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. 2021 May;25(35):1–234. doi: 10.3310/hta25350

Modelling tool to support decision-making in the NHS Health Check programme: workshops, systematic review and co-production with users.

Martin O'Flaherty, Ffion Lloyd-Williams, Simon Capewell, Angela Boland, Michelle Maden, Brendan Collins, Piotr Bandosz, Lirije Hyseni, Chris Kypridemos
PMCID: PMC8201571  PMID: 34076574

Abstract

BACKGROUND

Local authorities in England commission the NHS Health Check programme to invite everyone aged 40-74 years without pre-existing conditions for risk assessment and eventual intervention, if needed. However, the programme's effectiveness, cost-effectiveness and equity impact remain uncertain.

AIM

To develop a validated open-access flexible web-based model that enables local commissioners to quantify the cost-effectiveness and potential for equitable population health gain of the NHS Health Check programme.

OBJECTIVES

The objectives were as follows: (1) co-produce with stakeholders the desirable features of the user-friendly model; (2) update the evidence base to support model and scenario development; (3) further develop our computational model to allow for developments and changes to the NHS Health Check programme and the diseases it addresses; (4) assess the effectiveness, cost-effectiveness and equity of alternative strategies for implementation to illustrate the use of the tool; and (5) propose a sustainability and implementation plan to deploy our user-friendly computational model at the local level.

DESIGN

Co-production workshops surveying the best-performing local authorities and a systematic literature review of strategies to increase uptake of screening programmes informed model use and development. We then co-produced the workHORSE (working Health Outcomes Research Simulation Environment) model to estimate the health, economic and equity impact of different NHS Health Check programme implementations, using illustrative-use cases.

SETTING

Local authorities in England.

PARTICIPANTS

Stakeholders from local authorities, Public Health England, the NHS, the British Heart Foundation, academia and other organisations participated in the workshops. For the local authorities survey, we invited 16 of the best-performing local authorities in England.

INTERVENTIONS

The user interface allows users to vary key parameters that represent programme activities (i.e. invitation, uptake, prescriptions and referrals). Scenarios can be compared with each other.

MAIN OUTCOME MEASURES

Disease cases and case-years prevented or postponed, incremental cost-effectiveness ratios, net monetary benefit and change in slope index of inequality.

RESULTS

The survey of best-performing local authorities revealed a diversity of effective approaches to maximise the coverage and uptake of NHS Health Check programme, with no distinct 'best buy'. The umbrella literature review identified a range of effective single interventions. However, these generally need to be combined to maximally improve uptake and health gains. A validated dynamic, stochastic microsimulation model, built on robust epidemiology, enabled service options analysis. Analyses of three contrasting illustrative cases estimated the health, economic and equity impact of optimising the Health Checks, and the added value of obtaining detailed local data. Optimising the programme in Liverpool can become cost-effective and equitable, but simply changing the invitation method will require other programme changes to improve its performance. Detailed data inputs can benefit local analysis.

LIMITATIONS

Although the approach is extremely flexible, it is complex and requires substantial amounts of data, alongside expertise to both maintain and run.

CONCLUSIONS

Our project showed that the workHORSE model could be used to estimate the health, economic and equity impact comprehensively at local authority level. It has the potential for further development as a commissioning tool and to stimulate broader discussions on the role of these tools in real-world decision-making.

FUTURE WORK

Future work should focus on improving user interactions with the model, modelling simulation standards, and adapting workHORSE for evaluation, design and implementation support.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42019132087.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 35. See the NIHR Journals Library website for further project information.

Plain language summary

The NHS Health Check programme is available for adults aged 40–74 years in England to find the early risk of heart disease, cancers, lung disease and dementia, and lower that risk. However, some studies have suggested that the current scheme could perhaps be improved. We systematically looked at previous studies to understand what makes a screening programme successful. We also contacted local authorities with the best NHS Health Check programmes to find out how they were being delivered so well. The most successful local authorities highlighted a wide variety of methods for achieving success. All had concrete plans in place for delivery, including different approaches for encouraging more adults to participate. We further developed our existing computer model into a web-based tool [workHORSE (working Health Outcomes Research Simulation Environment)]. This tool can help those responsible for commissioning NHS Health Checks to further improve the delivery of their local programme. We held four workshops with relevant professionals to develop the workHORSE model. These workshops resulted in a useful ‘real-world’ tool for local commissioners: a tool that can calculate the current and potential future benefits of different programmes. We used the model to show how commissioners can explore and compare a variety of different programmes. We found that combining several improvements can be useful. However, this provides modest benefits in improving health and value for money. At the same time, the impact on reducing inequalities is less clear and depends on the interventions used. Our results suggest that: a variety of successful approaches can be used to help increase the uptake of screening programmes such as NHS Health Checksjointly developing a computer model with end-users leads to a more user-friendly and relevant model to improve the programmethe stage is now set for further work to identify the best approach in each local area.

