Top

BMC Health Services Research

Published in:

Open Access 01-12-2019 | Public Health | Research article

PRIMEtime CE: a multistate life table model for estimating the cost-effectiveness of interventions affecting diet and physical activity

Authors: Adam D. M. Briggs, Linda J. Cobiac, Jane Wolstenholme, Peter Scarborough

Published in: BMC Health Services Research | Issue 1/2019

Abstract

Background

Non-communicable diseases are the leading cause of death in England, and poor diet and physical inactivity are two of the principle behavioural risk factors. In the context of increasingly constrained financial resources, decision makers in England need to be able to compare the potential costs and health outcomes of different public health policies aimed at improving these risk factors in order to know where to invest so that they can maximise population health. This paper describes PRIMEtime CE, a multistate life table cost-effectiveness model that can directly compare interventions affecting multiple disease outcomes.

Methods

The multistate life table model, PRIMEtime Cost Effectiveness (PRIMEtime CE), is developed from the Preventable Risk Integrated ModEl (PRIME) and the PRIMEtime model. PRIMEtime CE uses routinely available data to estimate how changing diet and physical activity in England affects morbidity and mortality from heart disease, stroke, diabetes, liver disease, and cancers either directly or via raised blood pressure, cholesterol, and body weight.

Results

Model outcomes are change in quality adjusted life years, and change in English National Health Service and social care costs.

Conclusion

This paper describes PRIMEtime CE and highlights its main strengths and limitations. The model can be used to compare any number of public policies affecting diet and physical activity, allowing decision makers to understand how they can maximise population health with limited financial resources.

Available only for authorised users

Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. 2019. https://vizhub.healthdata.org/gbd-compare/. Accessed 27 Feb 2019.

World Health Organization Regional Office for Europe. The case for investing in public health. Copenhagen; 2014. http://www.euro.who.int/__data/assets/pdf_file/0009/278073/Case-Investing-Public-Health.pdf

Owen L, Morgan A, Fischer A, Ellis S, Hoy A, Kelly MP. The cost-effectiveness of public health interventions. J Public Health (Oxf). 2012;34:37–45. https://doi.org/10.1093/pubmed/fdr075.CrossRef

World Health Organization. Interventions on diet and physical activity: what works: summary report. Geneva; 2009. http://www.who.int/dietphysicalactivity/summary-report-09.pdf

NHS England. The NHS Long Term Plan. London: NHS England; 2019.

Department of Health and Social Care. Prevention is better than cure: our vision to health you live well for longer. London: Department of Health and Social Care; 2018.

UK Government. Health and Social Care Act 2012 c.7. 2012. http://www.legislation.gov.uk/ukpga/2012/7/contents/enacted.

Local Government Association. Future funding outlook for councils 2019/20. Interim 2015 update. London; 2015. https://www.local.gov.uk/sites/default/files/documents/future-funding-outlook-co-18b.pdf

House of Commons Health Committee. Public health post-2013. London; 2016. https://www.publications.parliament.uk/pa/cm201617/cmselect/cmhealth/140/14002.htm?utm_source=140&utm_medium=fullbullet&utm_campaign=modulereports

10.

van Gils PF, Tariq L, Verschuuren M, van den Berg M. Cost-effectiveness research on preventive interventions: a survey of the publications in 2008. Eur J Pub Health. 2011;21:260–4.CrossRef

11.

Masters R, Anwar E, Collins B, Cookson R, Capewell S. Return on investment of public health interventions: a systematic review. J Epidemiol Community Health. 2017;71:827–34. https://doi.org/10.1136/jech-2016-208141.CrossRefPubMed

12.

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. https://doi.org/10.1016/j.jval.2012.06.012.CrossRefPubMed

13.

National Institute for Health and Care Excellence. Developing NICE guidelines: the manual. London; 2014. https://www.nice.org.uk/media/default/about/what-we-do/our-programmes/developing-nice-guidelines-the-manual.pdf

14.

