Long-term wine consumption is related to cardiovascular mortality and life expectancy independently of moderate alcohol intake: the Zutphen Study

Background: Light to moderate alcohol intake lowers the risk of cardiovascular mortality, but whether this protective effect can be attributed to a specific type of beverage remains unclear. Moreover, little is known about the effects of long-term alcohol intake on life expectancy. Methods: The impact of long-term alcohol intake and types of alcoholic beverages consumed on cardiovascular mortality and life expectancy at age 50 was investigated in the Zutphen Study, a cohort of 1373 men born between 1900 and 1920 and examined repeatedly between 1960 and 2000. Hazard ratios (HRs) for total alcohol intake and alcohol from wine, beer and spirits were obtained from time-dependent Cox regression models. Life expectancy at age 50 was calculated from areas under survival curves. Results: Long-term light alcohol intake, that is =20 g per day, compared with no alcohol, was strongly and inversely associated with cerebrovascular (HR 0.43, 95% CI 0.26 to 0.70), total cardiovascular (HR 0.70, 95% CI 0.55 to 0.89) and all-cause mortality (HR 0.75, 95% CI 0.63 to 0.91). Independent of total alcohol intake, long-term wine consumption of, on average, less than half a glass per day was strongly and inversely associated with coronary heart disease (HR 0.61, 95% CI 0.41 to 0.89), total cardiovascular (HR 0.68, 95% CI 0.53 to 0.86) and all-cause mortality (HR 0.73, 95% CI 0.62 to 0.87). These results could not be explained by differences in socioeconomic status. Life expectancy was about 5 years longer in men who consumed wine compared with those who did not use alcoholic beverages. Conclusion: Long-term light alcohol intake lowered cardiovascular and all-cause mortality risk and increased life expectancy. Light wine consumption was associated with 5 years longer life expectancy; however, more studies are needed to verify this resul

Rekenen met kennis : analyse en synthese in voedingsonderzoek

Inaugurele rede over het gebruik van statistiek bij voedingsonderzoek bij de mens

The effectiveness of chronic care management for heart failure: meta-regression analyses to explain the heterogeneity in outcomes

Objective: 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.\ud \ud Data Sources: Reviews and primary studies that evaluated chronic care management interventions.\ud \ud 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 (CCM) components.\ud \ud Principal findings: Our meta-analysis showed that chronic care management reduces mortality by a mean of 18% (95% CI: 0.72-0.94) and hospitalization by a mean of 18% (95% CI: 0.76-0.93) and improves quality of life by 7.14 points (95% CI: -9.55 - -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.\ud \ud Conclusion: 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 CCM 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

Near normal HbA1c with stable glucose homeostasis: the ultimate target/aim of diabetes therapy

Background: The long-term longitudinal evidence for a relation between coffee intake and hypertension is relatively scarce. Objective: The objective was to assess whether coffee intake is associated with the incidence of hypertension. Design: This study was conducted on a cohort of 2985 men and 3383 women who had a baseline visit and follow-up visits after 6 and 11 y. Baseline coffee intake was ascertained with questionnaires and categorized into 0, > 0-3, > 3-6, and > 6 cups/d. Hypertension was defined as a mean systolic blood pressure (SBP) >= 140 mm Hg over both follow-up measurements, a mean diastolic blood pressure (DBP) >= 90 mm Hg over both follow-up measurements, or the use of antihypertensive medication at any follow-up measurement. Results: Coffee abstainers at baseline had a lower risk of hypertension than did those with a coffee intake of > 0-3 cups/d [odds ratio (OR): 0.54; 95% CI: 0.31, 0.92]. Women who drank > 6 cups/d had a lower risk than did women who drank > 0-3 cups/d (OR: 0.67; 95% CI: 0.46, 0.98). Subjects aged >= 39 y at baseline had 0.35 mm Hg (95% CI: -0.59, -0.11 mm Hg) lower SBP per cup intake/d and 0.11 mm Hg lower DBP (95% CI: -0.26, 0.03 mm Hg) than did those aged <39 y at baseline, although the difference in DBP was not statistically significant. Conclusions: Coffee abstinence is associated with a lower hypertension risk than is low coffee consumption. An inverse U-shaped relation between coffee intake and risk of hypertension was observed in the women

