How Much Money Is Spent On Preventive Health Care Obseity

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Enquiry Article

Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure

• Pieter H. M van Baal,
• Johan J Polder,
• Thou. Ardine de Wit,
• Rudolf T Hoogenveen,
• Talitha Fifty Feenstra,
• Hendriek C Boshuizen,
• Peter M Engelfriet,
• Werner B. F Brouwer

x

• Published: February 5, 2008
• https://doi.org/10.1371/journal.pmed.0050029

Figures

Abstract

Background

Obesity is a major cause of morbidity and mortality and is associated with high medical expenditures. Information technology has been suggested that obesity prevention could effect in price savings. The objective of this written report was to gauge the almanac and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention.

Methods and Findings

With a simulation model, lifetime health-care costs were estimated for a accomplice of obese people aged 20 y at baseline. To appraise the bear upon of obesity, comparisons were made with similar cohorts of smokers and "salubrious-living" persons (defined as nonsmokers with a torso mass index between 18.v and 25). Except for relative hazard values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the furnishings of epidemiologic parameters and toll definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Considering of differences in life expectancy, withal, lifetime health expenditure was highest among good for you-living people and everyman for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and price definitions did not change these conclusions.

Conclusions

Although effective obesity prevention leads to a decrease in costs of obesity-related diseases, this subtract is offset by toll increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-constructive way of improving public health, simply it is not a cure for increasing health expenditures.

Citation: van Baal PHM, Polder JJ, de Wit GA, Hoogenveen RT, Feenstra TL, Boshuizen HC, et al. (2008) Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Wellness Expenditure. PLoS Med 5(2): e29. https://doi.org/10.1371/journal.pmed.0050029

Bookish Editor: Andrew Prentice, London School of Hygiene & Tropical Medicine, United Kingdom

Received: June 20, 2007; Accustomed: Nov 30, 2007; Published: Feb 5, 2008

Copyright: © 2008 van Baal et al. This is an open up-admission article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatsoever medium, provided the original author and source are credited.

Funding: This work was funded by the Dutch Ministry of Health, Welfare and Sports. The funder did not have any office in study pattern, data collection and analysis, conclusion to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interest exist.

Abbreviations: BMI, body mass index; COI, cost of illness; RIVM-CDM, National Institute for Public Health and the Surround chronic affliction model; SHA, Organization of Health Accounts

Editors' Summary

Background.

Since the mid 1970s, the proportion of people who are obese (people who take an unhealthy amount of body fat) has increased sharply in many countries. One-third of all Usa adults, for example, are now classified every bit obese, and recent forecasts suggest that by 2025 half of US adults will be obese. A person is overweight if their body mass index (BMI, calculated by dividing their weight in kilograms by their tiptop in meters squared) is between 25 and xxx, and obese if BMI is greater than xxx. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight and obese individuals have an increased risk of developing many diseases, such equally diabetes, coronary heart affliction and stroke, and tend to dice younger. People get unhealthily fat by consuming food and drink that contains more energy than they need for their daily activities. In these circumstances, the torso converts the excess energy into fat for utilise at a afterward date. Obesity tin can be prevented, therefore, by having a good for you diet and exercising regularly.

Why Was This Written report Done?

Because obesity causes and so much affliction and premature death, many governments have public-health policies that aim to forestall obesity. Conspicuously, the improvement in health associated with the prevention of obesity is a worthwhile goal in itself simply the prevention of obesity might as well reduce national spending on medical care. It would practise this, the statement goes, past reducing the amount of money spent on treating the diseases for which obesity is a adventure cistron. All the same, some experts take suggested that these short-term savings might exist showtime past spending on treating the diseases that would occur during the extra lifespan experienced by not-obese individuals. In this study, therefore, the researchers accept used a calculator model to calculate yearly and lifetime medical costs associated with obesity in The Netherlands.

What Did the Researchers Do and Notice?