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References

  1. Murray CJ, Richards MA, Newton JN, Fenton KA, Anderson HR, Atkinson C, et al. UK health performance: findings of the Global Burden of Disease Study 2010. Lancet 2013;381:997–1020. https://doi.org/10.1016/S0140-6736(13)60355-4 doi: 10.1016/S0140-6736(13)60355-4. [DOI] [PubMed]
  2. NHS Expert Scientific and Clinical Advisory Panel. Emerging Evidence on the NHS Health Check: Findings and Recommendations. URL: www.healthcheck.nhs.uk/seecmsfile/?id=292 (accessed 10 October 2019).
  3. Kontis V, Mathers CD, Rehm J, Stevens GA, Shield KD, Bonita R, et al. Contribution of six risk factors to achieving the 25 × 25 non-communicable disease mortality reduction target: a modelling study. Lancet 2014;384:427–37. https://doi.org/10.1016/S0140-6736(14)60616-4 doi: 10.1016/S0140-6736(14)60616-4. [DOI] [PubMed]
  4. NHS England. Cardiovascular Disease (CVD). URL: www.england.nhs.uk/ourwork/clinical-policy/cvd/ (accessed 17 January 2020).
  5. Usher-Smith J, Martin A, Harte E, MacLure C, Meads C, Saunders C, et al. NHS Health Check Programme Rapid Evidence Synthesis. Cambridge: University of Cambridge; 2017.
  6. Robson J, Dostal I, Sheikh A, Eldridge S, Madurasinghe V, Griffiths C, et al. The NHS Health Check in England: an evaluation of the first 4 years. BMJ Open 2016;6:e008840. https://doi.org/10.1136/bmjopen-2015-008840 doi: 10.1136/bmjopen-2015-008840. [DOI] [PMC free article] [PubMed]
  7. Department of Health Vascular Policy Team. Economic Modelling for Vascular Checks 2008. London: Department for Health and Social Care; 2008.
  8. Hinde S, Bojke L, Richardson G, Retat L, Webber L. The cost-effectiveness of population Health Checks: have the NHS Health Checks been unfairly maligned? J Public Health 2017;25:425–31. https://doi.org/10.1007/s10389-017-0801-8 doi: 10.1007/s10389-017-0801-8. [DOI] [PMC free article] [PubMed]
  9. Crossan C, Lord J, Ryan R, Nherera L, Marshall T. Cost effectiveness of case-finding strategies for primary prevention of cardiovascular disease: a modelling study. Br J Gen Pract 2017;67:e67–e77. https://doi.org/10.3399/bjgp16X687973 doi: 10.3399/bjgp16X687973. [DOI] [PMC free article] [PubMed]
  10. Chang KC, Vamos EP, Palladino R, Majeed A, Lee JT, Millett C. Impact of the NHS Health Check on inequalities in cardiovascular disease risk: a difference-in-differences matching analysis. J Epidemiol Community Health 2019;73:11–18. https://doi.org/10.1136/jech-2018-210961 doi: 10.1136/jech-2018-210961. [DOI] [PubMed]
  11. Kypridemos C, Collins B, McHale P, Bromley H, Parvulescu P, Capewell S, O’Flaherty M. Future cost-effectiveness and equity of the NHS Health Check cardiovascular disease prevention programme: microsimulation modelling using data from Liverpool, UK. PLOS Med 2018;15:e1002573. https://doi.org/10.1371/journal.pmed.1002573 doi: 10.1371/journal.pmed.1002573. [DOI] [PMC free article] [PubMed]
  12. Ministry of Housing, Communities and Local Government. Local Authority Revenue Expenditure and Financing England: 2018 to 2019. Budget Individual Local Authority Data. URL: www.gov.uk/government/statistics/local-authority-revenue-expenditure-and-financing-england-2018-to-2019-budget-individual-local-authority-data (accessed 17 January 2020).
  13. UK Government. The Local Authorities (Public Health Functions and Entry to Premises by Local Healthwatch Representatives) Regulations 2013. URL: www.legislation.gov.uk/uksi/2013/351/contents/made (accessed 17 January 2020).
  14. NHS England. NHS Long Term Plan. URL: www.longtermplan.nhs.uk (accessed 12 February 2020).
  15. Artac M, Dalton AR, Babu H, Bates S, Millett C, Majeed A. Primary care and population factors associated with NHS Health Check coverage: a national cross-sectional study. J Public Health 2013;35:431–9. https://doi.org/10.1093/pubmed/fdt069 doi: 10.1093/pubmed/fdt069. [DOI] [PubMed]
  16. McNaughton RJ, Shucksmith J. Reasons for (non)compliance with intervention following identification of ‘high-risk’ status in the NHS Health Check programme. J Public Health 2015;37:218–25. https://doi.org/10.1093/pubmed/fdu066 doi: 10.1093/pubmed/fdu066. [DOI] [PMC free article] [PubMed]
  17. Baker C, Loughren EA, Crone D, Kallfa N. A process evaluation of the NHS Health Check care pathway in a primary care setting. J Public Health 2015;37:202–9. https://doi.org/10.1093/pubmed/fdv053 doi: 10.1093/pubmed/fdv053. [DOI] [PubMed]
  18. Dalton AR, Soljak M, Samarasundera E, Millett C, Majeed A. Prevalence of cardiovascular disease risk amongst the population eligible for the NHS Health Check Programme. Eur J Prev Cardiol 2013;20:142–50. https://doi.org/10.1177/1741826711428797 doi: 10.1177/1741826711428797. [DOI] [PubMed]
  19. Dalton AR, Bottle A, Okoro C, Majeed A, Millett C. Uptake of the NHS Health Checks programme in a deprived, culturally diverse setting: cross-sectional study. J Public Health 2011;33:422–9. https://doi.org/10.1093/pubmed/fdr034 doi: 10.1093/pubmed/fdr034. [DOI] [PubMed]
  20. Robson J, Dostal I, Madurasinghe V, Sheikh A, Hull S, Boomla K, et al. The NHS Health Check programme: implementation in east London 2009–2011. BMJ Open 2015;5:e007578. https://doi.org/10.1136/bmjopen-2015-007578 doi: 10.1136/bmjopen-2015-007578. [DOI] [PMC free article] [PubMed]
  21. Ebrahim S, Taylor F, Ward K, Beswick A, Burke M, Davey Smith G. Multiple risk factor interventions for primary prevention of coronary heart disease. Cochrane Database Syst Rev 2011;1:CD001561. https://doi.org/10.1002/14651858.CD001561.pub3 doi: 10.1002/14651858.CD001561.pub3. [DOI] [PMC free article] [PubMed]
  22. Jørgensen T, Jacobsen RK, Toft U, Aadahl M, Glümer C, Pisinger C. Effect of screening and lifestyle counselling on incidence of ischaemic heart disease in general population: Inter99 randomised trial. BMJ 2014;348:g3617. https://doi.org/10.1136/bmj.g3617 doi: 10.1136/bmj.g3617. [DOI] [PMC free article] [PubMed]
  23. Lee JT, Lawson KD, Wan Y, Majeed A, Morris S, Soljak M, Millett C. Are cardiovascular disease risk assessment and management programmes cost effective? A systematic review of the evidence. Prev Med 2017;99:49–57. https://doi.org/10.1016/j.ypmed.2017.01.005 doi: 10.1016/j.ypmed.2017.01.005. [DOI] [PubMed]
  24. Government Office for Science. Computational Modelling: Blackett Review. URL: www.gov.uk/government/publications/computational-modelling-blackett-review (accessed 31 January 2020).
  25. Davis SM. Chief Medical Officer Annual Report 2018: Better Health Within Reach. URL: www.gov.uk/government/publications/chief-medical-officer-annual-report-2018-better-health-within-reach (accessed 17 January 2020).
  26. Mytton OT, Jackson C, Steinacher A, Goodman A, Langenberg C, Griffin S, et al. The current and potential health benefits of the National Health Service Health Check cardiovascular disease prevention programme in England: a microsimulation study. PLOS Med 2018;15:e1002517. https://doi.org/10.1371/journal.pmed.1002517 doi: 10.1371/journal.pmed.1002517. [DOI] [PMC free article] [PubMed]
  27. Kypridemos C, Allen K, Hickey GL, Guzman-Castillo M, Bandosz P, Buchan I, et al. Cardiovascular screening to reduce the burden from cardiovascular disease: microsimulation study to quantify policy options. BMJ 2016;353:i2793. https://doi.org/10.1136/bmj.i2793 doi: 10.1136/bmj.i2793. [DOI] [PMC free article] [PubMed]
  28. Chartered Institute of Public Finance and Accountancy. Evaluating Preventative Investments in Public Health in England. URL: www.cipfa.org/policy-and-guidance/reports/evaluating-preventative-investments (accessed 26 November 2019).
  29. HM Treasury. The Green Book: Appraisal and Evaluation in Central Government. URL: www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-in-central-governent (accessed 31 January 2020).
  30. Allender S, Owen B, Kuhlberg J, Lowe J, Nagorcka-Smith P, Whelan J, Bell C. A community based systems diagram of obesity causes. PLOS ONE 2015;10:e0129683. https://doi.org/10.1371/journal.pone.0129683 doi: 10.1371/journal.pone.0129683. [DOI] [PMC free article] [PubMed]
  31. Frerichs L, Lich KH, Dave G, Corbie-Smith G. Integrating systems science and community-based participatory research to achieve health equity. Am J Public Health 2016;106:215–22. https://doi.org/10.2105/AJPH.2015.302944 doi: 10.2105/AJPH.2015.302944. [DOI] [PMC free article] [PubMed]
  32. Freebairn L, Atkinson JA, Kelly PM, McDonnell G, Rychetnik L. Decision makers’ experience of participatory dynamic simulation modelling: methods for public health policy. BMC Med Inform Decis Mak 2018;18:131. https://doi.org/10.1186/s12911-018-0707-6 doi: 10.1186/s12911-018-0707-6. [DOI] [PMC free article] [PubMed]
  33. van der Graaf P, Forrest LF, Adams J, Shucksmith J, White M. How do public health professionals view and engage with research? A qualitative interview study and stakeholder workshop engaging public health professionals and researchers. BMC Public Health 2017;17:892. https://doi.org/10.1186/s12889-017-4896-1 doi: 10.1186/s12889-017-4896-1. [DOI] [PMC free article] [PubMed]
