Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models
Background: Modelling studies have been widely used to inform human papillomavirus (HPV) vaccination policy decisions; however, many models exist and it is not known whether they produce consistent predictions of population-level effectiveness and herd effects. We did a systematic review and meta-an...
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2016-11-01
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Series: | The Lancet Public Health |
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Dr Prof Marc Brisson, PhD Élodie Bénard, MSc Mélanie Drolet, PhD Johannes A Bogaards, PhD Iacopo Baussano, MD Simopekka Vänskä, PhD Mark Jit, PhD Prof Marie-Claude Boily, PhD Megan A Smith, MPH Johannes Berkhof, PhD Prof Karen Canfell, PhD Harrell W Chesson, PhD Emily A Burger, PhD Yoon H Choi, PhD Prof Birgitte Freiesleben De Blasio, PhD Sake J De Vlas, PhD Giorgio Guzzetta, PhD Jan A C Hontelez, PhD Johannes Horn, Dipl Biomath Martin R Jepsen, PhD Prof Jane J Kim, PhD Fulvio Lazzarato, MSc Suzette M Matthijsse, MSc Rafael Mikolajczyk, MD Andrew Pavelyev, PhD Matthew Pillsbury, PhD Leigh Anne Shafer, PhD Stephen P Tully, PhD Hugo C Turner, PhD Cara Usher, PhD Prof Cathal Walsh, PhD |
spellingShingle |
Dr Prof Marc Brisson, PhD Élodie Bénard, MSc Mélanie Drolet, PhD Johannes A Bogaards, PhD Iacopo Baussano, MD Simopekka Vänskä, PhD Mark Jit, PhD Prof Marie-Claude Boily, PhD Megan A Smith, MPH Johannes Berkhof, PhD Prof Karen Canfell, PhD Harrell W Chesson, PhD Emily A Burger, PhD Yoon H Choi, PhD Prof Birgitte Freiesleben De Blasio, PhD Sake J De Vlas, PhD Giorgio Guzzetta, PhD Jan A C Hontelez, PhD Johannes Horn, Dipl Biomath Martin R Jepsen, PhD Prof Jane J Kim, PhD Fulvio Lazzarato, MSc Suzette M Matthijsse, MSc Rafael Mikolajczyk, MD Andrew Pavelyev, PhD Matthew Pillsbury, PhD Leigh Anne Shafer, PhD Stephen P Tully, PhD Hugo C Turner, PhD Cara Usher, PhD Prof Cathal Walsh, PhD Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models The Lancet Public Health |
author_facet |
Dr Prof Marc Brisson, PhD Élodie Bénard, MSc Mélanie Drolet, PhD Johannes A Bogaards, PhD Iacopo Baussano, MD Simopekka Vänskä, PhD Mark Jit, PhD Prof Marie-Claude Boily, PhD Megan A Smith, MPH Johannes Berkhof, PhD Prof Karen Canfell, PhD Harrell W Chesson, PhD Emily A Burger, PhD Yoon H Choi, PhD Prof Birgitte Freiesleben De Blasio, PhD Sake J De Vlas, PhD Giorgio Guzzetta, PhD Jan A C Hontelez, PhD Johannes Horn, Dipl Biomath Martin R Jepsen, PhD Prof Jane J Kim, PhD Fulvio Lazzarato, MSc Suzette M Matthijsse, MSc Rafael Mikolajczyk, MD Andrew Pavelyev, PhD Matthew Pillsbury, PhD Leigh Anne Shafer, PhD Stephen P Tully, PhD Hugo C Turner, PhD Cara Usher, PhD Prof Cathal Walsh, PhD |
author_sort |
Dr Prof Marc Brisson, PhD |
title |
Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models |
title_short |
Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models |
title_full |
Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models |
title_fullStr |
Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models |
title_full_unstemmed |
Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models |
title_sort |
population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models |
publisher |
Elsevier |
series |
The Lancet Public Health |
issn |
2468-2667 |
publishDate |
2016-11-01 |
description |
Background: Modelling studies have been widely used to inform human papillomavirus (HPV) vaccination policy decisions; however, many models exist and it is not known whether they produce consistent predictions of population-level effectiveness and herd effects. We did a systematic review and meta-analysis of model predictions of the long-term population-level effectiveness of vaccination against HPV 16, 18, 6, and 11 infection in women and men, to examine the variability in predicted herd effects, incremental benefit of vaccinating boys, and potential for HPV-vaccine-type elimination.