The researchers used their model to estimate the number of surviving individuals and the occurrence of various diseases for three hypothetical groups of men and women, examining data from the age of twenty until the time when the model predicted that everyone had died. The "obese" grouping consisted of never-smoking people with a BMI of more than xxx; the "healthy-living" group consisted of never-smoking people with a salubrious weight; the "smoking" group consisted of lifetime smokers with a good for you weight. Data from the Netherlands on the costs of illness were fed into the model to calculate the yearly and lifetime health-care costs of all three groups. The model predicted that until the age of 56, yearly health costs were highest for obese people and lowest for healthy-living people. At older ages, the highest yearly costs were incurred past the smoking group. However, because of differences in life expectancy (life expectancy at age xx was five years less for the obese grouping, and 8 years less for the smoking grouping, compared to the healthy-living group), total lifetime health spending was greatest for the healthy-living people, lowest for the smokers, and intermediate for the obese people.

What Practise These Findings Mean?

Equally with all mathematical models such every bit this, the accuracy of these findings depend on how well the model reflects real life and the data fed into it. In this case, the model does not take into account varying degrees of obesity, which are likely to touch lifetime health-care costs, nor indirect costs of obesity such equally reduced productivity. All the same, these findings propose that although effective obesity prevention reduces the costs of obesity-related diseases, this reduction is offset by the increased costs of diseases unrelated to obesity that occur during the extra years of life gained by slimming downwardly.

Introduction

Because obesity is best-selling as a major cause of morbidity and mortality [1,2], prevention of obesity is a target of health policy in many countries [three,4]. At the aforementioned time, many countries struggle to control ever-increasing health-intendance expenditures. The Organization for Economic Cooperation and Evolution suggested in 2005 that both goals could be accomplished simultaneously, since "well-designed public wellness programmes may contribute to the prevention of illness and help relieve some of the price pressures on wellness intendance systems" [v]. Such a promise of better health equaling lower costs is non new [6], yet is debatable. In fact, for smoking it has been argued that successful prevention will in the end increase expenditure exactly because it is successful [7,viii]. The caption for this hypothesis is that the life-years gained by prevention are not all lived in total health. While effective prevention will lead to a decrease in run a risk factor-related diseases, which may result in savings, these savings may be offset by cost increases related to an increase in diseases in life-years gained. Therefore, prevention may induce more health-care costs in the long run than it saves in the short run. Whether this possibility is truthful, however, will strongly depend on the adventure factor concerned. An important determinant is whether this risk factor primarily causes relatively cheap lethal diseases or rather expensive chronic ones [ix]. Since the diseases associated with obesity differ from those associated with smoking it is worthwhile to investigate whether or non prevention of obesity might indeed, as is sometimes suggested, relieve financial pressures on health-care systems. If it does not, of grade, it does not imply that preventing obesity is not worthwhile, since the associated health gain is valuable in itself, for social club and the individuals concerned.

In recent years several estimates of health-care costs attributable to obesity have been published [iv,x–twenty]. Not only do such estimates vary enormously because of differences in methodology and definitions of health-care costs, these studies practise not take into account the additional costs of "substitute" diseases that might occur during life-years gained. To our knowledge only two studies used the appropriate lifetime perspective [xix,20], while only one [20] took into account medical costs of substitute diseases in life-years gained. It concluded that obesity causes higher lifetime medical costs, implying that prevention in this area tin can indeed upshot in cost savings.

In this study we present new estimates of annual and lifetime health-care costs of obesity in Kingdom of the netherlands, and make comparisons betwixt cohorts of people with different patterns of morbidity and mortality—namely, on the i paw smokers and on the other "good for you-living" people. This comparison provides two clear reference points for the example of obesity. A cohort approach was called to avoid blurring the comparison past demographic heterogeneity and to permit for a lifetime perspective. We included both the costs of diseases straight associated with obesity and smoking and those of other diseases that tend to occur equally life-years are gained.

Methods

To estimate almanac and lifetime health-care costs provisional on the presence of gamble factors, the National Constitute for Public Wellness and the Surround chronic affliction model (RIVM-CDM) was used. The RIVM-CDM is a dynamic population model that describes the life grade of cohorts in terms of transitions between risk factor classes and changes between disease states over time. Smoking classes distinguished in the model are never-smokers, current smokers, and one-time smokers. Body weight is modeled in three classes using trunk mass alphabetize (BMI) as an indicator: xviii.5 ≤ BMI < 25 (normal weight), 25 ≤ BMI < xxx (overweight), BMI ≥ 30 (obese). The RIVM-CDM has been used in illness projections and price effectiveness analyses [21–25]. With the model we estimated survivor numbers and disease prevalence numbers for three different hypothetical cohorts consisting of 500 men and 500 women aged twenty y at baseline: (ane) an "obese" cohort, never-smoking men and women aged 20 with a BMI above 30; (two) a "healthy-living" cohort, never-smoking men and women aged twenty with normal weight (18.five ≤ BMI < 25); and (3) a "smoking" cohort, men and women anile 20 with normal weight who had smoked throughout their life.

