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Healthcare Costs and Obesity Prevention

Drug Costs and Other Sector-Specific Consequences


Background: Obesity is a major contributor to the overall burden of disease (also reducing life expectancy) and associated with high medical costs due to obesity-related diseases. However, obesity prevention, while reducing obesity-related morbidity and mortality, may not result in overall healthcare cost savings because of additional costs in life-years gained. Sector-specific financial consequences of preventing obesity are less well documented, for pharmaceutical spending as well as for other healthcare segments.

Objective: To estimate the effect of obesity prevention on annual and lifetime drug spending as well as other sector-specific expenditures, i.e. the hospital segment, long-term care segment and primary healthcare.

Methods: The RIVM (Dutch National Institute for Public Health and the Environment) Chronic Disease Model and Dutch cost of illness data were used to simulate, using a Markov-type model approach, the lifetime expenditures in the pharmaceutical segment and three other healthcare segments for a hypothetical cohort of obese (body mass index [BMI] ≥30 kg/m2), non-smoking people with a starting age of 20 years. In order to assess the sector-specific consequences of obesity prevention, these costs were compared with the costs of two other similar cohorts, i.e. a ‘healthy-living’ cohort (non-smoking and a BMI ≥18.5 and <25 kg/m2) and a smoking cohort. To assert whether preventing obesity results in cost savings in any of the segments, net present values were estimated using different discount rates. Sensitivity analyses were conducted across key input values and using a broader definition of healthcare.

Results: Lifetime drug expenditures are higher for obese people than for ‘healthy-living’ people, despite shorter life expectancy for the obese. Obesity prevention results in savings on drugs for obesity-related diseases until the age of 74 years, which outweigh additional drug costs for diseases unrelated to obesity in life-years gained. Furthermore, obesity prevention will increase long-term care expenditures substantially, while savings in the other healthcare segments are small or non-existent. Discounting costs more heavily or using lower relative mortality risks for obesity would make obesity prevention a relatively more attractive strategy in terms of healthcare costs, especially for the long-term care segment. Application of a broader definition of healthcare costs has the opposite effect.

Conclusions: Obesity prevention will likely result in savings in the pharmaceutical segment, but substantial additional costs for long-term care. These are important considerations for policy makers concerned with the future sustainability of the healthcare system.

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  1. 1.

    This implies that obesity affects both morbidity and mortality. While there is some debate about whether this is also true for the latter, much evidence suggests that obesity may indeed affect life expectancy negatively, especially among younger adults.[6,8,1416]

  2. 2.

    One study that does use a lifetime approach similar to the one followed by van Baal et al.[13] is the previously mentioned study by Allison et al.[11] These authors concluded that obesity prevention may lead to cost savings. Van Baal et al.[13] offer several possible explanations for their different findings.

  3. 3.

    It is important to note that prevention may sometimes increase total healthcare expenditure due to an increase of related medical costs that are induced in life-years gained alone.[19]

  4. 4.

    Consumers may buy supplementary healthcare insurance from private health insurers for care that is not covered by either of the two Acts, i.e. care that is covered in the third compartment (e.g. cosmetic surgery and physiotherapy).

  5. 5.

    Among other things, the CDM has previously been used for projections of risk factors and disease prevalence rates, and cost-effectiveness analyses.[32,33]

  6. 6.

    For example, smoking increases the chance of getting lung cancer, which subsequently increases the risk of dying. As a consequence, the life expectancy of smokers in the model is lower than the life expectancy of non-smokers.

  7. 7.

    The obese cohort is modelled as non-smokers to facilitate a clear interpretation of the substitution of diseases and its associated costs. Moreover, due to interactions between both risk factors with regard to mortality, it would pose additional data demands.

  8. 8.

    Other definitions of healthcare costs available in the Netherlands are the Dutch Health and Social Care Accounts used by Statistic Netherlands (CBS) and the Budgetary Scheme of Care used by the Dutch Ministry of Health, Welfare and Sports. The first definition takes the broadest, societal perspective including some welfare costs and, for example, costs of housing and day nursery, while the latter includes costs that fall under the ministerial responsibility.

  9. 9.

    Note that we focus here on the consequences of prevention for healthcare costs, not on the costs of prevention itself. Therefore, we assume that the preventive intervention that would lead to this eradication (i.e. completely preventing obesity), is costless. Obviously, such interventions are hard to come by.

  10. 10.

    Discount rates for costs differ for different jurisdictions and national guidelines for pharmacoeconomic research. Discount rates usually range from 3% to 5%, although sensitivity analyses including discount rates from 0% to 6% are largely prescribed.[4042]


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This study was part of the project ‘Living longer in good health’, which was financially supported by Netspar. The opinions expressed in the paper are those of the authors.

The authors have no conflicts of interest that are directly relevant to the content of this study.

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Correspondence to David R. Rappange.

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Rappange, D.R., Brouwer, W.B.F., Hoogenveen, R.T. et al. Healthcare Costs and Obesity Prevention. Pharmacoeconomics 27, 1031–1044 (2009). https://doi.org/10.2165/11319900-000000000-00000

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  • Obesity Prevention
  • Body Mass Index Level
  • Total Healthcare Expenditure
  • Relative Mortality Risk
  • Obese Cohort