Abstract
Background
Limited evidence is available on the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints. Decision analytic modelling may be an appropriate method to synthesise evidence from multiple sources, and overcomes issues with trial-based economic evaluations.
Objective
The aim was to describe the reporting of methods and objectives utilised in existing decision analytic modelling studies that assess the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints.
Methods
Decision analytic modelling studies investigating the use of any imaging modality for people of any age with back, neck, knee, or shoulder complaints were included. No restrictions on comparators were applied, and included studies were required to estimate both costs and benefits. A systematic search (5 January 2023) of four databases was conducted with no date limits imposed. Methodological and knowledge gaps were identified through a narrative summary.
Results
Eighteen studies were included. Methodological issues were identified relating to the poor reporting of methods, and measures of effectiveness did not incorporate changes in quantity and/or quality of life (cost-utility analysis in only ten of 18 studies). Included studies, particularly those investigating back or neck complaints, focused on conditions that were of low prevalence but have a serious impact on health (i.e. cervical spine trauma, cancer-related back pain).
Conclusions
Future models should pay particular attention to the identified methodological and knowledge gaps. Investment in the health technology assessment of these commonly utilised diagnostic imaging services is needed to justify the current level of utilisation and ensure that these services represent value for money.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
There is a paucity of high-quality modelling studies investigating the cost-effectiveness of diagnostic imaging for common musculoskeletal complaints despite its widespread use. |
There are few high-quality modelling studies that adhere to modelling guidelines, limiting our understanding of whether diagnostic imaging for back, neck, knee, and shoulder complaints represent value for money. |
1 Introduction
Diagnostic imaging is performed to aid diagnosis and therapeutic decision-making with the aim of improving patient outcomes. A large proportion of imaging requests by general practitioners are for musculoskeletal problems such as back, neck, knee, and shoulder pain [1]. Clinicians may request imaging to rule in or out serious pathology (e.g. malignancy, infection) and/or to identify conditions that might require specific treatment (e.g. fracture). Yet, a substantial proportion of tests are performed without an appropriate clinical indication and, therefore, provide little benefit to the patient and may cause harm [1,2,3]. Despite their frequent use, diagnostic imaging for these common musculoskeletal problems have not been subjected to rigorous processes of health technology assessment like emerging drugs or surgical procedures [4].
The value of diagnostic imaging can be assessed within clinical trials where patients undergo either a new or existing test, and patient-reported health outcomes and costs of the consequent management are measured [5, 6]. The benefit of these test-treatment trials is that they capture all aspects of value generated by a diagnostic test. However, diagnostic imaging has a small indirect effect on patient outcomes that is influenced by the prevalence of the condition and the marginal difference in diagnostic accuracy [7, 8]. In a clinical trial that is comparing two diagnostic tests for example, only 4% of the sample population will be affected by the new test when the prevalence of the disease is 20% and the marginal difference in sensitivity is 20% [8]. Clinical trials would need to recruit a prohibitively large sample size to overcome this dilution effect and provide an effect estimate for patient-reported health outcomes with adequate precision [8, 9]. Further, clinical trials are typically underpowered for cost-effectiveness analyses to report differences in effect and costs [9]. Alternative methods are needed to ascertain the cost-effectiveness of diagnostic imaging.
Decision analytic modelling is one method to estimate the cost-effectiveness of diagnostic tests [6, 10]. This technique synthesises available data on the characteristics of a diagnostic test (i.e. diagnostic accuracy, changes in diagnosis, changes in treatment modality) and its subsequent impact on outcomes and costs, to ascertain whether its use represents value for money to the healthcare system and society [11]. High-quality decision analytic models can aid policy-makers in answering resource allocation questions.
The objective of this scoping review was to describe the methods used in existing decision analytic models that investigate the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints. We also aimed to describe the objectives of these studies in terms of the clinical conditions of interest, resource allocation questions addressed, test-treatment pathway, and the incorporation of safety issues. The methodological and knowledge gaps identified in this review will identify opportunities for future research that aim to improve the use of diagnostic imaging for back, neck, knee, and shoulder complaints.
2 Methods
This scoping review was conducted according to the methodology proposed by Levac et al. [12] and Peters et al. [13]. The review methods and results are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews (PRISMA-ScR) [14].
