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1.  Long-term health outcomes and cost-effectiveness of a computer-tailored physical activity intervention among people aged over fifty: modelling the results of a randomized controlled trial 
BMC Public Health  2014;14(1):1099.
Background
Physical inactivity is a significant predictor of several chronic diseases, becoming more prevalent as people age. Since the aging population increases demands on healthcare budgets, effectively stimulating physical activity (PA) against acceptable costs is of major relevance. This study provides insight into long-term health outcomes and cost-effectiveness of a tailored PA intervention among adults aged over fifty.
Methods
Intervention participants (N = 1729) received tailored advice three times within four months, targeting the psychosocial determinants of PA. The intervention was delivered in different conditions (i.e. print-delivered versus Web-based, and with or without additional information on local PA opportunities). In a clustered RCT, the effects of the different intervention conditions were compared to each other and to a control group. Effects on weekly Metabolic Equivalents (MET)-hours of PA obtained one year after the intervention started were extrapolated to long-term outcomes (5-year, 10-year and lifetime horizons) in terms of health effects and quality-adjusted life years (QALYs) and its effect on healthcare costs, using a computer simulation model. Combining the model outcomes with intervention cost estimates, this study provides insight into the long-term cost-effectiveness of the intervention. Incremental cost-effectiveness ratios (ICERs) were calculated.
Results
For all extrapolated time horizons, the printed and the Web-based intervention resulted in decreased incidence numbers for diabetes, colon cancer, breast cancer, acute myocardial infarctions, and stroke and increased QALYs as a result of increased PA. Considering a societal Willingness-to-Pay of €20,000/QALY, on a lifetime horizon the printed (ICER = €7,500/QALY) as well as the Web-based interventions (ICER = €10,100/QALY) were cost-effective. On a 5-year time horizon, the Web-based intervention was preferred over the printed intervention. On a 10-year and lifetime horizon, the printed intervention was the preferred intervention condition, since the monetary savings of the Web-based intervention did no longer outweigh its lower effects. Adding environmental information resulted in a lower cost-effectiveness.
Conclusion
A tailored PA intervention in a printed delivery mode, without environmental information, has the most potential for being cost-effective in adults aged over 50.
Trial registration
The current study was registered at the Dutch Trial Register (NTR2297; April 26th 2010).
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2458-14-1099) contains supplementary material, which is available to authorized users.
doi:10.1186/1471-2458-14-1099
PMCID: PMC4221676  PMID: 25342517
Cost-effectiveness; Modelling; Quality of life; Disease incidence; Physical activity; Tailored intervention; Print-delivered; Web-based
2.  Cost-effectiveness of counseling and pedometer use to increase physical activity in the Netherlands: a modeling study 
Background
Counseling in combination with pedometer use has proven to be effective in increasing physical activity and improving health outcomes. We investigated the cost-effectiveness of this intervention targeted at one million insufficiently active adults who visit their general practitioner in the Netherlands.
Methods
We used the RIVM chronic disease model to estimate the long-term effects of increased physical activity on the future health care costs and quality adjusted life years (QALY) gained, from a health care perspective.
Results
The intervention resulted in almost 6000 people shifting to more favorable physical-activity levels, and in 5100 life years and 6100 QALYs gained, at an additional total cost of EUR 67.6 million. The incremental cost-effectiveness ratio (ICER) was EUR 13,200 per life year gained and EUR 11,100 per QALY gained. The intervention has a probability of 0.66 to be cost-effective if a QALY gained is valued at the Dutch informal threshold for cost-effectiveness of preventive intervention of EUR 20,000. A sensitivity analysis showed substantial uncertainty of ICER values.
Conclusion
Counseling in combination with pedometer use aiming to increase physical activity may be a cost-effective intervention. However, the intervention only yields relatively small health benefits in the Netherlands.
doi:10.1186/1478-7547-10-13
PMCID: PMC3495195  PMID: 23006466
Economic evaluation; Prevention; Modeling; Counseling; Pedometer use; Physical activity; Primary care
3.  Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention 
Background
Diabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients.
Methods
A mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed.
Results
Full implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients.
Conclusions
Major health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.
doi:10.1186/1478-7547-9-14
PMCID: PMC3200148  PMID: 21974836
4.  Cell Selection as Driving Force in Lung and Colon Carcinogenesis 
Cancer research  2010;70(17):6797-6803.
