Economic evaluation

Introduction

The aims of the economic analyses were to estimate the expected resource use and related costs associated with delivery of HeLP (i.e. the HeLP intervention) in a policy-relevant setting, and to estimate the cost-effectiveness of the HeLP intervention where it is shown to be an effective intervention compared with usual practice alone.

Given the effectiveness profile for the HeLP intervention, presented in Chapter 3, and the absence of statistically significant differences on the primary outcome measure of BMI SDS, we begin here with a summary statement on the cost-effectiveness of HeLP versus usual practice. We find that there is no expectation, using the framework for economic evaluation developed/described below, of improvements in the likely incidence of weight-related health events [e.g. coronary heart disease (CHD)], or related cost savings to the health and social care system, from the HeLP intervention. As described in the following sections, HeLP incurs additional costs for delivery of the intervention with no identified or expected health gains, using the analytical framework developed and presented here, and therefore usual practice ‘dominates’ HeLP in a resource allocation context (i.e. HeLP is not cost-effective compared with usual practice).

Although we highlight the results of the cost-effectiveness analysis at the outset in this chapter, for completeness, and to inform the reader and to inform future research, we provide detail in this chapter on the methods used, and the results of the cost analysis estimating the cost for delivery of HeLP. We also provide a narrative and detail on the development of a decision-analytic modelling framework developed to assess the cost-effectiveness of HeLP, using this to provide illustrative and exploratory cost-effectiveness analyses.

Below we summarise the methods used to estimate resource use and costs for delivery of HeLP, and the methods used to develop a decision-analytic modelling framework. We then present results for these two areas of research. When describing development of a modelling framework, we present summary details of a literature review of model-based economic evaluations that model childhood obesity interventions through adult years, and we describe the development of a modelling framework in this context, and the areas of evidence synthesis required to populate the model.

Methods

Estimating resource use and costs for delivery of the HeLP intervention

In summary, the HeLP intervention has 24 components, with activities delivered as either school level (i.e. delivered once regardless of school size) (n = 5) or class level (n = 19) (Table 17). Key to the intervention are the drama activities, made up of eight components. Further details on the HeLP intervention and usual care comparator are given in Chapter 2.

The research questions here are:

• What is the estimated resource use and cost associated with delivery of the HeLP intervention?
• What is the estimated mean cost of HeLP per participant? (Per school? Per class? Per cohort of schools?)

Informed through prior development research, and through conduct of the earlier exploratory RCT of HeLP,27,28 the delivery of the HeLP intervention involves resource use and cost primarily for the staff inputs required during delivery (contact and non-contact activities), for HeLP co-ordinators, the drama co-ordinator, actors and activity/workshop co-ordinators. Teaching staff are present for some activities and take an active role in others, but based on findings from prior pilot research their time input is not considered additional/incremental input over and above their expected teaching role. Therefore, costs associated with teaching input are not included in the base-case cost analysis; time inputs are described, and these are included in a sensitivity analysis.

Additional resource use and costs are incurred in the training of the delivery staff and for materials/consumables used to support the delivery of the intervention.

Data on resource use for delivery of the HeLP intervention were collected within the trial through completion of contact sheets by HeLP co-ordinators at every contact, by component of HeLP, describing who was involved (staff type) in the contact (component of HeLP) and the time inputs for each person involved, including time associated with planning/preparation and related travel time. Given the importance of the drama components in the HeLP intervention, alongside the data collected by HeLP co-ordinator contact sheets, the drama co-ordinator provided details of the estimated resource use associated with the delivery of drama across the HeLP intervention.

Estimates for resource use associated with the training required for those staff engaged in delivery of the HeLP intervention were provided by the HeLP study co-ordinator based on their experiences in the running of the trial. Estimates of materials/consumables needed to support delivery of the HeLP intervention are based on within-trial requirements and projected estimates for future delivery.

Estimates of resource use by unit (e.g. time/hours) are combined with published national unit costs, or cost estimates, to estimate the mean costs for delivery of HeLP by school and by class, and to estimate the mean participant-level costs for delivery of the HeLP intervention. Costs are presented in 2015 UK pounds sterling. Unit costs for staff inputs are predominantly taken from those published Unit Costs for Health and Social Care staff grades reported by Curtis and Burns.57 Table 18 reports the units costs used in the analyses, the source of the unit cost and/or the assumptions used in allocating unit costs to estimates of resource use. Cost estimates were determined in a ‘bottom-up’ approach, based on detailed micro-level resource use data collected within the trial.

‘Other’ resources/costs involved with the delivery of the drama intervention included:

• costumes (drama) – it was assumed that three sets of drama costumes at £80 per set would be required (based on £20 per character)
• materials (equipment including fizzy drinks, water and laminated cards) – it was assumed that three sets of materials would be required at a cost of £150 per set
• public liability – annual cost of £250 (based on guidance from drama provider).

These other additional costs (totalling £940 across the cohort of 27 classes) were assumed to be for a 12-month period and were distributed across the cohort in base-case analyses.

Development of modelling framework (Exeter Obesity Model) to estimate the cost-effectiveness of the HeLP intervention versus usual practice

As set out in the prespecified economic analysis plan,43 the framework for estimating the cost-effectiveness of the HeLP intervention is based on development of and subsequent use of a decision-analytic model to predict the future costs and benefits associated with an expected between-group difference in the HeLP RCT primary outcome measure of BMI SDS. A two-stage economic model has been developed, described in more detail below, to predict future adult weight status, from weight status profiles at 24-month follow-up in the HeLP RCT (BMI SDS) at age 11–12 years, and thereafter, in stage 2, to predict a profile of future weight-related health events (e.g. T2DM or CHD) as a function of the predicted adult weight status profiles (i.e. for treatment cohort participants vs. controls). The aim of the modelling framework – the Exeter Obesity Model – was to capture the difference in costs and outcomes, over time in adult years, associated with weight status profiles at 24-month follow-up for treatment and control participants in the HeLP RCT at age 11–12 years.

A decision-analytic model was developed based on a review of published models in this area (i.e. modelling impacts of childhood obesity interventions through adult years) and discussions across stakeholder groups (see detail of the PAG in Chapter 2). The model was informed and populated based on literature review for parameter inputs, and against good practice modelling guidance.59,60 The aim of the model-based framework was to estimate the cost-effectiveness of the HeLP intervention, assumed at this stage to be a relatively low-cost public health intervention, versus usual practice. The setting is a UK public health setting, involving a school-based intervention as a means of having an impact on future adult weight status and health status through reduced incidence of adverse health events. The decision-analytic context is the question on the cost-effectiveness of the HeLP intervention versus usual practice, and the modelling framework was not intended to be an accurate prediction of the life experiences of children by weight status through adult years. The development of a model-based framework was set out conceptually in a prespecified economic analysis plan.43 A cost-effectiveness analysis was expected to synthesis data on predicted mean incremental costs and benefits, taking a primary perspective as that of the NHS and Personal Social Services (i.e. third-party payer), consistent with the methods recommended by the National Institute for Health and Care Excellence (NICE).61 The primary economic end point was cost per quality-adjusted life-year (QALY), with a cost-effectiveness analysis also to be presented against cost per life-year gained and cost per weight-related event avoided.