Decision analytic models that investigated the use of diagnostic imaging for people of any age with back, neck, shoulder, or knee pain were included. There was no restriction on type of imaging modality that was being investigated. However, studies that investigated diagnostic imaging screening programmes of healthy individuals (e.g. use of dual-energy x-ray absorptiometry to screen for osteoporosis) or studies in which imaging was part of an intervention (e.g. ultrasound-guided injections) were excluded. There was no restriction on comparators. Studies that undertook cost-effectiveness, cost-utility, or cost-benefit analyses were included irrespective of the type of decision analytic modelling undertaken (e.g. decision tree, Markov modelling including Markov microsimulations, and discrete event simulation). There were no restrictions based upon health outcomes (e.g. quality-adjusted life years [QALYs], cases detected). We excluded conference proceedings and abstracts, and non-English language studies.
2.1 Search Methods
The search strategy was developed in consultation with a medical librarian. We performed database searches of MEDLINE (OVID), Embase (OVID), NHS Economic Evaluation Database, and the Health Technology Assessment database. All databases were searched from inception to 2nd of July 2021 and updated on the 5th of January 2023. The search strategy is outlined in Appendix A (see the electronic supplementary material).
2.2 Data Selection
Titles and abstracts of records retrieved from searches were screened for eligibility by two independent reviewers (SD, CB). The same two reviewers then independently determined eligibility from the full-text reports of potentially eligible studies. Discrepancies were resolved through discussion or via consultation with a third review author (LG).
2.3 Data Charting and Synthesis
We developed a data charting sheet to extract the relevant information from the included studies. The development of this data charting sheet was based on best practice methodological guidelines for economic evaluations [15] and decision analytic models [16] and an iterative process where the data charting sheet was updated to ensure that the data extracted were consistent with the research question and purpose [12]. Data were extracted by two authors (SD, CB), and any disagreements were resolved through discussion or with a third review author (RB) as required. The following study characteristics were charted:
-
Details of study: Study aims, decision analytic method, type of economic analysis, intervention and comparator, perspective, country, setting, time horizon, cycle length (if appropriate), discounting (if appropriate), funding, and competing interests of study authors.
-
Participants: Site of complaint investigated, condition of interest, and demographic information of the clinical scenario.
-
Model characteristics: Assumptions used for model structure, imaging modality/techniques investigated, interventions based on test outcomes, key health states, model inputs and source of these inputs (i.e. prevalence of condition, diagnostic accuracy of imaging, resource use, utility values, or other outcome measures), and cycle length (if appropriate).
-
Results: Costs and outcomes of each arm, incremental cost-effectiveness ratio for all comparisons (e.g. cost per QALY, cost per extra case detected, etc.), and results of sensitivity analyses if performed to identify predictors of costs and effects.
2.4 Summary of Findings
The study methods and modelling techniques were summarised for all included studies. The objectives and aims of all included studies were mapped within four broad themes identified by the iterative process of data charting: (1) the condition investigated (e.g. non-specific diagnoses, conditions of low prevalence); (2) the resource allocation question addressed (e.g. imaging A vs. imaging B, imaging vs. no imaging); (3) test-treatment pathway (e.g. surgery, palliative care); and (4) the assessment of safety issues related to the use of diagnostic imaging (e.g. radiation, risk of litigation). The outcomes from these themes were described for all included studies and separately for each of the sites of clinical complaints. A methodological appraisal was not conducted as this is beyond the scope of scoping reviews [17].
3 Results
Of the 5772 studies screened for potential eligibility, 18 studies satisfied the eligibility criteria (Fig. 1). Six investigated the use of diagnostic imaging for back complaints, five were for neck complaints, three were for the knee, and four were for the shoulder (Fig. 1).
Seven studies were published within the last 5 years (2018–2022), three were published in the preceding 5 years (2013–2017), and the remaining eight studies were published more than a decade ago (before 2012) (Table 1). Most studies were performed in the United States (n = 15, 83.3%), two were from Canada and one was from Australia. The clinical setting of included studies was distributed across primary care [18,19,20,21,22], outpatient orthopaedic clinics [21, 23,24,25,26,27,28,29], and emergency departments [30,31,32,33,34], with one study set in a rheumatology clinic [35].
Magnetic resonance imaging (MRI) was most commonly investigated (n = 13, 72.2%), including three studies for back complaints [18, 25, 35], three studies for neck complaints [31, 33, 34], and seven studies for knee [21, 23, 27] and shoulder [24, 26, 28, 29] complaints. Computed tomography and x-ray were only investigated in studies of back and neck complaints [18,19,20, 22, 25, 30, 32, 35]. Two studies focused on ultrasound [24, 28] and two studies on arthrography [26, 29] for shoulder complaints.
