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This chapter presents the methodology of health economic evaluations within the field of oral health and more specifically discusses the need for cost-effectiveness analyses of various caries preventive programs. It also deals with prioritized questions of limited resources and effective implementation of caries preventive technologies. As the societal cost of caries preventive interventions may not equal the direct cost of the intervention, the cost-effectiveness results depend also on the perspective and time-horizon of the analysis, and several such key issues are presented. The chapter also presents some basics about simulation modeling techniques and how to deal with uncertainty in health economic evaluations. An example of a cost-effectiveness analysis of a caries preventive program and aspects to consider is furthermore provided, to guide in the economic evaluation. Some evidences of the cost-effectiveness of various caries preventive programs are presented. However, there are some methodological weaknesses in many of the studies, and more research is needed to determine the value of such programs.
*Address all correspondence to: thomas.davidson@liu.se
1. Introduction
Oral diseases have a significant impact on the global economy, resulting in a burden of over US$700 billion, which includes both direct and indirect costs [1]. This amounts to almost 5% of the global health expenditure. Dental caries is responsible for 15% of this burden, but the actual percentage may be higher due to its association with other oral diseases [2]. Consequently, dental caries presents substantial economic challenges for individuals and healthcare systems alike.
At the same time, dental caries can result in significant health losses, from physical pain and tooth loss to psychological and financial burdens. Hence, there are many reasons to prevent dental caries.
Reducing the incidence of dental caries may be achieved through various programs. However, implementation of these programs requires investment of resources, both private and public. As resources are limited, it is essential to allocate them judiciously. Prioritizing the allocation of resources based on criteria such as effectiveness, ethics, and cost-effectiveness is important.
Comprehensive health economic evaluations play a crucial role in prioritizing healthcare services. Although a significant portion of oral health services is privately paid, there is still a need for guidance in the cost-effective use of resources. Many countries subsidize dentistry to some extent, and it is important that public resources are used efficiently. Moreover, public dental health programs may require health economic evaluations as they involve societal investments.
Health economic evaluations provide decision-makers with valuable information to allocate resources efficiently, develop guidelines, and fund research. They can also identify potential disparities in access to preventive care and oral health outcomes across different population groups. Decision-makers can use this information to direct certain programs to vulnerable groups to improve overall oral health equity.
This chapter presents health economic evaluations, different types of analyses, and some methodological issues such as the perspective and time horizon of the analysis. It briefly explains how to calculate the costs and outcomes of a program, how cost-effectiveness should be understood and interpreted, and the need for simulation studies and uncertainty analyses. A fictive example about the cost-effectiveness of a fluoride varnishing program in school is also presented. Finally, some general findings about the cost-effectiveness of caries preventive programs are discussed.
When it comes to health economic evaluations, there are various types of analyses. The main difference between them is how the outcomes are handled. In general, all analyses aim to determine the opportunity cost of a particular healthcare program in relation to its additional effects. Opportunity cost refers to the benefits that resources could have yielded if spent in the most optimal way. To determine the opportunity cost of a program, it should be compared with the best alternative [3].
Commonly used health economic analyses are:
Cost analysis (CA)
Cost-minimization analysis (CMA)
Cost-effectiveness analysis (CEA)
Cost-utility analysis (CUA)
Cost-benefit analysis (CBA)
Budget impact analysis (BI)
A cost analysis is a type of evaluation that focuses only on measuring the costs of a program and is not intended to be used for comparing different programs. It does not require a comparator since its purpose is only to determine the cost of a particular program.
A cost-minimization analysis, on the other hand, can be used for prioritizing between different programs. This type of analysis assumes that the consequences of the compared programs are identical. Therefore, it studies the costs of at least two programs and recommends the one with the lowest cost. Ideally, there should be evidence supporting the assumption of equal outcomes to use this analysis; otherwise, it may lead to unintentional rationing.
A cost-effectiveness analysis is used when the outcomes are expected to differ. The outcome measure in such an analysis could be any measure that is relevant to the treatment analyzed, such as life-years gained, the number of infections, prevented decayed, missing, or filled teeth (DMFT), and so on.
