watkinson.

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