Results

Estimating the resource use and cost of the HeLP intervention

The HeLP intervention was delivered to children in 16 schools, reflecting the delivery of the intervention to 27 classes (Table 19). Within-trial data on resource use for delivery were from 719 HeLP co-ordinator contact sheets, representing data on 94% of expected contacts with schools and accounting for school- and class-level activities.

Summary data on aggregate resource use across all 27 classes, by staff type and with time (hours) by type of time input (preparation, task and travel), are presented in Table 20. Data by staff type and by school-class configuration are presented in Table 21, showing economies of scale as school size increases. Table 22 reports the estimated total costs for delivery of HeLP across all 16 schools (and 27 classes). Overall, it is estimated to cost approximately £144,749 for delivery of HeLP to the 16 schools, with 27 classes, giving a mean estimated cost per child at £214 (n = 676). Drama staff costs make up 41% of the estimated delivery costs, with HeLP co-ordinator costs accounting for 37% of the overall estimated costs for delivery (when combined, these primary cost components are 78% of the costs for delivery of HeLP).

Estimates of the cost for training required for staff involved in delivery of HeLP, and other costs, are based on expected requirements for delivery across a cohort of 16 schools (27 classes), with training for four HeLP co-ordinators (one trainer), two drama facilitators and eight (two teams) actors. The details of the cost estimates of training are provided in Appendix 7.

Estimated delivery costs for HeLP by school-class configuration are presented in Table 23, providing an estimate by school size (i.e. schools with one, two or three classes by year group). The estimated mean costs by class, and by child per class, show a mean cost per class (per child) that reduces as the school size increases, from £5724 (£229) for a school with one class to £15,390 (£205) for a school with three classes. However, it is expected that the provision and delivery of HeLP would be at the level of geographical region, involving a mix of school sizes.

Sensitivity analysis

The following sensitivity analyses were conducted:

• sensitivity analysis 1 – input; actors’ cost per hour increased (from £11.20 to £25.34)
• sensitivity analysis 2 – input; inclusion of teacher time/costs.

Sensitivity analysis 1: actors unit costs uprated to £25.34 per hour (from £11.20 per hour)

The sensitivity analysis was conducted using a unit cost of £25.34 per hour for actor time inputs. This hourly rate is based on guidance from the drama provider for the potential salary level for professional actors, but is aligned here with salary structure for a similarly paid health-care worker using the Agenda for Change band 5 for health staff57 (see Table 20) and unit costs from Curtis and Burns.57

In this scenario, the total costs for the delivery of HeLP increase markedly, from £214.13 per participant to £258.43 per participant, based on the aggregate costing framework (Table 24), and the total (mean) costs increase by school-class scenario from £5724 (£229) and £15,390 (£205) for schools with one class and three classes, respectively, to £6782 (£274) and £18,643 (£248).

Sensitivity analysis 2: inclusion of teacher time/costs, added to base-case estimates

Based on prior consideration and pilot research, it is not expected that teacher time inputs around HeLP activities are additional/incremental to usual activities (i.e. usual teaching practice). However, we have explored this in sensitivity analysis, and we have estimated costs here including teacher time with a unit cost of £21 per hour (£160 per day) for teacher time (assuming any additional time inputs will be covered using a supply teacher). This hourly rate is based on published guidance from the National Association of Schoolmasters Union of Women Teachers58 on supply teacher rates of payment (see Table 18).

In this scenario, the total costs for the delivery of HeLP increase modestly, by < £13 per child, from £214.13 per participant to £227 per participant, based on the aggregate costing framework (Table 25), and the total (mean) costs increase by school-class scenario from £5724 (£229) and £15,390 (£205) for schools with one class and three classes, respectively, to £6090 (£243) and £16,249 (£216).

A framework for the economic evaluation of HeLP

Here we describe the development of the Exeter Obesity Model, a decision-analytic model-based framework for assessment of the cost-effectiveness of the HeLP school-based intervention versus usual practice in children aged 9–10 years. The development of the model was based on a review of model-based economic evaluations of interventions related to childhood obesity, with a focus on models tracking from childhood through to adult years, and a summary of this systematic literature search is presented below. Thereafter we document the conceptual structure and the operational development of the model, describing data sources and the populating of the model to address the primary research question of whether or not the HeLP intervention could be considered cost-effective compared with usual practice.

Review of decision-analytic models for use in cost-effectiveness analyses on obesity

A systematic review of model-based economic evaluations of interventions related to childhood obesity, as a means of preventing obesity in adults, was conducted in order to inform the structure and development of a modelling framework suitable for assessing the cost-effectiveness of the HeLP intervention.

Methods

Search strategy

Systematic searches were conducted in several databases (including MEDLINE, EMBASE, PsycINFO, NHS Economic Evaluation Database and Database of Abstracts of Reviews of Effects). The keywords and medical subject headings (MeSH) for obesity were combined with keywords for cost and economic analysis to capture relevant economic evaluations in all databases. The search strategy is detailed in Appendix 8. In addition, references within the included studies were reviewed to identify potentially overlooked studies. The searches were date limited 1990–2012 (original search). Language restrictions were not applied. Update searches were conducted in 2015.

Eligibility criteria

Titles and abstracts for all articles identified by the literature search were screened. Studies were eligible for inclusion if they were economic evaluations (cost–benefit, cost–utility, cost-effectiveness and cost–consequence analyses) of interventions related to child/adolescent overweight/obesity as a means of preventing obesity in adults. Next, full texts of all articles deemed eligible from the previous stage were reviewed to further confirm their eligibility. At this stage, the exclusion criteria were burden-of-illness studies, cost analyses, study types other than economic evaluations, review articles, editorials or reports, and articles not written in English. Studies published as abstracts or conference presentations were included only if sufficient details were presented to allow both an appraisal of the methodology and an assessment of the results to be undertaken.

Data extraction and synthesis

For each study included in the final review, data were extracted into a table. The information obtained included authors, country, study year, comparators, population characteristics, study design, time horizon, perspective, data sources, effectiveness, cost, sensitivity analysis and study conclusions. The identified modelling studies are summarised, and the approaches available to model disease progression over time are discussed in a summary descriptive review.

Results

Studies identified

A total of seven models (reported in 11 publications) were included in the final review.62–72 The study identification process is summarised in Figure 5.

FIGURE 5 The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. (Figure not reproduced here; see the original article.)

The study characteristics for the seven included models are summarised in Table 26.