Cohorts were imitation until everybody in the cohort had died. The methods and input data we used to judge survivor and disease prevalence numbers for the different cohorts with the model were discussed in depth elsewhere [26] (run into likewise Table S1 and Texts S1 and S2 for more information on the RIVM-CDM). In short, chance factors were linked to 22 obesity- and/or smoking-related chronic diseases through relative risks of disease incidence for each risk factor level, to model the chain leading from risk factor to illness to death. In addition, chance factor levels influence mortality directly through bloodshed from diseases that are not explicitly modeled. The diseases modeled account for roughly sixty% of total morbidity [27] and mortality, and fifteen% of full health-care costs in The Netherlands [28]. The RIVM-CDM is programmed as a deterministic Markov model, i.e., the simulation model calculates the expected outcomes in one run. Therefore, more replications would not meliorate the results, which differs from a so-called microsimulation or Monte Carlo simulation model. We chose 500 men and 500 women purely for convenience.

No ideals committee approving was required for this study.

Cost of affliction (COI) data from The Netherlands for 2003 were used to estimate health-care expenditure for the different cohorts [28]. The 2003 COI written report was a sequel to earlier COI 1999 studies in the Netherlands [29–31] and COI estimates were made using the health-care cost definitions of the System of Health Accounts (SHA) for reasons of international comparability of costs [32]. Average annual costs per patient having a sure disease were calculated by dividing full almanac costs by Dutch prevalence numbers for each illness in 2003. Health-care costs for the different cohorts were and then calculated as follows. Kickoff, the annual disease costs per patient were multiplied by RIVM-CDM projections of future prevalence numbers for each chronic disease in the model. Then, to calculate wellness-care costs for all "other" diseases, the numbers of survivors were multiplied by age- and sexual practice-specific cost profiles of "remaining" costs. These latter are the difference betwixt total health-care costs and the costs of the diseases incorporated explicitly in the model. These costs include, for case, the costs of mental and behavioral disorders. Finally, these two categories of costs, 1 related and the other unrelated to the risk factor under study, were added to estimate almanac costs. To summate lifetime health-care costs of the 3 unlike cohorts [33], annual costs were added over time. To reflect the concept of time preference, meaning that an amount of money spent or saved in the future is worth less than the same amount today, internet present values were calculated using discount rates of iii% and 4%. Using the differences in lifetime health-care costs compared to the good for you-living accomplice nosotros calculated whether or not avoidance of obesity and smoking resulted in lower health-care costs.

To investigate the robustness of our results with respect to future changes in disease epidemiology and health-care costs, and different definitions of health-care costs, a serial of sensitivity analyses were performed by estimating the lifetime wellness-care costs in different scenarios:

Scenario 1.

Assumes a yearly decrease of 1% in the incidence and mortality rates for all diseases included in the model. This is roughly the aforementioned yearly decrease as was used in the Global Brunt of Affliction projections of global mortality and burden of illness [34].

Scenario two.

Assumes a yearly subtract in all relative risks of the obese and smoking accomplice to reflect selective disease prevention efforts in smokers and obese as has been observed in the past [35,36]. This was done using the following formula: RR(t) = {RR(t − 1) − 1} × 0.99 + 1 where RR(t) is the relative take chances in yr t.

Scenario 3.

Assumes a yearly increase of 1% in health-care costs for all diseases per person.

Scenario iv.

Adopts a broader definition of health-care costs (like the 1 ordinarily used in The Netherlands [37]), which includes a broader range of long-term and residential intendance than in the SHA every bit used in baseline estimates, which is specially relevant in case of increased longevity.

Scenario five.

Adopts a narrower definition of health-care costs by excluding all expenditure on nursing and residential care mentioned in the SHA definition. These costs can cause substantial variation in cost-of-illness estimations between countries [37]. The narrower set of costs improves the international comparability of the figures presented.

Scenario 6.

