BACKGROUND: A modification to the case-control study design has become popular to assess vaccine effectiveness (VE) against viral infections. Subjects with symptomatic illness seeking medical care are tested by a highly specific polymerase chain reaction (PCR) assay for the detection of the infection of interest. Cases are subjects testing positive for the virus; those testing negative represent the comparison group. Influenza and rotavirus VE studies using this design are often termed "test-negative case-control" studies, but this design has not been formally described or evaluated. We explicitly state several assumptions of the design and examine the conditions under which VE estimates derived with it are valid and unbiased. METHODS: We derived mathematical expressions for VE estimators obtained using this design and examined their statistical properties. We used simulation methods to test the validity of the estimators and illustrate their performance using an influenza VE study as an example. RESULTS: Because the marginal ratio of cases to non-cases is unknown during enrollment, this design is not a traditional case-control study; we suggest the name "case test-negative" design. Under sets of increasingly general assumptions, we found that the case test-negative design can provide unbiased VE estimates. However, differences in health care-seeking behavior among cases and non-cases by vaccine status, strong viral interference, or modification of the probability of symptomatic illness by vaccine status can bias VE estimates. CONCLUSIONS: Vaccine effectiveness estimates derived from case test-negative studies are valid and unbiased under a wide range of assumptions. However, if vaccinated cases are less severely ill and seek care less frequently than unvaccinated cases, then an appropriate adjustment for illness severity is required to avoid bias in effectiveness estimates. Viral interference will lead to a non-trivial bias in the vaccine effectiveness estimate from case test-negative studies only when incidence of influenza is extremely high and duration of transient non-specific immunity is long.
BACKGROUND: A modification to the case-control study design has become popular to assess vaccine effectiveness (VE) against viral infections. Subjects with symptomatic illness seeking medical care are tested by a highly specific polymerase chain reaction (PCR) assay for the detection of the infection of interest. Cases are subjects testing positive for the virus; those testing negative represent the comparison group. Influenza and rotavirus VE studies using this design are often termed "test-negative case-control" studies, but this design has not been formally described or evaluated. We explicitly state several assumptions of the design and examine the conditions under which VE estimates derived with it are valid and unbiased. METHODS: We derived mathematical expressions for VE estimators obtained using this design and examined their statistical properties. We used simulation methods to test the validity of the estimators and illustrate their performance using an influenza VE study as an example. RESULTS: Because the marginal ratio of cases to non-cases is unknown during enrollment, this design is not a traditional case-control study; we suggest the name "case test-negative" design. Under sets of increasingly general assumptions, we found that the case test-negative design can provide unbiased VE estimates. However, differences in health care-seeking behavior among cases and non-cases by vaccine status, strong viral interference, or modification of the probability of symptomatic illness by vaccine status can bias VE estimates. CONCLUSIONS: Vaccine effectiveness estimates derived from case test-negative studies are valid and unbiased under a wide range of assumptions. However, if vaccinated cases are less severely ill and seek care less frequently than unvaccinated cases, then an appropriate adjustment for illness severity is required to avoid bias in effectiveness estimates. Viral interference will lead to a non-trivial bias in the vaccine effectiveness estimate from case test-negative studies only when incidence of influenza is extremely high and duration of transient non-specific immunity is long.
Authors: Joshua G Petrie; Caroline Cheng; Ryan E Malosh; Jeffrey J VanWormer; Brendan Flannery; Richard K Zimmerman; Manjusha Gaglani; Michael L Jackson; Jennifer P King; Mary Patricia Nowalk; Joyce Benoit; Anne Robertson; Swathi N Thaker; Arnold S Monto; Suzanne E Ohmit Journal: Clin Infect Dis Date: 2015-11-12 Impact factor: 9.079
Authors: Kate Russell; Jessie R Chung; Arnold S Monto; Emily T Martin; Edward A Belongia; Huong Q McLean; Manjusha Gaglani; Kempapura Murthy; Richard K Zimmerman; Mary Patricia Nowalk; Michael L Jackson; Lisa A Jackson; Brendan Flannery Journal: Vaccine Date: 2018-02-28 Impact factor: 3.641
Authors: Jill M Ferdinands; Manjusha Gaglani; Emily T Martin; Don Middleton; Arnold S Monto; Kempapura Murthy; Fernanda P Silveira; H Keipp Talbot; Richard Zimmerman; Elif Alyanak; Courtney Strickland; Sarah Spencer; Alicia M Fry Journal: J Infect Dis Date: 2019-09-13 Impact factor: 5.226
Authors: Suzanne E Ohmit; Joshua G Petrie; Ryan E Malosh; Alicia M Fry; Mark G Thompson; Arnold S Monto Journal: J Infect Dis Date: 2014-11-21 Impact factor: 5.226
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Authors: Huong Q McLean; Herve Caspard; Marie R Griffin; Manjusha Gaglani; Timothy R Peters; Katherine A Poehling; Christopher S Ambrose; Edward A Belongia Journal: JAMA Netw Open Date: 2018-10-05
Authors: Andrea S Gershon; Hannah Chung; Joan Porter; Michael A Campitelli; Sarah A Buchan; Kevin L Schwartz; Natasha S Crowcroft; Aaron Campigotto; Jonathan B Gubbay; Timothy Karnauchow; Kevin Katz; Allison J McGeer; J Dayre McNally; David C Richardson; Susan E Richardson; Laura C Rosella; Andrew E Simor; Marek Smieja; George Zahariadis; Jeffrey C Kwong Journal: J Infect Dis Date: 2020-01-01 Impact factor: 5.226
Authors: Mark G Thompson; Jessie Clippard; Joshua G Petrie; Michael L Jackson; Huong Q McLean; Manjusha Gaglani; Evelyn C Reis; Brendan Flannery; Arnold S Monto; Lisa Jackson; Edward A Belongia; Kempapura Murthy; Richard K Zimmerman; Swathi Thaker; Alicia M Fry Journal: Pediatr Infect Dis J Date: 2016-03 Impact factor: 2.129