Guidelines for multi-model comparisons of the impact of infectious disease interventions.

Cost-effectiveness Decision-making Harmonisation Impact modelling Infectious diseases Interventions Mathematical modelling Model comparisons Policy

Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
19 08 2019
Historique:
received: 09 05 2019
accepted: 02 08 2019
entrez: 20 8 2019
pubmed: 20 8 2019
medline: 7 1 2020
Statut: epublish

Résumé

Despite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. Herein, we present guidelines to provide a structured approach to comparisons of multiple models of interventions against infectious diseases. The primary target audience for these guidelines are researchers carrying out model comparison studies and policy-makers using model comparison studies to inform policy decisions. The consensus process used for the development of the guidelines included a systematic review of existing model comparison studies on effectiveness and cost-effectiveness of vaccination, a 2-day meeting and guideline development workshop during which mathematical modellers from different disease areas critically discussed and debated the guideline content and wording, and several rounds of comments on sequential versions of the guidelines by all authors. The guidelines provide principles for multi-model comparisons, with specific practice statements on what modellers should do for six domains. The guidelines provide explanation and elaboration of the principles and practice statements as well as some examples to illustrate these. The principles are (1) the policy and research question - the model comparison should address a relevant, clearly defined policy question; (2) model identification and selection - the identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias; (3) harmonisation - standardisation of input data and outputs should be determined by the research question and value of the effort needed for this step; (4) exploring variability - between- and within-model variability and uncertainty should be explored; (5) presenting and pooling results - results should be presented in an appropriate way to support decision-making; and (6) interpretation - results should be interpreted to inform the policy question. These guidelines should help researchers plan, conduct and report model comparisons of infectious diseases and related interventions in a systematic and structured manner for the purpose of supporting health policy decisions. Adherence to these guidelines will contribute to greater consistency and objectivity in the approach and methods used in multi-model comparisons, and as such improve the quality of modelled evidence for policy.

Sections du résumé

BACKGROUND
Despite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. Herein, we present guidelines to provide a structured approach to comparisons of multiple models of interventions against infectious diseases. The primary target audience for these guidelines are researchers carrying out model comparison studies and policy-makers using model comparison studies to inform policy decisions.
METHODS
The consensus process used for the development of the guidelines included a systematic review of existing model comparison studies on effectiveness and cost-effectiveness of vaccination, a 2-day meeting and guideline development workshop during which mathematical modellers from different disease areas critically discussed and debated the guideline content and wording, and several rounds of comments on sequential versions of the guidelines by all authors.
RESULTS
The guidelines provide principles for multi-model comparisons, with specific practice statements on what modellers should do for six domains. The guidelines provide explanation and elaboration of the principles and practice statements as well as some examples to illustrate these. The principles are (1) the policy and research question - the model comparison should address a relevant, clearly defined policy question; (2) model identification and selection - the identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias; (3) harmonisation - standardisation of input data and outputs should be determined by the research question and value of the effort needed for this step; (4) exploring variability - between- and within-model variability and uncertainty should be explored; (5) presenting and pooling results - results should be presented in an appropriate way to support decision-making; and (6) interpretation - results should be interpreted to inform the policy question.
CONCLUSION
These guidelines should help researchers plan, conduct and report model comparisons of infectious diseases and related interventions in a systematic and structured manner for the purpose of supporting health policy decisions. Adherence to these guidelines will contribute to greater consistency and objectivity in the approach and methods used in multi-model comparisons, and as such improve the quality of modelled evidence for policy.