Marsh K, Phillips CJ, Fordham R, Bertranou E, Hale J. Estimating cost-effectiveness in public health: a summary of modelling and valuation methods. Heal Econ Rev. 2012;2:17.CrossRef

15.

Weatherly H, Drummond M, Claxton K, Cookson R, Ferguson B, Godfrey C, et al. Methods for assessing the cost-effectiveness of public health interventions: key challenges and recommendations. Health Policy. 2009;93:85–92. https://doi.org/10.1016/j.healthpol.2009.07.012.CrossRefPubMed

16.

Walker S, Griffin S, Asaria M, Tsuchiya A, Sculpher M. Striving for a societal perspective: a framework for economic evaluations when costs and effects fall on multiple sectors and decision makers. Appl Health Econ Health Policy. 2019. https://doi.org/10.1007/s40258-019-00481-8.

17.

Drummond M, Weatherly H, Claxton K, Cookson R, Ferguson B, Godfrey C, et al. Assessing the challenges of applying standard methods of economic evaluation to public health interventions. York; 2007. http://phrc.lshtm.ac.uk/papers/PHRC_D1-05_Final_Report.pdf

18.

Edwards RT, Charles JM, Lloyd-Williams H. Public health economics: a systematic review of guidance for the economic evaluation of public health interventions and discussion of key methodological issues. BMC Public Health. 2013;13:1001. https://doi.org/10.1186/1471-2458-13-1001.CrossRefPubMedPubMedCentral

19.

Payne K, McAllister M, Davies LM. Valuing the economic benefits of complex interventions: when maximising health is not sufficient. Health Econ. 2013;22:258–71. https://doi.org/10.1002/hec.2795.CrossRefPubMed

20.

Kelly MP, McDaid D, Ludbrook A, Powell J. Economic appraisal of public health interventions. London; 2005. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.3820&rep=rep1&type=pdf

21.

Shiell A, Hawe P, Gold L. Complex interventions or complex systems? Implications for health economic evaluation. BMJ. 2008;336:1281–3. https://doi.org/10.1136/bmj.39569.510521.AD.CrossRefPubMedPubMedCentral

22.

Cooper K, Brailsford SC, Davies R. Choice of modelling technique for evaluating health care interventions. J Oper Res Soc. 2007;58:168–76. https://doi.org/10.1057/palgrave.jors.2602230.CrossRef

23.

Squires H, Chilcott J, Akehurst R, Burr J, Kelly MP. A systematic literature review of the key challenges for developing the structure of public health economic models. Int J Public Health. 2016;61:289–98. https://doi.org/10.1007/s00038-015-0775-7.CrossRefPubMed

24.

Briggs ADM, Wolstenholme J, Blakely T, Scarborough P. Choosing an epidemiological model structure for the economic evaluation of non-communicable disease public health interventions. Popul Health Metrics. 2016;14:17. https://doi.org/10.1186/s12963-016-0085-1.CrossRef

25.

Kaltenthaler E, Tappenden P, Paisley S, Squires H. NICE DSU technical support document 13: Idenfitfying and reviewing evidence to inform the conceptualisation and population of cost-effectiveness models. Sheffield: National Institute for Health and Care Excellence (NICE); 2011.

26.

Kelly M, Morgan A, Ellis S, Younger T, Huntley J, Swann C. Evidence based public health: a review of the experience of the National Institute of health and clinical excellence (NICE) of developing public health guidance in England. Soc Sci Med. 2010;71:1056–62. https://doi.org/10.1016/j.socscimed.2010.06.032.CrossRefPubMed

27.

Palmer AJ, Clarke P, Gray A, Leal J, Lloyd A, Grant D, et al. Computer modeling of diabetes and its complications: a report on the fifth Mount Hood challenge meeting. Value Health. 2013;16:670–85. https://doi.org/10.1016/j.jval.2013.01.002.CrossRefPubMed

28.

