Analysis of the article by Nuijten on the Cost-Effectiveness of a Specific Treatment for Children - Assignment Example

Critical Review of Article by Nuijten et al. This paper is a critical review of the article by Nuijten, Wittenberg, and Lebmeier (2007) on the cost effectiveness of a specific treatment for children. The review focuses on the health economics model and the method of analysis used and proposes alternative methods and models according to experiences in other parts of the world. The paper has two parts. The first part summarises the original paper highlighting the method of health economics analysis and includes a critical review of the contents and findings. The second part considers globally accepted methods of health economics strategies that provide alternatives in analysing and evaluating similar problems and concludes with a proposal that could be considered an alternative approach to the modelling, analysis, and presentation of the results appropriate for this case. Summary and Critical Review of Nuijten et al. (2007) Nuijten et al. (2007) studied the cost-effectiveness of using Palivizumab as a preventative treatment against severe respiratory syncytial virus (RSV) in children with bronchopulmonary dysplasia (BPD) and congenital heart disease (CHD). Given the medical research finding that Palivizumab could reduce the incidence of future hospitalisation of children with BPD and CHD, the research question that needed answering was whether it was more economical for treatment to be administered to children at risk of RSV infection at an earlier age, instead of withholding treatment until some 20% of children get infected at a later age, eventually ending up in the hospital because of severe infection, and some of whom do not survive. The study therefore aimed to determine whether an ounce of prevention is, indeed, more economical than a pound of cure. Using a decision tree model, the authors adopted the perspective of the National Health Service (NHS) of the UK, used data from published literature, Palivizumab clinical trials, and official UK price/tariffs lists, and availed of national population statistics to develop an econometric model to investigate the issue. The authors also adopted a societal perspective scenario analysis taking into account the lost productivity resulting from RSV-related mortality, administrative costs of the treatment, hospital care for RSV infections, and the cost of asthma treatment. The model used was a decision tree, a series of nodes each with two branches and extending out to six levels (see Figure 1 in Nuijten et al., 2007, p. 57). The costs and benefits at each branch and level are calculated based on probabilities and data gathered from clinical trials. The resulting figures for cost/QALY given the two options of treatment and no-treatment are summarised (see Table VII in Nuijten et al., 2007, p. 66) and discounted at 3.5% for scenario analysis resulting in two sets of ICER values (see Table VIII in Nuijten et al., 2007, p. 67). A key variable to assess the benefit of Palivizumab treatment was the number of RSV hospitalisations avoided extrapolated to the number of life-years gained measured in terms of computed Quality Adjusted Life Years (QALYs) discounted by 3.5%. Research results showed that in preterm infants and children with BPD, treatment with Palivizumab in comparison with no prophylaxis had an incremental cost-effectiveness ratio (ICER) of £7,042/QALY without discounting outcomes, increasing to £16,720/QALY after discounting. In babies with CHD, Palivizumab use resulted in an ICER of £2,427/QALY without discounting and £6,664/QALY after discounting. Sensitivity analyses confirmed the model’s robustness, and scenario analysis showed that including indirect costs further improved the cost-effectiveness of Palivizumab. The authors concluded that Palivizumab prophylaxis against severe RSV infection in children at high risk may be cost effective from the NHS perspective, and that the positive clinical and economic benefits may persist beyond one RSV season. Kernick (1998) and Drummond (2005) provide a framework for evaluating health economics studies. They proposed asking basic questions that: (1) define the viewpoint or perspective of the study, e.g., the societal, government health care, purchasing authority, or general practitioner dimensions; (2) assess the relevance of alternative treatments considered; (3) test the reliability of the economic data collected and the model used; (4) determine whether costs were measured using credible sources; (5) identify the output measures used; and (6) allow decision-makers to judge whether research results are generalisable or applicable to other situations such as private practice or in other locations. Answering these questions would give a realistic assessment of the objectivity and applicability of the study and likewise point out the study’s limitations, if any, so that the practitioners would be able to approach the issue under investigation in a more scientific manner characterised by academic rigor and truthfulness. In evaluating any health economics study, therefore, there are ten points that should be investigated and that affect scientific credibility. The first problem with economic studies is publication bias where a study is funded by a pharmaceutical company or institution that has a stake in publishing