Key Considerations in Health Economic Evaluations of High‐Intensity Speech Intervention for Children With Cleft Palate

ABSTRACT

Purpose

This paper aims to provide speech and language pathologists and researchers with practical, educational guidance on designing health economic evaluations (HEEs) to assess the cost‐effectiveness of high‐intensity speech intervention (HISI) for children with cleft palate with or without cleft lip (CP±L).

Method

Key methodological considerations for designing HEEs of HISI for children with CP±L were identified by drawing on established frameworks. The foundational framework by Drummond et al. (2015) and the CHEERS reporting standards served as guiding structures for organizing and synthesizing principles relevant to conducting rigorous and transparent HEEs. Based on this conceptual synthesis, this paper provides practical recommendations to support researchers in implementing high‐quality HEEs in the context of HISI for children with CP±L.

Conclusions

Despite growing interest in HISI, rigorous evidence on its cost‐effectiveness is still lacking. Well‐designed HEEs are needed to inform policymakers about the efficient allocation of healthcare resources. Clear evidence on cost‐effectiveness is essential to ensure that implementation and reimbursement decisions are based on interventions that are both clinically effective and economically justified.

WHAT THIS PAPER ADDS

What is already known on this subject

• High‐intensity speech intervention (HISI) is increasingly used in the management of speech sound errors in children with cleft palate with or without cleft lip (CP±L), with growing evidence supporting its clinical effectiveness. However, despite rising healthcare costs and pressure on effective service delivery, evidence on the cost‐effectiveness of HISI remains limited. Health economic evaluations (HEEs) are well established in health research but are underutilised in speech and language therapy, and existing studies often show considerable methodological variability and limited transparency in reporting.

What this study adds to existing knowledge

• This paper provides structured, practical guidance for designing rigorous HEEs of HISI for children with CP±L. Drawing on established frameworks and the CHEERS reporting standards, it synthesises key methodological considerations tailored to speech and language therapy research. The study clarifies how HEEs can be systematically integrated into intervention research in this field and offers concrete recommendations to improve methodological quality, transparency, and comparability of future economic evaluations of speech interventions for children with CP±L.

What are the clinical implications of this study?

• By supporting the design of high‐quality HEEs, this paper enables speech and language pathologists and researchers to generate evidence on the economic value of HISI. Such evidence can inform service planning and reimbursement decisions, supporting efficient allocation of resources in cleft care. Ultimately, improved economic evidence may facilitate wider, equitable implementation of effective interventions while ensuring sustainability of speech and language therapy services for children with CP±L.

1. Introduction

A cleft of the palate with or without a cleft of the lip (CP±L) represents one of the most common congenital craniofacial anomalies, with an estimated global incidence of approximately 1 in 700 live births (Emodi et al. 2022; Sommerlad 2002). Beyond the primary orofacial malformation, CP±L is associated with a complex and longitudinal care pathway that typically extends well beyond initial surgical repair of the cleft (Wells‐Durand et al. 2025). Children with CP±L frequently present with multifaceted clinical needs, including feeding difficulties, recurrent otitis media with effusion, dental and orthodontic anomalies, and persistent speech errors, necessitating longitudinal treatment from an interdisciplinary team (Asllanaj et al. 2017; Bessell et al. 2013; Imbery et al. 2017; Miller 2011). The condition, therefore, imposes a significant and enduring burden not only on individuals with CP±L and their families, but also on healthcare systems responsible for organizing and delivering coordinated, resource‐intensive care over time (Alansari et al. 2014).

The mean societal costs of interdisciplinary treatment for a child with CP±L, including costs to healthcare systems and out‐of‐pocket costs, can vary considerably. This variation is influenced by country‐specific factors, such as differences in treatment protocols, healthcare organization, and insurance policies, as well as study‐specific factors, including the types of costs considered and the timeframe of the analysis. For example, in the Netherlands, the average total treatment cost for a child with CP±L from birth to 24 years of age was estimated at €40,859 (Apon et al. 2024). This study included only the hospital's costs for consultations, diagnostic procedures, and surgical procedures performed within the hospital. Other relevant costs such as patient out‐of‐pocket expenses and out‐of‐hospital treatment costs (e.g., speech and language therapy) were not included. Boulet et al. (2009) estimated the total yearly cost of insurance reimbursements and out‐of‐pocket expenditures for the interdisciplinary treatment of a child with CP±L at USD 15,270, from birth to 10 years of age, in the United States. They stated that these costs are potentially an underestimation, as data were collected from only one insurer, and, in the United States, it is possible for individuals to have multiple health insurance plans. In contrast to Apon et al. (2024), this study included healthcare utilization both inside and outside the hospital, providing a more comprehensive estimate of the total costs associated with treatment. In Canada, the estimated cost of treating a child with CP±L up to 18 years of age was approximately $73,398 (currency not specified; unclear whether CAD or USD) (Wells‐Durand et al. 2025). This study accounted for all costs incurred by the patient, provider, and the healthcare system. Additionally, indirect costs for patients, such as travel expenses, parking, and caregiver absenteeism, were included in the analysis. However, the authors noted that the analysis remains incomplete, as certain data, such as additional appointments with orthodontists or speech and language pathologists (SLPs) at private clinics, were unavailable.