Summary of the literature identified on the modelling of the cost-effectiveness of interventions to address obesity in children

The results from the literature review indicated that seven studies (reported in 11 publications) used model-based economic evaluations to assess the cost-effectiveness of interventions aimed at reducing obesity in children. Most of the models identified in the review were from North America,62,63,67–72 with two European studies64,65 (one being a multisite study across six countries in Europe65) and one from New Zealand.66 The studies varied in terms of the modelling approach used, the range of health states (including weight-related events) included in the models, the techniques used to predict weight status in adulthood as a function of childhood weight status and the outcomes included in the models. Nine of the studies64–72 were published in 2012 or later, and of note in the current report is that the initial searching of the literature in 2012 was used as the basis for the conceptual modelling framework for HeLP, as set out below, and it was, therefore, based on a limited literature (as described below and in Table 26). However, a number of the more recent studies adopt the methodological framework used in the earlier literature, which has influenced the conceptual framework used for the HeLP-specific research.

Before 2012 the evidence base for the assessment of the cost-effectiveness of interventions aimed at reducing obesity in children was small. Two models were identified.62,63 Five models (reported in nine publications) were subsequently identified that were published between 2012 and 2015,64–67,69 with the majority of these published in the last 2 years.

The earlier models62,63 adopted a two-stage approach. The first stage used a decision tree structure and affixed a probability estimate that an individual (child) may change weight status between childhood and adult years. These predictions for adult weight status were based on longitudinal data from either Whitaker et al.73 or NHANES I (National Health and Nutrition Examination Survey) (epidemiological study data).74 The second stage of the model used a Markov-type approach, allocating the cohort in adult years to states by weight status (overweight, not overweight) and followed people up over time (through annual cycles) to compare a treatment versus control group in order to estimate the number of cases that were overweight in each group, and the medical care costs averted and number of QALYs saved per case of adulthood overweight prevented. Two models published in recent years65,66 also adopted a similar two-stage approach. In one model,65 the first stage takes a similar approach to that in Wang et al.62 and Brown et al.63 (i.e. the long-term extrapolation of the intervention effect on childhood weight status to adult age based on longitudinal data from Venn et al.75). The later models65,66 adopted a different approach in the second stage. Rather than estimating the medical care costs averted per case of adulthood overweight prevented, both studies projected the incidence of chronic diseases associated with obesity and mortality in adulthood. A core set of weight-related events were included in the models: CHD, stroke, T2DM, breast cancer and colorectal cancer (CRC). The remaining three models64,67,69 used epidemiological models, statistical64,69 and prevalence profile67 approaches. These models present as potentially useful modelling frameworks for the assessment of interventions for childhood obesity, but at the present time the detail reported for these approaches was not sufficient to fully understand the specific detail and methods employed, and, importantly, the detail available did not allow us to replicate the methods used.

Model development (cost-effectiveness framework)

Introduction/context

The effectiveness outcomes in the definitive trial of HeLP versus usual practice (e.g. BMI SDS) do not provide a strong basis for the direct estimation and presentation of trial-based cost-effectiveness analysis, with decision-makers requiring estimates of cost per QALY and or cost per life-year saved (i.e. longer-term outcomes) to consider and to compare results of cost-effectiveness analyses across a wide range of health-care interventions. Therefore, a modelling framework was developed to assess the cost-effectiveness of the HeLP intervention versus usual practice, in a UK school/education setting, in preventing childhood overweight and obesity. The model development process was informed and guided by the need to extrapolate from effectiveness data from the HeLP RCT (at 24-month follow-up) over a longer-term and policy-relevant time horizon. That is, the modelling aimed to predict the expected impact of a change in childhood BMI SDS (primary outcome), and any resulting change in the distribution of children by weight status (intervention vs. control) over time.

At the outset the HeLP intervention was expected to be a relatively low-cost (per child) intervention, and the objective was to develop a simple model to estimate the cost-effectiveness of HeLP. At the time of developing the model, the literature was sparse and there was no readily available modelling framework publicly available. We sought to develop a simple decision-analytic model, building on the published literature in this area and using weight-related health events of primary importance (when there was robust evidence of the relationship between incidence of events and weight status, applicable to a general population analysis), to assess the cost-effectiveness of the HeLP intervention.

Model structure (summary)

The model development process is described in further detail below. In summary, the final model is a two-stage model (Figure 6). In stage 1 it predicts adult weight status (healthy weight, overweight and obese) from childhood weight status (BMI SD centile), and in stage 2 it predicts against a set of weight-related health events in adult years. A simplifying assumption is the use of only three weight status categories in adults: (1) healthy weight, (2) overweight and (3) obese. The use of three states, and the terminology used here (i.e. healthy weight), is consistent with the identified published model-based economic evaluations and also the available longitudinal data on the tracking of obesity from childhood to adult years. We acknowledge, and note here, that a small percentage of people will be underweight; however, for simplicity they are part of the group referred to as healthy weight and are treated in the same way as others in the healthy weight status category.

FIGURE 6 Model overview. HW, healthy weight; OB, obese; OW, overweight; WS, weight status. (Figure not reproduced here; see the original article.)

In stage 1 of the model, a decision tree structure is used to predict adult weight status as a function of childhood weight status. The model starts with a cohort of children distributed by weight status per the UK90 growth reference standards51 using clinical assessment classifications (≤ 91st centile; > 91st to ≤ 95th centile; > 95th to ≤ 98th centile; and > 98th centile), and predicts the expected distribution of the cohort by weight status in adulthood. Data from the UK longitudinal study reported by Power et al.,76 which followed up participants from age 7 to age 33 years [see detail in Model development (stage 1): predicting adult weight status from child weight status], are used to predict adult weight status. Based on data reported by Power et al.,76 the probability of children from a particular weight category remaining or changing weight status at the age of 33 years was estimated. These probabilities were applied to the starting cohort of children distributed by weight status in order to predict the likely distribution of people in adulthood, by weight status category. The impact of interventions, in this instance HeLP, can be considered/integrated in this first stage of the model via the specification of the starting distribution of children by weight status (e.g. an effective intervention will have fewer children in BMI SD centile categories associated with overweight and/or obese weight status). The model structure uses (starts with) inputs for a base cohort distribution of children by weight status categories and applies effectiveness data (e.g. hazard rates/relative risks) to proportions of children in categories of weight status associated with overweight and obesity in order to simulate the reported treatment effectiveness in a comparison of the same cohort of people both with and without the intervention (effectiveness).

