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A Decision Chart for Assessing and Improving the Transferability of Economic Evaluation Results Between Countries

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Abstract

Objective: To develop a user-friendly tool for managing the transfer of economic evaluation results.

Methods: Factors that may influence the transfer of health economic study results were systematically identified and the way they impact on transferability was investigated. A transferability decision chart was developed that includes: knock out criteria; a checklist based on the transferability factors; and methods for improving transferability and for assessing the uncertainty of transferred results. This approach was tested on various international cost-effectiveness studies in the areas of interventional cardiology, vaccination and screening.

Results: The transfer of study results is possible pending the outcomes of the transferability check and necessary adjustments. Transferability factors can be grouped into areas of methodological, healthcare system and population characteristics. Different levels of effort are required for analysis of factors, ranging from very low (e.g. discount rate) to very high (e.g. practice variation). The impact of differences of most transferability factors can be estimated via the key health economic determinants: capacity utilisation, effectiveness, productivity loss and returns to scale.

Depending on the outcomes of the transferability check a correction of the study results for inflation and for differences related to currencies or purchasing power might be sufficient. Otherwise, modelling-based adjustments might be necessary, requiring the (re-)building and sometimes structural modification of the study model. For determination of the most essential adjustments, a univariate sensitivity analysis seems appropriate. If not all relevant study parameters can be substituted with country-specific ones, multivariate or probabilistic sensitivity analysis seems to be a promising way to quantify the uncertainty associated with a transfer. If study results cannot be transferred, the transfer of study models or designs should be investigated as this can significantly save time when conducting a new study.

Conclusions: Our transferability decision chart is a transparent and user-friendly tool for assessing and improving the transferability of economic evaluation results. A state of the art description of the methodology in a study, providing detailed components for calculation, is not only essential for determining its transferability but also for improving it via modelling adjustments.

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The authors have advised there was no external funding to support this study and therefore, the authors have no conflicts of interest that are directly relevant to the content of this study.

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Appendix

Appendix

Glossary of Transferability Factors

Methodological Characteristics

Perspective

The perspective may influence the measurement of costs and effects. While for example productivity costs are not important for the healthcare system they are of great interest to society. Furthermore, the same perspective might lead to different outcomes in the decision country than in the study country. If the coinsurance rate differs between two countries (e.g. health expenses are either fully, partially, or not at all covered by health insurance) the medical costs of a programme will also differ from the perspective of the health insurance, or that of the patient. While most economic evaluation guidelines recommend the societal perspective, e.g. American (Panel on Cost-Effectiveness in Health and Medicine),[58] Australian,[71] Canadian,[72] Dutch,[73] and German,[74] the UK National Institute for Clinical Excellence (NICE)[75] recommends the societal perspective for effects and the perspective of the NHS and personal social service decision-maker for costs. A quick overview of the different international guidelines can be found at the International Society for Pharmacoeconomic and Outcomes Research (ISPOR) website (http://www.ispor.org).

Discount Rate

Different discount rates may have a major impact on the CER of interventions if future costs or effects are related to the intervention. For example, a vaccination programme incurs immediate costs but future savings. Thus, the use of a higher discount rate in the study country will lead to an overestimation of the CER in the decision country. Authorities in different countries ask for different discount rates, e.g. 3% for the US (Panel on Cost-Effectiveness in Health and Medicine),[58] 4% for The Netherlands,[73] and 5% for Canada[72] and Germany.[74] Unlike other countries, the UK asks for different discount rates for costs (3.5%)[76] and effects (1.5%).[75]

Medical Cost Approach

There are different costing methods for direct costs. Direct costs can be estimated by using charges, fees, per diem costs and market prices as well as on different levels of aggregation. They may include overhead and capital costs which can also be measured in various ways.[77] If a hospital is interested in the cost effectiveness of a new diagnosis unit for their intensive care unit the results from a study using per diem costs would not be appropriate because of the high aggregation level of the costs. Instead, detailed cost utilisation and valuation data would be necessary.