Although surgical procedures and surgeon consultations likely account for a substantial proportion of the total costs, paramedical care also represents a significant and indispensable component, reflecting its critical role in the comprehensive management and long‐term support of individuals with CP±L (Wells‐Durand et al. 2025). SLPs are among the most frequently consulted paramedical professionals, as speech errors may persist even after successful surgical closure of the palate (Wells‐Durand et al. 2025). Two broad types of speech errors can occur in children with CP±L, namely passive speech errors and active speech errors (Harding and Grunwell 1998; Pereira and Sell 2023). Passive speech errors arise from structural deficits, resulting in speech errors such as hypernasality, nasal airflow disorders, and the nasal realization of plosives or the production of weak, nasalized consonants (Harding and Grunwell 1998; Pereira and Sell 2023). In contrast, active speech errors reflect learned compensatory behaviours that are intended to overcome the effects of impaired oronasal coupling. Such strategies typically involve changes in the place or manner of articulation, e.g., glottal productions or active nasal fricatives (Harding and Grunwell 1998; Pereira and Sell 2023). Passive speech errors usually necessitate surgery, whereas active speech errors can be effectively treated with speech therapy (Kummer 2011). However, among children with CP±L who experienced speech difficulties and received speech and language therapy, only 21% achieve age‐appropriate speech production skills despite receiving speech and language therapy, as reported in the systematic review by Sand et al. (2022). This means that approximately 80% of the children face ongoing speech difficulties despite intervention (Sand et al. 2022). As a result, prolonged speech therapy is frequently necessary, with treatment durations spanning months to years, thereby imposing significant costs and resource demands on families and healthcare systems (Wells‐Durand et al. 2025).

Precise data on the costs associated with traditional speech intervention remain limited. Existing estimates frequently represent only rough approximations based on defined intervention periods in clinical trials, meaning that these estimates do not necessarily reflect the total costs of speech intervention for children with CP±L, as speech errors may not yet be fully resolved and further improvement in speech skills may still be possible. For example, Prathanee (2011) reported an estimated cost of USD 3,912.49 per child for 48 treatment sessions delivered over a period of four to five years at a speech centre in Thailand. Such spacing of treatment sessions reflects the traditional intervention model in Thailand, where waiting lists are long and the number of certified SLPs is limited. This treatment schedule corresponds to an average interval of approximately 1.5 months between sessions, reflecting a very low‐intensity service delivery model. This example illustrates how traditional therapy paradigms may be shaped by service delivery constraints, meaning that clinicians in certain contexts may be required to adopt lower‐intensity treatment schedules, which can substantially influence both the duration and the overall costs of intervention. In addition, qualitative studies have reported that financial considerations often play a significant role in access to and continuity of speech therapy. According to Alighieri et al. (2020), parents in Flanders (i.e., the northern part of Belgium) indicated that speech therapy represented a substantial monthly expense for the family, even when partial reimbursement was available. Similarly, Williams et al. (2021) highlighted that in the United Kingdom, clinical judgment and child‐centred care are important components of care, but due to resource constraints, SLPs were often forced to base their intervention decisions on cost considerations. Unfortunately, no specific information on costs was provided.

Effective and evidence‐based therapy is essential to improve outcomes for children with CP±L. Currently, in the European context, speech intervention for children with CP±L is typically delivered once or twice per week over several months or even years (Baker and McLeod 2011; Maas et al. 2008; Mullen and Schooling 2010). This model is generally classified as low‐intensity speech intervention (LISI). While LISI remains widely implemented in clinical practice, its prolonged nature may entail cumulative demands on healthcare resources, families, and service delivery systems. Extended treatment trajectories may be associated with increased therapist contact hours over time, indirect costs for families (e.g., travel, time investment, and school absenteeism), and reduced service accessibility, including long waiting lists that may result in delayed initiation of therapy (Baigorri et al. 2021). As a result, several authors have suggested that the current practice should be reconsidered to address existing gaps.

Therefore, increasing attention has been devoted in recent decades to delineating the parameters that define treatment intensity, with the objective of optimizing speech interventions and outcomes. One of these parameters is dose frequency, which refers to how often therapy is delivered within a given period (e.g., once or twice per week) (Warren et al. 2007). In this context, HISI has been proposed as an alternative approach. HISI involves providing therapy sessions with a higher frequency across a shorter timeframe than LISI. For example, therapy can be scheduled daily over several consecutive days or weeks.

