The Brighton declaration arose out of a one day workshop held in Brighton in September 2013 as part of the Society for Social Medicine annual conference. The workshop convened UK based non-communicable disease modellers to discuss the challenges and opportunities for non-communicable disease modelling in the UK. The declaration describes the value and importance of non-communicable disease modelling, both for research and for informing health policy. The declaration also describes challenges and issues for non-communicable disease modelling. The declaration has been endorsed by many non-communicable disease modellers in the UK

Adam D. M. Briggs

James Woodcock

Klim McPherson

Laura Webber

Oliver T. Mytton

Peter Scarborough

Simon Capewell

PubMed

Crossref

COMMENTARYThe Brighton declaration: the value of non-communicable diseasemodelling in population health sciencesLaura Webber • Oliver T. Mytton • Adam D. M. Briggs •James Woodcock • Peter Scarborough • Klim McPherson •Simon CapewellReceived: 8 August 2014 / Accepted: 24 November 2014 / Published online: 13 December 2014 The Author(s) 2014. This article is published with open access at Springerlink.comThe Brighton declaration arose out of a one day workshopheld in Brighton in September 2013 as part of the Societyfor Social Medicine annual conference. The workshopconvened UK based non-communicable disease modellersto discuss the challenges and opportunities for non-com-municable disease modelling in the UK. The declarationdescribes the value and importance of non-communicabledisease modelling, both for research and for informinghealth policy. The declaration also describes challengesand issues for non-communicable disease modelling. Thedeclaration has been endorsed by many non-communicabledisease modellers in the UK.BackgroundOver 60 % of global deaths are attributable to non-com-municable diseases (NCDs) [1]. With most developingcountries experiencing a shift in disease burden away fromcommunicable disease to NCDs, this contribution isexpected to grow. To respond effectively, it will be crucialto understand these epidemics better: both how the burdenof disease is anticipated to change over time based oncurrent trends (e.g. demographic change, changes in riskfactor prevalence, or changes in diseases incidence), andthe effects that different interventions might have. This isimportant for planning health services and for developingan evidence base to inform public health policies aimed atreducing the burden of disease. While modelling is oftennot well understood and is frequently criticised, we arguethat modelling NCDs has an important role to play ininforming how society responds their increasing burden onpopulation health.What is non-communicable disease modelling?Models simplify reality—a good model represents thoseparts of reality that matter and leaves out those parts that donot. Modelling is the development and use of these modelsto understand how different inputs (e.g. behaviours) affectdifferent outcomes (e.g. disease). NCD modelling is amethod for estimating the extent to which changes in oneor more risk factor (e.g. smoking and diet) affects diseaseand health. It has two broad uses, health impact modelling(understanding the effect of prevention, screening ortreatment interventions on health) [2] and forecasting(estimating disease trends based on demographic change orpredicted changes in risk factors, including makingL. WebberUK Health Forum, Fleetbank House, 2-6 Salisbury Square,London EC4Y 8JX, UKO. T. Mytton  J. WoodcockUKCRC Centre for Diet and Activity Research, MRCEpidemiology Unit, Institute of Metabolic Science, Universityof Cambridge School of Medicine, Cambridge CB2 0QQ, UKA. D. M. Briggs (&)  P. ScarboroughBritish Heart Foundation Centre on Population Approaches forNon-Communicable Disease Prevention, Nuffield Departmentof Population Health, University of Oxford, New RichardsBuilding, Old Road Campus, Headington, Oxford OX3 7LF, UKe-mail: adam.briggs@dph.ox.ac.ukK. McPhersonNuffield Department of Obstetrics and Gynaecology, Universityof Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UKS. CapewellDepartment of Public Health and Policy, Institute of Psychology,Health and Society, University of Liverpool, WaterhouseBuilding, Block B, 2nd Floor, Liverpool L69 3BX, UK123Eur J Epidemiol (2014) 29:867–870DOI 10.1007/s10654-014-9978-0allowance for competing risk). Such modelling is some-times extended to consider the costs to society or healthsystems that arise from disease, and the potential savingsthat may accrue from interventions. NCD models use awide range of research methods and can answer a range ofquestions [3–6]. NCD modelling in the UK is performed by(and used by) a variety of institutions, academic researchunits, non-government organisations, as well as publicbodies. Although such modelling is not new, increasedavailability of datasets, improved computational power anduse of new methods is opening up new possibilities [7].The need and use for non-communicable diseasemodellingModelling is already widely used to inform decisionmaking in both the public and private sector. Within publichealth it has long been recognised that the greatest influ-ences on population health lie outside the health sector [8].NCD modelling has, for example, been influential in thedebate on minimum unit pricing [9] and is being used tointegrate health into transport planning [10, 11]. NCDmodelling may also be used to inform decisions on prior-ities for investment, for example modelling cost-effec-tiveness of different interventions for primary preventionof cardiovascular disease [12] or different approaches toscreening [13, 14], as well as predict future trends in dis-ease burden [13], which are important in planning healthservice provision.Modelling enables us to estimate the long term andpopulation-wide health effects of interventions. Theseeffects are rarely observed in a single research studybecause of a variety of other influences on the observedoutcome over time, and true randomised experiments areoften not practical. While modelling studies can be cheaperand quicker than real world studies, they should be seen asa complement to such studies, and may help us get the bestvalue out of such studies. It is often most appropriate tomodel when we already have a good understanding of thesystem being