tetano
Editor, Senior Moderator
Vaccine. 2014 Oct 27;32(51):6884-6901. doi: 10.1016/j.vaccine.2014.08.090. [Epub ahead of print]
Are influenza-associated morbidity and mortality estimates for those ≥65 in statistical databases accurate, and an appropriate test of influenza vaccine effectiveness?
Thomas RE.
Author information
Abstract
PURPOSES:
To assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.
PRINCIPAL RESULTS:
Laboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.
EVIDENCE FOR VACCINE EFFECTIVENESS WAS SOUGHT FROM:
(1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled. Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.
MAJOR CONCLUSIONS:
Improving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths). Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.
Copyright ? 2014 Elsevier Ltd. All rights reserved.
KEYWORDS:
Databases; Influenza; Influenza vaccine effectiveness; Morbidity and mortality; RCTs; Statistical models
PMID:
25454864
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/25454864
Are influenza-associated morbidity and mortality estimates for those ≥65 in statistical databases accurate, and an appropriate test of influenza vaccine effectiveness?
Thomas RE.
Author information
Abstract
PURPOSES:
To assess the accuracy of estimates using statistical databases of influenza-associated morbidity and mortality, and precisely measure influenza vaccine effectiveness.
PRINCIPAL RESULTS:
Laboratory testing of influenza is incomplete. Death certificates under-report influenza. Statistical database models are used as an alternative to randomised controlled trials (RCTs) to assess influenza vaccine effectiveness. Evidence of the accuracy of influenza morbidity and mortality estimates was sought from: (1) Studies comparing statistical models. For four studies Poisson and ARIMA models produced higher estimates than Serfling, and Serfling higher than GLM. Which model is more accurate is unknown. (2) Studies controlling confounders. Fourteen studies mostly controlled one confounder (one controlled comorbidities), and limited control of confounders limits accuracy.
EVIDENCE FOR VACCINE EFFECTIVENESS WAS SOUGHT FROM:
(1) Studies of regions with increasing vaccination rates. Of five studies two controlled for confounders and one found a positive vaccination effect. Three studies did not control confounders and two found no effect of vaccination. (2) Studies controlling multiple confounders. Of thirteen studies only two found a positive vaccine effect and no mortality differences between vaccinees and non-vaccinees in non-influenza seasons, showing confounders were controlled. Key problems are insufficient testing for influenza, using influenza-like illness, heterogeneity of seasonal and pandemic influenza, population aging, and incomplete confounder control (co-morbidities, frailty, vaccination history) and failure to demonstrate control of confounders by proving no mortality differences between vaccinees and non-vaccinees in non-influenza seasons.
MAJOR CONCLUSIONS:
Improving model accuracy requires proof of no mortality differences in pre-influenza periods between the vaccinated and non-vaccinated groups, and reduction in influenza morbidity and mortality in seasons with a good vaccine match, more virulent strains, in the younger elderly with less immune senescence, and specific outcomes (laboratory-confirmed outcomes, pneumonia deaths). Proving influenza vaccine effectiveness requires appropriately powered RCTs, testing participants with RT-PCR tests, and comprehensively monitoring morbidity and mortality.
Copyright ? 2014 Elsevier Ltd. All rights reserved.
KEYWORDS:
Databases; Influenza; Influenza vaccine effectiveness; Morbidity and mortality; RCTs; Statistical models
PMID:
25454864
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/25454864