1 Introduction
Self-harm is commonly defined in the UK and Europe as any form of non-fatal self-poisoning or self-injury (such as cutting, taking an overdose, hanging, self-strangulation, jumping from a height and running into traffic), regardless of the motivation or degree of intention to die. This definition would include US definitions of non-suicidal self-injury and suicidal behaviour. Self-harm in adolescents is a major public health issue with one in ten adolescents self-harming each year [
1]. Individuals with mental disorders are heavy users of public health services and require emotional support and care from their family [
2,
3]. Their disorders are likely to affect other family members’ health and own healthcare needs, especially because individuals with mental health conditions face elevated rates of all-cause mortality and this places a huge burden of costs and life-years lost on the family and the community [
4].
It appears that the magnitude of spillovers on the health of other family members is the greatest in parents of ill children [
5,
6]. Beyond the effect of caring for an ill child on parents’ health [
7], treatments that are provided to a self-harming child may have various spillover effects for the family. Indeed, psychotherapeutic treatments such as family-based therapies are often used with self-harming adolescents; they rely on individuals’ relational network, involve parents, caregivers, brothers and sisters, or other close relatives and friends in the therapies to improve clinical outcomes [
8], and typically aim at maximizing cohesion, attachment and support while moderating parental control [
9]. Therapy sessions do not necessarily include all family members, but it is expected that they will have an impact beyond the identified patient.
Some prior economic evaluations of psychotherapeutic interventions in young people have examined the impact of the therapy on the adolescent/child patient and on relatives participating in the therapy. These studies collected parents or carers’ outcomes and used them as additional outcomes of interest in a cost-effectiveness analysis (CEA) [
10‐
12], whilst only two studies combined child and parents’ outcomes. Bodden et al. [
13] used a compound summary of anxiety-specific scores of the child, mother and father, as part of the sensitivity analyses. Their analysis measured the cost-effectiveness per anxiety-free family by including the costs related to the child and other family members’ anxiety as self-reported in cost diaries. Cottrell et al. [
14] used the same data as this article over an 18-month follow-up and aggregated quality-adjusted life-years (QALYs) of the adolescent and one parent as a sum in a sensitivity analysis. Their application relied on the strong assumption that QALYs can be summed across individuals. This assumption has been used in other studies in child health [
15] and is consistent with research showing benefits to other family members involved in mental health family treatment [
16,
17]. However, such considerations require a more thorough discussion of the interdependence between the utility functions of the adolescent and the parent, and the most appropriate method to include the overall health benefits.
The National Institute for Health and Care Excellence (NICE) reference case underlines that the perspective on outcomes considers “all direct health effects, whether for patients or, when relevant, carers” [
18]; however, there is no consensus on how these health effects should be measured and valued. Wittenberg and Posser [
19] offered a summary of the evidence on the measurement and incorporation of health spillover of illness on family members or caregivers across health conditions, as a disutility. In their review, methods to measure spillovers included three different types: (1) a direct measure of disutility of family members; (2) a relative measure of family members’ utility with a comparison to a control group; or (3) an estimation of the utility of family members in a hypothetical scenario in which the patient is healthy or does not require caregiving.
In empirical economic evaluation studies, health spillovers have been included either as accrued health benefits [
20‐
22] or as an estimated multiplier parameter, which adjusts the patient’s health gain with a spillover for the rest of a wider network (including parents, carers, spouses and other relevant individuals) [
23,
24]. Whilst the first method uses a health-related quality of life (HRQoL) questionnaire and directly elicited utilities, the multiplier effect is based on a regression model using observational or primary data collection and consists of two multiplier effects.
In this article, we use data from a multi-centre, individually randomised controlled trial comparing family therapy (FT) with treatment as usual (TAU) as an intervention for self-harming adolescents aged 11–17 years [
25] as a case study. Both the adolescent and one parent
1 reported their HRQoL as part of the trial across repeated follow-up points. We undertake a within-trial CEA incorporating parental health spillover effects using alternative quantification methods. We add to the growing literature in three ways. First, we investigate the association between the health utility of the parent and a self-harming adolescent as part of an explanatory regression model using the preference-based HRQoL scores of both the adolescent and one parent. Second, we present a comparative analysis of alternative spillover quantification methods as part of an economic evaluation, bringing together the dyadic and the regression-based perspectives. Finally, we discuss how health spillovers could be adjusted including benefits to the rest of the family using an equivalence scale (ES) to adjust parental health gain.