Productivity Cost Approach

Productivity costs are typically measured using either the human-capital approach or the friction-cost method. The human-capital approach is the standard approach in most countries except The Netherlands where the friction-cost method[78,79] is required.[73] The US Panel on Cost-Effectiveness in Health and Medicine recommends measuring productivity costs as part of the QALYs.[58] The human-capital approach and the friction-cost method lead to comparable results only if the productivity loss duration is less than or equal to the friction time. Otherwise, the human-capital approach leads to higher productivity costs than the friction method.

In addition, the loss of productivity can be valued differently. Commonly used approaches are labour costs or wages for paid work and opportunity costs or substitution costs for unpaid work such as household work.

Healthcare System Characteristics

This area includes various factors related to the structure of the healthcare system, for instance market structure, government regulation, and staff characteristics. These factors are all more or less interrelated and some of them are difficult to check. However, all of them may ultimately influence three important factors that can be investigated: absolute and relative prices in healthcare; practice variation; and technology availability. In turn, absolute and relative prices in healthcare influence the costs in the CER, while practice variation and technology availability may influence both costs and effects. In the following sections, these three important factors are described. In addition, examples are given of underlying causes for differences in these factors.

Absolute and Relative Prices in Healthcare

Absolute as well as relative prices may strongly differ between countries and over time. For example, in 1996 the absolute unit costs of pH measurement for the management of upper gastrointestinal disease were 11% in Germany, 130% in Sweden and 121% in Switzerland, of the respective costs in the UK.[41]

In a multinational resource costing study strong relative cost differences were found. In Italy 1 day in the intensive care unit costs approximately 2.5-fold as much as a gastroscopy, while this ratio was about 6 : 1 in Australia.[9] Similar results have been reported by other researchers e.g. Grover et al.[38] and Rutten-van-Mölken et al.[8]

Practice Variation

Total hip replacement is a good example of practice variation between countries. Patients in Japan are more likely to be treated under regional anaesthesia and to receive a blood transfusion than in England.[42] Another example is the use of intensive cardiac procedures in patients with acute ischaemic syndromes. Yusuf et al.[80] found significant differences in the rate of invasive cardiac catheterisation and revascularisation procedures between different countries. Rates were highest in Brazil and the US, intermediate in Canada and Australia, and lowest in Hungary and Poland. Furthermore, in 1999, the overall average length of acute hospitalisation for all causes was 5.9 days in the US but 9.2 days in The Netherlands and 9.9 days in Germany,[62] suggesting strong practice variations.

Hospitalisation rates, hospitalisation duration, dosage regimens, and the timing of interventions are typical parameters that can give an indication of international practice variation. Practice variation may influence the effectiveness of the technology, if different treatment practices have different outcomes. It obviously influences the number of units of each type of healthcare but it may also affect productivity loss and healthcare costs. For instance, shorter hospitalisation implying a more intense treatment that renders higher hospital costs per day.

Technology Availability

A lower level of technology availability (such as lower numbers of computer tomographs per capita) leads to lower case numbers and thus may affect study results through capacity utilisation and returns to scale. A longer distance to the next technology provider may cause a higher productivity loss due to increased travelling time of the patient. The existence and length of waiting lists in some countries, such as The Netherlands or the UK, as well as the different range of licensed healthcare goods and generic drugs, indicates possible variations in the availability of a technology.

If a new technology is investigated, the speed of dissemination and thus accessibility is crucial. Differences in the speed of technology dissemination between countries exist, e.g. the adoption of laparoscopic cholecystectomy started 1 year earlier in The Netherlands than in Denmark and it took around 3 years for complete diffusion in The Netherlands compared with about 4.5 years in Denmark.[81]

Factors that influence absolute and relative prices in healthcare, practice variation and technology availability are:

  1. (i)

    Market Structure and Regulation: Market structures are typically characterised by concepts such as perfect competition, monopolistic competition, oligopoly, and monopoly. Market structure and regulations may influence absolute and relative costs. For example, prices for the same drug may differ between countries, dependent upon specific rules. Danzon and Chao[63] found that countries with strict price regulations (France, Italy, and Japan) systematically have lower prices for older molecules and global products than less-regulated countries (USA, UK). There is also some evidence that increased penetration of managed care lowers hospital prices and physician incomes as managed care organisations seem to pay lower fees than indemnity insurers for selected procedures such as mammograms.[82]

    Differences in incentives for healthcare providers and consumers may also cause practice variation. One recent study demonstrated that the level of managed care penetration of a healthcare system is positively associated with increases in quality, measured by in-hospital complication rates.[83] It also found that higher hospital market share and market concentration decreases quality, supporting the view that hospitals with higher market share exercise market power through a reduction in quality.[83] Another study showed that non-profit hospitals are more technically efficient than for-profit hospitals and public hospitals, and that increased managed care insurance is associated with improved technical efficiency.[84]

    Finally, market structure and regulation influence technology availability, e.g. whether and to what extent capital-intensive healthcare (transplantation, cancer treatment) is accessible.