  34. Morton KL, Atkin AJ, Corder K, Suhrcke M, Turner D, van Sluijs EM. Engaging stakeholders and target groups in prioritising a public health intervention: the Creating Active School Environments (CASE) online Delphi study. BMJ Open 2017;7:e013340. https://doi.org/10.1136/bmjopen-2016-013340 doi: 10.1136/bmjopen-2016-013340. [DOI] [PMC free article] [PubMed]
  35. Cairney P, Oliver K. Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy? Health Res Policy Syst 2017;15:35. https://doi.org/10.1186/s12961-017-0192-x doi: 10.1186/s12961-017-0192-x. [DOI] [PMC free article] [PubMed]
  36. Oliver K, Kothari A, Mays N. The dark side of coproduction: do the costs outweigh the benefits for health research? Health Res Policy Syst 2019;17:33. https://doi.org/10.1186/s12961-019-0432-3 doi: 10.1186/s12961-019-0432-3. [DOI] [PMC free article] [PubMed]
  37. Andersen DF, Richardson GP. Scripts for group model building. Syst Dyn Rev 1997;13:107–29. https://doi.org/10.1002/(SICI)1099-1727(199722)13:2<107::AID-SDR120>3.0.CO;2-7 doi: 10.1002/(SICI)1099-1727(199722)13:2&#x0003c;107::AID-SDR120&#x0003e;3.0.CO;2-7. [DOI]
  38. WikiBooks. Scriptapedia. URL: https://en.wikibooks.org/wiki/Scriptapedia (accessed 17 January 2020).
  39. Hovmand PS, Andersen DF, Rouwette E, Richardson GP, Rux K, Calhoun A. Group model-building ‘scripts’ as a collaborative planning tool. Syst Res Behav Sci 2012;29:179–93. https://doi.org/10.1002/sres.2105 doi: 10.1002/sres.2105. [DOI]
  40. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006;3:77–101. https://doi.org/10.1191/1478088706qp063oa doi: 10.1191/1478088706qp063oa. [DOI]
  41. Barker D, Clegg R. Fast-Track: A Rad Approach (Case Method). Boston, MA: Addison–Wesley; 1994.
  42. Lloyd-Williams F, Hyseni L, Guzman-Castillo M, Kypridemos C, Collins B, Capewell S, et al. Evaluating stakeholder involvement in building a decision support tool for NHS Health Checks: co-producing the WorkHORSE study. BMC Med Inform Decis Mak 2020;20:182. https://doi.org/10.1186/s12911-020-01205-y doi: 10.1186/s12911-020-01205-y. [DOI] [PMC free article] [PubMed]
  43. Hyseni L, Guzman-Castillo M, Kypridemos C, Collins B, Schwaller E, Capewell S, et al. Engaging with stakeholders to inform the development of a decision-support tool for the NHS health check programme: qualitative study. BMC Health Serv Res 2020;20:394. https://doi.org/10.1186/s12913-020-05268-5 doi: 10.1186/s12913-020-05268-5. [DOI] [PMC free article] [PubMed]
  44. Raffle AE, Mackie A, Gray JAM. Screening: Evidence and Practice. 2nd edn. New York, NY: Oxford University Press; 2019. https://doi.org/10.1093/med/9780198805984.001.0001 doi: 10.1093/med/9780198805984.001.0001. [DOI]
  45. UK Government. Population Screening Programmes: Detailed Information. URL: www.gov.uk/topic/population-screening-programmes (accessed 2 December 2019).
  46. Ferroni E, Camilloni L, Jimenez B, Furnari G, Borgia P, Guasticchi G, et al. How to increase uptake in oncologic screening: a systematic review of studies comparing population-based screening programs and spontaneous access. Prev Med 2012;55:587–96. https://doi.org/10.1016/j.ypmed.2012.10.007 doi: 10.1016/j.ypmed.2012.10.007. [DOI] [PubMed]
  47. Ponti, A, Anttilla A, Ronco G, Senore C. Cancer Screening in the European Union. Report on the Implementation of Council Recommendation on Cancer Screening. Brussels: European Commission; 2017.
  48. NHS England. Review of National Cancer Screening Programmes in England. URL: www.england.nhs.uk/publication/terms-of-reference-review-national-cancer-screening-programmes-england/ (accessed 31 January 2020).
  49. Public Health England. NHS Population Screening: Inequalities Strategy. URL: www.gov.uk/government/publications/nhs-population-screening-inequalities-strategy (accessed 13 February 2020).
  50. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009;339:b2535. https://doi.org/10.1136/bmj.b2535 doi: 10.1136/bmj.b2535. [DOI] [PMC free article] [PubMed]
  51. Aromataris E, Fernandez R, Godfrey CM, Holly C, Khalil H, Tungpunkom P. Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. Int J Evid Based Healthc 2015;13:132–40. https://doi.org/10.1097/XEB.0000000000000055 doi: 10.1097/XEB.0000000000000055. [DOI] [PubMed]
  52. Rat C, Latour C, Rousseau R, Gaultier A, Pogu C, Edwards A, Nguyen JM. Interventions to increase uptake of faecal tests for colorectal cancer screening: a systematic review. Eur J Cancer Prev 2018;27:227–36. https://doi.org/10.1097/CEJ.0000000000000344 doi: 10.1097/CEJ.0000000000000344. [DOI] [PubMed]
  53. Teo CH, Ling CJ, Ng CJ. Improving health screening uptake in men: a systematic review and meta-analysis. Am J Prev Med 2018;54:133–43. https://doi.org/10.1016/j.amepre.2017.08.028 doi: 10.1016/j.amepre.2017.08.028. [DOI] [PubMed]
  54. Cheong AT, Liew SM, Khoo EM, Mohd Zaidi NF, Chinna K. Are interventions to increase the uptake of screening for cardiovascular disease risk factors effective? A systematic review and meta-analysis. BMC Fam Pract 2017;18:4. https://doi.org/10.1186/s12875-016-0579-8 doi: 10.1186/s12875-016-0579-8. [DOI] [PMC free article] [PubMed]
  55. Verdoodt F, Jentschke M, Hillemanns P, Racey CS, Snijders PJF, Arbyn M. Reaching women who do not participate in the regular cervical cancer screening programme by offering self-sampling kits: a systematic review and meta-analysis of randomised trials. Eur J Cancer Oxf Engl 1990 2015;51:2375–85. https://doi.org/10.1016/j.ejca.2015.07.006 doi: 10.1016/j.ejca.2015.07.006. [DOI] [PubMed]
  56. Camilloni L, Ferroni E, Cendales BJ, Pezzarossi A, Furnari G, Borgia P, et al. Methods to increase participation in organised screening programs: a systematic review. BMC Public Health 2013;13:464. https://doi.org/10.1186/1471-2458-13-464 doi: 10.1186/1471-2458-13-464. [DOI] [PMC free article] [PubMed]
  57. Everett T, Bryant A, Griffin MF, Martin-Hirsch PP, Forbes CA, Jepson RG. Interventions targeted at women to encourage the uptake of cervical screening. Cochrane Database Syst Rev 2011;5:CD002834. https://doi.org/10.1002/14651858.CD002834.pub2 doi: 10.1002/14651858.CD002834.pub2. [DOI] [PMC free article] [PubMed]
  58. Khalid-de Bakker C, Jonkers D, Smits K, Mesters I, Masclee A, Stockbrügger R. Participation in colorectal cancer screening trials after first-time invitation: a systematic review. Endoscopy 2011;43:1059–86. https://doi.org/10.1055/s-0031-1291430 doi: 10.1055/s-0031-1291430. [DOI] [PubMed]
  59. Denhaerynck K, Lesaffre E, Baele J, Cortebeeck K, Van Overstraete E, Buntinx F. Mammography screening attendance: meta-analysis of the effect of direct-contact invitation. Am J Prev Med 2003;25:195–203. https://doi.org/10.1016/S0749-3797(03)00201-0 doi: 10.1016/S0749-3797(03)00201-0. [DOI] [PubMed]
  60. Black ME, Yamada J, Mann V. A systematic literature review of the effectiveness of community-based strategies to increase cervical cancer screening. Can J Public Health 2002;93:386–93. https://doi.org/10.1007/BF03404575 doi: 10.1007/BF03404575. [DOI] [PMC free article] [PubMed]
  61. Bonfill X, Marzo M, Pladevall M, Martí J, Emparanza JI. Strategies for increasing women participation in community breast cancer screening. Cochrane Database Syst Rev 2001;1:CD002943. doi: 10.1002/14651858.CD002943. [DOI] [PMC free article] [PubMed]
  62. Jepson R, Clegg A, Forbes C, Lewis R, Sowden A, Kleijnen J. The determinants of screening uptake and interventions for increasing uptake: a systematic review. Health Technol Assess 2000;4(14). https://doi.org/10.3310/hta4140 doi: 10.3310/hta4140. [DOI] [PubMed]
  63. Sin JP, Leger AS. Interventions to Increase Breast Screening Uptake: Do They Make Any Difference? York: Centre for Reviews and Dissemination; 1999. https://doi.org/10.1136/jms.6.4.170 doi: 10.1136/jms.6.4.170. [DOI] [PubMed]
  64. Issaka RB, Avila P, Whitaker E, Bent S, Somsouk M. Population health interventions to improve colorectal cancer screening by fecal immunochemical tests: a systematic review. Prev Med 2019;118:113–21. https://doi.org/10.1016/j.ypmed.2018.10.021 doi: 10.1016/j.ypmed.2018.10.021. [DOI] [PMC free article] [PubMed]
  65. Dougherty MK, Brenner AT, Crockett SD, Gupta S, Wheeler SB, Coker-Schwimmer M, et al. Evaluation of interventions intended to increase colorectal cancer screening rates in the united states: a systematic review and meta-analysis. JAMA Intern Med 2018;178:1645–58. https://doi.org/10.1001/jamainternmed.2018.4637 doi: 10.1001/jamainternmed.2018.4637. [DOI] [PMC free article] [PubMed]
  66. Rees I, Jones D, Chen H, Macleod U. Interventions to improve the uptake of cervical cancer screening among lower socioeconomic groups: a systematic review. Prev Med 2018;111:323–35. https://doi.org/10.1016/j.ypmed.2017.11.019 doi: 10.1016/j.ypmed.2017.11.019. [DOI] [PubMed]
  67. Uy C, Lopez J, Trinh-Shevrin C, Kwon SC, Sherman SE, Liang PS. Text messaging interventions on cancer screening rates: a systematic review. J Med Internet Res 2017;19:e296. https://doi.org/10.2196/jmir.7893 doi: 10.2196/jmir.7893. [DOI] [PMC free article] [PubMed]