Since the pioneering research by Albery and Enderby 1984, an increasing but limited body of evidence has examined the application of HISI in children with CP±L. Various models for addressing HISI have been proposed, ranging from intensive speech camps and traditional individual therapy delivered by an SLP to interventions carried out by individuals close to the child (e.g., parents) under the supervision of a SLP (Alighieri et al. 2021; Pamplona et al. 2005; Prathanee 2011; Sweeney et al. 2020). With regard to speech camps, Pamplona et al. (2005) examined a 3‐week speech summer camp intervention in 45 Mexican children with CP±L, providing 4 hours of therapy per day, 5 days a week for 3 weeks. A matched control group of 45 children with CP±L received two 1‐hour sessions per week for 12 months in small groups of 2 to 3. Although not statistically significant, the summer‐camp group showed a trend toward better speech outcomes, specifically exhibiting fewer active speech errors after intervention than children receiving LISI. Similarly, Prathanee (2011) reported that a 4‐day speech camp (36 therapy sessions of 30 min each) followed by a 1‐day follow‐up session (12 therapy sessions of 30 min) significantly reduced the number of active speech errors in 12 Thai children. However, no comparison with a control group was conducted, and therefore this result should be interpreted with caution, as the level of evidence is considered weak. Alighieri et al. (2021) investigated the effectiveness of HISI compared to LISI. The authors concluded that delivering five individual 1‐hour sessions per week for 2 weeks was more effective than providing one individual 1‐hour session per week for 10 weeks in 12 Belgian Dutch‐speaking children with CP±L. Children in the HISI group achieved superior outcomes in speech understandability and acceptability, cleft speech characteristics (e.g., the presence of particular types of active speech errors), and HRQoL. Finally, Sweeney et al. (2020) demonstrated that Parent‐Led, Therapist‐Supervised Articulation Therapy (PLAT) constitutes a promising high‐intensity strategy. In this model, parents were trained and supported by a SLP to implement a therapy program specifically tailored to their child. Over a 12‐week period, parents in the intervention group delivered therapy five times per week for 10–15 min per day. The control group received 1‐hour sessions with a SLP every two weeks over the same period. The results indicated that PLAT was as effective as conventional LISI in terms of speech outcomes and activity and participation measures for children with CP±L. Children in the PLAT group had fewer direct contact hours with a SLP, while the total duration of the intervention period was the same for both groups, this approach may represent a viable alternative in contexts with long waiting lists or to reduce the financial burden of therapy.

Although further research is needed to confirm findings from previous studies and to strengthen the evidence base across different HISI models, the existing evidence suggesting that various models of HISI may be at least as effective as LISI provides a strong rationale for conducting HEEs. Such evaluations can offer clearer insight into the financial implications of different treatment intensities, thereby informing resource allocation decisions and potentially supporting more equitable access to effective speech and language intervention for children with CP±L.

In the past, various authors have proposed that HISI may be cost‐saving compared to LISI, as faster speech improvement could reduce the number of sessions required (Pamplona et al. 2005; Prathanee 2011). While the evidence base is still limited, some studies have sought to support this assumption using empirical data. Pamplona et al. (2005) reported that the 3‐week high‐intensive summer camp (corresponding to 20 hours of therapy) described above was more than four times less expensive than conventional LISI. The average out‐of‐pocket cost per child for LISI was USD 412. For the entire summer camp, the average total cost per patient was USD 100. Unfortunately, it was not clearly specified whether these costs also included additional expenses, such as meals during the speech camp, or whether they referred exclusively to expenses directly related to speech therapy sessions. Similarly, Prathanee (2011) concluded that the average cost of their 4‐day HISI model and one day follow‐up (corresponding to 24 hours of therapy) was USD 412.82. This amount covered all expenses, ranging from the costs of the speech therapy itself to travel, food, and accommodation for the patient and their family during the speech camp. In contrast, providing the same number of sessions as conventional LISI in a speech centre was estimated to cost USD 3,912.49, which is more than nine times higher than that of the HISI approach. It was not clarified whether this amount included only the costs of the therapy sessions themselves or also other costs, such as travel expenses. Although these studies report both the costs and effectiveness of the interventions, the absence of a formal comparative assessment of cost‐effectiveness significantly limits their policy relevance. As no full HEE was conducted, key methodological elements, such as specifying a perspective or calculating incremental cost‐effectiveness ratios, are understandably absent.

Against this background, gaining insight into the costs associated with different intervention intensities is essential to inform decisions regarding optimal service delivery. Despite this need, no full HEEs have yet been conducted comparing intervention intensities for children with CP±L, leaving a critical gap in the evidence base required for informed decision‐making. As the application of HEEs remains relatively limited in speech and language pathology, this paper outlines how HEEs in this area can be systematically designed to produce robust and transparent evidence. Accordingly, the purpose of this paper is to provide SLPs and researchers with practical, educational information on designing rigorous HEEs to assess the cost‐effectiveness of HISI for children with CP±L.

2. Evaluating the Cost‐Effectiveness and Cost‐Utility of High‐Intensity Speech Intervention

To promote methodological rigor and completeness in conducting HEEs, as well as transparency and consistency in reporting, the foundational textbook by Drummond et al. (2015) and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist were adopted as guiding frameworks for the conduct and presentation of such evaluations (Drummond et al. 2015; Husereau et al. 2022; Husereau et al. 2013). Building on these frameworks, the following sections introduce key methodological considerations for the design and execution of HEEs in this context, with particular emphasis on principles that enhance the credibility and relevance of evaluations for decision‐makers. The identified key aspects are discussed and applied specifically to HEEs evaluating HISI for children with CP±L.