Uses relative mortality risk estimates for persons with xxx ≤ BMI < 35, published past Flegal et al. [38] as input for the simulation model for the obese cohort. Mortality estimates vary substantially every bit a role of BMI in the higher ranges beyond the cutoff BMI value of thirty. Lumping together all values above 30 into one category masks this significant variation in mortality and thus also in lifetime health-care costs, maybe leading to biased estimates. In fact, there still is scientific debate almost the exact values of the bloodshed risks associated with different levels of BMI. The article past Flegal et al. [38] attracted much attention because their estimates of the excess mortality associated with obesity were much lower than previously idea.

Scenario 7.

Uses relative mortality adventure estimates for persons with a BMI ≥ 35, published by Flegal et al. [38] as input for the simulation model for the obese cohort.

Results

Table 1 shows remaining life expectancy and the lifetime wellness-care costs for the three cohorts, specified past disease category.

The obese cohort has the highest health-care costs for diabetes and musculoskeletal diseases compared to the other cohorts. Lifetime costs for cancers other than lung cancer are equal for all cohorts. Despite differences in life expectancy, the costs for stroke are like for all cohorts. The most pronounced departure in costs occurs in the category "costs of other diseases," which is purely the result of unlike life expectancies.

Figure 1 displays average almanac wellness-care costs per healthy-living person, smoker, and obese person. At all ages, smokers and obese people incur more costs than do salubrious-living persons. Until historic period 56, average almanac health-intendance costs are highest for an obese person. In higher age groups smokers are more than expensive.

Despite the higher annual costs of the obese and smoking cohorts, the healthy-living cohort incurs highest lifetime costs, due to its higher life expectancy, as shown in Table i. Furthermore, the greatest differences in wellness-care costs are non caused by smoking- and obesity-related diseases, but by the other, unrelated, diseases that occur as life-years are gained (Table 1). Therefore, successful prevention of obesity and smoking would upshot in lower health-intendance costs in the short run (assuming no costs of prevention), but in the long run they would result in higher costs.

To zoom in on what might happen to health-care costs if successful prevention converts the obese and smoking cohort to a healthy-living cohort, Effigy ii displays the differences in total health-intendance costs over fourth dimension between the obese and smoking cohorts compared to the healthy-living cohort. Figure 2 shows that in approximately the starting time 50 years afterwards the hypothetical lifestyle change of the cohort, cost savings are realized through the reduced incidence of smoking- and high-BMI–related diseases. Later on this period, additional wellness-care costs occur during life-years gained. The initial savings are higher for the converted obese accomplice, primarily the result of savings due to a reduced incidence of diabetes and of nonlethal diseases such as osteoarthritis and lower-dorsum pain. Furthermore, Effigy two demonstrates that the initial savings weigh more heavily than do additional costs in the long run if costs are discounted. Cumulative differences in wellness-care costs are lower for obesity prevention than for smoking prevention: at disbelieve rates of, respectively, 3% and 4% successful smoking prevention would issue in additional health-care costs of €vii.i and €3.iv million (assuming costless intervention). For obesity prevention these figures would amount to €1.8 and €1.0 meg. Only for discount rates to a higher place 4.seven% would complimentary obesity prevention be price saving. For smoking prevention to be toll saving, the discount rate for costs should exist at least five.7%

Figure 2. Differences in Aggregated Health-Care Costs over Time if Successful Prevention Converts the Obese and Smoking Cohort into a Healthy-Living Cohort (Undiscounted and with a Discount Rate of 3%)

https://doi.org/ten.1371/periodical.pmed.0050029.g002

Tabular array ii displays the results of the sensitivity analyses. Expected health-care costs for all cohorts, and relative differences betwixt cohorts increase in scenario 1 (decreasing incidence and bloodshed rates) due to increases in life expectancy. In scenario two (decreasing relative risks), differences between the cohorts become less pronounced. In scenario 3 (increasing health-care costs) absolute estimates of lifetime health-care costs and differences between the cohorts increase. This is due to the fact that the yearly increase in health-care costs will be by and large felt at older ages. Nether the broader Dutch definition of wellness-intendance costs (scenario 4), differences between cohorts increase. Excluding costs of nursing homes (scenario 5) attenuates the differences between cohorts. Estimates of lifetime wellness-care costs using lower relative mortality risks for the obese cohort as input narrow the differences between the obese and good for you-living cohort (scenarios 6 and 7). The rank order of lifetime wellness-care costs for the cohorts, however, is the aforementioned in all scenarios.