modelled, both its structures and its param-eters. Often in this way modelling studies are being used tointegrate evidence from different studies and differentdomains. Modelling can also inform empirical studies, forexample to identify assumptions or key parts of evidencethat may have a significant impact on model outcome(direct empirical study of these assumptions may givegreater confidence in the model and its predictions).Modelling may also be used to estimate likely effect sizesto inform the size of evaluative studies.For example our understanding of the effectiveness andcost-effectiveness of the current UK Breast CancerScreening programme is informed by modelling [14, 15].While estimates of the effectiveness of screening could bederived from past trials, these occurred many years ago,when incidence was lower, diagnostic technology different,and treatment options fewer. Our knowledge of the systemand the parameters within the system is relatively good. Italso serves as an example of modelling being comple-mentary to, and used alongside, ‘‘empirical’’ evidence.While such modelling does not preclude the need for atrial, it can give a more rapid indication of where the costsand benefits lie. Indeed, estimates could be updated on aperiodic basis as disease trends change and technologyevolves. Further, modelling could demonstrate that theexisting evidence is sufficient, such that further empiricalstudies may not be necessary.Criticisms of non-communicable disease modellingModelling work is often criticised. This may relate tocriticisms of the underlying assumptions within the model:either the particular values (parameters) used or the mod-el’s structure (the assumed underlying relationships orcausal model). Some of these assumptions may be implicitwithin the model, and there is a risk that they may not bereadily identified as assumptions by those building (andusing) the model. This can be particularly problematicwhen there is either no or limited evidence to support theseassumptions. Sometimes the criticism relates to the veryidea of modelling, that modelling is by definition, uncer-tain, resulting in the view that the model will lead tounrealistic estimates. These fears may be compounded if aclear description of the model, ideally one that is accessibleto a wide audience, is not given. Developing such a cleardescription is not always straightforward and requires theability to articulate many of the key assumptions sur-rounding model structure (including implicit assumptions)to non-specialist audiences.While criticisms of the underlying assumptions maysometimes be justified, sensitivity analyses can be used toidentify which explicit assumptions are critical to themodel (in terms of having a significant impact on the modeloutcome), and uncertainty analyses can quantify uncer-tainty surrounding the model’s parameters and structure;both are important for calibration and validation of themodel. Within the bounds of expected values someunderlying assumptions and parameters may have littleeffect on the model results even though the uncertaintysurrounding those particular parameters may be high [16].While there may be times that uncertainty is so great thatthe results cannot be used in decision making, we believe abest estimate can often be better than no estimate. Failureto model health outcomes may mean failure to considerhealth, alongside many of the other outcomes that may be868 L. Webber et al.123modelled (and considered) for prospective policies, forexample employment, revenues, and carbon emissions, andill health is expensive especially in the future. It might alsomean that decision making is informed by decision makers’best guesses as to what the costs and benefits are—effec-tively ‘‘implicit modelling’’ which is unstructured, non-transparent, and not reproducible.Challenges for modellingA number of challenges have been identified:• perception that conclusions are largely assumptionbased and that underlying (and sometimes implicit)assumptions are being hidden or chosen in such a wayto support a particular outcome;• conceptualising and communicating issues of uncer-tainty, particularly to policy makers;• communicating to both technical and non-technicalaudiences how the modelling was undertaken and thenature of the underlying assumptions;• presenting study results in an accessible way to reachan audience that may not be familiar with technicaldetails and issues of modelling;• journals having difficulty identifying appropriate han-dling editors and referees with correct technical expertise;• journal editors lacking familiarity with modellingapproaches and being unsure how to appraise quality;• findings being reported too strongly, particularly whereunacknowledged or implicit assumptions may under-mine the validity of the model.Need for standardised reporting guidelinesWe recognise that some of these issues might be resolvedby improved and standardised reporting of modellingstudies. Transparent acknowledgements of assumptionsand limitations (including using non-technical language)enables a more thorough and robust peer-review and allowsreaders to assess the model’s quality [17–19]. Unfortu-nately, to our knowledge there is no reporting guideline(e.g. Enhancing the QUAlity and Transparency Of healthResearch (EQUATOR) network guidelines: www.equator-network.org) for NCD modelling nor are we aware of anyequivalent reporting standards.Health economics, which often draws on modelling, hasreporting guidelines and some of these cover modelling[e.g. Consolidated Health Economic Evaluation ReportingStandards (CHEERS)] [20]. There are also separate stan-dards for the reporting of health economic modelling [21].While these guidelines may partially address the needs ofNCD modelling, we do not think they are sufficient.Besides having a large focus on economic outcomes, theeconomic modelling literature to which these standardsapply is predominantly concerned with the cost-effective-ness of treatment of diseases on health and outcomes (forwhich the evidence is typically drawn from randomisedcontrolled trials), rather than the effect of risk factormodification on health (for which the evidence may comefrom trials but tends to be drawn from observationalstudies). Moreover the health economic guidelines tend notto be used when reporting NCD modelling, particularly inthe absence