In stage 2, a Markov model is used to model weight-related health events over time by adult weight status (Figure 7). Weight-related health events included in the model are T2DM, CHD, stroke and CRC [see detail in Model development (stage 2): predicting the impact of overweight and obesity on incidence of weight-related health events]. The model cycle length is 1 year and has a 30-year time horizon. The time horizon is chosen to reflect a longer-term adult time horizon, over which adult weight-related health events may be predicted (i.e. over age 33–62 years). Adults enter the model in one of three event-free health states depending on their weight status (healthy weight, overweight or obese). In the base-case model, adult overweight and obese weight status categories are BMI of 25.0–30.0 kg/m² and BMI of > 30.0 kg/m², respectively, with healthy weight being BMI of < 25 kg/m². Each year (1-year cycle) adults are at risk of remaining in the event-free health state, developing a weight-related event, or death. The model assumes that adults who experience an event will remain in that health state until death and are not exposed to any other events; this is a simplifying assumption, consistent with previously published model-based evaluations. People in the event-free health state are exposed to general population all-cause mortality risks. People in states defined by weight-related events are exposed to condition-specific mortality risks [see Model development (stage 2): predicting the impact of overweight and obesity on incidence of weight-related health events]. The costs and outcomes associated with weight-related events are estimated based on a review of the published literature; costs are presented as 2015 UK pounds sterling (£). Data on mortality are from national data sources (all-cause mortality) and from published literature on event-related mortality. Future costs and benefits are discounted at 3.5% per annum in the base-case analysis, with the option to vary this in sensitivity analyses, and the perspective of the analysis, consistent with the context of the decision problem, is that of the UK NHS and Personal Social Services (third-party payer perspective). The decision-analytic context is guided by the UK NHS decision-making context, using methods guidance for conduct of economic evaluations issued by NICE.77

FIGURE 7 Markov model structure. Costs by event/state: costCHD, costT2DM, costCRC and costStroke. Health state values (QALY weights) by event/state: hsvGenPop, hsvCHD, hsvT2DM, hsvCRC and hsvStroke. HW, healthy weight; OW, overweight. (Figure not reproduced here; see the original article.)

Model development (stage 1): predicting adult weight status from child weight status

Longitudinal studies were used to inform the prediction of the proportion of children that will be healthy weight, overweight or obese in adulthood based on their childhood weight status.

A literature search was undertaken to identify systematic reviews that reviewed the literature concerning the tracking of childhood weight status into adulthood. A total of three systematic reviews were identified.3,78,79 Owing to methodological differences between the reviews, the bibliographies of included studies for all three reviews were scrutinised for cohort studies reporting cohort data tracking childhood weight status into adulthood. A total of eight studies were identified.75,76,80–85 Of these, four75,76,80,85 presented data projecting weight status from childhood into adulthood and included classifications by weight status (healthy weight, overweight), and were therefore selected for consideration to populate the model. The remaining four studies were excluded, either because they provided no usable data81,82,84 or because the data were for a child age range that was inappropriate for the current research.83 The study characteristics for the included studies are presented in Table 27, and a summary description is provided below.

Power et al. 1997 (1958 UK birth cohort)

Power et al.76 followed up a cohort of children who were born in Britain in March 1958 (the 1958 UK birth cohort). In this longitudinal study, height and weight measurements were taken at various points between the ages of 7 and 33 years. The weight status of children was defined by the UK90 growth reference standards.51 At the end of the data collection, measurements for analysis were available for 11,212 adults. Adult BMI was categorised as healthy weight (20.0–24.9 kg/m²), overweight (25.0–30.0 kg/m²) and obese (> 30.0 kg/m²). From the study, the authors reported the weight status of an adult aged 33 years as a function of child weight status at the age of 11 years.

Wright et al. 2001

Wright et al.80 reported data on a UK cohort of children who were born in Newcastle upon Tyne between May and June 1947. Height and weight measurements were taken at 9, 13 and 50 years. Weight status of children was defined by UK90 growth reference standards. The analyses were based on the proportion of adults who were overweight or obese at the age of 50 years as a result of weight status at 9 and 13 years, respectively.

Laitinen et al. 2001

Laitinen et al.85 followed a cohort of children from birth year 1966 in northern Finland. Height and weight measurements were taken at birth, and at 1, 14, and 31 years of age. Data at 14 years were obtained from a questionnaire that was sent to participants, and follow-up data at 31 years (1997–8) were obtained from a questionnaire, a clinical examination or both. Adolescence (age 14 years) overweight was defined as a BMI ≥ 85th to < 95th percentile and obesity as a BMI ≥ 95th percentile. Adult BMI at 31 years was classified as follows: underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²) and obese (≥ 30.0 kg/m²). The analysis was restricted to individuals for whom BMI data were available for all measurement points (males, n = 2876; females, n = 3404).

Venn et al. 2007

Venn et al.75 examined overweight and obesity in Australian children followed through to adulthood. A cohort study of 8498 children aged 7–15 years participated in the 1985 Australian Schools Health and Fitness Survey. Of these, 4571 completed a follow-up questionnaire at age 24–34 years in 2001–5. Height and weight were measured in 1985, and self-reported at follow-up. The accuracy of self-reported data was checked for 1185 participants. Overweight and obesity in childhood were defined according to international standard definitions for BMI, and, in adulthood, as a BMI of 25–29.9 kg/m² and ≥ 30 kg/m², respectively, after correcting for self-report error.

Summary

The longitudinal studies identified here presented data on tracking of weight status from childhood into adulthood. The results presented for each of the identified studies indicate that childhood overweight/obesity persists into adulthood, endorsing the broader evidence base which supports the use of childhood weight status as a strong predictor of adult weight status in a policy context (e.g. Whitaker et al.73). There were methodological differences between the included studies, for example the number of participants included in the studies, the ages at which authors predicted weight status to and from, prevalence of overweight and obese across the studies, the thresholds used to define weight status as a child, the centile categories used to describe the distribution of weight status, and the growth reference population. All studies have limitations; however, these are considered to be high-quality longitudinal studies.

Data from each of these four longitudinal studies have been used to predict adult weight status by childhood weight status, represented in the form of transition probabilities across the defined child to adult weight status categories (Table 28). For stage 1 of the Exeter Obesity Model transition probabilities derived from the longitudinal data reported by Power et al.,76 for the 1958 British birth cohort, are used in the base-case analysis (model structure), predicting the proportions of children (aged 11–12 years) expected to be healthy weight, overweight or obese at 33 years. These probabilities were applied to the distribution of children at the start of the model, expected to be informed by the 24-month follow-up data in the HeLP study, in order to predict the expected distribution of the cohort by adult weight status at the age of 33 years.

Data from Wright et al.80 (British cohort data to age 50 years) and Laitinen et al.85 (Finnish cohort data to age 31 years) were set out for potential use in sensitivity analysis to assess structural uncertainty (see Table 28). Although not specifically appropriate for sensitivity analyses, the estimate transit probabilities using data reported by Venn et al.75 (Australian cohort data to age 29 years) are presented to provide a comparison between a relatively recent set of data and the data from other cohort studies76,80 that were collected in earlier years.