3.1 Summary of Study Methods
Reporting of methods was poor in some studies, which limits replication and the generalisability of research findings (Table 2). Issues included absence of the reporting model structure (two studies [20, 35]), time horizon (four studies [19, 25,26,27]), discount rate (six studies [19, 21, 25,26,27, 31]), and cycle length for Markov models (three of four Markov models [23, 33, 34]).
Six studies conducted their analysis from a societal perspective [21, 27, 30, 32,33,34], four of which investigated the cost-effectiveness of diagnostic imaging for traumatic cervical spine injuries [30, 32,33,34]. Nine considered a healthcare perspective with some slight variations applied [18, 19, 23, 24, 27,28,29, 31, 35]. For instance, Ertel et al. [31] modified their perspective to include the costs of litigation following a missed diagnosis. Two studies did not report the perspective taken [20, 26]; however, based on the type of included costs, it can be assumed both took a healthcare perspective.
There were also a wide range of time horizons employed when stated. A lifetime horizon was employed by five studies [30,31,32,33,34], all of which investigated the cost-effectiveness of diagnostic imaging for traumatic cervical spine injuries. Time horizons for the remaining studies ranged from 8 weeks to 10 years.
3.2 Summary of Study Objectives
Figure 2 shows the number of studies categorised within the four broad themes for all included studies and across each of the symptomatic anatomical sites. Within the clinical condition of interest theme, eight studies (44.4%) investigated conditions that have a low prevalence within the relevant setting, but when present have a significant long-term health impact. These studies investigated cervical spine trauma [30,31,32,33,34], cancer-related low back pain [18, 19], and axial spondyloarthritis [35]. Three studies [20, 22, 25], all of which investigated neck or back complaints, investigated non-specific presentations (i.e. acute low back pain, non-emergent spinal disorders). All studies investigating conditions of the knee and shoulder focused on imaging findings that are commonly present in asymptomatic individuals (i.e. meniscal injuries, rotator cuff tears, labral tears).
Within the resource allocation question addressed by the study theme, the majority of studies (n = 9) investigated the comparative effectiveness of a range of diagnostic modalities [18, 23, 24, 26, 28,29,30, 32, 35] or compared diagnostic imaging to no imaging [20, 21, 27, 28, 31, 33, 34]. Interventions aimed at reducing unnecessary imaging were assessed in two studies [22, 25].
For the third theme, a clear test-surgery pathway was outlined in eight studies [21, 23,24,25,26,27,28,29], including all seven studies for knee and shoulder complaints. Four studies did not specify the treatment that was provided following the imaging test yet included patient health outcomes [20, 30,31,32]. Three decision analytic models, all for neck or back complaints, solely incorporated palliative care to manage symptoms and maintain a level of quality of life rather than treat the conditions (e.g. radiotherapy and chemotherapy for cancer-related back pain, standard care following tetraplegia) [18, 33, 34].
Safety issues related to the use of diagnostic imaging were incorporated in five decision analytic models, all of which focused on neck or back complaints [18, 22, 31, 32, 36]. The increased risk of cancer due to radiation from X-ray or computed tomography (CT) imaging was most commonly considered either by incorporating the probability, costs, and consequence of radiation-induced malignancy [18, 32] or as an outcome measure for interventions that aimed to reduce unnecessary imaging [22, 36]. The probability and cost of litigation following a missed diagnosis were considered in two studies, both of which were assessing the use of imaging to detect cervical spine injury following trauma [31, 32]. One study considered the probability, costs, and consequences of adverse events related to diagnostic imaging itself, such as complications in transport to, or patient positioning in, the MRI scanner [31].
A summary of results from included studies is included in Appendix B (see the electronic supplementary material).
4 Discussion
This review identified several methodological issues and knowledge gaps in the application of decision analytic modelling to investigate the cost-effectiveness of diagnostic imaging for common musculoskeletal conditions. Limitations in the economic analysis performed and perspective taken were identified. Further, knowledge gaps were identified with eight of the 18 studies published over 10 years ago and a focus on clinical conditions that are rare within the clinical setting of interest. The results of this scoping review identify opportunities for future research that address critical evidence gaps.
Ten of the 18 included studies (55.6%) conducted a cost-utility analysis, which is comparatively lower than that reported in a systematic review summarising decision analytic modelling studies of diagnostic tests for various other health conditions. Yang et al. [10] found that a cost-utility analysis had been conducted in 95% of studies. However, this high proportion may be because they confined their search to Health Technology Assessment reports within the UK National Institute for Health Research that are generally considered of good quality.