  68. Albrow R, Blomberg K, Kitchener H, Brabin L, Patnick J, Tishelman C, et al. Interventions to improve cervical cancer screening uptake amongst young women: a systematic review. Acta Oncol 2014;53:445–51. https://doi.org/10.3109/0284186X.2013.869618 doi: 10.3109/0284186X.2013.869618. [DOI] [PubMed]
  69. Sabatino SA, Lawrence B, Elder R, Mercer SL, Wilson KM, DeVinney B, et al. Effectiveness of interventions to increase screening for breast, cervical, and colorectal cancers: nine updated systematic reviews for the guide to community preventive services. Am J Prev Med 2012;43:97–118. https://doi.org/10.1016/j.amepre.2012.04.009 doi: 10.1016/j.amepre.2012.04.009. [DOI] [PubMed]
  70. Brouwers MC, De Vito C, Bahirathan L, Carol A, Carroll JC, Cotterchio M, et al. What implementation interventions increase cancer screening rates? A systematic review. Implement Sci 2011;6:111. https://doi.org/10.1186/1748-5908-6-111 doi: 10.1186/1748-5908-6-111. [DOI] [PMC free article] [PubMed]
  71. Vernon SW, McQueen A, Tiro JA, del Junco DJ. Interventions to promote repeat breast cancer screening with mammography: a systematic review and meta-analysis. J Natl Cancer Inst 2010;102:1023–39. https://doi.org/10.1093/jnci/djq223 doi: 10.1093/jnci/djq223. [DOI] [PMC free article] [PubMed]
  72. Stone EG, Morton SC, Hulscher ME, Maglione MA, Roth EA, Grimshaw JM, et al. Interventions that increase use of adult immunization and cancer screening services: a meta-analysis. Ann Intern Med 2002;136:641–51. https://doi.org/10.7326/0003-4819-136-9-200205070-00006 doi: 10.7326/0003-4819-136-9-200205070-00006. [DOI] [PubMed]
  73. Tseng DS, Cox E, Plane MB, Hla KM. Efficacy of patient letter reminders on cervical cancer screening: a meta-analysis. J Gen Intern Med 2001;16:563–8. https://doi.org/10.1046/j.1525-1497.2001.016008567.x doi: 10.1046/j.1525-1497.2001.016008567.x. [DOI] [PMC free article] [PubMed]
  74. Yabroff KR, Mandelblatt JS. Interventions targeted toward patients to increase mammography use. Cancer Epidemiol Biomark Prev Publ 1999;8:749–57. [PubMed]
  75. Lu M, Moritz S, Lorenzetti D, Sykes L, Straus S, Quan H. A systematic review of interventions to increase breast and cervical cancer screening uptake among Asian women. BMC Public Health 2012;12:413. https://doi.org/10.1186/1471-2458-12-413 doi: 10.1186/1471-2458-12-413. [DOI] [PMC free article] [PubMed]
  76. Han HR, Lee JE, Kim J, Hedlin HK, Song H, Kim MT. A meta-analysis of interventions to promote mammography among ethnic minority women. Nurs Res 2009;58:246–54. https://doi.org/10.1097/NNR.0b013e3181ac0f7f doi: 10.1097/NNR.0b013e3181ac0f7f. [DOI] [PMC free article] [PubMed]
  77. Han HR, Kim J, Lee JE, Hedlin HK, Song H, Song Y, Kim MT. Interventions that increase use of Pap tests among ethnic minority women: a meta-analysis. Psychooncology 2011;20:341–51. https://doi.org/10.1002/pon.1754 doi: 10.1002/pon.1754. [DOI] [PMC free article] [PubMed]
  78. Legler J, Meissner HI, Coyne C, Breen N, Chollette V, Rimer BK. The effectiveness of interventions to promote mammography among women with historically lower rates of screening. Cancer Epidemiol Biomarkers Prev 2002;11:59–71. [PubMed]
  79. Musa J, Achenbach CJ, O’Dwyer LC, Evans CT, McHugh M, Hou L, et al. Effect of cervical cancer education and provider recommendation for screening on screening rates: a systematic review and meta-analysis. PLOS ONE 2017;12:e0183924. https://doi.org/10.1371/journal.pone.0183924 doi: 10.1371/journal.pone.0183924. [DOI] [PMC free article] [PubMed]
  80. Jager M, Demb J, Asghar A, Selby K, Mello EM, Heskett KM, et al. Mailed outreach is superior to usual care alone for colorectal cancer screening in the USA: a systematic review and meta-analysis. Dig Dis Sci 2019;64:2489–96. https://doi.org/10.1007/s10620-019-05587-6 doi: 10.1007/s10620-019-05587-6. [DOI] [PMC free article] [PubMed]
  81. Mandelblatt JS, Yabroff KR. Effectiveness of interventions designed to increase mammography use: a meta-analysis of provider-targeted strategies. Cancer Epidemiol Biomarkers Prev 1999;8:759–67. [PubMed]
  82. Gardner MP, Adams A, Jeffreys M. Interventions to increase the uptake of mammography amongst low income women: a systematic review and meta-analysis. PLOS ONE 2013;8:e55574. https://doi.org/10.1371/journal.pone.0055574 doi: 10.1371/journal.pone.0055574. [DOI] [PMC free article] [PubMed]
  83. Anastasi N, Lusher J. The impact of breast cancer awareness interventions on breast screening uptake among women in the United Kingdom: a systematic review. J Health Psychol 2019;24:113–24. https://doi.org/10.1177/1359105317697812 doi: 10.1177/1359105317697812. [DOI] [PubMed]
  84. Agide FD, Garmaroudi G, Sadeghi R, Shakibazadeh E, Yaseri M, Koricha ZB, Tigabu BM. A systematic review of the effectiveness of health education interventions to increase cervical cancer screening uptake. Eur J Public Health 2018;28:1156–62. https://doi.org/10.1093/eurpub/cky197 doi: 10.1093/eurpub/cky197. [DOI] [PubMed]
  85. Volk RJ, Linder SK, Lopez-Olivo MA, Kamath GR, Reuland DS, Saraykar SS, et al. Patient decision aids for colorectal cancer screening: a systematic review and meta-analysis. Am J Prev Med 2016;51:779–91. https://doi.org/10.1016/j.amepre.2016.06.022 doi: 10.1016/j.amepre.2016.06.022. [DOI] [PMC free article] [PubMed]
  86. Ivlev I, Jerabkova S, Mishra M, Cook LA, Eden KB. Prostate cancer screening patient decision aids: a systematic review and meta-analysis. Am J Prev Med 2018;55:896–907. https://doi.org/10.1016/j.amepre.2018.06.016 doi: 10.1016/j.amepre.2018.06.016. [DOI] [PMC free article] [PubMed]
  87. Volk RJ, Hawley ST, Kneuper S, Holden EW, Stroud LA, Cooper CP, et al. Trials of decision aids for prostate cancer screening: a systematic review. Am J Prev Med 2007;33:428–34. https://doi.org/10.1016/j.amepre.2007.07.030 doi: 10.1016/j.amepre.2007.07.030. [DOI] [PubMed]
  88. Evans R, Edwards A, Brett J, Bradburn M, Watson E, Austoker J, Elwyn G. Reduction in uptake of PSA tests following decision aids: systematic review of current aids and their evaluations. Patient Educ Couns 2005;58:13–26. https://doi.org/10.1016/j.pec.2004.06.009 doi: 10.1016/j.pec.2004.06.009. [DOI] [PubMed]
  89. Kelly C, Pericleous M, Hendy J, de Lusignan S, Ahmed A, Vandrevala T, Ala A. Interventions to improve the uptake of screening across a range of conditions in ethnic minority groups: a systematic review. Int J Clin Pract 2018;72:13202. https://doi.org/10.1111/ijcp.13202 doi: 10.1111/ijcp.13202. [DOI] [PubMed]
  90. Hou SI, Sealy DA, Kabiru CW. Closing the disparity gap: cancer screening interventions among Asians – a systematic literature review. Asian Pac J Cancer Prev 2011;12:3133–9. [PubMed]
  91. Usher-Smith JA, Silarova B, Sharp SJ, Mills K, Griffin SJ. Effect of interventions incorporating personalised cancer risk information on intentions and behaviour: a systematic review and meta-analysis of randomised controlled trials. BMJ Open 2018;8:e017717. https://doi.org/10.1136/bmjopen-2017-017717 doi: 10.1136/bmjopen-2017-017717. [DOI] [PMC free article] [PubMed]
  92. Edwards AG, Naik G, Ahmed H, Elwyn GJ, Pickles T, Hood K, Playle R. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev 2013;2:CD001865. https://doi.org/10.1002/14651858.CD001865.pub3 doi: 10.1002/14651858.CD001865.pub3. [DOI] [PMC free article] [PubMed]
  93. Albada A, Ausems MG, Bensing JM, van Dulmen S. Tailored information about cancer risk and screening: a systematic review. Patient Educ Couns 2009;77:155–71. https://doi.org/10.1016/j.pec.2009.03.005 doi: 10.1016/j.pec.2009.03.005. [DOI] [PubMed]
  94. Bellhouse S, McWilliams L, Firth J, Yorke J, French DP. Are community-based health worker interventions an effective approach for early diagnosis of cancer? A systematic review and meta-analysis. Psychooncology 2018;27:1089–99. https://doi.org/10.1002/pon.4575 doi: 10.1002/pon.4575. [DOI] [PubMed]
  95. Wells KJ, Luque JS, Miladinovic B, Vargas N, Asvat Y, Roetzheim RG, Kumar A. Do community health worker interventions improve rates of screening mammography in the United States? A systematic review. Cancer Epidemiol Biomarkers Prev 2011;20:1580–98. https://doi.org/10.1158/1055-9965.EPI-11-0276 doi: 10.1158/1055-9965.EPI-11-0276. [DOI] [PMC free article] [PubMed]
  96. Mojica CM, Parra-Medina D, Vernon S. Interventions promoting colorectal cancer screening among Latino men: a systematic review. Prev Chronic Dis 2018;15:E31. https://doi.org/10.5888/pcd15.170218 doi: 10.5888/pcd15.170218. [DOI] [PMC free article] [PubMed]
  97. Agide FD, Sadeghi R, Garmaroudi G, Tigabu BM. A systematic review of health promotion interventions to increase breast cancer screening uptake: from the last 12 years. Eur J Public Health 2018;28:1149–55. https://doi.org/10.1093/eurpub/ckx231 doi: 10.1093/eurpub/ckx231. [DOI] [PMC free article] [PubMed]
