Appraisal of the 2-Year Cost-Effectiveness of 3 Options to Treat Lumbar Spinal Stenosis Patients Study - Article Example

Critical appraisal of modelling based articles: The 2-Year Cost-Effectiveness of 3 options to Treat Lumbar Spinal Stenosis Patients Introduction The study, “The 2-Year Cost-Effectiveness of 3 options to Treat Lumbar Spinal Stenosis Patients” was a study that was undertaken to evaluate the cost-effectiveness of three different methods of treating Lumbar Spinal Stenosis. The study concluded that the minimally invasive lumbar decompression (mild) was the most cost-effective method for treating the problem (Udeh, B. et al. 2014:1). The technique applied in the analysis was the quality-adjusted life years (QALY) technique. Therefore, this discussion seeks to conduct a critical appraisal of this study, with a view to evaluating the modeling aspect of the study, in order to establish whether the results are convincing, and whether there were other alternative methods that could have been suitable for this study. Study summary The degenerative changes of the spine may result to Lumbar spinal stenosis (LSS), which may lead to various neurotic problems, eventually causing a reduction in the quality of life (Udeh, B. et al. 2014:1). Consequently, there are various methods that are applied towards the treatment of this problem, which vary significantly in their cost implication. In this respect, different methods have been developed with the two major methods applied being epidural steroid injections (ESI) and laminectomy surgery (Udeh, B. et al. 2014:2). While the epidural steroid injections (ESI) is known to be a cheaper method, it is also known to be ineffective, considering that it is only suitably applicable at the early stages of the intervention, but must be applied repeatedly. On the other hand, the Laminectomy surgery is found to be more expensive, and yet prone to high rates of complications (Udeh, B. et al. 2014:2). Thus, the study was undertaken to evaluate whether another method, the minimally invasive lumbar decompression (mild), which is found to be another applicable alternative was more cost-effective. The comparison applied a decision-analytic model, which compared the minimally invasive lumbar decompression (mild) to either Laminectomy surgery or steroid injections (ESI), to find out which method would emerge as more cost effective. The model of analysis applied a population analysis comprising of population of Lumbar spinal stenosis (LSS) patients with symptoms ranging from moderate to severe (Udeh, B. et al. 2014:4). The costs that were put into consideration included the initial costs of the procedure undertaken to treat the LSS, the costs of the reap procedures undertaken after the first procedure failed and the costs of alternative procedure, in case the initial and the repeat procedures had failed. Thus, the analysis only considered patients who had failed in conservative therapy, and applied the quality-adjusted life years (QALY) technique, to assess the improvement of the life of the patients within 2 years after each procedure (Udeh, B. et al. 2014:5). The findings indicated that the minimally invasive lumbar decompression (mild) was the most cost effective method at $43,760, followed by ESI which indicated an additional cost of $37,758 and lastly the Laminectomy surgery $125,985 per QALY emerged as the least cost-effective procedure for addressing Lumbar spinal stenosis problem (Udeh, B. et al. 2014:8). Analysis The modeling framework that was applied by the study was not comprehensively flawless, considering that the study considered the lack of relief and the minimal level of relief offered by the minimally invasive method of treating Lumbar Spinal Stenosis (LSS) Patients to be equal (Udeh, B. et al. 2014:3). Placing the treatment procedures with minimal treatment relief together with the treatment procedures that had completely no effect in the same batch means that there was no accuracy in the assessment of the costs. In this respect, there was no elaborate framework that could separate the levels of effectiveness of the minimally invasive lumbar decompression procedure, such that the methods could be assessed based on its levels of effectiveness, compared with the existing circumstances that defined the treatment (CRD, 2014:n.p.). Instead, the modeling framework applied by the study placed the minimal treatment of LSS and the complete lack of treat under one batch. Thus, the patients who did not register any improvement at all after undergoing the minimal invasive procedure and those who registered minimal treatment were categorized as the same. The problem with this modeling framework is that there was a high level