5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Comparing the Clinical and Economic Outcomes Associated with Adjuvanted versus High-Dose Trivalent Influenza Vaccine among Adults Aged ≥ 65 Years in the US during the 2019–20 Influenza Season—A Retrospective Cohort Analysis

      , , , , , ,
      Vaccines
      MDPI AG

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The burden of influenza is disproportionally higher among older adults. We evaluated the relative vaccine effectiveness (rVE) of adjuvanted trivalent (aIIV3) compared to high-dose trivalent influenza vaccine (HD-IIV3e) against influenza and cardio-respiratory disease (CRD)-related hospitalizations/ER visits among adults ≥65 years during the 2019–2020 influenza season. Economic outcomes were also compared. A retrospective cohort analysis was conducted using prescription, professional fee claims, and hospital data. Inverse probability of treatment weighting (IPTW) was used to adjust for confounding. IPTW-adjusted Poisson regression was used to evaluate the adjusted rVE of aIIV3 versus HD-IIV3e. All-cause and influenza-related healthcare resource utilization (HCRU) and costs were examined post-IPTW. Recycled predictions from generalized linear models were used to estimate adjusted costs. Adjusted analysis showed that aIIV3 (n = 798,987) was similarly effective compared to HD-IIV3e (n = 1,655,979) in preventing influenza-related hospitalizations/ER visits (rVE 3.1%; 95% CI: −2.8%; 8.6%), hospitalizations due to any cause (−0.7%; 95% CI: −1.6%; 0.3%), and any CRD-related hospitalization/ER visit (0.9%; 95% CI: 0.01%; 1.7%). Adjusted HCRU and annualized costs were also statistically insignificant between the two cohorts. The adjusted clinical and economic outcomes evaluated in this study were comparable between aIIV3 and HD-IIV3e during the 2019–2020 influenza season.

          Related collections

          Most cited references24

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

          The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

            The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Efficacy of high-dose versus standard-dose influenza vaccine in older adults.

              As compared with a standard-dose vaccine, a high-dose, trivalent, inactivated influenza vaccine (IIV3-HD) improves antibody responses to influenza among adults 65 years of age or older. This study evaluated whether IIV3-HD also improves protection against laboratory-confirmed influenza illness.
                Bookmark

                Author and article information

                Contributors
                Journal
                VBSABP
                Vaccines
                Vaccines
                MDPI AG
                2076-393X
                October 2021
                October 08 2021
                : 9
                : 10
                : 1146
                Article
                10.3390/vaccines9101146
                34696254
                d4fccc38-5acc-4426-b55b-703bdd8fd1b4
                © 2021

                https://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article

                scite_

                Similar content220

                Cited by8

                Most referenced authors238