2 Self-Harm Intervention: Family Therapy (SHIFT) Trial Case Study
The self-harm intervention: family therapy (SHIFT) study was a randomised controlled trial conducted in local child and adolescent mental health services in Yorkshire, Greater Manchester and London for adolescents aged 11–17 years who had self-harmed twice. Participants were randomly allocated to receive FT or TAU. The objective of the trial was to assess whether FT would reduce the number of times the adolescents attended hospital with further self-harm. The trial results are reported elsewhere [
14].
Personal characteristics were collected at baseline including the adolescent’s sex, age, and type and number of self-harm episodes, as well as the sex and age of their parent. Additional information was collected on the adolescent’s mental health using the Hopelessness Scale for Children [
26], the parent’s emotion toward the adolescent using the Family Questionnaire [
27], the parent’s viewpoint on the family atmosphere through the McMaster Family Assessment Device [
28] and the parents’ General Health Questionnaire (GHQ-12) [
29]. All these measurements are defined in Table
1. The adolescent’s HRQoL was measured by the EuroQoL 5 Dimensions 3 Levels (EQ-5D-3L) [
30] whilst the parent’s HRQol was determined by the Health Utility Index (HUI2) [
31,
32]. The original research proposal considered HUI2 as the HRQoL measure for both the parent and the adolescent following the NICE guidelines at the time [
33,
34]. However, we carried out a pilot study [
35] on a sample of 49 adolescents aged 11–18 years to test the ability of children to deal with the concepts and language used in the EQ-5D-3L and HUI2. We found that EQ-5D-3L had the least amount of missing data and presented limited problematic wording for that age group; therefore, the EQ-5D-3L was eventually used to measure the HRQoL of adolescents in the trial. However, the parents’ HRQoL instrument was not changed.
Table 1
Description of clinical scores
Adolescent | Hopelessness Scale for Children: A measure of the degree to which adolescents have negative expectancies about themselves and the future. It consists of 17 items with true or false responses, providing a single overall score with higher scores reflecting greater negative expectations towards the future |
Parent | Family Questionnaire: A 20-item self-report questionnaire relating to the different ways in which families try to cope with everyday problems. It consists of a single overall score with higher scores indicating greater levels of expressed emotion directed at the adolescent by the parent |
| McMaster Family Assessment Device: A measurement of family functioning across 60 items on six different dimensions: Problem Solving, Communication, Roles, Affective Responsiveness, Affective Involvement and Behaviour Control. A higher total score is indicative of poorer family functioning |
| GHQ-12: A measure of current mental health focusing on two major areas: the inability to carry out normal functions and the appearance of new and distressing experiences. High total scores are indicative of greater psychological distress |
An adolescent’s responses to the EQ-5D-3L were converted into health-state utility scores using national tariff values [
36]. Similarly, a parent’s responses to HUI2 were converted into health-state utility values [
32,
37]. The area under the curve approach was used to calculate QALYs for the adolescent and the parent.
Resource use of health services was self-reported by the adolescent and/or his or her parent. Accident and emergency visits and inpatient stays of the adolescent were available from National Health Service digital records. Resource use was combined with national unit costs distinguishing, where possible, by a self-harm and not self-harm-related event leading to hospitalisation [
38]. Psychotropic medication costs were calculated using trial medication records. The intervention costs were calculated separately for each treatment arm using information on the type and duration of the therapies sessions available from the trial records [
14,
39].
Eight hundred and thirty-two adolescents and their parents were recruited in the trial (417 in TAU and 415 in FT). This article focuses on the first 12-month follow-up, thus discounting is not required. Missing utility scores and total health and hospital services costs at 6 and 12 months were imputed using multiple imputations via chained equations [
40‐
42]. Imputations were based on a number of demographic and clinical predictors; the process is described elsewhere [
39]. Missing utility (4%) and clinical scores (3%) at baseline were not imputed. The sample used in the main analysis is 731 adolescents and their parent (359 in TAU and 372 in FT). As part of the sensitivity analysis, the analysis was also carried out on the complete case sample; the sample reduced to 206 adolescents and their parent (73 in TAU and 133 in FT).
4 Results
4.1 Regression Models
Descriptive statistics are presented in Table
2. At baseline, more than two thirds of the adolescents were female with about three self-harm episodes over the duration of the trial. Self-harm was caused by self-injury for over 70% of the adolescents with more than 50% reporting some problems with anxiety/depression. For parents, 86% were mothers with an average age of 42 years (see Table
3). Parent’s average GHQ-12 was 8.52 (standard deviation 5.38), which is within the distressed range (4–12) but lower than the level of psychological distress observed in a sample of caregivers of a dependent relative [
47].