A cost-utility analysis is similar to a cost-effectiveness analysis, but it relates outcomes to some kind of values that represent utility or quality of life (QoL), most often quality-adjusted life-years (QALYs).
Another type of analysis is the cost-benefit analysis, which uses monetary terms for both the costs and the outcomes. This is often measured by studying the willingness to pay. If the outcomes are valued higher than the costs in such an analysis, this means that the treatment has a positive net benefit and, hence, that it should be implemented. Such monetary outcomes may be especially relevant in dental care, as people are generally used to paying most of the costs themselves, in contrast to other healthcare fields where much of the cost is often covered by society or insurance companies.
The analyses presented above can be complemented by a budget impact analysis [4], which evaluates how the introduction of a new program affects one or several budgets and what other consequences are expected for the main actors. All types of health economic analyses may be suitable for economic evaluation of caries preventive programs. Which one to use depends on what question the evaluation strives to answer.
2.2 Perspective and time horizon of the evaluation
A health economic analysis can be performed from different perspectives. The healthcare and societal perspectives are the most used perspectives. The healthcare perspective covers all costs and effects that occur within the healthcare system such as clinic costs, material used, overhead costs, and so on. On the other hand, the societal perspective includes all costs and effects on society, regardless of who they impact. This perspective also considers indirect costs such as productivity loss and the need for informal care. Other perspectives may include the third-party payer, the clinic’s, or the patient’s perspective. Although there is no international agreement on what perspective to use [5], the health care and societal perspectives are the two most common in various national guidelines. Generally, the perspective used depends on the analysis’s aim to provide information.
Most methodological guidelines in health economic evaluations agree that the analysis of costs and effects should have a time horizon that is long enough to reflect all significant differences in costs or outcomes related to the assessed program. This means that evaluations in, for example, prosthetics should include all future costs and effects related to the treatment, which may be a lifetime. The same applies to caries preventive programs, as this may affect the future incidence of new caries. However, the time horizon of the analysis should also be based on the level of data that exists and the uncertainty around that data. For instance, a decision-maker who considers a caries preventive program at school may not find a lifetime horizon to be relevant or trustworthy. Instead, a time horizon of, for example, 5 years could be more relevant.
It is essential to discount costs and effects that occur in the future annually to reflect their values at the time of the analysis. Although the discount rates may vary between guidelines, a rate of 3% per year is commonly used.
2.3 Costs
A program’s cost goes beyond its price tag. The costs incurred are determined by the resources utilized and should be evaluated based on what those resources could have been used for elsewhere (the opportunity cost). It is best if all resources used are quantified and presented in natural units before being valued, to ensure transparency and transferability to other settings.
There are various ways to classify costs, but they are usually categorized as direct and indirect costs, or initial treatment and maintenance costs. Direct costs refer to resources such as dental personnel, all supplies and material used, dental clinic, administrative, and patient costs. Indirect costs are productivity losses linked to a treatment or health condition. Maintenance costs include all treatment costs that are not part of the initial treatment.
To accurately reflect the direct costs, the number of minutes the dental team spends on patients should be measured and valued. If a societal perspective is sought, all the time used by patients (and significant others, if applicable) should also be included. If the program requires multiple visits to the dental clinic, all time spent needs to be added up. The value of patients’ time may be difficult to estimate, but it should reflect their opportunity cost. If the time used by patients would have otherwise been spent on paid production, the cost of having a person employed should be used (using the human capital method). If time off is used, the opportunity cost of this time off should be used, sometimes valued at 30% of paid production.
2.4 Outcomes
There are three different types of outcome measures that can be used to evaluate programs: clinical outcomes (including intermediate outcomes), measures of quality of life (QoL), and monetary outcomes. The choice of which measure to use depends on the purpose of the analysis.
Health economic evaluations allow for any kind of outcome measure to be used, but it is important that the chosen measure is relevant to the program being evaluated. For example, when evaluating a caries preventive program, the outcome measure should reflect the aims of the program. This might include factors such as the amount of caries, dmft/DMFT, pain, esthetics, or QoL.