  98. Secginli S, Nahcivan NO, Gunes G, Fernandez R. Interventions promoting breast cancer screening among Turkish women with global implications: a systematic review. Worldviews Evid Based Nurs 2017;14:316–23. https://doi.org/10.1111/wvn.12245 doi: 10.1111/wvn.12245. [DOI] [PubMed]
  99. Baron RC, Rimer BK, Breslow RA, Coates RJ, Kerner J, Melillo S, et al. Client-directed interventions to increase community demand for breast, cervical, and colorectal cancer screening a systematic review. Am J Prev Med 2008;35:34–55. https://doi.org/10.1016/j.amepre.2008.04.002 doi: 10.1016/j.amepre.2008.04.002. [DOI] [PubMed]
  100. Luque JS, Logan A, Soulen G, Armeson KE, Garrett DM, Davila CB, Ford ME. Systematic review of mammography screening educational interventions for Hispanic women in the United States. J Cancer Educ 2019;34:412–22. https://doi.org/10.1007/s13187-018-1321-0 doi: 10.1007/s13187-018-1321-0. [DOI] [PMC free article] [PubMed]
  101. Saei Ghare Naz M, Kariman N, Ebadi A, Ozgoli G, Ghasemi V, Rashidi Fakari F. Educational interventions for cervical cancer screening behavior of women: a systematic review. Asian Pac J Cancer Prev 2018;19:875–84. https://doi.org/10.22034/APJCP.2018.19.4.875 doi: 10.22034/APJCP.2018.19.4.875. [DOI] [PMC free article] [PubMed]
  102. Chan DN, So WK. A systematic review of randomised controlled trials examining the effectiveness of breast and cervical cancer screening interventions for ethnic minority women. Eur J Oncol Nurs 2015;19:536–53. https://doi.org/10.1016/j.ejon.2015.02.015 doi: 10.1016/j.ejon.2015.02.015. [DOI] [PubMed]
  103. Corcoran J, Dattalo P, Crowley M. Cervical cancer screening interventions for U.S. Latinas: a systematic review. Health Soc Work 2012;37:197–205. https://doi.org/10.1093/hsw/hls035 doi: 10.1093/hsw/hls035. [DOI] [PubMed]
  104. Morrow JB, Dallo FJ, Julka M. Community-based colorectal cancer screening trials with multi-ethnic groups: a systematic review. J Community Health 2010;35:592–601. https://doi.org/10.1007/s10900-010-9247-4 doi: 10.1007/s10900-010-9247-4. [DOI] [PubMed]
  105. Siddiqui MR, Sajid MS, Khatri K, Kanri B, Cheek E, Baig MK. The role of physician reminders in faecal occult blood testing for colorectal cancer screening. Eur J Gen Pract 2011;17:221–8. https://doi.org/10.3109/13814788.2011.601412 doi: 10.3109/13814788.2011.601412. [DOI] [PubMed]
  106. Baron RC, Melillo S, Rimer BK, Coates RJ, Kerner J, Habarta N, et al. Intervention to increase recommendation and delivery of screening for breast, cervical, and colorectal cancers by healthcare providers a systematic review of provider reminders. Am J Prev Med 2010;38:110–17. https://doi.org/10.1016/j.amepre.2009.09.031 doi: 10.1016/j.amepre.2009.09.031. [DOI] [PubMed]
  107. Copeland VC, Kim YJ, Eack SM. Effectiveness of interventions for breast cancer screening in African American women: a meta-analysis. Health Serv Res 2018;53:3170–88. https://doi.org/10.1111/1475-6773.12806 doi: 10.1111/1475-6773.12806. [DOI] [PMC free article] [PubMed]
  108. Escribà-Agüir V, Rodríguez-Gómez M, Ruiz-Pérez I. Effectiveness of patient-targeted interventions to promote cancer screening among ethnic minorities: a systematic review. Cancer Epidemiol 2016;44:22–39. https://doi.org/10.1016/j.canep.2016.07.009 doi: 10.1016/j.canep.2016.07.009. [DOI] [PubMed]
  109. Donnelly TT, Hwang J. Breast cancer screening interventions for Arabic women: a literature review. J Immigr Minor Health 2015;17:925–39. https://doi.org/10.1007/s10903-013-9902-9 doi: 10.1007/s10903-013-9902-9. [DOI] [PMC free article] [PubMed]
  110. Escoffery C, Rodgers KC, Kegler MC, Haardörfer R, Howard DH, Liang S, et al. A systematic review of special events to promote breast, cervical and colorectal cancer screening in the United States. BMC Public Health 2014;14:274. https://doi.org/10.1186/1471-2458-14-274 doi: 10.1186/1471-2458-14-274. [DOI] [PMC free article] [PubMed]
  111. Corcoran J, Dattalo P, Crowley M. Interventions to increase mammography rates among U.S. Latinas: a systematic review. J Womens Health 2010;19:1281–8. https://doi.org/10.1089/jwh.2009.1621 doi: 10.1089/jwh.2009.1621. [DOI] [PubMed]
  112. The Health Foundation. Complex Adaptive Systems. URL: www.health.org.uk/publications/complex-adaptive-systems (accessed 5 February 2020).
  113. Katerndahl DA. Lessons from Jurassic Park: patients as complex adaptive systems. J Eval Clin Pract 2009;15:755–60. https://doi.org/10.1111/j.1365-2753.2009.01228.x doi: 10.1111/j.1365-2753.2009.01228.x. [DOI] [PubMed]
  114. Javanparast S, Ward P, Young G, Wilson C, Carter S, Misan G, et al. How equitable are colorectal cancer screening programs which include FOBTs? A review of qualitative and quantitative studies. Prev Med 2010;50:165–72. https://doi.org/10.1016/j.ypmed.2010.02.003 doi: 10.1016/j.ypmed.2010.02.003. [DOI] [PubMed]
  115. Macintyre S. Inequalities in Health in Scotland: What Are They and What Can We Do About Them. 2007. URL: http://eprints.gla.ac.uk/81903/ (accessed 17 January 2020).
  116. White M, Adams J, Heywood P. How and Why Do Interventions That Increase Health Overall Widen Inequalities Within Populations? Bristol: Policy Press; 2009. https://doi.org/10.1332/policypress/9781847423207.003.0005 doi: 10.1332/policypress/9781847423207.003.0005. [DOI]
  117. Asaria M, Griffin S, Cookson R, Whyte S, Tappenden P. Distributional cost-effectiveness analysis of health care programmes – a methodological case study of the UK Bowel Cancer Screening Programme. Health Econ 2015;24:742–54. https://doi.org/10.1002/hec.3058 doi: 10.1002/hec.3058. [DOI] [PubMed]
  118. Rutter H, Savona N, Glonti K, Bibby J, Cummins S, Finegood DT, et al. The need for a complex systems model of evidence for public health. Lancet 2017;390:2602–4. https://doi.org/10.1016/S0140-6736(17)31267-9 doi: 10.1016/S0140-6736(17)31267-9. [DOI] [PubMed]
  119. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci 2011;6:42. https://doi.org/10.1186/1748-5908-6-42 doi: 10.1186/1748-5908-6-42. [DOI] [PMC free article] [PubMed]
  120. Perry C, Chhatralia K, Damesick D, Hobden S, Volpe L. Behavioural Insights in Health Care. URL: www.health.org.uk/publications/behavioural-insights-in-health-care (accessed 31 January 2020).
  121. Mindell J, Biddulph JP, Hirani V, Stamatakis E, Craig R, Nunn S, Shelton N. Cohort profile: the Health Survey for England. Int J Epidemiol 2012;41:1585–93. https://doi.org/10.1093/ije/dyr199 doi: 10.1093/ije/dyr199. [DOI] [PubMed]
  122. Great Britain. Health and Social Care Act 2012. London: The Stationery Office; 2012. https://doi.org/10.12968/eqhe.2012.1.7.5 doi: 10.12968/eqhe.2012.1.7.5. [DOI]
  123. Office for National Statistics. Lower Layer Super Output Area Population Estimates (Supporting Information). URL: www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/lowersuperoutputareamidyearpopulationestimates (accessed 14 December 2018).
  124. Office for National Statistics. Ethnic Group by Sex by Age. 2013. URL: www.nomisweb.co.uk/census/2011/lc2101ew (accessed 11 February 2020).
  125. Office for National Statistics. Population Projections – Local Authorities: SNPP Z1. URL: www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/datasets/localauthoritiesinenglandz1 (accessed 13 December 2018).
  126. Office for National Statistics. National Population Projections. URL: www.ons.gov.uk/ons/publications/all-releases.html?definition=tcm%3A77-21600 (accessed 13 March 2014).
  127. UK Data Service. Health Survey for England, 2003. URL: https://doi.org/10.5255/UKDA-SN-5098-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-5098-1. [DOI]
  128. UK Data Service. Health Survey for England, 2004. URL: https://doi.org/10.5255/UKDA-SN-5439-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-5439-1. [DOI]
  129. UK Data Service. Health Survey for England, 2005. URL:: https://doi.org/10.5255/UKDA-SN-5675-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-5675-1. [DOI]
  130. UK Data Service. Health Survey for England, 2006. URL: https://doi.org/10.5255/UKDA-SN-5809-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-5809-1. [DOI]
  131. UK Data Service. Health Survey for England, 2007. URL: https://doi.org/10.5255/UKDA-SN-6112-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-6112-1. [DOI]
  132. UK Data Service. Health Survey for England, 2008. URL: https://doi.org/10.5255/UKDA-SN-6397-2 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-6397-2. [DOI]
  133. UK Data Service. Health Survey for England, 2009. URL: https://doi.org/10.5255/UKDA-SN-6732-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-6732-1. [DOI]
  134. UK Data Service. Health Survey for England, 2010. URL: https://doi.org/10.5255/UKDA-SN-6986-2 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-6986-2. [DOI]
  135. UK Data Service. Health Survey for England, 2011. URL: https://doi.org/10.5255/UKDA-SN-7260-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-7260-1. [DOI]
