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Research Article

Cost-Effectiveness of Interventions to Promote Physical Activity: A Modelling Study

• l.cobiac@uq.edu.au

Affiliation: Centre for Burden of Disease and Cost-Effectiveness, School of Population Health, The University of Queensland, Herston, Queensland, Australia

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• Affiliation: Centre for Burden of Disease and Cost-Effectiveness, School of Population Health, The University of Queensland, Herston, Queensland, Australia

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• Affiliation: Centre for Burden of Disease and Cost-Effectiveness, School of Population Health, The University of Queensland, Herston, Queensland, Australia

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• Published: July 14, 2009
• DOI: 10.1371/journal.pmed.1000110

Abstract

Background

Physical inactivity is a key risk factor for chronic disease, but a growing number of people are not achieving the recommended levels of physical activity necessary for good health. Australians are no exception; despite Australia's image as a sporting nation, with success at the elite level, the majority of Australians do not get enough physical activity. There are many options for intervention, from individually tailored advice, such as counselling from a general practitioner, to population-wide approaches, such as mass media campaigns, but the most cost-effective mix of interventions is unknown. In this study we evaluate the cost-effectiveness of interventions to promote physical activity.

Methods and Findings

Intervention Pathway Analysis

The optimal pathway for implementation of interventions is developed using a generalised cost-effectiveness approach [32]. We first derive the disease incidence rates that would have occurred in 2003 if none of the interventions under evaluation (Table 2) were in place. This scenario is referred to as the “partial null.” The cost-effectiveness of each intervention is then evaluated in comparison with the partial null to determine the order of interventions in the pathway, from most cost-effective to least cost-effective. Finally, the cost-effectiveness of each intervention combination in the pathway is evaluated in comparison with the partial null. From this we derive an incremental cost-effectiveness ratio (ICER) for each intervention, which reflects the cost-effectiveness of adding the intervention to the pathway.

Sensitivity Analysis

The sustainability of intervention health effects over time is an important parameter in the cost-effectiveness analysis, but there are currently too few studies with long-term participant follow-up (e.g., greater than two years) to quantify the sustainability of the physical activity effect associated with each of the interventions. In our base case analysis we assume that the intervention effects on physical activity are sustained for the first year, but decay exponentially at a rate of 50% per annum thereafter; therefore, there will be virtually no intervention effect after five years. Sensitivity of the intervention pathway to this assumption is evaluated by varying decay rates between 0% (lifelong behaviour change) and 100% (behaviour change reversed after the first year).

Results

Intervention Cost-Effectiveness

There is large variability in the health gain that can be achieved with different methods of intervention. The number of DALYs averted ranges from 740 (95% uncertainty interval [UI] 110–1,900) for internet-based intervention to 23,000 (95%UI 7,600–40,000) for a mass media campaign (Table 3). Intervention costs also vary substantially, ranging from AUS$13 million (95%UI AUS$11 million to 16 million) for the mass media campaign to AUS$410 million (95%UI AUS$210 million to 570 million) for the TravelSmart individualised marketing program.

The interventions predominantly fall in the northeast and southeast quadrants of the cost-effectiveness plane (Figure 1), indicating a high probability of improvements in population health with increased expenditure on physical activity intervention or, in some cases, with a net cost-saving due to physical activity intervention.

Two interventions stand out as being most effective and most cost-effective—the mass media campaign and the pedometer program. Both of these interventions are dominant and have a 100% probability of being cost-saving (Table 4).

The first four interventions in the pathway—pedometers, mass media, internet-based intervention and GP prescription—are under the AUS$50,000 per DALY threshold for cost-effectiveness under all decay scenarios (Table 6). The key differences between the sensitivity scenarios and the base case results are that GP referral is cost-effective when decay is slower than the 50% assumed in the primary analysis, and that the TravelSmart program is no longer cost-effective at maximum (i.e., 100%) decay. Discussion Intervention to encourage an increase in physical activity participation is highly recommended in Australia. Potential reductions in costs of treating ischaemic heart disease, stroke, diabetes, breast cancer, and colon cancer mean that there is a high probability of cost-savings from a health sector perspective. Taken as a package of interventions, all six physical activity interventions could lead to a substantial improvement in population health at under AUS$50,000 per DALY.

Cost-effectiveness of the package is not highly sensitive to the sustainability of behavioural changes (total package is under AUS$50,000 per DALY at maximum rates of decay). However, it is likely that some interventions will lead to a more sustained effect than others, and this could affect the order of implementation in the pathway. It is also possible that there will be synergistic effects with implementation of multiple interventions, which could improve the sustainability of intervention effects on physical activity over time, thus increasing cost-effectiveness of the intervention package. However, this may well be countered by a decrease in effectiveness of each additional intervention, due to the increasing proportion of the population less willing or able to change their physical activity behaviour. When modelled from the selected studies of intervention effectiveness, intervention programs that encourage use of pedometers and mass media-based community campaigns are the most cost-effective strategies to implement and are very likely to be cost-saving. We found that these interventions have the potential to deliver large health benefits to the population, despite the seemingly small or nonsignificant effects on physical activity behaviour when measured at a population level [34],[35]. Overall, our intervention cost-effectiveness ratios, which ranged from dominant up to AUS$75,000 per DALY, were not as favourable as the entirely dominant results reported by NICE for ten GP prescription and referral interventions in the United Kingdom [21]. This may be because the NICE analysis did not include patient costs of time and travel, and assumed a slower decay in physical activity behaviour change (50% of participants assumed to maintain change in behaviour long enough to experience health benefits e.g. 20 y for a 25-y-old), but there were also other differences in modelling methods and assumptions (e.g., discount rates) that may have influenced the more favourable NICE results.