of generalization, which then means that the assessment of the cost for each intervention was inefficient, considering the fact that where the minimally invasive procedure derived some minimal benefits, the benefits were not considered in the final calculation of the cost-effectiveness of the procedure (Urvij M. et al., 2013:597). This way, the modeling framework served to increase the costs consideration of the procedure, since there was no actual analysis of the cost of each treatment procedure. The quality-adjusted life-years (QALYs) modeling framework is associated with the limitation of data accuracy uncertainty. The modeling framework for this study applied a combination of both the minimally invasive lumbar decompression trials data and the hospital published data in relation to the other two methods; epidural steroid injections (ESI) and the Laminectomy surgery (Udeh, B. et al. 2014:4). In this respect, one major weakness associated with the data is that, it was not uniformly obtained. The combination of published data with the data collected for the minimally invasive procedure means that the accuracy of the data is put into question, considering that a study can only be accurate, where it has collected uniform data for all the methods to be considered from the same patients and during the same study period (Newby & Hill, 2003:147). The modeling framework used in this case comprised the newly collected data from the minimally invasive procedure trials, with the old data that was collected in a time that is different from the study period in question. Even while assuming that the data from the two different study periods considered in the study were accurate, the time difference creates the disparity in the cost implications, effectively affecting the cost analysis that was eventually undertaken (Brosnan & Swint, 2001:13). The other major problem associated with the modeling framework that was applied in this study is that; the actual sources of the published hospital data in relation to both the epidural steroid injections (ESI) and the Laminectomy surgery (LS) interventions is not clearly stated (CRD, 2014:n.p.). This means that it is not certain to trace the actual patients from whom the data in these two methods was collected. Thus, the accuracy of such data is put into question. This effectively means that it is difficult to assess the accuracy of the costs that are associated with these two study methods. Therefore, the accuracy of the costs associated with the two methods whose data was obtained from the hospital published data becomes difficult to evaluate in the final cost implications. Consequently, the lack of certainty regarding the accuracy of the data means that the overall cost analysis lacks certainty, and thus the final findings cannot be relied upon (Albright, Adelson & Pollack, 2008:27). In this respect, the only data that can be relied upon to give accurate cost analysis results is the data from the minimally invasive trials, considering that this data was collected during the actual study period. However, while this method can give accurate cost assessment in relation to the QALY modeling framework that is applied, the problem is that this study applies a comparative model, where the data that is gathered in one method of assessing the cost implication of the Lumbar Spinal Stenosis has to be compared with the data collected in the other two LSS treatment methods, so that a final cost analysis can be reached (Udeh, B. et al. 2014:7). Thus, considering that the data collected from the epidural steroid injections (ESI) and the Laminectomy surgery (LS) is not accurate and therefore cannot be relied upon, the final costs analysis is also doubtful, and thus unreliable. The other major problem that is associated with the modeling framework that was applied in this study is the disparity in the nature of the sample population that was applied for the different methods. The study that was undertaken to evaluate the cost effectiveness of the three methods of treating Lumbar Spinal Stenosis did not have a uniform sample population in all the three methods, considering that the sample population of the patients used for both the epidural steroid injections (ESI) and the Laminectomy surgery (LS) were less severely affected, compared to the sample population that was used in the minimally invasive lumbar decompression trials (CRD, 2014:n.p.). As a result, the study required that the data collected from the three set of population samples be harmonized, through having the QALY gain estimates for the two methods reduced by 25%, so that the cost analysis could be undertaken in a manner reflecting some sense of uniformity in the sample (Udeh, B. et al. 2014:4). However, the problem with this measure undertaken to ensure that the QALY gain estimates for the three methods applied towards establishing the most cost effective method of treating LSS is that, the basis of reducing the QALY gain estimates for both the epidural steroid injections (ESI) and the Laminectomy surgery (LS) has not been explained satisfactorily by the study. In this respect, there is a high level of uncertainty in relation to whether the modification done to the data by reducing their QALY gain estimates by 25% had unintended effect on the costs implications that the two methods produced. This way, there is uncertainty regarding whether the QALY gain estimates modification done either affected the cost analysis favorably or unfavorably, thus prejudicing the final results of the cost effectiveness analysis for this study (Kaminski, & Banse, 2013:1872). Further, the modeling framework applied in this study is associated with another assumption that serves to increase the uncertainty of the accuracy of the study. The measure of benefit applied under the study is the QALY gained over the 2-year period that the study put into consideration. However, the QALY gain estimates were discounted at a discount rate of 3% per year, so that the results of the benefits attainable from the three different treatment methods could be effectively moderated (Udeh, B. et al. 2014:2). The problem associated with this aspect of the study is that; first, the basis of discounting the QALY gain estimates at the rate of 3% has not been sufficiently explained. Additionally, there are always errors that are associated with any discounting rate that is done while assessing the transfer of either gains or costs from one year to the other, considering that the application of the discounting rate is not an accurate basis of calculation, but a mere assumption basis (NICHSR, 2014:n.p.). Therefore, the discounting rate of 3% that was applied in the study could affect the QALY gain estimates either favorably or unfavorably, meaning that the gains eventually determined could either be under estimated or overestimated. This will eventually reflect in the final cost effectiveness analysis presented in the study findings, considering that if there was an overestimation, the study finding will reflect more QALY gains than was correct, while in the situation where there was an underestimation, the QALY benefits will be understated (Brosnan & Swint, 2001:15). This will affect the final cost analysis by either reducing or increasing the costs associated with the different methods of LSS treatment, thus giving rise to overall inaccuracy of cost-effectiveness methods, and thus resulting to misleading conclusions on the favorable method that is less costly in treating the LSS. The cost data analysis done by the modeling framework that was applied in the study also indicates the likelihood of the final results being inaccurate, and thus unreliable. This is because, the modeling framework eliminated all the complications that arose within the first 90 days of the intervention to treat the LSS, considering that such complications were not reimbursed by the Medicare (Udeh, B. et al. 2014:4). The problem associated with this modeling of the cost-effectiveness assessment is that; the failure to include the complications that occurred within the first 90 days of the treatment intervention means that the actual costs assessment was inaccurate and therefore faulty, since the costs incurred during this period were completely eliminated from the final costs analysis of the study. With some costs implications missing in the costs analysis, the final cost results that were arrived at had the limitation of being under assessed. This is because, the final costs results had left out some cost implications, and thus made the costs of treating the LSS under the different methods less expensive in reporting, yet the actual cost implications of the interventions could be much higher (Albright, Adelson & Pollack, 2008:22). Additionally, the failure to include the costs of complications arising from the first 90 days means that the accuracy regarding which method was more cost-effective was also misleading, since the costs incurred within that period could differ and thus change the overall cost implication for each method (CRD, 2014:n.p.). Further, the study also applied a One-way sensitivity analyses while analyzing the sensitivity of the results to changes in different variables (Udeh, B. et al. 2014:6). This in turn means that the sensitivity analysis was not adequate, since it ought to have been a two-way analysis to comprehensively assess the sensitivity of the results on how different