the favourable results of the study. It is notable that the Nuijten et al. study was funded by Abbott GmbH & Co. of Germany, the maker of the Synagis brand of Palivizumab (Whitehall et al., 2001). This makes the credibility of the study results questionable. There are, however, nine other points that need investigation to make a more objective assessment. The second point is the type of analysis. The Nuijten et al. study indicates a cost effectiveness perspective but ends up using the cost utility perspective of the treatment by allocating quality of life values and combining quantity and quality of life with the use of QALY. As Kernick (1998, p. 1663) pointed out, “the cost utility method has the advantage that different interventions can be compared across a broad range of choices in resource allocation…(but) a number of methodological problems remain.” The following points indicate what these problems are. The third point is whether costs are related to outcomes. In an ideal study, the economic analysis is undertaken together with controlled trials, which may not be always possible. The Nuijten et al. study used clinical results from various sources dated 1993 (Groothuis), 1997 (PREVENT), and 1998 (Simoes et al.). Since the sources are almost a decade old, the precision and relation of the costs would be doubtful. The fourth point is the time scale used, and on this point, the Nuijten et al. study has a strong point. The period of the follow-up must be long enough to capture all the clinical outcomes and the results must be available within a reasonable period of time, considering too the resource implications of the study itself. This is the purpose for discounting of results, which the study incorporates, using a 3.5% discount rate to establish a comparable time frame for the economic evaluation of the intervention. However, considering that the UK economy during the period 1993-2003 when the study was conducted averaged 2.5%, falling within a range from a low 1.9% in 2001 to a high 3.1% in 1996 (ONS, 2008), the discount rate used is on the conservative side at 3.5%. The effect of using this higher figure would tend to over-estimate the costs and also the benefits and the calculation of the QALYs. Provided that the same rate is used for the computations and that the figure used is stated clearly (as it is in the Nuijten et al. study), then this should not pose much of a problem. The perspective from which the economic data is analysed is likewise stated clearly as being that of the NHS, which leads this writer to think that the company-funded study had as one of its main (though partly hidden) objective that of calling the attention of the UK government to subsidise the administration of Palivizumab for all children in the UK. The purpose may be to justify spending these amounts now so that the government could avoid higher health care costs due to hospitalisation of infected children in the future. The societal perspective was likewise included, taking into consideration the future lost productivity of the child and the parents arising from RSV-related mortality. Included also were other general practice costs such as the administration of the medicine, hospital care, and the cost of asthma treatment, one of the interventions arising from early RSV infection in children. One of the methodological problems mentioned previously and that applies to this study is the fact that the NHS is not a perfect market, calling into question the validity of the cot measurements used, although this may be due to methodological uncertainty rather than inefficiencies (Kernick, 1998, p. 1664). Whilst the NHS’s inefficiencies during the period of the study are extensively known and documented (NHS, 2006), critiquing economic studies must take into account that the use of prices as a proxy for costs must be done carefully. The Nuijten study considers direct costs arising from Palivizumab treatment and indirect costs from loss of productivity whilst parents are in the hospital caring for children. There are also some factors that are difficult to value, such as mortality or the stress that parents go through when their children are sick. This is where sensitivity analysis is useful in determining the accuracy of data and to test the study’s conclusions against the range of values and findings. The Nuijten et al. study used sensitivity analysis to test the model used, but the authors noted that the probabilistic analysis only reflected mortality during hospitalisation and not the long-term mortality resulting from RSV infection. The probably reason is the lack of data that would lead to more precise value calculations. The seventh point considers the importance of marginal analysis, the incremental benefit obtained from an increment in cost, rather than placing emphasis on the average cost and benefit ratios. Incremental cost effectiveness is more useful in decision-making, more so if the research is conducted from the perspective of the NHS. The Nuijten et al. study made full use of the ICER, which compares the incremental difference in cost-effectiveness ratios (CER) of two interventions, each with its own calculated values of costs and health benefits. The CER measures the value of the benefit that could be derived for every unit cost of the intervention and is a good way of determining whether an intervention is economically