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Clinical Studies and Practice

Cost effectiveness of primary care referral to a commercial provider for weight loss treatment, relative to standard care: a modelled lifetime analysis

Abstract

Background:

Because of the high prevalence of overweight and obesity, there is a need to identify cost-effective approaches for weight loss in primary care and community settings.

Objective:

To evaluate the long-term cost effectiveness of a commercial weight loss programme (Weight Watchers) (CP) compared with standard care (SC), as defined by national guidelines.

Methods:

A Markov model was developed to calculate the incremental cost-effectiveness ratio (ICER), expressed as the cost per quality-adjusted life year (QALY) over the lifetime. The probabilities and quality-of-life utilities of outcomes were extrapolated from trial data using estimates from the published literature. A health sector perspective was adopted.

Results:

Over a patient’s lifetime, the CP resulted in an incremental cost saving of AUD 70 per patient, and an incremental 0.03 QALYs gained per patient. As such, the CP was found to be the dominant treatment, being more effective and less costly than SC (95% confidence interval: dominant to 6225 per QALY). Despite the CP delaying the onset of diabetes by 10 months, there was no significant difference in the incidence of type 2 diabetes, with the CP achieving <0.1% fewer cases than SC over the lifetime.

Conclusion:

The modelled results suggest that referral to community-based interventions may provide a highly cost-effective approach for those at high risk of weight-related comorbidities.

Introduction

The prevalence of overweight and obesity is placing a substantial burden on health-care resources, particularly in developed countries.1 Because of limited health-care budgets, policy makers are seeking evidence of the cost effectiveness of interventions before publicly subsidising programmes. It is therefore imperative that obesity management programmes that are both efficacious and cost effective are identified and implemented.

A partnership between primary care providers and commercial organisations presents a practical approach for a population-based weight loss programme, whereby participants can benefit from early lifestyle intervention for weight management. Our recent randomised controlled trial (RCT) involving three countries (Australia, United Kingdom and Germany) showed that referral to a commercial weight loss community intervention programme (Weight Watchers) (CP) produced greater weight loss over 1 year than standard care (SC).2 These results confirmed observational data3, 4 that a shared care approach between primary care providers and commercial organisations has the potential to deliver weight management programmes scalable to a national level in a community setting.

Importantly, this shared care approach was shown to be cost effective based on a prospectively designed within-trial analysis.5 The key strengths of the within-trial cost-effectiveness analysis were the certainty of the results as they were based directly on observed trial cost and efficacy data. The limitations were the short time horizon for analysis (1 year), the assumption that all weight loss was maintained, and that several cost offsets including reduced rates of obesity-related disease were not captured.5 Collection of follow-up data at 2 years has since been performed6 that enables us to report on weight regain statistics after cessation of the weight loss intervention.

Previous estimates of the cost of the CP have been reported, but were based on small studies with limited data.7 The aim of this study was to develop a decision analytic model to estimate the long-term effectiveness and cost effectiveness of the CP compared with SC in reducing rates of obesity and obesity-related disease in a population of overweight and obese adults. The model builds on our within-trial cost-effectiveness analysis5 by extrapolating costs and outcomes from the 2-year RCT6 to the lifetime of the trial population.

Materials and methods

A decision model was developed to estimate the lifetime costs and health outcomes of the CP compared with SC in a simulated cohort of overweight and obese patients. The baseline characteristics of the modelled cohort and the weight loss results for the initial 2 years of the model were imputed directly from data collected in the RCT.2, 6

The net effectiveness of each strategy was quantified in terms of quality-adjusted life years (QALYs). This involved weighting the time spent in each health state by the health-related quality-of-life value (utility) associated with that state (where 0=death and 1=full health). Incremental cost effectiveness was measured in terms of the cost per QALY gained. A health sector perspective was adopted and an annual discount rate of 3.5% was applied to all future costs and health outcomes.