Table 2
Adolescents’ characteristics at baseline
Sex, n (%) |
Male | 93 (12) | 48 (13) | 45 (12) |
Female | 661 (88) | 323 (87) | 338 (88) |
Age, years, n (%) |
11–14 | 396 (53) | 195 (53) | 201 (52) |
15–17 | 358 (47) | 176 (47) | 182 (48) |
Centre, n (%) |
Yorkshire | 272 (36) | 135 (36) | 137 (36) |
Manchester | 267 (35) | 132 (36) | 135 (35) |
London | 215 (29) | 104 (28) | 111 (29) |
Total no. of self-harm episodes, mean (SD) | 2.92 (21.51) | 3.26 (28.59) | 2.60 (10.95) |
Type of index episode, n (%) |
Self-poisoning | 170 (23) | 83 (22) | 87 (23) |
Self-injury | 533 (71) | 262 (71) | 271 (71) |
Combined | 51 (7) | 26 (7) | 25 (7) |
Source of referral (from hospital), n (%) |
Yes | 274 (36) | 130 (35) | 144 (38) |
No | 480 (64) | 241 (65) | 239 (62) |
EQ-5D-3L score (overall), mean (SD) | 0.68 (0.27) | 0.68 (0.26) | 0.68 (0.28) |
Hopelessness Scale for Children scorea, mean (SD) | 7.39 (4.26) | 7.21 (4.29) | 7.56 (4.22) |
Table 3
Parents’ characteristics at baseline
Sex, n (%) |
Male | 89 (12) | 47 (13) | 42 (11) |
Female | 665 (88) | 324 (87) | 341 (89) |
Relationship to adolescent, n (%) |
Father | 85 (11) | 47 (13) | 38 (10) |
Foster parent | 2 (0) | 1 (0) | 1 (0) |
Guardian | 11 (1) | 3 (1) | 8 (2) |
Mother | 649 (86) | 318 (86) | 331 (86) |
Step-father | 2 (0) | 0 (0) | 2 (1) |
Step-mother | 5 (1) | 2 (1) | 3 (1) |
Agea, years, mean (SD) | 42.38 (6.42) | 42.40 (6.18) | 42.36 (6.64) |
HUI score (overall), mean (SD) | 0.71 (0.28) | 0.70 (0.28) | 0.72 (0.27) |
McMaster Family Assessment Deviceb, mean (SD) | 2.20 (0.37) | 2.21 (0.37) | 2.20 (0.36) |
Family Questionnairec, mean (SD) | 52.86 (10.75) | 52.88 (10.79) | 52.84 (10.72) |
Parent GHQd, mean (SD) | 5.70 (4.07) | 6.07 (4.07) | 5.33 (4.04) |
Table
4 shows the mean utility scores for adolescents and their parent at baseline, 6 and 12 months, overall and by treatment arm. For the adolescents, utility scores increase monotonically over the 12 months and regardless of the treatment arm. Differences in utility scores between arms were significant at 6 and 12 months favouring FT. The difference from baseline appears to be slightly larger in FT than in TAU (on average 0.145 vs. 0.095). The parent’s utility also shows an increase in the overall HUI2 score at 6 and 12 months from baseline; this increase however is much smaller than for the adolescent (on average 0.045 vs. 0.12) and is not significant when distinguished by treatment arm.
Table 4
Adolescent’s and parent’s health-related quality of life by time period (N = 754)
Overall, mean (SD)
|
Adolescent’s EQ-5D-3L score | 0.68 (0.27) | 0.78 (0.17) | 0.80 (0.19) |
Parent’s HUI score | 0.71 (0.28) | 0.76 (0.23) | 0.78 (0.23) |
Family therapy
|
Adolescent’s EQ-5D-3L score | 0.68 (0.28) | 0.80 (0.17) | 0.81 (0.19) |
Parent’s HUI score | 0.72 (0.27) | 0.77 (0.23) | 0.78 (0.23) |
Treatment as usual
|
Adolescent’s EQ-5D-3L score | 0.68 (0.26) | 0.76 (0.17) | 0.78 (0.18) |
Parent’s HUI score | 0.70 (0.28) | 0.76 (0.22) | 0.78 (0.23) |
Difference FT vs. TAU, mean (SD)
|
Adolescent’s EQ-5D-3L score | − 0.003 (0.20) | 0.043*** (0.01) | 0.036** (0.14) |
Parent’s HUI score | 0.019 (0.02) | 0.014 (0.03) | 0.000 (0.02) |
Table
5 presents the Tobit regression results of the parent’s HRQoL; the association between the parent’s and adolescent’s health varies across time points and model specifications. We find a significant and positive association with the parent’s health at 6 months and 12 months in Model 2 while in Model 1, the parent’s health is positively associated with the adolescent’s HRQoL at 6 months only. This is in line with prior studies on the experience of parents’ caregiving for an ill child [
5,
7,
48], and carers of people with mental health disorders [
3].