  136. UK Data Service. Health Survey for England, 2012. URL: https://doi.org/10.5255/UKDA-SN-7480-1 (accessed 1 May 2014). doi: 10.5255/UKDA-SN-7480-1. [DOI]
  137. UK Data Service. Health Survey for England, 2013. URL: https://doi.org/10.5255/UKDA-SN-7649-1 (accessed 1 May 2016). doi: 10.5255/UKDA-SN-7649-1. [DOI]
  138. UK Data Service. Health Survey for England, 2014. URL: https://doi.org/10.5255/UKDA-SN-7919-1 (accessed 1 May 2016). doi: 10.5255/UKDA-SN-7919-1. [DOI]
  139. Rahman A. Estimating small area health-related characteristics of populations: a methodological review. Geospat Health 2017;12:495. https://doi.org/10.4081/gh.2017.495 doi: 10.4081/gh.2017.495. [DOI] [PubMed]
  140. Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F. Flexible Regression and Smoothing: Using GAMLSS in R. Boca Raton, FL: CRC Press/Taylor & Francis Group; 2017. https://doi.org/10.1201/b21973 doi: 10.1201/b21973. [DOI]
  141. Rigby RA, Stasinopoulos MD, Heller GZ, De Bastiani F. Distributions for Modelling Location, Scale, and Shape Using GAMLSS in R. New York, NY: CRC Press, LLC; 2019. https://doi.org/10.1201/9780429298547 doi: 10.1201/9780429298547. [DOI]
  142. Suen S, Goldhaber-Fiebert JD, Basu S. Matching microsimulation risk factor correlations to cross-sectional data: the shortest distance method. Med Decis Making 2018;38:452–64. https://doi.org/10.1177/0272989X17741635 doi: 10.1177/0272989X17741635. [DOI] [PMC free article] [PubMed]
  143. Embrechts P, Lindskog F, Mcneil A. Modelling Dependence with Copulas and Applications to Risk Management. In Rachev ST, editor. Handbook of Heavy Tailed Distributions in Finance. Amsterdam: Elsevier; 2003. pp. 329–84.
  144. Levin ML. The occurrence of lung cancer in man. Acta – Unio Int Contra Cancrum 1953;9:531–41. [PubMed]
  145. World Health Organization. International Statistical Classification Of Diseases and Related Health Problems – 10th Revision. Fifth Edition. Geneva; 2016.
  146. Pujades-Rodriguez M, Timmis A, Stogiannis D, Rapsomaniki E, Denaxas S, Shah A, et al. Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention. PLOS One 2014;9:e104671. https://doi.org/10.1371/journal.pone.0104671 doi: 10.1371/journal.pone.0104671. [DOI] [PMC free article] [PubMed]
  147. Barendregt JJ, Van Oortmarssen GJ, Vos T, Murray CJ. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metr 2003;1:4. https://doi.org/10.1186/1478-7954-1-4 doi: 10.1186/1478-7954-1-4. [DOI] [PMC free article] [PubMed]
  148. 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. https://doi.org/10.1016/S0140-6736(12)61766-8 doi: 10.1016/S0140-6736(12)61766-8. [DOI] [PMC free article] [PubMed]
  149. Boshuizen HC, Lhachimi SK, van Baal PH, Hoogenveen RT, Smit HA, Mackenbach JP, Nusselder WJ. The DYNAMO-HIA model: an efficient implementation of a risk factor/chronic disease Markov model for use in Health Impact Assessment (HIA). Demography 2012;49:1259–83. https://doi.org/10.1007/s13524-012-0122-z doi: 10.1007/s13524-012-0122-z. [DOI] [PubMed]
  150. Hippisley-Cox J, Coupland C, Brindle P. The performance of seven QPrediction risk scores in an independent external sample of patients from general practice: a validation study. BMJ Open 2014;4:e005809. https://doi.org/10.1136/bmjopen-2014-005809 doi: 10.1136/bmjopen-2014-005809. [DOI] [PMC free article] [PubMed]
  151. Hyndman RJ. R Package ‘Demography’: Forecasting Mortality, Fertility, Migration and Population Data. 2017. URL: https://cran.r-project.org/web/packages/demography/demography.pdf (accessed 10 October 2019).
  152. Hyndman RJ, Shahid Ullah M. Robust forecasting of mortality and fertility rates: a functional data approach. Comput Stat Data Anal 2007;51:4942–56. https://doi.org/10.1016/j.csda.2006.07.028 doi: 10.1016/j.csda.2006.07.028. [DOI]
  153. Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women. Lancet 2017;389:1229–37. https://doi.org/10.1016/S0140-6736(16)32380-7 doi: 10.1016/S0140-6736(16)32380-7. [DOI] [PMC free article] [PubMed]
  154. Law MR, Wald NJ, Rudnicka AR. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis. BMJ 2003;326:1423. https://doi.org/10.1136/bmj.326.7404.1423 doi: 10.1136/bmj.326.7404.1423. [DOI] [PMC free article] [PubMed]
  155. Sattar N, Preiss D, Murray HM, Welsh P, Buckley BM, de Craen AJ, et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 2010;375:735–42. https://doi.org/10.1016/S0140-6736(09)61965-6 doi: 10.1016/S0140-6736(09)61965-6. [DOI] [PubMed]
  156. King W, Lacey A, White J, Farewell D, Dunstan F, Fone D. Socioeconomic inequality in medication persistence in primary and secondary prevention of coronary heart disease – a population-wide electronic cohort study. PLOS ONE 2018;13:e0194081. https://doi.org/10.1371/journal.pone.0194081 doi: 10.1371/journal.pone.0194081. [DOI] [PMC free article] [PubMed]
  157. Wallach-Kildemoes H, Andersen M, Diderichsen F, Lange T. Adherence to preventive statin therapy according to socioeconomic position. Eur J Clin Pharmacol 2013;69:1553–63. https://doi.org/10.1007/s00228-013-1488-6 doi: 10.1007/s00228-013-1488-6. [DOI] [PubMed]
  158. Janssen B, Szende A. Population Norms for the EQ-5D. In Szende A, Janssen B, Cabases J, editors. Self-Reported Population Health: An International Perspective Based on EQ-5D. Dordrecht: Springer Netherlands; 2014. pp. 19–30.
  159. 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 doi: 10.1177/0272989X11401031. [DOI] [PubMed]
  160. Licchetta M, Stelmach M. Fiscal Sustainability Analytical Paper: Fiscal Sustainability and Public Spending on Health. London: Office for Budget Responsibility; 2016.
  161. Davies A, Bardsley M, Davies S, Georghiou T. Understanding Patterns of Health and Social Care at the End of Life. London: Nuffield Trust; 2017. URL: www.nuffieldtrust.org.uk/research/understanding-patterns-of-health-and-social-care-at-the-end-of-life (accessed 21 November 2019).
  162. Rowen D, Dixon S, Hernández-Alava M, Mukuria C. Estimating informal care inputs associated with EQ-5D for use in economic evaluation. Eur J Health Econ 2016;17:733–44. https://doi.org/10.1007/s10198-015-0718-5 doi: 10.1007/s10198-015-0718-5. [DOI] [PubMed]
  163. 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 doi: 10.1002/hec.3130. [DOI] [PubMed]
  164. Collins B, Kypridemos C, Cookson R, Parvulescu P, McHale P, Guzman-Castillo M, et al. Universal or targeted cardiovascular screening? Modelling study using a sector-specific distributional cost effectiveness analysis. Prev Med 2020;130:105879. https://doi.org/10.1016/j.ypmed.2019.105879 doi: 10.1016/j.ypmed.2019.105879. [DOI] [PubMed]
  165. Mackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med 1997;44:757–71. https://doi.org/10.1016/S0277-9536(96)00073-1 doi: 10.1016/S0277-9536(96)00073-1. [DOI] [PubMed]
  166. Collins B, Griffin S, Asaria M, Capewell S, Love-Koh J, Kypridemos C, et al. How Do We Include Health Inequality Impacts in Economic Analysis of Policy Options? URL: www.york.ac.uk/che/news/news-2018/how-do-we-include-health-inequality-impacts/ (accessed 31 January 2020).
  167. Groot Koerkamp B, Stijnen T, Weinstein MC, Hunink MG. The combined analysis of uncertainty and patient heterogeneity in medical decision models. Med Decis Making 2011;31:650–61. https://doi.org/10.1177/0272989X10381282 doi: 10.1177/0272989X10381282. [DOI] [PubMed]
  168. Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD, ISPOR-SMDM Modeling Good Research Practices Task Force. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force – 6. Value Health 2012;15:835–42. https://doi.org/10.1016/j.jval.2012.04.014 doi: 10.1016/j.jval.2012.04.014. [DOI] [PubMed]
  169. Krogsbøll LT, Jørgensen KJ, Gøtzsche PC. General health checks in adults for reducing morbidity and mortality from disease. Cochrane Database Syst Rev 2019;1:CD009009. https://doi.org/10.1002/14651858.CD009009.pub3 doi: 10.1002/14651858.CD009009.pub3. [DOI]
  170. Gidlow C, Ellis N, Randall J, Cowap L, Smith G, Iqbal Z, Kumar J. Method of invitation and geographical proximity as predictors of NHS Health Check uptake. J Public Health 2015;37:195–201. https://doi.org/10.1093/pubmed/fdu092 doi: 10.1093/pubmed/fdu092. [DOI] [PMC free article] [PubMed]
  171. Attwood S, Morton K, Sutton S. Exploring equity in uptake of the NHS Health Check and a nested physical activity intervention trial. J Public Health 2016;38:560–8. https://doi.org/10.1093/pubmed/fdv070 doi: 10.1093/pubmed/fdv070. [DOI] [PMC free article] [PubMed]
  172. Public Health England. Cardiovascular Disease Profiles. URL: https://fingertips.phe.org.uk/profile-group/cardiovascular-disease-diabetes-kidney-disease/profile/cardiovascular/data#page/0/page-options/ovw-do-0 (accessed 1 March 2021).
  173. Sallis A, Gold N, Agbebiyi A, James RJE, Berry D, Bonus A, et al. Increasing uptake of National Health Service Health Checks in primary care: a pragmatic randomized controlled trial of enhanced invitation letters in Northamptonshire, England. J Public Health 2021;43:e92–e99. https://doi.org/10.1093/pubmed/fdz134 doi: 10.1093/pubmed/fdz134. [DOI] [PubMed]