Carcinogenesis is the result of mutations and subsequent clonal expansions of mutated, selectively advantageous cells. To investigate the relative contributions of mutation versus cell selection in tumorigenesis, we compared two mathematical models of carcinogenesis in two different cancer types: lung and colon. One approach is based on a population genetics model, the Wright-Fisher process, whereas the other approach is the two-stage clonal expansion model. We compared the dynamics of tumorigenesis predicted by the two models in terms of the time period until the first malignant cell appears, which will subsequently form a tumor. The mean waiting time to cancer has been calculated approximately for the evolutionary colon cancer model. Here, we derive new analytic approximations to the median waiting time for the two-stage lung cancer model and for a multistage approximation to the Wright-Fisher process. Both equations show that the waiting time to cancer is dominated by the selective advantage per mutation and the net clonal expansion rate, respectively, whereas the mutation rate has less effect. Our comparisons support the idea that the main driving force in lung and colon carcinogenesis is Darwinian cell selection.
doi:10.1158/0008-5472.CAN-09-4392
PMCID: PMC3085130  PMID: 20656803
5.  Co-occurrence of diabetes, myocardial infarction, stroke, and cancer: quantifying age patterns in the Dutch population using health survey data 
Background
The high prevalence of chronic diseases in Western countries implies that the presence of multiple chronic diseases within one person is common. Especially at older ages, when the likelihood of having a chronic disease increases, the co-occurrence of distinct diseases will be encountered more frequently. The aim of this study was to estimate the age-specific prevalence of multimorbidity in the general population. In particular, we investigate to what extent specific pairs of diseases cluster within people and how this deviates from what is to be expected under the assumption of the independent occurrence of diseases (i.e., sheer coincidence).
Methods
We used data from a Dutch health survey to estimate the prevalence of pairs of chronic diseases specified by age. Diseases we focused on were diabetes, myocardial infarction, stroke, and cancer. Multinomial P-splines were fitted to the data to model the relation between age and disease status (single versus two diseases). To assess to what extent co-occurrence cannot be explained by independent occurrence, we estimated observed/expected co-occurrence ratios using predictions of the fitted regression models.
Results
Prevalence increased with age for all disease pairs. For all disease pairs, prevalence at most ages was much higher than is to be expected on the basis of coincidence. Observed/expected ratios of disease combinations decreased with age.
Conclusion
Common chronic diseases co-occur in one individual more frequently than is due to chance. In monitoring the occurrence of diseases among the population at large, such multimorbidity is insufficiently taken into account.
doi:10.1186/1478-7954-9-51
PMCID: PMC3175448  PMID: 21884614
multimorbidity; comorbidity; diabetes; cancer; cardiovascular disease; stroke; P-splines
6.  Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty 
BMC Public Health  2011;11:163.
Background
Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases.
Methods
Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000.
Results
Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank.
Conclusion
Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.
doi:10.1186/1471-2458-11-163
PMCID: PMC3064641  PMID: 21406092
incidence; prevalence; Monte Carlo simulation; uncertainty
7.  Association between lung function and exacerbation frequency in patients with COPD 
Purpose:
To quantify the relationship between severity of chronic obstructive pulmonary disease (COPD) as expressed by Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage and the annual exacerbation frequency in patients with COPD.
Methods:
We performed a systematic literature review to identify randomized controlled trials and cohort studies reporting the exacerbation frequency in COPD patients receiving usual care or placebo. Annual frequencies were determined for total exacerbations defined by an increased use of health care (event-based), total exacerbations defined by an increase of symptoms, and severe exacerbations defined by a hospitalization. The association between the mean forced expiratory volume in one second (FEV1)% predicted of study populations and the exacerbation frequencies was estimated using weighted log linear regression with random effects. The regression equations were applied to the mean FEV1% predicted for each GOLD stage to estimate the frequency per stage.
Results:
Thirty-seven relevant studies were found, with 43 reports of total exacerbation frequency (event-based, n = 19; symptom-based, n = 24) and 14 reports of frequency of severe exacerbations. Annual event-based exacerbation frequencies per GOLD stage were estimated at 0.82 (95% confidence interval 0.46–1.49) for mild, 1.17 (0.93–1.50) for moderate, 1.61 (1.51–1.74) for severe, and 2.10 (1.51–2.94) for very severe COPD. Annual symptom-based frequencies were 1.15 (95% confidence interval 0.67–2.07), 1.44 (1.14–1.87), 1.76 (1.70–1.88), and 2.09 (1.57–2.82), respectively. For severe exacerbations, annual frequencies were 0.11 (95% confidence interval 0.02–0.56), 0.16 (0.07–0.33), 0.22 (0.20–0.23), and 0.28 (0.14–0.63), respectively. Study duration or type of study (cohort versus trial) did not significantly affect the outcomes.
