| Title:
|
Lives saved with vaccination for 10 pathogens across 112 countries in a pre-COVID-19 world |
| Abstract:
|
BACKGROUND: Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b hepatitis B human papillomavirus Japanese encephalitis measles Neisseria meningitidis serogroup A rotavirus rubella Streptococcus pneumoniae and yellow fever over the years 20002030 across 112 countries. METHODS: Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios. RESULTS: We estimate 97 (95%CrI[80 120]) million deaths would be averted due to vaccination activities over 20002030 with 50 (95%CrI[41 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030 we estimate 52 (95%CrI[41 69]) million more deaths would occur over their lifetimes without vaccination against these diseases. CONCLUSIONS: This study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future. FUNDING: VIMC is jointly funded by Gavi the Vaccine Alliance and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortiums modelling groups (VIMC-funded institutions represented in this paper: Imperial College London London School of Hygiene and Tropical Medicine Oxford University Clinical Research Unit Public Health England Johns Hopkins University The Pennsylvania State University Center for Disease Analysis Foundation Kaiser Permanente Washington University of Cambridge University of Notre Dame Harvard University Conservatoire National des Arts et Mtiers Emory University National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH MJ XL SE-L JT KW NMF KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development which supported aspects of VIMC's work (MRC grant number: MR/R015600/1). JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation. This paper was compiled by all coauthors including two coauthors from Gavi. Other funders had no role in study design data collection data analysis data interpretation or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. |
| Published:
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2021-07-13 |
| Journal:
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eLife |
| DOI:
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10.7554/elife.67635 |
| DOI_URL:
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http://doi.org/10.7554/elife.67635 |
| Author Name:
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Toor Jaspreet |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/toor_jaspreet |
| Author Name:
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Echeverria Londono Susy |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/echeverria_londono_susy |
| Author Name:
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Li Xiang |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/li_xiang |
| Author Name:
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Abbas Kaja |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/abbas_kaja |
| Author Name:
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Carter Emily D |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/carter_emily_d |
| Author Name:
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Clapham Hannah E |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/clapham_hannah_e |
| Author Name:
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Clark Andrew |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/clark_andrew |
| Author Name:
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de Villiers Margaret J |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/de_villiers_margaret_j |
| Author Name:
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Eilertson Kirsten |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/eilertson_kirsten |
| Author Name:
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Ferrari Matthew |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/ferrari_matthew |
| Author Name:
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Gamkrelidze Ivane |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/gamkrelidze_ivane |
| Author Name:
|
Hallett Timothy B |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/hallett_timothy_b |
| Author Name:
|
Hinsley Wes R |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/hinsley_wes_r |
| Author Name:
|
Hogan Daniel |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/hogan_daniel |
| Author Name:
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Huber John H |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/huber_john_h |
| Author Name:
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Jackson Michael L |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/jackson_michael_l |
| Author Name:
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Jean Kevin |
| Author link:
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https://covid19-data.nist.gov/pid/rest/local/author/jean_kevin |
| Author Name:
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Jit Mark |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/jit_mark |
| Author Name:
|
Karachaliou Andromachi |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/karachaliou_andromachi |
| Author Name:
|
Klepac Petra |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/klepac_petra |
| Author Name:
|
Kraay Alicia |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/kraay_alicia |
| Author Name:
|
Lessler Justin |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/lessler_justin |
| Author Name:
|
Li Xi |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/li_xi |
| Author Name:
|
Lopman Benjamin A |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/lopman_benjamin_a |
| Author Name:
|
Mengistu Tewodaj |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/mengistu_tewodaj |
| Author Name:
|
Metcalf C Jessica E |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/metcalf_c_jessica_e |
| Author Name:
|
Moore Sean M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/moore_sean_m |
| Author Name:
|
Nayagam Shevanthi |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/nayagam_shevanthi |
| Author Name:
|
Papadopoulos Timos |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/papadopoulos_timos |
| Author Name:
|
Perkins T Alex |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/perkins_t_alex |
| Author Name:
|
Portnoy Allison |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/portnoy_allison |
| Author Name:
|
Razavi Homie |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/razavi_homie |
| Author Name:
|
Razavi Shearer Devin |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/razavi_shearer_devin |
| Author Name:
|
Resch Stephen |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/resch_stephen |
| Author Name:
|
Sanderson Colin |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/sanderson_colin |
| Author Name:
|
Sweet Steven |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/sweet_steven |
| Author Name:
|
Tam Yvonne |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/tam_yvonne |
| Author Name:
|
Tanvir Hira |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/tanvir_hira |
| Author Name:
|
Tran Minh Quan |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/tran_minh_quan |
| Author Name:
|
Trotter Caroline L |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/trotter_caroline_l |
| Author Name:
|
Truelove Shaun A |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/truelove_shaun_a |
| Author Name:
|
Vynnycky Emilia |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/vynnycky_emilia |
| Author Name:
|
Walker Neff |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/walker_neff |
| Author Name:
|
Winter Amy |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/winter_amy |
| Author Name:
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Woodruff Kim |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/woodruff_kim |
| Author Name:
|
Ferguson Neil M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/ferguson_neil_m |
| Author Name:
|
Gaythorpe Katy AM |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/gaythorpe_katy_am |
| sha:
|
0ff733e4e6d6d92e7f9ba2241e9e91d2552c95e5 |
| license:
|
cc-by |
| license_url:
|
https://creativecommons.org/licenses/by/4.0/ |
| source_x:
|
Medline; PMC; WHO |
| source_x_url:
|
https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/https://www.who.int/ |
| pubmed_id:
|
34253291 |
| pubmed_id_url:
|
https://www.ncbi.nlm.nih.gov/pubmed/34253291 |
| pmcid:
|
PMC8277373 |
| pmcid_url:
|
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277373 |
| url:
|
https://doi.org/10.7554/elife.67635
https://www.ncbi.nlm.nih.gov/pubmed/34253291/ |
| has_full_text:
|
TRUE |
| Keywords Extracted from Text Content:
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Neisseria meningitidis serogroup A
Haemophilus influenzae type b
Echeverria-Londono
rotavirus
human papillomavirus
Streptococcus pneumoniae
rubella
https://doi.org/10.7554/eLife.67635 1
measles
children
BD
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MCV2
Sindh province
EPI
swh:1:rev:0da13630968de0863f38294a1c234c5947baf97e
2-3
Merlin
Varaine
Hib pneumonia
NPNM
hepatocellular carcinoma
f R
Vaccine
children
cervical
line
per-contact
re-vaccinate
Eye
Hib
Immune Reactive
mother
LMICs
Echeverria-Londono
cross-pathogen
VIMC-wide
GVAP
hepatitis B
chest x-ray
Excelbased
HPV vaccine
Herd
83:1
b 1
DTP1
measles dynaMICE
IHME GBD 2019
infants
Hib meningitis
survivors
Fangjun
JEV infections
Rotavirus vaccines
201910gavi -version 5
people
humans
Velázquez
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Geneva, 2005
non-primary infections
pre-vaccine
Consortium (
encephalitis virus
Vaccine-induced
fowl
CHB
HBsAg+ HBeAg-at
Clermont
IgG
DTP
Hib [
tenofovir
Susceptible-Infected-Recovered (SIR
beta
Georgakopoulou
LP DAAC NASA
Burkina Faso
Rubella
Nicoara
Rota [
measles
Rota,
V
gavi73
HepB3
PAHO
Encephalitis
b 1;i
HBsAg+ HBeAg-mothers
non-human primates
HPV-16/18
JE
Figure 2
PSA
Hib, measles
0Á26
b-PERT
VPDs
Liver Cancer
HBeAg-negative
NRI
VIMC
rotavirus vaccines
FVP
Longbottom
2021a
95%CrI[6.1
SIR
Appendix 5-figure 3
hepatitis B virus
Hib non-pneumonia/non-meningitis
non-vaccine HPV
DynaMICE
Culex tritaeniorhynchus
BAL
Hib vaccine
b S
CrIs
CLT
hepatitis B core antigen
hepatitis B.
