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Bad science in health economics: complementary medicine, costs and mortality

By Chris Sampson, David Whitehurst and Andrew Street

In December 2012, an article was published in The European Journal of Health Economics with the title ‘Patients whose GP knows complementary medicine tend to have lower costs and live longer’. We spotted a number of shortcomings in the analysis and reporting, to which we felt a response was worthwhile. Subsequently the authors of the original piece, Professor Peter Kooreman and Dr Erik Baars, wrote a reply. In this blog post we summarise the debate and offer some concluding thoughts.

The study

The study employed a large dataset (n~150,000) from a Dutch health insurer. The objective of the study was “to explore the cost-effectiveness of CAM compared with conventional medicine”. The study sought to find out whether different levels of cost or mortality were observed depending on whether or not an individual’s general practitioner (GP) was trained in complementary and alternative medicine (CAM). The authors specifically looked at GPs trained in anthroposophy, homeopathy and acupuncture.

The authors implemented both a linear and log-linear regression model to estimate the cost differences associated with different types of CAM-training. Separate regressions were carried out for each type of CAM, for four different age groups and for five different cost categories. This gave a total of 120 different coefficients (2 (models) x 3 (CAM approaches) x 4 (age groups) x 5 (cost categories)) for the cost difference associated with CAM-training. Eighteen (15%) of these coefficients were negative (indicating positive findings attributable to CAM training) and statistically significant at the 5% level. Three (2.5%) coefficients showed a greater cost associated with CAM training.

For mortality effects, the authors implemented both a fixed effects logit and a fixed effects linear probability model (LPM). In this case the groups were split by sex and, again, by type of CAM-training; additionally an overall effect of CAM-training was included. This gave a total of 24 different coefficients for the mortality difference associated with CAM-training. Four (16.7%) of these were lower and statistically significant at the 5% level; all from the LPM.

The authors concluded that “patients whose GP has additional CAM training have 0–30% lower healthcare costs and mortality rates, depending on age groups and type of CAM”; adding that “since the differences are obtained while controlling for confounders… the lower costs and longer lives are unlikely to be related to differences in socioeconomic status.”

The study’s faults

A major problem with the study is one of selection. Selection is important in this study; there is selection of individuals who decide whether or not to register with CAM-trained GPs and selection of GPs who choose to pursue CAM. Patients that register with CAM-trained GPs may have different characteristics from those who do not, and exhibit different levels of cost and mortality as a result of these characteristics, rather than of CAM itself. The risk-adjustment the authors perform is the only way they deal with selection, and the set of risk-adjusters is very small; including only age, gender and postal code. The authors defend their position by citing a paper suggesting that selection bias might operate in the other direction. Neither we nor the authors can prove this one way or another. To thoroughly address selection, a larger set of risk-adjusters should be included and an approach such as propensity score matching would have been superior to the model adopted by the authors.

In reporting and reflecting upon their analyses, the authors do not recognise the problems associated with multiple testing. The authors appear to misunderstand the familywise error rate and the implications of this for the results that are currently shown as statistically significant. The authors should have accounted for this, using a method such as the Bonferroni correction.

The primary claims of the study are that patients with CAM-trained GPs had “0–30% lower costs” and “0–30% lower mortality rates”. These claims can be found throughout the original study, including the title, and in the authors’ subsequent dealings with the media. We believe that the first claim is a ‘cherry-picked’ finding; the second is simply false.

With regard to costs, as identified in the authors’ reply, the 30% relates specifically to patients “aged 75 and above with an anthroposophic GP-CAM”. But there are some coefficients that show a greater cost associated with CAM-trained GPs. Yet the paper’s title and publicity statements focus on this significant result alone. This is not an accurate reflection of the cost implications for patients in general, and highlighting this cherry-picked result is a misleading representation of the overall effects. A more appropriate way of reporting the results would have been to present the expected cost differences across the whole sample.

The analysis of mortality is simply incorrect. Mortality risk is bounded by 0,1 but the linear probability model is unbounded; making it inappropriate to model mortality data. The logit model is designed for binary outcomes, and when this is employed the significance of the mortality differences disappears or is less than 5%. But even the logit is inappropriate for these data because mortality is an infrequent event (around 3% of the sample died). A probit model would be preferable and we suspect that, had a probit been employed, no significant differences would be found. In short, the ‘significant’ effects that the authors identify are due to incorrect model specification.

