Applied Health Economics and Health Policy

, Volume 15, Issue 6, pp 763–772 | Cite as

Economic Evaluation of Interventions for Children with Neurodevelopmental Disorders: Opportunities and Challenges

Open Access
Leading Article

Abstract

Economic evaluation is a tool used to inform decision makers on the efficiency of comparative healthcare interventions and inform resource allocation decisions. There is a growing need for the use of economic evaluations to assess the value of interventions for children with neurodevelopmental disorders (NDDs), a population that has increasing demands for healthcare services. Unfortunately, few evaluations have been conducted to date, perhaps stemming from challenges in applying existing economic evaluation methodologies in this heterogeneous population. Opportunities exist to innovate methods to address key challenges in conducting economic evaluations of interventions for children with NDDs. In this paper, we discuss important considerations and highlight areas for future work. This includes the paucity of appropriate instruments for measuring outcomes meaningful to children with NDDs and their families, difficulties in the measurement of costs due to service utilization in a wide variety of sectors, complexities in the measurement of caregiver and family effects and considerations in estimating long-term productivity costs. Innovation and application of evaluation approaches in these areas will help inform decisions around whether the resources currently spent on interventions for children with NDDs represent good value for money, or whether greater benefits for children could be generated by spending money in other ways.

Key Points for Decision Makers

Productivity costs or the loss of value of gross earnings resulting from NDDs for either a child with NDD or their caregivers/family should be incorporated.

Outcome measures need to be assessed for their ability to capture meaningful clinical change for the NDD population.

The evolving dependency relationships between the health of a child with NDDs and the quality of life of family members needs to be incorporated into the evaluations.

1 Background

Economic evaluation provides information on costs and consequences of healthcare interventions to prioritize interventions that could improve population health [1]. The decision problem addressed by economic evaluation considers the choice between different types of healthcare interventions by assessing the value for money, given a budget constraint. Neurodevelopmental disorders (NDDs) are a heterogeneous group of conditions with onset in the developmental period, characterized by impairments in personal, social, educational or occupational functioning. Manifesting in early development and ranging from specific limitations of learning or control of executive functions to global impairments of social skills and intelligence, these functional limitations have implications for the well-being of the child, the family, and society [2]. When resources are limited, decision makers are increasingly turning to evidence generated by economic evaluation to make decisions about resource allocation, but little evidence exists regarding economic evaluations of NDD interventions [3, 4].

While the number of economic evaluations in child health grows rapidly [5], few economic evaluations are specific to NDDs. Beyond the core challenges all evaluations working with a pediatric population face [6, 7, 8, 9, 10], NDDs warrant specific consideration. In this paper, we highlight some key challenges and opportunities for this population related to developing outcome measures meaningful to children with NDD [8, 11], costing service utilization in different sectors [1], incorporation of caregiver and family effects [12] and estimating long-term productivity costs [7].

2 Economic Evaluation Methods

2.1 Perspective and Time Horizon

The perspective in an economic evaluation provides a framework for analysis and determines what costs and effects to include and how to value them. The healthcare sector perspective includes formal healthcare costs borne by third-party payers or paid for out-of-pocket by families. The Guidelines for the Economic Evaluation of Health Technologies in Canada (CADTH) recommends reporting a reference case from a publicly funded healthcare payer perspective [13]. It should reflect the costs incurred by Canadian public payer and health effects for patients and their informal caregivers.

NDD interventions are complex and often include non-health outcomes such as education performance [14], employment [15], (social) participation [16], criminal activities [17], better captured by adopting a societal perspective. The societal perspective is a broader perspective, capturing health and non-health costs or effects such as time costs of children and families in seeking and receiving care, time costs of informal (unpaid) caregivers, transportation costs, effects on future productivity and consumption. The National Institute for Health and Care Excellence (NICE) recommends a broader perspective regarding cost, indicating that the cost in a reference case should use National Health Services and personal social services perspectives. NICE advises that the outcomes in a reference case should measure all the direct health effects relevant to patients and carers [18]. The second panel on cost-effectiveness in health and medicine in the USA (USA panel on CEA) recommends reporting the reference case analysis from a healthcare sector or a societal perspective [19].

For children with NDD, a lifespan time horizon can be important because the impact of interventions is expected to manifest in adulthood and throughout their lifespan. The time horizon should reflect resources consumed and outcomes experienced over the lifetime of participants as part of, or as a result of, an intervention. The USA panel on CEA, CADTH, and NICE recommend adopting a time horizon long enough to capture all relevant differences in the future costs and outcomes associated with the alternatives compared. However, longer time horizons introduce higher uncertainty because of variation in discount rates [20]. When creating long-term and lifetime models to capture the effects of an intervention, different discount rates should be considered [21].

2.2 Measurement of Costs

Costing involves the systematic identification, measurement, and valuation of resources used in the intervention and the comparators, and should reflect the opportunity cost for children with a NDD and their families utilizing these resources. Costs can be categorized into costs of care, and productivity costs. Costs of care can include expenditure on healthcare, therapeutic, behavioral or educational services, transportation, caregivers and other special needs services for a child with a NDD. Productivity costs include a reduction in the ability of either the child with a NDD (when they become adults), or parent of a child with a NDD, to sustain paid employment.

Children with NDDs need a range of services during their lifetime; often extending beyond healthcare services, utilization occurs in social services, rehabilitation, the education and criminal justice systems. A systematic review on the economic consequences of child and adolescent mental illness in the UK found that only 6% of the costs fall on the health system, and the majority of costs were from outside (social services, productivity, education and the criminal justice system) [22]. Furthermore, the needs of services for children with NDD diagnosis change as setting and age change [10]. For instance, a child with autism spectrum disorder (ASD) may require a home-based special educator or early intervention therapies in early childhood [23], special education during school years [24] and employment support or vocational training during the transition into adulthood [25]. Consequently, compiling and quantifying service usage in monetary units for an economic evaluation can be challenging [26]. While adopting a narrower public health-care system perspective might be an option, failure to incorporate other relevant sources and settings may bias the results when the majority of service usage is outside the healthcare system.