country-bycountry
under-5s
HBsAg+ HBeAg-
serum bactericidal antibody
Gavi
individuals
encephalitis
Bill
measles Pennsylvania
Fetal
Immune
liver cancer
RCV
SARS-COV-2 vaccines
focal RI
light-touch
HPV 16/18
HPV vaccines
mothers
http://primetool.org/
GSK
4-11
post-2030
95%CrI[0.47
SARS-COV-2 vaccine
FOIs
immune 'M
ð1 þ acosð2ptÞÞ
HBV vaccine
Nigeria (Appendix 5- figure 7
Rota
age/year
Zgó rniak-Nowosielska
JEV
HepB
DCC
D bSÀfS
NRI for
Jit
hosts'status
Jackson
CrI
hepatitis B e antigen
rice
colonization/disease
Infants
RI-only (
Health Survey
b R
FVPs
land
Hadler
WHO-UNICEF
Neisseria meningitidis
GAMS
birth-dose
Hasso-Agopsowicz
HCC
hepatitis B surface antigen
rubella immunoglobulin G
Finkenstadt
UNICEF
HBeAg+/ HBsAg+
people's
Pol3
95%CrI[120
HBsAg
95%CrI[2.9
Papillomavirus Rapid Interface
lockdown
Tristan de Espino
perspectives-either
HBsAg+
BD vaccine
cervical cancer
e t
campaign-based vaccinations
b 2
WorldPop
hepatitis
Tristan de Espino, 1989
Appendix 5-figure 2
YFV
b 0;i
rotavirus vaccine
rotavirus infections
RI
coverage/FVPs
TAP
Quaglio
HBsAg+ HBeAg+
f S
cancer
HBV
NRI years Y v
HPV
rubella
WHO/UNICEF
herd
RCV1
RStan
b a;j;t 1⁄4 b
Lingani
Liver
a=2
VPD
anti-HBc
matrix
rotavirus
liver
Prem
NPIs
Zeshan
Chad
rotavirus gastroenteritis
Beta
zero-dose children
WHO-AFRO
Vaccines
UNWPP
EndHep2030
1-5, 6-11, 12-23
human
https://dhsprogram.com
FVP S
Non-routine
Rota, RI
GLO-BOCAN 2020
b 1 0:7b 2 0:7b 2 b 2
HBIG
WUENIC
low-viral
Campagne
b a;j
hepatitis B vaccine
WUENIC DTP
zone
UPHIA
Gambia
DTP3
PCV13 serotypes
pre-COVID-19
NRI, D S
AOM
HBeAg
contact matrix
HepB-attributable
Measles
LVL
S. pneumoniae
Tartof
pre-infectious
infection/disease
95%CrI[4100
MACs
Eric Mast
non-vaccine
NRI denotes non-routine immunisations
Vynnycky
GDP
FVP R
95%CrI[5.9
E1⁄2Á
cervical cancers
MenAfriVac
2019a
Chandir
persons
vimc98
age-contact
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Appendix 5-figure 1
COVID-19
GHDx, 2019
serotypes
RCV2
UNIVAC
GBD 2019
measles/measles-rubella
Congo
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VIMC-wide
FitzJohn
Appendix 5-figure 2
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2021a
HepB
persons
FVPs
measles |
| Extracted Text Content in Record:
|
First 5000 Characters:Background: Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, Toor, Echeverria-Londono, Li, et al. eLife 2021;10:e67635. DOI: https://doi.org/10.7554/eLife.67635 1 of 63 RESEARCH ARTICLE rubella, Streptococcus pneumoniae, and yellow fever over the years 2000-2030 across 112 countries. Methods: Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios. Results: We estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000-2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases.
Conclusions: This study represents the largest assessment of vaccine impact before COVID-19related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future.
Vaccines play a vital role in immunising populations worldwide to provide protection against a wide range of diseases. In 1974, the World Health Organisation (WHO) launched the Expanded Programme on Immunisation (EPI) with a goal of universal access to all relevant vaccines for all at risk (Keja et al., 1988) . To further increase momentum on vaccine coverage, Gavi, the Vaccine Alliance, was created in 2000 with a goal of providing vaccines to save lives and protect people's health (Bill & Melinda Gates Foundation, 2020; Zerhouni, 2019) . Over the past two decades, vaccination programmes have expanded across low-and middle-income countries (LMICs), significantly reducing morbidity and mortality related to vaccine preventable diseases (VPDs). As of 2019, Gavi has helped immunise over 822 million children through routine programmes and provided over 1.1 billion vaccinations through campaigns in supported countries (Gavi, the Vaccine Alliance, 2019) . Despite this immense progress, almost one in five (15.2 million) children in Gavi-supported countries remain under-immunised with the third dose of the essential childhood vaccination containing diphtheriatetanus-pertussis vaccine (DTP3), 10.6 million of these children are zero-dose children, that is, having not received their first dose of DTP (Zerhouni, 2019) .