  174. Department of Health and Social Care. Advancing our Health: Prevention in the 2020s – Consultation Document. London: Department of Health and Social Care; 2019.
  175. Palmer AJ, Si L, Tew M, Hua X, Willis MS, Asseburg C, et al. Computer modeling of diabetes and its transparency: a report on the eighth mount hood challenge. Value Health 2018;21:724–31. https://doi.org/10.1016/j.jval.2018.02.002 doi: 10.1016/j.jval.2018.02.002. [DOI] [PMC free article] [PubMed]
  176. Bandosz P, Ahmadi-Abhari S, Guzman-Castillo M, Pearson-Stuttard J, Collins B, Whittaker H, et al. Potential impact of diabetes prevention on mortality and future burden of dementia and disability: a modelling study. Diabetologia 2020;63:104–15. https://doi.org/10.1007/s00125-019-05015-4 doi: 10.1007/s00125-019-05015-4. [DOI] [PMC free article] [PubMed]
  177. Capewell S, O’Flaherty M. Rapid mortality falls after risk-factor changes in populations. Lancet 2011;378:752–3. https://doi.org/10.1016/S0140-6736(10)62302-1 doi: 10.1016/S0140-6736(10)62302-1. [DOI] [PubMed]
  178. Crandall JP, Mather K, Rajpathak SN, Goldberg RB, Watson K, Foo S, et al. Statin use and risk of developing diabetes: results from the Diabetes Prevention Program. BMJ Open Diabetes Res Care 2017;5:e000438. https://doi.org/10.1136/bmjdrc-2017-000438 doi: 10.1136/bmjdrc-2017-000438. [DOI] [PMC free article] [PubMed]
  179. Casula M, Mozzanica F, Scotti L, Tragni E, Pirillo A, Corrao G, Catapano AL. Statin use and risk of new-onset diabetes: a meta-analysis of observational studies. Nutr Metab Cardiovasc Dis 2017;27:396–406. https://doi.org/10.1016/j.numecd.2017.03.001 doi: 10.1016/j.numecd.2017.03.001. [DOI] [PubMed]
  180. Capewell S. Will screening individuals at high risk of cardiovascular events deliver large benefits? No. BMJ 2008;337:a1395. https://doi.org/10.1136/bmj.a1395 doi: 10.1136/bmj.a1395. [DOI] [PubMed]
  181. Calder M, Craig C, Culley D, de Cani R, Donnelly CA, Douglas R, et al. Computational modelling for decision-making: where, why, what, who and how. R Soc Open Sci 2018;5:172096. https://doi.org/10.1098/rsos.172096 doi: 10.1098/rsos.172096. [DOI] [PMC free article] [PubMed]
  182. Gilbert N, Ahrweiler P, Barbrook-Johnson P, Narasimhan KP, Wilkinson H. Computational modelling of public policy: reflections on practice. J Artif Soc Soc Simul 2018;21:14. https://doi.org/10.18564/jasss.3669 doi: 10.18564/jasss.3669. [DOI]
  183. Ruiz M, Zabaleta N, Elorza U. Decision Making Through Simulation in Public Policy Management Field. INTED 2016 10th annual International Technology, Education and Development Conference, Valencia, Spain, 7–9 March 2016. https://doi.org/10.21125/inted.2016.0911 doi: 10.21125/inted.2016.0911. [DOI]
  184. Kneale D, Rojas-García A, Raine R, Thomas J. The use of evidence in English local public health decision-making: a systematic scoping review. Implement Sci 2017;12:53. https://doi.org/10.1186/s13012-017-0577-9 doi: 10.1186/s13012-017-0577-9. [DOI] [PMC free article] [PubMed]
  185. Public Health England. Recent Trends in Mortality in England: Review and Data Packs. London: NHS England; 2018.
  186. Tricco AC, Antony J, Zarin W, Strifler L, Ghassemi M, Ivory J, et al. A scoping review of rapid review methods. BMC Med 2015;13:224. https://doi.org/10.1186/s12916-015-0465-6 doi: 10.1186/s12916-015-0465-6. [DOI] [PMC free article] [PubMed]
  187. Spaniol MJ, Rowland NJ. Defining scenario. Futur Foresight Sci 2019;1:e3. https://doi.org/10.1002/ffo2.3 doi: 10.1002/ffo2.3. [DOI]
  188. Durance P, Godet M. Scenario building: uses and abuses. Technol Forecast Soc Change 2010;77:1488–92. https://doi.org/10.1016/j.techfore.2010.06.007 doi: 10.1016/j.techfore.2010.06.007. [DOI]
  189. Brailsford SC, Bolt T, Connell C, Klein JH, Patel B. Stakeholder Engagement in Health Care Simulation. WSC ’09: Winter Simulation Conference, Austin, TX, USA, December 2009. https://doi.org/10.1109/WSC.2009.5429190 doi: 10.1109/WSC.2009.5429190. [DOI]
  190. Jahangirian M, Taylor SJE, Eatock J, Stergioulas LK, Taylor PM. Causal study of low stakeholder engagement in healthcare simulation projects. J Oper Res Soc 2015;66:369–79. https://doi.org/10.1057/jors.2014.1 doi: 10.1057/jors.2014.1. [DOI]
  191. Jahangirian M, Borsci S, Shah SGS, Taylor SJE. Causal factors of low stakeholder engagement: a survey of expert opinions in the context of healthcare simulation projects. Simulation 2015;91:511–26. https://doi.org/10.1177/0037549715583150 doi: 10.1177/0037549715583150. [DOI]
  192. Caro JJ, Briggs AH, Siebert U, Kuntz KM, ISPOR-SMDM Modeling Good Research Practices Task Force. 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 doi: 10.1016/j.jval.2012.06.012. [DOI] [PubMed]
  193. Capewell S, Capewell A. An effectiveness hierarchy of preventive interventions: neglected paradigm or self-evident truth? J Public Health 2018;40:350–8. https://doi.org/10.1093/pubmed/fdx055 doi: 10.1093/pubmed/fdx055. [DOI] [PubMed]
  194. Adams J, Mytton O, White M, Monsivais P. Why are some population interventions for diet and obesity more equitable and effective than others? The role of individual agency. PLOS Med 2016;13:e1001990. https://doi.org/10.1371/journal.pmed.1001990 doi: 10.1371/journal.pmed.1001990. [DOI] [PMC free article] [PubMed]
  195. Moore GF, Evans RE, Hawkins J, Littlecott H, Melendez-Torres GJ, Bonell C, Murphy S. From complex social interventions to interventions in complex social systems: future directions and unresolved questions for intervention development and evaluation. Evaluation 2019;25:23–45. https://doi.org/10.1177/1356389018803219 doi: 10.1177/1356389018803219. [DOI] [PMC free article] [PubMed]
  196. Frew E, Breheny K. Methods for public health economic evaluation: a Delphi survey of decision makers in English and Welsh local government. Health Econ 2019;28:1052–63. https://doi.org/10.1002/hec.3916 doi: 10.1002/hec.3916. [DOI] [PubMed]
  197. Mahase E. Changes to NHS health checks must be evidence based and beneficial, say GPs. BMJ 2019;366:l5201. https://doi.org/10.1136/bmj.l5201 doi: 10.1136/bmj.l5201. [DOI] [PubMed]
  198. Dias L, Morton A, Quigley J. Elicitation – The Science and Art of Structuring Judgement. New York, NY: Springer; 2018. https://doi.org/10.1007/978-3-319-65052-4 doi: 10.1007/978-3-319-65052-4. [DOI]
  199. O’Hagan A. Expert knowledge elicitation: subjective but scientific. Am Stat 2019;73:69–81. https://doi.org/10.1080/00031305.2018.1518265 doi: 10.1080/00031305.2018.1518265. [DOI]
  200. Gillespie DO, Allen K, Guzman-Castillo M, Bandosz P, Moreira P, McGill R, et al. The health equity and effectiveness of policy options to reduce dietary salt intake in England: policy forecast. PLOS ONE 2015;10:e0127927. https://doi.org/10.1371/journal.pone.0127927 doi: 10.1371/journal.pone.0127927. [DOI] [PMC free article] [PubMed]
  201. O’Hagan T. SHELF: Version 4. URL: www.tonyohagan.co.uk/shelf/SHELF4.html (accessed 28 September 2020).
  202. Webber L, Mytton OT, Briggs AD, Woodcock J, Scarborough P, McPherson K, Capewell S. The Brighton declaration: the value of non-communicable disease modelling in population health sciences. Eur J Epidemiol 2014;29:867–70. https://doi.org/10.1007/s10654-014-9978-0 doi: 10.1007/s10654-014-9978-0. [DOI] [PMC free article] [PubMed]
  203. Sexton E, McLoughlin A, Williams DJ, Merriman NA, Donnelly N, Rohde D, et al. Systematic review and meta-analysis of the prevalence of cognitive impairment no dementia in the first-year post-stroke. Eur Stroke J 2019:4;160–71. https://doi.org/10.1177/2396987318825484 doi: 10.1177/2396987318825484. [DOI] [PMC free article] [PubMed]
  204. Public Health England. Technical Document for Sub-National English Atrial Fibrillation Prevalence Estimates. URL: www.gov.uk/government/publications/atrial-fibrillation-prevalence-estimates-for-local-populations (accessed 3 March 2021).
  205. Symmons D, Turner G, Webb R, Asten P, Barrett E, Lunt M, et al. The prevalence of rheumatoid arthritis in the United Kingdom: new estimates for a new century. Rheumatology 2002;41:793–800. https://doi.org/10.1093/rheumatology/41.7.793 doi: 10.1093/rheumatology/41.7.793. [DOI] [PubMed]
  206. Ezzati M, Henley SJ, Thun MJ, Lopez AD. Role of smoking in global and regional cardiovascular mortality. Circulation 2005;112:489–97. https://doi.org/10.1161/CIRCULATIONAHA.104.521708 doi: 10.1161/CIRCULATIONAHA.104.521708. [DOI] [PubMed]
  207. Macacu A, Autier P, Boniol M, Boyle P. Active and passive smoking and risk of breast cancer: a meta-analysis. Breast Cancer Res Treat 2015;154:213–24. https://doi.org/10.1007/s10549-015-3628-4 doi: 10.1007/s10549-015-3628-4. [DOI] [PubMed]
  208. Huxley RR, Woodward M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: a systematic review and meta-analysis of prospective cohort studies. Lancet 2011;378:1297–305. https://doi.org/10.1016/S0140-6736(11)60781-2 doi: 10.1016/S0140-6736(11)60781-2. [DOI] [PubMed]