2.1. Applying HEEs in Speech and Language Therapy for Children with CP±L: General Overview

Full HEEs involve a comparison of two or more alternatives by assessing both their costs and health outcomes (Husereau et al. 2022). The two most commonly used types of full HEEs are cost‐utility analysis and cost‐effectiveness analysis (Shields and Elvidge 2020). While cost‐effectiveness analysis compares costs to health outcomes measured in natural units (e.g., number of therapy sessions required or the percentage of consonants correct (PCC)), cost‐utility analysis is a specific type of cost‐effectiveness analysis that quantifies health outcomes using a utility‐based metric, namely quality‐adjusted life years (QALYs) (Gray and Wilkinson 2016). The QALY is a generic outcome measure used to assess how effective a health intervention is. It combines both quality and quantity of life, thereby capturing the multidimensional benefits of an intervention (Drummond and Jefferson 1996; Gray and Wilkinson 2016). Given that HEEs are relatively novel in the field of speech and language therapy, particularly for children with CP±L, key considerations relevant to both cost‐utility analysis and cost‐effectiveness analysis will be discussed, as each offers distinct added value. Both approaches are structured similarly, so the paragraphs below apply to both cost‐utility analysis and cost‐effectiveness analysis. To support clarity, Table 1 provides an overview of the main similarities and differences between cost‐utility analysis and cost‐effectiveness analysis. Any specific differences (e.g., in outcome selection) are explicitly indicated where relevant in the following paragraphs.

TABLE 1.

Overview of core features of cost‐effectiveness analysis and cost‐utility analysis.

	Cost‐effectiveness analysis	Cost‐utility analysis
Definition	Economic evaluation comparing costs and outcomes measured in natural health units	Economic evaluation comparing costs and outcomes measured in utility‐based units combining quantity and quality of life
Outcome measure of effectiveness	condition‐specific clinical outcomes: natural units	Utility‐based: QALYs
Cost components	Determined by the chosen analytic perspective (e.g., healthcare, societal)
Primary outcome measure	Incremental Cost‐Effectiveness Ratio: cost per natural unit	Incremental Cost‐Utility Ratio: cost per QALY gained
Data requirements	Clinical effectiveness data are sufficient	Additional data on health state utilities required
Comparability across studies	Limited: outcomes are condition‐specific	High: QALYs allow comparison across different interventions and populations

2.2. Selecting Relevant Comparators

Within the scope of a HEE, the intervention under investigation must be explicitly defined, followed by the selection of an appropriate comparator against which its clinical and economic value can be assessed (Husereau et al. 2022). Within the context of HISI, the ‘intervention’ is defined as HISI, with comparators including standard care, such as LISI or a waiting list condition (e.g., in regions without access to SLPs), as well as alternative delivery modalities of HISI (e.g., speech camps or group‐based versus individual intervention formats). Given the potential for substantial variation in how speech intervention is operationalized, both in terms of therapeutic content and intensity, it is crucial to provide a detailed description of the actual intervention and the comparator(s). Against this background, the parameters proposed by Warren et al. (2007) offer a useful framework for characterizing intervention intensity. Warren et al. (2007) describe intervention intensity using six parameters: (1) dose form (i.e., the type of task or activity through which teaching episodes are delivered, such as play‐based therapy or drill exercises), (2) dose (i.e., the number of teaching episodes per session), (3) dose frequency (i.e., the number of sessions per unit of time), (4) session duration (i.e., the length of each session), (5) total intervention duration (i.e., the overall intervention period), and (6) cumulative intervention intensity (i.e., the product of dose, dose frequency, and total intervention duration).

2.3. Resource Use and Costs: The Impact of Study Perspective

The perspective of a HEE delineates the viewpoint from which costs and consequences are assessed. Consequently, the perspective adopted in a cost‐utility analysis or cost‐effectiveness analysis fundamentally determines both the cost categories included and the stakeholders whose interests are represented in the evaluation (Sittimart et al. 2024). More specifically, the perspective specifies whose costs and outcomes are considered relevant (e.g., those of the healthcare system, the patient, or society as a whole), and therefore directly shapes the scope and interpretation of the economic evaluation.

In general, two broad cost categories can be identified, namely direct and indirect costs. Direct costs can be further subdivided into medical and non‐medical costs. Direct medical costs refer to expenses directly related to a disease or medical condition and may be incurred by different stakeholders depending on the analytic perspective, such as the healthcare payer (e.g., health insurer, health authority, trust, or government agency) or the patient (Drummond et al. 2015; Husereau et al. 2022). Direct non‐medical costs are costs that occur outside healthcare but are directly associated with the condition and its treatment (Sabermahani et al. 2021; Sittimart et al. 2024). In the context of speech and language intervention, direct medical costs refer to the expenses associated with therapy sessions and assessments, whereas direct non‐medical costs may include travel expenses to attend therapy. Indirect costs encompass productivity losses, for example, parental absenteeism from work to accompany their child to speech and language therapy (Sabermahani et al. 2021; Sittimart et al. 2024).