The beneficial effect of vaccination programmes cannot be assessed directly as the counterfactual, that is, the situation without vaccination, cannot be observed. Hence, models of disease risk and the impact of vaccination activities play a vital role in assessing the current burden, examining the effect of previous activities and projecting the future situation. The Vaccine Impact Modelling estimates. Twenty-one mathematical models were used to inform the estimates with two models per pathogen (except HepB which has three models) thereby increasing robustness and capturing structural uncertainty within the analyses. There is substantial variation in modelling approach due to both the differences in pathogen dynamics and inherent uncertainties in modelling disease risk. The model characteristics vary in their type, from static cohort to transmission-dynamic models; their complexity, for example in their representation of age effects; and their calibration and validation methods. A brief overview of pathogens is provided in Table 1 with detailed model descriptions provided in Appendix 2.2 (HepB [Nayagam et al., 2016] , HPV [Goldie et al., 2008; Abbas et al., 2020b] , Hib [Clark et al., 2019a; Walker et al., 2013a] , JE [Quan et al., 2020] , Measles [Chen et al., 2012] , MenA [Karachaliou et al., 2015; Tartof et al., 2013] , PCV [Walker et al., 2013a; Clark et al., 2019a] , Rota [Pitzer et al., 2012; Clark et al., 2019a] , Rubella [Boulianne et al., 1995; Vynnycky et al., 2019] , YF [Gaythorpe et al., 2021a] ).
Each modelling group provided estimates of age-stratified disease burden at national level for three scenarios: no vaccination, only routine vaccination (routine immunisations; RI) and, where appropriate, both RI and non-routine vaccination (non-routine immunisations; NRI, such as multi-age cohort vaccinations for HPV, and catch-up campaigns for measles). Disease burden was quantified in terms of deaths and DALYs. DALYs measure the years of healthy life lost due to premature death and disability from the disease, and are the sum of years of life lost (YLLs) through premature mortality and years lived with disability (YLDs). No discounting or weighting was applied in the calculation of DALYs. For rubella, only disease burden from congenital rubella syndrome (CRS) was included and the models differed in the inc |
| Keywords Extracted from PMC Text:
|
Rota, RI
95%CrI[6.1
WHO/UNICEF
zero-dose children
EPI
HepB-attributable
CrIs
95%CrI[0.47
2000–2019
RI-only (
HepB
Gavi
Rota [
VPDs
Figure 2
COVID-19
measles Pennsylvania
human
Streptococcus pneumoniae
pre-COVID-19
measles
Hib
DTP
children
Vynnycky
Vaccine
's
rubella
Measles
MCV2
Appendix 5—figure 4
95%CrI[4100
GBD 2019
WUENIC
Neisseria meningitidis serogroup A
re-vaccinate
people's
tetanus
HPV
FVP
coronavirus 2019
rotavirus
Deghmane
GVAP
human papillomavirus
FVPs
Tartof
95%CrI[120
LMICs
2019a
2021a]
hepatitis B (HepB
VIMC-wide
Appendix 5—figure 2
Bill
Haemophilus influenzae type b (Hib
Nigeria (Appendix 5—figure 7
Appendix 5—figure 9
Rota,
CrI
Echeverria-Londono
Hib, measles
DTP3
RI
Vaccines
encephalitis
Rota
5—figure
Appendix 5—figure 3
measles dynaMICE
NRI for
NPIs
Rubella
line
UNWPP
JE
NRI
post-2030
95%CrI[2.9
Hib [
GHDx, 2019
VIMC
95%CrI[5.9 |
| Extracted PMC Text Content in Record:
|
First 5000 Characters:Vaccines play a vital role in immunising populations worldwide to provide protection against a wide range of diseases. In 1974, the World Health Organisation (WHO) launched the Expanded Programme on Immunisation (EPI) with a goal of universal access to all relevant vaccines for all at risk (Keja et al., 1988). To further increase momentum on vaccine coverage, Gavi, the Vaccine Alliance, was created in 2000 with a goal of providing vaccines to save lives and protect people's health (Bill & Melinda Gates Foundation, 2020; Zerhouni, 2019). Over the past two decades, vaccination programmes have expanded across low- and middle-income countries (LMICs), significantly reducing morbidity and mortality related to vaccine preventable diseases (VPDs). As of 2019, Gavi has helped immunise over 822 million children through routine programmes and provided over 1.1 billion vaccinations through campaigns in supported countries (Gavi, the Vaccine Alliance, 2019). Despite this immense progress, almost one in five (15.2 million) children in Gavi-supported countries remain under-immunised with the third dose of the essential childhood vaccination containing diphtheria-tetanus-pertussis vaccine (DTP3), 10.6 million of these children are zero-dose children, that is, having not received their first dose of DTP (Zerhouni, 2019).