variables changed. This way, some levels of uncertainty is also projected in the study. The final problem associated with the modeling framework in this study is the problem of results presentation. Under this aspect, the modeling framework does not accurately report the cost implications for the treatment methods. This is because; the study reports the costs for the Laminectomy surgery (LS) intervention as $13,771, while cost for the epidural steroid intervention was found to be $7,888 and that of the minimally invasive treatment as $5,458 (Udeh, B. et al. 2014:6). While the Laminectomy surgery (LS) was effectively eliminated as the LSS intervention method that has the least cost effectiveness, there was a problem in the reporting of the most cost effective method, since the epidural steroid intervention had the overall incremental cost-effectiveness ratio, which stood at $37,758 and therefore should have been reported as the most cost-effective method (CRD, 2014:n.p.). Instead, the modeling framework reported the minimally invasive lumbar decompression as the most cost-effective method, because it had a higher average cost-effectiveness ratio, thus resulting in misleading and misinterpreted conclusion. Alternative approaches to the modeling, analysis and presentation of the results In relation to all the limitations and weaknesses identified in the modeling framework that was applied in this study, it is suggested that an alternative methods could have been applied to analyze the cost-effectiveness of the three methods in question, for the intervention and treatment of LSS. Thus, the Cost-benefit analysis (CBA) could have been more appropriate. This is because, CBA is a more comprehensive method of analyzing benefits and costs, which comprehensively balances the overall costs and benefits of each option, regardless of whatever the cost components are, and whoever incurred them (PHAST, 2011:n.p.). In this respect, the Cost-benefit analysis (CBA) could have included the costs of the first 90 days that was omitted in the current study modeling framework. Further, the Cost-benefit analysis (CBA) could have been suitable for this analysis, considering that technique could have summed up the total costs involved in the three different treatment methods, as opposed to considering either the incremental or the average cost ratios (Robinson, 1993:924). This way, it could be easier to make a more accurate assessment, since the total cost and benefit implication could be accurately assessed. References Albright, A. L., Adelson, P. D., & Pollack, I. F. (2008). Principles and practice of pediatric neurosurgery. New York: Thieme. Brosnan, C., & Swint, J. (2001). Cost Analysis: Concepts and Application. Public Health Nursing, 18(1), 13-18. Centre for Reviews and Dissemination. 2014. Analysis: The 2-year cost-effectiveness of 3 options to treat lumbar spinal stenosis patients. University of York. http://www.crd.york.ac.uk/CRDWeb/ShowRecord.asp?ID=22014001405#.Uyr1x6CsJCl Johnson, J. (2010). Treat your own spinal stenosis. Indianapolis, IN: Dog Ear Pub. Kim, D. H., Vaccaro, A. R., & Fessler, R. G. (2005). Spinal instrumentation: Surgical techniques. New York: Thieme. Kaminski, L., & Banse, X. (2013). Time spent per patient in lumbar spinal stenosis surgery. European Spine Journal, 22(8), 1868-1876. National Information Center on Health Services Research and Health Care Technology (NICHSR). January 6, 2014. HTA 101: IV. COST ANALYSIS METHODS. https://www.nlm.nih.gov/nichsr/hta101/ta10106.html Newby, D. D., & Hill, S. S. (2003). Use of pharmacoeconomics in prescribing research. Part 2: cost-minimization analysis – when are two therapies equal?. Journal Of Clinical Pharmacy & Therapeutics, 28(2), 145-150. Public Health Action Support Team (PHAST). (2011). Techniques of economic appraisal (including cost-effectiveness analysis and modelling, cost-utility analysis, option appraisal and cost-benefit analysis, the measurement of health benefits in terms of QALYs and related measures e.g. DALYs). http://www.healthknowledge.org.uk/public-health-textbook/medical-sociology-policy-economics/4d-health-economics/economic-appraisal Robinson, R. (1993). Cost-benefit analysis. BMJ: British Medical Journal (International Edition), 307(6909), 924. Udeh, B. L et al (2014) The 2-Year Cost-Effectiveness of 3 options to Treat Lumbar Spinal Stenosis Patients. Pain Practice, online publication. Udeh, BL et al (2014) The 2-Year Cost-Effectiveness of 3 options to Treat Lumbar Spinal Stenosis Patients. Pain Practice, 1-10. Urvij M. et al. (2013). Readmission Rates after Decompression Surgery in Patients with Lumbar Spinal Stenosis among Medicare Beneficiaries. Disclosures Spine 7, 591-596. Read More