Give-and-take

In this study we have shown that, although obese people induce high medical costs during their lives, their lifetime health-care costs are lower than those of healthy-living people simply higher than those of smokers. Obesity increases the gamble of diseases such equally diabetes and coronary eye disease, thereby increasing wellness-care utilization only decreasing life expectancy. Successful prevention of obesity, in turn, increases life expectancy. Unfortunately, these life-years gained are non lived in full health and come up at a price: people suffer from other diseases, which increases wellness-care costs. Obesity prevention, just like smoking prevention, volition non stem the tide of increasing health-care expenditures. The underlying mechanism is that at that place is a substitution of cheap, lethal diseases toward less lethal, and therefore more than costly, diseases [9]. Every bit smoking is in particular related to lethal (and relatively inexpensive) diseases, the ratio of toll savings from a reduced incidence of hazard factor–related diseases to the medical costs in life-years gained is more favorable for obesity prevention than for smoking prevention.

The simulation model we used to written report the lifetime medical costs associated with obesity employs data and assumptions similar to those used to summate and then-called "attributable fractions" [39] that serve to decide which proportion of health intendance may be attributed to particular risk factors [ten]. The main difference between the two approaches is that our model tin can have into business relationship differences in life expectancy. Using the same simulation model and methodology employed in this paper nosotros calculated that 2.0% of total health-intendance costs in Holland in 2003 could exist attributed to overweight (BMI > 25). For smoking, this percentage equaled 3.vii%. Given differences in overweight and smoking prevalence between the U.s.a. and The netherlands, these figures compare well with previous research [4,viii,10–18]. With respect to lifetime medical costs for smokers, our results are in line with other studies that used a lifetime perspective. Barendregt et al. [vii], using Dutch information, and Sloane et al. [8], in the United states of america wellness-care setting, both found that the high medical costs of smoking-related diseases are more than first by lower survival of smokers. For costs attributable to obesity, only i previous study used a like methodology, employing a lifetime perspective and including all medical costs. In dissimilarity to our analysis, it concluded that obesity increases lifetime medical costs [20]. This discrepancy may be explained in several ways. First, Allison and colleagues truncated their analysis at age 85, which—even bold no differences in mortality between cohorts afterwards this age—biases the results toward relatively higher lifetime medical costs attributable to obesity. 2nd, they based their estimates on another report in which the costs attributable to obesity were not stratified by age group [ten]; to compensate for this lack of information they hypothesized an age slope. Third, their age-related costs increased more than gradually, which may be due to a narrower definition of health-care costs. Fourth, information technology could well exist the case that our price definition was broader and included more so-called costs of care instead of cure, which are related to historic period rather than to affliction per se.

Some aspects of our study methodology need to be emphasized. Commencement, in the simulation model employed, affliction incidence rates are coupled to risk gene levels. Linking costs per affliction to the estimated disease prevalence over fourth dimension so allows for an explicit causal link between BMI condition and health-care costs. This is an important indicate, since in studies using individual-level data comprising both BMI and wellness-intendance use, the causality of the relationship between BMI and wellness-care utilize is usually left unspecified [eleven,15,xl]. As a result, the observed differences between the groups might accept been associated with confounding variables, e.m., socioeconomic status.

Second, the wellness-care costs employed in the model were a function of age and illness condition but not of proximity to death, which has been proposed equally an important determinant of health-care costs [41–43]. However, by modeling most master causes of decease (coronary heart disease, stroke, and unlike types of cancers) in our example, nosotros implicitly have taken into account "time to death" as an important explanatory variable of health-care costs, since postponement of these lethal diseases through prevention besides postpones the costs of these diseases.

3rd, nosotros assumed that costs per patient for each risk factor–related disease are equal, irrespective of risk factor status. This similarity might non always exist the case. For example, treatment costs of lower-back pain could depend on BMI status.

