Introduction

Developmental disabilities (DDs) are a heterogenous group of conditions characterized by impairments that affect a child’s physical, learning, or behavioural functioning [1]. Globally, 53 million children under the age of 5 years have DDs such as sensory impairment, intellectual disability, epilepsy, and autism spectrum disorder (ASD) [2]. With the lack of improvement of the global burden of developmental disabilities over the last three decades [2], the socioemotional consequences, academic challenges and economic cost to society will be sustained or may worsen over time [3]. More specific to DDs, neurodevelopmental disorders (NDDs) occur in the developmental period and induce deficits that produce impairments of functioning (personal, academic, occupational or social) [4]. They include intellectual disability (ID); ASD; Attention-Deficit/Hyperactivity Disorder (ADHD); communication disorders; neurodevelopmental motor disorders; and specific learning disorders [4]. The prevalence of NDDs has rarely been assessed as a whole, with existing literature highlighting multimorbidity as a norm [3]. The majority of children with DDs lack access to care especially in Africa, due to inadequate capacity of skilled human resource [5,6]. With a focus on NDDs, substantial evidence shows that parents can learn skills to effectively improve their child’s development and positive behaviour [7].

Caregiver-focused interventions have been developed to train and educate parents/carers of children with developmental disabilities to support them in their child’s development [8]. Caregiver interventions are often centred on increasing parental responsiveness to improve the child’s behaviour and communication outcomes [9–12]. Parent skills training models have shown to be low-intensity in implementation due to less practitioner time requirement, the narrower focus of parenting strategies, and are adaptable for clinic, home, groups or individuals [13,14]. Further, interventions in early childhood are shown to have the strongest impact on improving the child’s long-term outcomes and parent outcomes [10–12]. Therefore, the need to explore the childhood specific strategies is important to reduce the overall economic burden of the illness. As such, the use of economic evaluations on health interventions and strategies for developmental disabilities has steadily gained interest. The economic evaluation data provides policy makers with information on the best value for money for the interventions [15]. Considering resource limitations, it is important that health interventions are effective in reducing the burden of disease. Economic evaluations can provide this information, and it is important to assess the current scope of evaluations of child specific interventions to inform future policy decisions.

Economic evaluation is an approach that contributes evidence on the economic costs and outcomes of health interventions to inform efficient resource allocation within a budget constraint. [16]. Policy strategies to decrease the impact of developmental disabilities are optimally effective when they are informed by evidence that determines how to improve efficacy, efficiency and cost-effectiveness of interventions, and their translation into clinical practice [17]. With a goal of maximizing health and well-being, economic analyses are needed alongside effectiveness evidence for policy makers to identify the best options for child and mental health resources [18]. Costs and cost-effectiveness data for caregiver interventions for children with DDs is scarce. Lamsal et al (2017) highlight few economic evaluations that have been conducted to value interventions for children with NDDs resulting from challenges in applying economic evaluation methodologies for this heterogenous population [1]. For instance, Penner et al (2015) conducted a cost effectiveness analysis on a parent delivered pre-diagnosis intervention for ASD and noted the limited choice of outcome measures for ASD and availability of this data for the analysis [19]. Similarly, Byford et al (2015) carried out a cost effectiveness analysis on a parent-directed intervention for pre-school children with ASD and highlighted the absence of a suitable generic measure and societal value for a unit improvement in the ASD score as a limitation to conducting similar studies [20]. Further, Rodgers et al (2020) conducted a review on early intensive behaviour interventions for autistic children and underlined limited effectiveness evidence which constrained economic evaluation analysis for the interventions [21]. A review by Kularatna et al (2022) identified twelve model based economic evaluations for care for NDDs, however none modelled the impact on families and caregiver [22]. Evidently, the unavailability of quality data is a major limitation of economic evaluation analyses for these interventions.

For childhood intervention programmes, some of the methodological challenges are related to cost and outcome measurement and valuation, the requirements for sensitivity analyses, the decision rules adopted by decision makers and the interpretation of results considering contextual factors [23,24]. Given these factors, there is a notable gap in literature that provides an overview of economic evaluations for caregiver interventions for children with DDs. The aim of this scoping review is to map the body of literature and describe the nature of evidence available on the economic evaluations for caregiver interventions for NDDs. Additionally, the review appraised the quality of studies included and discussed their methodological challenges and ways to mitigate them. By outlining the available evidence, we aim to inform the paediatric economic evaluation methodological approaches utilized and associated challenges and build on existing literature on interventions for child and adolescent mental healthcare.