justifiable. Given two different possible transitions from the same base case, e.g., non-treatment or an existing health budget that produces a certain level of health, an intervention with a lower ICER would be a more attractive option because it would give a higher incremental benefit at a lower incremental cost. Were economic resources unlimited, using cost-effectiveness ratios would not be necessary, but because of the financial pressures on government health services to utilise their resources more efficiently, CER calculations are needed to justify increases in health services budgets and resource allocation decisions. In addition, health resources must be distributed not only across interventions but also across population groups in such a way that the highest possible level of over-all health is attained. This has given rise to the usefulness of calculating incremental CER to determine the cost-effectiveness of a wide spectrum of interventions within the general population and across several countries. Calculating the CER for a specific intervention and comparing it with other CER figures is a helpful way to look at the relative cost of health care in the face of several possible alternatives and choices. However, this cost-effectiveness method is not without its limitations because such mathematical calculations, no matter how precise they may seem, could not capture the total value of health or its absence, or the value of a terminated life. In addition, the use of such precise methods is not without political consequences inasmuch as the numerical data are utilised to support policy decisions that may not be free of subjective rationalisation. For example, politicians with their own agendas may justify cutting off budgetary support for specific interventions (special needs children or people with HIV-AIDS) on supposedly rational grounds according to what they may consider as objective and scientific bases. Prioritising health care interventions is a complex issue, and cost-effectiveness measures are a good starting point, presenting a useful tool for health services practitioners in making decisions. These should, however, be aware that the economics of health services have several technical shortcomings, such as integrity of data, inconsistency of econometric methods and models, and the applicability of findings (in this case, the use of Palivizumab on children) beyond the context of the original study. The main reason, as Hutubessy et al. (2003) argued is that “the relative costs and effects of interventions for a wide range of diseases and risk factors need to be determined in order to identify the optimal mix of interventions that will meet the overall objectives of the health system, such as the maximisation of health itself or the equitable distribution of health gains across the population. Nuijten et al. (2007, pp. 68-69) provided examples in their methodology where they resorted to conservative assumptions in their calculations. First, they excluded the cost of asthma treatment because not doing so led to only a moderately higher ICER for Palivizumab prophylaxis for all patient populations. Second, including indirect costs (or losses in productivity) showed a substantial improvement in the CER. Third, they assumed that prophylaxis only affected the occurrence of RSV hospitalisation but does not influence the severity of the RSV infection, although clinical trials suggested otherwise. Each set of assumptions has an impact on the econometric model and influence the CER in a certain way. This would likewise affect the over-all findings and conclusions of the research study. Provided these are made clear (and they are), those who peruse the findings would be able to formulate their own insights into the usefulness of the findings. In fact, Nuijten et al. in their conclusion point out the study’s strengths and shortcomings and suggest possible avenues for further research. The key strength of their study is the use of cost/QALY that incorporates health outcomes or benefits according to the latest National Institute of Health and Clinical Excellence (NICE) guidelines compared to other previous studies that only used cost per avoided hospitalisation. Whilst the use of QALY provides a measurable means for calculating the value of health gains arising from interventions and can be used in setting priorities, the values assigned to the quality of life component is subjective and may not necessarily reflect the same priorities and values of patients receiving the health care intervention. Although QALY provides a basis for comparing the extra length of life gained from an intervention and the quality of the extension of life for the same problem, it may also be limited because some critics cite its lack of sensitivity to other diseases (e.g., which is worse, to die from malaria or RSV, from HIV/AIDS or malnutrition?) that have higher mortality and greater morbidity. Thus, using QALY may be up-to-date, but it can also over-simplify what is essentially a complex set of issues and may not be readily acceptable because it is too easy. Moreover, the authors also warned that the use of ICER measures is not a generally accepted cost-effectiveness outcome since no thresholds are available, and that the interpretation of the cost effectiveness based only on the avoidance of hospitalisation is subjective and must be