Hoogendoorn M, Feenstra TL, Asukai Y, Briggs AH, Hansen RN, Leidl R, et al. External validation of health economic decision models for chronic obstructive pulmonary disease (COPD): report of the third COPD modeling meeting. Value Health. 2017;20:397–403. https://doi.org/10.1016/j.jval.2016.10.016.CrossRefPubMed

29.

Mandelblatt JS, Stout NK, Schechter CB, van den Broek JJ, Miglioretti DL, Krapcho M, et al. Collaborative modeling of the benefits and harms associated with different U.S. breast Cancer screening strategies. Ann Intern Med. 2016;164:215–25. https://doi.org/10.7326/M15-1536.CrossRefPubMedPubMedCentral

30.

Scarborough P, Harrington RA, Mizdrak A, Zhou LM, Doherty A, Scarborough P, et al. The preventable Risk integrated ModEl and its use to estimate the health impact of public health policy scenarios. Scientifica (Cairo). 2014;2014:748750. https://doi.org/10.1155/2014/748750.CrossRef

31.

Cobiac LJ, Scarborough P, Kaur A, Rayner M. The Eatwell guide: modelling the health implications of incorporating new sugar and fibre guidelines. PLoS One. 2016;11:e0167859. https://doi.org/10.1371/journal.pone.0167859.CrossRefPubMedPubMedCentral

32.

Cobiac LJ, Vos T, Veerman JL. Cost-effectiveness of interventions to promote fruit and vegetable consumption. PLoS One. 2010;5:e14148. https://doi.org/10.1371/journal.pone.0014148.CrossRefPubMedPubMedCentral

33.

Cobiac LJ, Vos T, Barendregt JJ. Cost-effectiveness of interventions to promote physical activity: a modelling study. PLoS Med. 2009;6:e1000110. https://doi.org/10.1371/journal.pmed.1000110.CrossRefPubMedPubMedCentral

34.

Blakely T, Cobiac LJ, Cleghorn CL, Pearson AL, van der Deen FS, Kvizhinadze G, et al. Health, health inequality, and cost impacts of annual increases in tobacco tax: multistate life table modeling in New Zealand. PLoS Med. 2015;12:e1001856. https://doi.org/10.1371/journal.pmed.1001856.CrossRefPubMedPubMedCentral

35.

Barendregt JJ. EpiGearXL. 2016. http://www.epigear.com/Products/EpigearXL/epigearxl.html.

36.

Barendregt JJ. Ersatz. 2016. http://www.epigear.com/index_files/ersatz.html.

37.

Microsoft. Microsoft Excel. 2013.

38.

Smolina K, Wright FL, Rayner M, Goldacre MJ. Incidence and 30-day case fatality for acute myocardial infarction in England in 2010: national-linked database study. Eur J Pub Health. 2012;22:848–53. https://doi.org/10.1093/eurpub/ckr196.CrossRef

39.

Rothwell PM, Coull AJ, Silver LE, Fairhead JF, Giles MF, Lovelock CE, et al. Population-based study of event-rate, incidence, case fatality, and mortality for all acute vascular events in all arterial territories (Oxford vascular study). Lancet. 2005;366:1773–83. https://doi.org/10.1016/S0140-6736(05)67702-1.CrossRefPubMed

40.

Office for National Statistics. Mortality in England and Wales. London; 2015. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathregistrationssummarytablesenglandandwalesreferencetables

41.

World Health Organization. DisMod II. 2014. http://www.epigear.com/index_files/dismod_ii.html.

42.

Lee S, Shafe ACE, Cowie MR. UK stroke incidence, mortality and cardiovascular risk management 1999-2008: time-trend analysis from the general practice research database. BMJ Open. 2011;1:e000269. https://doi.org/10.1136/bmjopen-2011-000269.CrossRefPubMedPubMedCentral

43.