Productivity costs are often captured as the loss of value of gross earnings resulting from the NDD for the child, parents (or caregivers) and other family members relative to a comparator [27]. A lack of social skills, adequate training, appropriate working environment and education, or appropriate supports [28], means that adolescents with a NDD have enormous difficulties in getting a job, and keeping it, as they transition from school to employment. For example, in Canada, the employment rate of working-age adults with a developmental disability (22.3%) was less than one-third of the rate for people without a disability (73.6%) [29].

The impact on productivity for parents and caregivers can be considered in terms of time spent in formal or informal labor markets. Primary caregivers of children with a NDD often must give up work, reduce working hours, or change jobs to provide support and care. An estimated 85% of individuals with ASD need some measure of care and assistance from their parents and families for the duration of their lives [30]. The employment stability of family members in the household (including parents and siblings) is also affected by the health of a child with a NDD [31]. Mothers of children with disabilities are 3–11% less likely to work, and the effect (13–15%) is larger if the child is severely disabled [32].

When compiling and quantifying service usage in monetary terms, there are two main challenges. First, establishing a causal relationship can be difficult in practice. For example, distinguishing the hours of care provided by caregivers related to NDD compared to usual care is a challenge. Second, quantifying productivity for children in terms of the child’s future earnings is problematic. Persons with a NDD make less money on average than persons without a NDD [29]. While not unique to NDD, incorporating these productivity costs in an economic evaluation warrants careful consideration. For example, if we use a human capital approach to estimate the productivity costs, then costs associated with each day of lost work will be lower for an individual with NDD than for individuals without NDD. Given the high productivity costs for wealthy individuals, the economic evaluation would prioritize interventions for affluent individuals rather than interventions more prevalent among those who earn lower wages. However, failure to incorporate the impacts of early interventions on productivity in adulthood could potentially underestimate the effectiveness of the intervention.

2.3 Measurement of Outcomes (Effectiveness)

Measurement of health outcomes in children with NDDs typically involves assessment of physical, social, psychological and cognitive functioning. Outcomes should reflect the experience and well-being of children with NDDs who are utilizing supports and services. For reference case analysis, all three guidelines (USA panel on CEA, CADTH, and NICE) recommend reporting the health consequences of changes in morbidity or mortality in quality-adjusted life years (QALYs) [10, 11, 12].

Deriving a QALY in pediatric populations is a challenge. In a recent review of pediatric economic evaluations published between 1980 and 2014, only 24.9% were cost-utility analyses with the majority being cost-effectiveness analyses (63.9%) [5]. To date, very few evaluations of interventions for children with a NDD have reported QALYs, due to challenges with measurement. Instruments used in economic evaluation of interventions for NDD are summarized in Table 1 (general health profile measures) and Table 2 (preference-based measures) [11, 33]. Typically, natural units such as dependency-free life years [34, 35] clinical- or disease-specific outcomes [36, 37, 38] or general health profile measures (Table 1) that are familiar to clinicians and patients are used to measure consequences of NDD interventions. A recent cost-effectiveness analysis of a communication-focused therapy for pre-school children with ASD highlights challenges with clinical or disease-specific measures [37]. The primary outcome measure reflected autism symptom severity, and without the societal value relative to a unit improvement, it is difficult to compare interventions even within NDDs.
Table 1

Examples of general health profile measures applicable to children with neurodevelopmental disorders (NDDs)

Instrumentsa

Domains

Number of items/questionnaires

Age

Raters

Used in children with NDD

Child Health and Illness Profile (CHIP): CHIP-CE and CHIP-AE [65]

CHIP-CE: satisfaction, comfort, resilience, risk avoidance

CHIP-AE: satisfaction, discomfort, risk avoidance and resilience

45 and 108

CHIP-CE: 6–11 years

CHIP-AE: 11–17 years

Child self-report/proxy report (parent)

ASD [66]

Child Health Questionnaire

(CHQ): CHQ-PF28, CHQ-PF50 andCHQ-CF87 [67]

Physical functioning, role/social limitations, general health Perceptions, bodily pain/discomfort, family activities, parent impact, mental health, self-esteem, general behavior, family cohesion and change in health

28, 50 and 87

CHQ-PF28: 4–11 years

CHQ-PF 50:5-18 years

CHQ-CF87: 10 years or above

Child self-report/proxy report (parent)

Epilepsy [68], ADHD [69]

Functional Status II-R [70]

Communications, mobility, mood, energy, play, sleep, eating and toileting patterns

43 and 14

0–16 years

Child self-report/proxy report (parent)

Not found

Generic Children’s Quality of life Measure (GCQ) [71]

Worry, happiness, relationships with parents, general satisfaction, support, health/appearance, attainments

25

6–14 years

Child self-report

Not found

Health Status Questionnaire [72]

Malformation, neuromotor function, seizure, hearing, communication, vision, cognitive and other physical disability

8 clinical domains

2 years or older

Proxy report (parents, healthcare professionals)

Not found

KIDSCREEN: KIDSCREEN-52, KIDSCREEN-27 and KIDSCREEN-10 [73]

Physical well-being, psychological well- being, moods and emotions, autonomy, parents, relations and home life, peers and social support, school environment, bullying and financial support

52, 27 and 10

8–18 years

Child self-report/proxy report

ASD [74]

KINDL Questionnaire [75]

Psychological well-being, social relationships, physical functioning, everyday life activities

24 and disease specific module

3–17 years

Child self-reported/proxy report (parent)

CP [76]

Pediatric Quality of Life Inventory (PedsQLTM) [77]

Physical, social, emotional and school

23 and 35

2–18 years

Child self-report/proxy report (parent)

ASD [78], Cerebellar malformations [79]

The Inventory of Measuring Quality of Life in Children and Adolescents (ILK questionnaire) [80]

School, family, social contact with peers, interests and recreational activities, physical health, psychological health, overall assessment of the quality of life, exposure to diagnostic and therapeutic

9 thematic areas

6–18 years

Child self-report/proxy report (adults and their medical doctors or therapists)

ASD [81]

The TNO-AZL Questionnaires for Children’s Health-Related Quality-of-Life (TACQOL) [82]

General physical functioning/complaints; functioning: motor, daily, cognitive, social, global emotional (negative and positive)

56 and 43

6–15 years

Child self-report/proxy-report (others administered by parents)

Not found

ASD autism spectrum disorder, ADHD attention-deficit-hyperactivity disorder, CP cerebral palsy

aThis is not a comprehensive list of the general health profile measures, but gives examples of instruments used most commonly in the field currently

Table 2

Examples of preference-based health-related quality-of-life (HRQoL) measures applicable to children with neurodevelopmental disorders

Instrumentsa

Domains

Number of items/questionnaires 

Age

Raters

Used in children with NDD?