A study may be conducted from one or more explicitly defined perspectives, such as the patient perspective (i.e., considering only costs incurred by the patient), the health insurance perspective (i.e., considering direct medical costs covered by the health insurer), the payer's perspective (i.e., considering direct medical costs covered by parties financing healthcare delivery, and potentially also costs borne by patients, depending on the definition applied), or the societal perspective (i.e., encompassing all costs associated with the intervention, whether health‐related or not, including both direct and indirect costs) (Husereau et al. 2022; Kim et al. 2020; Sittimart et al. 2024).

The choice of perspective should align with the purpose of the analysis. If the primary goal is to inform the health insurer for reimbursement of a specific speech and language intervention modality for children with CP±L, adopting the health insurance perspective is most appropriate. When the therapy is already reimbursed under current regulations but not consistently implemented in practice, a societal perspective may be preferable. This approach allows for the assessment of broader economic and social consequences of non‐implementation, thereby strengthening the case for full and systematic adoption.

Once the perspective has been determined, resource use for both the intervention and the comparator can be identified and quantified based on the relevant cost components. Data on healthcare utilization (e.g., diagnostic tests and therapy consultations) may be obtained from various sources, such as patient records, administrative databases, participant questionnaires, or literature sources. Resource items can be valued by multiplying their quantity by an appropriate unit cost, for example derived from national costing guidelines, or condition‐specific costs can be taken directly from literature. In some cases, studies report costs at an aggregated level from peer‐reviewed or grey literature without providing transparent information on the underlying resource use. When standard prices are not available, market prices or micro‐costing methods can be applied to ensure accurate valuation (Husereau et al. 2022).

2.4. Selection, Measurement, and Valuation of Health Effects

The health outcomes of an intervention and comparator(s) can be assessed in two main ways: using utilities (cost‐utility analysis) or natural units (cost‐effectiveness analysis) (Drummond et al. 2015). Utilities are numerical values representing the desirability of specific health states, typically anchored on a scale where 0 corresponds to death and 1 represents full health, with possible negative values for states considered worse than death (Prieto and Sacristan 2003). These values are derived from preference‐based utility measures, which are HRQoL questionnaires that incorporate population‐based preferences for different health states using specific valuation methods (Drummond et al. 2015). Although many widely used preference‐based measures are generic, such as the EuroQol five‐dimensional questionnaire (EQ‐5D) (EuroQol Research Foundation 2020) or the 36‐item short form health survey (SF‐36) (Ware and Sherbourne 1992), some condition‐specific instruments can also be adapted to preference‐based forms. Thus, preference‐based instruments are defined by the presence of a value set derived from societal preferences that allows health states to be converted into utility scores, which capture the quality dimension of health at a given point in time (Husereau et al. 2022). QALYs build upon these utility values by incorporating the time an individual spends in each health state. Formally, QALYs are calculated by multiplying the utility score by the duration (often in years) spent in that state, and summing the results over the relevant time horizon (i.e., the period over which all relevant costs and health outcomes are measured), reflecting both the quality and quantity of life (Prieto and Sacristan 2003).

QALYs are a well‐established metric in health economics, allowing comparisons across different health conditions and interventions (Shields and Elvidge 2020). However, their relevance for speech and language interventions is questionable. Utility measures derived from generic questionnaires may not fully capture improvements in communication, participation, and well‐being that result from therapy. For example, gains in speech understandability may substantially enhance social interaction but are frequently underrepresented in generic HRQoL questionnaires, because such measures typically place limited emphasis on communication‐related outcomes. The key challenges in using QALYs for this field include accurately describing communication‐related health states and assigning valid utility values to them. These challenges highlight the need to consider whether QALY frameworks could be revised to incorporate communication‐specific aspects of quality of life, measured with tools sensitive to the changes expected in speech and language therapy.

Utilities may also be obtained from condition‐specific HRQoL outcome measures, with responses subsequently converted into utility values, frequently using established mapping algorithms (Drummond et al. 2015). Mapping refers to the process of statistically linking health outcomes from condition‐specific HRQoL instruments to preference‐based utility measures, such as the EQ‐5D. This requires the availability of both a condition‐specific outcome measure and a generic, preference‐based outcome measure (Wailoo et al. 2017). For children with CP±L, several condition‐specific patient‐reported outcome measures (PROMs) are available to assess HRQoL, such as the CLEFT‐Q and the Velopharyngeal Insufficiency Effects on Life Outcomes (VELO) questionnaire (Skirko et al. 2012; Wong Riff et al. 2017). At present, however, HRQoL scores obtained from these condition‐specific instruments cannot be directly converted into utility values, as they do not include a preference‐based valuation set (i.e. population‐derived weights required to generate utilities). In the future, it would be valuable to develop methods such as mapping strategies to enable the use of condition‐specific HRQoL‐scores for utility estimation, as this would address the issue of the limited sensitivity of generic HRQoL instruments in children with CP±L. This would require datasets in which both a generic and a condition‐specific HRQoL questionnaire are administered to the same participants (Wailoo et al. 2017). In the meantime, we therefore encourage the concurrent administration of both generic and condition‐specific instruments, as this would allow the creation of datasets that could subsequently be used to develop mapping strategies.