of an economic component to the modelling, asthey are perceived as inappropriate. Consequently, webelieve there is a need for specific non-communicabledisease reporting guidelines within our field.Modelling has an important role in public health andhealth policy. This may grow in the future, open access todata and increased computing power are facilitating thedevelopment of more sophisticated models. We recognisethere is a need to improve the reporting of our work interms of transparency and standardised reporting of bothmodels and model results. It is also important that wecontinue to work in a collaborative fashion to develop thescience to support modelling and capacity within the UK toundertake such work.Acknowledgments The following academics collaborated with theauthors to finalise this article are and acknowledged as co-signatorieson its content. The authors are extremely grateful for their input.University of Cambridge: Ali Abbas, Marko Tanio; University ofEdinburgh: Dr Susannah McLean; UK Health Forum: Martin Brown,Tim Marsh, Marco Mesa-Frias, Lise Retat; Imperial College London:Anthony Laverty; The London School of Hygiene and TropicalMedicine: Zaid Chalabi; University College London: Luz SanchezRomero; University of Oxford: Anja Mizdrak, Mike Rayner, MarcoSpringmann; University of Sheffield: Alan Brennan, James Chilcott,John Holmes, Petra Meier, John Mooney; University of Southampton:Grant Aitken. ADMB and OTM are funded by the Wellcome Trust.PS is funded by the British Heart Foundation. JW is funded by anMRC Population Health Scientist Fellowship.Conflict of interest The authors declare that there is no conflict ofinterest that could be perceived as prejudicing the impartiality of theresearch reported.Open Access This article is distributed under the terms of theCreative Commons Attribution License which permits any use, dis-tribution, and reproduction in any medium, provided the originalauthor(s) and the source are credited.References1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, AboyansV, Abraham J, Adair T, Aggarwal R, Ahn SY, Alvarado M,Anderson HR, Anderson LM, Andrews KG, Atkinson C, BaddourLM, Barker-Collo S, Bartels DH, Bell ML, Benjamin EJ, BennettD, Bhalla K, Bikbov B, Bin Abdulhak A, Birbeck G, Blyth F,The Brighton declaration 869123Bolliger I, Boufous S, Bucello C, Burch M, et al. Global andregional mortality from 235 causes of death for 20 age groups in,1990 and 2010: a systematic analysis for the global burden ofdisease study 2010. Lancet. 2012;380:2095–128.2. Stout NK, Knudsen AB, Kong CY, McMahon PM, Gazelle GS.Calibration methods used in cancer simulation models and sug-gested reporting guidelines. Pharmacoeconomics. 2009;27:533–45.3. Astolfi R, Lorenzoni L, Oderkirk J. Informing policy makers aboutfuture health spending: a comparative analysis of forecastingmethods in OECD countries. Health Policy. 2012;107:1–10.4. Barhak J, Isaman DJM, Ye W, Lee D. Chronic disease modelingand simulation software. J Biomed Inform. 2010;43:791–9.5. Kopec JA, Sayre EC, Flanagan WM, Fines P, Cibere J, RahmanMM, Bansback NJ, Anis AH, Jordan JM, Sobolev B, AghajanianJ, Kang W, Greidanus NV, Garbuz DS, Hawker GA, Badley EM.Development of a population-based microsimulation model ofosteoarthritis in Canada. Osteoarthr Cartil. 2010;18:303–11.6. Unal B, Capewell S, Critchley JA. Coronary heart disease policymodels: a systematic review. BMC Public Health. 2006;6:213.7. Maglio PP, Mabry PL. Agent-based models and systems scienceapproaches to public health. Am J Prev Med. 2011;40:392–4.8. World Health Organization. The Ottawa charter for health pro-motion. Geneva. 1986.9. Purshouse RC, Meier PS, Brennan A, Taylor KB, Rafia R. Esti-mated effect of alcohol pricing policies on health and healtheconomic outcomes in England: an epidemiological model.Lancet. 2010;375:1355–64.10. Jarrett J, Woodcock J, Griffiths UK, Chalabi Z, Edwards P,Roberts I, Haines A. Effect of increasing active travel in urbanEngland and wales on costs to the National Health Service.Lancet. 2012;379:2198–205.11. Kahlmeier S, Racioppi F, Cavill N, Rutter H, Oja P. ‘‘Health inall policies’’ in practice: guidance and tools to quantifying thehealth effects of cycling and walking. J Phys Act Health.2010;7(Suppl 1):S120–5.12. Cobiac LJ, Magnus A, Lim S, Barendregt JJ, Carter R, Vos T.Which interventions offer best value for money in primary pre-vention of cardiovascular disease? PLoS ONE. 2012;7:e41842.13. McPherson K, Marsh T, Brown M. Foresight. Tackling obesities:future choices—modelling future trends in obesity and the impacton health. 2nd ed. London: Government Office for Science; 2007.14. Marmot MG, Altman DG, Cameron DA, Dewar JA, ThompsonSG, Wilcox M. The benefits and harms of breast cancer screen-ing: an independent review. Br J Cancer. 2013;108:2205–40.15. Pharoah PDP, Sewell B, Fitzsimmons D, Bennett HS, PashayanN. Cost effectiveness of the NHS breast screening programme:life table model. BMJ. 2013;346:f2618.16. Woodcock J, Tainio M, Cheshire J, O’Brien O, Goodman A.Health effects of the London bicycle sharing system: healthimpact modelling study. BMJ. 2014;348:g425.17. Kopec JA, Fine`s P, Manuel DG, Buckeridge DL, Flanagan WM,Oderkirk J, Abrahamowicz M, Harper S, Sharif B, Okhmatovs-kaia A, Sayre EC, Rahman MM, Wolfson MC. Validation ofpopulation-based disease simulation models: a review of conceptsand methods. BMC Public Health. 2010;10:710.18. Weinstein MC, O’Brien B, Hornberger J, Jackson J, JohannessonM, McCabe C, Luce BR. Principles of good practice for decisionanalytic modeling in health-care evaluation: report of the ISPORtask force on good research practices-modeling studies. ValueHealth. 2003;6:9–17.19. Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Rie-msma R, Woolacoot N, Glanville J. Review of guidelines forgood practice in decision-analytic modelling in health technologyassessment. Health Technol Assess. 2004, 8: iii–iv, ix–xi: 1–158.20. Husereau D, Drummond M, Petrou S, Carswell C, Moher D,Greenberg D, Augustovski F, Briggs AH, Mauskopf J, Loder E.Consolidated health economic evaluation reporting standards(CHEERS) statement. BMJ. 2013;346:f1049.21. Caro JJ, Briggs AH, Siebert U, Kuntz KM. Modeling goodresearch practices—overview: a report of the ISPOR-SMDMmodeling good research practices task force–1. Value Health.2012;15:796–803.870 L. Webber et al.123

