Author information
1Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK; Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Wildau, Germany.
2Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK.
3London School of Hygiene & Tropical Medicine, London, UK; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
4London School of Hygiene & Tropical Medicine, London, UK.
5Center for Infectious Disease Dynamics, Pennsylvania State University, Pennsylvania, PA, USA.
6London School of Hygiene & Tropical Medicine, London, UK; School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, China.
7Veterinary Medicine, University of Cambridge, Cambridge, UK.
8Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
9Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK; Section of Hepatology and Gastroenterology, Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
10UK Health Security Agency, London, UK.
11Center for Health Decision Science, T H Chan School of Public Health, Harvard University, Boston, MA, USA.
12Department of Epidemiology and Biostatistics and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA.
13School of Public Health, Yale University, New Haven, CT, USA.
14Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
15Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam; Nuffield Department of Medicine, Oxford University, Oxford, UK.
16Rollins School of Public Health, Emory University, Atlanta, GA, USA.
17Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; School of Medicine, Washington University, St Louis, MO, USA.
18Laboratoire Modélisation, épidémiologie, et surveillance des risques sanitaires and Unit Cnam risques infectieux et émergents, Institut Pasteur, Conservatoire National des Arts et Metiers, Paris, France.
19International Vaccine Institute, Seoul, South Korea.
20Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
21Department of Immunization, Vaccines, and Biologicals, WHO, Geneva, Switzerland.
22Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK; Veterinary Medicine, University of Cambridge, Cambridge, UK.
23Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK. Electronic address: k.gaythorpe@imperial.ac.uk.
Abstract
Background: There have been declines in global immunisation coverage due to the COVID-19 pandemic. Recovery has begun but is geographically variable. This disruption has led to under-immunised cohorts and interrupted progress in reducing vaccine-preventable disease burden. There have, so far, been few studies of the effects of coverage disruption on vaccine effects. We aimed to quantify the effects of vaccine-coverage disruption on routine and campaign immunisation services, identify cohorts and regions that could particularly benefit from catch-up activities, and establish if losses in effect could be recovered.
Methods: For this modelling study, we used modelling groups from the Vaccine Impact Modelling Consortium from 112 low-income and middle-income countries to estimate vaccine effect for 14 pathogens. One set of modelling estimates used vaccine-coverage data from 1937 to 2021 for a subset of vaccine-preventable, outbreak-prone or priority diseases (ie, measles, rubella, hepatitis B, human papillomavirus [HPV], meningitis A, and yellow fever) to examine mitigation measures, hereafter referred to as recovery runs. The second set of estimates were conducted with vaccine-coverage data from 1937 to 2020, used to calculate effect ratios (ie, the burden averted per dose) for all 14 included vaccines and diseases, hereafter referred to as full runs. Both runs were modelled from Jan 1, 2000, to Dec 31, 2100. Countries were included if they were in the Gavi, the Vaccine Alliance portfolio; had notable burden; or had notable strategic vaccination activities. These countries represented the majority of global vaccine-preventable disease burden. Vaccine coverage was informed by historical estimates from WHO-UNICEF Estimates of National Immunization Coverage and the immunisation repository of WHO for data up to and including 2021. From 2022 onwards, we estimated coverage on the basis of guidance about campaign frequency, non-linear assumptions about the recovery of routine immunisation to pre-disruption magnitude, and 2030 endpoints informed by the WHO Immunization Agenda 2030 aims and expert consultation. We examined three main scenarios: no disruption, baseline recovery, and baseline recovery and catch-up.
Findings: We estimated that disruption to measles, rubella, HPV, hepatitis B, meningitis A, and yellow fever vaccination could lead to 49 119 additional deaths (95% credible interval [CrI] 17 248-134 941) during calendar years 2020-30, largely due to measles. For years of vaccination 2020-30 for all 14 pathogens, disruption could lead to a 2·66% (95% CrI 2·52-2·81) reduction in long-term effect from 37 378 194 deaths averted (34 450 249-40 241 202) to 36 410 559 deaths averted (33 515 397-39 241 799). We estimated that catch-up activities could avert 78·9% (40·4-151·4) of excess deaths between calendar years 2023 and 2030 (ie, 18 900 [7037-60 223] of 25 356 [9859-75 073]).
Interpretation: Our results highlight the importance of the timing of catch-up activities, considering estimated burden to improve vaccine coverage in affected cohorts. We estimated that mitigation measures for measles and yellow fever were particularly effective at reducing excess burden in the short term. Additionally, the high long-term effect of HPV vaccine as an important cervical-cancer prevention tool warrants continued immunisation efforts after disruption.
Funding: The Vaccine Impact Modelling Consortium, funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation.
Translations: For the Arabic, Chinese, French, Portguese and Spanish translations of the abstract see Supplementary Materials section.