Daniel Hungerford1, Roberto Vivancos2, Jonathan M Read3, Laura J Bonnett4, Naor Bar-Zeev5, Miren Iturriza-Gómara6, Nigel A Cunliffe7, Neil French8. 1. The Centre for Global Vaccine Research, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK; Field Epidemiology Services, National Infection Service, Public Health England, Suite 3b, Third Floor, The Cunard Building, Water Street, Liverpool L3 1DS, UK; NIHR Health Protection Research Unit in Gastrointestinal Infections, The Farr Institute@HeRC, University of Liverpool, 2nd Floor, Block F, Waterhouse Buildings, 1-5 Brownlow Street, Liverpool L69 3GL, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK. Electronic address: d.hungerford@liverpool.ac.uk. 2. Field Epidemiology Services, National Infection Service, Public Health England, Suite 3b, Third Floor, The Cunard Building, Water Street, Liverpool L3 1DS, UK; NIHR Health Protection Research Unit in Gastrointestinal Infections, The Farr Institute@HeRC, University of Liverpool, 2nd Floor, Block F, Waterhouse Buildings, 1-5 Brownlow Street, Liverpool L69 3GL, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK. Electronic address: roberto.vicancos@phe.gov.uk. 3. NIHR Health Protection Research Unit in Gastrointestinal Infections, The Farr Institute@HeRC, University of Liverpool, 2nd Floor, Block F, Waterhouse Buildings, 1-5 Brownlow Street, Liverpool L69 3GL, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK; Centre for Health Informatics, Computing and Statistics, Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YW, UK. Electronic address: jonathan.read@lancaster.ac.uk. 4. Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Block F, Waterhouse Buildings, 1-5 Brownlow Street, Liverpool L69 3GL, UK. Electronic address: ljbcmshe@liverpool.ac.uk. 5. The Centre for Global Vaccine Research, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK; International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, 415 N Washington Street 5th Floor, Baltimore, MD 21231, USA. 6. The Centre for Global Vaccine Research, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK; NIHR Health Protection Research Unit in Gastrointestinal Infections, The Farr Institute@HeRC, University of Liverpool, 2nd Floor, Block F, Waterhouse Buildings, 1-5 Brownlow Street, Liverpool L69 3GL, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK. Electronic address: miren@liverpool.ac.uk. 7. The Centre for Global Vaccine Research, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK. Electronic address: nigelc@liverpool.ac.uk. 8. The Centre for Global Vaccine Research, Institute of Infection and Global Health, University of Liverpool, Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK. Electronic address: french@liverpool.ac.uk.
Abstract
BACKGROUND: Measuring vaccine effectiveness (VE) relies on the use of observational study designs. However, achieving robust estimates of direct and indirect VE is frequently compromised by bias, particularly when using syndromic diagnoses of low-specificity. METHODS: In order to mitigate confounding between the measured outcome and vaccine uptake, we developed a method to balance comparator populations using individual-level propensity scoring derived from the vaccine-exposed population, and applied it to the unexposed comparator population. Indirect VE was estimated by comparing the unvaccinated vaccine-exposed group with a propensity score-simulated unvaccinated, unexposed group. Direct VE was derived by removing indirect VE from the overall VE. We applied this method to an evaluation of the effectiveness of infant rotavirus vaccination in the UK. Using a general practice cohort of 45,259 live births between May 2010 and December 2015, we calculated indirect and direct VE against consultations for acute gastroenteritis using conventional and vaccination-propensity adjustment comparator populations. RESULTS: The overall VE during the rotavirus-season (January-May) calculated using mixed-effects Cox regression was 30% [95% confidence intervals (95% CI: 25,35%)]. Use of conventional comparator populations resulted in implausible VE estimates -14% (95% CI: -41,7%) for direct and 29% (95% CI: 14,42%) for indirect effects. Applying our alternative method, direct VE was 26% (95% CI: 1,45%) and indirect VE was 8% (95% CI: -19,29%). CONCLUSIONS: Estimating VE using propensity score simulated comparator populations, particularly for studies using routine health data with syndromic, low-specificity endpoints will aid accurate measurement of the broader public health impact of a vaccine programme.
BACKGROUND: Measuring vaccine effectiveness (VE) relies on the use of observational study designs. However, achieving robust estimates of direct and indirect VE is frequently compromised by bias, particularly when using syndromic diagnoses of low-specificity. METHODS: In order to mitigate confounding between the measured outcome and vaccine uptake, we developed a method to balance comparator populations using individual-level propensity scoring derived from the vaccine-exposed population, and applied it to the unexposed comparator population. Indirect VE was estimated by comparing the unvaccinated vaccine-exposed group with a propensity score-simulated unvaccinated, unexposed group. Direct VE was derived by removing indirect VE from the overall VE. We applied this method to an evaluation of the effectiveness of infant rotavirus vaccination in the UK. Using a general practice cohort of 45,259 live births between May 2010 and December 2015, we calculated indirect and direct VE against consultations for acute gastroenteritis using conventional and vaccination-propensity adjustment comparator populations. RESULTS: The overall VE during the rotavirus-season (January-May) calculated using mixed-effects Cox regression was 30% [95% confidence intervals (95% CI: 25,35%)]. Use of conventional comparator populations resulted in implausible VE estimates -14% (95% CI: -41,7%) for direct and 29% (95% CI: 14,42%) for indirect effects. Applying our alternative method, direct VE was 26% (95% CI: 1,45%) and indirect VE was 8% (95% CI: -19,29%). CONCLUSIONS: Estimating VE using propensity score simulated comparator populations, particularly for studies using routine health data with syndromic, low-specificity endpoints will aid accurate measurement of the broader public health impact of a vaccine programme.
Authors: Jemma L Walker; Nick J Andrews; Christina J Atchison; Sarah Collins; David J Allen; Mary E Ramsay; Shamez N Ladhani; Sara L Thomas Journal: Vaccine X Date: 2019-01-04
Authors: Ellen Rafferty; Xiaoyan Guo; Bruce McDonald; Lawrence W Svenson; Shannon Elizabeth MacDonald Journal: BMJ Open Date: 2019-11-02 Impact factor: 2.692