16 Dimensional (16D) [83]

Mobility, vision, hearing, breathing, sleeping, eating, speech, excretion, school and hobbies, mental function, discomfort and symptoms, depression, distress, vitality, appearance and friends

16

12–15 years

Child self-report

Specific language impairment [84], Prader-Willi syndrome [85]

17 Dimensional (17D) [86]

Mobility, vision, hearing, breathing, sleeping, eating, speech, excretion, school and hobbies, discomfort and symptoms, depression, vitality, appearance, friends, concentration, anxiety, learning and memory

17

8–11 years

Child self-report

Specific language impairment [87]

Child Health Utility 9D (CHU9D) [88]

Worried, sad, pain, tired, annoyed, schoolwork/homework, sleep, daily routine and ability to join in activities

9 dimensions with 5 levels of response options per dimension

7–11 years

Child self-report

Not found

Euro QoL five-dimension questionnaire for youth (EQ-5D-Y)/EQ-5D[89]

Mobility, looking after myself, doing usual activities, having pain or discomfort, feeling worried, sad or unhappy

5 dimensions with 3 levels of response options per dimension and Visual Analogue Scale (VAS)-0 (the worst health you can imagine)-100 (the best health you can imagine)

4–11 years (4–7: proxy version, 8–11: self-report)

Child self-report/proxy report

ADHD [90], Spina bifida [91]

Health utilities index (HUI): HUI Mark 1, HUI Mark 2 and HUI Mark 3 [92]

HUI2: sensation, mobility, emotion, cognition, self-care, pain and fertility

HUI3: vision, hearing, speech, ambulation, dexterity, cognition and pain

15 and 40

5 years and older (5–8, 8–12 and 13+ years)

Child self-report/proxy report (parent)

ASD [39], FASD [93]

Quality of well-being scale, self-administered (QWB-SA) [94]

Mobility, physical activity and social activity

N/A

N/A

Child self-report

ASD [39]

Standard gamble (SG) [95]

N/A

N/A

N/A

Child self-report/proxy report (parent)

ADHD [96, 97]

Time trade off (TTO) [98]

N/A

N/A

N/A

Child self-report/proxy report (parent)

Not found

ADHD attention-deficit-hyperactivity disorder, ASD autism spectrum disorder, FASD fetal alcohol spectrum disorder, N/A not applicable

aThis is not a comprehensive list of the preference-based HRQoL, but gives examples of instruments most commonly used in the field currently

Many general health profile measures are adapted from measures developed for adult populations and not designed for NDDs. Each instrument has different domains, and adapting to a NDD intervention can be difficult. For example, the Child Health Questionnaire (CHQ), KIDSCREEN and Child Health and Illness Profile (CHIP) have a significant number of items related to social dimensions such as school, family, and peers, whereas the Pediatric Quality of Life Inventory (PedsQL) has more items that focus on physical and emotional functioning (Table 1). A recent study by Janssens et al looked at these instruments in the context of NDD and found a lack of evidence on responsiveness and measurement error, making it difficult to distinguish meaningful (clinically important) change [11]. Of the instruments assessed in this study, the PedsQL and CHQ were most evaluated in NDD populations, but findings were not satisfactory. Ultimately, the main disadvantage of using either a general health profile or disease-specific measure for economic evaluation is the lack of standardization into a scale that can elicit a QALY.

Few studies relating to children with a NDD have used preference-based measures as an outcome measure (Table 2). There are also differences in the domains captured in preference-based measures. The reliability and validity of preference-based instruments in children with NDD need to be examined. For instance, a study by Tilford et al [39] comparing preference-based and clinical measures in ASD, found that the Health Utilities Index (HUI)-3 to be a good measure of effectiveness and should be included in clinical trials. Other studies are needed to assess these instruments in other NDDs for their ability to capture meaningful clinical changes.

Using direct approaches such as standard gamble or time trade-off for deriving a QALY is another approach, but can be as problematic for children with NDD as in other pediatric populations. Children with a NDD are more likely to experience emotional and behavioral dysregulation, social exclusion, isolation and poorer academic performance than their peers who do not have a disability [40]. This may influence a child’s understanding of health and well-being, meaning they may not have the capacity to understand the concept of time in a way that would allow them to indicate their preference.

If preference-based measures are not feasible, general health profile measures can be converted into QALYs using mapping [41]. This is an emerging opportunity in the field of NDDs as it allows for adaptation of data collected from commonly used general health profile measures such as the KIDSCREEN or PedsQL to estimate a QALY. Algorithms for mapping general health profile measures into health utilities have been reported for some conditions [42]; however, very few algorithms have been created specifically for children with NDD [43].

Ideally, outcome measures should reflect a child’s experience and perception. Due to feasibility, reliability, and validity issues, proxies such as parents, caregivers and healthcare professionals are used. Proxy reporters can be effective for visible signs and symptoms but are less accurate for subjective measures such as quality of life (QoL), emotion, and utility. Reporting is impacted by the proxy’s knowledge, experiences, and expectations [6]. This is particularly important as each NDD has a unique etiology and wide heterogeneity of symptoms and co-morbidities such as mental health considerations [44].