Although the use of utilities is preferred in HEEs, it is questionable whether they would yield reliable results in children with CP±L, given the limited sensitivity of generic questionnaires and the current inability to use condition‐specific HRQoL instruments to derive utilities. Therefore, alternative approaches may be considered. One such approach to evaluate outcomes relies on natural outcome measures. Natural outcome measures or units refer to concrete, directly measurable outcomes specific to the intervention (Angevine and Berven 2014; Husereau et al. 2022). Drummond et al. (2015) stated that the use of natural outcomes can be considered appropriate when there is a clearly defined primary objective of therapy. For children with speech sound disorders, such as children with CP±L, the main goal of therapy is to achieve intelligible speech that is functional in everyday communication (Howard and Lohmander 2011). One outcome measure that is widely recognized for assessing articulatory proficiency is the PCC, for which numerous methodological variations have been developed and are currently utilized (Shriberg et al. 1997). Although PCC does not directly measure speech intelligibility, higher PCC scores generally align with better intelligibility, particularly in clinician‐based measures (Chen et al. 2025; Kappen et al. 2017). It is therefore considered a valid measure for evaluating progress in speech production. In addition, PCC is an objective, clinician‐rated measure rather than a PROM, making it less susceptible to subjective bias and highly suitable for standardized assessment. The use of PCC is also consistent with the internationally standardized outcome measures for speech assessment in children with CP±L, proposed by the International Consortium for Health Outcomes Measurement (ICHOM) (Allori et al. 2017). In the context of HEEs, PCC can serve as natural unit outcomes. In recent decades, there has been increasing attention to the use of condition‐specific PROMs to asses HRQoL (Howard and Lohmander 2011). These instruments could potentially also serve as natural outcome measures in HEE. However, applying such an approach would restrict comparability to HEEs conducted in children with CP±L (Drummond et al. 2015). This limited comparability arises because condition‐specific PROMs, while clinically relevant, do not allow broader cross‐condition comparisons and because generic HRQoL instruments are generally not sensitive enough to capture changes in speech‐related functioning. For these reasons, we currently recommend using PCC as the primary outcome measure for HEE in children with CP±L. Although the use of PCC (and natural outcome measures in general) does not enable full comparability across all HEEs, it does allow comparison with other studies assessing speech sound disorders. In addition, we recommend integrating both a condition‐specific and a generic HRQoL measure into the assessment protocol, as this will facilitate future mapping between clinical outcomes and utilities.

2.5. Selection of Outcome

The primary outcome of a HEE is the ratio of the incremental costs to the incremental health effects. When QALYs (i.e., a measure that combines utilities, reflecting preference‐based health outcomes, and the time spent in a given health state) are used, the incremental cost‐utility ratio is calculated. In contrast, when effectiveness is measured in natural units (e.g., number of therapy sessions required or PCC), the incremental cost‐effectiveness ratio is calculated. The incremental cost‐utility ratio expresses the additional cost per additional QALY gained, while the incremental cost‐effectiveness ratio expresses the additional cost per unit of the specific natural outcome achieved (Abbott et al. 2022). In both cases, the calculation combines these effectiveness measures with the corresponding cost data to determine cost‐effectiveness. Both ratios are calculated in the same way, using the following formula: incremental cost‐utility ratio or incremental cost‐effectiveness ratio = (Cost_intervention—Cost_control) / (Effect_intervention—Effect_control) (Annemans 2018). To apply this formula appropriately, it is necessary to determine the precise magnitude of both costs and effects for each treatment option. Therefore, it is essential to consider the availability and quality of data.

Information on costs and effects may be derived directly from existing data sources, such as existing datasets, published literature, or administrative databases. However, for cleft therapy interventions, currently available data are limited and may not capture all relevant cost components, outcomes, or quality of life measures required for a comprehensive HEE. One potential approach is therefore to combine existing data sources with additional primary data collection. Primary short‐term data can be obtained through the conduct of a randomized clinical trial (RCT) (Drummond et al. 2015). Such a study would allow the prospective collection of detailed information on costs, outcomes, and resource use, thereby addressing current evidence gaps and supporting a prospective HEE.

When cost and effect data are collected within a RCT, a HEE can be performed directly alongside the trial (i.e., a trial‐based HEE). In this case, the analysis is based on empirically observed costs and outcomes among study participants. Nevertheless, this approach may introduce several challenges. Protocol‐induced costs and outcomes, as well as the potential impact of exclusion criteria and participant dropout may limit the generalizability of findings (Annemans 2018). Moreover, RCTs typically have a limited duration, which may not provide sufficient insight into the long‐term effects of an intervention. These factors constitute important considerations that must be carefully addressed when designing a RCT for HEEs, for example by minimizing protocol‐driven activities that do not reflect routine clinical care.

