Structuring and validating a cost-effectiveness model of primary asthma prevention amongst children

Expert panel sessions and model structuring

Consultation with experts, just as personal experience or a review of the literature, may provide the background information needed when developing a model [28]. For this paper, experts were consulted to provide biomedical insight into the natural course of asthma and its care in the Netherlands. We developed a protocol (Additional File 1) on conducting the expert panel held as round-table sessions [29]. The protocol identified relevant inputs and expected outcomes of each session, including the guiding questions to structure the discussions. Following a review of relevant health technology assessment (HTA) literature [28–30], the protocol consists of the following elements:

The foremost criterion for expert selection was the extent of their expertise and experience with asthma and its prevention in children and/or research experience in the field of efficacy and cost-effectiveness [29]. This was discussed within the research group which resulted in nominations for candidate experts. Once identified and contacted, the experts' willingness to participate and ability to attend the sessions [29] were also considered. The experts received copies of the protocol for their guidance to the process. Initially, only two sessions were planned but because of logistical issues as well as significant changes in the model structure after consulting the research group, another session was added. Consensus was not formalised as strict unanimity on discussion points because of the explorative nature of the round-table discussions [29].

The expert panel sessions were conducted in 2010 at Maastricht University, the Netherlands. Eight expert nominees were contacted but the final combination of experts were two from general practice and (paediatric) pulmonology and one each from epidemiology and HTA. During the first session, the HTA expert joined in by telephone and thereafter kept contact through e-mail. Because of unavailability, one pulmonologist and one general practitioner (GP) dropped out after the first and second sessions, respectively.

The first session was meant to frame the decision problem and generate assumptions needed to determine the structure of the model. Input to this session was an overview of the research line as background for the decision problem and purpose of the model. The experts were asked about other aspects of the decision framework covering the natural history of the disease, gold standards of measure, possible interaction and recursion of disease states, as well as appropriate comparators. Since time horizon, population, and perspective were already naturally defined by the existing clinical studies from the PREVention of ASthma amongst Children (PREVASC) research line [14–18], these elements were not solicited from the experts. The first session resulted in a set of assumptions and a decision framework.

After the first expert panel session, the literature was systematically searched for existing models that could be adapted for the current decision problem [31]. When a new model was required, the type was chosen using guidelines and recommendations in the literature [21–24]. Consideration was given to the purpose (single-use vs. programmatic), level of simulation (individual vs. cohort), relationship between individuals (independent vs. interacting), and frequency of outcomes/health states (recursive vs. non-recursive). The model structure was drawn based on the type of model, the decision framework and assumptions, and published recommendations [32].

The last sessions were meant to refine the model structure and determine its descriptive validity. For the second session, the input was an overview of the modelling process and the general types of models, to provide some information to the experts with limited exposure to economic modelling. It was only given at that time so that the generation of assumptions and the framing of the decision problem during the first session would not be restricted by bias for one model or another. During this second session, the model structure was scrutinised on how well it reflected the set of assumptions and decision framework from the first expert panel session. The outcome at this stage was a list of comments for improving the correspondence of the model structure with the set of assumptions and decision framework. On the third and last expert panel session, the input was the latest model structure which was again evaluated. After satisfying the descriptive validity, the model was finalised.

Validating the model

Descriptive validity (i.e. whether the essential characteristics of reality is captured in the model [33–35]) was determined during the second and third expert panel sessions. This validation was carried out with the experts' review of how the model structure was able to incorporate the assumptions about the development and current care of the disease into a simplified structure. Specifically, it was determined during the sessions whether the model was parsimonious and sufficient to answer the decision problem [36]. This was an iterative process with the experts after the initial session.

In the absence of comparable models that evaluate allergen-avoidance measures in the primary prevention of asthma in children, we focused instead on the parallelisms (i.e. similarities) between characteristics of the model structure, namely: model type, time horizon, perspective, population, intervention, comparator, and health states/effect measures [37]. The other characteristics are related to the natural history of a disease and its treatment which are currently important determinants in the chain of choices that lead to the selection of the model structure [37]. Aside from including studies on the secondary and tertiary prevention of asthma (i.e. preventing allergic sensitisation from progressing to asthma or preventing the exacerbations of established asthma [1]) we proceeded with this "parallel validation" by increasing the scope to studies on lower respiratory tract infections. These infections in infants, particularly with the respiratory syncytial virus, have been associated with the development of asthma later on [38]. To test for the parallelisms, we first performed a systematic search of the literature to identify the relevant studies. The search was conducted in the PubMed databases based on an extensive search strategy for economic evaluations [39, 40]. The database was searched until 08 September 2010 and was restricted to English-language records of titles and abstracts of evaluations that used decision-analytic modelling. Only evaluations of at least two alternatives on asthma prevention which relate costs to consequences [41] were included in the review. Following the screening process (Figure 1), the results were reviewed by one of the co-authors and any uncertainty was resolved by direct discussion. Technical or internal validity of the calculations and consistency of formulae and parameter inputs with the assumptions were checked.