Methods: We searched MEDLINE and Embase for transmission-dynamic modelling studies published between Jan 1, 2009, and April 28, 2015, that predicted the population-level impact of vaccination on HPV 6, 11, 16, and 18 infections in high-income countries. We contacted authors to determine whether they were willing to produce new predictions for standardised scenarios. Strategies investigated were girls-only vaccination and girls and boys vaccination at age 12 years. Base-case vaccine characteristics were 100% efficacy and lifetime protection. We did sensitivity analyses by varying vaccination coverage, vaccine efficacy, and duration of protection. For all scenarios we pooled model predictions of relative reductions in HPV prevalence (RRprev) over time after vaccination and summarised results using the median and 10th and 90th percentiles (80% uncertainty intervals [UI]).
Findings: 16 of 19 eligible models from ten high-income countries provided predictions. Under base-case assumptions, 40% vaccination coverage and girls-only vaccination, the RRprev of HPV 16 among women and men was 0·53 (80% UI 0·46–0·68) and 0·36 (0·28–0·61), respectively, after 70 years. With 80% girls-only vaccination coverage, the RRprev of HPV 16 among women and men was 0·93 (0·90–1·00) and 0·83 (0·75–1·00), respectively. Vaccinating boys in addition to girls increased the RRprev of HPV 16 among women and men by 0·18 (0·13–0·32) and 0·35 (0·27–0·39) for 40% coverage, and 0·07 (0·00–0·10) and 0·16 (0·01–0·25) for 80% coverage, respectively. The RRprev were greater for HPV 6, 11, and 18 than for HPV 16 for all scenarios investigated. Finally at 80% coverage, most models predicted that girls and boys vaccination would eliminate HPV 6, 11, 16, and 18, with a median RRprev of 1·00 for women and men for all four HPV types. Variability in pooled findings was low, but increased with lower vaccination coverage and shorter vaccine protection (from lifetime to 20 years).
Interpretation: Although HPV models differ in structure, data used for calibration, and settings, our population-level predictions were generally concordant and suggest that strong herd effects are expected from vaccinating girls only, even with coverage as low as 20%. Elimination of HPV 16, 18, 6, and 11 is possible if 80% coverage in girls and boys is reached and if high vaccine efficacy is maintained over time.
Funding: Canadian Institutes of Health Research. |
url |
http://www.sciencedirect.com/science/article/pii/S2468266716300019 |
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doaj-e632cdc25d5b46cba0279961eb33a9e42020-11-25T02:20:16ZengElsevierThe Lancet Public Health2468-26672016-11-0111e8e1710.1016/S2468-2667(16)30001-9Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic modelsDr Prof Marc Brisson, PhD0Élodie Bénard, MSc1Mélanie Drolet, PhD2Johannes A Bogaards, PhD3Iacopo Baussano, MD4Simopekka Vänskä, PhD5Mark Jit, PhD6Prof Marie-Claude Boily, PhD7Megan A Smith, MPH8Johannes Berkhof, PhD9Prof Karen Canfell, PhD10Harrell W Chesson, PhD11Emily A Burger, PhD12Yoon H Choi, PhD13Prof Birgitte Freiesleben De Blasio, PhD14Sake J De Vlas, PhD15Giorgio Guzzetta, PhD16Jan A C Hontelez, PhD17Johannes Horn, Dipl Biomath18Martin R Jepsen, PhD19Prof Jane J Kim, PhD20Fulvio Lazzarato, MSc21Suzette M Matthijsse, MSc22Rafael Mikolajczyk, MD23Andrew Pavelyev, PhD24Matthew Pillsbury, PhD25Leigh Anne Shafer, PhD26Stephen P Tully, PhD27Hugo C Turner, PhD28Cara Usher, PhD29Prof Cathal Walsh, PhD30Centre de recherche du CHU de Québec—Université Laval, Quebec City, QC, CanadaCentre de recherche du CHU de Québec—Université Laval, Quebec City, QC, CanadaCentre de recherche du CHU de Québec—Université Laval, Quebec City, QC, CanadaCentre for Infectious Disease Control, National Institute of Public Health and the Environment, Bilthoven, NetherlandsInfection and Cancer Epidemiology Group, International Agency for Research on Cancer, Lyon, FranceVaccination Programme Unit, National Institute for Health and Welfare, Helsinki, FinlandModelling and Economics Unit, Public Health England, London, UKDépartement de médecine sociale et préventive, Université Laval, Quebec City, QC, CanadaCancer Research Division, Cancer Council NSW, Sydney, NSW, AustraliaDepartment of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, NetherlandsCancer Research Division, Cancer Council NSW, Sydney, NSW, AustraliaDivision of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USACenter for Health Decision Science, Harvard T H Chan School of Public Health, Boston, MA, USANational