Cost-utility evaluations are preferred as they quantify the benefit of interventions in terms of the quantity and quality of life, generally expressed as QALYs. This allows findings to be compared across populations and conditions, and enables decision-makers to weigh the opportunity cost of implementing competing interventions [37]—for example, CT imaging for people with low back pain compared to CT imaging for those with respiratory issues. Willingness-to-pay thresholds are also available for cost-utility analyses in multiple countries, which enable decision-makers to identify interventions that represent value for money [38]. Only two studies included in this review stated the absence of cost-utility analysis as a limitation [19, 27], with Suarez-Almazor et al. [27] justifying the exclusion of this type of analysis due to an absence of data. The methodological quality of decision analytic models can be improved by ensuring that the benefit of diagnostic imaging is valued using QALYs instead of focusing on outcomes related to the diagnostic process [39].
Cost-effectiveness analyses identified in this review primarily focused on the number of cases detected. These studies are limited as they overestimate the benefit of imaging in detecting a condition and disregard any harms that may occur [40]. Individuals with a false positive diagnosis may undergo further testing (e.g. invasive biopsy of cancerous lesion) and/or receive unnecessary treatment. Harms due to overdiagnosis (detection of abnormalities that generate a diagnosis that does not result in patient benefit or may cause harm [41]) will also impact the cost-effectiveness of diagnostic imaging [42, 43]. It is critical that future models incorporate all downstream consequences following imaging to accurately estimate the costs and benefits.
While productivity losses are commonly associated with musculoskeletal complaints [44,45,46,47], few included studies assessed costs and benefits from a societal perspective [48]. Decision-makers and Health Technology Assessment organisations (e.g. Medicare Services Advisory Committee in Australia, National Institute for Health Research Health Technology Assessment programme in the United Kingdom) prefer economic evaluations from a healthcare perspective [49] as their role is allocating finite healthcare resources. However, the Second Panel on Cost-Effectiveness in Health and Medicine recommended that economic evaluations include both a healthcare and societal perspective reference case to assess how interventions may effect non-health related costs (e.g. productivity, unpaid caregiver time costs) [48]. Diagnostic imaging may impact these non-health-related costs and effect cost-effectiveness ratios. For example, Suarez-Almazor et al. [27] compared the cost-effectiveness of MRI to knee arthroscopy for internal knee derangement from both a healthcare and societal perspective. The incremental cost-effectiveness ratio for MRI was reported to be US$41 per arthroscopy avoided from a healthcare perspective and cost-saving (US$201) when analysed from a societal perspective. Regardless of what perspective is chosen, future studies need to ensure that the relevant information, including costs, is included based on the chosen perspective. Two studies inappropriately excluded productivity losses when taking a societal perspective [21, 30], and costs from litigation due to a missed diagnosis were included in a healthcare perspective [31]. Reporting cost information relevant to the study perspective is important to ensure that cost-effectiveness ratios can be compared between studies.
Imaging requests for common musculoskeletal complaints are increasing and suggest the inappropriate use of imaging [1, 50]. Concerns for the sustainability of diagnostic imaging expenditure have led health systems to seek interventions that reduce inappropriate imaging [51, 52]. Our review included only two studies, both in low back pain, that assessed the cost-effectiveness of interventions addressing inappropriate imaging, and neither included patient health outcomes [22, 25]. An absence of primary studies that assess patient outcomes may have contributed to this knowledge gap [53, 54]. Well-designed models can aid policy-makers in improving the efficient use of diagnostic imaging, and the exploration of uncertainties in model inputs can help direct future research (i.e. sensitivity and value of information analyses [55]).
The current applicability of decision analytic models that have investigated the cost-effectiveness of diagnostic imaging for knee and shoulder complaints are questionable. All seven studies outlined a clear test-treatment pathway where surgery was performed in positive cases [21, 23, 24, 26, 27]. These positive cases were based on pathoanatomical abnormalities observed on imaging that are frequently observed in asymptomatic individuals [56, 57]. Further, surgery for these conditions has been shown to be ineffective [58,59,60,61] and contrary to current clinical practice guidelines [62, 63]. This indicates that decision analytic models may need to be updated to accepted test-treatment pathways outlined in clinical practice guidelines.