Model operation

Once the decision problem was framed and the model's structure visualised, its operational feasibility or technical validity [35] was tested by populating it with data from the PREVASC studies. Detailed description of the PREVASC programme (Figure 2) can be found elsewhere [14–18]. Children were included in the studies based on risk for first-degree familial asthma history (i.e. positive or negative, PFH or NFH respectively) [14, 15]. Phase 1 and 2 studies investigated the clinical effects of multifaceted primary asthma prevention at age two and six years, respectively. It was found that over the short or long term, there were no significant reductions of asthma diagnosis between intervention and control groups [16]. However, a subgroup of the PREVASC children seemed to benefit from the prevention programme [14]. Phase 3 was a natural history study which found that there were more respiratory-tract morbidities and atopy in PFH than NFH children in the first two years of life [19, 20].

Direct medical as well as intervention costs were assessed and valued using standardised unit prices for the Netherlands indexed using 2009 figures [43]. These direct medical costs include GP and specialist consultations for asthma-related complaints, any related hospital admissions, the diagnostic tests (i.e. chest x-ray and radioimmunoassay test for allergens), and medication use. For those who received intervention, costs on research nurse visits, the use of house dust mite-impermeable materials for the beds of participants and parents, hypoallergenic formula feeding, and education materials in the form of brochures were included. The model outcome was the incremental cost-effectiveness ratio which was assessed for various threshold values (€0 - €70,000 per asthma case avoided).

Expert panel and sessions

Clinical insight into the natural course of asthma, diagnosis, as well as current care and alternatives were provided by experts from the first three fields. The HTA expert provided insight into important economic considerations such as how costs and consequences can be incorporated in the comparison of alternatives. The involvement of the experts in the development of the model structure is illustrated in Figure 3.

Decision framework and assumptions for the model structure

Although differentiating between phenotypes was recognised as invaluable in refining asthma diagnosis, it was contended that the diagnosis of asthma alone was sufficient and necessary to determine the value of primary prevention at the health care level. Even if diagnosis were to be established at age six years, it was assumed that the pathological processes may have begun long before diagnosis. Furthermore, these processes may continue even in the absence of clinical signs and symptoms at later years. However, they noted that diagnosis of asthma prior to age six years is difficult or changes over time. Patients would be seen when the flare of symptoms as well as the pathologic process are subsiding which may not give a very accurate picture of the disease. At a later age, clinical remission may occur during pubescence or it may recur during the adult years. The difficulty, they said, is compounded by a lack of gold standard for its diagnosis. Around age six years, large groups of children who may have asthma are symptom-free but physiologic changes such as exaggerated inflammatory reactions and airway remodelling may already be occurring which could be detected using objective measurements. According to the experts, bronchioalveolar lavage and biopsies are the most reliable tests for airway inflammation. But since the procedures are also invasive and difficult to perform in practice, those were considered primarily research more than routine clinical tools. In contrast, the experts said that spirometry is common in the general practice setting and suitable in documenting abnormal lung function described by reversibility and hyperresponsiveness of the bronchial tubes. Combining objective lung function tests with symptomatology seemed to them to be the best approach to asthma diagnosis to date. However, they said that since symptomatic children in the Netherlands receive asthma medication, particularly ICS, an objective diagnosis at age six years may be contaminated. It was then assumed that children who received ICS therapy three months prior to the tests are asthmatic when either test of reversibility or hyperreactivity is positive. When both tests are negative, diagnosis could go either way. Amongst children who did not receive ICS therapy, positive hyperreactivity was assumed to signify presence of asthma and negative hyperreactivity meant absence of asthma. It was also assumed that evaluating the costs and consequences over a time horizon of six years after birth would be sufficient since this is the age when lung function tests would be first reliable in children.