The most commonly used outcome measure in healthcare programs is quality adjusted life year (QALY). This measure combines the value of a health state with the time spent in that state, providing scores on a scale with common anchor points. However, if QALY is to be used in caries preventive programs, it is important to consider whether it can capture oral health-related quality of life (OHRQoL) aspects [6].
It is necessary to choose outcome measures carefully, as they are used to inform decision-makers about the cost-effectiveness of proposed programs. If the chosen measure is not relevant to the decision-maker, then the analysis will not be useful. Therefore, it is important to consider the purpose of the analysis and who the decision-maker is when choosing an outcome measure.
If the evaluation’s focus is on determining the cost-effectiveness of a program, then a cost-effectiveness analysis or cost-utility analysis is generally required. In this type of analysis, an incremental cost-effectiveness ratio (ICER) is calculated by dividing the difference in costs between at least two alternative programs with the difference in effects between the programs.
For instance, if program A costs $100 per participant and causes 1 DMFT and program B costs $50 per participant and causes 1.5 DMFT, this means that program A costs an additional $50 and prevents 0.5 DMFT compared to program B. The ICER would then show that it costs an additional $100 per DMFT prevented. Whether this is cost-effective or not depends on the willingness to pay to prevent DMFT. However, no such willingness-to-pay value exists, which makes it difficult to state if program A is cost-effective.
In the case where the outcome measure instead is QALY, the same program A may yield 0.80 QALY and program B 0.79 QALY, resulting in an increase of 0.01 QALY. The ICER of program A compared with program B would then be $5000 per QALY gained (as shown in Eq. (1) below). Since QALY has been used in many studies and previous decisions, we know that the willingness to pay per QALY gained use to be much higher than $5000. Therefore, program A can be considered cost-effective compared with program B.
Eq. (1). Calculation of the incremental cost-effectiveness ratio (ICER) of program a compared with program B is as shown below:
A cost-effectiveness analysis can be represented on a cost-effectiveness plane, as shown in Figure 1 below. The ICER can be located in any of the four quadrants, A, B, C, or D. Only in quadrant A or D, it is straightforward to determine whether the program being evaluated is cost-effective, as it leads to either increased costs and lower effects or decreased costs and higher effects. However, in quadrant B or C, it depends on how much one is willing to pay for improved effects. The maximum willingness to pay for an additional effect can be shown on an acceptance curve, and all estimates below this curve are considered cost-effective. The star in the figure represents the potential ICER of program A compared with program B in Eq. (1) above, indicating that program A is deemed cost-effective.
Figure 1.
The cost effectiveness plane. = ICER of program a compared with program B.
The main purpose of health economic analyses is to assist decision-makers in prioritizing between different programs or treatments. It is crucial to provide relevant and reliable information for this purpose, which often requires the use of simulation models and uncertainty analyses. In this part, we will briefly introduce these concepts.
4.1 Simulation models
Simulation involves creating models that replicate real-world scenarios to assess the potential impact of different programs on costs and outcomes. These models can be used to estimate the long-term costs and effects of a program and to synthesize available data from different sources [7]. Simulation models can also be used to generalize results from one context to another and to go from intermediate outcomes to other outcomes. There are three main types of simulation models used in health economic evaluations: decision trees, Markov models, and discrete event simulation.
Decision trees are the simplest type of model and are useful when the timeframe is short and when the process is not complex. For instance, a decision tree may be relevant in a caries preventive program to model how many people received caries with different programs. However, it is not suitable if you want to follow the caried tooth for a long time.
A Markov model is built around different states, in which a patient can stay or transfer to another state with certain probabilities. Such a model can be useful for dental caries as it may have a state of a sound tooth, another state with enamel caries, another one with dental caries, and so on. The model can run for many cycles, and the tooth (or the patient) may move between these states, causing costs or effects continuously, which can be summed up.