Randomised controlled trial

A multicentre RCT was undertaken whereby overweight and obese adults were recruited by their general practitioners (GPs) and randomised to receive 1 year of access to either the CP or SC by a primary care provider in Australia, United Kingdom and Germany.2 Participants randomised to the CP group received vouchers to attend a weekly community CP meeting (Weight Watchers). Those randomised to SC received weight loss advice delivered by a GP/primary care professional at their local medical practice. The frequency of SC visits was at the discretion of the GP and the participant. All participants were aged 18 years with a body mass index (BMI) of 27–35 kg m−2, and had at least one risk factor for obesity-related disease. Risk factors included central adiposity (waist circumference >88 cm in women and >102 cm in men); type 2 diabetes without insulin treatment; family history of type 2 diabetes; previous gestational diabetes; impaired glucose tolerance or impaired fasting glycaemia; mild-to-moderate dyslipidemia (defined by national guidelines), or treatment for dyslipidemia; treatment for hypertension; polycystic ovarian syndrome or infertility without apparent cause other than weight; lower-limb osteoarthritis; or abdominal hernia. A full list of inclusion and exclusion criteria, as well as a more in-depth description of the two intervention groups, are reported with the primary findings from the study.2 After the 1-year intervention, participants were then followed-up at 18 and 24 months, during which time they could self-select their method of weight management, or do nothing.6

Model structure

The Markov process defines a set of discrete health states associated with overweight and obesity, and a set of probabilities that govern the likelihood of transitioning from one state to another at the end of each 1-year cycle (see Figure 1).8 Each health state was assigned an estimate of the cost required to provide typical health care over the cycle and a utility weighting that reflected the QALYs gained per cycle (defined under ‘Resource Use and Costs’). A half-cycle correction was used in the Markov process. The model was constructed using TreeAge Pro Suite 2008 (Williamstown, MA, USA) with links to Excel.

Figure 1
figure1

Health states included in the Markov model following a CP versus SC care for weight loss treatment.

Transition probabilities and health state utilities

All transition probabilities and health state utilities are listed in Table 1. Consistent with the baseline data collected in the RCT, patients entering the model had an average age of 47 years2 and were assigned to begin the model in either an overweight, obese or type 2 diabetes health state. All patients remained in the model until death or until they reached the age of 99 years.

Table 1 Annualised parameter estimates used in Markov model

Trial data were used to assign the probabilities of transitioning between health states at the end of the intervention period (1 year), and at the end of follow-up (2 years). Based on the 2-year follow-up trial data at 1 year post completion of the interventions, patients experienced an average weight regain equivalent to 0.09 BMI points per month for the CP group and 0.03 BMI points per month for the SC group. As the entry criterion for the study was a BMI of 27–35 kg m−2, those with a BMI of <27 kg m−2 (<26.55 kg m−2) at the end of the intervention and at the end of follow-up were considered to be in a normal BMI range.

Based on a meta-regression performed by Dansinger et al.9 (whereby at 5.5 years post intervention, no residual weight loss remained) it was assumed that participants in both groups had a weight regain of 0.03 BMI points per month after the end of the 2-year follow-up. In our study, this was equivalent to 1 kg per year. When applied from the conclusion of the trial follow-up, this assumption resulted in participants in the CP group being projected to regain all their weight loss by 5 years post intervention as compared with 4 years post intervention for participants in the SC group. It was assumed for both groups that no residual weight loss remained indefinitely.

Comparing our methodology used with other literature, this was seen to be a conservative weight regain approach post 2-year follow-up. Ara et al.10 reported an increase in BMI of 0.175 per year for women for an equivalent nondiabetic cohort, which is less than the increase in BMI that we modelled (0.36 BMI units per year). Furthermore, as shown by the results from the 10-year Diabetes Prevention Program (DPP) Outcomes Study,11 weight regain is shown to slow down over time (after 2 years) for a similar cohort of patients. Data from the DPP shows that a 2-kg weight loss is maintained up to 10 years;11 however, this was not assumed for our study.