Holden SH, Barnett AH, Peters JR, Jenkins-Jones S, Poole CD, Morgan CL, et al. The incidence of type 2 diabetes in the United Kingdom from 1991 to 2010. Diabetes Obes Metab. 2013;15:844–52. https://doi.org/10.1111/dom.12123.CrossRefPubMed

44.

Health and Social Care Information Centre. National Diabetes Audit 2011–12. London; 2013. https://webarchive.nationalarchives.gov.uk/20180307201242/http://digital.nhs.uk/catalogue/PUB12421

45.

Ratib S, West J, Crooks CJ, Fleming KM. Diagnosis of liver cirrhosis in England, a cohort study, 1998-2009: a comparison with cancer. Am J Gastroenterol. 2014;109:190–8. https://doi.org/10.1038/ajg.2013.405.CrossRefPubMed

46.

Office for National Statistics. Cancer registration statistics, England: 2012. London; 2014. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/cancerregistrationstatisticsengland/2014-06-19

47.

Salomon JA, Murray CJL. The epidemiologic transition revisited: compositional models for causes of death by age and sex. Popul Dev Rev. 2002;28:205–28. https://doi.org/10.1111/j.1728-4457.2002.00205.x.CrossRef

48.

Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3:e442. https://doi.org/10.1371/journal.pmed.0030442.CrossRefPubMedPubMedCentral

49.

NatCen Social Research, MRC Human Nutrition Research, University College London Medical School. National Diet and nutrition survey years 1-4, 2008/09-2011/12. [data collection]. 7th ed; 2015. https://doi.org/10.5255/UKDA-SN-6533-6.CrossRef

50.

Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380:2224–60.CrossRefPubMedPubMedCentral

51.

Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr. 2006;136:2588–93.CrossRefPubMed

52.

Dauchet L, Amouyel P, Dallongeville J. Fruit and vegetable consumption and risk of stroke: a meta-analysis of cohort studies. Neurology. 2005;65:1193–7.CrossRefPubMed

53.

Vieira AR, Abar L, Vingeliene S, Chan DSM, Aune D, Navarro-Rosenblatt D, et al. Fruits, vegetables and lung cancer risk: a systematic review and meta-analysis. Ann Oncol. 2016;27:81–96. https://doi.org/10.1093/annonc/mdv381.CrossRefPubMed

54.

Threapleton DE, Greenwood DC, Evans CEL, Cleghorn CL, Nykjaer C, Woodhead C, et al. Dietary fibre intake and risk of cardiovascular disease: systematic review and meta-analysis. BMJ. 2013;347:f6879. https://doi.org/10.1136/bmj.f6879.CrossRefPubMedPubMedCentral

55.

Aune D, Chan DSM, Greenwood DC, Vieira AR, Rosenblatt DAN, Vieira R, et al. Dietary fiber and breast cancer risk: a systematic review and meta-analysis of prospective studies. Ann Oncol. 2012;23:1394–402. https://doi.org/10.1093/annonc/mdr589.CrossRefPubMed

56.

Aune D, Chan DSM, Lau R, Vieira R, Greenwood DC, Kampman E, et al. Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose-response meta-analysis of prospective studies. Br Med J. 2011;343:d6617.CrossRef

57.

Zhang Z, Xu G, Ma M, Yang J, Liu X. Dietary fiber intake reduces risk for gastric cancer: a meta-analysis. Gastroenterology. 2013;145:113–120.e3. https://doi.org/10.1053/j.gastro.2013.04.001.CrossRefPubMed

58.

Aune D, Chan DSM, Vieira AR, Navarro Rosenblatt DA, Vieira R, Greenwood DC, et al. Red and processed meat intake and risk of colorectal adenomas: a systematic review and meta-analysis of epidemiological studies. Cancer Causes Control. 2013;24:611–27. https://doi.org/10.1007/s10552-012-0139-z.CrossRefPubMed

59.

