Assessing the impact of confounding (measured and unmeasured) in a case-control study to examine the increased risk of pandemic A/H1N1 associated with receipt of the 2008-9 seasonal influenza vaccine.
Identifieur interne : 000488 ( Main/Corpus ); précédent : 000487; suivant : 000489Assessing the impact of confounding (measured and unmeasured) in a case-control study to examine the increased risk of pandemic A/H1N1 associated with receipt of the 2008-9 seasonal influenza vaccine.
Auteurs : Laura C. Rosella ; Rolf H H. Groenwold ; Natasha S. CrowcroftSource :
- Vaccine [ 1873-2518 ] ; 2011.
English descriptors
- KwdEn :
- Adolescent, Adult, Case-Control Studies, Child, Child, Preschool, Confidence Intervals, Confounding Factors, Epidemiologic, Female, Humans, Infant, Influenza A Virus, H1N1 Subtype, Influenza Vaccines (administration & dosage), Influenza Vaccines (immunology), Influenza, Human (epidemiology), Influenza, Human (prevention & control), Logistic Models, Male, Middle Aged, Multivariate Analysis, Odds Ratio, Ontario, Pandemics, Risk Factors, Sensitivity and Specificity, Young Adult.
- MESH :
- chemical , administration & dosage : Influenza Vaccines.
- chemical , immunology : Influenza Vaccines.
- geographic : Ontario.
- epidemiology : Influenza, Human.
- prevention & control : Influenza, Human.
- Adolescent, Adult, Case-Control Studies, Child, Child, Preschool, Confidence Intervals, Confounding Factors, Epidemiologic, Female, Humans, Infant, Influenza A Virus, H1N1 Subtype, Logistic Models, Male, Middle Aged, Multivariate Analysis, Odds Ratio, Pandemics, Risk Factors, Sensitivity and Specificity, Young Adult.
Abstract
BACKGROUND
This study examines the role of measured and unmeasured confounding in the relationship between the 2008-9 seasonal influenza vaccine and pandemic H1N1 (pH1N1) influenza virus.
METHODS
Data were taken from a test-negative case-control study of 462 lab confirmed pandemic A/H1N1 (pH1N1) cases and 484 test-negative controls. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were derived using multivariate logistic regression. The analysis was repeated using propensity matching. A sensitivity analysis was conducted to quantify the impact of a hypothetical unmeasured confounder.
RESULTS
Cases were more likely to have received the seasonal influenza vaccine after adjusting for multiple confounders using multivariate regression (OR 1.82, 95% CI: 1.25-2.65), using propensity matching (OR 1.86, 95% CI: 1.19-2.92) and in subsequent sensitivity analyses. An unmeasured confounder would need a prevalence of 20%, an odds ratio with the vaccine and pH1N1 of ≥3.5 and ≥3.0 (respectively) to result in a non-significant association. Using a prevalence of 40% the respective associations were 3.0 and 2.5.
CONCLUSION
A significant positive association between the seasonal influenza vaccine and lab confirmed pH1N1 was observed after considering multiple confounders and using different methods for confounder adjustment. This was not likely explained by an unmeasured confounder given the prevalence and strength of association needed to result in a non-significant association.