Finally, discrete event simulation is generally more advanced and requires data on a patient level. It allows the study of systems or processes whose state changes discretely over time.
4.2 Uncertainty
Uncertainty is a common challenge in health economic evaluations due to factors such as variations in patient outcomes, differences in care settings, and uncertainty in cost estimates. To ensure the reliability and application of evaluation results, it is important to address and quantify uncertainty. Sensitivity analysis is a fundamental tool for assessing the impact of uncertain parameters on the results of economic evaluations.
Sensitivity analysis involves varying key inputs and assumptions, such as treatment costs, disease prevalence, and treatment effectiveness, to identify the variables that have the greatest impact on outcomes. This process allows researchers to understand the strength of the results and make better-informed decisions.
There are different types of uncertainty that need to be dealt with differently. Parameter uncertainty relates to the fact that the true value is unknown, and this can be tested with statistical tests. Methodological uncertainty relates to all the methodological choices that must be made in an economic evaluation, and the significance of this can be tested by varying those choices. Structural uncertainty relates to how the decision is labeled, the comparator chosen, and the simulation model created. This type of uncertainty is often the most difficult to test.
Another type of sensitivity analysis is called bootstrapping, which is a statistical resampling technique used to estimate the uncertainty around cost and outcomes. It involves drawing multiple random samples with replacement from the original data to generate a distribution of cost and effectiveness estimates. This method provides confidence intervals around the point estimates, which help to understand the level of certainty in evaluation results.
Probabilistic sensitivity analysis goes further than traditional sensitivity analysis by including probability distributions for uncertain parameters. Instead of using single point estimates, a probabilistic sensitivity analysis assigns probability distributions to these parameters and performs thousands of simulation runs to generate a range of possible outcomes with associated probabilities.
In summary, simulation techniques and uncertainty analysis are important components of health economic evaluations. By using sophisticated simulation techniques and carefully addressing uncertainty, researchers can increase the credibility and utility of their findings. This can guide decision-makers in making evidence-based decisions that improve oral health outcomes and optimize resource allocation in dental care systems.
5. An example: fluoride varnishing program in school
Here follows an example of an economic evaluation of a caries preventive program and some aspects to consider. In this example, we evaluate a fictitious caries preventive program from a health economic perspective. A decision-maker is considering starting a fluoride varnishing program in schools for 12-year-old children. The main reason for this is that dental health is uneven, and high-risk children are difficult to reach. A program within schools has a high chance of capturing these hard-to-reach children. The comparator is no such program (treatment as usual).
We have chosen a cost-effectiveness analysis with the number of DMFT as the outcome measure. We have chosen a societal perspective and a time horizon of 5 years to capture the wide effects over a long time. The direct cost of each application has been found to be relatively low as a whole class can be treated within a short time. The cost includes the varnish used, the time used by dental personnel, and the costs for the person to get to the school. The cost of the involved children’s time is set to zero as only a few minutes of school time are used.
Assuming the program would cost $50 per involved child and the varnish would lead to fewer DMFT compared to treatment as usual for children with moderate or high risk of caries, we need to study the relation between increased costs and improved outcomes to examine the cost-effectiveness. For children with low risk, the program only reduces DMFT by 0.1 compared to treatment as usual. For children with moderate or high risk, the number of DMFT is reduced by 0.4 and 1.0, respectively; see Table 1. This reduction in DMFT furthermore leads to reduced costs; that is why the cost difference in Table 1 is lower than the cost of the program. However, the difference in cost is above zero for all risk groups, which means that the program will not lead to savings.
Risk of caries
Long term results (5 years)
Low risk
Moderate risk
High risk
Cost difference ($)
45
30
10
DMFT difference
−0.1
−0.4
−1.0
ICER(cost per DMFT prevented)
450
75
10
Table 1.
Presentation of the incremental cost-effectiveness ratio of the example.
From Table 1 we can see that the most favorable ICER is achieved for the group with a high risk of caries, but we could not state whether this is cost-effective or not as we do not know the willingness to pay per DMFT prevented.
