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1.  The Effectiveness of Chronic Care Management for Heart Failure: Meta-Regression Analyses to Explain the Heterogeneity in Outcomes 
Health Services Research  2012;47(5):1926-1959.
To support decision making on how to best redesign chronic care by studying the heterogeneity in effectiveness across chronic care management evaluations for heart failure.
Data Sources
Reviews and primary studies that evaluated chronic care management interventions.
Study Design
A systematic review including meta-regression analyses to investigate three potential sources of heterogeneity in effectiveness: study quality, length of follow-up, and number of chronic care model components.
Principal Findings
Our meta-analysis showed that chronic care management reduces mortality by a mean of 18 percent (95 percent CI: 0.72–0.94) and hospitalization by a mean of 18 percent (95 percent CI: 0.76–0.93) and improves quality of life by 7.14 points (95 percent CI: −9.55 to −4.72) on the Minnesota Living with Heart Failure questionnaire. We could not explain the considerable differences in hospitalization and quality of life across the studies.
Chronic care management significantly reduces mortality. Positive effects on hospitalization and quality of life were shown, however, with substantial heterogeneity in effectiveness. This heterogeneity is not explained by study quality, length of follow-up, or the number of chronic care model components. More attention to the development and implementation of chronic care management is needed to support informed decision making on how to best redesign chronic care.
PMCID: PMC3513612  PMID: 22417281
Heart failure; chronic care management; quality improvement; statistical heterogeneity; systematic review
2.  Mortality and life expectancy in relation to long‐term cigarette, cigar and pipe smoking: The Zutphen Study 
Tobacco Control  2007;16(2):107-113.
Study objective
To study the effect of long‐term smoking on all‐cause and cause‐specific mortality, and to estimate the effects of cigarette and cigar or pipe smoking on life expectancy.
A long‐term prospective cohort study.
Zutphen, The Netherlands.
1373 men from the Zutphen Study, born between 1900 and 1920 and studied between 1960 and 2000.
Hazard ratios for the type of smoking, amount and duration of cigarette smoking, obtained from a time‐dependent Cox regression model. Absolute health effects of smoking are expressed as differences in life expectancy and the number of disease‐free years of life.
Main results
Duration of cigarette smoking was strongly associated with mortality from cardiovascular disease, lung cancer and chronic obstructive pulmonary disease, whereas both the number of cigarettes smoked as well as duration of cigarette smoking were strongly associated with all‐cause mortality. Average cigarette smoking reduced the total life expectancy by 6.8 years, whereas heavy cigarette smoking reduced the total life expectancy by 8.8 years. The number of total life‐years lost due to cigar or pipe smoking was 4.7 years. Moreover, cigarette smoking reduced the number of disease‐free life‐years by 5.8 years, and cigar or pipe smoking by 5.2 years. Stopping cigarette smoking at age 40 increased the life expectancy by 4.6 years, while the number of disease‐free life‐years was increased by 3.0 years.
Cigar or pipe smoking reduces life expectancy to a lesser extent than cigarette smoking. Both the number of cigarettes smoked and duration of smoking are strongly associated with mortality risk and the number of life‐years lost. Stopping smoking after age 40 has major health benefits.
PMCID: PMC2598467  PMID: 17400948
3.  Stability of dietary patterns assessed with reduced rank regression; the Zutphen Elderly Study 
Nutrition Journal  2014;13:30.
Reduced rank regression (RRR) combines exploratory analysis with a-priori knowledge by including risk factors in the model. Dietary patterns, derived from RRR analysis, can be interpreted by the chosen risk factor profile and give an indication of positive or adverse health effects for a specific disease. Our aim was to assess the stability of dietary patterns derived by RRR over time.
We used data from 467 men, aged 64–85 years, participating in the 1985 and 1990 examination rounds of the Zutphen Elderly Study. Backwards regression on risk factors and food groups was applied prior to the RRR analysis to exclude food groups with low predictability (from 36 to 19 food groups) for the chosen risk factor profile. For the final RRR analysis, dietary intake data from 19 food groups as predictor variables and 6 established risk factors for cardiovascular diseases (body mass index, systolic and diastolic blood pressure, high density lipoprotein and total cholesterol levels, and uric acid) were used.
Three RRR dietary patterns were derived for both examination years: a “(low in) cereal fibre pattern”, an “alcohol pattern” and an “inconsistent pattern”. The “(low in) cereal fibre pattern” was most stable over time, with a correlation coefficient of 0.47 (95% CI: 0.38-0.53) between 1985 and 1990 measurements.