The limitations of QALYs in capturing non-health outcomes has resulted in some researchers calling for more comprehensive outcome measures [45, 46]. Sen’s “Capability approach” has been proposed as one alternative [47, 48, 49]. The capability approach is based on the view of living as a combination of various ‘doings and beings,’ with the QoL to be assessed in terms of the “capability to achieve valuable functioning” [50], p31. The essence of the capability approach is that “an individual’s well-being should not be measured according to what they actually do (that is their function) but what they can do (their capabilities) [49], p850. In this context, the capability approach offers the potential for a richer set of dimensions in the evaluation of interventions for children with NDD [47]. A number of capability-based instruments/questionnaires (Investigating Choice Experiments Capability Measure [ICECAP], Oxford Capability Instruments [OxCAP], Adult Social Care Outcome Toolkit [ASCOT], etc.) have been developed for use in healthcare. However, application of this approach in economic evaluations of interventions for children with NDDs requires development and testing of the capability-based questionnaire/instruments for this heterogeneous population.

2.4 Caregivers and Family Effects

The notion that children are not ‘isolated individuals,’ but rather have a social circle of parents, siblings, other relatives and friends has emerged in the health economic literature [12]. There are two main effects, described as ‘caregiving effects’ and ‘family effects,’ which are spillover effects. The caregiving effect refers to the impact of a child’s health on the QoL and economic well-being of a caregiver, and family effects refer more broadly to the impact of a child’s health on QoL and economic well-being of family members, applying to parents, siblings and other relatives living in the household. The importance of incorporating spillover effects in economic evaluations of health interventions has been described [51, 52]. Several guidelines (US panel on CEA, CADTH and NICE) recommend including spillover effects for caregivers in the reference case analysis [13, 18, 19]; however, applications to specific conditions and treatments remain limited.

An intervention can have positive and negative effects on the QoL and economic well-being of both the child with NDD and their caregivers and family members. Parents of children with NDD report poorer physical and mental health and experience higher levels of family distress than parents who are not raising children with NDD [53]. A longitudinal population study looked at the impact on the maternal and paternal health of having a child with a disability in the household. After controlling for previous health status and other sociodemographic characteristics, the health of mothers declined compared to fathers [54]. Caregivers of children with NDD experience more health and psychological problems such as stress, problematic family functioning, migraine headaches and asthma compared to caregivers of neurotypical children [53]. Informal caregivers’ health suffers as the severity of disability and needs of patient increases [55, 56]. Siblings of children with a chronic illness may also be impacted, as a meta-analysis showed fewer peer activities and lowered developmental cognitive scores compared to siblings without a chronic illness [57].

Acknowledging these dynamic, complex and changing dependency relationships between a child’s health and the QoL of family members, a number of researchers have proposed different methods of incorporating family and caregiver effects into economic evaluation. Basu and Meltzer developed a theoretical framework based on a utility function for measuring family spillover effects and have shown how medical treatment could provide direct and indirect effects on the welfare of all family members [58]. Al-Janabi et al advanced research on health spillovers by developing a framework, which involves the inclusion of two multiplier effects: multiplier effects for health benefits generated and displaced by a new intervention [59].

A few methodological approaches for isolating and measuring spillover health effects of caregivers and family members (separately) have been introduced in the literature. Brouwer et al and Poley et al have used the EQ-5D to measures the health-related QoL of rheumatoid arthritis caregivers and parents providing informal care to young patients with congenital anomalies [56, 60]. They found that effects of illness were extended to other members of the family. Similarly, using a modified time trade-off method, Basu et al evaluated the impact on the QoL of partners due to a patient experiencing prostate cancer [61]. The spouse of the patient was asked to trade off his or her life based on their expected burden if the patient developed one of the prostate cancer-related health states. Using standard gamble (SG) methods, Prosser et al asked family members of Alzheimer/dementia, arthritis, cancer and depression patients to value the spillover effects [62] and found that effects of illness extend beyond the individual patient to caregivers, children, a spouse, and family members. Spillover effects on caregivers can also be measured using the CarerQol, a carer-related QoL instrument [63, 64]. While numerous methods to incorporate caregiver and family effects have been suggested in the literature, there is a lack of standardized methodology, and more empirical and theoretical work related to NDD is warranted.

3 Conclusion

There is an urgent need for identifying or developing the most appropriate instruments or methods that take into consideration the measurement challenges present for economic evaluations of interventions for children with a NDD. This paper is the first step to highlighting these challenges and outlines considerations when conducting an economic evaluation in this field. Children with NDD present unique challenges for health-specific outcome measures, as they often lack cognitive, communication and social abilities to respond to questionnaires on health and well-being. Furthermore, there is uncertainty about the measurement of costs, for instance when and how to measure costs for caregivers, and how to estimate and incorporate productivity costs in economic evaluation. Further research on methods to measure spillover effects on family members and caregivers and productivity costs of NDD is needed. Understanding the heterogeneity of children with NDDs in terms of their patterns of healthcare utilization, resources used, and dependency relationships with caregivers is important to ensure the measurement of costs and consequences are more representative in the economic evaluation.

Notes

Acknowledgements

The authors would like to thank Dr. David Whitehurst, Simon Fraser University and Dr. Mike Paulden, University of Alberta for providing comments and feedback during the preparation of the manuscript. We gratefully acknowledge the contributions from Kids Brain Health Network funded through The Networks of Centers of Excellence Program and CHILD-BRIGHT funded by CIHR.

Author Contribution

Both authors contributed to the manuscript. RL led the manuscript drafting and JZ leading conception of the manuscript, drafting and revisions.

Compliance with Ethical Standards

Ethical approval

No specific ethical approval was sought for this leading article.

Conflict of interest

Ramesh Lamsal and Jennifer Zwicker work on economic evaluations of interventions for children with NDD.

Funding

RL and JZ are supported by grants from the Kids Brain Health Network, a Network of Center of Excellence and CHILD-BRIGHT, funded by CIHR. The Social Policy and Health Research Unit is supported by the Sinneave Family Foundation.