The probabilities of developing type 2 diabetes for those with normal, overweight and obese BMI ranges were sourced from the 2005 AusDiab Report.12 Age-specific annual mortality rates for patients with and without type 2 diabetes were sourced from Magliano et al.14 (also reported by Keating et al.13) that combined data from the AusDiab study with national Australian mortality data.

Utility values for each health state in the Markov model were based on patient responses to the IWQOL-Lite (Impact of Weight on Quality of Life Questionnaire-Lite)15, 16 that was collected as part of our trial on five occasions over 2 years (baseline, months 6, 12, 18 and 24). A utility score was derived from the patient responses to the questionnaire using the algorithm described by Brazier et al.17

Resource use and costs

All resource use and costs are listed in Table 2. Programme costs associated with the CP group were based on the market price of attending the programme and were sourced directly from Weight Watchers Australia (www.weightwatchers.com.au). This consisted of a monthly payment plan and included unlimited access to meetings and online electronic web tools. The cost of the referral visit to the CP was also included in the costing. For the SC group, the cost applied was that of a consultation lasting 20 min with a GP. There were no programme costs assigned for either group during the follow-up period (beyond 1 year) as the choice of weight loss method, if any, was not recorded.

Table 2 Cost inputs for Markov model

The mean annual health-care costs for patients living with type 2 diabetes were sourced from the Australian Diabetes, Obesity and Lifestyle Study.18 Average medication costs for those in each BMI range (healthy, overweight and obese) were from a study by Colagiuri et al.19

All costs were measured in Australian dollars (AUD). Costs sourced from alternative years were presented in 2010–2011 values by applying the relevant price inflators or deflators.20

Uncertainty analysis

A probabilistic sensitivity analysis was undertaken to examine the effect of multiparameter uncertainty around estimates of costs, utilities and probabilities. A Monte Carlo simulation of the patient cohort with 10 000 iterations was used to estimate the 95% uncertainty interval around the mean incremental cost-effectiveness ratio (ICER) as well as probabilities of acceptable cost-effectiveness thresholds.

Scenario analysis

The cost-effectiveness analysis was based on commercial pricing decisions (prices sourced from Weight Watchers website) as the use of existing market prices is considered the most practical approach in costing analyses.8 A scenario analysis was performed to examine the cost-effectiveness impacts of reducing programme costs in Australia to the equivalent of those from the UK National Health Service (NHS) Weight Watchers referral scheme (GBP 45 for 12 sessions4). As the CP is identical across countries and the cost to deliver the intervention similar, we assumed that this cost would represent the likely Australian government cost of publicly subsidising the programme. The total cost applied was based on an attendance of 36 CP sessions over 12 months (GBP 135–12 session cost multiplied by 3). The Weight Watchers NHS referral scheme was used as it is a system currently in place.

Sensitivity analysis

A sensitivity analysis was performed to estimate the cost effectiveness of the CP when including the costs associated with patient travel to attend either CP or SC consultations. The number of participant visits to their primary care provider for weight loss advice (SC group), or to the CP (CP group), was recorded throughout the 1-year study. Those attending the CP had 3 times more visits over the 1-year treatment period than those receiving SC (Table 2). Patients were assumed to have travelled within a 10-km radius to either their local CP or primary care clinic. Opportunity costs of employment were not considered because participants could attend their intervention outside working hours, during their lunch break or on weekends. Childcare costs were not considered as children of any age are welcome at the CP meetings and can accompany their parent to a SC visit.

Results

Modelled results

Using base case assumptions, the CP resulted in an additional 50 life years gained per 1000 patients treated, and an additional 50 years spent in a normal BMI range per 1000 patients (Table 3). The average onset of type 2 diabetes in the CP group was delayed by 10.29 months (0.85 years) when compared with patients in SC. However, there was no statistically significant difference (<0.1%) in the probability of developing type 2 diabetes over the remainder of the patient’s lifetime (34.95% and 34.99% for the CP and SC groups, respectively).