European Journal of Epidemiology

The Brighton declaration: the value of non-communicable disease modelling in population health sciences

Springer - Publisher Connector

COMMENTARY
The Brighton declaration: the value of non-communicable disease
modelling in population health sciences
Laura Webber • Oliver T. Mytton • Adam D. M. Briggs •
James Woodcock • Peter Scarborough • Klim McPherson •
Simon Capewell
Received: 8 August 2014 / Accepted: 24 November 2014 / Published online: 13 December 2014
 The Author(s) 2014. This article is published with open access at Springerlink.com
The Brighton declaration arose out of a one day workshop
held in Brighton in September 2013 as part of the Society
for Social Medicine annual conference. The workshop
convened UK based non-communicable disease modellers
to discuss the challenges and opportunities for non-com-
municable disease modelling in the UK. The declaration
describes the value and importance of non-communicable
disease modelling, both for research and for informing
health policy. The declaration also describes challenges
and issues for non-communicable disease modelling. The
declaration has been endorsed by many non-communicable
disease modellers in the UK.
Background
Over 60 % of global deaths are attributable to non-com-
municable diseases (NCDs) [1]. With most developing
countries experiencing a shift in disease burden away from
communicable disease to NCDs, this contribution is
expected to grow. To respond effectively, it will be crucial
to understand these epidemics better: both how the burden
of disease is anticipated to change over time based on
current trends (e.g. demographic change, changes in risk
factor prevalence, or changes in diseases incidence), and
the effects that different interventions might have. This is
important for planning health services and for developing
an evidence base to inform public health policies aimed at
reducing the burden of disease. While modelling is often
not well understood and is frequently criticised, we argue
that modelling NCDs has an important role to play in
informing how society responds their increasing burden on
population health.
What is non-communicable disease modelling?
Models simplify reality—a good model represents those
parts of reality that matter and leaves out those parts that do
not. Modelling is the development and use of these models
to understand how different inputs (e.g. behaviours) affect
different outcomes (e.g. disease). NCD modelling is a
method for estimating the extent to which changes in one
or more risk factor (e.g. smoking and diet) affects disease
and health. It has two broad uses, health impact modelling
(understanding the effect of prevention, screening or
treatment interventions on health) [2] and forecasting
(estimating disease trends based on demographic change or
predicted changes in risk factors, including making
L. Webber
UK Health Forum, Fleetbank House, 2-6 Salisbury Square,
London EC4Y 8JX, UK
O. T. Mytton  J. Woodcock
UKCRC Centre for Diet and Activity Research, MRC
Epidemiology Unit, Institute of Metabolic Science, University
of Cambridge School of Medicine, Cambridge CB2 0QQ, UK
A. D. M. Briggs (&)  P. Scarborough
British Heart Foundation Centre on Population Approaches for
Non-Communicable Disease Prevention, Nuffield Department
of Population Health, University of Oxford, New Richards
Building, Old Road Campus, Headington, Oxford OX3 7LF, UK
e-mail: adam.briggs@dph.ox.ac.uk
K. McPherson
Nuffield Department of Obstetrics and Gynaecology, University
of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
S. Capewell
Department of Public Health and Policy, Institute of Psychology,
Health and Society, University of Liverpool, Waterhouse
Building, Block B, 2nd Floor, Liverpool L69 3BX, UK
123
Eur J Epidemiol (2014) 29:867–870
DOI 10.1007/s10654-014-9978-0
allowance for competing risk). Such modelling is some-
times extended to consider the costs to society or health
systems that arise from disease, and the potential savings
that may accrue from interventions. NCD models use a
wide range of research methods and can answer a range of
questions [3–6]. NCD modelling in the UK is performed by
(and used by) a variety of institutions, academic research
units, non-government organisations, as well as public
bodies. Although such modelling is not new, increased
availability of datasets, improved computational power and
use of new methods is opening up new possibilities [7].
The need and use for non-communicable disease
modelling
Modelling is already widely used to inform decision
making in both the public and private sector. Within public
health it has long been recognised that the greatest influ-
ences on population health lie outside the health sector [8].
NCD modelling has, for example, been influential in the
debate on minimum unit pricing [9] and is being used to
integrate health into transport planning [10, 11]. NCD
modelling may also be used to inform decisions on prior-
ities for investment, for example modelling cost-effec-
tiveness of different interventions for primary prevention
of cardiovascular disease [12] or different approaches to
screening [13, 14], as well as predict future trends in dis-
ease burden [13], which are important in planning health
service provision.
Modelling enables us to estimate the long term and
population-wide health effects of interventions. These
effects are rarely observed in a single research study
because of a variety of other influences on the observed
outcome over time, and true randomised experiments are
often not practical. While modelling studies can be cheaper
and quicker than real world studies, they should be seen as
a complement to such studies, and may help us get the best
value out of such studies. It is often most appropriate to
model when we already have a good understanding of the
system being modelled, both its structures and its param-
eters. Often in this way modelling studies are being used to
integrate evidence from different studies and different
domains. Modelling can also inform empirical studies, for
example to identify assumptions or key parts of evidence
that may have a significant impact on model outcome
(direct empirical study of these assumptions may give
greater confidence in the model and its predictions).
Modelling may also be used to estimate likely effect sizes
to inform the size of evaluative studies.
For example our understanding of the effectiveness and
cost-effectiveness of the current UK Breast Cancer
Screening programme is informed by modelling [14, 15].
While estimates of the effectiveness of screening could be
derived from past trials, these occurred many years ago,
when incidence was lower, diagnostic technology different,
and treatment options fewer. Our knowledge of the system
and the parameters within the system is relatively good. It
also serves as an example of modelling being comple-
mentary to, and used alongside, ‘‘empirical’’ evidence.
While such modelling does not preclude the need for a
trial, it can give a more rapid indication of where the costs
and benefits lie. Indeed, estimates could be updated on a
periodic basis as disease trends change and technology
evolves. Further, modelling could demonstrate that the
existing evidence is sufficient, such that further empirical
studies may not be necessary.
Criticisms of non-communicable disease modelling
Modelling work is often criticised. This may relate to
criticisms of the underlying assumptions within the model:
either the particular values (parameters) used or the mod-
el’s structure (the assumed underlying relationships or
causal model). Some of these assumptions may be implicit
within the model, and there is a risk that they may not be
readily identified as assumptions by those building (and
using) the model. This can be particularly problematic
when there is either no or limited evidence to support these
assumptions. Sometimes the criticism relates to the very
idea of modelling, that modelling is by definition, uncer-
tain, resulting in the view that the model will lead to
unrealistic estimates. These fears may be compounded if a
clear description of the model, ideally one that is accessible
to a wide audience, is not given. Developing such a clear
description is not always straightforward and requires the
ability to articulate many of the key assumptions sur-
rounding model structure (including implicit assumptions)
to non-specialist audiences.
While criticisms of the underlying assumptions may
sometimes be justified, sensitivity analyses can be used to
identify which explicit assumptions are critical to the
model (in terms of having a significant impact on the model
outcome), and uncertainty analyses can quantify uncer-
tainty surrounding the model’s parameters and structure;
both are important for calibration and validation of the
model. Within the bounds of expected values some
underlying assumptions and parameters may have little
effect on the model results even though the uncertainty
surrounding those particular parameters may be high [16].
While there may be times that uncertainty is so great that
the results cannot be used in decision making, we believe a
best estimate can often be better than no estimate. Failure
to model health outcomes may mean failure to consider
health, alongside many of the other outcomes that may be
868 L. Webber et al.
123
modelled (and considered) for prospective policies, for