Song P, Lu M, Yin Q, Wu L, Zhang D, Fu B, et al. Red meat consumption and stomach cancer risk: a meta-analysis. J Cancer Res Clin Oncol. 2014;140:979–92. https://doi.org/10.1007/s00432-014-1637-z.CrossRefPubMed

60.

Aune D, Ursin G, Veierød MB. Meat consumption and the risk of type 2 diabetes: a systematic review and meta-analysis of cohort studies. Diabetologia. 2009;52:2277–87. https://doi.org/10.1007/s00125-009-1481-x.CrossRefPubMed

61.

Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJL, et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009;6:e1000058. https://doi.org/10.1371/journal.pmed.1000058.CrossRefPubMedPubMedCentral

62.

Prospective Studies Collaboration, Lewington S, Whitlock G, Clarke R, Sherliker P, Emberson J, et al. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet. 2007;370:1829–39. https://doi.org/10.1016/S0140-6736(07)61778-4.CrossRef

63.

Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Prospective studies collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903–13.CrossRefPubMed

64.

Prospective Studies Collaboration, Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373:1083–96. https://doi.org/10.1016/S0140-6736(09)60318-4. CrossRefPubMedCentral

65.

Norat T, Aune D, Vieira AR, Chan D, Navarro Rosenblatt D, Vieira R. The associations between food, nutrition and physical activity and the Risk of pancreatic Cancer. In: WCRF/AICR systematic literature review continuous update project report. London; 2011. http://www.wcrf.org/sites/default/files/SLR_pancreatic_cancer_2011.pdf.

66.

Peters SAE, Huxley RR, Woodward M. Diabetes as risk factor for incident coronary heart disease in women compared with men: a systematic review and meta-analysis of 64 cohorts including 858,507 individuals and 28,203 coronary events. Diabetologia. 2014;57:1542–51. https://doi.org/10.1007/s00125-014-3260-6.CrossRefPubMed

67.

Peters SAE, Huxley RR, Woodward M. Diabetes as a risk factor for stroke in women compared with men: a systematic review and meta-analysis of 64 cohorts, including 775 385 individuals and 12 539 strokes. Lancet. 2014;383:1973–80. https://doi.org/10.1016/S0140-6736(14)60040-4.CrossRefPubMed

68.

Sport England. Active people survey, 2010–2011; 2013. https://doi.org/10.5255/UKDA-SN-7138-3.CrossRef

69.

Wahid A, Manek N, Nichols M, Kelly P, Foster C, Webster P, et al. Quantifying the association between physical activity and cardiovascular disease and diabetes: a systematic review and meta-analysis. J Am Heart Assoc. 2016;5:e002495. https://doi.org/10.1161/JAHA.115.002495.CrossRefPubMedPubMedCentral

70.

Wahid A, Manek N, Nichols M, Kelly P, Foster C, Webster P, et al. Quantifying the association between physical activity and cancer: a systematic review and meta-analysis. 2016.

71.

Clarke R, Frost C, Collins R, Appleby F, Peto R. Dietary lipids and blood cholesterol: quantitative meta-analysis of metabolic ward studies. BMJ. 1997;314:112–7.CrossRefPubMedPubMedCentral

72.

He FJ, Li J, MacGregor GA. Effect of longer term modest salt reduction on blood pressure: Cochrane systematic review and meta-analysis of randomised trials. BMJ. 2013;346:f1325. https://doi.org/10.1136/bmj.f1325.CrossRefPubMed

73.

Christiansen E, Garby L. Prediction of body weight changes caused by changes in energy balance. Eur J Clin Investig. 2002;32:826–30.CrossRef

74.

GBD 2013 Risk Factors Collaborators, Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2015;386:2287–323. https://doi.org/10.1016/S0140-6736(15)00128-2.CrossRefPubMedCentral

75.