Table 5
Relative health spillover: results of Tobit regression model of the parent’s health-related quality of life with adolescent’s EuroQoL 5 Dimensions 3 Levels (EQ-5D-3L) score (full sample with imputations for missing data)
Adolescent | | | | | | |
EQ-5D-3L | − 0.0077 | 0.2913*** | 0.0881 | 0.0558 | 0.2997*** | 0.1272** |
Female | 0.0895* | 0.0516* | 0.0332 | 0.0470 | 0.0314 | 0.0136 |
Age 15–17 years vs. 11–14 years | − 0.0231 | 0.0183 | 0.0162 | 0.0130 | 0.0078 | 0.0398 |
Type of index episode (ref. self-poisoning) |
Self-injury | | | | 0.0336 | 0.0752*** | 0.0119 |
Combined | | | | 0.0495 | 0.0594 | 0.0078 |
Repeated SH episodes (ref. >3 events) | | | − 0.0357 | − 0.0565 | 0.0263 |
Hopelessness Scale for Children score | | | | 0.0108*** | 0.0047* | 0.0050* |
Parent |
McMaster Family Assessment Device | | | | − 0.1031** | − 0.0702** | − 0.0763* |
Family Questionnaire | | | | − 0.0033** | − 0.0027** | − 0.0027* |
Parent GHQ | | | | − 0.0378*** | − 0.0157*** | − 0.0150*** |
Female | − 0.1062** | − 0.0550* | − 0.0511 | − 0.0218 | − 0.0213 | − 0.0101 |
Centre |
Manchester | | | | − 0.0865*** | − 0.0237 | − 0.0097 |
London | | | | − 0.0781** | − 0.0062 | 0.0045 |
Constant | 0.8176*** | 0.5914*** | 0.7564*** | 1.3256*** | 0.9341*** | 0.9594*** |
Sigma | 0.3382 | 0.2536 | 0.2900 | 0.2702 | 0.2267 | 0.2699 |
Observations | 754 | 754 | 754 | 731 | 731 | 731 |
Pseudo R-squared | 0.017 | 0.084 | 0.010 | 0.426 | 0.459 | 0.151 |
The parent’s HRQoL at every time point also appears to be negatively associated with a higher score of emotion within the family, of poor family functioning and of psychological distress as measured by GHQ-12, all three measured at baseline. The strong association between a parent’s utility and GHQ-12 has also been shown in other studies [
49]. Furthermore, parent’s health is positively and significantly associated with an adolescent’s higher score of hopelessness; however, this association substantially reduces in magnitude and significance over time.
4.2 Spillover Effects in Cost-Effectiveness Analysis
Table
6 presents the incremental cost-effectiveness ratios (ICERs) and their respective probabilities of cost-effectiveness using the base-case analysis when only the adolescent’s QALY gain is considered along with the five regression-based alternative spillover quantifications
4 and the ES-based spillover quantification with five alternative elasticity values. Costs used in the analysis are summarised in Table
10 of the “
Appendix”. Because we did not collect healthcare costs for the parent, we note that the costs for each ICER are strictly identical and it is only the level of QALY gain that varies.