Model structure

A search of the literature for economic models of primary asthma prevention in children based on the expert-developed decision framework came up empty-handed (Table 3). This meant that a new model (Figure 4) had to be developed which began with the problem or situation of administering to children a primary prevention programme on asthma. Based on the expert sessions, it was determined that the decision tree structure suited the decision framework and assumptions. Identifying the target population for the intervention on the basis of risk became the first node for the intervention arm of the decision tree. It was assumed that NFH children would receive current care [44] whereas PFH children would be given the intervention either as a unifaceted or multifaceted prevention depicted by the second decision node coming from the PFH branch. A child would receive unifaceted prevention when only one measure against air- or food-borne allergens is employed and multifaceted when at least two measures against both triggers are used. It was also assumed that children could not shift between these two prevention categories. The model resulted in three situations of care: prevention with unifaceted or multifaceted approach and current situation care. For these situations, children were assumed to either remain healthy or manifest some asthma signs and symptoms. Consequent upon the clinical picture, the children may or may not receive pharmacotherapy as required. The primary outcome is either a positive or a negative objective diagnosis of asthma, which relies on reversibility and hyperreactivity tests and symptomatology.

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#2	Cost-effective*[Title/Abstract]	54952
#3	#1 OR #2	78623
#4	Journal Article[ptyp] AND English[lang]	14875628
#5	"Animals"[MeSH:NoExp]	4570915
#6	"Humans"[MeSH:NoExp]	1125512
#7	#5 NOT (#5 AND #6)	3397345
#8	(#3 AND #4) NOT #7	67344
#9	Asthma*[Title/Abstract] or "Asthma/prevention and control"[MeSH]	99545
#10	#8 AND #9	586
#11	#10 NOT Review[Ptyp]	406
#12	"primary prevention"[Title/Abstract] or "secondary prevention"[Title/Abstract]	16821
#13	#11 AND #12	2
#14	#10 AND model[Title/Abstract]	62

Validation of the model structure

Model operation

For this demonstration of the model's operation and internal validity, we have used solely the data from the PREVASC studies. Only cases with relevant diagnostic data were included in the modelling which whittled down the possible 443 PFH cases in the intervention and control group to 324 cases. Their compliance with at least two, one, or none of the measures against exposure to airborne and food allergens was the basis for respectively regrouping them into multifaceted (N = 259), unifaceted (N = 53), and control (N = 12). Branch probabilities were calculated and folded back for the expected outcomes and costs. Between the prevention approaches, the multifaceted had a higher costs but also a higher expected probability of asthma cases avoided than the unifaceted approach (€539.49 vs. €445.19 for costs and 0.93191 vs. 0.92610 for probability of asthma avoidance, respectively). This means that the multifaceted approach costs around €16,000 more to avoid one asthma case than in the unifaceted approach. However, when compared with the current situation, which has the lowest cost and highest probability (€302.30 and 0.96494), the two prevention approaches were strongly dominated. Even for various cost-effectiveness thresholds, the current situation dominated the two approaches.

The model structure

Use of models in health economic evaluation has increased over the years and most often for one of two things [50]. First, when the relevant clinical trial has not (yet) been conducted or economic data is missing or incomplete. Second, when the clinical trial used intermediate outcomes or when the follow-up period is short whilst the decision problem is concerned with endpoints long into the future. Though modelling allows decision makers to be critical and realistic about alternatives, to be conscious and clear of valorisation, and to reveal the relationships between inputs and outputs [51], models can seem like "black boxes" [33, 50] to consumers unless steps are made explicit and transparent. This was the rationale for explicitly reporting the steps we took in structuring our model.

In the primary prevention of asthma in children, several studies [8–11, 14–18] have provided information on effectiveness but none could singly determine whether primary prevention of asthma is cost-effective and whether a unifaceted or a multifaceted approach is better. Like others [52, 53], we have found that the literature is surprisingly silent regarding health economic studies on the value of health care strategies other than those concerned with asthma control. Consequently, one may cast a wary eye on the policy and care decisions relating to the prevention of asthma in children and questions whether the decisions were adequately informed. By synthesising the best available evidence through this model, we hope to take the first step toward properly evaluating and substantiating the choice amongst primary asthma prevention alternatives.

A number of structures could have been used in developing an economic model from regression equations to system dynamic models [21–24]. Some of the more common structures include decision trees, Markov-types, and discrete event simulations (DES) [21–24]. It has always been advocated that the choice of a model structure should be on the consideration of the simplest, and thus best, fit [34] between three elements: the decision problem, natural history of the disease, and current paradigms of care [33]. Since Markov-type models are used in decision problems with time-dependent or recurrent situations [21–24], these were considered inappropriate for this study primarily because asthma was assumed to be a stable diagnosis. Although asthma is understood to be a chronic condition, we considered that its diagnosis would not change in the six-year time frame of our study. This consideration is supported by findings that physiologic changes occur even for so-called mild cases who are more likely to be asymptomatic or only rarely symptomatic [54]. The various wheezing phenotypes could have been modelled instead as the recurring disease states of asthma because of their strong relationship with each other. Despite this, the experts did not think that wheezing was strictly causative of asthma. DES structures, which are more detailed and complicated extensions of Markov-type models [21–24], are similarly not appropriate for this study because asthma does not have a universally accepted measure of severity particularly in population-based studies [55]. Also, the choice for a more complex model should be balanced by any change to the results compared to use of a simpler model [33]. For these reasons, in combination with the decision framework, a decision tree structure was chosen and developed for our specific research question that aims to assess the cost-effectiveness of a programme to prevent the presence of asthma in children around age six years.