References

  1. 1.
    Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. 4th ed. New York: Oxford University Press; 2015.Google Scholar
  2. 2.
    American Psychiatric A. Diagnostic and statistical manual of mental disorders (DSM-5®): American Psychiatric Pub; 2013.Google Scholar
  3. 3.
    Bailey AJ. Where are the autism economists? Autism Res. 2009;2(5):245.CrossRefPubMedGoogle Scholar
  4. 4.
    Payakachat N, Tilford JM, Kovacs E, Kuhlthau K. Autism spectrum disorders: a review of measures for clinical, health services and cost–effectiveness applications. Expert Rev Pharmacoecon Outcomes Res. 2012;12(4):485–503.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Sullivan SM, Tsiplova K, Ungar WJ. A scoping review of pediatric economic evaluation 1980–2014: do trends over time reflect changing priorities in evaluation methods and childhood disease? Expert Rev Pharmacoecon Outcomes Res. 2016;16(5):599–607.CrossRefPubMedGoogle Scholar
  6. 6.
    Bevans KB, Forrest CB. The reliablity and validity of children’s and adolsecents’ self-reported health. In: Ungar WJ, editor. Economic evaluation in children health. New York: Oxford University Press; 2010. p. 34–54.Google Scholar
  7. 7.
    Petrou S, Gray R. Methodological challenges posed by economic evaluations of early childhood intervention programmes. Appl Health Econ Health Policy. 2005;4(3):175–81.CrossRefPubMedGoogle Scholar
  8. 8.
    Sung L, Petrou S, Ungar WJ. Measurement of health utilities in children. In: Ungar WJ, editor. Economic evaluation in children health. New York: Oxford University Press; 2010. p. 34–54.Google Scholar
  9. 9.
    Ungar WJ. Challenges in health state valuation in paediatric economic evaluation. PharmacoEconomics. 2011;29(8):641–52.CrossRefPubMedGoogle Scholar
  10. 10.
    Ungar WJ, Gerber A. The uniqueness of child health and challenges to measuring costs and consequences. In: Ungar WJ, editor. Economic evaluation in children health. New York: Oxford University Press; 2010. p. 3–32.Google Scholar
  11. 11.
    Janssens A, Rogers M, Gumm R, Jenkinson C, Tennant A, Logan S, Morris C. Measurement properties of multidimensional patient-reported outcome measures in neurodisability: a systematic review of evaluation studies. Dev Med Child Neurol. 2016;58(5):437–51.CrossRefPubMedGoogle Scholar
  12. 12.
    Brouwer W, van Exel J, Tilford JM. Incorporating caregiver and family effects in economic evaluations of child health. In: Ungar WJ, editor. Economic evaluation in children health. New York: Oxford University Press; 2010. p. 55–76.Google Scholar
  13. 13.
    Canadian Agency for Drugs and Technologies in H. Guidelines for the economic evaluation of health technologies. 4th ed. Ottawa: CADTH; 2017.Google Scholar
  14. 14.
    Loe IM, Feldman HM. Academic and educational outcomes of children with ADHD. J Pediatr Psychol. 2007;32(6):643–54.CrossRefPubMedGoogle Scholar
  15. 15.
    Wehman P, Schall C, McDonough J, Molinelli A, Riehle E, Ham W, et al. Project SEARCH for youth with autism spectrum disorders: Increasing competitive employment on transition from high school. J Posit Behav Interv. 2013;15(3):144–55.CrossRefGoogle Scholar
  16. 16.
    Murphy NA, Carbone PS. American Academy of Pediatrics Council on Children With D. Promoting the participation of children with disabilities in sports, recreation, and physical activities. Pediatrics. 2008;121(5):1057–61.CrossRefPubMedGoogle Scholar
  17. 17.
    Fletcher J, Wolfe B. Long-term consequences of childhood ADHD on criminal activities. J Mental Health Policy Econ. 2009;12(3):119–38.Google Scholar
  18. 18.
    National Institute of Health and Sciences(NICE). In: Guide to the methods of technology appraisal. 2013. https://www.nice.org.uk/process/pmg9/chapter/foreword. Accessed 1 Feb 2016.
  19. 19.
    Neumann PJ, Sanders GD, Russell LB, Siegel JE, Gaaniats TG, editors. Cost-effectiveness in health and medicine. 2nd ed. New York: Oxford University Press; 2017.Google Scholar
  20. 20.
    Newell RG, Pizer WA. Discounting the distant future: how much do uncertain rates increase valuations? J Environ Econ Manag. 2003;46(1):52–71.CrossRefGoogle Scholar
  21. 21.
    Smith DH, Gravelle H. The practice of discounting in economic evaluations of healthcare interventions. Int J Technol Assess Health Care. 2001;17(2):236–43.CrossRefPubMedGoogle Scholar
  22. 22.
    Suhrcke M, Pillas D, Selai C. Economic aspects of mental health in children and adolescents. Social cohesion for mental well-being among adolescents. Copenhagen: WHO Regional Office for Europe; 2008.Google Scholar
  23. 23.
    Anderson SR, Avery DL, DiPietro EK, Edwards GL, Christian WP. Intensive home-based early intervention with autistic children. Education and treatment of children. 1987;8(32):352–66.Google Scholar
  24. 24.
    Chasson GS, Harris GE, Neely WJ. Cost comparison of early intensive behavioral intervention and special education for children with autism. J Child Fam Stud. 2007;16(3):401–13.CrossRefGoogle Scholar
  25. 25.
    Hendricks D. Employment and adults with autism spectrum disorders: Challenges and strategies for success. J Vocational Rehabil. 2010;32(2):125–34.Google Scholar
  26. 26.
    Mayer S, Paulus ATG, Laszewska A, Simon J, Drost R, Ruwaard D, et al. Health-related resource-use measurement instruments for intersectoral costs and benefits in the education and criminal justice sectors. Pharmacoeconomics. 2017. doi: 10.1007/s40273-017-0522-4.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Van den Hout W. The value of productivity: human-capital versus friction-cost method. Ann Rheum Dis. 2010;69(Suppl 1):i89–91.CrossRefPubMedGoogle Scholar