example employment, revenues, and carbon emissions, and
ill health is expensive especially in the future. It might also
mean that decision making is informed by decision makers’
best guesses as to what the costs and benefits are—effec-
tively ‘‘implicit modelling’’ which is unstructured, non-
transparent, and not reproducible.
Challenges for modelling
A number of challenges have been identified:
• perception that conclusions are largely assumption
based and that underlying (and sometimes implicit)
assumptions are being hidden or chosen in such a way
to support a particular outcome;
• conceptualising and communicating issues of uncer-
tainty, particularly to policy makers;
• communicating to both technical and non-technical
audiences how the modelling was undertaken and the
nature of the underlying assumptions;
• presenting study results in an accessible way to reach
an audience that may not be familiar with technical
details and issues of modelling;
• journals having difficulty identifying appropriate han-
dling editors and referees with correct technical expertise;
• journal editors lacking familiarity with modelling
approaches and being unsure how to appraise quality;
• findings being reported too strongly, particularly where
unacknowledged or implicit assumptions may under-
mine the validity of the model.
Need for standardised reporting guidelines
We recognise that some of these issues might be resolved
by improved and standardised reporting of modelling
studies. Transparent acknowledgements of assumptions
and limitations (including using non-technical language)
enables a more thorough and robust peer-review and allows
readers to assess the model’s quality [17–19]. Unfortu-
nately, to our knowledge there is no reporting guideline
(e.g. Enhancing the QUAlity and Transparency Of health
Research (EQUATOR) network guidelines: www.equator-
network.org) for NCD modelling nor are we aware of any
equivalent reporting standards.
Health economics, which often draws on modelling, has
reporting guidelines and some of these cover modelling
[e.g. Consolidated Health Economic Evaluation Reporting
Standards (CHEERS)] [20]. There are also separate stan-
dards for the reporting of health economic modelling [21].
While these guidelines may partially address the needs of
NCD modelling, we do not think they are sufficient.
Besides having a large focus on economic outcomes, the
economic modelling literature to which these standards
apply is predominantly concerned with the cost-effective-
ness of treatment of diseases on health and outcomes (for
which the evidence is typically drawn from randomised
controlled trials), rather than the effect of risk factor
modification on health (for which the evidence may come
from trials but tends to be drawn from observational
studies). Moreover the health economic guidelines tend not
to be used when reporting NCD modelling, particularly in
the absence of an economic component to the modelling, as
they are perceived as inappropriate. Consequently, we
believe there is a need for specific non-communicable
disease reporting guidelines within our field.
Modelling has an important role in public health and
health policy. This may grow in the future, open access to
data and increased computing power are facilitating the
development of more sophisticated models. We recognise
there is a need to improve the reporting of our work in
terms of transparency and standardised reporting of both
models and model results. It is also important that we
continue to work in a collaborative fashion to develop the
science to support modelling and capacity within the UK to
undertake such work.
Acknowledgments The following academics collaborated with the
authors to finalise this article are and acknowledged as co-signatories
on its content. The authors are extremely grateful for their input.
University of Cambridge: Ali Abbas, Marko Tanio; University of
Edinburgh: Dr Susannah McLean; UK Health Forum: Martin Brown,
Tim Marsh, Marco Mesa-Frias, Lise Retat; Imperial College London:
Anthony Laverty; The London School of Hygiene and Tropical
Medicine: Zaid Chalabi; University College London: Luz Sanchez
Romero; University of Oxford: Anja Mizdrak, Mike Rayner, Marco
Springmann; University of Sheffield: Alan Brennan, James Chilcott,
John Holmes, Petra Meier, John Mooney; University of Southampton:
Grant Aitken. ADMB and OTM are funded by the Wellcome Trust.
PS is funded by the British Heart Foundation. JW is funded by an
MRC Population Health Scientist Fellowship.
Conflict of interest The authors declare that there is no conflict of
interest that could be perceived as prejudicing the impartiality of the
research reported.
Open Access This article is distributed under the terms of the
Creative Commons Attribution License which permits any use, dis-
tribution, and reproduction in any medium, provided the original
author(s) and the source are credited.
References
1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans
V, Abraham J, Adair T, Aggarwal R, Ahn SY, Alvarado M,
Anderson HR, Anderson LM, Andrews KG, Atkinson C, Baddour
LM, Barker-Collo S, Bartels DH, Bell ML, Benjamin EJ, Bennett
D, Bhalla K, Bikbov B, Bin Abdulhak A, Birbeck G, Blyth F,
The Brighton declaration 869
123
Bolliger I, Boufous S, Bucello C, Burch M, et al. Global and
regional mortality from 235 causes of death for 20 age groups in,
1990 and 2010: a systematic analysis for the global burden of
disease study 2010. Lancet. 2012;380:2095–128.
2. Stout NK, Knudsen AB, Kong CY, McMahon PM, Gazelle GS.
Calibration methods used in cancer simulation models and sug-
gested reporting guidelines. Pharmacoeconomics. 2009;27:533–45.
3. Astolfi R, Lorenzoni L, Oderkirk J. Informing policy makers about
future health spending: a comparative analysis of forecasting
methods in OECD countries. Health Policy. 2012;107:1–10.
4. Barhak J, Isaman DJM, Ye W, Lee D. Chronic disease modeling
and simulation software. J Biomed Inform. 2010;43:791–9.
5. Kopec JA, Sayre EC, Flanagan WM, Fines P, Cibere J, Rahman
MM, Bansback NJ, Anis AH, Jordan JM, Sobolev B, Aghajanian
J, Kang W, Greidanus NV, Garbuz DS, Hawker GA, Badley EM.
Development of a population-based microsimulation model of
osteoarthritis in Canada. Osteoarthr Cartil. 2010;18:303–11.
6. Unal B, Capewell S, Critchley JA. Coronary heart disease policy
models: a systematic review. BMC Public Health. 2006;6:213.
7. Maglio PP, Mabry PL. Agent-based models and systems science
approaches to public health. Am J Prev Med. 2011;40:392–4.
8. World Health Organization. The Ottawa charter for health pro-
motion. Geneva. 1986.
9. Purshouse RC, Meier PS, Brennan A, Taylor KB, Rafia R. Esti-
mated effect of alcohol pricing policies on health and health
economic outcomes in England: an epidemiological model.
Lancet. 2010;375:1355–64.
10. Jarrett J, Woodcock J, Griffiths UK, Chalabi Z, Edwards P,
Roberts I, Haines A. Effect of increasing active travel in urban
England and wales on costs to the National Health Service.
Lancet. 2012;379:2198–205.
11. Kahlmeier S, Racioppi F, Cavill N, Rutter H, Oja P. ‘‘Health in
all policies’’ in practice: guidance and tools to quantifying the
health effects of cycling and walking. J Phys Act Health.
2010;7(Suppl 1):S120–5.
12. Cobiac LJ, Magnus A, Lim S, Barendregt JJ, Carter R, Vos T.
Which interventions offer best value for money in primary pre-
vention of cardiovascular disease? PLoS ONE. 2012;7:e41842.
13. McPherson K, Marsh T, Brown M. Foresight. Tackling obesities:
future choices—modelling future trends in obesity and the impact
on health. 2nd ed. London: Government Office for Science; 2007.
14. Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson
SG, Wilcox M. The benefits and harms of breast cancer screen-
ing: an independent review. Br J Cancer. 2013;108:2205–40.
15. Pharoah PDP, Sewell B, Fitzsimmons D, Bennett HS, Pashayan
N. Cost effectiveness of the NHS breast screening programme:
life table model. BMJ. 2013;346:f2618.
16. Woodcock J, Tainio M, Cheshire J, O’Brien O, Goodman A.
Health effects of the London bicycle sharing system: health
impact modelling study. BMJ. 2014;348:g425.
17. Kopec JA, Fine`s P, Manuel DG, Buckeridge DL, Flanagan WM,
Oderkirk J, Abrahamowicz M, Harper S, Sharif B, Okhmatovs-
kaia A, Sayre EC, Rahman MM, Wolfson MC. Validation of
population-based disease simulation models: a review of concepts
and methods. BMC Public Health. 2010;10:710.
18. Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson
M, McCabe C, Luce BR. Principles of good practice for decision
analytic modeling in health-care evaluation: report of the ISPOR
task force on good research practices-modeling studies. Value
Health. 2003;6:9–17.
19. Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Rie-
msma R, Woolacoot N, Glanville J. Review of guidelines for
good practice in decision-analytic modelling in health technology
assessment. Health Technol Assess. 2004, 8: iii–iv, ix–xi: 1–158.
20. Husereau D, Drummond M, Petrou S, Carswell C, Moher D,
Greenberg D, Augustovski F, Briggs AH, Mauskopf J, Loder E.
Consolidated health economic evaluation reporting standards
(CHEERS) statement. BMJ. 2013;346:f1049.
21. Caro JJ, Briggs AH, Siebert U, Kuntz KM. Modeling good
research practices—overview: a report of the ISPOR-SMDM
modeling good research practices task force–1. Value Health.
2012;15:796–803.
870 L. Webber et al.
123