Office for National Statistics. Population estimates for UK, England and Wales, Scotland and Northern Ireland, revised: mid-2012, mid-2013 and mid-2014. London; 2016. https://www.ons.gov.uk/releases/populationestimatesforukenglandandwalesscotlandandnorthernirelandrevisedmid2012mid2013andmid2014

76.

University of California B (USA), Max Planck Institute for Demographic Research (Germany). Human Mortality Database. http://www.mortality.org/. Accessed 27 Feb 2019.

77.

NHS England. 2013–14 CCG Programme budgeting benchmarking tool. London; 2015. https://www.england.nhs.uk/wp-content/uploads/2015/06/13-14-ccg-prog-bug-benchmarking-tool.xlsm

78.

NHS England. Technical guide to allocation formulae and pace of change. Leeds; 2016. https://www.england.nhs.uk/wp-content/uploads/2016/04/1-allctins-16-17-tech-guid-formulae-v1.pdf

79.

Claxton K, Sculpher M, Palmer S, Culyer AJ. Causes for concern: is NICE failing to uphold its responsibilities to all NHS patients? Health Econ. 2015;24:1–7. https://doi.org/10.1002/hec.3130.CrossRefPubMed

80.

Sullivan PW, Slejko JF, Sculpher MJ, Ghushchyan V. Catalogue of EQ-5D scores for the United Kingdom. Med Decis Mak. 2011;31:800–4. https://doi.org/10.1177/0272989X11401031.CrossRef

81.

Squires H, Chilcott J, Akehurst R, Burr J, Kelly MP. A framework for developing the structure of public health economic models. Value Health. 2016;19:588–601.CrossRefPubMed

82.

Ainsworth B, Haskell W, Herrmann S, Meckes N, Bassett D, Tudor-Locke C, et al. 2011 compendium of physical activities. Med Sci Sports Exerc. 2011;43:1575–81. https://doi.org/10.1249/MSS.0b013e31821ece12.CrossRefPubMed

83.

Department of Health. Start Active, Stay Active: a report on physical activity from the four home countries’ Chief Medical Officers. London; 2011. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/216370/dh_128210.pdf

84.

Cobiac L, Scarborough P. Translating the World Health Organization 25x25 goals into a United Kingdom context: the PROMISE modelling study. BMJ Open. 2017;7:e012805.CrossRefPubMedPubMedCentral

85.

Briggs ADM, Scarborough P, Wolstenholme J. Estimating comparable English healthcare costs for multiple diseases and unrelated future costs for use in health and public health economic modelling. PLoS One. 2018;13:e0197257. https://doi.org/10.1371/journal.pone.0197257.CrossRefPubMedPubMedCentral

86.

Luengo-Fernandez R, Leal J, Gray A, Sullivan R. Economic burden of cancer across the European Union: a population-based cost analysis. Lancet Oncol. 2013;14:1165–74. https://doi.org/10.1016/S1470-2045(13)70442-X.CrossRefPubMed

87.

PSSRU. Personal Social Services Research Unit. 2015. https://www.pssru.ac.uk/. Accessed 27 Feb 2019.

88.

The Money Advice Service. Means tests for help with care costs – how they work - money advice service. 2019. https://www.moneyadviceservice.org.uk/en/articles/means-tests-for-help-with-care-costs-how-they-work. Accessed 27 Feb 2019.

89.

Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35:1095–108.CrossRefPubMed

90.

Papaioannou D, Brazier J, Paisley S. NICE DSU technical support document 9: the identification, review and synthesis of health state utility values from the literature. Sheffield; 2010.

91.

ICD10Data.com. ICD10Data.com. 2016. http://www.icd10data.com/. Accessed 27 Feb 2019.

92.

Sullivan PW, Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Med Decis Mak. 2006;26:410–20. https://doi.org/10.1177/0272989X06290495.CrossRef

93.

Briggs ADM, Wolstenholme J, Scarborough P. Estimating the cost-effectiveness of salt reformulation and increasing access to leisure centres in England, with PRIMEtime CE model validation using the AdViSHE tool. BMC Health Serv Res. 2019. https://doi.org/10.1186/s12913-019-4292-x.