  28. 28.
    Dudley C, Nicholas DB, Zwicker J. What do we know about improving employment outcomes for individuals with autism spectrum disorder? 2015;8(32):1–36.Google Scholar
  29. 29.
    Volkmar FR, Pauls D. Autism. Lancet (London, England). 2003;362(9390):1133-41.Google Scholar
  30. 30.
    Reichman NE, Corman H, Noonan K. Impact of child disability on the family. Matern Child Health J. 2008;12(6):679–83.CrossRefPubMedGoogle Scholar
  31. 31.
    Stabile M, Allin S. The economic costs of childhood disability. Futur Child. 2012;22(1):65–96.CrossRefGoogle Scholar
  32. 32.
    Zwicker J, Zaresani A, Emery JCH. Describing heterogeneity of unmet needs among adults with a developmental disability: an examination of the 2012 Canadian survey on disability. Res Dev Disabil. 2017;65:1–11.CrossRefPubMedGoogle Scholar
  33. 33.
    Chen G, Ratcliffe J. A review of the development and application of generic multi-attribute utility instruments for paediatric populations. Pharmacoeconomics. 2015;33(10):1013–28.CrossRefPubMedGoogle Scholar
  34. 34.
    Motiwala SS, Gupta S, Lilly MB, Ungar WJ, Coyte PC. The cost-effectiveness of expanding intensive behavioural intervention to all autistic children in Ontario: in the past year, several court cases have been brought against provincial governments to increase funding for Intensive Behavioural Intervention (IBI). This economic evaluation examines the costs and consequences of expanding an IBI program. Healthcare Policy. 2006;1(2):135.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Penner M, Rayar M, Bashir N, Roberts SW, Hancock-Howard RL, Coyte PC. Cost-effectiveness analysis comparing pre-diagnosis autism spectrum disorder (ASD)-Targeted Intervention with Ontario’s autism intervention program. J Autism Dev Disord. 2015;45(9):2833–47.CrossRefPubMedGoogle Scholar
  36. 36.
    Comans T, Mihala G, Sakzewski L, Boyd RN, Scuffham P. The cost-effectiveness of a web-based multimodal therapy for unilateral cerebral palsy: the Mitii randomized controlled trial. Dev Med Child Neurol. 2017;59(7):756–61.CrossRefPubMedGoogle Scholar
  37. 37.
    Byford S, Cary M, Barrett B, Aldred CR, Charman T, Howlin P, et al. Cost-effectiveness analysis of a communication-focused therapy for pre-school children with autism: results from a randomised controlled trial. BMC Psychiatry. 2015;21(15):316.CrossRefGoogle Scholar
  38. 38.
    Sayal K, Taylor JA, Valentine A, Guo B, Sampson CJ, Sellman E, et al. Effectiveness and cost-effectiveness of a brief school-based group programme for parents of children at risk of ADHD: a cluster randomised controlled trial. Child Care Health Dev. 2016;42(4):521–33.CrossRefPubMedGoogle Scholar
  39. 39.
    Tilford JM, Payakachat N, Kovacs E, Pyne JM, Brouwer W, Nick TG, et al. Preference-based health-related quality-of-life outcomes in children with autism spectrum disorders: a comparison of generic instruments. Pharmacoeconomics. 2012;30(8):661–79.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Wagner JL, Wilson DA, Smith G, Malek A, Selassie AW. Neurodevelopmental and mental health comorbidities in children and adolescents with epilepsy and migraine: a response to identified research gaps. Dev Med Child Neurol. 2015;57(1):45–52.CrossRefPubMedGoogle Scholar
  41. 41.
    Longworth L, Rowen D. NICE DSU technical support document 10: the use of mapping methods to estimate health state utility values. Sheffield: Decision Support Unit, ScHARR, University of Sheffield; 2011.Google Scholar
  42. 42.
    Dakin H. Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database. Health and quality of life outcomes. 2013;11(1):151.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Payakachat N, Tilford JM, Kuhlthau KA, Exel N, Kovacs E, Bellando J, et al. Predicting health utilities for children with autism spectrum disorders. Autism Res. 2014;7(6):649–63.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Gurney JG, McPheeters ML, Davis MM. Parental report of health conditions and health care use among children with and without autism: National Survey of Children’s Health. Arch Pediatr Adolesc Med. 2006;160(8):825–30.CrossRefPubMedGoogle Scholar
  45. 45.
    Brazier J, Tsuchiya A. Improving cross-sector comparisons: going beyond the health-related QALY. Appl Health Econ Health Policy. 2015;13(6):557–65.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Lorgelly PK, Lawson KD, Fenwick EAL, Briggs AH. Outcome measurement in economic evaluations of public health interventions: a role for the capability approach? Int J Environ Res Public Health. 2010;7(5):2274–89.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Coast J, Smith R, Lorgelly P. Should the capability approach be applied in health economics? Health Econ. 2008;17(6):667–70.CrossRefPubMedGoogle Scholar
  48. 48.
    Karimi M, Brazier J, Basarir H. The capability approach: a critical review of its application in health economics. Value Health. 2016;19(6):795–9.CrossRefPubMedGoogle Scholar
  49. 49.
    Lorgelly PK. Choice of outcome measure in an economic evaluation: a potential role for the capability approach. PharmacoEconomics. 2015;33(8):849–55.CrossRefPubMedGoogle Scholar
  50. 50.
    Sen A. Capability and well-being. In: N. Sen, editor. The quality of life. Oxford: Clarendon Press; 1993.Google Scholar
  51. 51.
    Bobinac A, van Exel NJ, Rutten FF, Brouwer WB. Health effects in significant others: separating family and care-giving effects. Med Decis Mak. 2011;31(2):292–8.CrossRefGoogle Scholar