A strength of this review was the broad search strategy that ensured a comprehensive summary of the published literature to identify methodological and knowledge gaps. A limitation of this review was that we did not formally assess the methodological quality of the included studies as this is not standard practice for scoping reviews. However, we did summarise the reporting of critical methods. There are aspects of methodological quality that are relevant, such as the use of confounding data in Markov models [64], but have not been assessed in the current review. Further, we did not assess the appropriateness and quality of source inputs used in the included studies. In future, a formal assessment of model inputs may be useful to identify areas requiring further research.
5 Conclusion
We found a paucity of high-quality decision analytic modelling studies investigating the cost-effectiveness of diagnostic imaging for common musculoskeletal complaints. The identified methodological flaws and knowledge gaps can be used to inform future studies. With increased scrutiny of the rising cost of healthcare and the need to identify strategies that improve its efficiency, investment in health technology assessments of diagnostic imaging services using decision analytic modelling may aid in research prioritisation and may provide useful insights for decision-makers [65]. However, it is critical that future investigations adhere to modelling guidelines [15, 66] to produce high-quality evidence that aids decision-makers in efficiently allocating diagnostic imaging resources.
References
Britt H, Miller GC, Valenti L, et al. Evaluation of imaging ordering by general practitioners in Australia, 2002–03 to 2011–12. General practice series no. 35. Sydney: Sydney University Press; 2014.
Jenkins HJ, Downie AS, Maher CG, et al. Imaging for low back pain: is clinical use consistent with guidelines? A systematic review and meta-analysis. Spine J. 2018;18:2266–77. https://doi.org/10.1016/j.spinee.2018.05.004.
O’Sullivan JW, Albasri A, Nicholson BD, et al. Overtesting and undertesting in primary care: a systematic review and meta-analysis. BMJ Open. 2018;8: e018557. https://doi.org/10.1136/bmjopen-2017-018557.
Demehri S, Recht MP, Lee CI. Comparative effectiveness research in musculoskeletal imaging. Semin Musculoskelet Radiol. 2017;21:17–22. https://doi.org/10.1055/s-0036-1597250.
di Ruffano LF, Dinnes J, Sitch AJ, et al. Test-treatment RCTs are susceptible to bias: a review of the methodological quality of randomized trials that evaluate diagnostic tests. BMC Med Res Methodol. 2017;17:35. https://doi.org/10.1186/s12874-016-0287-z.
Sculpher MJ, Claxton K, Drummond M, et al. Whither trial-based economic evaluation for health care decision making? Health Econ. 2006;15:677–87. https://doi.org/10.1002/hec.1093.
Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Med Decis Making. 1991;11:88–94. https://doi.org/10.1177/0272989X9101100203.
di Ruffano LF, Deeks JJ. Test-treatment RCTs are sheep in wolves’ clothing (Letter commenting on: J Clin Epidemiol. 2014;67:612-21). J Clin Epidemiol. 2016;69:266–7. https://doi.org/10.1016/j.jclinepi.2015.06.013.
Briggs A. Economic evaluation and clinical trials: size matters: the need for greater power in cost analyses poses an ethical dilemma. BMJ. 2000;321:1362–3. https://doi.org/10.1136/bmj.321.7273.1362.
Yang Y, Abel L, Buchanan J, et al. Use of decision modelling in economic evaluations of diagnostic tests: an appraisal and review of health technology assessments in the UK. Pharmacoecon Open. 2019;3:281–91. https://doi.org/10.1007/s41669-018-0109-9.
Sailer AM, van Zwam WH, Wildberger JE, et al. Cost-effectiveness modelling in diagnostic imaging: a stepwise approach. Eur Radiol. 2015;25:3629–37. https://doi.org/10.1007/s00330-015-3770-8.
Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5:69. https://doi.org/10.1186/1748-5908-5-69.
Peters MDJ, Godfrey CM, Khalil H, et al. Guidance for conducting systematic scoping reviews. JBI Evid Implement. 2015;13:141–6. https://doi.org/10.1097/XEB.0000000000000050.
Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467–73. https://doi.org/10.7326/M18-0850.
Husereau D, Drummond M, Augustovski F, et al. Consolidated health economic evaluation reporting standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMJ. 2022;376: e067975. https://doi.org/10.1136/bmj-2021-067975.
Peñaloza Ramos MC, Barton P, Jowett S, et al. A systematic review of research guidelines in decision-analytic modeling. Value Health. 2015;18:512–29. https://doi.org/10.1016/j.jval.2014.12.014.
Munn Z, Peters MDJ, Stern C, et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18:143. https://doi.org/10.1186/s12874-018-0611-x.