Ten-Year Blood Pressure Trajectories, Cardiovascular Mortality, and Life Years Lost in 2 Extinction Cohorts: the Minnesota Business and Professional Men Study and the Zutphen Study

Background Blood pressure (BP) trajectories derived from measurements repeated over years have low measurement error and may improve cardiovascular disease prediction compared to single, average, and usual BP (single BP adjusted for regression dilution). We characterized 10-year BP trajectories and examined their association with cardiovascular mortality, all-cause mortality, and life years lost. Methods and Results Data from 2 prospective and nearly extinct cohorts of middle-aged men—the Minnesota Business and Professional Men Study (n=261) and the Zutphen Study (n=632)—were used. BP was measured annually during 1947–1957 in Minnesota and 1960–1970 in Zutphen. BP trajectories were identified by latent mixture modeling. Cox proportional hazards and linear regression models examined BP trajectories with cardiovascular mortality, all-cause mortality, and life years lost. Associations were adjusted for age, serum cholesterol, smoking, and diabetes mellitus. Mean initial age was about 50 years in both cohorts. After 10 years of BP measurements, men were followed until death on average 20 years later. All Minnesota men and 98% of Zutphen men died. Four BP trajectories were identified, in which mean systolic BP increased by 5 to 49 mm Hg in Minnesota and 5 to 20 mm Hg in Zutphen between age 50 and 60. The third systolic BP trajectories were associated with 2 to 4 times higher cardiovascular mortality risk, 2 times higher all-cause mortality risk, and 4 to 8 life years lost, compared to the first trajectory. Conclusions Ten-year BP trajectories were the strongest predictors, among different BP measures, of cardiovascular mortality, all-cause mortality, and life years lost in Minnesota. However, average BP was the strongest predictor in Zutphen

Do intensive care data on respiratory infections reflect influenza epidemics?

Objectives 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. Methods 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. Results 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. Conclusions 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

Potential impact of reduced tobacco use on life and health expectancies in Belgium

Objectives: We investigated the potential impact of reduced tobacco use scenarios on total life expectancy and health expectancies, i.e., healthy life years and unhealthy life years. Methods: Data from the Belgian Health Interview Survey 2013 were used to estimate smoking and disability prevalence. Disability was based on the Global Activity Limitation Indicator. We used DYNAMO-HIA to quantify the impacts of risk factor changes and to compare the “business-as-usual” with alternative scenarios. Results: The “business-as-usual” scenario estimated that in 2028 the 15-year-old men/women would live additional 50/52 years without disability and 14/17 years with disability. The “smoking-free population” scenario added 3.4/2.8 healthy life years and reduced unhealthy life years by 0.79/1.9. Scenarios combining the prevention of smoking initiation with smoking cessation programs are the most effective, yielding the largest increase in healthy life years (1.9/1.7) and the largest decrease in unhealthy life years (− 0.80/− 1.47). Conclusions: Health impact assessment tools provide different scenarios for

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model

Sensitivity analysis of state-transition models: how to deal with a large number of inputs

State-transition models are employed to project future prevalence rates of risk factors and diseases within populations. Sensitivity analysis should be performed to assess the reliability of the results but often the number of inputs of the model is so huge, and running the model is so time-consuming, that not all methods of sensitivity analysis are practically available. Screening methods detect which inputs have a major influence on the outputs. We briefly review the available screening methods, and discuss one in particular, Morris' OAT Design. We applied the method under different assumptions to a module of the RIVM Chronic Diseases Model, where we projected the rates of never smokers, former smokers and current smokers in time up to the year 2050, based on smoking rates, start, stop and quit rates from 2003 and information on selective mortality in smokers from the literature. Different assumptions with regard to the interval of the inputs used for screeing led to different conclusions, especially with regard to the importance of quit and relapse rates versus initial prevalence rates. This should not to be read as a lack of validity of the method, but it shows that any sensitivity method cannot be automated in a form that runs without expert guidance on the range