Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Association between lung function and exacerbation frequency in patients with COPD

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. 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 (FEV(1))% 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 FEV(1)% predicted for each GOLD stage to estimate the frequency per stage. 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. This study provides an estimate of the exacerbation frequency per GOLD stage, which can be used for health economic and modeling purposes

Environmental Impact Determinants: An Empirical Analysis based on the STIRPAT Model

AbstractThis paper attempt to investigate the impact of economic and population growth, urbanization level, energy intensity and Kyoto protocol obligations on carbon dioxide emissions using the STIRPAT model (STochastic Impacts by Regression on Population, Affluence and Technology). Our sample of countries is decomposed into groups according to the revenue level and the analyzed period extends from 1980 through 2010. Using several methods to estimate panel data, we find that there is a significant effect of economic growth, population growth, urbanization level and Kyoto protocol on emissions level and this effect depends on the revenue level

Future Burden and Costs of Smoking-Related Disease in the Netherlands: A Dynamic Modeling Approach

AbstractObjectivesIn this article, we explore the future health gain of different policy measures to reduce smoking prevalence: health education campaigns specifically aimed at keeping (young) people from starting to smoke, campaigns aimed at persuading smokers to quit, and tax measures.MethodsWe drew up different policy scenarios based on evaluations of several health promotion campaigns. Implementing these into the dynamic multistate models, we simulated smoking prevalence, loss of life-years, and costs for several decades into the next century.ResultsIn the short run, campaigns aimed at potential “quitters” appear to be most effective in terms of health gain. However, their effect fades away after several decades, while campaigns aimed at young “starters” or tax measures in the end yield a larger and more lasting decrease in smoking attributable disease burden.ConclusionDynamic modeling is very useful tool in calculating costs and effects of preventive public health measures

Bounded-width polynomial-size branching programs recognize exactly those languages in NC1

AbstractWe show that any language recognized by an NC1 circuit (fan-in 2, depth O(log n)) can be recognized by a width-5 polynomial-size branching program. As any bounded-width polynomial-size branching program can be simulated by an NC1 circuit, we have that the class of languages recognized by such programs is exactly nonuniform NC1. Further, following Ruzzo (J. Comput. System Sci. 22 (1981), 365–383) and Cook (Inform. and Control 64 (1985) 2–22), if the branching programs are restricted to be ATIME(logn)-uniform, they recognize the same languages as do ATIME(log n)-uniform NC1 circuits, that is, those languages in ATIME(log n). We also extend the method of proof to investigate the complexity of the word problem for a fixed permutation group and show that polynomial size circuits of width 4 also recognize exactly nonuniform NC1

Co-occurrence of diabetes, myocardial infarction, stroke, and cancer: quantifying age patterns in the Dutch population using health survey data

<p>Abstract</p> <p>Background</p> <p>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).</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

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

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

The cost-effectiveness of increasing alcohol taxes: a modelling study

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>Focusing on health-care costs and health consequences for drinkers, an alcohol tax increase is a cost-effective policy instrument.</p