To synthesize evidence from one or more sources, decision‐analytic models can be used. Models such as decision trees or Markov models enable the systematic assessment of the costs and effects of alternative treatment strategies (Drummond et al. 2015). At this moment, we consider a decision tree to be the most appropriate modelling approach. This framework enables transparent analysis of the most relevant short‐ to medium‐term outcomes. Decision trees can be used when data are available from a single study, such as a well‐designed RCT, but they can also incorporate data from multiple sources. Moreover, the possible trajectories of speech therapy are generally limited and can be represented as a finite set of mutually exclusive outcomes (e.g., resolution of active speech errors or need for ongoing therapy), which can be adequately captured within a decision tree. Because decision trees require fewer input parameters (and thus less data) than state‐transition models such as a Markov model, this approach reduces the risk that data limitations compromise the credibility of the analysis (Drummond et al. 2015). Future research could extend this framework to state‐transition models, such as a Markov model, once sufficient evidence on long‐term outcomes and resource use becomes available. Markov models typically rely on data from multiple sources to inform transition probabilities, costs, and health outcomes. Regardless of the specific modelling approach chosen, it is essential to ensure that the model is appropriately validated. This can be achieved by assessing its face validity, internal validity, cross validity, external validity, and predictive validity (Eddy et al. 2012). Face validity refers to how plausible and credible the model appears to experts and stakeholders (e.g., clinicians, researcher, or policy makers). Internal validity assesses whether the model is internally consistent and correctly implemented, while cross validity evaluates the model's consistency by comparing it with other existing models addressing the same research question. External validation tests the model by comparing its predictions in a real‐world context, such as a clinical trial, with the observed outcomes. Finally, predictive validity assesses the model's ability to accurately predict future outcomes (Eddy et al. 2012).

To conclude, To mitigate the limitations of trail‐ and model‐based analyses, researchers are encouraged to design RCTs that better reflect clinical practice (e.g., by using broader inclusion criteria or more pragmatic trial designs) and to complement short‐term trial data with real‐world evidence, modelling approaches, or indirect treatment comparisons when direct evidence is limited. This combined strategy can enhance the external validity of findings and provide a more comprehensive basis for HEEs.

2.6. Threshold Values for Cost‐Effectiveness in Health Care

Once the ratio between costs and effects has been calculated, the cost‐effectiveness of an intervention can be assessed. Generally, an increase in the ratio indicates a decrease in cost‐effectiveness (Abbott et al. 2022). A formal evaluation, however, requires that health outcomes are expressed in standardized utility measures, such as QALYs (Drummond et al. 2015). While incremental cost‐effectiveness ratios derived from outcome measures other than utilities may provide valuable insights, they do not permit definitive judgments about cost‐effectiveness, as such measures are often not directly compatible with established willingness‐to‐pay thresholds. The willingness‐to‐pay threshold refers to the maximum amount a decision‐maker (e.g., a healthcare system or payer) is willing to pay for a unit of health gain, such as one QALY. It is used as a benchmark to determine whether an intervention can be considered cost‐effective (Drummond et al. 2015).

Predefined thresholds for assessing cost‐effectiveness can take various forms. They are often expressed as fixed monetary amounts or based on relative to gross domestic product per capita (i.e., the average gross domestic product per person in a country) (Kazibwe et al. 2022). When gross domestic product per capita is used, the World Health Organization recommends a threshold of one to three times the national annual gross domestic product per capita (Drummond et al. 2015). Reference values for acceptable cost‐effectiveness consequently tend to differ across countries. Therefore, it is recommended to carefully verify the threshold values employed when conducting or comparing HEEs. These results should ultimately be interpreted within transparent, consistent, and context‐specific decision‐making frameworks that involve relevant stakeholders (Bertram et al. 2016). The systematic review by Cameron et al. (2018) provides an overview of threshold values applied in 17 countries worldwide. However, in certain countries, such as Belgium, no official cost‐effectiveness threshold exists. Assessments are instead informed by broader contextual considerations, such as prevailing budgetary constraints (Neyt et al. 2025).

Additionally, even when interventions are already reimbursed within the existing healthcare system, HEEs can remain relevant. In Belgium, both HISI and LISI are reimbursable, as children with CP±L are allocated a predefined number of reimbursed therapy sessions. Consequently, the relevance of a HEE in this context does not lie in determining eligibility for reimbursement, but rather in assessing the efficient allocation and organization of limited therapeutic resources.

