Evidence map›Paper›PMID 39474443›Full record

ArticleOpen forum infectious diseases2024

Quantifying and Adjusting for Confounding From Health-Seeking Behavior and Health Care Access in Observational Research.

Sophie Graham, Jemma L Walker, Nick Andrews, William J Hulme, Dorothea Nitsch, Edward P K Parker, Helen I McDonald

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sophie GrahamFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0002-7770-1863
Jemma L WalkerFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0003-3728-9509
Nick AndrewsHealth Protection Research Unit in Vaccines and Immunisation, National Institute for Health and Care Research, London, UK.
William J HulmeThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-9162-4999
Dorothea NitschFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0001-5767-248X
Edward P K ParkerFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0001-5847-109X
Helen I McDonaldFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0003-0576-2015

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Health-seeking behavior and health care access (HSB/HCA) are recognized confounders in many observational studies but are not directly measurable in electronic health records. We used proxy markers of HSB/HCA to quantify and adjust for confounding in observational studies of influenza and COVID-19 vaccine effectiveness (VE). Methods: This cohort study used primary care data prelinked to secondary care and death data in England. We included individuals aged ≥66 years on 1 September 2019 and assessed influenza VE in the 2019-2020 season and early COVID-19 VE (December 2020-March 2021). VE was estimated with sequential adjustment for demographics, comorbidities, and 14 markers of HSB/HCA. Influenza vaccination in the 2019-2020 season was also considered a negative control exposure against COVID-19 before COVID-19 vaccine rollout. Results: We included 1 991 284, 1 796 667, and 1 946 943 individuals in the influenza, COVID-19, and negative control exposure populations, respectively. Markers of HSB/HCA were positively correlated with influenza and COVID-19 vaccine uptake. For influenza, adjusting for HSB/HCA markers in addition to demographics and comorbidities increased VE against influenza-like illness from -1.5% (95% CI, -3.2% to .1%) to 7.1% (95% CI, 5.4%-8.7%) with a less apparent trend for more severe outcomes. For COVID-19, adjusting for HSB/HCA markers did not change VE estimates against infection or severe disease (eg, 2 doses of BNT162b2 against infection: 82.8% [95% CI, 78.4%-86.3%] to 83.1% [95% CI, 78.7%-86.5%]). Adjusting for HSB/HCA markers removed bias in the negative control exposure analysis (-7.5% [95% CI, -10.6% to -4.5%] vs -2.1% [95% CI, -6.0% to 1.7%] before vs after adjusting for HSB/HCA markers). Conclusions: Markers of HSB/HCA can be used to quantify and account for confounding in observational vaccine studies.

Indexed as

confounding from health-seeking behavior and health care accessCOVID-19influenzavaccine effectivenessvaccines

Identifiers

PMID39474443
PMCPMC11518854

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.