Model development (stage 2): predicting the impact of overweight and obesity on incidence of weight-related health events

Weight-related events

Stage 2 of the model starts with a cohort of people with a given distribution by weight status (healthy weight, overweight or obese), as described in stage 1. All persons start stage 2 as disease free (i.e. free of the major weight-related health events modelled in stage 2). Stage 2 uses CHD, stroke, T2DM and CRC as the weight-related health events of interest. The selection and inclusion of these four events is considered a conservative assumption (i.e. it is expected that the incidence of a greater number of health events will be influenced by weight status). The selection and inclusion of these four events is based on the review of the previously published model-based economic evaluations, discussed above, which typically included a small core set of weight-related health events similar to those selected here, and consideration of the data on incidence of events by weight status (see below), plus discussion of the conceptual modelling framework across the HeLP stakeholder group(s). Of importance when identifying health events for inclusion in the model was the simplifying assumption in the model that once a person experienced a condition they were then not exposed further to additional events. This led to a focus on the main primary health conditions, rather than events such as hypertension, which, although relatively frequent (and serious), was considered to be analogous to an intermediate outcome increasing the future risk of events such as CHD, stroke and T2DM.

Incidence (probability) of the weight-related health events was informed using a targeted keyword literature search (in MEDLINE) to identify studies that estimated the incidence of comorbidities related to obesity and overweight. The search strategy was structured using the following terms: (obesity OR adipose tissue OR body mass index OR body composition) AND (comorbidit$OR multimorbidit$OR chronic disease). No date limits or language restrictions were applied to the search. Eligible studies were systematic reviews of cohort studies, reporting risk estimates based on the incidence of weight-related events including T2DM, cardiovascular disease (including hypertension, coronary artery disease, congestive heart failure, pulmonary embolism, stroke and dislipidaemia) and CRC. The searches retrieved 126 titles and abstracts. Ten reviews were considered at full-text stage. Of these, only one was considered eligible for inclusion.88

Guh et al.88 conducted a literature search to provide an estimate of the incidence of each comorbidity related to obesity and overweight using a meta-analysis. Twenty comorbidities were initially included in this analysis: cancer (kidney, colorectal, prostate, ovarian, uterine/endometrial, esophageal, pancreatic and postmenopausal breast), T2DM, cardiovascular disease (hypertension, coronary artery disease, congestive heart failure, pulmonary embolism, stroke and dislipidaemia), gallbladder disease, chronic back pain, osteoarthritis, asthma and sleep apnoea.

For each of the weight-related events evaluated in Guh et al.,88 we sourced the studies included in the review and extracted the data on number of incident cases and person-years from the primary papers contributing to the reported pooled relative risk estimate. We used these data to estimate the incidence of the events in the reference group (i.e. healthy weight), assuming a male-to-female gender ratio of 1 : 1. Using these raw data on incidence in the reference group, we then applied the risk ratios reported by Guh et al.88 to estimate the incidence of the weight-related events in people with weight status at overweight and obese (Table 29).

Mortality data

People are exposed to an annual mortality risk in each cycle of the model in stage 2. People in the disease-free state (no weight-related event) are subject to all-cause mortality risks based on age-specific data from the UK Office for National Statistics,89 using data reported for 2012–14, assuming a cohort of people with a gender ratio of 1 : 1.

Data on mortality risks by health condition, whereby people are in health states defined by the weight-related health events, were informed by a targeted literature search of the published literature. We searched the literature using terms for the population of interest (i.e. T2DM, CHD, stroke and CRC) with terms for mortality rate using the MEDLINE database in December 2015. Table 30 presents the data that we have used to populate the model on mortality risks for the health events. For stroke and CRC, we have used a mortality input for year 1 of the event, together with a separate mortality parameter for subsequent years in that condition/health state. This structure on mortality data is consistent with the findings from the literature search.

Health state values (QALY weights)

Data used in the model on health state values to derive QALYs are reported in Table 31. The health state value used for the ‘event free’ state (0.86) is based on UK EuroQol-5 Dimensions (EQ-5D) data, consistent with methods guidance from NICE,61 and is the general population mean health state value reported by Kind et al.94 Data inputs for other model states were based on a literature review to identify published systematic reviews reporting health state values (utilities) associated with the health states (events, conditions) used in the model. The search method involved targeted keyword searching in MEDLINE (December 2015) for CHD, T2DM, stroke and CRC. Terms for health state values (‘health state value$’ and ‘EQ-5D’ and ‘EQ5D’) were combined with terms for the specified conditions of interest. A filter was applied to the searches to identify systematic reviews (review or ‘systematic review’ or ‘literature review’ or meta$). In addition, we used the data reported in the model-based economic evaluations for obesity identified earlier (see Table 26). In considering the appropriateness of the identified health state values, the following attributes were taken into account: the population describing the health state (e.g. age, sex, disease severity), the utility value elicitation technique (e.g. time trade-off, standard gamble, visual analogue score), the sample size and the country. Further detail on the literature identified is presented below. The data used in the model for health state values are broadly in line with published estimates used in other public health modelling contexts (e.g. see studies reported in Table 27).

Summary of literature identified, and selection of health state values as model inputs

Type 2 diabetes mellitus

One systematic review was identified98 that identified a set of health state/utility values for one or more complications in people with diabetes. The methodology of identified studies was considered against the requirements of the UK NICE reference case (2013) (i.e. criteria included use of EQ-5D questionnaire value set or other established measure of preference, health states should be determined by patients, and valuation performed using a societal valuation algorithm [preferences of general public]). The authors created a preferred input set of utility values. Of 16,574 records identified, they report on 61 studies, of which we considered 19 to be consistent with the NICE criteria.

The studies included in the Beaudet et al.98 review were scrutinised and baseline T2DM utility values were extracted. For the base case for the current Exeter Obesity Model, the utility value of 0.78 reported in the study by Clarke et al.95 was considered to be the most methodologically robust, given its large sample size, T2DM-specific population and use of the EQ-5D in a UK population. This estimate is also broadly consistent with other published estimates for T2DM without complications (e.g. NICE99).

Coronary heart disease

One systematic review was identified100 that reviewed utility data based on EQ-5D in cardiovascular disease. Sixty articles were identified that reported EQ-5D scores (health state values). There was wide variation in terms of cardiovascular disease subgroups, disease stage, age distribution and other methodological aspects. The majority of studies reported EQ-5D index scores using the UK-based algorithm. The majority of identified studies reported data for ischaemic heart disease and cerebrovascular disease. Stratification by disease severity was possible for ischaemic heart disease and heart failure participants.

Supplementary searches in PubMed identified one additional study96 reporting health state values for cardiovascular conditions, also distinguishing between acute impact including the cardiovascular event and the chronic post-event impact. Three cardiovascular conditions were described: stroke, acute coronary syndrome and heart failure. One-year acute health states represented the event and its immediate impact, and post-event health states represented chronic impact. Values were from a sample (n = 200) of the UK general population. The mean time trade-off health state value for acute coronary syndrome was 0.67 (SD 0.34) and for chronic post event health states was 0.82 (0.17).