94.

Kelly FJ, Kelly J. London air quality: a real world experiment in progress. Biomarkers. 2009;14(Suppl 1):5–11. https://doi.org/10.1080/13547500902965252. https://doi.org/10.1186/s12913-019-4237-4.

95.

Marsh K, Ganz ML, Hsu J, Strandberg-Larsen M, Gonzalez RP, Lund N. Expanding health technology assessments to include effects on the environment. Value Health. 2016;19:249–54. https://doi.org/10.1016/j.jval.2015.11.008.CrossRefPubMed

96.

Mallender J, Bertranou E, Owen L, Lester-George A, Jhita T, Roberts S. Physical Activity Return on Investment Tool. London; 2013. https://www.nice.org.uk/about/what-we-do/into-practice/return-on-investment-tools/physical-activity-return-on-investment-tool

97.

Matrix. Estimating return on investment for interventions and strategies to increase physical activity - Technical Report. 2014. https://www.nice.org.uk/Media/Default/About/what-we-do/Into-practice/Return-on-Investment/NICE-return-on-investment-physical-activity-technical-report.pdf.

98.

The University of Queensland School of Population Health. Assessing Cost Effectiveness (ACE) in Prevention Study. 2019. https://public-health.uq.edu.au/research/centres/past-centres/assessing-cost-effectiveness-ace-prevention-study. Accessed 27 Feb 2019.

99.

The University of Otago. Burden of Disease Epidemiology, Equity & Cost-Effectiveness Programme (BODE³). 2019. https://www.otago.ac.nz/wellington/departments/publichealth/research/bode3/. Accessed 27 Feb 2019.

100.

Wilson N, Nghiem N, Eyles H, Mhurchu CN, Shields E, Cobiac LJ, et al. Modeling health gains and cost savings for ten dietary salt reduction targets. Nutr J. 2015;15:44. https://doi.org/10.1186/s12937-016-0161-1.CrossRef

101.

Salomon JA, Vos T, Hogan DR, Gagnon M, Naghavi M, Mokdad A, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the global burden of disease study 2010. Lancet. 2012;380:2129–43. https://doi.org/10.1016/S0140-6736(12)61680-8.CrossRefPubMed

102.

Begg S, Vos T, Barker B, Stevenson C, Stanley L, Lopez A. The burden of disease and injury in Australia 2003. PHE 82. Canberra: Australian Institute of Health and Welfare; 2007.

103.

Australian Institute of Health and Welfare. AIHW disease costs and impacts study data Canberra; 2001.

Title: PRIMEtime CE: a multistate life table model for estimating the cost-effectiveness of interventions affecting diet and physical activity
Authors: Adam D. M. Briggs
Linda J. Cobiac
Jane Wolstenholme
Peter Scarborough
Publication date: 01-12-2019
Publisher: BioMed Central
Keyword: Public Health
Published in: BMC Health Services Research / Issue 1/2019
Electronic ISSN: 1472-6963
DOI: https://doi.org/10.1186/s12913-019-4237-4

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

PRIMEtime CE: a multistate life table model for estimating the cost-effectiveness of interventions affecting diet and physical activity

Abstract

Background

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2019

High admission rates and heavy inpatient service costs of urban tuberculosis patients in eastern China

Development of a patient reported outcome measures for measuring the impact of visual impairment following stroke

Rapid evaluation for health and social care innovations: challenges for “quick wins” using interrupted time series

Patient consultation rate and clinical and NHS outcomes: a cross-sectional analysis of English primary care data from 2.7 million patients in 238 practices

Determinants of health workers intention to use malaria rapid diagnostic test in Kintampo North Municipality, Ghana - a cross-sectional study

Study protocol to investigate the efficacy of normalisation of Advance Care Planning (ACP) for people with chronic diseases in acute and community settings: a quasi-experimental design