Estimates of 2012/13 influenza vaccine effectiveness using the case test-negative control design with different influenza negative control groups

Nunes, Baltazar; Machado, Ausenda; Guiomar, Raquel; Pechirra, Pedro; Conde, P.; Cristóvão, Paula; Falcão, Isabel

doi:10.1016/j.vaccine.2014.06.053

Cited by 17 publications

(21 citation statements)

References 27 publications

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“…However, in one study, the comparison group comprised patients who tested negative for influenza and positive for a non-influenza respiratory virus [67]. In addition, seven studies included sensitivity analyses comparing influenza-positive patients with non-influenza respiratory virus-positive patients as well as pan-negative patients [19, 26, 47, 60, 82, 84, 109]. …”

Section: Resultsmentioning

confidence: 99%

“…Five studies did not include some kind of adjustment for age either because they used a stepwise model building approach [36, 40, 62, 92] or restricted the age group studied [72]. Age was usually specified as a categorical variable (n=67), the choices of which varied considerably, but was also used as a linear term (n=9 [12, 29, 47, 59, 80, 81, 108, 87, 88, 95]), cubic spline (n=2 [60, 97]), a matching variable (n=3 [14, 64]) and in one instance as a quadratic term [26]. …”

Section: Resultsmentioning

confidence: 99%

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Potential of the test-negative design for measuring influenza vaccine effectiveness: a systematic review

Sullivan¹,

Feng²,

Cowling³

2014

Expert Review of Vaccines

159

146

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SUMMARY Background The test-negative design is a variant of the case control study being increasingly used to study influenza vaccine effectiveness. In these studies, patients with influenza-like illness are tested for influenza. Vaccine coverage is compared between those testing positive versus those testing negative to estimate vaccine effectiveness. Objectives We reviewed features in the design, analysis and reporting of 85 published test-negative studies. Data sources Studies were identified from PubMed, reference lists and email updates. Study eligibility All studies using the test-negative design reporting end-of-season estimates were included. Study appraisal Design features that may affect the validity and comparability of reported estimates were reviewed, including setting, study period, source population, case definition, exposure and outcome ascertainment and statistical model. Results There was considerable variation in the analytic approach, with 68 unique statistical models identified among the studies. Conclusion Harmonisation of analytic approaches may improve the potential for pooling vaccine effectiveness estimates.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Potential of the test-negative design for measuring influenza vaccine effectiveness: a systematic review

Sullivan¹,

Feng²,

Cowling³

2014

Expert Review of Vaccines

159

146

View full text Add to dashboard Cite

show abstract

“…29,44 Three other studies that recruited relatively smaller numbers of patients in single influenza seasons found larger differences in vaccine effectiveness point estimates by alternative control groups with overlapping confidence intervals. 21,31,45 Although virus interference was given as a possible explanation for differences in vaccine effectiveness estimates by control group, those studies may have been underpowered to identify real effects. Previous studies have suggested that choosing a control group testing positive for another respiratory virus might be preferred to one based only on testing negative for influenza because detection of other viruses confirms adequate swab quality, especially for older children or adults, who can have lower levels of virus shedding compared to young children.…”

Section: Discussionmentioning

confidence: 99%

“…16-20 Some studies have suggested that virus interference may occur in some seasons but not others, or may occur after epidemics of some influenza types/subtypes but not others. 16,21 This phenomenon is still controversial and is not consistently observed. The biological mechanism causing the ecological phenomenon of virus interference could be temporary non-specific immunity following an episode of acute respiratory infection, due to stimulation of the innate immune response during and for a short time after infection.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Virus Interference in a Test-negative Study of Influenza Vaccine Effectiveness