considered with caution (Nuijten et al., 2007, p. 69). The eighth point that should be considered in making an evaluation of the Nuijten et al. study is on how the outputs are measured. Whilst measuring mortality is the simplest approach, measuring all the costs and benefits of an intervention may be difficult, which makes the use of QALY helpful. QALY is an absolute measure that is of interest because it hints at the quality of life during those additional years gained as the result of intervention. On this issue, the Nuijten et al. study exhibited one of its strong points. An important consideration in this study is the fact that the health of children was the focus of the intervention. Nuijten et al. are optimistic that all the children who may be saved from the prophylaxis would be productive and offer a positive contribution to UK society. This gives the study an unrealistic dimension, because it is possible that the child would grow up to be less productive, perhaps even turn out to be someone who would prove to be a menace to society, capable of performing actions that would wipe out any benefits gained beforehand from the treatment. This sociological perspective may not seem popular and show one of the methodological problems that health economics studies must include in their calculations. Of course, it is also possible that any negative productivity effects may be overwhelmingly balanced by the positive contributions of others who are cured by the treatment, but the assumption that all children would become productive must be tested against actual data in the past for similar interventions, such as mandatory immunisation measures. Although the fact that the UK economy continues to grow provides the hint of evidence that past interventions have positive productivity effects, it could also not be denied that part of the productivity is due to the contribution of immigrants who were not recipients of the NHS’s past interventions. The ninth, and second to the last, point for the analysis concerns the comparator that is used for the study. As Kernick (1998, p. 1664) pointed out, an ideal study should compare all competing interventions, including that of doing nothing. The Nuijten et al. study compared Palivizumab prophylaxis with several base case scenarios using scenario analysis, but the “do nothing” scenario was not included (Nuijten et al., 2007, p. 59) because the researchers did not assign a cost to the no prophylaxis arm of the model. The effect of this methodology is to underestimate the total cost, overestimate the CER and ICER, and therefore enhances the viability of the treatment. It also assumes that the new treatment is as effective as the existing treatment. This brings back a cloud to the credibility of the results, even though the researchers are forward with their assumptions, even that of admitting that the cost of adverse events were not considered because “the clinical trials did not show any difference between prophylaxis and no prophylaxis based on 1998 and 2003 data” (p. 59). Although this critique focuses on the modelling aspect and assumes that the results of clinical trials are correct, the assumption that the costs are the same is rather difficult to believe and makes the study results doubtful. The last point is the external validity, or the applicability of the test results to other situations. The article’s title is clear that the research focused on the UK experience, using NHS cost data, and from the NHS perspective. Thus, there is no claim that the results are applicable to other countries, for the obvious reason that cost structures may be different and that there may be other factors affecting the morbidity of the youth population. Give these considerations and the model used, the study, while doubtful in its objectivity, provides an empirical data set that is limited in application to the cost effectiveness of RSV in high-risk children in the UK. Nevertheless, it presents a good perspective in studying other diseases using the same methodology and may be utilised to study similar cases in other countries, not only those related to RSV morbidity. Other Models of Health Economics Analysis The World Health Organization (WHO) came out (Tan-Torres Edejer et al., 2003) with a comprehensive set of guidelines for measuring the cost effectiveness of health care interventions. They cited several models that serve to enable health service policy makers in the task of prioritising the allocation of scarce economic resources. Amongst the models discussed were: 1. Traditional Cost Effectiveness Analysis (CEA), a form of incremental analysis that determines how a new or additional intervention compares in terms of efficiency in the use of resources with the current mix of interventions that address a singular set of health issues. 2. Generalized Cost Effectiveness Analysis (GCEA) allows the maximisation of generalizing or applying results in different settings, regardless of each country’s socio-economic status, helping health services institutions to apply experiences and lessons learned in other countries at the fastest possible time and lowest possible costs. GCEA can do this by evaluating interventions in comparison with the base case of doing nothing or no prophylaxis, encompassing traditional CEA and including or not productivity costs. 