Hollingworth W, Gray DT, Martin BI, et al. Rapid magnetic resonance imaging for diagnosing cancer-related low back pain. J Gen Intern Med. 2003;18:303–12. https://doi.org/10.1046/j.1525-1497.2003.20633.x.
Joines JD, McNutt RA, Carey TS, et al. Finding cancer in primary care outpatients with low back pain. J Gen Intern Med. 2001;16:14–23. https://doi.org/10.1111/j.1525-1497.2001.00249.x.
Liang M, Komaroff AL. Roentgenograms in primary care patients with acute low back pain: a cost-effectiveness analysis. Arch Intern Med. 1982;142:1108–12.
Mather RC, Garrett WE, Cole BJ, et al. Cost-effectiveness analysis of the diagnosis of meniscus tears. Am J Sports Med. 2015;43:128–37. https://doi.org/10.1177/0363546514557937.
Morgan T, Wu J, Ovchinikova L, et al. A national intervention to reduce imaging for low back pain by general practitioners: a retrospective economic program evaluation using Medicare Benefits Schedule data. BMC Health Serv Res. 2019;19:983. https://doi.org/10.1186/s12913-019-4773-y.
Amin N, McIntyre L, Carter T, et al. Cost-effectiveness analysis of needle arthroscopy versus magnetic resonance imaging in the diagnosis and treatment of meniscal tears of the knee. Arthrosc. 2019;35:554-562.e13. https://doi.org/10.1016/j.arthro.2018.09.030.
Gyftopoulos S, Guja KE, Subhas N, et al. Cost-effectiveness of magnetic resonance imaging versus ultrasound for the detection of symptomatic full-thickness supraspinatus tendon tears. J Shoulder Elbow Surg. 2017;26:2067–77. https://doi.org/10.1016/j.jse.2017.07.012.
Kim JSM, Dong JZ, Brener S, et al. Cost-effectiveness analysis of a reduction in diagnostic imaging in degenerative spinal disorders. Healthc Policy 2011. https://www.longwoods.com/content/22619/healthcare-policy/cost-effectiveness-analysis-of-a-reduction-in-diagnostic-imaging-in-degenerative-spinal-disorders (accessed 24 Nov 2021).
Oh DK, Yoon YC, Kwon JW, et al. Comparison of indirect isotropic mr arthrography and conventional MR arthrography of labral lesions and rotator cuff tears: a prospective study. Am J Roentgen. 2009;192:473–9. https://doi.org/10.2214/AJR.08.1223.
Suarez-Almazor ME, Kaul P, Kendall CJ, et al. The cost-effectiveness of magnetic resonance imaging for patients with internal derangement of the knee. Int J Technol Assess Health Care. 1999;15:392–405.
Levin JM, Wickman J, Lazarides AL, et al. Is advanced imaging to assess rotator cuff integrity before shoulder arthroplasty cost-effective? A decision modeling study. Clin Orthopaed Relat Res. 2022;480:1129. https://doi.org/10.1097/CORR.0000000000002110.
Gyftopoulos S, Conroy J, Koo J, et al. Imaging of patients suspected of SLAP tear: a cost-effectiveness study. Am J Roentgen. 2022;218:227–33. https://doi.org/10.2214/AJR.21.26420.
Blackmore CC, Ramsey SD, Mann FA, et al. Cervical spine screening with CT in trauma patients: a cost-effectiveness analysis. Radiology. 1999;212:117–25. https://doi.org/10.1148/radiology.212.1.r99jl08117.
Ertel AE, Robinson BRH, Eckman MH. Cost-effectiveness of cervical spine clearance interventions with litigation and long-term-care implications in obtunded adult patients following blunt injury. J Trauma Acute Care Surg. 2016;81:897–904. https://doi.org/10.1097/TA.0000000000001243.
Overmann KM, Robinson BRH, Eckman MH. Cervical spine evaluation in pediatric trauma: a cost-effectiveness analysis. Am J Emerg Med. 2020;38:2347–55. https://doi.org/10.1016/j.ajem.2019.11.051.
Wu X, Malhotra A, Geng B, et al. Cost-effectiveness of magnetic resonance imaging in cervical clearance of obtunded blunt trauma after a normal computed tomographic finding. JAMA Surg. 2018;153:625–32. https://doi.org/10.1001/jamasurg.2018.0099.
Wu X, Malhotra A, Geng B, et al. Cost-effectiveness of magnetic resonance imaging in cervical spine clearance of neurologically intact patients with blunt trauma. Ann Emerg Med. 2018;71:64–73. https://doi.org/10.1016/j.annemergmed.2017.07.006.