Quaternary, it is important to stress that we have focused solely on health-care costs related to smoking and obesity, ignoring broader cost categories and consequences of these hazard factors to society. It is likely, however, that these impacts will be substantial. For example, reduced morbidity in people of working age may improve productivity and thus result in sizeable productivity gains in club (e.thousand., [44]). In the case of smoking and obesity, these indirect costs could well exist higher than the direct medical costs [8,18]. Moreover, from a societal perspective, other potentially substantial costs and consequences need to exist considered, such as those related to informal care, the damage due to fires caused by smoking, or the reduced well-existence of family members due to morbidity and premature decease. These unlike cost categories emphasize the influence the perspective taken in economic analyses has on the conclusions. From a welfare economic perspective the societal perspective is, in fact, the about relevant [45], although in do many evaluations take a narrower perspective, which more closely conforms to the perspective almost relevant to the decision-maker they are trying to inform [46].

5th, in our simulation model there are no gradations of obesity, which are critical to relative risks, mortality impact, and thus also to lifetime wellness-care costs. However, our sensitivity analyses revealed that even if mortality risks for the obese group were based solely on the grouping thirty ≤ BMI < 35, lifetime wellness-care costs of the obese would still be lower than those of healthy-living persons.

Finally, nosotros assumed that no transitions occur between risk cistron classes over time. In reality, of class, transitions betwixt classes practice occur: some smokers quit (and some of them might start once again later) and obese people of course might lose and regain weight over time.

A remaining and most important question is whether prevention should be cost-saving in gild to be attractive. Obviously, the answer is that it need non be cost-saving: similar other forms of care information technology "only" needs to be cost-effective. Bonneux et al. [9] fabricated this very clear: "The aim of wellness care is non to relieve money merely to save people from preventable suffering and death. Any potential savings on health care costs would be icing on the cake." If prevention can bring boosted health to a population at relatively low costs, it is a good candidate for funding [47]. Even so, the present study demonstrates that sound estimates of medical costs in life-years gained should be taken into business relationship in cost-effectiveness analysis of prevention. In this respect it is interesting to annotation that in the area of smoking cessation and weight loss, favorable cost-effectiveness results have been shown even if medical costs in life-years gained are taken into account [22,26,33]. Prevention may therefore not be a cure for increasing expenditures—instead it may well be a price-effective cure for much morbidity and bloodshed and, importantly, contribute to the health of nations.

Supporting Information

Author Contributions

PHMvB had the original idea for the paper, carried out the analyses, and drafted the initial manuscript. RTH developed the simulation model. All authors contributed essentially in developing and writing the paper. HCB is the guarantor.