Table 6
Incremental cost-effectiveness ratios (ICERs) with alternative spillover quantifications
Base-case analysis
| |
TAU | 3750.59 (198.34) | 0.745 (0.008) | | |
FT | 4957.75 (194.13) | 0.774 (0.008) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.030*** (0.011) | 40,453.30 | (0.080–0.263) |
Quantification 1: Relative health spillover (Model 2, Table
5
)
| |
TAU | 3750.59 (198.34) | 0.940 (0.008) | | |
FT | 4957.75 (194.13) | 0.970 (0.008) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.030*** (0.011) | 40,453.30 | (0.073–0.284) |
Quantification 2: Relative health spillover per treatment arm (Model 2, Table
6
)
| |
TAU | 3750.59 (198.34) | 1.001 (0.008) | | |
FT | 4957.75 (194.13) | 0.934 (0.008) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | − 0.067***(0.011) | Dominated | (0.000–0.000) |
Quantification 3: Absolute health spillover (Model 2, Table
7
)
| |
TAU | 3750.59 (198.34) | 0.943 (0.008) | | |
FT | 4957.75 (194.13) | 0.972 (0.008) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.030** (0.012) | 40,838.11 | (0.083–0.267) |
Quantification 4: Absolute health spillover per treatment arm (Model 2, Table
8
)
| |
TAU | 3750.59 (198.34) | 1.056 (0.019) | | |
FT | 4957.75 (194.13) | 0.905 (0.010) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | − 0.150***(0.021) | Dominated | (0.001–0.004) |
Quantification 5: Additive health spillover
b
| |
TAU | 3750.59 (198.34) | 1.492 (0.015) | | |
FT | 4957.75 (194.13) | 1.536 (0.014) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.044** (0.020) | 27,166.45 | (0.297–0.568) |
Equivalence scale health spillover with
\(a = 0\)
| |
TAU | 3750.59 (198.34) | 1.492 (0.015) | | |
FT | 4957.75 (194.13) | 1.536 (0.014) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.044** (0.020) | 27,166.45 | (0.279–0.539) |
Equivalence scale health spillover with
\(a = 0.3\)
| |
TAU | 3750.59 (198.34) | 1.351 (0.013) | | |
FT | 4957.75 (194.13) | 1.393 (0.013) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1,207.16*** (277.53) | 0.042** (0.018) | 28,951.77 | (0.245–0.531) |
Equivalence scale health spillover with
\(a = 0.5\)
| |
TAU | 3750.59 (198.34) | 1.273 (0.012) | | |
FT | 4957.75 (194.13) | 1.313 (0.012) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.040** (0.020) | 30,058.05 | (0.202–0.487) |
Equivalence scale health spillover with
\(a = 0.8\)
| |
TAU | 3750.59 (198.34) | 1.174 (0.011) | | |
FT | 4957.75 (194.13) | 1.212 (0.011) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.038** (0.015) | 31,581.72 | (0.169–0.475) |
Equivalence scale spillover with
\(a = 1\)
| |
TAU | 3750.59 (198.34) | 1.118 (0.010) | | |
FT | 4957.75 (194.13) | 1.155 (0.010) | | |
|
Incremental costs
|
Incremental QALY
| | |
FT vs. TAU | 1207.16*** (277.53) | 0.037** (0.015) | 32,504.48 | (0.164–0.426) |
Results from the base-case analysis indicate that adolescents in FT incurred £1207 higher costs on average and gained 0.030 extra QALYs than the adolescents in TAU, which is equivalent to an extra 10.95 days of perfect health annually. The ICER from this analysis (£40,453 per QALY) is above the recommended threshold range specified for NICE decision making in England and Wales (£20,000–£30,000 per QALY gain), indicating that FT is unlikely to be cost-effective. When considering the relative parental health spillover independently of the treatment arm using quantification 1, the ICER is almost identical to the one obtained from the base-case analysis. However, when accounting for the direct involvement of the parents in the FT arm (quantification 2), parents and adolescents continue to incur higher costs on average but with 24.5 fewer days of perfect health (loss of 0.067 QALYs annually) than those in TAU and therefore indicating that FT is dominated by TAU.
The ICER remains above the nationally recommended threshold when we control for the absolute parental health spillover using the number of repeated self-harm events at 12 months (£40,838), implying that FT is not cost effective. If we further control for any heterogeneity in the absolute parental health spillover, FT is dominated by TAU with adolescents and parents in the FT arm incurring 54.8 fewer days of perfect health (loss of 0.150 QALYs annually) than those in the TAU arm. Any of the regression-based quantifications indicate that FT is unlikely to be cost effective. However, the ICER reduces to £27,167 per QALY when we simply sum the adolescent’s and parent’s QALYs (quantification 5), demonstrating a potential for FT to bring 16.1 extra days at full health annually for both the adolescent and the parent and a value within the NICE threshold range.
As expected, quantification 5 is equivalent to the quantification with an ES using an elasticity of a = 0. The value of the elasticity a directly impacts on the average QALY gains, and the higher the elasticity, the lower the cumulated QALY gain and thus the higher the ICER. For smaller values of the elasticity a (less than 0.5), the quantifications using an ES show an ICER within the NICE cost-effectiveness range. The probability of FT to be cost effective is higher when using an ES to quantify spillover than with regression-based spillover quantifications; at £20,000 it is between 16 and 28% with an ES vs. 0–7% with regressions. At £30,000, it respectively reaches 43–54% vs. 0–28%.
