Health economists search for ways to stretch healthcare budgets

When the need for healthcare and social services is great, but resources are running low, it is especially important to focus on measures that provide the most efficient return for the money. SBU’s health economists work to inform decisions by comparing the benefits of various interventions with their costs.

Medical and Social Science & Practice

The SBU newsletter presents and disseminates the results of the SBU reports, describes ongoing projects at the agency, informs about assessment projects at sister organisations, and promotes interest in scientific assessments and critical reviews of methods in health care and social services.

Reading time approx. 10 minutes Published: Publication type:
Share:

Euro bill folded av as a stair When demand for healthcare and social services exceeds what society can provide, resources must be wisely managed. There simply is not enough to go around. Economists commonly refer to opportunity cost to describe the value or benefit of the opportunity given up each time a decision is made to spend limited resources on a different option, ie, the “sacrifice” that is incurred.

Policymakers constantly set priorities among different options. But the huge expenses during the COVID-19 pandemic brought such issues into the spotlight. What interventions must be eliminated to make ends meet? Where should the resources be obtained in order for healthcare and social services to provide care for these patients?
In Sweden, priorities in publicly funded health care must be set based on the ethical platform, which was adopted by the Swedish Riksdag in 1997. It encompasses the ethical principles of human dignity, needs and solidarity, and cost-effectiveness.
When SBU conducts a cost effectiveness analysis, the focus is on what interventions provide the most health for the money. According to the corresponding principle of the ethical platform, health care has a duty to utilise its resources as efficiently as possible.

This discipline of knowledge is called health economics and is a subdiscipline of economics. This field applies knowledge and theories about human behaviour and values, as well as the organisation of health care and its financing. Health economics usually deals with comparing various interventions with regard to their costs and effects on health and quality of life. Similar assessments are also conducted regarding interventions within social services, although in such cases the effects are not limited to health.
Cost-effectiveness analysis entails comparing costs and outcomes of two or more interventions. An intervention characterised by both lower costs and better outcomes than a different alternative is considered to be dominant. In such cases, choice of intervention is simple from the standpoint of health economics. However, in many cases, more effective interventions are also more expensive. The health economist will then want to determine whether the more effective intervention is worth the increased cost.
The methodology used to measure and analyse cost-effectiveness may vary. The outcome measure preferred by health economists is known as quality-adjusted life-years (QALYs). This measure considers not only how long a patient with a particular medical condition lives, but also the quality of life during this period.

Quality of life, also known as QALY weights, is usually represented on a scale ranging from 0 to 1, where 0 corresponds to death and 1 designates perfect health. In order to indicate how treatment effects both length of life and quality of life, the number of life-years gained is multiplied by the estimated average quality of life. For example, a treatment that prolongs life by an average of five years with an average quality of life weight of 0.7 yields 5 x 0.7 = 3.5 QALYs.
QALYs are widely used in health economics, regardless of what disease is under analysis, as a universal measure of health outcomes for different conditions and treatments. The idea is to be able to compare how much health can be achieved for a given cost, even when analysing completely different treatments and conditions. Such comparisons, however, require that the estimate of quality of life concerning different conditions, i.e. the QALY weight to be used, is completely accurate and universally valid. This is one of the factors that determines whether or not a health economic calculation represents a correct portrayal.
The QALY weight can be calculated using either direct or indirect methodology. Direct methods include in part standard gamble and time trade-off, where people are asked to choose between different scenarios, and in part visual analogue scales, where people rate the state of their health on a scale from best possible to worst possible. In contrast, indirect methods rely on responses to questionnaires called quality of life instruments (e.g. EQ-5D, SF-6D and HUI-3). The responses are converted into QALY weights using a scoring system known as a tariff, which in turn was obtained using one of the direct methods.

When reviewing health economic analyses, it is important to assess how the QALY weights were calculated, based on the quality of life instrument and valuation system used. It is important to know the category to which the people who completed the questionnaire belonged – for example, whether the quality of life associated with the condition has been assessed by the general public (i.e. hypothetically by people with no personal experience of the condition), by subject matter experts (i.e. people with professional knowledge and experience), or by people or patients who actually have the condition. Quality of life is often rated higher by those who actually live with the condition than by the general population, who can only imagine what the situation must be like.*

Health economists use many types of analytical methods; see the sidebar. Selection of methodology depends on the question the analysis must answer, but also on available data. When health care most choose between two equally effective interventions that entail equivalent risks, a cost-minimisation analysis may serve the purpose well. When the choice comes down to alternative methods that mainly affect mortality, in some cases a cost-effectiveness analysis using life-years as an outcome measure may suffice. If the concern is with treatment of chronic conditions that pose no direct threat to life, it will be necessary to consider impact on quality of life as well. The is when a cost-benefit analysis comes into play.
The outcome when comparing health economic aspects of two interventions is often presented as an incremental cost-effectiveness ratio (ICER) – the ratio between difference in cost and difference in effectiveness. This ratio denotes the cost of gaining one additional unit of effect (e.g. a life-year gained) when choosing one intervention over another.
When discussing the ICER of an intervention, health economists usually consider it in relation to the amount of money that society has seemed willing to pay for a particular unit of effect, such as a QALY. This amount is referred the willingness-to-pay threshold. Although this threshold can be analysed using scientific methodology, the actual value is determined by societal values and policymakers – not by researchers.