The beneficial effect of vaccination programmes cannot be assessed directly as the counterfactual, that is, the situation without vaccination, cannot be observed. Hence, models of disease risk and the impact of vaccination activities play a vital role in assessing the current burden, examining the effect of previous activities and projecting the future situation. The Vaccine Impact Modelling Consortium (VIMC), established in 2016, aims to deliver an effective, transparent and sustainable approach to generating disease burden and vaccine impact estimates (Imperial College London, 2021). The VIMC consists of twenty-one independent research groups which provide estimates of disease burden and vaccine impact across 112 LMICs for 10 pathogens, namely hepatitis B (HepB), Haemophilus influenzae type b (Hib), human papillomavirus (HPV), Japanese encephalitis (JE), measles, Neisseria meningitidis serogroup A (MenA), Streptococcus pneumoniae (PCV), rotavirus (Rota), rubella, and yellow fever (YF).
There are various ways of calculating the impact of vaccination (Echeverria-Londono et al., 2021). The burden averted by vaccination can be estimated in terms of the number of cases, deaths and disability adjusted life years (DALYs) averted. Vaccine impact is commonly presented by calendar year, that is, the number of lives saved by vaccination in a particular year or by birth cohort, that is, the number of lives saved by vaccination over the lifetime of individuals born in a particular year. Previous work by the VIMC on these 10 pathogens estimated that 69 million deaths would be averted by vaccination over calendar years 2000–2030 across 98 LMICs, with 120 million deaths averted over the lifetime of birth cohorts born between 2000 and 2030 (Li et al., 2019). The WHO estimates that immunisation currently prevents 2–3 million deaths every year (World Health Organisation, 2021), similarly Ehreth, 2003 estimated 3 million deaths averted due to vaccination for pathogens such as measles, YF, HepB, diptheria, Hib, pertussis, neonatal tetanus and poliomyelitis.
Although attributing vaccine impact to calendar year or birth cohort is intuitive and commonly used, these methods fail to capture the impact of a specific year's vaccination activities traced over the lifetime of those vaccinated. It is beneficial to examine the impact corresponding to a vaccination activity so that the cost and benefit of each intervention can be appropriately calculated. The impact by year of vaccination activity method, developed by the VIMC, estimates the number of individuals that will be saved due to a particular year's vaccination activities (Echeverria-Londono et al., 2021). This method addresses the issue of attributing impact to the vaccination activity that caused it without repeatedly rerunning modelling scenarios which, whilst the optimal approach, is extremely computationally intensive. As such, we can approximate the potential effect of one year's worth of activity.
The first human case of coronavirus 2019 (COVID-19) was reported in December 2019 and has subsequently affected vaccination and healthcare worldwide. Whilst the effect of COVID-19 is not the focus of the current study, we acknowledge the huge influence the global pandemic has had and will have for years to come. Preliminary work has begun on quantifying the effect of disruption on vaccination activities and on assessing the benefit of continuing routine infant immunisation in times of COVID-19 (Abbas et al., 2020a; Gaythorpe et al., 2021b). There is also evidence that the rise in non-pharmaceutical interventions (NPIs, e.g. social distancing) associated with the pandemic may re |
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