Dietary patterns as measured by RRR, after backwards regression, are reasonably stable over a period of five years. Thus, RRR appears to be an attractive method to measure long-term dietary exposure for nutritional epidemiological studies, with one dietary measurement at baseline.
PMCID: PMC4021363  PMID: 24690194
Dietary pattern; Stability; Elderly; Exploratory reduced rank regression analyses; Confirmatory reduced rank regression analyses
4.  Do Intensive Care Data on Respiratory Infections Reflect Influenza Epidemics? 
PLoS ONE  2013;8(12):e83854.
Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics.
We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003–2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI.
Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51.
ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities.
PMCID: PMC3877112  PMID: 24391837
5.  Disaster exposure as a risk factor for mental health problems, eighteen months, four and ten years post-disaster – a longitudinal study 
BMC Psychiatry  2012;12:147.
Disaster experiences have been associated with higher prevalence rates of (mental) health problems. The objective of this study was to examine the independent relation between a series of single disaster experiences versus the independent predictive value of a accumulation of disaster experiences, i.e. a sum score of experiences and symptoms of distress and post-traumatic stress disorder (PTSD).
Survivors of a fireworks disaster participated in a longitudinal study and completed a questionnaire three weeks (wave 1), eighteen months (wave 2) and four years post-disaster (wave 3). Ten years post-disaster (wave 4) the respondents consisted of native Dutch survivors only. Main outcome measures were general distress and symptoms of PTSD.
Degree of disaster exposure (sum score) and some disaster-related experiences (such as house destroyed, injured, confusion) were related to distress at waves 2 and 3. This relation was mediated by distress at an earlier point in time. None of the individual disaster-related experiences was independently related to symptoms of distress. The association between the degree of disaster exposure and symptoms of PTSD at waves 2 and 3 was still statistically significant after controlling for symptoms of distress and PTSD at earlier point in time. The variable ‘house destroyed’ was the only factor that was independently related to symptoms of PTSD at wave 2. Ten years after the disaster, disaster exposure was mediated by symptoms of PTSD at waves 2 and 3. Disaster exposure was not independently related to symptoms of PTSD ten years post-disaster.
Until 4 years after the disaster, degree of exposure (a sum score) was a risk factor for PTSD symptoms while none of the individual disaster experiences could be identified as an independent risk factor. Ten years post-disaster, disaster exposure was no longer an independent risk factor for symptoms of PTSD. Since symptoms of PTSD and distress at earlier waves perpetuate the symptoms at later waves, health care workers should aim their resources at those who still have symptoms after one and a half year post-disaster, to prevent health problems at medium and long-term.
PMCID: PMC3557213  PMID: 22989093
Disasters; Longitudinal studies; Risk Factors; Stress, Psychological; Stress Disorders, Post-Traumatic
6.  DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments 
PLoS ONE  2012;7(5):e33317.
Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies.
Methods and Results
DYNAMO-HIA quantifies the impact of user-specified risk-factor changes on multiple diseases and in turn on overall population health, comparing one reference scenario with one or more intervention scenarios. The Markov-based modeling approach allows for explicit risk-factor states and simulation of a real-life population. A built-in parameter estimation module ensures that only standard population-level epidemiological evidence is required, i.e. data on incidence, prevalence, relative risks, and mortality. DYNAMO-HIA provides a rich output of summary measures – e.g. life expectancy and disease-free life expectancy – and detailed data – e.g. prevalences and mortality/survival rates – by age, sex, and risk-factor status over time. DYNAMO-HIA is controlled via a graphical user interface and is publicly available from the internet, ensuring general accessibility. We illustrate the use of DYNAMO-HIA with two example applications: a policy causing an overall increase in alcohol consumption and quantifying the disease-burden of smoking.
By combining modest data needs with general accessibility and user friendliness within the causal framework of HIA, DYNAMO-HIA is a potential standard tool for health impact assessment based on epidemiologic evidence.
PMCID: PMC3349723  PMID: 22590491
7.  Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool 
PLoS ONE  2012;7(2):e32363.
There are several types of tobacco control interventions/policies which can change future smoking exposure. The most basic intervention types are 1) smoking cessation interventions 2) preventing smoking initiation and 3) implementation of a nationwide policy affecting quitters and starters simultaneously. The possibility for dynamic quantification of such different interventions is key for comparing the timing and size of their effects.