The Brighton declaration arose out of a one day workshop
held in Brighton in September 2013 as part of the Society
for Social Medicine annual conference. The workshop
convened UK based non-communicable disease modellers
to discuss the challenges and opportunities for non-communicable
disease modelling in the UK. The declaration
describes the value and importance of non-communicable
disease modelling, both for research and for informing
health policy. The declaration also describes challenges
and issues for non-communicable disease modelling. The
declaration has been endorsed by many non-communicable
disease modellers in the UK

Apollo (Cambridge)

COMMENTARYThe Brighton declaration: the value of non-communicable diseasemodelling in population health sciencesLaura Webber • Oliver T. Mytton • Adam D. M. Briggs •James Woodcock • Peter Scarborough • Klim McPherson •Simon CapewellReceived: 8 August 2014 / Accepted: 24 November 2014 / Published online: 13 December 2014 The Author(s) 2014. This article is published with open access at Springerlink.comThe Brighton declaration arose out of a one day workshopheld in Brighton in September 2013 as part of the Societyfor Social Medicine annual conference. The workshopconvened UK based non-communicable disease modellersto discuss the challenges and opportunities for non-com-municable disease modelling in the UK. The declarationdescribes the value and importance of non-communicabledisease modelling, both for research and for informinghealth policy. The declaration also describes challengesand issues for non-communicable disease modelling. Thedeclaration has been endorsed by many non-communicabledisease modellers in the UK.BackgroundOver 60 % of global deaths are attributable to non-com-municable diseases (NCDs) [1]. With most developingcountries experiencing a shift in disease burden away fromcommunicable disease to NCDs, this contribution isexpected to grow. To respond effectively, it will be crucialto understand these epidemics better: both how the burdenof disease is anticipated to change over time based oncurrent trends (e.g. demographic change, changes in riskfactor prevalence, or changes in diseases incidence), andthe effects that different interventions might have. This isimportant for planning health services and for developingan evidence base to inform public health policies aimed atreducing the burden of disease. While modelling is oftennot well understood and is frequently criticised, we arguethat modelling NCDs has an important role to play ininforming how society responds their increasing burden onpopulation health.What is non-communicable disease modelling?Models simplify reality—a good model represents thoseparts of reality that matter and leaves out those parts that donot. Modelling is the development and use of these modelsto understand how different inputs (e.g. behaviours) affectdifferent outcomes (e.g. disease). NCD modelling is amethod for estimating the extent to which changes in oneor more risk factor (e.g. smoking and diet) affects diseaseand health. It has two broad uses, health impact modelling(understanding the effect of prevention, screening ortreatment interventions on health) [2] and forecasting(estimating disease trends based on demographic change orpredicted changes in risk factors, including makingL. WebberUK Health Forum, Fleetbank House, 2-6 Salisbury Square,London EC4Y 8JX, UKO. T. Mytton  J. WoodcockUKCRC Centre for Diet and Activity Research, MRCEpidemiology Unit, Institute of Metabolic Science, Universityof Cambridge School of Medicine, Cambridge CB2 0QQ, UKA. D. M. Briggs (&)  P. ScarboroughBritish Heart Foundation Centre on Population Approaches forNon-Communicable Disease Prevention, Nuffield Departmentof Population Health, University of Oxford, New RichardsBuilding, Old Road Campus, Headington, Oxford OX3 7LF, UKe-mail: adam.briggs@dph.ox.ac.ukK. McPhersonNuffield Department of Obstetrics and Gynaecology, Universityof Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UKS. CapewellDepartment of Public Health and Policy, Institute of Psychology,Health and Society, University of Liverpool, WaterhouseBuilding, Block B, 2nd Floor, Liverpool L69 3BX, UK123Eur J Epidemiol (2014) 29:867–870DOI 10.1007/s10654-014-9978-0allowance for competing risk). Such modelling is some-times extended to consider the costs to society or healthsystems that arise from disease, and the potential savingsthat may accrue from interventions. NCD models use awide range of research methods and can answer a range ofquestions [3–6]. NCD modelling in the UK is performed by(and used by) a variety of institutions, academic researchunits, non-government organisations, as well as publicbodies. Although such modelling is not new, increasedavailability of datasets, improved computational power anduse of new methods is opening up new possibilities [7].The need and use for non-communicable diseasemodellingModelling is already widely used to inform decisionmaking in both the public and private sector. Within publichealth it has long been recognised that the greatest influ-ences on population health lie outside the health sector [8].NCD modelling has, for example, been influential in thedebate on minimum unit pricing [9] and is being used tointegrate health into transport planning [10, 11]. NCDmodelling may also be used to inform decisions on prior-ities for investment, for example modelling cost-effec-tiveness of different interventions for primary preventionof cardiovascular disease [12] or different approaches toscreening [13, 14], as well as predict future trends in dis-ease burden [13], which are important in planning healthservice provision.Modelling enables us to estimate the long term andpopulation-wide health effects of interventions. Theseeffects are rarely observed in a single research studybecause of a variety of other influences on the observedoutcome over time, and true randomised experiments areoften not practical. While modelling studies can be cheaperand quicker than real world studies, they should be seen asa complement to such studies, and may help us get the bestvalue out of such studies. It is often most appropriate tomodel when we already have a good understanding of thesystem being modelled, both its structures and its param-eters. Often in this way modelling studies are being used tointegrate evidence from different studies and differentdomains. Modelling can also inform empirical studies, forexample to identify assumptions or key parts of evidencethat may have a significant impact on model