From this perspective, a hypothetical HEE comparing HISI and LISI in children with CP±L may serve to illustrate how cost‐effectiveness findings can inform policy‐relevant decisions. For example, if HISI were associated with higher short‐term costs due to increased therapy intensity, but resulted in superior long‐term improvements in speech outcomes and HRQoL, this could translate into a favourable incremental cost‐effectiveness ratio. In the absence of an explicit willingness‐to‐pay threshold, such findings would need to be interpreted within the Belgian healthcare context, taking into account factors such as the paediatric nature of the condition, the long‐term impact of speech outcomes on educational attainment and social participation, budget impact, and the opportunity costs associated with alternative uses of speech and language care resources.

2.7. Additional Considerations

Any model is, by definition, a simplification of reality and therefore subject to assumptions and potential sources of uncertainty, particularly concerning methodological aspects and the selection of input parameters such as costs, utility values and clinical outcomes (Jain et al. 2011). Consequently, an analysis can never be entirely comprehensive. It is therefore essential to clearly describe the methods used to handle uncertainty, as well as the assumptions underlying the adopted model and analysis, in order to ensure transparency and reproducibility.

To test the robustness of the HEE results, sensitivity analyses are strongly recommended. One‐way sensitivity analysis allows the impact on the incremental cost‐utility ratio or incremental cost‐effectiveness ratio of varying a single parameter at a time to be explored, thereby identifying key drivers of cost‐effectiveness. This is often visualized by means of a Tornado diagram (Drummond et al. 2015). A tornado diagram is a visualization of the results from a one‐way sensitivity analysis that ranks model parameters by the magnitude of their individual impact on the Incremental Cost Effectiveness Ratio, when varying the values of those input parameters separately (Drummond et al. 2015). Probabilistic sensitivity analysis, on the other hand, simultaneously incorporates uncertainty across multiple parameters and provides a more comprehensive assessment of the robustness of the analysis, often represented through incremental cost‐effectiveness planes and cost‐effectiveness acceptability curves (Jain et al. 2011). A more detailed discussion of these methods can be found in Drummond et al. (2015).

In addition to the inherent uncertainty associated with HEE outcomes, it is crucial to acknowledge that these results are highly context‐ and country‐specific. Variations in healthcare system structures, intervention costs, population characteristics, and methodological standards, as well as differences in the threshold values applied for cost‐effectiveness, can substantially influence the results. Consequently, HEE findings should not be directly extrapolated across settings without proper adaptation. When interpreting or designing a new HEE, it is essential to critically assess the underlying assumptions, data sources, and analytical methods. Consulting country‐specific methodological guidelines, can provide valuable support in this process.

3. Discussion

In light of the recognized importance of health investments and given that healthcare budgets are relatively limited and subject to ongoing pressures for cost savings, there has been a marked shift toward ensuring efficient health care budget utilization in recent decades (Littlejohns et al. 2012). This has prompted policymakers to favour interventions with the highest demonstrated efficiency in improving health outcomes relative to their costs (Bertram et al. 2021). Within this context, HEEs have become a key methodological tool for informing decision‐making. By systematically comparing the costs and outcomes of alternative interventions, HEEs generate robust evidence that supports transparent, accountable, and evidence‐informed resource allocation decisions by stakeholders such as policymakers, payers, and healthcare providers.

Within specific clinical and organizational contexts, HEEs may further inform discussions on the relative value of alternative intervention models. For example, economic evidence may indicate that HISI constitutes a valuable or efficient alternative to LISI under certain circumstances, depending on factors such as service delivery constraints, implementation feasibility, caregiver burden, or expected treatment gains. In these cases, HEEs can help determine whether HISI represents an acceptable or advantageous option within a given healthcare context.

Importantly, there is no single “correct” approach to conducting a HEE. The design of a HEE is inherently dependent on the predefined objectives of the analysis, the decision context, and the (policy) questions being addressed. Choices regarding the type of economic evaluation (e.g., cost‐effectiveness or cost‐utility), the analytical perspective (e.g., healthcare system or societal), and the selection of outcome measures can substantially influence study findings. Consequently, the appropriateness and interpretation of an HEE must always be considered in light of its methodological choices and contextual assumptions.

To support methodological decision‐making in this field, a flowchart (Figure 1) was added outlining key recommendations for conducting HEEs of HISI in children with CP±L. This flowchart provides a structured overview of critical analytical steps and considerations, aiming to guide researchers and decision‐makers in aligning economic evaluations with both clinical practice and policy‐relevant objectives.

FIGURE 1.

Flowchart summarizing key considerations for HEEs of HISI in children with CP±L.

4. Conclusion

While HISI for children with CP±L shows promising effects on speech outcomes and HRQoL, evidence regarding their cost‐effectiveness remains limited. Given the substantial and prolonged therapy needs in this population, HEEs are essential to inform policy decisions and support efficient and equitable allocation of resources. To this end, this paper provided an overview to guide researchers in designing rigorous HEEs in this field. Generating robust evidence on the economic value of these interventions is critical to enable their effective implementation in routine clinical practice and ultimately to improve outcomes for children with CP±L.

Conflicts of Interest

The authors declare that there are no conflicts of interest to report.

Data Availability Statement

Data sharing is not applicable to this paper as no datasets were generated or analysed in the present study.