Methods and Results
We developed a software tool, DYNAMO-HIA, which allows for a quantitative comparison of the health impact of different policy scenarios. We illustrate the outcomes of the tool for the three typical types of tobacco control interventions if these were applied in the Netherlands. The tool was used to model the effects of different types of smoking interventions on future smoking prevalence and on health outcomes, comparing these three scenarios with the business-as-usual scenario. The necessary data input was obtained from the DYNAMO-HIA database which was assembled as part of this project. All smoking interventions will be effective in the long run. The population-wide strategy will be most effective in both the short and long term. The smoking cessation scenario will be second-most effective in the short run, though in the long run the smoking initiation scenario will become almost as effective. Interventions aimed at preventing the initiation of smoking need a long time horizon to become manifest in terms of health effects. The outcomes strongly depend on the groups targeted by the intervention.
We calculated how much more effective the population-wide strategy is, in both the short and long term, compared to quit smoking interventions and measures aimed at preventing the initiation of smoking. By allowing a great variety of user-specified choices, the DYNAMO-HIA tool is a powerful instrument by which the consequences of different tobacco control policies and interventions can be assessed.
PMCID: PMC3285691  PMID: 22384230
8.  Participation in and attitude towards the national immunization program in the Netherlands: data from population-based questionnaires 
BMC Public Health  2012;12:57.
Knowledge about the determinants of participation and attitude towards the National Immunisation Program (NIP) may be helpful in tailoring information campaigns for this program. Our aim was to determine which factors were associated with nonparticipation in the NIP and which ones were associated with parents' intention to accept remaining vaccinations. Further, we analyzed possible changes in opinion on vaccination over a 10 year period.
We used questionnaire data from two independent, population-based, cross-sectional surveys performed in 1995-96 and 2006-07. For the 2006-07 survey, logistic regression modelling was used to evaluate what factors were associated with nonparticipation and with parents' intention to accept remaining vaccinations. We used multivariate multinomial logistic regression modelling to compare the results between the two surveys.
Ninety-five percent of parents reported that they or their child (had) participated in the NIP. Similarly, 95% reported they intended to accept remaining vaccinations. Ethnicity, religion, income, educational level and anthroposophic beliefs were important determinants of nonparticipation in the NIP. Parental concerns that played a role in whether or not they would accept remaining vaccinations included safety of vaccinations, maximum number of injections, whether vaccinations protect the health of one's child and whether vaccinating healthy children is necessary. Although about 90% reported their opinion towards vaccination had not changed, a larger proportion of participants reported to be less inclined to accept vaccination in 2006-07 than in 1995-96.
Most participants had a positive attitude towards vaccination, although some had doubts. Groups with a lower income or educational level or of non-Western descent participated less in the NIP than those with a high income or educational level or indigenous Dutch and have been less well identified previously. Particular attention ought to be given to these groups as they contribute in large measure to the rate of nonparticipation in the NIP, i.e., to a greater extent than well-known vaccine refusers such as specific religious groups and anthroposophics. Our finding that the proportion of the population inclined to accept vaccinations is smaller than it was 10 years ago highlights the need to increase knowledge about attitudes and beliefs regarding the NIP.
PMCID: PMC3298495  PMID: 22264347
9.  Co-occurrence of diabetes, myocardial infarction, stroke, and cancer: quantifying age patterns in the Dutch population using health survey data 
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).
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.
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.
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.
PMCID: PMC3175448  PMID: 21884614
multimorbidity; comorbidity; diabetes; cancer; cardiovascular disease; stroke; P-splines
10.  Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty 
BMC Public Health  2011;11:163.
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.
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.
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.
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.
PMCID: PMC3064641  PMID: 21406092
incidence; prevalence; Monte Carlo simulation; uncertainty
11.  Fitting additive Poisson models 
This paper describes how to fit an additive Poisson model using standard software. It is illustrated with SAS code, but can be similarly used for other software packages.
PMCID: PMC2914659  PMID: 20646285
12.  Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure 
PLoS Medicine  2008;5(2):e29.
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.
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
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
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)
PMCID: PMC2225430  PMID: 18254654
13.  Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation 
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.
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.
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.
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.
PMCID: PMC2267164  PMID: 18190684
14.  Estimating health-adjusted life expectancy conditional on risk factors: results for smoking and obesity 
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.
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.
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.
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.
PMCID: PMC1636666  PMID: 17083719
15.  The association between noise exposure and blood pressure and ischemic heart disease: a meta-analysis. 
Environmental Health Perspectives  2002;110(3):307-317.