  2. (ii)

    Staff Characteristics and Effects of Learning: The knowledge, skills and productivity of the (non-) medical staff may differ between countries. Several study results suggest that effects of learning can be found in the healthcare sector and lead to significant increases of quality.[8589] Furthermore, Ho[90] found a negative association between annual PTCA volume and average costs for performing PTCA per patient in Californian hospitals which is either attributable to learning by doing or economies of scale.

  3. (iii)

    Incentives to Healthcare Providers: Because of the specific nature of healthcare, supplier-induced demand is likely to play a dominant role. Supplier-induced demand is a result of market failure due to imperfect information[91] and may be stimulated by retrospective payment systems (e.g. fee for service) or prevented by prospective payment systems (e.g. based on diagnosis-related groups [DRG]) of healthcare providers.

    The healthcare system’s structure affects incentives for supplier-induced demand, which in turn causes practice variation. One study suggests that performance-based reimbursement of physicians might not only result in a greater cost awareness and shorter average length of stay but also in a worse quality of care.[92] Norton et al.[93] found that the prospective average price per inpatient stay is positively correlated with the inpatient length of stay. Greenfield et al.[94] showed that solo practice/single-specialty groups based on fee-for-service had 41% more hospitalisations than health maintenance organisations in three major US cities.

  4. (iv)

    Place of Technology: Several interventions can be performed on an inpatient or outpatient basis, in an academic or general hospital or by a nurse, GP, or specialist. This is an immediate cause of practice variation and may also affect absolute and relative prices in healthcare. Grieve et al.[40] showed that during the mid nineties, most stroke patients in London (UK) were admitted to general wards while in Copenhagen (Denmark) they were directly admitted to stroke or neurology units despite the fact that the case-mix in London was generally more severe.

Population Characteristics

Disease Incidence/Prevalence

Given that a technology has high fixed costs but rather low variable costs its utilisation frequency has a high impact on its (average) unit costs. For variable capacity, utilisation frequency is also linked to the returns to scale. For screening tests with a specificity <1 the prevalence has a major impact on the CER of a screening programme. In the case of low prevalence, several healthy individuals may have false positive results and be treated. This incurs avoidable costs, and possible negative health effects and associated productivity loss if the treatment yields negative adverse effects. Furthermore, for some infectious diseases the incidence and prevalence might be important for the spread of the disease in the population (see later section ‘Disease Spread’).

Disease incidence and prevalence can vary strongly worldwide. Population characteristics such as genetic predisposition (e.g. for sickle cell anaemia and malaria), life-style (e.g. intravenous drug use and risk for hepatitis C virus or skiing and bone fractures), environment (e.g. UV light and skin cancer), emigration and immigration (e.g. incidence of hepatitis B virus or HIV), and travelling (e.g. hepatitis A virus, tropical diseases) might determine the incidence/prevalence of diseases.

Case-Mix

The case-mix of the target population, such as age,[26] sex, race, education, co-morbidity, severity of disease and risk factors, (e.g. resistance to antibacterials) may have a strong impact on the effectiveness of most technologies. The susceptibility to disease might depend on age (e.g. some types of diabetes mellitus), sex (e.g. sex-specific diseases like ectopic pregnancy) and co-morbidity (e.g. HIV and cytomegalovirus infection). The average age of women giving birth correlates positively with the risk that her offspring will suffer from genetic diseases like Down’s Syndrome. The case-mix also strongly influences the progression of disease, i.e. whether a patient might enter a specific disease state early, later or never. A recent study by Liao et al.[95] indicated that there might be differences in healthcare-seeking behaviour between Japan and the US which might also lead to differences in the delay for healthcare. Depending on the disease (e.g. acute myocardial infarction) this delay might cause a more severe case-mix.