It is important to note that with any quantification method, both cost differences between FT and TAU and QALY differences are significant. The same analyses were performed on the complete case sample to test the sensitivity of the results to missing data imputations (see Table
11 of the “
Appendix”). The ICER estimations for each spillover quantification are all larger (between £34,071 and £45,842) with broader standard deviations for both costs and QALYs. It is remarkable that the differences between quantifications present the same pattern as the main analysis.
5 Discussion
We showed that a parent’s HRQoL is associated with the health of a self-harming adolescent. We investigated how health spillover for the parent could be included in CEA using alternative quantifications based on estimated coefficients and QALY valuations. Sensitivity analyses revealed that the valuation technique had a considerable impact on the magnitude of the QALY and could change the inference about the most cost-effective alternative in a trial. We made two propositions in this article. Proposition 1 suggests that health gains are only aggregated at the household level when the QALY gain for the patient is positive or equal to zero. Proposition 2 suggests the use of an ES to convert a distribution of observed health spillover across other household members into an extra health gain to be added to the patient’s QALY gain. We illustrated the use of an ES with a set of alternative elasticity values. There are several advantages with the use of an ES. First, an ES has been widely used in the literature to measure household social welfare [
44‐
46]. Second, health spillover measured either as a QALY gain from a utility score or a utility parameter generated from a regression model could be summed and transformed into an extra health gain using the ES. Third, the ES adapts to data availability and thus every family relative with observed health outcomes can be included. Finally, one could transform easily the ES to account for family members’ proximity to the patient including an individual weight in the same way it is achieved with income equivalence scales.
5 This methodological proposition will require further scrutiny in future research.
5.1 Limitations
Our study presents limitations. The trial study used two different HRQoL instruments to measure the adolescent and the parent’s quality of life. For the purpose of the spillover quantification, we assumed that utilities and QALYs generated from two different generic measures were of the same nature and meaning and could be combined. However, these two measures are quite different in descriptive content and in valuation technique. While the EQ-5D covers dimensions of physical, mental and general health and is valued with Time Trade-Off, HUI2 additionally considers impairments in vision, hearing, and dexterity and is valued using standard gamble and visual analogue scaling. Research has shown a moderate level of agreement between HRQoL measures in various condition-specific groups [
50‐
53]. The assumption according to which the two preference-based measures can be combined in our spillover quantifications could potentially be biased. For example, if EQ-5D-3L tends to provide lower mean utility estimates than HUI2, this would imply for our study that quantification 5 and the ES quantification with
a = 0 lead to an aggregation of health gains where the parent’s QALY gain from the intervention is relatively higher than for the adolescent’s (patient’s) QALY gain, and thus the patient is not the main beneficiary (though respecting proposition 1 ensures that the patient is the priority for the healthcare decision making). In this context, head-to-head comparisons between preference-based HRQoL instruments will be useful to develop potential measurement corrections to ensure comparability between utilities and QALYs when measuring health spillover.
Methodologically, the reverse correlation with a focus on the impact of a parent’s health on an adolescent’s health could have been of interest to study. Moreover, several authors [
13,
17,
54] have argued that potential healthcare cost savings are transferred to others when treating one family member using family-based psychotherapy; it would be ideal to include the healthcare resource use of the parent had they been available in the data.
Conceptually, we investigated how social externalities such as the health effects on other individuals could be introduced into the framework of a CEA; to some extent, this questions whether a cost-utility analysis is appropriate or whether a cost-benefit analysis with distributional weights should be considered. We did not enter into this debate and assumed that a cost-utility analysis would remain the preferred method for the health spillover quantification [
55].
Admittedly, our proposition to rely on an ES is a pragmatic choice. The adoption of a unique scale that would be identical for any CEA would have the advantage of facilitating the generation of evidence that is comparable between individuals and between cost-utility analyses.
Acknowledgements
We are grateful to Bryony Dawkins, John O’Dwyer, Laetitia Schmitt, Paula Boston, and participants to the LAGV International Conference in Public Economics in Aix en Provence in June and the EuHEA conference in Maastricht in 2018 for comments and feedback on an earlier version of this article. Two anonymous reviewers and the editors provided us with rich comments that helped improve the article. We thank Josephine Aikpitanyi for her help with literature references.
Compliance with Ethical Standards