Table 3 Markov model results (5-year and lifetime means per patient)

After discounting costs and benefits by 3.5%, the CP cost an additional AUD 123 over the first 5 years post treatment. This produced an ICER of AUD 11 260 per QALY. Over a patient’s lifetime, the CP resulted in an incremental cost saving of AUD 70 per patient, and an incremental 0.03 QALYs gained per patient. As such, the CP was found to be the dominant treatment, being more effective and less costly than SC. The 95% confidence interval ranged from dominant to 6225 per QALY.

Probabilistic sensitivity analysis

The results of the Monte Carlo analysis for the cost effectiveness of the CP relative to SC are presented on a cost-effectiveness plane in Figure 2. Based on this analysis, the probability of the CP being a cost-effective treatment at an ICER of AUD 50 000 was 77.9%.

Figure 2
figure2

Incremental cost-effectiveness scatterplot of the commercial weight loss programme vs standard care.

Scenario analysis

When the programme costs of the CP in Australia were reduced to the equivalent of the Weight Watchers NHS referral scheme, our base case results were strengthened and the CP remained the dominant intervention.

Sensitivity analysis

When including the costs associated with patient travel in our analysis, programme costs increased to AUD 1170 and AUD 508 for the CP and SC groups, respectively. This resulted in an ICER of AUD 6389 per QALY (95% uncertainty interval: CP dominant to 10 925 per QALY) for the CP relative to SC.

Discussion

Although the CP was associated with a higher initial cost than SC, these extra costs were offset by the longer-term health benefits associated with an increased rate of weight loss (and therefore lower health-care resource use) when patients were followed over the lifetime. The CP was therefore a dominant intervention (that is, both more effective and less costly than SC). The lifetime analysis presented here extrapolates from the results of the within-trial cost-effectiveness analysis conducted alongside the RCT, where an ICER of AUD 18 085 per QALY was reported.5

When the commercial price of the CP was re-evaluated in accordance with the likely costs of the programme if it were to be funded through the health system, the base case result was strengthened and the CP remained the dominant option. The costs of patient travel were estimated in a separate sensitivity analysis as those attending the CP had more frequent visits than those receiving SC (3 times more visits over 1 year), which may have had a large contribution to the success of the CP. When these costs were included in the analysis, the CP became more expensive than SC, but remained highly cost effective with an ICER of AUD 6389 that was well below the commonly accepted threshold of $50 000 per QALY.21, 22, 23 It may be argued that patient time to attend the CP or GP consultations should also be included; however, a large benefit of these services is that they are available at lunchtimes, after work hours and on weekends, thereby minimising the opportunity costs of paid employment.

When GPs are given adequate support services such as those in the SC arm of our study, as well as the Counterweight Programme in the United Kingdom,24 they (GPs) have been found to achieve a weight loss of 3 kg at 1 year. Although this approach is cost effective when compared with background rates of population weight gain,25 our results indicated that the CP provided a relatively more effective and less costly long-term approach. Despite the higher initial programme costs associated with the CP, and no significant difference in overall incidence of type 2 diabetes between groups (<0.1%), the quality-of-life benefits and lower levels of resource use associated with patients being in a healthier BMI range resulted in the CP being cost saving over the lifetime. Importantly, this shared care approach has the potential to be delivered on a large scale and in community settings. This suggests that we should be using the CP as a cost-effective means for community weight control and it is a suitable support resource for GPs to refer patients to. However, the CP may be beyond the financial reach of a substantial portion of the population, particularly those who need it most.26

Although other studies have reported on the cost effectiveness of the CP, these were small in scale and reliant on several assumptions.7 However, cost-effectiveness ratios for other adult weight loss interventions that incorporated dietary and exercise counselling have been published. The DASH and low-fat diet programmes (as reported by Forster et al.27) were found to have incremental cost-effectiveness ratios of AUD 12 000 and AUD 13 000 per disability-adjusted life year, respectively, when patient time and travel were not included.27 An economic evaluation of weight loss interventions in overweight and obese women found the most cost-effective option to be a diet, exercise and behavioural modification programme at a cost of USD 12 600 per QALY.28 Although there are challenges in comparing with previous cost-effectiveness analyses for reducing overweight and obesity in an adult population (including differences in costing perspectives, timeframe for outcomes measured, modelling methods, discounting rates and assumptions around the sustainability of intervention effects), the CP, being a cost saving intervention, is highly favourable when compared with other diet and exercise interventions.