DOI: 10.1016/j.vaccine.2011.09.132
PubMed: 22001885
Links to Exploration step
pubmed:22001885Le document en format XML
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<author><name sortKey="Rosella, Laura C" sort="Rosella, Laura C" uniqKey="Rosella L" first="Laura C" last="Rosella">Laura C. Rosella</name>
<affiliation><nlm:affiliation>Public Health Ontario, Toronto, Ontario, Canada. laura.rosella@oahpp.ca</nlm:affiliation>
</affiliation>
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<author><name sortKey="Groenwold, Rolf H H" sort="Groenwold, Rolf H H" uniqKey="Groenwold R" first="Rolf H H" last="Groenwold">Rolf H H. Groenwold</name>
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<author><name sortKey="Crowcroft, Natasha S" sort="Crowcroft, Natasha S" uniqKey="Crowcroft N" first="Natasha S" last="Crowcroft">Natasha S. Crowcroft</name>
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<author><name sortKey="Rosella, Laura C" sort="Rosella, Laura C" uniqKey="Rosella L" first="Laura C" last="Rosella">Laura C. Rosella</name>
<affiliation><nlm:affiliation>Public Health Ontario, Toronto, Ontario, Canada. laura.rosella@oahpp.ca</nlm:affiliation>
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<author><name sortKey="Groenwold, Rolf H H" sort="Groenwold, Rolf H H" uniqKey="Groenwold R" first="Rolf H H" last="Groenwold">Rolf H H. Groenwold</name>
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<author><name sortKey="Crowcroft, Natasha S" sort="Crowcroft, Natasha S" uniqKey="Crowcroft N" first="Natasha S" last="Crowcroft">Natasha S. Crowcroft</name>
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<term>Adult</term>
<term>Case-Control Studies</term>
<term>Child</term>
<term>Child, Preschool</term>
<term>Confidence Intervals</term>
<term>Confounding Factors, Epidemiologic</term>
<term>Female</term>
<term>Humans</term>
<term>Infant</term>
<term>Influenza A Virus, H1N1 Subtype</term>
<term>Influenza Vaccines (administration & dosage)</term>
<term>Influenza Vaccines (immunology)</term>
<term>Influenza, Human (epidemiology)</term>
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<term>Logistic Models</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Multivariate Analysis</term>
<term>Odds Ratio</term>
<term>Ontario</term>
<term>Pandemics</term>
<term>Risk Factors</term>
<term>Sensitivity and Specificity</term>
<term>Young Adult</term>
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<term>Adult</term>
<term>Case-Control Studies</term>
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<term>Child, Preschool</term>
<term>Confidence Intervals</term>
<term>Confounding Factors, Epidemiologic</term>
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<term>Infant</term>
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<term>Middle Aged</term>
<term>Multivariate Analysis</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>This study examines the role of measured and unmeasured confounding in the relationship between the 2008-9 seasonal influenza vaccine and pandemic H1N1 (pH1N1) influenza virus.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>Data were taken from a test-negative case-control study of 462 lab confirmed pandemic A/H1N1 (pH1N1) cases and 484 test-negative controls. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were derived using multivariate logistic regression. The analysis was repeated using propensity matching. A sensitivity analysis was conducted to quantify the impact of a hypothetical unmeasured confounder.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>Cases were more likely to have received the seasonal influenza vaccine after adjusting for multiple confounders using multivariate regression (OR 1.82, 95% CI: 1.25-2.65), using propensity matching (OR 1.86, 95% CI: 1.19-2.92) and in subsequent sensitivity analyses. An unmeasured confounder would need a prevalence of 20%, an odds ratio with the vaccine and pH1N1 of ≥3.5 and ≥3.0 (respectively) to result in a non-significant association. Using a prevalence of 40% the respective associations were 3.0 and 2.5.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSION</b>
</p>
<p>A significant positive association between the seasonal influenza vaccine and lab confirmed pH1N1 was observed after considering multiple confounders and using different methods for confounder adjustment. This was not likely explained by an unmeasured confounder given the prevalence and strength of association needed to result in a non-significant association.</p>
</div>
</front>
</TEI>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">This study examines the role of measured and unmeasured confounding in the relationship between the 2008-9 seasonal influenza vaccine and pandemic H1N1 (pH1N1) influenza virus.</AbstractText>
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<AbstractText Label="RESULTS" NlmCategory="RESULTS">Cases were more likely to have received the seasonal influenza vaccine after adjusting for multiple confounders using multivariate regression (OR 1.82, 95% CI: 1.25-2.65), using propensity matching (OR 1.86, 95% CI: 1.19-2.92) and in subsequent sensitivity analyses. An unmeasured confounder would need a prevalence of 20%, an odds ratio with the vaccine and pH1N1 of ≥3.5 and ≥3.0 (respectively) to result in a non-significant association. Using a prevalence of 40% the respective associations were 3.0 and 2.5.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">A significant positive association between the seasonal influenza vaccine and lab confirmed pH1N1 was observed after considering multiple confounders and using different methods for confounder adjustment. This was not likely explained by an unmeasured confounder given the prevalence and strength of association needed to result in a non-significant association.</AbstractText>
<CopyrightInformation>Copyright © 2011 Elsevier Ltd. All rights reserved.</CopyrightInformation>
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<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rosella</LastName>
<ForeName>Laura C</ForeName>
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<Author ValidYN="Y"><LastName>Crowcroft</LastName>
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