Conclusion:
This study provides an estimate of the exacerbation frequency per GOLD stage, which can be used for health economic and modeling purposes.
doi:10.2147/COPD.S13826
PMCID: PMC3008329  PMID: 21191438
COPD; exacerbations; disease severity; GOLD; review; regression
8.  Cost-Effectiveness of Lifestyle Modification in Diabetic Patients 
Diabetes Care  2009;32(8):1453-1458.
OBJECTIVE
To explore the potential long-term health and economic consequences of lifestyle interventions for diabetic patients.
RESEARCH DESIGN AND METHODS
A literature search was performed to identify interventions for diabetic patients in which lifestyle issues were addressed. We selected recent (2003–2008), randomized controlled trials with a minimum follow-up of 12 months. The long-term outcomes for these interventions, if implemented in the Dutch diabetic population, were simulated with a computer-based model. Costs and effects were discounted at, respectively, 4 and 1.5% annually. A lifelong time horizon was applied. Probabilistic sensitivity analyses were performed, taking account of variability in intervention costs and (long-term) treatment effects.
RESULTS
Seven trials with 147–5,145 participants met our predefined criteria. All interventions improved cardiovascular risk factors at ≥1 year follow-up and were projected to reduce cardiovascular morbidity over lifetime. The interventions resulted in an average gain of 0.01–0.14 quality-adjusted life-years (QALYs) per participant. Health benefits were generally achieved at reasonable costs (≤€50,000/QALY). A self-management education program (X-PERT) and physical activity counseling achieved the best results with ≥0.10 QALYs gained and ≥99% probability to be very cost-effective (≤€20,000/QALY).
CONCLUSIONS
Implementation of lifestyle interventions would probably yield important health benefits at reasonable costs. However, essential evidence for long-term maintenance of health benefits was limited. Future research should be focused on long-term effectiveness and multiple treatment strategies should be compared to determine incremental costs and benefits of one over the other.
doi:10.2337/dc09-0363
PMCID: PMC2713648  PMID: 19435958
9.  Cost-Effectiveness of an Opportunistic Screening Programme and Brief Intervention for Excessive Alcohol Use in Primary Care 
PLoS ONE  2009;4(5):e5696.
Background
Effective prevention of excessive alcohol use has the potential to reduce the public burden of disease considerably. We investigated the cost-effectiveness of Screening and Brief Intervention (SBI) for excessive alcohol use in primary care in the Netherlands, which is targeted at early detection and treatment of ‘at-risk’ drinkers.
Methodology and Results
We compared a SBI scenario (opportunistic screening and brief intervention for ‘at-risk’ drinkers) in general practices with the current practice scenario (no SBI) in the Netherlands. We used the RIVM Chronic Disease Model (CDM) to extrapolate from decreased alcohol consumption to effects on health care costs and Quality Adjusted Life Years (QALYs) gained. Probabilistic sensitivity analysis was employed to study the effect of uncertainty in the model parameters. In total, 56,000 QALYs were gained at an additional cost of €298,000,000 due to providing alcohol SBI in the target population, resulting in a cost-effectiveness ratio of €5,400 per QALY gained.
Conclusion
Prevention of excessive alcohol use by implementing SBI for excessive alcohol use in primary care settings appears to be cost-effective.
doi:10.1371/journal.pone.0005696
PMCID: PMC2682644  PMID: 19479081
10.  The cost-effectiveness of increasing alcohol taxes: a modelling study 
BMC Medicine  2008;6:36.
Background
Excessive alcohol use increases risks of chronic diseases such as coronary heart disease and several types of cancer, with associated losses of quality of life and life-years. Alcohol taxes can be considered as a public health instrument as they are known to be able to decrease alcohol consumption. In this paper, we estimate the cost-effectiveness of an alcohol tax increase for the entire Dutch population from a health-care perspective focusing on health benefits and health-care costs in alcohol users.
Methods
The chronic disease model of the National Institute for Public Health and the Environment was used to extrapolate from decreased alcohol consumption due to tax increases to effects on health-care costs, life-years gained and quality-adjusted life-years gained, A Dutch scenario in which tax increases for beer are planned, and a Swedish scenario representing one of the highest alcohol taxes in Europe, were compared with current practice in the Netherlands. To estimate cost-effectiveness ratios, yearly differences in model outcomes between intervention and current practice scenarios were discounted and added over the time horizon of 100 years to find net present values for incremental life-years gained, quality-adjusted life-years gained, and health-care costs.