References

1. 1. Haslam DW, James WP (2005) Obesity. Lancet 366: 1197–1209.
   • View Commodity
   • Google Scholar
2. 2. Rodgers A, Ezzati M, Vander Hoorn South, Lopez Advertizement, Lin RB, Murray CJ (2004) Distribution of major health risks: findings from the Global Burden of Disease written report. PLoS Med 1: e27.
   • View Article
   • Google Scholar
3. iii. Groves T (2006) Pandemic obesity in Europe. BMJ 333: 1081.
   • View Commodity
   • Google Scholar
4. iv. Roth J, Qiang X, Marban SL, Redelt H, Lowell BC (2004) The obesity pandemic: where have nosotros been and where are nosotros going? Obes Res 12(Suppl 2): 88S–101S.
   • View Commodity
   • Google Scholar
5. 5. Arrangement for Economic Cooperation and Development (2005) Health at a glance. Paris: OECD.
6. 6. Fries JF, Koop CE, Beadle CE, Cooper PP, England MJ, et al. (1993) Reducing health care costs by reducing the need and demand for medical services. The Wellness Project Consortium. North Engl J Med 329: 321–325.
   • View Article
   • Google Scholar
7. 7. Barendregt JJ, Bonneux 50, van der Maas PJ (1997) The health intendance costs of smoking. Northward Engl J Med 337: 1052–1057.
   • View Article
   • Google Scholar
8. 8. Sloan FAOJ, Picone K, Conover C, Taylor DH (2004) The toll of smoking. Massachusetts: MIT Press.
9. 9. Bonneux L, Barendregt JJ, Nusselder WJ, der Maas PJ (1998) Preventing fatal diseases increases healthcare costs: crusade elimination life table approach. BMJ 316: 26–29.
   • View Article
   • Google Scholar
10. 10. Wolf AM, Colditz GA (1998) Current estimates of the economic cost of obesity in the Usa. Obes Res half dozen: 97–106.
    • View Commodity
    • Google Scholar
11. eleven. Sturm R (2002) The effects of obesity, smoking, and drinking on medical problems and costs. Health Aff (Millwood) 21: 245–253.
    • View Article
    • Google Scholar
12. 12. Colditz GA (1999) Economic costs of obesity and inactivity. Med Sci Sports Exerc 31: S663–S667.
    • View Article
    • Google Scholar
13. 13. Finkelstein EA, Fiebelkorn IC, Wang G (2003) National medical spending attributable to overweight and obesity: how much, and who's paying? Health Aff (Millwood). Suppl Web Exclusives, W3-219-26.
14. 14. Andreyeva T, Sturm R, Ringel JS (2004) Moderate and astringent obesity have large differences in health intendance costs. Obes Res 12: 1936–1943.
    • View Article
    • Google Scholar
15. 15. Arterburn DE, Maciejewski ML, Tsevat J (2005) Impact of morbid obesity on medical expenditures in adults. Int J Obes (Lond) 29: 334–339.
    • View Article
    • Google Scholar
16. xvi. Thorpe KE, Florence CS, Howard DH, Joski P (2004) The impact of obesity on rise medical spending. Health Aff (Millwood). Suppl Spider web Exclusives, W4-480-6.
17. 17. Wang G, Dietz WH (2002) Economic burden of obesity in youths aged half dozen to 17 years: 1979–1999. Pediatrics 109: E81.
    • View Article
    • Google Scholar
18. 18. National Audit Office (2001) Tackling obesity in England. Study by the comptroller and auditor general. London: NAO.
19. 19. Thompson D, Edelsberg J, Colditz GA, Bird AP, Oster G (1911) Lifetime wellness and economic consequences of obesity. Curvation Intern Med 159: 2177–2183.
    • View Article
    • Google Scholar
20. xx. Allison DB, Zannolli R, Narayan KM (1999) The straight health care costs of obesity in the U.s.. Am J Public Health 89: 1194–1199.
    • View Commodity
    • Google Scholar
21. 21. Struijs JN, van Genugten ML, Evers SM, Ament AJ, Baan CA, et al. (2005) Modeling the future burden of stroke in The Netherlands: impact of aging, smoking, and hypertension. Stroke 36: 1648–1655.
    • View Article
    • Google Scholar
22. 22. Jacobs-van der Bruggen MA, Bos G, Bemelmans WJ, Hoogenveen RT, Vijgen SM, et al. (2007) Lifestyle interventions are cost-effective in people with different levels of diabetes chance: results from a modeling written report. Diabetes Care 30: 128–134.
    • View Article
    • Google Scholar
23. 23. Feenstra TL, Hamberg-van Reenen HH, Hoogenveen RT, Rutten-van Molken MP (2005) Price-effectiveness of face up-to-face smoking cessation interventions: a dynamic modeling study. Value Health 8: 178–190.
    • View Article
    • Google Scholar
24. 24. van Baal PH, Brouwer WB, Hoogenveen RT, Feenstra TL (2007) Increasing tobacco taxes: a cheap tool to increase public wellness. Health Policy 82: 142–52.
    • View Article
    • Google Scholar
25. 25. Feenstra TL, van Genugten ML, Hoogenveen RT, Wouters EF, Rutten-van Molken MP (2001) The impact of aging and smoking on the future burden of chronic obstructive pulmonary disease: a model analysis in the Netherlands. Am J Respir Crit Care Med 164: 590–596.