It has been suggested that noise exposure is associated with blood pressure changes and ischemic heart disease risk, but epidemiologic evidence is still limited. Furthermore, most reviews investigating these relations were not carried out in a systematic way, which makes them more prone to bias. We conducted a meta-analysis of 43 epidemiologic studies published between 1970 and 1999 that investigate the relation between noise exposure (both occupational and community) and blood pressure and/or ischemic heart disease (International Classification of Diseases, Ninth Revision, codes 410-414). We studied a wide range of effects, from blood pressure changes to a myocardial infarction. With respect to the association between noise exposure and blood pressure, small blood pressure differences were evident. Our meta-analysis showed a significant association for both occupational noise exposure and air traffic noise exposure and hypertension: We estimated relative risks per 5 dB(A) noise increase of 1.14 (1.01-1.29) and 1.26 (1.14-1.39), respectively. Air traffic noise exposure was positively associated with the consultation of a general practitioner or specialist, the use of cardiovascular medicines, and angina pectoris. In cross-sectional studies, road traffic noise exposure increases the risk of myocardial infarction and total ischemic heart disease. Although we can conclude that noise exposure can contribute to the prevalence of cardiovascular disease, the evidence for a relation between noise exposure and ischemic heart disease is still inconclusive because of the limitations in exposure characterization, adjustment for important confounders, and the occurrence of publication bias.
PMCID: PMC1240772  PMID: 11882483
16.  A Large Outbreak of Legionnaires’ Disease at a Flower Show, the Netherlands, 1999 
Emerging Infectious Diseases  2002;8(1):37-43.
In 1999, an outbreak of Legionnaires’ disease affected many visitors to a flower show in the Netherlands. To identify the source of the outbreak, we performed an environmental investigation, as well as a case-control study among visitors and a serologic cohort study among exhibitors to measure exposure to possible sources. Of 77,061 visitors, 188 became ill (133 confirmed and 55 probable cases), for an attack rate of 0.23% for visitors and 0.61% for exhibitors. Two whirlpool spas in halls 3 and 4 of the exhibition and a sprinkler in hall 8 were culture positive for Legionella pneumophila. One of three genotypes found in both whirlpool spas was identical to the isolates from 28 of 29 culture-positive patients. Persons who paused at the whirlpool spa in hall 3 were at increased risk for becoming ill. This study illustrates that whirlpool spas may be an important health hazard if disinfection fails.
PMCID: PMC2730281  PMID: 11749746
Legionnaires’ disease; outbreaks; environmental microbiology; pulsed-field gel electrophoresis; case-control studies; cohort studies; seroepidemiologic studies
17.  Blood pressure and mortality in elderly people aged 85 and older: community based study 
BMJ : British Medical Journal  1998;316(7147):1780-1784.
Objective: To determine whether the inverse relation between blood pressure and all cause mortality in elderly people over 85 years of age can be explained by adjusting for health status, and to determine whether high blood pressure is a risk factor for mortality when the effects of poor health are accounted for.
Design: 5 to 7 year follow up of community residents aged 85 years and older.
Setting: Leiden, the Netherlands.
Subjects: 835 subjects whose blood pressure was recorded between 1987 and 1989.
Main outcome measure: All cause mortality.
Results: An inverse relation between blood pressure and all cause mortality was observed. For diastolic blood pressure crude 5 year all cause mortality decreased from 88% (52/59) (95% confidence interval 79% to 95%) in those with diastolic blood pressures <65 mm Hg to 59% (27/46) (44% to 72%) in those with diastolic pressures >100 mm Hg. For systolic blood pressure crude 5 year all cause mortality decreased from 85% (95/112) (78% to 91%) in those with systolic pressures <125 mm Hg to 59% (13/22) (38% to 78%) in those with systolic pressures >200 mm Hg. This decrease was no longer significant after adjustment for indicators of poor health. No relation existed between blood pressure and mortality from cardiovascular causes or stroke after adjustment for age and sex, but after adjustment for age, sex, and indicators of poor health there was a positive relation between diastolic blood pressure and mortality from both cardiovascular causes and stroke.
Conclusion: The inverse relation between blood pressure and all cause mortality in elderly people over 85 is associated with health status.
Key messages Among community residents aged 85 and older there was a paradoxical inverse relation between blood pressure and all cause mortality: higher blood pressure was associated with lower mortality This inverse relation seems mainly to be due to higher mortality in those with low blood pressure; low blood pressure seems to be caused by poor health There was no longer a significant relation between blood pressure and all cause mortality after adjusting for health status. However, there was a positive relation between diastolic blood pressure and mortality from both cardiovascular causes and stroke Treating hypertension does not shorten life expectancy among elderly people aged 85 and older, and it might prevent disability from stroke
PMCID: PMC28576  PMID: 9624064

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