The current cost-effectiveness analysis limited itself to the use of the clinical data from the PREVASC studies to demonstrate the model's operation and evaluate its internal validity. The results of this demonstration showed that based on the probability of developing asthma, the current situation dominated either approach to primary prevention of asthma in children. It is notable though that a multifaceted approach was better than a unifaceted one. Use of the PREVASC data enabled technical validation by debugging and refining the model to handle problems like empty cells. For this feasibility assessment of the model, it should be noted that originally, there was no distinction made between the unifaceted and multifaceted groups in the PREVASC studies. The distinction was made only between control and intervention.

Value of expert panels

Experts are frequently involved in modelling studies to provide the initial model structure, validate the final structure, estimate values of transition probabilities, usage, and cost units [29]. Their reported involvement is, however, uneven [25]. Many are unclear in their reports even of the extent of expert participation in the process. In this paper, we have presented a detailed account of the explicit inputs from experts and their role in structuring a model. We have taken a naïve approach to reduce the bias of the modeller in the selection of one model structure over another but more importantly to benefit from the multi-disciplinary interaction between experts. The round-table discussions provided the opportunity for a dynamic engagement of the experts critical to framing the decision problem. Through the multidisciplinary experts, we hope to have gained better insight into daily practice which is not always possible through systematic reviews alone. All of these should contribute to the improvement of the descriptive and eventual face validity of the model's structure through the iterative reflection on the elicited assumptions. An important consideration is the selection of experts since the breadth and depth of their (practical) knowledge would determine the richness of the endeavour. This could be aided by a brief review of the literature to identify potential experts who are often cited.

Eliciting information and feedback from a panel of experts may range from informal consultations, to round-table discussions, and then Delphi approaches [25, 29]. The first approach simply and usually provides background information which may or may not be used. It is typically unilateral and high-handed. Round-table discussions are usually semi-structured sessions where participants are less restricted to explore issues and arguments without necessarily concluding in a consensus which is in contrast to the Delphi approaches. Although Delphi approaches may be seen as the most "democratic" approach to elicit expert opinion because of the intermediary, it is also a handicap since some element of exploration is lost as statements or opinions are "edited" by the intermediary. In round-table discussions, model developers may benefit from the dynamic and personal interaction with and amongst the experts so that no significantly relevant opinion, alternative, or scenario is left out of the model. There is, however, a caveat to round-table discussions: more dominant experts may "drown" the opinions of other experts so the modeller must be able to deftly control the panel. We have tried to address this partly through the imposition of a time limit for the discussions. Also, contributions of each expert were actively sought.

Parallel validation

Given that novel models do not have comparable models on the same decision problem, the standard validation techniques on the face, convergent, and predictive attributes of this model could not be ascertained at this stage of our study. The comparison of the outputs between the developed model and other models on the same decision problem [33–35] usually covered in the discussion section of a scientific report [56] hints at the prevalent approach to validate novel models. Indeed, validation of novel models "is superseded by the concept of model credibility" particularly because complete validation of a model is not possible [57]. Face validity through expert opinion has been suggested as an approach to validate the structure of novel models. Another solution that we propose and have described here may be through drawing parallelisms with other models, which is to perform what we refer as "parallel validation". Similar to convergent validation in testing between model corroboration [33–35], the distinguishing feature of parallel validation stems from the absence of a model built around the same decision problem. The proposed validation compares other models with the same structure but concern different diseases, populations, or interventions. This is therefore a qualitative consideration of the parallelisms from one model to another. Structural comparisons are admittedly more difficult than comparing input parameters [58]. Nevertheless, decision makers still need to make judicious choices with the highest possible level of confidence. Existing standards on modelling and reporting have been used to serve as markers of similarity and dissimilarity [59]. Figuratively, one can speak of models on the same decision problems as coming from one nuclear family. Parallel validation may be a viable way to compare the structure of one model to a model from a different but closely related family, even if only qualitatively. How modest the literature on (cost-) effectiveness of pharmacotherapies may be, there is even less on prevention [52–54]. Parallel validation reveals that more needs to be done to increase the literature on economic evaluation of primary prevention of asthma amongst children.