For the base case in the Exeter Obesity Model, the time trade-off value of 0.67 (SD 0.34) reported by Matza et al.96 was considered the most reflective of the health state in the model. This is in line with estimates used in other models of this type.101–103

Stroke

One systematic review was identified104 that reviewed evidence on health state values for stroke. Mean utilities of major stroke (Rankin scale 4–5) and minor stroke (Rankin scale 2–3) were presented, stratified by study design and elicitation method. A total of 23 studies were included in the review. Study populations in the included studies were healthy people, people at risk of stroke or stroke survivors. The studies included in the review reporting data from general public (non-patient, healthy participants) were considered most relevant to the health state used in the Exeter Obesity Model.105–108 However, two of these studies105,108 used only the visual analogue scale to elicit utilities, and the remaining studies used mixed samples and had relatively small sample sizes. The supplementary searches carried out to identify utility values for CHD identified one paper96 that also reported health state values for stroke. In that study, a number of health state values are reported, including a mean (SD) value of 0.52 (0.38) for chronic stroke. We also reviewed previous health technology appraisals and clinical guidelines reported by NICE for stroke, identifying data presented for stroke in the appraisal of apixaban for the prevention of stroke and systemic embolism in people with non-valvular atrial fibrillation.103,109

Considering the varied evidence available, we use the data reported by Matza et al.96 in the base case for the model presented here. This estimate is consistent with health state values used for stroke in prior public health modelling contexts, such as exercise referral schemes.102,110

Colorectal cancer

One systematic review was identified97 that reviewed evidence on utility weights for CRC health states. Twenty-six articles met the inclusion criteria for the review. The authors report a mean EQ-5D health state value of 0.76 (based on 33 reported utilities), and this is used in the current model framework. This estimate is broadly consistent with data used to inform the NICE technology appraisal of drugs CRC,111 aligned to first-line and second-line treatment health states.111

Cost data

As stated previously, the perspective applied for the economic analyses is that of the UK NHS and Personal Social Services, and cost estimates included for model inputs are consistent with that perspective (i.e. third-party payer).

Intervention costs (HeLP)

The additional cost included for the delivery of the HeLP intervention, in the base-case analysis, is the estimated mean per participant cost of £214.14 (see Table 22).

Costs for disease states (weight-related events)

An annual estimate of costs associated with model states by condition (weight-related event) is applied in the model when people are in these states. The estimates of annual costs used are presented in Table 32. The estimates used are informed by a literature search to identify published systematic reviews of the literature in each of the disease areas.

Targeted literature searches in MEDLINE and web searches combining terms for the population of interest (i.e. T2DM, CHD, stroke and CRC) with terms for costs (e.g. cost, annual cost, per patient cost, UK cost) were conducted in December 2015. Cost estimates used in the model were inflated/uprated to 2014/15 prices, when appropriate, using inflation indices from Curtis and Burns.57

Type 2 diabetes mellitus

Kanavos et al.112 report costs for diabetes (direct and indirect) across France, Germany, Italy, Spain and the UK. Estimates include inpatient, outpatient, primary and community costs, and medication-related costs. Annual UK costs for T2DM are estimated at £3717 mean per-person costs. These costs are based on high-quality data from large retrospective cohort studies for primary care and outpatient activity116 and inpatient costs.117

Coronary heart disease

A report from NHS Scotland113 presents estimates of the resource use and costs associated with cardiovascular disease-related diagnoses. The cost per patient for CHD was estimated as £2852. This estimate was based on a comprehensive costing methodology, assessing the average cost per patient for the following principal diagnoses: unstable angina, £1760; acute myocardial infarction, £3990; subsequent myocardial infarction, £4240; chronic ischaemic heart disease unspecified, £2765; and stable angina, £1500.

Stroke

Saka et al.114 reported detailed data on the resource use and cost associated with stroke, estimating societal and direct costs of care. Direct care costs include diagnosis, inpatient care and outpatient care and are based on data from a population-based register. The data are predominantly presented as aggregate burden/cost of illness results; however, when considering an estimate of cost per case/per stroke patient (applying assumptions/methods from Bosanquet and Franks118), the authors present estimates of £2800 and £17,500 for costs associated with a rapid recovery case and a case with disability but discharged into the community. As a conservative assumption here we use the cost estimate of £2800 for the treatment of stroke events arising in the model presented here. This cost is applied as a one-off treatment cost (model payoff), rather than an annual treatment cost.

Colorectal cancer

A report from Cancer Research UK115 presents mean estimated costs for CRC of £8441, covering diagnosis and treatment costs, across disease stages 1–4. Cost estimates are based on a mapping of national treatment guidelines, national data sets and clinical audit data. This cost estimate is similar to the data presented by Trueman et al.119 in a report commissioned by the UK Department of Health (mean cost approximately £8800).

Modelling summary

The model structure has been described above, and the parameter inputs to populate the model have been presented together with a description of methods and rationale for data inputs and assumptions used. Appendix 9 presents a summary of the model parameters and inputs used.

The Exeter Obesity Model has been developed as part of the research funded alongside the NIHR-funded clinical trial (RCT) on HeLP versus usual practice. The model is set out here as a parsimonious modelling framework appropriate for estimating cost-effectiveness for HeLP versus usual practice, given the expectation of a relatively low-cost intervention being used in a public health context, with benefits accruing over the longer term (i.e. in adult years, 20–50 years after the intervention is delivered). The specific focus of the model development has been the use of key weight-related events as outcomes of interest in the model, the use of published data to populate the model, and the presentation of cost-effectiveness analyses using cost per QALY, and cost per life-year saved, in order to inform health policy and decision-making in a UK context. The conceptual model and the simple model design are based on the need to answer the a priori research question on whether or not HeLP is cost-effective versus usual practice, that is, whether it represents value for money to a UK third-party payer (such as the NHS) when assessed against commonly used estimates of willingness to pay for health benefits (i.e. cost-per-QALY thresholds).77

However, as referred to above, the HeLP intervention is not effective compared with usual practice, and therefore the modelling framework is presented here as a potentially useful generic contribution to the methods available to model the effectiveness and cost-effectiveness of interventions for childhood obesity, where it is important to extrapolate from relatively short-term effectiveness outcomes to longer-term impacts of interventions, into and over adult years.

Cost-effectiveness analyses (HeLP vs. usual practice)

The above sections have presented results of the estimates of the costs associated with delivery of the HeLP intervention, and have described the modelling framework set out to estimate the cost-effectiveness of the HeLP intervention versus usual practice.