3. Intervention mix constrained CEA (IMC-CEA) is a subset of traditional CEA and involves the evaluation of new interventions in comparison with the current mix of interventions. Although practical, IMC-CEA does not help policy-makers much in knowing whether this is the best way to use limited health resources. 4. The use of computerised tools that allow the collection and reporting of standardised data. Amongst these are (a) CostIt, a costing template which records cost data in a form that is useful to analysts and policy-makers; (b) PopMod, or Population Model, which automatically calculates the effectiveness of interventions for a standardised population in terms of outcome indicators such as QALYs and DALYs (disability-adjusted life-years averted); and (c) MCLeague, a programme that uses Monte Carlo simulation principles to demonstrate the uncertainty over costs and effects to decision-makers by using stochastic league tables (Hutubessy et al., 2001). These methods provide information beyond those used in traditional cost-effectiveness analysis, including the probability for each intervention in the optimal mix for given levels of resource availability. Health economics is an important tool of government policy-making and, like all the other economic sciences, could be considered both a science and an art. It combines the precision of mathematics with the art of presenting the results in a manner that decision-makers could comprehend. The Nuijten et al. article critique pointed out several shortcomings and strong points, and whilst the study is somewhat tainted by publishing bias that gives it a self-serving perspective, there are ways the data gathering, utilisation, and presentation could be improved in future studies. Crafting stochastic league tables (see Appendix for an example) requires large amounts of information, but the resulting ease with which different interventions could be compared as to their costs and health effects, mean and sample ICER values for all possible combinations of interventions, and the cost effectiveness of these combinations given varying levels of resource allocation. The methodology for constructing a league table with the resultant findings is explained in detail by Hutubessy et al. (2001). Using Monte Carlo simulation for the sensitivity analysis would allow the construction of intervention models that are more complex but that are more precise in estimating cost effectiveness because they diminish the degree of uncertainty in costs and effects. This method allows an intervention to have a greater impact on its probability of inclusion in the optimal mix that would maximise population health for a given level of resources. The resulting league tables help risk-neutral policy-makers decide the extent to which they should trade off efficiency gains with other goals of the health system. Bibliography Baltussen, R.M.P.M., Hutubessy, R.C.W., Evans, D.B. & Murray, C.J.L. (2002) Uncertainty in cost-effectiveness analysis: Probabilistic uncertainty analysis and stochastic league tables. International Journal of Technology Assessment in Health Care, 18, pp. 112-119. Drummond, M.F. (2005) Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press. Groothuis, J.R., Simoes, E.A., Levin, M.J. et al. (1993) prophylactic administration of RSV immune globulin to high-risk infants and young children: RSV immune globulin study group. New England Journal of Medicine, 329, pp. 1542-30. Hutubessy, R.C.W., Baltussen, R.M.P.M., Evans, D.B., Barendregt, J.J. & Murray, C.J.L. (2001) Stochastic league tables: Communicating cost-effectiveness results to decision-makers. Health Economics, 10, pp. 473-477. Hutubessy, R.C.W., Chisholm, D., Tan-Torres Edejer, T. & WHO-CHOICE (2003) Generalized cost-effectiveness analysis for national-level priority setting in the health sector. Cost Effectiveness and Resource Allocation, 1 (8). Kernick, D.P. (1998) Economic evaluation in health: A thumbnail sketch. British Medical Journal, 316, pp. 1663-65. NHS (National Health Service) (2006) U.K Health Report: Focus on health. M. Bajekal, V. Osborne, M. Yar, & H. Meltzer, Eds. Basingstoke: Palgrave Macmillan. Nuijten, M.J.C., Wittenberg, W. & Lebmeier, M. (2007) Cost effectiveness of Palivizumab for respiratory syncytial virus prophylaxis in high-risk children: A UK analysis. Pharmacoeconomics, 25 (1), pp. 55-71. ONS (Office of National Statistics) (2008) Annual inflation rates. Available from: http://www.statistics.gov.uk/cci [24 January 2008]. PREVENT Study Group (1997) Reduction of RSV hospitalization among premature infants with bronchopulmonary dysplasia using RSV immune globulin prophylaxis. Pediatrics, 99, pp. 93-9. Simoes, E.A., Sondheimer, H.M., & Top, F.H. (1998) RSV immune globulin for prophylaxis against RSV disease in infants and children with congenital heart disease: Cardiac study group. Journal of Pediatrics, 133 (4), pp. 492-9. Tan-Torres Edejer, T., Baltussen, R.M.P.M., Adam, T., Hutubessy, R.C.W., Acharya, A., Evans, D.B. & Murray, C.J.L. (Eds.) (2003) Making choices in health: WHO guide to cost-effectiveness analysis. Geneva: World Health Organization. Whitehall, J.S., Bolisetty, S., Whitehall, P., Francis, F., Norton, R. & Patole, S.K. (2001) High rate of indigenous bronchialitis and Palivizumab. Journal of Paediatrics and Child Health, 37 (4), pp. 416-17. Appendix: Example of a Stochastic League Table Source: Table 3, Baltussen et al. (2002) Read More