  209. Wolf PA, D’Agostino RB, Kannel WB, Bonita R, Belanger AJ. Cigarette smoking as a risk factor for stroke: the Framingham study. JAMA 1988;259:1025–9. https://doi.org/10.1001/jama.1988.03720070025028 doi: 10.1001/jama.1988.03720070025028. [DOI] [PubMed]
  210. Forey BA, Thornton AJ, Lee PN. Systematic review with meta-analysis of the epidemiological evidence relating smoking to COPD, chronic bronchitis and emphysema. BMC Pulm Med 2011;11:36. https://doi.org/10.1186/1471-2466-11-36 doi: 10.1186/1471-2466-11-36. [DOI] [PMC free article] [PubMed]
  211. Liang PS, Chen T-Y, Giovannucci E. Cigarette smoking and colorectal cancer incidence and mortality: systematic review and meta-analysis. Int J Cancer 2009;124:2406–15. https://doi.org/10.1002/ijc.24191 doi: 10.1002/ijc.24191. [DOI] [PubMed]
  212. Tammemägi MC, Church TR, Hocking WG, Silvestri GA, Kvale PA, Riley TL, et al. Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts. PLOS Med 2014;11:e1001764. https://doi.org/10.1371/journal.pmed.1001764 doi: 10.1371/journal.pmed.1001764. [DOI] [PMC free article] [PubMed]
  213. He J, Vupputuri S, Allen K, Prerost MR, Hughes J, Whelton PK. Passive smoking and the risk of coronary heart disease – a meta-analysis of epidemiologic studies. N Engl J Med 1999;340:920–6. https://doi.org/10.1056/NEJM199903253401204 doi: 10.1056/NEJM199903253401204. [DOI] [PubMed]
  214. Oono IP, Mackay DF, Pell JP. Meta-analysis of the association between secondhand smoke exposure and stroke. J Public Health 2011;33:496–502. https://doi.org/10.1093/pubmed/fdr025 doi: 10.1093/pubmed/fdr025. [DOI] [PubMed]
  215. Fischer F, Kraemer A. Meta-analysis of the association between second-hand smoke exposure and ischaemic heart diseases, COPD and stroke. BMC Public Health 2015;15:1202. https://doi.org/10.1186/s12889-015-2489-4 doi: 10.1186/s12889-015-2489-4. [DOI] [PMC free article] [PubMed]
  216. Kim CH, Lee Y-CA, Hung RJ, McNallan SR, Cote ML, Lim W-Y, et al. Exposure to secondhand tobacco smoke and lung cancer by histological type: a pooled analysis of the International Lung Cancer Consortium (ILCCO). Int J Cancer 2014;135:1918–30. https://doi.org/10.1002/ijc.28835 doi: 10.1002/ijc.28835. [DOI] [PMC free article] [PubMed]
  217. Global Health Data Exchange. Global Burden of Disease Study 2016 (GBD 2016) Data Resources. URL: http://ghdx.healthdata.org/gbd-2016 (accessed 3 March 2021).
  218. 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. https://doi.org/10.1016/S0140-6736(02)11911-8 doi: 10.1016/S0140-6736(02)11911-8. [DOI] [PubMed]
  219. 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 doi: 10.1016/S0140-6736(07)61778-4. [DOI] [PubMed]
  220. The Emerging Risk Factors Collaboration. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet 2011;377:1085–95. https://doi.org/10.1016/S0140-6736(11)60105-0 doi: 10.1016/S0140-6736(11)60105-0. [DOI] [PMC free article] [PubMed]
  221. The Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 2010;375:2215–22. https://doi.org/10.1016/S0140-6736(10)60484-9 doi: 10.1016/S0140-6736(10)60484-9. [DOI] [PMC free article] [PubMed]
  222. Bull FC, Armstrong TP, Dixon T, Ham S, Neiman A, Pratt M. Comparative Quantification of Health Risks. Chapter 10: Physical Inactivity. Geneva: World Health Organization; 2004.
  223. 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. https://doi.org/10.1093/jn/136.10.2588 doi: 10.1093/jn/136.10.2588. [DOI] [PubMed]
  224. 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. https://doi.org/10.1212/01.wnl.0000180600.09719.53 doi: 10.1212/01.wnl.0000180600.09719.53. [DOI] [PubMed]
  225. Wang Y, Li F, Wang Z, Qiu T, Shen Y, Wang M. Fruit and vegetable consumption and risk of lung cancer: a dose–response meta-analysis of prospective cohort studies. Lung Cancer 2015;88:124–30. https://doi.org/10.1016/j.lungcan.2015.02.015 doi: 10.1016/j.lungcan.2015.02.015. [DOI] [PubMed]
  226. 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 doi: 10.1093/annonc/mdv381. [DOI] [PubMed]
  227. Vieira J, Mathur R, Shah AD, Pujades-Rodriguez M, Denaxas S, Smeeth L, et al. Ethnicity and the first diagnosis of a wide range of cardiovascular diseases: associations in a linked electronic health record cohort of 1 million patients. PLOS ONE 2017;12:e0178945. https://doi.org/10.1371/journal.pone.0178945 doi: 10.1371/journal.pone.0178945. [DOI] [PMC free article] [PubMed]
  228. Christiansen CB, Gerds TA, Olesen JB, Kristensen SL, Lamberts M, Lip GYH, et al. Atrial fibrillation and risk of stroke: a nationwide cohort study. Europace 2016;18:1689–97. https://doi.org/10.1093/europace/euv401 doi: 10.1093/europace/euv401. [DOI] [PubMed]
  229. Singer DE, Chang Y, Borowsky LH, Fang MC, Pomernacki NK, Udaltsova N, et al. A new risk scheme to predict ischemic stroke and other thromboembolism in atrial fibrillation: the ATRIA study stroke risk score. J Am Heart Assoc 2013;2:e000250. https://doi.org/10.1161/JAHA.113.000250 doi: 10.1161/JAHA.113.000250. [DOI] [PMC free article] [PubMed]
  230. Wobus DZ, Olin GL. Health Care Expenses: Poor, Near Poor, and Low Income People in the United States Civilian Noninstitutionalized Population, 2002. Rockville, MD: Agency for Healthcare Research and Quality; 2005.
  231. HM Treasury. GDP Deflators at Market Prices, and Money GDP. URL: www.gov.uk/government/collections/gdp-deflators-at-market-prices-and-money-gdp (accessed 1 October 2019).
  232. National Institute for Health and Care Excellence. Hypertension Update: Appendix J Cost Effectiveness Analysis. URL: www.nice.org.uk/guidance/cg127/documents/hypertension-update-appendix-j-costeffectiveness-analysis-diagnosis2 (accessed 10 February 2020).
  233. Regional Drug and Therapeutics Centre. Cost Comparison Charts August 2019. Newcastle upon Tyne: Regional Drug and Therapeutics Centre; 2019.
  234. Stewart S, Murphy NF, Murphy N, Walker A, McGuire A, McMurray JJ. Cost of an emerging epidemic: an economic analysis of atrial fibrillation in the UK. Heart 2004;90:286–92. https://doi.org/10.1136/hrt.2002.008748 doi: 10.1136/hrt.2002.008748. [DOI] [PMC free article] [PubMed]
  235. Hex N, Bartlett C, Wright D, Taylor M, Varley D. Estimating the current and future costs of type 1 and type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet Med 2012;29:855–62. https://doi.org/10.1111/j.1464-5491.2012.03698.x doi: 10.1111/j.1464-5491.2012.03698.x. [DOI] [PubMed]
  236. Luengo-Fernández R, Leal J, Gray A, Petersen S, Rayner M. Cost of cardiovascular diseases in the United Kingdom. Heart 2006;92:1384–9. https://doi.org/10.1136/hrt.2005.072173 doi: 10.1136/hrt.2005.072173. [DOI] [PMC free article] [PubMed]
  237. Ward S, Lloyd Jones M, Pandor A, Holmes M, Ara R, Ryan A, et al. A systematic review and economic evaluation of statins for the prevention of coronary events. Health Technol Assess 2007;11(14). https://doi.org/10.3310/hta11140 doi: 10.3310/hta11140. [DOI] [PubMed]
  238. Punekar YS, Shukla A, Müllerova H. COPD management costs according to the frequency of COPD exacerbations in UK primary care. Int J Chron Obstruct Pulmon Dis 2014;9:65–73. https://doi.org/10.2147/COPD.S54417 doi: 10.2147/COPD.S54417. [DOI] [PMC free article] [PubMed]
  239. Kerr M, Bray B, Medcalf J, O’Donoghue DJ, Matthews B. Estimating the financial cost of chronic kidney disease to the NHS in England. Nephrol Dial Transplant 2012;27:iii73-80. https://doi.org/10.1093/ndt/gfs269 doi: 10.1093/ndt/gfs269. [DOI] [PMC free article] [PubMed]
  240. Luengo-Fernandez R, Leal J, Gray AM. Cost of dementia in the pre-enlargement countries of the European Union. J Alzheimers Dis 2011;27:187–96. https://doi.org/10.3233/JAD-2011-102019 doi: 10.3233/JAD-2011-102019. [DOI] [PubMed]
  241. Laudicella M, Walsh B, Burns E, Smith PC. Cost of care for cancer patients in England: evidence from population-based patient-level data. Br J Cancer 2016;114:1286–92. https://doi.org/10.1038/bjc.2016.77 doi: 10.1038/bjc.2016.77. [DOI] [PMC free article] [PubMed]
  242. Lee S, Shafe AC, 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 doi: 10.1136/bmjopen-2011-000269. [DOI] [PMC free article] [PubMed]
  243. Prince M, Knapp M, Guerchet M, McCrone P, Prina M, Comas-Herrera A, et al. Dementia UK: Second Edition – Overview 2014. URL: www.alzheimers.org.uk/sites/default/files/migrate/downloads/dementia_uk_update.pdf (accessed 10 October 2019).
  244. Miners A, Cairns J, Wailoo A. Department of Health Proposals for Including Wider Societal Benefits into Value Based Pricing: A Description and Critique. London: NICE Decision Support Unit; 2013.
  245. Office for National Statistics. Employee Earnings in the UK Statistical Bulletins. URL: www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2018 (accessed 1 October 2019).
  246. Office of National Statistics. Unemployment. URL: www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment (accessed 21 November 2019).
  247. GOV.UK. Family Resources Survey. URL: www.gov.uk/government/collections/family-resources-survey--2 (accessed 2 September 2020).

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