Results
In the Swedish scenario, many more quality-adjusted life-years were gained than in the Dutch scenario, but both scenarios had almost equal incremental cost-effectiveness ratios: €5100 per quality-adjusted life-year and €5300 per quality-adjusted life-year, respectively.
Conclusion
Focusing on health-care costs and health consequences for drinkers, an alcohol tax increase is a cost-effective policy instrument.
doi:10.1186/1741-7015-6-36
PMCID: PMC2637894  PMID: 19040717
11.  Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure 
PLoS Medicine  2008;5(2):e29.
Background
Obesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this study was to estimate the annual 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 cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and “healthy-living” persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions.
Conclusions
Although effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.
Using a simulation model, Pieter van Baal and colleagues conclude that obesity prevention leads to a decrease in costs of obesity-related diseases, but this is offset by cost increases due to diseases unrelated to obesity in life-years gained.
Editors' Summary
Background.
Since the mid 1970s, the proportion of people who are obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. One-third of all US adults, for example, are now classified as 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 height in meters squared) is between 25 and 30, and obese if BMI is greater than 30. 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 as diabetes, coronary heart disease and stroke, and tend to die younger. People become unhealthily fat by consuming food and drink that contains more energy than they need for their daily activities. In these circumstances, the body converts the excess energy into fat for use at a later date. Obesity can be prevented, therefore, by having a healthy diet and exercising regularly.
Why Was This Study Done?
Because obesity causes so much illness and premature death, many governments have public-health policies that aim to prevent obesity. Clearly, the improvement in health associated with the prevention of obesity is a worthwhile goal in itself but the prevention of obesity might also reduce national spending on medical care. It would do this, the argument goes, by reducing the amount of money spent on treating the diseases for which obesity is a risk factor. However, some experts have suggested that these short-term savings might be offset by spending on treating the diseases that would occur during the extra lifespan experienced by non-obese individuals. In this study, therefore, the researchers have used a computer model to calculate yearly and lifetime medical costs associated with obesity in The Netherlands.
What Did the Researchers Do and Find?
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 20 until the time when the model predicted that everyone had died. The “obese” group consisted of never-smoking people with a BMI of more than 30; the “healthy-living” group consisted of never-smoking people with a healthy weight; the “smoking” group consisted of lifetime smokers with a healthy 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 by the smoking group. However, because of differences in life expectancy (life expectancy at age 20 was 5 years less for the obese group, and 8 years less for the smoking group, 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 Do These Findings Mean?
As with all mathematical models such as 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 affect lifetime health-care costs, nor indirect costs of obesity such as reduced productivity. Nevertheless, these findings suggest 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 down.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/doi:10.1371/journal.pmed.0050029.
The MedlinePlus encyclopedia has a page on obesity (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of obesity (in English and Spanish)
The UK National Health Service's health Web site (NHS Direct) provides information about obesity
The International Obesity Taskforce provides information about preventing obesity
The UK Foods Standards Agency, the United States Department of Agriculture, and Shaping America's Health all provide useful advice about healthy eating
The Netherlands National Institute for Public Health and the Environment (RIVM) Web site provides more information on the cost of illness and illness prevention in the Netherlands (in English and Dutch)
doi:10.1371/journal.pmed.0050029
PMCID: PMC2225430  PMID: 18254654
12.  Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation 
Background
To support health policy makers in setting priorities, quantifying the potential effects of tobacco control on the burden of disease is useful. However, smoking is related to a variety of diseases and the dynamic effects of smoking cessation on the incidence of these diseases differ. Furthermore, many people who quit smoking relapse, most of them within a relatively short period.
Methods
In this paper, a method is presented for calculating the effects of smoking cessation interventions on disease incidence that allows to deal with relapse and the effect of time since quitting. A simulation model is described that links smoking to the incidence of 14 smoking related diseases. To demonstrate the model, health effects are estimated of two interventions in which part of current smokers in the Netherlands quits smoking.
To illustrate the advantages of the model its results are compared with those of two simpler versions of the model. In one version we assumed no relapse after quitting and equal incidence rates for all former smokers. In the second version, incidence rates depend on time since cessation, but we assumed still no relapse after quitting.
