Differences in COVID-19 vaccination coverage by occupation in England: a national linked data study
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Abstract
Monitoring differences in COVID-19 vaccination uptake in different groups is crucial to help inform the policy response to the pandemic. A key data gap is the absence of data on uptake by occupation. This study investigates differences in vaccination rates by occupation in England, using nationwide population-level data.
Methods
We calculated the proportion of people who had received three COVID-19 vaccinations (assessed on 28 February 2022) by detailed occupational categories in adults aged 18–64 and estimated adjusted ORs to examine whether these differences were driven by occupation or other factors, such as education. We also examined whether vaccination rates differed by ability to work from home.
Results
Our study population included 15 456 651 adults aged 18–64 years. Vaccination rates differed markedly by occupation, being higher in health professionals (84.7%) and teaching and other educational professionals (83.6%) and lowest in people working in elementary trades and related occupations (57.6%). We found substantial differences in vaccination rates looking at finer occupational groups. Adjusting for other factors likely to be linked to occupation and vaccination, such as education, did not substantially alter the results. Vaccination rates were associated with ability to work from home, the rate being higher in occupations which can be done from home. Many occupations with low vaccination rates also involved contact with the public or with vulnerable people
Conclusions
Increasing vaccination coverage in occupations with low vaccination rates is crucial to help protecting the public and control infection. Efforts should be made to increase vaccination rates in occupations that cannot be done from home and involve contact with the public.
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SciScore for 10.1101/2021.11.10.21266124: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable We adjusted for sex (Male, Female), age (five-year age band), region, ethnicity (White British, Bangladeshi, Black African, Black Caribbean, Chinese, Indian, Mixed, Other, Pakistani, White other), disability status (non-disabled, disabled and limited a little, disabled and limited a lot), highest level of qualification (Level 4+, Level 3, Apprenticeship, Level 2, Level 1, other, no qualification) and pre-existing conditions (1+) based on the QCovid risk model (See Table S2 for more detail). Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open …
SciScore for 10.1101/2021.11.10.21266124: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable We adjusted for sex (Male, Female), age (five-year age band), region, ethnicity (White British, Bangladeshi, Black African, Black Caribbean, Chinese, Indian, Mixed, Other, Pakistani, White other), disability status (non-disabled, disabled and limited a little, disabled and limited a lot), highest level of qualification (Level 4+, Level 3, Apprenticeship, Level 2, Level 1, other, no qualification) and pre-existing conditions (1+) based on the QCovid risk model (See Table S2 for more detail). Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: A major strength of this study is the use of nationwide linked popula1tion-level data from clinical records and the 2011 Census. This study is the first to examine how vaccination rates vary by occupation using population-level data. Because information on occupation is not collected in electronic health records, we used data from the Census to assess people’s occupation. Some surveys collect data on both occupations and vaccination but face the issue that non-response is likely correlated with the propensity to be vaccinated. Having population level data based on electronic health records and the Census, which is mandatory and has a high response rate, we were able to accurately estimate vaccination rates for detailed occupational groups. The main limitation of our study is that the information on occupation is nine years out of date. Our exposure is therefore likely to be misclassified for a proportion of people, because they have left the labour force or changed occupation since 2011, especially during the pandemic. To mitigate measurement error, we restricted our analysis to people aged 40-64 years, who have a relatively high occupational stability, as shown in an analysis of a large longitudinal household survey [7]. Exposure misclassification is nonetheless likely result in underestimating the differences in vaccination rates between occupations, especially if the exposure misclassification is random. Turnout rate may be greater in some occupa...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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