A Method to Estimate the Chronic Health Impact of Air Pollutants in U.S. Residences

Background: Indoor air pollutants (IAPs) cause multiple health impacts. Prioritizing mitigation options that differentially affect individual pollutants and comparing IAPs with other environmental health hazards require a common metric of harm

Spectroscopic survey of the Galaxy with Gaia I. Design and performance of the Radial Velocity Spectrometer

The definition and optimisation studies for the Gaia satellite spectrograph, the Radial Velocity Spectrometer (RVS), converged in late 2002 with the adoption of the instrument baseline. This paper reviews the characteristics of the selected configuration and presents its expected performance. The RVS is a 2.0 by 1.6 degree integral field spectrograph, dispersing the light of all sources entering its field of view with a resolving power R=11 500 over the wavelength range [848, 874] nm. The RVS will continuously and repeatedly scan the sky during the 5 years of the Gaia mission. On average, each source will be observed 102 times over this period. The RVS will collect the spectra of about 100-150 million stars up to magnitude V~17-18. At the end of the mission, the RVS will provide radial velocities with precisions of ~2 km/s at V=15 and \~15-20 km/s at V=17, for a solar metallicity G5 dwarf. The RVS will also provide rotational velocities, with precisions (at the end of the mission) for late type stars of sigma_vsini ~5 km/s at V~15 as well as atmospheric parameters up to V~14-15. The individual abundances of elements such as Silicon and Magnesium, vital for the understanding of Galactic evolution, will be obtained up to V~12-13. Finally, the presence of the 862.0 nm Diffuse Interstellar Band (DIB) in the RVS wavelength range will make it possible to derive the three dimensional structure of the interstellar reddening.Comment: 17 pages, 9 figures, accepted for publication in MNRAS. Fig. 1,2,4,5, 6 in degraded resolution; available in full resolution at http://blackwell-synergy.com/links/doi/10.1111/j.1365-2966.2004.08282.x/pd

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

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

Spectroscopic survey of the Galaxy with Gaia- I. Design and performance of the Radial Velocity Spectrometer

The definition and optimization studies for the Gaia satellite spectrograph, the ‘radial velocity spectrometer' (RVS), converged in late 2002 with the adoption of the instrument baseline. This paper reviews the characteristics of the selected configuration and presents its expected performance. The RVS is a 2.0 × 1.6 degree integral field spectrograph, dispersing the light of all sources entering its field of view with a resolving power R=λ/Δλ= 11 500 over the wavelength range [848, 874] nm. The RVS will continuously and repeatedly scan the sky during the 5‐yr Gaia mission. On average, each source will be observed 102 times over this period. The RVS will collect the spectra of about 100-150 million stars up to magnitude V≃ 17-18. At the end of the mission, the RVS will provide radial velocities with precisions of ∼2 km s−1 at V= 15 and ∼15-20 km s−1 at V= 17, for a solar‐metallicity G5 dwarf. The RVS will also provide rotational velocities, with precisions (at the end of the mission) for late‐type stars of σvsin i≃ 5 km s−1 at V≃ 15 as well as atmospheric parameters up to V≃ 14-15. The individual abundances of elements such as silicon and magnesium, vital for the understanding of Galactic evolution, will be obtained up to V≃ 12-13. Finally, the presence of the 862.0‐nm diffuse interstellar band (DIB) in the RVS wavelength range will make it possible to derive the three‐dimensional structure of the interstellar reddenin

Obstetrical outcome valuations by patients, professionals, and laypersons: Differences within and between groups using three valuation methods

Background: Decision-making can be based on treatment preferences of the patient, the doctor, or by guidelines based on lay people's preferences. We compared valuations assigned by three groups: patients, obstetrical care professionals, and laypersons, for health states involving both mother and (unborn) child. Our aim was to compare the valuations of different groups using different valuation methods and complex obstetric health outcome vignettes that involve both maternal and neonatal ou

Dietary fruits and vegetables and cardiovascular diseases risk

Diet is likely to be an important determinant of cardiovascular disease (CVD) risk. In this article, we will review the evidence linking the consumption of fruit and vegetables and CVD risk. The initial evidence that fruit and vegetable consumption has a protective effect against CVD came from observational studies. However, uncertainty remains about the magnitude of the benefit of fruit and vegetable intake on the occurrence of CVD and whether the optimal intake is five portions or greater. Results from randomized controlled trials do not show conclusively that fruit and vegetable intake protects against CVD, in part because the dietary interventions have been of limited intensity to enable optimal analysis of their putative effects. The protective mechanisms of fruit and vegetables may not only include some of the known bioactive nutrient effects dependent on their antioxidant, anti-inflammatory, and electrolyte properties, but also include their functional properties, such as low glycemic load and energy density. Taken together, the totality of the evidence accumulated so far does appear to support the notion that increased intake of fruits and vegetables may reduce cardiovascular risk. It is clear that fruit and vegetables should be eaten as part of a balanced diet, as a source of vitamins, fiber, minerals, and phytochemicals. The evidence now suggests that a complicated set of several nutrients may interact with genetic factors to influence CVD risk. Therefore, it may be more important to focus on whole foods and dietary patterns rather than individual nutrients to successfully impact on CVD risk reduction. A clearer understanding of the relationship between fruit and vegetable intake and cardiovascular risk would provide health professionals with significant information in terms of public health and clinical practice

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