There are various ways to define and study threshold values, but no definitive threshold value has been determined for Sweden. According to the health economics literature, there is in fact considerable variation – one study estimated that individuals are willing to sacrifice between SEK 150,000 and 350,000 in consumption in order to gain one additional QALY, while another came up with an amount of SEK 2.4 million. In line with the ethical platform for priority-setting adopted by the Riksdag, willingness to pay within the Swedish healthcare system is also influenced by other factors, such as severity of the condition, rarity of the disease and magnitude of the treatment response, as well as the reliability of the health economic analysis.

The costs of the disease and its care are usually divided into direct and indirect costs. Direct costs are incurred as a direct result of care and treatment – staff, premises, equipment and costs attributed to the patient. Indirect costs refer to resources that are indirectly lost as a result of the disease or treatment, such as impaired ability to work or loss of production, in cases where people are unable to work due to the disease or the treatment. Loss of production also includes what is known as sickness presence, when the individual works but is less productive than previously because of illness or injury. The types of costs included in a health economic analysis depend on the type of intervention being assessed, and the perspective to be applied – for example, a health care perspective or a societal perspective. The principle of human dignity contained in the ethical platform also influences what costs are included.
Agencies such as SBU view the situation from a societal perspective to show the total costs and effects for society at large, not just for a particular sector. Costs and effects must be taken into account regardless of where they arise, yet it is common practice to describe how costs and effects are distributed among the different actors. How indirect costs affect cost-effectiveness is also addressed.

When SBU evaluates health economic aspects, the first step is usually to carry out a review of published health economic studies. The Agency reviews both empirical studies, those which are designed to gather data on both costs and effects within the context of the same study, and modelling analyses, which combine efficacy data from clinical trials (or meta-analyses of such trials) with data on costs and risks of disease from other sources.
Model analyses require certain assumptions and cannot replace empirical studies. They are primarily used in an attempt to predict costs and effects over a longer timeframe than that covered by current studies. They are also used when efficacy studies or data concerning costs and QALYs are unavailable. The most common methods used in model analysis are called decision trees and Markov models.
One important issue when reviewing health economic studies is to consider the risk of inappropriate influence on the results, such as certain cases of industry sponsorship. Since the calculations are often carried out in other countries, it also becomes necessary to ascertain whether the data that were used appear to deviate significantly from Swedish conditions, and whether Swedish data would have yielded a similar result. Countries may differ significantly in questions such as organisation, costs, disease prevalence, mortality and quality of life.

Models must be subjected to thorough sensitivity analysis in order to ascertain the reliability of the results. In this way, authors must demonstrate how robust the results are when certain conditions, data and assumptions change. For example, they may investigate the effect on results when certain outlying data are discarded or replaced with alternatives. Sometimes a probabilistic sensitivity analysis is conducted in which the uncertainties associated with different values and assumptions are concomitantly analysed in order to determine the combined uncertainty.
The current health economic literature cannot always provide an answer to the policy questions posed by SBU projects. In such cases, SBU can team up with subject matter experts to conduct its own analyses of cost-effectiveness, based on clinical studies and Swedish cost data. In some cases, it may be sufficient for the agency to study the costs associated with different interventions in relation to efficacy studies in order to be able to assess the cost-effectiveness of the interventions. In other cases, complete model analyses may be necessary.
The reliability of the results is of course a key issue when conducting health economic calculations. The reliability of health economic outcomes (e.g. days of care) in randomised studies can be evidence-graded, just as with medical outcomes. When it comes to cost-effectiveness, the problem becomes more difficult since different outcomes are aggregated.
In health economics, as in other fields of research, it is paramount that numbers are never perceived as being more reliable or accurate than they actually are. RL

* Aronsson M, et al. Differences between hypothetical and experience-based value sets for EQ-5D used in Sweden: Implications for decision makers. Scand J Public Health. 2015;43:848-54.


Reading tips

  • Socialdepartmentet (1995), Vårdens svåra val. Prioriteringsutredningens slutbetänkande, SOU 1995:5.
  • Socialdepartementet (1996/97), Prioriteringar inom hälso- och sjukvården. Proposition, 1996/97:60.
  • SBU:s metodbok, www.sbu.se/metodbok



Page published