Conversely, our cost-effectiveness ratios were mostly more favourable than the cost-effectiveness ratios recently reported for seven physical activity intervention programs in the US [25]. Costs per QALY ranged from US$14,000 (2003AUS$19,000) to US$69,000 (2003AUS$91,000) for the physical activity promotion interventions, which included two community-wide campaigns, two social support walking programs, two individually adapted behaviour change programs, and one program to enhance access to a more active environment (e.g., new bicycle paths, fitness centre, etc.). Some additional costs were included in the US analysis (e.g., patient out-of-pocket expenses for physical activity clothing and equipment) contributing to relatively high intervention costs per person, which may have led to the less favourable US results, but there were also many other differences in analysis methods and assumptions, such as a shorter time horizon (40 y), additional medical inflation on disease costs (8% per annum), and more sustained effects on behaviour (33% to 50% decay in the second year, with maintenance of effect thereafter), which complicate interpretation of the contrasting results.

However, the NICE results, the US study, and our own analyses together provide good evidence that physical activity intervention can be cost-effective in the UK, US, and Australia. Over 20 different intervention programs have now been evaluated in these countries, with only four programs exceeding a cost-effectiveness threshold of AUS$50,000. To our knowledge, no other studies have evaluated cost-effectiveness of this package of interventions. However, two studies have previously evaluated GP prescription intervention based on the randomised controlled trial of New Zealand's Green Prescription program [36], reporting costs per QALY of NZ$2,100 (2003AUS$2,000) [19] and dominant [21]. Although not directly comparable to each other or to our cost per DALY of AUS$11,000 due to different analysis methods and assumptions (e.g., discount rates), the growing number of analyses reporting cost-effectiveness under varying methods and assumptions strengthens the argument for this particular physical activity intervention as a cost-effective measure for improving public health. However, the results for the Green Prescription program do not necessarily reflect the cost-effectiveness of all physical activity prescription programs (many of which have not shown a significant effect on physical activity behaviour [37][40]).

A number of limitations must be taken into account when evaluating the results of this research. For example, because of inconsistent physical activity outcome measures and a limited number of randomised controlled trials, for some interventions we selected single intervention studies for cost-effectiveness analysis rather than combining multiple trials in a meta-analysis. Therefore, while the results reflect the cost-effectiveness of the interventions that were evaluated, and based on the best available evidence, they should not be generalised to all interventions of a similar type.

In addition, although interventions were evaluated as if implemented for one year, in some cases it was necessary to include studies of less than one-year duration. While it would be preferable to include only those studies with follow-up data at one year, this would exclude a number of interventions from cost-effectiveness analysis (e.g., active transport interventions, community mass media and pedometer programs, etc.), and potentially bias cost-effectiveness analyses toward the more targeted interventions (e.g., general practice interventions) for which longer-term studies are more readily available. We have included shorter-duration studies in the interests of modelling a wide range of interventions, but acknowledge that these interventions may not prove to be as cost-effective if subsequent intervention studies find a significant drop in effectiveness at one year.

Furthermore, the level of evidence underlying the measures of intervention effect is relatively weak. For example, evaluation of the mass media campaign effect on population health was based only on the results of a single quasi-experimental study, and evaluation of the pedometer program effect on population health was based on a meta-analysis that included only 277 participants in total. In addition, it is likely that those who volunteered to participate in the pedometer trials were more active or more motivated to change their activity behaviour than the general population, leading to a more favourable estimate of cost-effectiveness than might actually occur with rollout of a pedometer program across Australia. Further randomised controlled trials, using consistent measures of physical activity behaviour, would improve our confidence in both the relative position of interventions in the pathway and the overall magnitude of the health gain that could be achieved.

There are a number of other unknowns in modelling physical activity that may have influenced our cost-effectiveness results. Due to the reliance on (mainly) observational studies in the meta-analyses of relative risks of disease by Bull et al. [1], it is possible that the risk would not be fully reversible for those increasing their physical activity in response to an intervention. It is also plausible that there is a time lag between change in physical activity behaviour and change in risk, which may be relatively short for cardiovascular diseases [41], but longer for cancers [42]. These factors could lead to an overestimate of cost-effectiveness ratios. We do, however, incorporate an attenuation of cancer risk by age (see Table I in Text S2), which Bull et al. [1] based on ischaemic heart disease data, that might be an overestimate of attenuation and may, therefore, offset risk reversibility and lag effects.

Nevertheless, the research illustrates how combining physical activity interventions in a cost-effectiveness expansion pathway can provide guidance to policymakers in identifying the most cost-effective approaches to decreasing the burden of disease due to physical inactivity, based on the best available evidence. For Australia, based on current evidence, it is likely that the package of interventions would not only be cost-effective but very likely cost-saving to the health sector, leading to substantial improvements in health for the Australian population.

Supporting Information

Text S1.

Physical activity interventions.

doi:10.1371/journal.pmed.1000110.s001

(0.18 MB DOC)

Text S2.

Cost-effectiveness modelling methods.

doi:10.1371/journal.pmed.1000110.s002

(0.16 MB DOC)

Text S3.

Input parameters and uncertainty.

doi:10.1371/journal.pmed.1000110.s003

(0.15 MB DOC)

Acknowledgments

Thanks to C. Jackson and P. Van Baal for their comments on the manuscript.

Author Contributions

ICMJE criteria for authorship read and met: LJC TV JJB. Agree with the manuscript's results and conclusions: LJC TV JJB. Designed the experiments/the study: LJC TV. Analyzed the data: LJC. Collected data/did experiments for the study: LJC TV. Wrote the first draft of the paper: LJC. Contributed to the writing of the paper: LJC TV JJB. Developed the modelling methods: JJB.

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