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Frequency and impact of confounding by indication and healthy vaccinee bias in observational studies assessing influenza vaccine effectiveness: a systematic review

Overview of attention for article published in BMC Infectious Diseases, October 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
7 tweeters

Citations

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34 Dimensions

Readers on

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68 Mendeley
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1 CiteULike
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Title
Frequency and impact of confounding by indication and healthy vaccinee bias in observational studies assessing influenza vaccine effectiveness: a systematic review
Published in
BMC Infectious Diseases, October 2015
DOI 10.1186/s12879-015-1154-y
Pubmed ID
Authors

Cornelius Remschmidt, Ole Wichmann, Thomas Harder

Abstract

Evidence on influenza vaccine effectiveness (VE) is commonly derived from observational studies. However, these studies are prone to confounding by indication and healthy vaccinee bias. We aimed to systematically investigate these two forms of confounding/bias. Systematic review of observational studies reporting influenza VE and indicators for bias and confounding. We assessed risk of confounding by indication and healthy vaccinee bias for each study and calculated ratios of odds ratios (crude/adjusted) to quantify the effect of confounder adjustment. VE-estimates during and outside influenza seasons were compared to assess residual confounding by healthy vaccinee effects. We identified 23 studies reporting on 11 outcomes. Of these, 19 (83 %) showed high risk of bias: Fourteen due to confounding by indication, two for healthy vaccinee bias, and three studies showed both forms of confounding/bias. Adjustment for confounders increased VE on average by 12 % (95 % CI: 7-17 %; all-cause mortality), 9 % (95 % CI: 4-14 %; all-cause hospitalization) and 7 % (95 % CI: 4-10 %; influenza-like illness). Despite adjustment, nine studies showed residual confounding as indicated by significant off-season VE-estimates. These were observed for five outcomes, but more frequently for all-cause mortality as compared to other outcomes (p = 0.03) and in studies which indicated healthy vaccinee bias at baseline (p = 0.01). Both confounding by indication and healthy vaccinee bias are likely to operate simultaneously in observational studies on influenza VE. Although adjustment can correct for confounding by indication to some extent, the resulting estimates are still prone to healthy vaccinee bias, at least as long as unspecific outcomes like all-cause mortality are used. Therefore, cohort studies using administrative data bases with unspecific outcomes should no longer be used to measure the effects of influenza vaccination.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 26%
Student > Master 15 22%
Student > Ph. D. Student 10 15%
Student > Bachelor 6 9%
Other 5 7%
Other 12 18%
Unknown 2 3%
Readers by discipline Count As %
Medicine and Dentistry 34 50%
Agricultural and Biological Sciences 5 7%
Nursing and Health Professions 5 7%
Social Sciences 4 6%
Psychology 3 4%
Other 8 12%
Unknown 9 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 January 2020.
All research outputs
#4,178,284
of 15,363,529 outputs
Outputs from BMC Infectious Diseases
#1,279
of 5,622 outputs
Outputs of similar age
#78,106
of 286,361 outputs
Outputs of similar age from BMC Infectious Diseases
#130
of 585 outputs
Altmetric has tracked 15,363,529 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 5,622 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 286,361 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 585 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.