Previous studies have found surgically induced weight loss to be a cost-effective approach for managing obesity and remission of type 2 diabetes.13, 29, 30 Compared with nonsurgical management of obesity, this was found to produce an ICER of GBP 11 000 per QALY,30 with a more recent systematic review confirming these results.29 However, the majority of ICERs reported for bariatric surgery are higher than those for other obesity management approaches. In this context, it would be a better use of government resources to support approaches, such as those reported here, that are low-cost programmes that help promote weight loss and delay the onset of type 2 diabetes.

The strength of the methodology applied in this study lies in its ability to synthesise trial evidence of efficacy, resource use and patient preferences associated with weight loss interventions in an explicit and transparent manner. This modelling approach allows us to extrapolate 2-year follow-up results from a robust community intervention to project the outcomes for patients over a lifetime. The estimates of the cost effectiveness are robust. This is unsurprising as minimal assumptions were required within the model and all the transition probabilities applied were based on the highest levels of evidence (RCTs and meta-analyses).

A limitation of our study was that only one clinical outcome (type 2 diabetes) was assessed. We chose to model the impacts on this condition alone as it was the only condition for which we had baseline prevalence rates, and because the link between BMI and type 2 diabetes has been well established. Exclusion of other obesity-related diseases where the evidence of the association with BMI is scarcer would have required more assumptions and hence have produced greater uncertainty in the cost-effectiveness estimates. However, if additional obesity-related diseases were to be included in the model, it is likely that our existing conclusions would be strengthened further.

Conclusion

The CP was found to be a more effective and less costly intervention than SC in overweight and obese individuals. It also delayed the onset of type 2 diabetes. This suggests that a greater emphasis on referral to commercial weight loss programmes may provide a highly cost-effective approach for those at high risk of weight-related comorbidities.

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Acknowledgements

We acknowledge the assistance of Crystal Lee who provided critical analysis of the draft manuscript; Kirsten Howard who reviewed the Markov model; and Catherine Keating for assistance with data acquisition.

Author contributions

NRF: acquisition of data, development of the Markov model, analysis and interpretation of data and writing of the manuscript; HC, SC and DS: development of the Markov model, interpretation of data, critical review and writing of the manuscript; SAJ and HH: study design and conception, obtained funding and critical review of the manuscript; IDC: study design and conception, obtained funding and writing the manuscript. All authors read and approved the final manuscript.

Trial Registration: This study is registered with the International Standard Register of Clinical Trials, ISRCTN 85485463.

Role of the Funding Source: This study was investigator initiated but funded by Weight Watchers International through a grant to the Medical Research Council (UK). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Correspondence to I D Caterson.

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Competing interests

NRF and IDC have received research grants for other clinical trials funded by Sanofi-Aventis, Allergan, Eli Lilly and Novo Nordisk. IDC was a board member for the SCOUT trial, is on the EXSCEL Operations Committee and has received payment for lectures from iNova Pharmaceuticals, Pfizer Australia and Servier Laboratories (Australia). SAJ has received research grants for other clinical trials from Sanofi-Aventis and Coca Cola, and is a member of the Tanita Medical Advisory Board and receives a fee for nutrition articles and lectures for Rosemary Conley Enterprises. HH is on the Advisory Board for Weight Watchers International and has received payment for lectures from Sara Lee, Novartis, Sanofi Aventis and Bristol-Myers Squibb.

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Fuller, N., Carter, H., Schofield, D. et al. Cost effectiveness of primary care referral to a commercial provider for weight loss treatment, relative to standard care: a modelled lifetime analysis. Int J Obes 38, 1104–1109 (2014). https://doi.org/10.1038/ijo.2013.227

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Keywords

  • Markov model
  • overweight
  • type 2 diabetes

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