Methods

We applied the scoping review approach proposed by Arksey and O’Malley [25], and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines for reporting this review. The review protocol was uploaded to Open Science Framework (OSF) https://osf.io/ymwr2.

Search strategy and selection criteria

An extensive literature search was conducted using electronic databases that included PubMed, PsycINFO, Web of Science, the International Network of Agencies for Health Technology Assessment (INAHTA), Paediatric Economic Database Evaluation (PEDE), CINAHL, Econ Lit through EBSCO from January 1993 to December 2023. The period was selected to capture a comprehensive picture within a relevant time frame with focus on a recent period to ensure findings reflect the most up-to-date evidence. Further, we searched databases of grey literature specifically google scholar, and references of the included studies. The final search was conducted on September 18^th, 2024. The search terms used in all search strategies were categorized into 4 blocks including: i) caregiver (parent, family); ii) interventions (e.g., training, programme, groups); iii) neurodevelopmental disorders (e.g., autism, intellectual disability); and iv) economic evaluation (e.g., cost utility analysis and cost effectiveness analysis). The details of the search concepts can be obtained from S1 and S2 Tables. All citations were imported into an electronic database (Endnote version X8) [26], where duplicates were removed. Studies were then uploaded into Rayyan (https://www.rayyan.ai/) for title and abstract screening. The inclusion criteria comprised of empirical studies that reported on an economic evaluation with a focus on caregiver interventions for neurodevelopmental disorders, in all geographical locations as summarized in Table 1. Studies that focused on physical and sensory impairments in children and studies that did not report on economic evaluation models of caregiver interventions for NDDs were excluded. Further, studies published beyond the time frame, were reviews, and in languages other than English were also excluded. The reason for exclusion of each study were noted. Two reviewers (AK and ED) independently screened titles and abstracts to assess relevance based on inclusion and exclusion criteria (Table 1). A third reviewer (EB) resolved any variations established. Abstracts included were then assessed for full-text inclusion. Full text articles eligible for inclusion were then selected for data extraction.

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Table 1. Inclusion and exclusion criteria summary.

https://doi.org/10.1371/journal.pgph.0003928.t001

Results

Article selection

The literature search identified 7,811 articles. After excluding duplicate studies, 7,255 studies remained for title and abstract screening. The screening based on title and abstract resulted in 38 articles eligible for full text screening. Most studies were excluded because they included a clinical condition with no NDD, they were not primary studies reporting economic evaluations results (e.g., reviews) or focused on clinical treatment rather than caregiver interventions for children with NDDs. After full-text screening, 20 studies were included for data extraction and quality assessment. Additional details are presented in the PRISMA flow diagram (Fig 1).

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Fig 1. PRISMA flow chart of the study selection process.

https://doi.org/10.1371/journal.pgph.0003928.g001

Overview of key characteristics

All the studies included were conducted in high income countries specifically UK (n = 7), Canada (n = 2), United States (n = 3), Australia (n = 1), Denmark (n = 1), Sweden (n = 3), Ireland (n = 1), Japan (n = 1), and multi-country (England, Ireland, Italy, Spain) (n = 1). Most of the studies focused on caregiver interventions for children with autism spectrum disorder (ASD) (n = 7), followed by attention deficit hyperactivity disorder (ADHD) (n = 6), disruptive behaviour with ADHD (n = 2), behavioural problems with ADHD (n = 3), intellectual disabilities (n = 1) and language delay (n = 1). The caregiver interventions were delivered through group format (n = 9), on individual basis (n = 8) or both platforms (n = 1). Two of the studies did not report on the intervention delivery method. Overall, the caregiver interventions targeted children ranging from 12 months to 12 years.

