Economic evaluation and modelling methods in the context of bottlenecks in the diagnosis and treatment of cancer
Project theme: Methods development
This is a joint project with the Policy Research Unit in Cancer Awareness, Screening and Early Diagnosis. Over recent years there has been an increased failure to meet waiting time targets with regards to the identification and treatment of cancers [1]. This situation has been further exacerbated by the COVID-19 pandemic [1–3]. Delayed identification and treatment of cancers can lead to adverse health outcomes and deaths [4,5]. Policies and interventions are needed to help reduce cancer waiting times to improve population health. These could include, for example, alternative stratification approaches to prioritize certain groups, technological solutions (e.g. use of triage tests such as the cytosponge for suspected Barrett’ oesophagus [6] or faecal calprotectin for suspect colorectal cancer [7]), increased staffing or task shifting.
To help inform decision making regarding the choice of policies and interventions, evidence on the resource requirements, costs, benefits and impacts on population health of the alternative approaches is required. Economic evaluation and decision analysis provide a framework for helping to inform the decisions by assessing the population health impact of the policies under consideration [8–10]. However, standard approaches to modelling, economic evaluation and decision analysis may not be appropriate for the evaluation of the policies and interventions being considered to help reduce cancer waiting times [10]. There may be multiple constraints on care (financial and non-financial) and the financial costs of particular staff (e.g. endoscopists) may not accurately reflect their value if they are particularly constrained [11–13]. Further, the interdependence of the different components of the system means they cannot be considered in isolation or the options under consideration are complex, can be used in combination and potentially evolve over time (e.g. a decision to increase staff may take several years to implement) [10,12,13]. As a result, novel approaches to economic evaluation bringing in insights from other fields, such as operational research, should be considered so that evidence on the impacts of the options available on population health can be generated [14–17].
Aims
This project will aim to develop approaches to the economic evaluation of policies and interventions which reduce waiting times in the diagnosis and/or treatment of cancer and apply them to a case study in one type of cancer to inform policy by:
i) Developing a case study in one area of cancer and evaluate potential policy options to alleviate the waiting times.
ii) Demonstrating how the methods developed can be applied to tackle waiting times for other cancers and other diseases.
Project Team
Simon Walker (EEPRU, York), Mark Sculpher (EEPRU, York), Marta Soares (EEPRU, York), Willie Hamilton (CASED PRU Exeter), Fiona Walter, (CASED PRU, QMUL), Daniel Vulkan, (CASED PRU, QMUL)
References:
1. Cancer waiting times | The Nuffield Trust [Internet]. [cited 2021 Jun 10]. Available from: https://www.nuffieldtrust.org.uk/resource/cancer-waiting-time-targets
2. Restarting endoscopy: how can rising waiting lists be addressed? – Hospital Times [Internet]. [cited 2021 Jun 4]. Available from: https://www.hospitaltimes.co.uk/restarting-endoscopy-how-can-rising-waiting-lists-be-addressed/
3. Waiting times soar for bowel cancer diagnostic tests | Bowel Cancer UK [Internet]. [cited 2021 Jun 4]. Available from: https://www.bowelcanceruk.org.uk/news-and-blogs/news/waiting-times-soar-for-bowel-cancer-diagnostic-tests/
4. Allgar VL, Neal RD. Delays in the diagnosis of six cancers: Analysis of data from the National Survey of NHS Patients: Cancer [Internet]. Br. J. Cancer. Nature Publishing Group; 2005 [cited 2021 Jun 7]. p. 1959–70. Available from: www.bjcancer.com
5. Hanna TP, King WD, Thibodeau S, Jalink M, Paulin GA, Harvey-Jones E, et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ [Internet]. NLM (Medline); 2020 [cited 2021 Jun 4];371:m4087. Available from: https://www.bmj.com/content/371/bmj.m4087
6. Heberle CR, Omidvari A-H, Ali A, Kroep S, Kong CY, Inadomi JM, et al. Cost-Effectiveness of Screening Patients with Gastroesophageal Reflux Disease for Barrett’s Esophagus With a Minimally Invasive Cell Sampling Device. Clin Gastroenterol Hepatol [Internet]. NIH Public Access; 2017 [cited 2021 Aug 17];15:1397. Available from: /pmc/articles/PMC5827938/
7. Turvill J, Aghahoseini A, Sivarajasingham N, Abbas K, Choudhry M, Polyzois K, et al. Faecal calprotectin in patients with suspected colorectal cancer: a diagnostic accuracy study. Br J Gen Pract [Internet]. British Journal of General Practice; 2016 [cited 2021 Aug 17];66:e499–506. Available from: https://bjgp.org/content/66/648/e499
8. Briggs A, Claxton K, Sculpher M. Decision Modelling for Health Economic Evaluation. Oxford: OUP; 2006.
9. Drummond M, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. Oxford: OUP; 2015.
10. Walker S, Fox A, Altunkaya J, Colbourn T, Drummond M, Griffin S, et al. Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making. Med Decis Making [Internet]. SAGE PublicationsSage CA: Los Angeles, CA; 2021 [cited 2021 Jun 3];272989X211016427. Available from: http://www.ncbi.nlm.nih.gov/pubmed/34041992
11. Wright SJ, Newman WG, Payne K. Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review. PharmacoEconomics 2019 378 [Internet]. Springer; 2019 [cited 2021 Oct 4];37:1011–27. Available from: https://link.springer.com/article/10.1007/s40273-019-00801-9
12. van Baal P, Morton A, Severens JL. Health care input constraints and cost effectiveness analysis decision rules. Soc Sci Med [Internet]. Pergamon; 2018 [cited 2018 Sep 24];200:59–64. Available from: https://www.sciencedirect.com/science/article/pii/S0277953618300261
13. Revill P, Walker S, Cambiano V, Phillips A, Sculpher MJ. Reflecting the real value of health care resources in modelling and cost-effectiveness studies—The example of viral load informed differentiated care. Law M, editor. PLoS One [Internet]. Public Library of Science; 2018 [cited 2018 Sep 24];13:e0190283. Available from: http://dx.plos.org/10.1371/journal.pone.0190283
14. Aspland E, Gartner D, Harper P. Clinical pathway modelling: a literature review. https://doi.org/101080/2047696520191652547 [Internet]. Taylor & Francis; 2019 [cited 2021 Oct 4];10:1–23. Available from: https://doi.org/10.1080/20476965.2019.1652547
15. Saville CE, Smith HK, Bijak K. Operational research techniques applied throughout cancer care services: a review. https://doi.org/101080/2047696520171414741 [Internet]. Taylor & Francis; 2018 [cited 2021 Oct 4];8:52–73. Available from: https://www.tandfonline.com/doi/abs/10.1080/20476965.2017.1414741
16. Arruda EF, Harper P, England T, Gartner D, Aspland E, Ourique FO, et al. Resource optimization for cancer pathways with aggregate diagnostic demand: a perishable inventory approach. IMA J Manag Math [Internet]. Oxford Academic; 2021 [cited 2021 Oct 4];32:221–36. Available from: https://academic.oup.com/imaman/article/32/2/221/5864939
17. MR D, W K, R T. A System Dynamics Simulation Applied to Healthcare: A Systematic Review. Int J Environ Res Public Health [Internet]. Int J Environ Res Public Health; 2020 [cited 2021 Oct 4];17:1–27. Available from: https://pubmed.ncbi.nlm.nih.gov/32784439/