outcome(direct empirical study of these assumptions may givegreater confidence in the model and its predictions).Modelling may also be used to estimate likely effect sizesto inform the size of evaluative studies.For example our understanding of the effectiveness andcost-effectiveness of the current UK Breast CancerScreening programme is informed by modelling [14, 15].While estimates of the effectiveness of screening could bederived from past trials, these occurred many years ago,when incidence was lower, diagnostic technology different,and treatment options fewer. Our knowledge of the systemand the parameters within the system is relatively good. Italso serves as an example of modelling being comple-mentary to, and used alongside, ‘‘empirical’’ evidence.While such modelling does not preclude the need for atrial, it can give a more rapid indication of where the costsand benefits lie. Indeed, estimates could be updated on aperiodic basis as disease trends change and technologyevolves. Further, modelling could demonstrate that theexisting evidence is sufficient, such that further empiricalstudies may not be necessary.Criticisms of non-communicable disease modellingModelling work is often criticised. This may relate tocriticisms of the underlying assumptions within the model:either the particular values (parameters) used or the mod-el’s structure (the assumed underlying relationships orcausal model). Some of these assumptions may be implicitwithin the model, and there is a risk that they may not bereadily identified as assumptions by those building (andusing) the model. This can be particularly problematicwhen there is either no or limited evidence to support theseassumptions. Sometimes the criticism relates to the veryidea of modelling, that modelling is by definition, uncer-tain, resulting in the view that the model will lead tounrealistic estimates. These fears may be compounded if aclear description of the model, ideally one that is accessibleto a wide audience, is not given. Developing such a cleardescription is not always straightforward and requires theability to articulate many of the key assumptions sur-rounding model structure (including implicit assumptions)to non-specialist audiences.While criticisms of the underlying assumptions maysometimes be justified, sensitivity analyses can be used toidentify which explicit assumptions are critical to themodel (in terms of having a significant impact on the modeloutcome), and uncertainty analyses can quantify uncer-tainty surrounding the model’s parameters and structure;both are important for calibration and validation of themodel. Within the bounds of expected values someunderlying assumptions and parameters may have littleeffect on the model results even though the uncertaintysurrounding those particular parameters may be high [16].While there may be times that uncertainty is so great thatthe results cannot be used in decision making, we believe abest estimate can often be better than no estimate. Failureto model health outcomes may mean failure to considerhealth, alongside many of the other outcomes that may be868 L. Webber et al.123modelled (and considered) for prospective policies, forexample employment, revenues, and carbon emissions, andill health is expensive especially in the future. It might alsomean that decision making is informed by decision makers’best guesses as to what the costs and benefits are—effec-tively ‘‘implicit modelling’’ which is unstructured, non-transparent, and not reproducible.Challenges for modellingA number of challenges have been identified:• perception that conclusions are largely assumptionbased and that underlying (and sometimes implicit)assumptions are being hidden or chosen in such a wayto support a particular outcome;• conceptualising and communicating issues of uncer-tainty, particularly to policy makers;• communicating to both technical and non-technicalaudiences how the modelling was undertaken and thenature of the underlying assumptions;• presenting study results in an accessible way to reachan audience that may not be familiar with technicaldetails and issues of modelling;• journals having difficulty identifying appropriate han-dling editors and referees with correct technical expertise;• journal editors lacking familiarity with modellingapproaches and being unsure how to appraise quality;• findings being reported too strongly, particularly whereunacknowledged or implicit assumptions may under-mine the validity of the model.Need for standardised reporting guidelinesWe recognise that some of these issues might be resolvedby improved and standardised reporting of modellingstudies. Transparent acknowledgements of assumptionsand limitations (including using non-technical language)enables a more thorough and robust peer-review and allowsreaders to assess the model’s quality [17–19]. Unfortu-nately, to our knowledge there is no reporting guideline(e.g. Enhancing the QUAlity and Transparency Of healthResearch (EQUATOR) network guidelines: www.equator-network.org) for NCD modelling nor are we aware of anyequivalent reporting standards.Health economics, which often draws on modelling, hasreporting guidelines and some of these cover modelling[e.g. Consolidated Health Economic Evaluation ReportingStandards (CHEERS)] [20]. There are also separate stan-dards for the reporting of health economic modelling [21].While these guidelines may partially address the needs ofNCD modelling, we do not think they are sufficient.Besides having a large focus on economic outcomes, theeconomic modelling literature to which these standardsapply is predominantly concerned with the cost-effective-ness of treatment of diseases on health and outcomes (forwhich the evidence is typically drawn from randomisedcontrolled trials), rather than the effect of risk factormodification on health (for which the evidence may comefrom trials but tends to be drawn from observationalstudies). Moreover the health economic guidelines tend notto be used when reporting NCD modelling, particularly inthe absence of an economic component to the modelling, asthey are perceived as inappropriate. Consequently, webelieve there is a need for specific non-communicabledisease reporting guidelines within our field.Modelling has an important role in public health andhealth policy. This may grow in the future, open access todata and increased computing power are facilitating thedevelopment of more sophisticated models. We recognisethere is a need to improve the reporting of our work interms of transparency and standardised