Of the reviewed studies, eleven reported a cost effectiveness analysis (CEA) [20,31,32,34,37,39,40,42,43,48,49], two studies carried out a cost utility analysis (CUA) [35, 46], one study performed a cost consequence analysis [50] and one study conducted a cost benefit analysis (CBA) [38]. However, two studies reported as CEAs [34, 40] were CUAs based on the measure of outcome as quality adjusted life years (QALYs) and one study conducted an additional CUA [48]. In addition to these, two studies conducted combined analyses as a cost analysis alongside a CEA [33], and a cost consequence analysis (CCA) alongside a CEA [41]. For partial economic evaluations, three studies conducted a costing analysis [36, 44, 45]. Fourteen were economic evaluations conducted alongside trials [20,31,32,34,36,40–46, 48, 49], whereas five studies were model based [33, 35, 38, 39, 50]. One study did not state the economic evaluation approach used [37].The time horizon for the majority of studies was relatively short, that is: 13 weeks for one study [43]; six to ten months for three studies [32, 36, 44]; and, one year for two studies [20, 48]. Eight studies extrapolated to a long-term period ranging from two years to 65 years [33, 35, 37–41, 50]. Six studies [31, 34, 42, 45, 46, 49] did not explicitly state the time horizon of the evaluations. In Table 2 we provide a detailed overview of the main characteristics of the included studies.

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Table 2. Overview of the key characteristics of the included economic evaluation studies.

https://doi.org/10.1371/journal.pgph.0003928.t002

Economic evaluation methods

Cost estimates.

We summarized the cost estimates associated with caregiver interventions implementation and the associated healthcare costs for children with DDs. Nine studies conducted evaluations from one perspective only: four studies adopted a health provider’s perspective [31, 35, 37, 45]; two studies adopted a government (insurer) payer perspective [33, 41]; and three studies adopted a societal perspective [40, 42, 43]. Nine studies reported results from more than one perspective [20, 34, 36, 39, 42, 46, 48–50]. The remaining two studies did not explicitly state the perspective adopted [38, 44].

The health provider costs mainly reported on the intervention implementation costs, for instance training, personnel, overheads, treatment costs amongst others. Among these, only three studies [31, 37, 48] reported on both start-up and implementation costs of the caregiver interventions. Notably, Scavenius et al (2020) reported only financial costs and excluded opportunity costs such as income loss or other sector benefits, as they were not tangible costs of the intervention implementation [31]. Cost inclusion or exclusion may impact the study results by increasing the costs of the intervention, but at the same time may also reduce the incremental costs of the intervention. Four studies [35, 38, 46, 48] captured the costs of other sectors, i.e., education, social and crime services. Furthermore, Nystrand et al (2019; 2020) included cost offsets as the treatment costs avoided with the reduction of the disease prevalence incurred by the health sector as a cost input [35, 38]. For societal costs, four studies [39, 40, 48, 49] mainly included either direct costs (health service costs, medication, childcare, travel) or indirect costs (productivity loss), whereas eight studies [20, 32, 34, 36, 42, 43, 46, 50] comprehensively captured both direct and indirect costs. Most studies explicitly stated the currency and reported year used except six studies [20, 32, 34, 44-46]. Discounting was not necessary in the studies which collected costs over a one-year time horizon or less. However, where necessary due to a longer time horizon adopted by twelve of the included studies, a discount rate ranging from 2% to 6% per annum was applied with the exception of two studies [37, 39]. Therefore, the cost-effectiveness results reported may be inaccurate for both studies.

Health outcome measures.

We report key effectiveness outcomes of caregiver interventions and comparators in the eligible studies in Table 4. Except for the cost analysis, the most frequently reported health outcomes for cost utility analysis included four studies reporting on QALYs [32, 34, 40, 48], and one study studies on disability adjusted life years (DALYs) [35]. Of the studies that reported QALYs, the generic health related quality of life (HRQoL) measures used were Child Health Utility 9D (CHU-9D) and EuroQoL 5 dimensions Youth (EQ5D-Y). Health outcomes reported in nine studies applying cost effectiveness analysis were mostly per case reduction and per outcome score improvement amongst others [20, 31, 33, 37, 41-43, 48, 49]. O’Farrelly et al (2021) carried out a short term CEA and a long term CUA, but with the small differences in the QALYs and no difference in cost between the trial groups, only the short term CEA was reported with per outcome score improvement as the health outcome [41]. Further, Penner et al (2015) reported dependency-free life years (DFLYs) as the outcome measure [39], while Nystrand et al (2020) reported the net present value for the CBA [38]. Additional details included in each of the reviewed studies are listed in Table 3.

