tetano
Editor, Senior Moderator
Am J Epidemiol
. 2024 Sep 17:kwae365.
doi: 10.1093/aje/kwae365. Online ahead of print. Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis
Nimalan Arinaminpathy[SUP] 1 [/SUP], Carrie Reed[SUP] 2 [/SUP], Matthew Biggerstaff[SUP] 2 [/SUP], Anna T Nguyen[SUP] 3 [/SUP], Tejas S Athni[SUP] 3 [/SUP], Benjamin F Arnold[SUP] 4 [/SUP], Alan Hubbard[SUP] 5 [/SUP], Art Reingold[SUP] 5 [/SUP], Jade Benjamin-Chung[SUP] 3 6 [/SUP]
Affiliations
Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.
Keywords: indirect effects; influenza; mathematical modelling; probabilistic bias analysis; triangulation; vaccines.
. 2024 Sep 17:kwae365.
doi: 10.1093/aje/kwae365. Online ahead of print. Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis
Nimalan Arinaminpathy[SUP] 1 [/SUP], Carrie Reed[SUP] 2 [/SUP], Matthew Biggerstaff[SUP] 2 [/SUP], Anna T Nguyen[SUP] 3 [/SUP], Tejas S Athni[SUP] 3 [/SUP], Benjamin F Arnold[SUP] 4 [/SUP], Alan Hubbard[SUP] 5 [/SUP], Art Reingold[SUP] 5 [/SUP], Jade Benjamin-Chung[SUP] 3 6 [/SUP]
Affiliations
- PMID: 39290087
- DOI: 10.1093/aje/kwae365
Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.
Keywords: indirect effects; influenza; mathematical modelling; probabilistic bias analysis; triangulation; vaccines.