Effectiveness of HeLP versus usual practice

Data presented in Chapter 3 have reported that there is no evidence that HeLP is more effective than usual practice. Table 9 reports no statistically significant differences between HeLP participants and controls at 24-month follow up on the primary outcome measure of BMI SDS. The Exeter Obesity Model estimates cost-effectiveness with a starting premise that an intervention has shown a difference in effectiveness that can be translated to a reduction in the relative risk associated with being in BMI SDS weight status categories associated with overweight and/or obesity, using the BMI centile categories applied in the base data drawn from Power et al.120 (see Table 28). This model structure allows a comparison of a cohort of children over time, with and without the intervention. The results from the HeLP RCT do not show reductions in the relative risk for being in states aligned to overweight and/or obesity, finding no statistically significant difference between the intervention group and the control group (see Table 11).

Cost-effectiveness of HeLP versus usual practice

Given the effectiveness profile for the HeLP intervention, there is no expectation, using the Exeter Obesity Model framework, that there will be any improvements in the likely incidence of the weight-related health events (CHD, stroke, T2DM and CRC), or cost savings to the health and social care system associated with weight-related events. This profile, together with a certain expectation that the introduction of the HeLP intervention is associated with additional resource use and costs, in the short term, leads to the clear conclusion that HeLP is not considered to be a cost-effective alternative to usual practice.

HeLP incurs additional costs with no identified or expected health gains, using the analytical framework developed and presented here, and therefore usual practice ‘dominates’ HeLP in a resource allocation context (Table 33). HeLP does not offer value for money to the UK health-care system, compared with usual practice.

As this is a clear and unambiguous outcome, consistent with agreement with the TSC, no further detailed cost-effectiveness analysis is presented here. We have presented detailed cost analysis and results on the estimated expected mean incremental cost per child for delivery of the HeLP intervention, together with estimates of expected additional costs per school (per class).

Exploratory cost-effectiveness analyses using the Exeter Obesity Model

Predicted results for control participants

In the HeLP RCT the distribution of children starting the trial and at 24-month follow-up by weight status categories, as centile categories used by Power et al.,120 are reported in Table 34, together with their predicted weight status profile as adults aged 33 years (using standard adult BMI categories).

When modelling the experiences of a cohort (n = 1000) of children defined using the weight status data for the control participants in the HeLP study, we predict that at age 33 years the cohort will result in an adult cohort with 49% overweight or obese adults and 51% healthy weight adults. We predict that 33% will be overweight and 15.5% will be obese, and this is consistent with the data reported by age in the Health Survey for England (2009 data).121 Over the 30-year follow-up period, adult-years, there are 252 weight-related events (CHD, stroke, T2DM and CRC). These 252 events comprise 76 cases of CHD, 105 cases T2DM, 28 cases of CRC and 42 cases of stroke; 675 participants remain event free at age 62–63 years. The mean per participant cost associated with those events is estimated at £1140 (£6821 undiscounted; £3145 discounted at 1.5% per annum). Over the 30-year time horizon (30 adult-years) where events, costs and event related outcomes are estimated, in addition to all-cause mortality, the mean number of life-years per participant is 26.75, prior to discounting (4.47 when discounted at 3.5% per annum; 12.34 at 1.5% per annum), and the mean number of QALYs is 22.78 prior to discounting (3.8 when discounted at 3.5% per annum; 10.5 at 1.5% per annum). Of note, overall we are modelling life expectancy over a 50-year time horizon, with specific payoffs (future costs and outcomes for health events) captured in the latter 30 years.

The modelling framework developed to assess the cost-effectiveness of interventions such as HeLP is dependent on identifying a beneficial (effective) intervention, with effectiveness reflected through an improved weight status profile in childhood. That mechanism of effect provides an opportunity to consider the future potential impact of a change in childhood weight status, into adult years, and the estimation of that effect over adult years through a reduction in future incidence of a core set of weight-related health events. When applying the data from the HeLP exploratory trial (crude unadjusted relative risks, by BMI SDS category), the modelling framework indicates that HeLP has the potential to be a cost-effective intervention (see scenario analyses E in Table 35). This is primarily due to the reduced number of participants in the HeLP exploratory trial intervention arm, compared with the number in the control arm, who ended up in the childhood weight status category associated with obesity. However, the sample size in the exploratory trial was very small, and the distributions by weights status in the intervention and control arms were not balanced at baseline, given that the study design was for an exploratory trial, and feasibility research.

Here, to illustrate the use of the modelling framework, and to illustrate the potential scenarios in which an intervention such as HeLP may be considered cost-effective, we present a series of scenario analyses in Table 35 describing alternative weight status profiles for a potential hypothetical treatment group, compared with the data reported for the HeLP control participants (standardised to a cohort of n = 1000).

In these exploratory analyses we have used the inputs on expected additional costs for the HeLP intervention, and the data on the weight status of the control participants in HeLP at 24-month follow-up, to explore the cost-effectiveness of the HeLP intervention at hypothetical scenarios on intervention effectiveness [i.e. relative risk(s) of being overweight and/or obese]. We use hypothetical estimates of the relative risk, between controls and intervention, and we present data on predicted distribution by adult weight status, and summary data on weight related events, cost per life-year saved and cost per QALY gained.

The exploratory results demonstrate that when the relative risks are ≥ 0.75 and above (≤ 25% reduction in proportion in BMI SDS centile category) the cost per QALY is above the commonly referred to UK NHS willingness to pay for a QALY, of between £20,000 and £30,000 per QALY.61 However, of note is that the number of children in the BMI SD centiles associated with overweight and obesity is relatively small (i.e. circa 6% in each category), which therefore requires a relatively high risk reduction in order to make a meaningful impact on the actual numbers of children, and thereafter adults, in the overweight and/or obesity related weight status categories (e.g. a relative risk of 0.75 for childhood obesity represents a difference of approximately 20 cases per 1000 children). We therefore suggest that relatively modest effectiveness results may lead to a scenario in which an intervention is cost-effective, regardless of the apparent magnitude of the estimated relative risk (for small proportions of the cohort). We advise that the exploratory results presented here should not be interpreted in a way that indicates that interventions may be cost-effective only when dramatic effects are seen. The exploratory results also illustrate that when considering the impacts of such a public health intervention over time, the mean incremental costs and outcomes are very small, and the cost per life-year gained, and cost per QALY gained, estimates are very sensitive to small changes in the mean incremental inputs to the cost-effectiveness ratios. Furthermore, the exploratory results highlight that the results are sensitive to the parameter used to discount future costs and outcomes, with cost-effectiveness estimates markedly different (more attractive) when using a discount rate of 1.5% per annum (vs. the base case 3.5% per annum).