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Table 3. Cost inputs associated with the caregiver interventions included in this review.

https://doi.org/10.1371/journal.pgph.0003928.t003

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Table 4. Clinical and economic outcomes of the economic evaluation studies included in this review.

https://doi.org/10.1371/journal.pgph.0003928.t004

Sensitivity analysis.

At least one form of sensitivity analysis was carried out in 14 of the eligible studies as shown in Table 4. Scenario analysis was carried out in two studies [40, 41], univariate sensitivity analysis was carried out in eight studies [33–36, 42–44, 48], two studies [39, 43] carried out a multivariate sensitivity analysis, one study carried out a deterministic sensitivity analysis [50] and probabilistic sensitivity analysis was carried out in five studies [33, 35, 37, 38, 41]. Six studies did not conduct sensitivity analysis to explore uncertainties around their results [20, 31, 32, 45, 46, 49].

Statistical analysis.

Of the trial based economic evaluations, twelve of these studies further applied statistical analysis. Nine studies [20, 31, 32, 34, 36, 40, 42, 48, 49] applied regression models and bootstrapping methods to analyse the differences in costs and outcomes, whilst two studies [20, 48] adjusted for missing data. With trials being designed to assess the effectiveness of a new intervention for a specific population and CEAs/CUAs designed to account for benefits for even those not receiving a new health intervention, statistical analyses should be applied to address methodological challenges that may arise with the joint analysis.

Economic evaluation findings and policy suggestions.

Evidence of the findings of the economic evaluations of the caregiver interventions are summarised in Table 5. Of the 9 CEA studies and 5 CUA studies, four studies [20, 34, 40, 41] did not report outcomes using incremental cost effectiveness ratios (ICERs) due to small to no differences in the outcomes. Several of the studies reported the intervention as cost-effective (n = 9) and cost-saving (n = 5) especially when considering the long-term effects. A few of the studies found the intervention less likely to be cost effective. Charman et al (2021) found the predictive parenting intervention more costly and less cost-effective than its comparator [34]. Similarly, Byford et al (2015) found the combination of pre-school autism communication therapy (PACT) and usual treatment to have higher costs and no significant difference in outcomes [20]. Page et al (2016) found medication to be recommended for treatment of ADHD as it maximizes the societal cost compared to group behavioural therapy [44]. With the willingness to pay threshold being arbitrary, most of the studies used varied thresholds to determine cost-effectiveness of the interventions.

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Table 5. Summary of findings and policy implications.

https://doi.org/10.1371/journal.pgph.0003928.t005

Discussion

This review identified economic evaluations of caregiver interventions for children with DDs and synthesized evidence on the methods adopted and their associated challenges. Twenty studies were identified that carried out both partial and full economic evaluations. Most of the studies evaluated interventions delivered through group programmes (47%) compared to individual based platforms (41%). One striking feature of this review is the heterogeneity of the economic evaluation approaches presented. The majority of studies (70%) were trial based economic evaluations, which are considered suitable for inferring cost-effectiveness for interventions with no prior evidence [51]. Although many of the included economic evaluations were rated average or good, many had methodological limitations, emphasising the challenges often faced in the conduct of economic evaluations of caregiver interventions for children with DDs. A few factors impact on the validity of conclusions drawn in economic evaluation such as accuracy of cost information, choice of discount rate, scope, modelling and time horizon and perspective. However, three main methodological challenges were identified from the literature as representative of the methodological issues more specific to conducting economic evaluations of caregiver interventions for young populations including time horizon analysis, measuring and valuing costs and measuring health outcomes.