  52. 52.
    Brouwer WB. Too important to ignore: informal caregivers and other significant others. PharmacoEconomics. 2006;24(1):39–41.CrossRefPubMedGoogle Scholar
  53. 53.
    Lach LM, Kohen DE, Garner RE, Brehaut JC, Miller AR, Klassen AF, et al. The health and psychosocial functioning of caregivers of children with neurodevelopmental disorders. Disabil Rehabil. 2009;31(9):741–52.CrossRefPubMedGoogle Scholar
  54. 54.
    Burton P, Lethbridge L, Phipps S. Children with disabilities and chronic conditions and longer-term parental health. J Socio-Econ. 2008;37(3):1168–86.CrossRefGoogle Scholar
  55. 55.
    Argimon JM, Limon E, Vila J, Cabezas C. Health-related quality of life in carers of patients with dementia. Fam Pract. 2004;21(4):454–7.CrossRefPubMedGoogle Scholar
  56. 56.
    Brouwer WB, van Exel NJ, van de Berg B, Dinant HJ, Koopmanschap MA, van den Bos GA. Burden of caregiving: evidence of objective burden, subjective burden, and quality of life impacts on informal caregivers of patients with rheumatoid arthritis. Arthritis Rheum. 2004;51(4):570–7.CrossRefPubMedGoogle Scholar
  57. 57.
    Sharpe D, Rossiter L. Siblings of children with a chronic illness: a meta-analysis. J Pediatr Psychol. 2002;27(8):699–710.CrossRefPubMedGoogle Scholar
  58. 58.
    Basu A, Meltzer D. Implications of spillover effects within the family for medical cost-effectiveness analysis. J Health Econ. 2005;24(4):751–73.CrossRefPubMedGoogle Scholar
  59. 59.
    Al-Janabi H, van Exel J, Brouwer W, Coast J. A framework for including family health spillovers in economic evaluation. Med Decis Mak. 2016;36(2):176–86.CrossRefGoogle Scholar
  60. 60.
    Poley MJ, Brouwer WBF, van Exel NJA, Tibboel D. Assessing health-related quality-of-life changes in informal caregivers: an evaluation in parents of children with major congenital anomalies. Qual Life Res. 2012;21(5):849–61.CrossRefPubMedGoogle Scholar
  61. 61.
    Basu A, Dale W, Elstein A, Meltzer D. A time tradeoff method for eliciting partner’s quality of life due to patient’s health states in prostate cancer. Med Decis Mak. 2010;30(3):355–65.CrossRefGoogle Scholar
  62. 62.
    Prosser LA, Lamarand K, Gebremariam A, Wittenberg E. Measuring family HRQoL spillover effects using direct health utility assessment. Med Decis Mak. 2015;35(1):81–93.CrossRefGoogle Scholar
  63. 63.
    Brouwer WB, van Exel NJ, Van Gorp B, Redekop WK. The CarerQol instrument: a new instrument to measure care-related quality of life of informal caregivers for use in economic evaluations. Qual Life Res. 2006;15(6):1005–21.CrossRefPubMedGoogle Scholar
  64. 64.
    Hoefman RJ, van Exel J, Brouwer WB. Measuring care-related quality of life of caregivers for use in economic evaluations: CarerQol Tariffs for Australia, Germany, Sweden, UK, and US. Pharmacoeconomics. 2017;35(4):469–78.CrossRefPubMedGoogle Scholar
  65. 65.
    Child Health and Illness P. Child Health and illness profile: a comprehensive assessment of health and functioning for children and adolescents. http://www.childhealthprofile.org/. Accessed on 31 Jul 2017.
  66. 66.
    Kuhlthau K, Kovacs E, Hall T, Clemmons T, Orlich F, Delahaye J, et al. Health-related quality of life for children with ASD: associations with behavioral characteristics. Res Autism Spectrum Disord. 2013;7(9):1035–42.CrossRefGoogle Scholar
  67. 67.
    Landgraf JM, Abetz L, Ware JE. The CHQ user’s manual. Boston: The Health Institute, New England Medical Center. 1996;571.Google Scholar
  68. 68.
    Sabaz M, Cairns D, Bleasel A, Lawson J, Grinton B, Scheffer I, et al. The health-related quality of life of childhood epilepsy syndromes. J Paediatr Child Health. 2003;39(9):690–6.CrossRefPubMedGoogle Scholar
  69. 69.
    Klassen AF. Quality of life of children with attention deficit hyperactivity disorder. Expert Rev Pharmacoecon Outcomes Res. 2005;5(1):95–103.CrossRefPubMedGoogle Scholar
  70. 70.
    Stein REK, Jessop DJ. Functional status II (R): a measure of child health status. Med Care. 1990;28(11):1041–55.CrossRefPubMedGoogle Scholar
  71. 71.
    Collier J, MacKinlay D, Phillips D. Norm values for the Generic Children’s Quality of Life Measure (GCQ) from a large school-based sample. Qual of Life Res. 2000;9(6):617–23.CrossRefGoogle Scholar
  72. 72.
    Jones HP, Guildea ZE, Stewart JH, Cartlidge PH. The Health Status Questionnaire: achieving concordance with published disability criteria. Arch Dis Child. 2002;86(1):15–20.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Ravens-Sieberer U, Gosch A, Rajmil L, Erhart M, Bruil J, Duer W, et al. KIDSCREEN-52 quality-of-life measure for children and adolescents. Expert Rev Pharmacoecon Outcomes Res. 2005;5(3):353–64.CrossRefPubMedGoogle Scholar
  74. 74.
    Baghdadli A, Brisot J, Henry V, Michelon C, Soussana M, Rattaz C, et al. Social skills improvement in children with high-functioning autism: a pilot randomized controlled trial. Eur Child Adolesc Psychiatry. 2013;22(7):433–42.CrossRefPubMedGoogle Scholar
  75. 75.
    Ravens-Sieberer U, Bullinger M. Assessing health-related quality of life in chronically ill children with the German KINDL: first psychometric and content analytical results. Qual Life Res. 1998;7(5):399–407.CrossRefPubMedGoogle Scholar
  76. 76.
    Okurowska-Zawada B, Kulak W, Otapowicz D, Sienkiewicz D, Paszko-Patej G, Wojtkowski J. Quality of life in children and adolescents with cerebral palsy and myelomeningocele. Pediatr Neurol. 2011;45(3):163–8.CrossRefPubMedGoogle Scholar