In their responses, the authors retreated from their original emphasis on the significance of the mortality results saying that “our results do not show any evidence that patients of GP-CAMs have higher mortality rates”. We agree with this re-statement. Nevertheless, the title of the paper remains “Patients whose GP knows complementary medicine tend to … live longer”, which the authors now appear to admit is false.

Closing remarks

The study was available in its current form, as well as earlier versions, long before it was published in the EJHE. As a result, the study’s inaccurate claims have been repeated in a number of papers that cite the work in relation to herbal medicine and CAM in primary care. The publicity sounding these claims, and the authors’ conduct with the media, has been discussed elsewhere (English translation).

We believe that the original study and the response pieces might be used as a case study to aid teaching. To this end we have provided material to the Health Economics Education website. In addition, please do consider commenting below to develop the discussion – whatever your thoughts on the matter. Do you see other flaws in the study design? Or maybe you think some of our comments are unfounded? Are there better ways of studying important questions such as these?

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Rationing and deprivation in risk sharing schemes

Here in the UK, NICE sometimes advises against the provision of particular drugs, by the NHS, on the grounds that evidence does not indicate them to be cost-effective. In some cases it appears that these ‘rejections’ are the result of insufficient data rather than comprehensive data against the use of the drug. On occasion the Department of Health has followed such decisions with a trial-in-practice patient access scheme; what are known as Risk Sharing Schemes. These allow for the provision of the drug, following an agreement with the manufacturer, and the possibility for evidence development.

One well-publicised risk sharing scheme is the Multiple Sclerosis Risk Sharing Scheme. The outcomes of this trial-in-practice remain uncertain, but the scheme has been described as a “costly failure“. In a study of participant data, a recent article demonstrated that the likelihood of individuals being offered treatment, as a part of the MS Risk Sharing Scheme, was positively related to their socio-economic status. This raises questions about the value of the results from a ‘trial-in-practice’ such as this.

Rationing and deprivation

That individuals’ deprivation levels or economic status can be a determinant of prescribing decisions is a well-documented phenomenon. Evidence exists in relation to treatment for colorectal cancer, glaucomalung cancer and the prescription of statins and antidementia drugs. Health economists often suppose that the most deprived individuals are also those most in need of health care, but the evidence from all the studies mentioned above highlights deprivation level as being a negative predictor of the levels of care received. In some cases there is good reason for this; those who are more deprived can sometimes tend to present later when care would be less effective, and there may also be relevant issues surrounding health literacy. However, in other cases such an explanation is not so obvious.

Experimental evidence

Trudy Owens and co’s study shows that risk sharing schemes can demonstrate similar characteristics, which I believe to be a point of concern. I would suggest that such schemes as the MS Risk Sharing Scheme can only be justified if they seek to produce experimental evidence of the cost-effectiveness of the intervention. Without this they will be little more than a back door means of provision for drugs that have not been demonstrated to be cost-effective. It seems obvious to me, therefore, that this research and experimental evidence, and the schemes themselves, must conform to a good study design. One would not tolerate a study that allowed practitioners to pick and choose individuals for treatment based on their subjective expectation of success. While this may contribute to the manufacturer’s aims of finding a cost-effective use of their drug it is hardly good science. If such a drug were accepted on to formularies, would doctors continue to (inadvertently or otherwise) discriminate based on deprivation status? Quite possibly, but we’d certainly highlight this as a problematic issue and it would be one reinforced by the poor quality trial-in-practice of the risk sharing scheme.

While some papers offer advice on the design and administration of risk sharing and evidence development schemes, there appear to be no studies addressing the problems caused by discrimination and rationing. It seems to me that there is a substantial gap in the research if we are to prevent risk sharing schemes becoming publicly-funded bad science.

Do you see value in risk sharing schemes? Are they likely to be more representative of practice than randomised trials? Is deprivation a reasonable basis for rationing?

 
1 Comment

Posted by on March 7, 2012 in Efficiency and Equity

 

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