Caregiver interventions for developmental disorders may result in long-term benefits and the choice of time horizon analysis should aim to capture all meaningful costs and outcomes. In our review seven studies extrapolated data over a one-year time horizon. One trial based economic evaluation study [43], modelled shorter timeframes due to lack of published data. However, the intervention showed a sustained effected with indication of it being even more cost-effective in later years of life. Similarly, O’Farrelly et al (2021) found that the short time horizon was a limitation as the participants were less likely to incur costs for service use relating to behavioural problems during the preschool period but instead when the child is older [41]. Current trial based economic evaluations have limited time horizons, often less than one year. Extending the time horizon of economic evaluations may lead to more favourable estimates [52], especially for interventions with long-term effects, as in the case of caregiver interventions. Two studies [39, 41] reported that the use of multiple data sources to extrapolate results over longer periods and to different population should be used with caution because this would require many assumptions and huge uncertainties which would likely produce poor estimates of the results. The uncertainties of parameters contribute to the overall model uncertainties whose potential impact can be assessed through scenario and sensitivity analyses, which are important considerations for economic evaluations [22]. Modelling studies may help address some of the issues of RCTs with longer period projections of the cost estimates and outcomes, and should use available evidence of real-world data and assumptions. Our review identified only four studies, which applied a decision analytical modelling approach such as decision trees or Markov models. Within any economic evaluation modelling, there is a balance between adequately simplifying complex health states and interventions to allow model parameterisation, while avoiding oversimplification to the extent that findings are not representative of the real-world context [22]. Even with diverseness of the methods applied, more than half (64%) of the reviewed studies found that the caregiver interventions were cost saving or cost-effective, while improving quality of life for the children and parents.

The studies reported varied levels of details of cost components used in the economic evaluations, with most studies including the major components as start-up and implementation costs. A few (18%) of the reviewed studies included costs from other sectors or cost offsets as important cross cutting and relevant costs which significantly contribute to wholistic care of children with DDs. Children with DDs require various services often extending beyond healthcare, for instance social services, education, rehabilitation, and criminal and justice systems [53]. Suhrcke et al (2008) conducted a systematic review demonstrating that for child and adolescent mental illness, only 6% of costs accounted for healthcare, with majority of the costs in other sectors (i.e., social services, education, productivity) [53]. Moreover, the service needs for children with DDs changes with the setting and age during their lifetime [24]. Therefore, quantifying service utilization for an economic evaluation of caregiver interventions may be challenging.

A prominent challenge related to cost measurement and valuation was the cost categories included. The studies identified exclusion of important cost inputs that would over- or underestimate the cost-effectiveness outcome. For societal costs, Scavenius et al (2020) did not account for productivity loss costs [31], and Nystrand et al (2019) did not include caregiver health and well-being costs and the costs of behaviour problem consequences in adulthood which would be significant cost offsets in the long term [35]. Regarding provider costs, Tsiplova et al (2022) excluded certain costs such as intervention development costs, and training costs (materials, coaches travel expenses, coach training) [36], and Nystrand et al (2020) included only medication costs in the healthcare resource use [38]. The accuracy of resources measurement was potentially limited by self-reporting of resource use for instance informal care and parent time costs in two studies [20, 43]. Another challenge noted was the lack of data which limited the costs included in the analysis. For instance, one study [43] was unable to assess all costs necessary for a full societal approach, another study [35] applied a narrow costing perspective, which likely missed different social impacts, because mental health interventions may have a broader societal impact, and interact with other sectors such as education, social services, justice, and voluntary sectors. Also, one other study [37] used a limited health system perspective and only cost offsets for one episode of care.

Notably, Penner et al (2015) found that caregiver costs were a significant cost driver in the cost-effectiveness estimates which further highlights the importance of accounting for all relevant costs [39].While inclusion of such costs may increase the intervention cost, the incremental costs of the intervention may also reduce. Adopting a narrower costing perspective may fail to incorporate other relevant sectors, which may bias study findings when most service utilization is beyond the healthcare system [1]. The economic benefits that accrued over time are realized not only by the participant and family involved, but also by other sectors and society in general [42]. In addition, the impact on productivity for caregivers may be considered in terms of time spent at work, and more often primary caregivers of children with DDs reduce working hours, give up work or change jobs to provide care [1]. Two main challenges with quantifying and valuing productivity are differentiating hours of care provided related to the DD compared to usual care and measuring productivity for children for their future earnings [54]. One of the reviewed studies [31] excluded productivity costs of parents and indicated possible underestimation, whereas two studies [35, 43] included parent time costs which added value to the findings. Although including productivity costs requires careful consideration, failure to capture productivity costs may potentially underestimate the resources use and effectiveness of the intervention [1].