Identifiants

pubmed: 31422772
doi: 10.1186/s12916-019-1403-9
pii: 10.1186/s12916-019-1403-9
pmc: PMC6699075
doi:

Types de publication

Guideline Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

163

Subventions

Organisme : World Health Organization
ID : 001
Pays : International
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 208812/Z/17/Z
Pays : United Kingdom

Références

PLoS Med. 2009 Sep;6(9):e1000109
pubmed: 19901974
BMJ. 2009 Jul 21;339:b2700
pubmed: 19622552
Value Health. 2018 Oct;21(10):1250-1258
pubmed: 30314627
Lancet Public Health. 2016 Nov;1(1):e8-e17
pubmed: 29253379
Epidemiol Infect. 1997 Oct;119(2):183-201
pubmed: 9363017
PLoS Med. 2012;9(7):e1001245
pubmed: 22802730
Lancet Glob Health. 2013 Dec 10;2(1):23-34
pubmed: 25083415
PLoS Med. 2016 Nov 29;13(11):e1002181
pubmed: 27898668
Value Health. 2012 Sep-Oct;15(6):843-50
pubmed: 22999134
Epidemics. 2017 Mar;18:16-28
pubmed: 28279452
Lancet Glob Health. 2016 Nov;4(11):e806-e815
pubmed: 27720688
Diabetes Care. 2007 Jun;30(6):1638-46
pubmed: 17526823
BMJ. 2013 Mar 25;346:f1049
pubmed: 23529982
Value Health. 2014 Jul;17(5):525-36
pubmed: 25128045
BMC Med. 2015 Dec 17;13:301
pubmed: 26675206
J R Soc Interface. 2012 Jan 7;9(66):136-46
pubmed: 21653569
N Engl J Med. 2005 Oct 27;353(17):1784-92
pubmed: 16251534
Vaccine. 2008 Aug 19;26 Suppl 10:K76-86
pubmed: 18847560
Proc Natl Acad Sci U S A. 2008 Mar 25;105(12):4639-44
pubmed: 18332436
BMC Med. 2011 May 12;9:54
pubmed: 21569406
BMC Med. 2011 May 12;9:55
pubmed: 21569407
BMC Med. 2011 May 12;9:53
pubmed: 21569402
Pharmacoeconomics. 2016 Mar;34(3):227-44
pubmed: 26477039
Pharmacoeconomics. 2011 May;29(5):371-86
pubmed: 21504239
Lancet. 2016 Jan 23;387(10016):367-375
pubmed: 26549466
Epidemics. 2017 Mar;18:1-3
pubmed: 28279450
F1000Res. 2017 Aug 29;6:1584
pubmed: 29552335
BMC Med. 2011 Jul 08;9:84
pubmed: 21740545
Wkly Epidemiol Rec. 2016 Jan 4;91(4):33-51
pubmed: 26829826
Vaccine. 2017 Oct 13;35(43):5835-5841
pubmed: 28941619
Wkly Epidemiol Rec. ;92(19):241-68
pubmed: 28530369

Auteurs

Saskia den Boon (S)

Department of Immunization, Vaccines and Biologicals, World Health Organization, Avenue Appia 20, CH-1211, Geneva 27, Switzerland.

Mark Jit (M)

Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
Modelling and Economics Unit, Public Health England, London, UK.
School of Public Health, University of Hong Kong, Hong Kong, SAR, China.

Marc Brisson (M)

Department of Social and Preventive Medicine, Université Laval, Quebec, Canada.

Graham Medley (G)

Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.

Philippe Beutels (P)

Centre for Health Economics Research & Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.

Richard White (R)

Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
TB Modelling Group, London School of Hygiene and Tropical Medicine, London, UK.

Stefan Flasche (S)

Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.

T Déirdre Hollingsworth (TD)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.

Tini Garske (T)

Faculty of Medicine, School of Public Health, Imperial College London, London, UK.

Virginia E Pitzer (VE)

Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, 06511, USA.

Martine Hoogendoorn (M)

Institute for Medical Technology Assessment (iMTA), Erasmus University Rotterdam, Rotterdam, The Netherlands.

Oliver Geffen (O)

Faculty of Medicine, School of Public Health, Imperial College London, London, UK.

Andrew Clark (A)

Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.

Jane Kim (J)

Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, USA.

Raymond Hutubessy (R)

Department of Immunization, Vaccines and Biologicals, World Health Organization, Avenue Appia 20, CH-1211, Geneva 27, Switzerland. hutubessyr@who.int.

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