Gorelik N, Tamizuddin F, Rodrigues TC, et al. Comparison between radiography and magnetic resonance imaging for the detection of sacroiliitis in the initial diagnosis of axial spondyloarthritis: a cost-effectiveness study. Skeletal Radiol. 2020;49:1581–8. https://doi.org/10.1007/s00256-020-03444-6.
Morgan T, Blogg S, Moorin R, et al. Economic evaluation of the NPS medicinewise program imaging for acute low back pain, cost- benefit analysis report. Sydney: NPS MedicineWise; 2016.
Rand LZ, Kesselheim AS. Controversy over using quality-adjusted life-years in cost-effectiveness analyses: a systematic literature review. Health Aff. 2021;40:1402–10. https://doi.org/10.1377/hlthaff.2021.00343.
McDougall JA, Furnback WE, Wang BCM, et al. Understanding the global measurement of willingness to pay in health. J Mark Access Health Policy. 2020;8:1717030. https://doi.org/10.1080/20016689.2020.1717030.
Newman-Toker DE, McDonald KM, Meltzer DO. How much diagnostic safety can we afford, and how should we decide? A health economics perspective. BMJ Qual Saf. 2013;22:11–20. https://doi.org/10.1136/bmjqs-2012-001616.
Aberegg SK, O’Brien JM. The normalization heuristic: an untested hypothesis that may misguide medical decisions. Med Hypotheses. 2009;72:745–8. https://doi.org/10.1016/j.mehy.2008.10.030.
Brodersen J, Schwartz LM, Heneghan C, et al. Overdiagnosis: what it is and what it isn’t. BMJ Evid-Based Med. 2018;23:1–3. https://doi.org/10.1136/ebmed-2017-110886.
Deveza LA, Matthews L, O’Connell R, et al. Is the use of knee magnetic resonance imaging one of the drivers of persistently high arthroscopy rates in older adults?—an analysis of national data in Australia. Osteoarthr Cartil. 2018;26:S249. https://doi.org/10.1016/j.joca.2018.02.511.
Lemmers GPG, van Lankveld W, Westert GP, et al. Imaging versus no imaging for low back pain: a systematic review, measuring costs, healthcare utilization and absence from work. Eur Spine J. 2019;28:937–50. https://doi.org/10.1007/s00586-019-05918-1.
Ackerman IN, Fotis K, Pearson L, et al. Impaired health-related quality of life, psychological distress, and productivity loss in younger people with persistent shoulder pain: a cross-sectional analysis. Disabil Rehabil. 2021;2:1–10. https://doi.org/10.1080/09638288.2021.1887376.
Agaliotis M, Mackey MG, Jan S, et al. Burden of reduced work productivity among people with chronic knee pain: a systematic review. Occup Environ Med. 2014;71:651–9. https://doi.org/10.1136/oemed-2013-101997.
Gedin F, Alexanderson K, Zethraeus N, et al. Productivity losses among people with back pain and among population-based references: a register-based study in Sweden. BMJ Open. 2020;10: e036638. https://doi.org/10.1136/bmjopen-2019-036638.
Rowell D, Connelly LB. Personal assistance, income and employment: the spinal injuries survey instrument (SISI) and its application in a sample of people with quadriplegia. Spinal Cord. 2008;46:417–24. https://doi.org/10.1038/sj.sc.3102157.
Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA. 2016;316:1093–103. https://doi.org/10.1001/jama.2016.12195.
Medical Services Advisory Committee. Guidelines for preparing assessments for the Medical Services Advisory Committee. 2021.http://msac.gov.au/internet/msac/publishing.nsf/Content/E0D4E4EDDE91EAC8CA2586E0007AFC75/$File/MSAC%20Guidelines-complete-16-FINAL(18May21).pdf
Downie A, Hancock M, Jenkins H, et al. How common is imaging for low back pain in primary and emergency care? Systematic review and meta-analysis of over 4 million imaging requests across 21 years. Br J Sports Med. 2020;54:642–51. https://doi.org/10.1136/bjsports-2018-100087.
Docking S, Haddock R, Buchbinder R. Australian health policies related to diagnostic imaging: too much of a good thing? Aust Health Rev. 2022. https://doi.org/10.1071/AH22064.
O’Connor DA, Glasziou P, Maher CG, et al. Effect of an individualized audit and feedback intervention on rates of musculoskeletal diagnostic imaging requests by australian general practitioners: a randomized clinical trial. JAMA. 2022;328:850–60. https://doi.org/10.1001/jama.2022.14587.