et al. 2017

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Background The observational test-negative study design is used to estimate vaccine effectiveness against influenza virus infection. An important assumption of the test-negative design is that vaccination does not affect the risk of infection with another virus. If such virus interference occurred, detection of other respiratory viruses would be more common among influenza vaccine recipients and vaccine effectiveness estimates could differ. We evaluated the potential for virus interference using data from the Influenza Incidence Surveillance Project. Methods From 2010 to 2013, outpatients presenting to clinics in 13 US jurisdictions with acute respiratory infections were tested for influenza and other respiratory viruses. We investigated whether virus interference might affect vaccine effectiveness estimates by first evaluating the sensitivity of estimates using alternative control groups that include or exclude patients with other respiratory virus detections by age group and early/middle/late stage of influenza seasons. Second, we evaluated the association between influenza vaccination receipt and other respiratory virus detection among influenza test negative patients. Results Influenza was detected in 3,743/10,650 patients (35%), and overall vaccine effectiveness was 47% (95% CI: 42%, 52%). Estimates using each control group were consistent overall or when stratified by age groups, and there were no differences among early, middle, or late phase during influenza season. We found no associations between detection of other respiratory viruses and receipt of influenza vaccination. Conclusions In this 3-year test-negative design study in an outpatient setting in the United States, we found no evidence of virus interference or impact on influenza vaccine effectiveness estimation.

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“…Several studies have shown that the definition of the control group in TND studies may influence the estimates of the IVE [12][13][14][15][16]. Three types of control groups have been used in TND studies: (1) all ILI patients tested negative for influenza virus infection (all influenza negative), (2) ILI patients tested negative for influenza virus but positive for another respiratory virus (noninfluenza virus positive), and (3) ILI patients tested negative for both influenza virus and other respiratory viruses (pan-negative) [11][12][13][14][15][16][17][18]. Although all influenza negative controls are commonly used, in several studies non-influenza virus positive controls have been used arguing that if another respiratory virus than influenza virus could be detected in the control group, the presence of misclassification is highly unlikely, as there is a confirmed infectious cause of ILI in both cases and controls.…”

Section: Introductionmentioning

confidence: 99%

Influenza vaccine effectiveness estimates in the Dutch population from 2003 to 2014: The test-negative design case-control study with different control groups

Doorn¹,

Darvishian²,

Dijkstra³

et al. 2017

Vaccine

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Information about influenza vaccine effectiveness (IVE) is important for vaccine strain selection and immunization policy decisions. The test-negative design (TND) case-control study is commonly used to obtain IVE estimates. However, the definition of the control patients may influence IVE estimates. We have conducted a TND study using the Dutch Sentinel Practices of NIVEL Primary Care Database which includes data from patients who consulted the General Practitioner (GP) for an episode of acute influenza-like illness (ILI) or acute respiratory infection (ARI) with known influenza vaccination status. Cases were patients tested positive for influenza virus. Controls were grouped into those who tested (1) negative for influenza virus (all influenza negative), (2) negative for influenza virus, but positive for respiratory syncytial virus, rhinovirus or enterovirus (non-influenza virus positive), and (3) negative for these four viruses (pan-negative). We estimated the IVE over all epidemic seasons from 2003/2004 through 2013/2014, pooled IVE for influenza vaccine partial/full matched and mismatched seasons and the individual seasons using generalized linear mixed-effect and multiple logistic regression models. The overall IVE adjusted for age, GP ILI/ARI diagnosis, chronic disease and respiratory allergy was 35% (95% CI: 15-48), 64% (95% CI: 49-75) and 21% (95% CI: -1 to 39) for all influenza negative, non-influenza virus positive and pan-negative controls, respectively. In both the main and subgroup analyses IVE estimates were the highest using non-influenza virus positive controls, likely due to limiting inclusion of controls without laboratory-confirmation of a virus causing the respiratory disease.

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Estimates of 2012/13 influenza vaccine effectiveness using the case test-negative control design with different influenza negative control groups

Cited by 17 publications

References 27 publications

Potential of the test-negative design for measuring influenza vaccine effectiveness: a systematic review

Potential of the test-negative design for measuring influenza vaccine effectiveness: a systematic review

Assessment of Virus Interference in a Test-negative Study of Influenza Vaccine Effectiveness

Influenza vaccine effectiveness estimates in the Dutch population from 2003 to 2014: The test-negative design case-control study with different control groups

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