    • View Article
    • Google Scholar
26. 26. van Baal PH, Hoogenveen RT, de Wit GA, Boshuizen HC (2006) Estimating health-adjusted life expectancy provisional on risk factors: results for smoking and obesity. Popul Health Metr 4: 14.
    • View Commodity
    • Google Scholar
27. 27. Statistics Netherlands (2006) Statline. Available: http://www.cbs.nl/en/figures/statline. Accessed xx December 2006.
28. 28. Slobbe LCJ, Kommer GJ, Smit JM, Groen J, Meerding WJ, et al. (2006) Costs of affliction in the Netherlands 2003. Bilthoven: Rijksinstituut voor Volksgezondheid en Milieu.
29. 29. Polder JJ, Meerding WJ, Bonneux 50, van der Maas PJ (2005) A cross-national perspective on price of affliction: a comparing of studies from Kingdom of the netherlands, Commonwealth of australia, Canada, Federal republic of germany, Great britain, and Sweden. Eur J Health Econ half dozen: 223–232.
    • View Commodity
    • Google Scholar
30. 30. Polder JJ, Bonneux L, Meerding WJ, van der Maas PJ (2002) Age-specific increases in health care costs. Eur J Public Health 12: 57–62.
    • View Article
    • Google Scholar
31. 31. Meerding WJ, Bonneux L, Polder JJ, Koopmanschap MA, van der Maas PJ (1998) Demographic and epidemiological determinants of healthcare costs in Netherlands: cost of illness study. BMJ 31: 111–115.
    • View Article
    • Google Scholar
32. 32. Arrangement for Economic Cooperation and Development (2000) A organization of health accounts. Version i.0. Paris: OECD.
33. 33. van Baal PH, Feenstra TL, Hoogenveen RT, Ardine de Wit G, Brouwer WB (2007) Unrelated medical care in life years gained and the cost utility of primary prevention: in search of a "perfect" cost-utility ratio. Health Econ 16: 421–433.
    • View Article
    • Google Scholar
34. 34. Mathers CD, Loncar D (2006) Projections of global mortality and brunt of disease from 2002 to 2030. PLoS Med three: e442.
    • View Article
    • Google Scholar
35. 35. Flim-flam CS, Coady Due south, Sorlie PD, Levy D, Meigs JB, et al. (2004) Trends in cardiovascular complications of diabetes. JAMA 292: 2495–2499.
    • View Article
    • Google Scholar
36. 36. Gregg EW, Cheng YJ, Cadwell BL, Imperatore Thou, Williams DE, et al. (2005) Secular trends in cardiovascular disease risk factors according to torso mass index in Us adults. JAMA 293: 1868–1874.
    • View Article
    • Google Scholar
37. 37. Heijink R, Koopmanschap MA, Polder JJ (2006) International comparison of cost of disease. Bilthoven: National Institute of Public Health and the Environment (RIVM).
38. 38. Flegal KM, Graubard BI, Williamson DF, Gail MH (1920) Excess deaths associated with underweight, overweight, and obesity. JAMA 293: 1861–1867.
    • View Article
    • Google Scholar
39. 39. Steenland Thousand, Armstrong B (2006) An overview of methods for computing the brunt of illness due to specific take chances factors. Epidemiology 17: 512–519.
    • View Article
    • Google Scholar
40. twoscore. Thompson D, Dark-brown JB, Nichols GA, Elmer PJ, Oster Thousand (2001) Torso mass alphabetize and time to come healthcare costs: a retrospective cohort study. Obes Res 9: 210–218.
    • View Article
    • Google Scholar
41. 41. Polder JJ, Barendregt JJ, van Oers H (2006) Health care costs in the last year of life—the Dutch experience. Soc Sci Med 63: 1720–1731.
    • View Article
    • Google Scholar
42. 42. Zweifel P, Felder South, Meiers K (1999) Ageing of population and health intendance expenditure: a cerise herring? Wellness Econ viii: 485–496.
    • View Article
    • Google Scholar
43. 43. Spillman BC, Lubitz J (2000) The effect of longevity on spending for astute and long-term intendance. N Engl J Med 342: 1409–1415.
    • View Article
    • Google Scholar
44. 44. Sullivan PW, Ghushchyan Five, Wyatt Hour, Wu EQ, Hill JO (2007) Productivity costs associated with cardiometabolic risk factor clusters in the U.s.a.. Value Wellness 10: 443–450.
    • View Article
    • Google Scholar
45. 45. Gold MR, Siegel JE, Russell LB, Weinstein MC (1996) Cost-effectiveness in health and medicine. New York: Oxford University Press.
46. 46. Brouwer WB, van Exel NJ, Baltussen RM, Rutten FF (2006) A dollar is a dollar is a dollar—or is information technology? Value Health 9: 341–347.
    • View Article
    • Google Scholar
47. 47. Roux L, Donaldson C (2004) Economics and obesity: costing the problem or evaluating solutions? Obes Res 12: 173–179.
    • View Article
    • Google Scholar

Source: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0050029

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