If all the ICER of the risk groups from the example are plotted in the cost effectiveness plane, we might more easily see the difference between the groups; see Figure 2. If the maximum willingness-to-pay per DMFT prevented would be $30, the program would only be cost-effective if directed toward the high caries risk group. If the maximum willingness-to-pay per DMFT prevented instead would be $100, the program would be considered cost-effective also if directed toward those with a moderate risk. The program is probably not cost-effective if directed toward those with a low risk of caries. However, a school-based program would probably include all these groups, perhaps showing an average in risks close to the moderate risk group.
Figure 2.
Illustration of the cost-effectiveness of the example in a cost-effectiveness plane. = ICER of program a compared with treatment as usual for three different risk groups.
6. Evidence of cost-effectiveness of caries preventive programs
There’s plenty of evidence to suggest that caries preventive programs are cost-effective, as shown by various studies and health economic evaluations over the years; for example [8, 9]. However, there are some methodological weaknesses in many of the studies, and more research is needed to determine the value of such programs [10].
Most of the studies have focused on children with a high risk of caries, with fluoride varnish being the most common intervention. While these interventions have been successful and cost-effective, they are not directly applicable to the general population where caries incidence may be lower [8].
There are a few interventions that have been found to be effective and cost-efficient for the general population, such as risk-based interventions [11], frequency of dental check-ups [12], and taxes on sugar [13]. However, there is a lack of studies using QALY as the outcome measured, which makes it challenging for decision-makers to prioritize caries preventive interventions.
Water fluoridation is probably the most efficient program, which has been found to save costs and reduce caries [14]. However, many countries or regions have made this program illegal for different reasons.
Additionally, it is important to note that the cost-effectiveness of caries preventive programs can vary depending on several factors, including the target population, the specific intervention, the dental care setting, and regional healthcare costs. Therefore, conducting local or regional health economic evaluations is necessary to tailor preventive programs to the specific context and population. Overall, more research is needed to determine the cost-effectiveness of caries preventive programs and to improve their quality.
The efficient use of scarce resources is crucial in dentistry, which is why health economic evaluations are necessary. In this chapter, we have discussed various methods to achieve this objective. Health economic evaluations can also help prevent socioeconomic inequalities in dental health and support research in preventive dental care. We have also discussed the different types of health economic evaluations and identified cost-effectiveness analysis, including cost-utility analysis, as the most used ones. However, before selecting a particular methodology, it is crucial to define the decision problem clearly. This involves identifying the patients, intervention, comparator, and outcomes (PICO), as well as determining the perspective of the analysis and the cost-effectiveness threshold.
To ensure that the analysis covers all the critical aspects, it is recommended to use checklists [3]. Nevertheless, these checklists should be used in the context of their influence on the decision-making process, rather than the number of boxes ticked. Some items in the checklist may not be relevant to the decision problem, while other important aspects may be overlooked. For instance, the analysis’s relevance to the decision problem, the inclusion of the best comparator, and the selection of the most relevant outcome measure are critical considerations.
It is essential to assess values that are important to patients when conducting health economic evaluations. The social function of esthetics, such as “kissing,” may be more important to patients than the appearance of their teeth. Similarly, patients may view the chewing function as “meal joy” rather than merely a dental function [15].
To provide relevant health economic evaluations in dentistry, well-developed simulation models are also necessary. These models should analyze programs over an appropriate time horizon, combine sources from different areas, and explore total uncertainty. Additionally, health economic evaluations in dentistry would benefit from estimating QALY values for various dental health states.
In conclusion, assessing the cost-effectiveness of caries preventive programs is crucial in evidence-based decision-making in oral health. These evaluations provide valuable insights into the financial feasibility, value for money, and the long-term impact of preventive interventions. By understanding the cost-effectiveness of different programs, policymakers and dental professionals can develop sustainable, efficient, and equitable strategies to combat dental caries effectively and promote better oral health outcomes for populations.
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Written By
Thomas Davidson
Submitted: 29 September 2023Reviewed: 24 October 2023Published: 22 November 2023
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