Results
Not taking into account time since smoking cessation on disease incidence rates results in biased estimates of the effects of interventions. The immediate public health effects are overestimated, since the health risk of quitters immediately drops to the mean level of all former smokers. However, the long-term public health effects are underestimated since after longer periods of time the effects of past smoking disappear and so surviving quitters start to resemble never smokers. On balance, total health gains of smoking cessation are underestimated if one does not account for the effect of time since cessation on disease incidence rates. Not taking into account relapse of quitters overestimates health gains substantially.
Conclusion
The results show that simulation models are sensitive to assumptions made in specifying the model. The model should be specified carefully in accordance with the questions it is supposed to answer. If the aim of the model is to estimate effects of smoking cessation interventions on mortality and morbidity, one should include relapse of quitters and dependency on time since cessation of incidence rates of smoking-related chronic diseases. A drawback of such models is that data requirements are extensive.
doi:10.1186/1478-7547-6-1
PMCID: PMC2267164  PMID: 18190684
13.  Estimating health-adjusted life expectancy conditional on risk factors: results for smoking and obesity 
Background
Smoking and obesity are risk factors causing a large burden of disease. To help formulate and prioritize among smoking and obesity prevention activities, estimations of health-adjusted life expectancy (HALE) for cohorts that differ solely in their lifestyle (e.g. smoking vs. non smoking) can provide valuable information. Furthermore, in combination with estimates of life expectancy (LE), it can be tested whether prevention of obesity and smoking results in compression of morbidity.
Methods
Using a dynamic population model that calculates the incidence of chronic disease conditional on epidemiological risk factors, we estimated LE and HALE at age 20 for a cohort of smokers with a normal weight (BMI < 25), a cohort of non-smoking obese people (BMI>30) and a cohort of 'healthy living' people (i.e. non smoking with a BMI < 25). Health state valuations for the different cohorts were calculated using the estimated disease prevalence rates in combination with data from the Dutch Burden of Disease study. Health state valuations are multiplied with life years to estimate HALE. Absolute compression of morbidity is defined as a reduction in unhealthy life expectancy (LE-HALE) and relative compression as a reduction in the proportion of life lived in good health (LE-HALE)/LE.
Results
Estimates of HALE are highest for a 'healthy living' cohort (54.8 years for men and 55.4 years for women at age 20). Differences in HALE compared to 'healthy living' men at age 20 are 7.8 and 4.6 for respectively smoking and obese men. Differences in HALE compared to 'healthy living' women at age 20 are 6.0 and 4.5 for respectively smoking and obese women. Unhealthy life expectancy is about equal for all cohorts, meaning that successful prevention would not result in absolute compression of morbidity. Sensitivity analyses demonstrate that although estimates of LE and HALE are sensitive to changes in disease epidemiology, differences in LE and HALE between the different cohorts are fairly robust. In most cases, elimination of smoking or obesity does not result in absolute compression of morbidity but slightly increases the part of life lived in good health.
Conclusion
Differences in HALE between smoking, obese and 'healthy living' cohorts are substantial and similar to differences in LE. However, our results do not indicate that substantial compression of morbidity is to be expected as a result of successful smoking or obesity prevention.
doi:10.1186/1478-7954-4-14
PMCID: PMC1636666  PMID: 17083719
14.  Disability weights for comorbidity and their influence on Health-adjusted Life Expectancy 
Background
Comorbidity complicates estimations of health-adjusted life expectancy (HALE) using disease prevalences and disability weights from Burden of Disease studies. Usually, the exact amount of comorbidity is unknown and no disability weights are defined for comorbidity.
Methods
Using data of the Dutch national burden of disease study, the effects of different methods to adjust for comorbidity on HALE calculations are estimated. The default multiplicative adjustment method to define disability weights for comorbidity is compared to HALE estimates without adjustment for comorbidity and to HALE estimates in which the amount of disability in patients with multiple diseases is solely determined by the disease that leads to most disability (the maximum adjustment method). To estimate the amount of comorbidity, independence between diseases is assumed.
Results
Compared to the multiplicative adjustment method, the maximum adjustment method lowers HALE estimates by 1.2 years for males and 1.9 years for females. Compared to no adjustment, a multiplicative adjustment lowers HALE estimates by 1.0 years for males and 1.4 years for females.
Conclusion
The differences in HALE caused by the different adjustment methods demonstrate that adjusting for comorbidity in HALE calculations is an important topic that needs more attention. More empirical research is needed to develop a more general theory as to how comorbidity influences disability.
doi:10.1186/1478-7954-4-1
PMCID: PMC1523368  PMID: 16606448

Results 1-14 (14)