Infection Service, Public Health England, London, UKOslo Centre for Biostatistics and Epidemiology, Division of Infectious Disease Control, Norwegian Institute of Public Health and Oslo Centre for Statistics and Epidemiology, Oslo, NorwayDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, NetherlandsFondazione Bruno Kessler, Trento, ItalyDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USAEpidemiological and Statistical Methods Research Group, Helmholtz Centre for Infection Research, Braunschweig, GermanySection for Geography, Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkCenter for Health Decision Science, Harvard T H Chan School of Public Health, Boston, MA, USAInfection and Cancer Epidemiology Group, International Agency for Research on Cancer, Lyon, FranceDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, NetherlandsEpidemiological and Statistical Methods Research Group, Helmholtz Centre for Infection Research, Braunschweig, GermanyMerck Research Laboratories, Rahway, NJ, USAMerck Research Laboratories, Rahway, NJ, USADepartment of Internal Medicine, University of Manitoba, Winnipeg, MB, CanadaInfection and Cancer Epidemiology Group, International Agency for Research on Cancer, Lyon, FranceDepartment of Infectious Disease Epidemiology, Imperial College, London, UKNational Centre for Pharmacoeconomics (NCPE Ireland), Dublin, IrelandDepartment of Mathematics and Statistics, University of Limerick, Limerick, IrelandBackground: Modelling studies have been widely used to inform human papillomavirus (HPV) vaccination policy decisions; however, many models exist and it is not known whether they produce consistent predictions of population-level effectiveness and herd effects. We did a systematic review and meta-analysis of model predictions of the long-term population-level effectiveness of vaccination against HPV 16, 18, 6, and 11 infection in women and men, to examine the variability in predicted herd effects, incremental benefit of vaccinating boys, and potential for HPV-vaccine-type elimination. Methods: We searched MEDLINE and Embase for transmission-dynamic modelling studies published between Jan 1, 2009, and April 28, 2015, that predicted the population-level impact of vaccination on HPV 6, 11, 16, and 18 infections in high-income countries. We contacted authors to determine whether they were willing to produce new predictions for standardised scenarios. Strategies investigated were girls-only vaccination and girls and boys vaccination at age 12 years. Base-case vaccine characteristics were 100% efficacy and lifetime protection. We did sensitivity analyses by varying vaccination coverage, vaccine efficacy, and duration of protection. For all scenarios we pooled model predictions of relative reductions in HPV prevalence (RRprev) over time after vaccination and summarised results using the median and 10th and 90th percentiles (80% uncertainty intervals [UI]). Findings: 16 of 19 eligible models from ten high-income countries provided predictions. Under base-case assumptions, 40% vaccination coverage and girls-only vaccination, the RRprev of HPV 16 among women and men was 0·53 (80% UI 0·46–0·68) and 0·36 (0·28–0·61), respectively, after 70 years. With 80% girls-only vaccination coverage, the RRprev of HPV 16 among women and men was 0·93 (0·90–1·00) and 0·83 (0·75–1·00), respectively. Vaccinating boys in addition to girls increased the RRprev of HPV 16 among women and men by 0·18 (0·13–0·32) and 0·35 (0·27–0·39) for 40% coverage, and 0·07 (0·00–0·10) and 0·16 (0·01–0·25) for 80% coverage, respectively. The RRprev were greater for HPV 6, 11, and 18 than for HPV 16 for all scenarios investigated. Finally at 80% coverage, most models predicted that girls and boys vaccination would eliminate HPV 6, 11, 16, and 18, with a median RRprev of 1·00 for women and men for all four HPV types. Variability in pooled findings was low, but increased with lower vaccination coverage and shorter vaccine protection (from lifetime to 20 years). Interpretation: Although HPV models differ in structure, data used for calibration, and settings, our population-level predictions were generally concordant and suggest that strong herd effects are expected from vaccinating girls only, even with coverage as low as 20%. Elimination of HPV 16, 18, 6, and 11 is possible if 80% coverage in girls and boys is reached and if high vaccine efficacy is maintained over time. Funding: Canadian Institutes of Health Research.http://www.sciencedirect.com/science/article/pii/S2468266716300019 |