Most of the included studies used cost effectiveness methods. Although informative, CEAs measure condition specific outcomes that are not directly comparable to other interventions targeting the same related problems or those across different diagnostic areas. In addition, the use of clinical measures undermines the likelihood of finding improvements that may be relevant to general well-being and everyday life, for instance improvement in quality of life. This is a key aspect of caregiver interventions that may have an impact on different areas of the children’s and parents’ lives. Therefore, cost utility studies which measure health outcomes with a generic health status that considers both mortality and the quality of life with morbidity would be preferred. In this review, only eight studies conducted CUAs with QALYs, DALYs and DFLYs used as the outcome measures. Measuring health related quality of life in children remains contentious because of the descriptive system or valuation perspective. For instance, measuring effectiveness of DDs is challenging and involves assessment of physical, behavioural, psychological, cognitive and social domains [1]. In this regard, our review highlighted methodological challenges towards measuring QALYs and DALYs in mental health and for the younger population.

First, the use of DALYs as an outcome may be limited by data availability on disease weights for specific DDs and the severity levels. For instance, Sampaio et al (2015) and Nystrand et al (2019) noted that conduct disorder (CD) doesn’t have differential severity weights, and significant assumptions on the disorder distribution and disability weights would be required to model changes in disorder severity [35, 55]. Consequently, children with some levels of CD who would benefit from the intervention and the benefits incurred by parents, siblings and teachers were excluded from the analysis, which would likely underestimate the health gains attributed to the reduction in disorder severity [55]. A better approach would specify weights based on disorder severity as a reflection of heterogeneity in health [56]. Second, the use of QALYs and the maximization approach to resource allocation in relation to mental health remains a contentious and understudied area [57]. Specifically, QALY measures may be “mis-valued”, insensitive and not specific to all mental health issues. Nonetheless, in failing to use QALYs there is risk of marginalization of mental health interventions in priority setting and budgeting processes. Third, there are challenges in methods of measuring and valuing health related quality of life in children, which have been studied to a limited extent than the adult population. Mihalopolous et al (2015) highlighted that the study was limited by the availability of adult population parameters which would not be representative of anxiety problems and the disease duration estimates in young children [58]. Based on the other health outcome measures applied in two studies [20, 41] it was noted that QALYs measures have mostly been applied in adult populations and may not be representative of the younger populations. In a recent review, some of the challenges highlighted included how to elicit informed preferences, the generation and use of combined adult and adolescent preferences and the appropriateness and acceptability of valuation tasks for children and adolescents [59]. Sayal et al (2016) utilized two measures CHU 9D and EQ5D-Y which showed slight variance in results likely from the differences in the valuation processes employed [32]. One study [35] used parental proxy rather than self-reported outcomes, which may be associated with misreporting. Thus, the use of different health related quality of life measures may show slight differences in the results, like one of the included studies [32]. Although the indirect approaches of utility measurement from multi-attribute utility instruments including EuroQol 5 dimensions (EQ-5D) and Health Utilities Index (HUI) are more appropriate to measure health status of children with DDs, deriving the parameter estimates can be challenging because of age-appropriateness, the domains, and methods to derive utilities [60]. Despite the challenges, there remains extensive opportunity for improvement of these estimates. Fourth, the application of utility values from other countries may be less accurate than country specific as highlighted in one of the reviewed studies [40]. In addition, there is limited data on utility values for some countries [38], or were condition-specific for instance per case reduction [39, 43], which limits the use of QALYs as outcome measures. The valuation of child and adolescent outcome measures remains a challenging research area that requires further empirical evidence to inform best practice.

Conclusion

This review mapped economic evaluations of caregiver interventions for children with DDs and highlighted the gap in evidence and methodological challenges. Whilst economic evaluation analyses in this area are scarce, emerging data from common DDs was promising in the quest for cost saving and cost-effective interventions that improve quality of life of both the child and parent/caregiver. Caregiver interventions are a promising avenue to strengthen access and reduce costs associated with health services for children with DDs. This review has provided an overview of evidence on DDs which is a growing priority across many areas of paediatrics. Future research should consider the development of appropriate outcome measures and measurement of all relevant costs for this heterogenous population. Prioritizing more economic evaluation studies in this area would inform decision-making on efficient resource allocation, promote inclusivity and equitable access of services for children with DDs within the health system.

Supporting information

Acknowledgments

For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Author Manuscript version arising from this submission.