  77. 77.
    Varni JW, Seid M, Kurtin PS. PedsQL™ 4.0: reliability and validity of the pediatric quality of life inventory™ version 4.0 generic core scales in healthy and patient populations. Med Care. 2001;39(8):800–12.CrossRefPubMedGoogle Scholar
  78. 78.
    Kuhlthau K, Orlich F, Hall TA, Sikora D, Kovacs EA, Delahaye J, et al. Health-related quality of life in children with autism spectrum disorders: results from the autism treatment network. J Autism Dev Disord. 2010;40(6):721–9.CrossRefPubMedGoogle Scholar
  79. 79.
    Bolduce ME, Du Plessis AJ, Sullivan N, Khwaja OS, Zhang X, Barnes K, et al. Spectrum of neurodevelopmental disabilities in children with cerebellar malformations. Dev Med Child Neurol. 2011;53(5):409–16.CrossRefGoogle Scholar
  80. 80.
    Mattejat F, Simon B, Konig U, Quaschner K, Barchewitz C, Felbel D, et al. Quality of life of children and adolescents with psychiatric disorders. Results of the 1st multicenter study with an inventory to assess the quality of life in children and adolescents. Zeitschrift fur Kinder- und Jugendpsychiatrie und Psychotherapie. 2003;31(4):293–303.CrossRefPubMedGoogle Scholar
  81. 81.
    Kamp-Becker I, Schroder J, Muehlan H, Remschmidt H, Becker K, Bachmann CJ. Health-related quality of life in children and adolescents with autism spectrum disorder. Z Kinder Jugendpsychiatr Psychother. 2011;39(2):123–31.CrossRefPubMedGoogle Scholar
  82. 82.
    Vogels T, Verrips GHW, Koopman HM, Theunissen NCM, Fekkes M, Kamphuis RP. TACQOL manual: parent form and child form. Leiden: Leiden Center for Child Health and Pediatrics LUMC-TNO; 2000.Google Scholar
  83. 83.
    Apajasalo M, Sintonen H, Holmberg C, Sinkkonen J, Aalberg V, Pihko H, et al. Quality of life in early adolescence: a sixteen dimensional health-related measure (16D). Quality of Life Research. 1996;5(2):205–11.CrossRefPubMedGoogle Scholar
  84. 84.
    Arkkila E, Räsänen P, Roine R, Sintonen H, Saar V, Vilkman E. Health-related quality of life of adolescents with childhood diagnosis of specific language impairment. Int J Pediatr Otorhinolaryngol. 2009;73(9):1288–96.CrossRefPubMedGoogle Scholar
  85. 85.
    Sipilä I, Sintonen H, Hietanen H, Apajasalo M, Alanne S, Viita AM, et al. Long-term effects of growth hormone therapy on patients with Prader–Willi syndrome. Acta paediatrica. 2010;99(11):1712–8.CrossRefPubMedGoogle Scholar
  86. 86.
    Apajasalo M, Rautonen J, Holmberg C, Sinkkonen J, Aalberg V, Pihko H, et al. Quality of life in pre-adolescence: a 17-dimensional health-related measure (17D). Qual Life Res. 1996;5(6):532–8.CrossRefPubMedGoogle Scholar
  87. 87.
    Arkkila E, Räsänen P, Roine R, Sintonen H, Saar V, Vilkman E. Health-related quality of life of children with specific language impairment aged 8–11. Folia phoniatrica et Logopaedica. 2011;63(1):27–35.CrossRefPubMedGoogle Scholar
  88. 88.
    Stevens K. Developing a descriptive system for a new preference-based measure of health-related quality of life for children. Qual Life Res. 2009;18(8):1105–13.CrossRefPubMedGoogle Scholar
  89. 89.
    Wille N, Badia X, Bonsel G, Burström K, Cavrini G, Devlin N, et al. Development of the EQ-5D-Y: a child-friendly version of the EQ-5D. Qual Life Res. 2010;19(6):875–86.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Matza LS, Secnik K, Mannix S, Sallee FR. Parent-proxy EQ-5D ratings of children with attention-deficit hyperactivity disorder in the US and the UK. Pharmacoeconomics. 2005;23(8):777–90.CrossRefPubMedGoogle Scholar
  91. 91.
    Kelly AE, Haddix AC, Scanlon KS, Helmick CG, Mulinare. Cost-effectiveness of strategies to prevent neural tube defects. In: MR Gold, JE Siegel, LB Russell, MC Weinstein, editors. Cost-effectiveness in health and medicine, New York: Oxford University Press; 1996.Google Scholar
  92. 92.
    Horsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI®): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003;1(1):54.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Stade BC, Stevens B, Ungar WJ, Beyene J, Koren G. Health-related quality of life of Canadian children and youth prenatally exposed to alcohol. Health Qual Life Outcomes. 2006;4(1):81.CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Seiber WJ, Groessl EJ, David KM, Ganiats TG, Kaplan RM. Quality of well being self-administered (QWB-SA) scale. San Diego: Health Services Research Center, University of California; 2008.Google Scholar
  95. 95.
    Gafni A. The standard gamble method: what is being measured and how it is interpreted. Health Serv Res. 1994;29(2):207.PubMedPubMedCentralGoogle Scholar
  96. 96.
    Matza LS, Secnik K, Rentz AM, Mannix S, Sallee FR, Gilbert D, et al. Assessment of health state utilities for attention-deficit/hyperactivity disorder in children using parent proxy report. Qual Life Res. 2005;14(3):735–47.PubMedGoogle Scholar
  97. 97.
    Secnik K, Matza LS, Cottrell S, Edgell E, Tilden D, Mannix S. Health state utilities for childhood attention-deficit/hyperactivity disorder based on parent preferences in the United Kingdom. Med Decis Mak. 2005;25(1):56–70.CrossRefGoogle Scholar
  98. 98.
    Gudex C. Time trade-off user manual: Props and self-completion method, Centre for Health Economics, University of York, Occasional Paper Series. 1994.Google Scholar

Copyright information

© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of Public PolicyUniversity of CalgaryCalgaryCanada
  2. 2.Faculty of KinesiologyUniversity of CalgaryCalgaryCanada

Personalised recommendations