French SD, Green S, Buchbinder R, et al. Interventions for improving the appropriate use of imaging in people with musculoskeletal conditions. Cochrane Database Syst Rev. 2010. https://doi.org/10.1002/14651858.CD006094.pub2.
Jenkins HJ, Hancock MJ, French SD, et al. Effectiveness of interventions designed to reduce the use of imaging for low-back pain: a systematic review. CMAJ. 2015;187:401–8. https://doi.org/10.1503/cmaj.141183.
Fenwick E, Steuten L, Knies S, et al. Value of information analysis for research decisions—an introduction: report 1 of the ISPOR value of information analysis emerging good practices task force. Value Health. 2020;23:139–50. https://doi.org/10.1016/j.jval.2020.01.001.
Culvenor AG, Øiestad BE, Hart HF, et al. Prevalence of knee osteoarthritis features on magnetic resonance imaging in asymptomatic uninjured adults: a systematic review and meta-analysis. Br J Sports Med. 2019;53:1268–78. https://doi.org/10.1136/bjsports-2018-099257.
Teunis T, Lubberts B, Reilly BT, et al. A systematic review and pooled analysis of the prevalence of rotator cuff disease with increasing age. J Shoulder Elbow Surg. 2014;23:1913–21. https://doi.org/10.1016/j.jse.2014.08.001.
Karjalainen TV, Jain NB, Page CM, et al. Subacromial decompression surgery for rotator cuff disease. Cochrane Database Syst Rev. 2019. https://doi.org/10.1002/14651858.CD005619.pub3.
Marsh JD, Birmingham TB, Giffin JR, et al. Cost-effectiveness analysis of arthroscopic surgery compared with non-operative management for osteoarthritis of the knee. BMJ Open. 2016;6: e009949. https://doi.org/10.1136/bmjopen-2015-009949.
Palmer JS, Monk AP, Hopewell S, et al. Surgical interventions for symptomatic mild to moderate knee osteoarthritis. Cochrane Database Syst Rev Published Online First. 2019. https://doi.org/10.1002/14651858.CD012128.pub2.
Sihvonen R, Paavola M, Malmivaara A, et al. Arthroscopic partial meniscectomy versus sham surgery for a degenerative meniscal tear. NEJM. 2013;369:2515–24. https://doi.org/10.1056/NEJMoa1305189.
Nelson AE, Allen KD, Golightly YM, et al. A systematic review of recommendations and guidelines for the management of osteoarthritis: the chronic osteoarthritis management initiative of the U.S. bone and joint initiative. Semin Arthritis Rheum. 2014;43:701–12. https://doi.org/10.1016/j.semarthrit.2013.11.012.
Siemieniuk RAC, Harris IA, Agoritsas T, et al. Arthroscopic surgery for degenerative knee arthritis and meniscal tears: a clinical practice guideline. BMJ. 2017. https://doi.org/10.1136/bmj.j1982.
Nuijten MJC, Rutten F. The incorporation of potential confounding variables in Markov models. Pharmacoeconomic. 2003;21:941–50. https://doi.org/10.2165/00019053-200321130-00003.
Productivity Commission. Efficiency in Health. Canberra: 2015. https://www.pc.gov.au/research/completed/efficiency-health/efficiency-health.pdf
Roberts M, Russell LB, Paltiel AD, et al. Conceptualizing a model: a report of the ISPOR-SMDM modeling good research practices task force–2. Med Decis Making. 2012;32:678–89. https://doi.org/10.1177/0272989X12454941.
Acknowledgements
The authors would like to acknowledge and thank Diane Horrigan for her assistance in developing the search strategy.
Funding
Open Access funding enabled and organized by CAUL and its Member Institutions.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Author contribution
SD, LG, and RB conceived the idea for the article, SD and CB performed the literature search and data analysis, and all authors drafted and critically revised the work.
Funding
This research did not receive any specific funding.
Conflict of interest
All authors declare that they have no conflicts of interest.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and material
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Code availability
Not applicable.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
About this article
Cite this article
Docking, S., Gao, L., Ademi, Z. et al. Use of Decision-Analytic Modelling to Assess the Cost-Effectiveness of Diagnostic Imaging of the Spine, Shoulder, and Knee: A Scoping Review. Appl Health Econ Health Policy 21, 467–475 (2023). https://doi.org/10.1007/s40258-023-00799-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40258-023-00799-4