Life Expectancy

Life expectancy often has a direct impact on effect parameters such as (quality adjusted) life years gained, especially in the case of technologies aimed at the treatment of chronic diseases or the prevention of fatal diseases. Additionally, it can also influence the costs, when they are connected to the remaining life expectancy.

Health-Status Preferences

Preferences for different health states and trade-offs between lifetime and life quality can vary between countries. As a result, different preferences have an impact on economic evaluations that consider changes in health quality.

In western countries, EuroQol health-status values derived by using the visual analogue scale (VAS) have been found to be broadly similar in The Netherlands, Norway, Sweden, and the UK.[96,97] On the other hand, statistically significant different values were found between The Netherlands and Germany[98] and between Finland and the US.[99] Even greater differences have been found between western European countries and Japan, suggesting that health-status values might be strongly related to culture.[35]

Acceptance, Compliance and Incentives to Patients

Acceptance of a medical technology is especially important for preventive measures such as screening or vaccination. Culture and religion are typical parameters that might influence acceptance, e.g. some religions are against vaccination or organ transplantation. The degree of intervention acceptance might strongly differ between countries. While condoms are an accepted prevention measure to control sexually transmitted diseases in many countries (e.g. Germany, The Netherlands) they are not accepted in other countries such as the Philippines.

The success of many technologies in which patients play an active role (e.g. regularly taking medicine, following a specific diet) can strongly depend on the patient’s compliance. In the Helsinki Heart Study it was shown that a lower compliance with the drug gemfibrozil directly resulted in a lower preventive effect (reduction in coronary risk).[100] Bleyer et al.[101] found significant differences in patient compliance with haemodialysis between the US and Sweden.

There are direct monetary incentives for consumers, such as bonus systems, in various healthcare systems. For example, the insurance coverage for specific dental procedures in Germany and The Netherlands is higher if the patient has had a dental check during the last years. Besides, there is a risk of moral hazard[102] if the costs will be (partly) covered by someone else, e.g. an insurer or the public. Moral hazard describes the change of health behaviour and healthcare consumption caused by cost coverage. The size of moral hazard may depend on parameters such as the level of insurance coverage (full, partial or no coverage), co-payments and the deductible.

Productivity and Work-Loss Time

The average productivity often strongly differs between countries resulting in differences in average labour costs or wages. The average productivity is affected by parameters such as labour force participation rate, unemployment rate and the percentage of part-time workers.

If the friction method is used for the determination of indirect costs, the length of the friction time can have a strong influence on the productivity cost estimate. The friction time depends strongly on the unemployment rate but also on the knowledge and skills of the average inhabitant of the country. As a result, the friction time is very likely to differ between most countries. There are several estimates for the average friction time for The Netherlands; about 3[73] or 4[103] months for 1998 and about 6 months for 1999.[104] For Germany, an average friction time of 71 days (old states) and 70 days (new states) has been reported for 1998.[105]

Furthermore, the average number of work-loss days per case of a specific disease is likely to vary between countries. Boonen et al.[36] found that for patients with ankylosing spondylitis the risk for being disabled and the average number of sick leave days are much higher in The Netherlands than in Belgium or France. Moreover, the risk for retiring early due to specific diseases might also be country specific.

Disease Spread

The spread of infectious diseases is a key indicator for the success of prevention measures. Many different parameters influence disease diffusion, such as prevalence, susceptibility and the intervention effectiveness. The susceptibility for a disease may depend on case mix, genetic predisposition, environment and life-style. The effectiveness of an intervention in decreasing the level of disease diffusion, and thus the force of infection, may depend on technology acceptance, technology availability, and compliance. Interventions that lead to a decreased force of infection can cause indirect protection effects which are also known as herd immunity effects for vaccination programmes.[106,107]

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Welte, R., Feenstra, T., Jager, H. et al. A Decision Chart for Assessing and Improving the Transferability of Economic Evaluation Results Between Countries. PharmacoEconomics 22, 857–876 (2004). https://doi.org/10.2165/00019053-200422130-00004

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