reporting of bothmodels and model results. It is also important that wecontinue to work in a collaborative fashion to develop thescience to support modelling and capacity within the UK toundertake such work.Acknowledgments The following academics collaborated with theauthors to finalise this article are and acknowledged as co-signatorieson its content. The authors are extremely grateful for their input.University of Cambridge: Ali Abbas, Marko Tanio; University ofEdinburgh: Dr Susannah McLean; UK Health Forum: Martin Brown,Tim Marsh, Marco Mesa-Frias, Lise Retat; Imperial College London:Anthony Laverty; The London School of Hygiene and TropicalMedicine: Zaid Chalabi; University College London: Luz SanchezRomero; University of Oxford: Anja Mizdrak, Mike Rayner, MarcoSpringmann; University of Sheffield: Alan Brennan, James Chilcott,John Holmes, Petra Meier, John Mooney; University of Southampton:Grant Aitken. ADMB and OTM are funded by the Wellcome Trust.PS is funded by the British Heart Foundation. JW is funded by anMRC Population Health Scientist Fellowship.Conflict of interest The authors declare that there is no conflict ofinterest that could be perceived as prejudicing the impartiality of theresearch reported.Open Access This article is distributed under the terms of theCreative Commons Attribution License which permits any use, dis-tribution, and reproduction in any medium, provided the originalauthor(s) and the source are credited.References1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, AboyansV, Abraham J, Adair T, Aggarwal R, Ahn SY, Alvarado M,Anderson HR, Anderson LM, Andrews KG, Atkinson C, BaddourLM, Barker-Collo S, Bartels DH, Bell ML, Benjamin EJ, BennettD, Bhalla K, Bikbov B, Bin Abdulhak A, Birbeck G, Blyth F,The Brighton declaration 869123Bolliger I, Boufous S, Bucello C, Burch M, et al. Global andregional mortality from 235 causes of death for 20 age groups in,1990 and 2010: a systematic analysis for the global burden ofdisease study 2010. Lancet. 2012;380:2095–128.2. Stout NK, Knudsen AB, Kong CY, McMahon PM, Gazelle GS.Calibration methods used in cancer simulation models and sug-gested reporting guidelines. Pharmacoeconomics. 2009;27:533–45.3. Astolfi R, Lorenzoni L, Oderkirk J. Informing policy makers aboutfuture health spending: a comparative analysis of forecastingmethods in OECD countries. Health Policy. 2012;107:1–10.4. Barhak J, Isaman DJM, Ye W, Lee D. Chronic disease modelingand simulation software. J Biomed Inform. 2010;43:791–9.5. Kopec JA, Sayre EC, Flanagan WM, Fines P, Cibere J, RahmanMM, Bansback NJ, Anis AH, Jordan JM, Sobolev B, AghajanianJ, Kang W, Greidanus NV, Garbuz DS, Hawker GA, Badley EM.Development of a population-based microsimulation model ofosteoarthritis in Canada. Osteoarthr Cartil. 2010;18:303–11.6. Unal B, Capewell S, Critchley JA. Coronary heart disease policymodels: a systematic review. BMC Public Health. 2006;6:213.7. Maglio PP, Mabry PL. Agent-based models and systems scienceapproaches to public health. Am J Prev Med. 2011;40:392–4.8. World Health Organization. The Ottawa charter for health pro-motion. Geneva. 1986.9. Purshouse RC, Meier PS, Brennan A, Taylor KB, Rafia R. Esti-mated effect of alcohol pricing policies on health and healtheconomic outcomes in England: an epidemiological model.Lancet. 2010;375:1355–64.10. Jarrett J, Woodcock J, Griffiths UK, Chalabi Z, Edwards P,Roberts I, Haines A. Effect of increasing active travel in urbanEngland and wales on costs to the National Health Service.Lancet. 2012;379:2198–205.11. Kahlmeier S, Racioppi F, Cavill N, Rutter H, Oja P. ‘‘Health inall policies’’ in practice: guidance and tools to quantifying thehealth effects of cycling and walking. J Phys Act Health.2010;7(Suppl 1):S120–5.12. Cobiac LJ, Magnus A, Lim S, Barendregt JJ, Carter R, Vos T.Which interventions offer best value for money in primary pre-vention of cardiovascular disease? PLoS ONE. 2012;7:e41842.13. McPherson K, Marsh T, Brown M. Foresight. Tackling obesities:future choices—modelling future trends in obesity and the impacton health. 2nd ed. London: Government Office for Science; 2007.14. Marmot MG, Altman DG, Cameron DA, Dewar JA, ThompsonSG, Wilcox M. The benefits and harms of breast cancer screen-ing: an independent review. Br J Cancer. 2013;108:2205–40.15. Pharoah PDP, Sewell B, Fitzsimmons D, Bennett HS, PashayanN. Cost effectiveness of the NHS breast screening programme:life table model. BMJ. 2013;346:f2618.16. Woodcock J, Tainio M, Cheshire J, O’Brien O, Goodman A.Health effects of the London bicycle sharing system: healthimpact modelling study. BMJ. 2014;348:g425.17. Kopec JA, Finès P, Manuel DG, Buckeridge DL, Flanagan WM,Oderkirk J, Abrahamowicz M, Harper S, Sharif B, Okhmatovs-kaia A, Sayre EC, Rahman MM, Wolfson MC. Validation ofpopulation-based disease simulation models: a review of conceptsand methods. BMC Public Health. 2010;10:710.18. Weinstein MC, O’Brien B, Hornberger J, Jackson J, JohannessonM, McCabe C, Luce BR. Principles of good practice for decisionanalytic modeling in health-care evaluation: report of the ISPORtask force on good research practices-modeling studies. ValueHealth. 2003;6:9–17.19. Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Rie-msma R, Woolacoot N, Glanville J. Review of guidelines forgood practice in decision-analytic modelling in health technologyassessment. Health Technol Assess. 2004, 8: iii–iv, ix–xi: 1–158.20. Husereau D, Drummond M, Petrou S, Carswell C, Moher D,Greenberg D, Augustovski F, Briggs AH, Mauskopf J, Loder E.Consolidated health economic evaluation reporting standards(CHEERS) statement. BMJ. 2013;346:f1049.21. Caro JJ, Briggs AH, Siebert U, Kuntz KM. Modeling goodresearch practices—overview: a report of the ISPOR-SMDMmodeling good research practices task force–1. Value Health.2012;15:796–803.870 L. Webber et al.123

English

The Brighton declaration: the value of non-communicable disease modelling in population health sciences.

Webber, Laura

Mytton, Oliver T.

Briggs, Adam D. M.

Woodcock, James

Scarborough, Peter

McPherson, Klim

Capewell, Simon

Warwick Research Archives Portal Repository

The Brighton declaration : the value of non-communicable disease modelling in population health sciences

Webber, L

Mytton, OT

Briggs, Adam

Woodcock, J

McPherson, Christopher

Capewell, S

Oxford University Research Archive

http://dx.doi.org/10.1007/s10654-014-9978-0

The Brighton declaration: the value of non-communicable disease modelling in population health sciences

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