Discussion

We have estimated the additional cost for delivery of the HeLP intervention to be £214 per participant, based on delivery to a cohort of schools (classes) similar to that in the HeLP study. The estimated cost is based on good-quality data collected within the trial, describing the staff inputs and resource use required to deliver the intervention, alongside realistic estimates of the costs associated with the resource inputs. The drama component, integral to the HeLP intervention, is the primary area of resource use and cost, and, together with the costs associated with the HeLP co-ordinator role, accounts for over three-quarters of the cost for delivery of HeLP. There are economies of scale when delivering HeLP in larger schools, given that some components are delivered once at the school level, with costs spread over a larger number of children receiving the intervention. We would anticipate that interventions such as HeLP would be commissioned (funded) across a cohort of schools, and in that setting we report an estimated mean cost per class of approximately £5350. This is a significant additional cost per class, at the level of the school budget, but we have shown in the illustrative cost-effectiveness analyses that a relatively small reduction in the predicted number of adverse weight-related health events (e.g. T2DM and CHD) in future adult years demonstrates that this investment represents value for money using a health and social care sector perspective.

However, in the trial we found no evidence that the HeLP intervention was effective in terms of a difference in mean BMI SDS and in preventing overweight or obesity at 24-month follow-up. Given the scenario of additional costs, together with an absence of demonstrated benefits in weight status, the result of the cost-effectiveness analyses is unambiguous, with usual care dominating the HeLP intervention.

As part of the research process, we have described the development of a framework for assessing the cost-effectiveness of the HeLP intervention (or similar interventions). We have described the Exeter Obesity Model, which builds on the published evidence base for methods surrounding the model-based economic evaluation of interventions for childhood obesity, in a public health context. When seeking to estimate the cost-effectiveness of interventions such as HeLP, there is no readily available decision-analytic modelling framework to utilise. We have therefore set down such a framework and described it in detail to support any future research in a similar context. The modelling framework is simple, using good-quality longitudinal data, and limiting its scope to a small number of health events where there is robust evidence that the incidence of disease has a strong association with weight status, and where there is a published meta-analysis reporting relative risks by weight status categories. However, the modelling framework makes a number of simplifying assumptions, which are deserving of further sensitivity analyses and research. We have not presented a full detailed set of cost-effectiveness results owing to the effectiveness scenario for the HeLP intervention. We had anticipated presenting cost-effectiveness analyses (results) when addressing structural uncertainty, and applying alternative estimates of the predictions of adult weight status as a function of childhood weight status (BMI SD centiles) (i.e. alternatives to the data use in the base-case analysis120). Such sensitivity analyses have not been undertaken, but we do report the estimated transition probabilities for alternative sources of data to inform future modelling developments. Based on our preliminary modelling, when using data from Wright et al.80 or Laitinen et al.85 (see Table 27) we would expect the outcome for estimates of cost-effectiveness (i.e. binary outcome where intervention is either considered cost-effective or not) to be similar to that when using the base case data from Power et al.120

Modelling the cost-effectiveness of public health interventions typically results in the use of a long-term time horizon, often over 30–40 years into the future, and given the expectation that interventions are population based with only a small number of identifiable beneficiaries (in terms of hard outcomes), the mean incremental differences in costs and outcomes are relatively small (averaged across a large target group for the intervention). This leads to difficulties in articulating the expected benefits at a population level, and to difficulties associated with sensitivity in cost- effectiveness ratios (e.g. cost per QALY estimates) to very small changes to the estimated incremental costs and effects. We see these scenarios played out here in the exploratory cost-effectiveness analyses and cost-per-life-year and cost-per-QALY estimates. For example, in scenario B (see Table 35), we report a gain of 10 life-years (approximately 6.5 QALYs) per 1000 children exposed to the HeLP intervention, owing to the prevention of approximately six adverse health events (including preventing five cases of T2DM). At a geographical region, such as in Devon, we would see this multiplied using the target population of 9- to 10-year-olds, approximately 10,000 children, and benefits would be in the region of 100 life-years saved per year. This estimate is adjusted using a discount rate, but an unadjusted (non discounted) estimate would be in the region of 520 life-years saved per 10,000 children receiving an intervention (or 240 life-years saved, using a discount rate of 1.5% per year). These illustrations are speculative and hypothetical, and scenario B assumes an effectiveness scenario with a relative risk of 0.5 for obesity, so we must urge caution here when we seek to illustrate a potential analytical framework (although the HeLP exploratory RCT suggested an effect that was much higher than that in this scenario).

A related characteristic of the modelling framework, although more specific to estimates of effectiveness, is that the magnitude of the relative risk estimates is likely to be quite sensitive to changes in weight status for relatively small numbers of children. That is, given the small numbers of children considered obese in the starting control cohort (e.g. approximately 60–80 per 1000 children), and the large numbers in other weight status categories, the relative risk statistic can be in the region of 0.5 when there are differences of only 20 children in differing directions of weight status (when comparing two similarly distributed cohorts). The exploratory data presented in Table 35 give some indication of this prevailing characteristic of the effectiveness inputs (albeit in a hypothetical setting), although further consideration is recommended.

Specific challenges in modelling the impacts of childhood obesity interventions, using the framework suggested here, include the profiles for the incidence of health events by weight status. The differences in the distributions by weight status are small in both child and adult populations, with only around one-fifth of the population likely to be overweight or obese. This, combined with the data available on the difference in rates of events by weight status, presents an analytical challenge. For example, the incidence of T2DM and CHD is approximately 13.3 cases and 4.8 cases, respectively, per 1000 person-years in obese states, compared with estimates of 1.7 cases and 2.4 cases per 1000 person-years in healthy weight status. Of note here is the 8.5-fold risk of T2DM for obese versus healthy weight status, compared with the twofold risk of CHD for obese versus healthy weight status. We see this reflected in the modelling results, with the vast majority of the differences in predicted events due to cases of T2DM, with very little difference in the incidence of the other events when comparing a potential treatment cohort with controls. Such results provide a rationale for modelling only those health events that exhibit a potentially meaningful difference in incidence by weight status (i.e. meaningful in a mathematical modelling context).

The model presented here has simplifying assumptions, which in many instances are due to the parsimonious structure of the modelling framework, although the absence of data in some areas is also a mitigating factor. We suggest that the model is likely to be sufficient to answer the research question on the cost-effectiveness of an intervention versus usual practice (or other), when the intervention is shown to be effective on weight status outcomes, but we do not propose this modelling framework for more detailed examination of the consequences of overweight and obesity.

The HeLP intervention has been shown to have positive outcomes in areas of secondary importance, and the overall feedback and satisfaction with HeLP from the school setting is very positive. That there is no specific policy-relevant evidence to indicate it is cost-effective, when considering weight status, points commissioners to the dominance of usual practice. However, given that there is a wide range of other interventions being used across the school setting, in the absence of evidence of their effectiveness and cost-effectiveness, it may be that commissioners consider the potential use of HeLP, or HeLP-like interventions, in a school-based setting, and if so here we have provided strong evidence on the resource use and cost associated with delivery of HeLP, and a framework that may be used in any further future assessment of HeLP or similar interventions.