Assessment and prevention of acute health effects of weather conditions in Europe, the PHEWE project: background, objectives, design

<p>Abstract</p> <p>Background</p> <p>The project "Assessment and prevention of acute health effects of weather conditions in Europe" (PHEWE) had the aim of assessing the association between weather conditions and acute health effects, during both warm and cold seasons in 16 European cities with widely differing climatic conditions and to provide information for public health policies.</p> <p>Methods</p> <p>The PHEWE project was a three-year pan-European collaboration between epidemiologists, meteorologists and experts in public health. Meteorological, air pollution and mortality data from 16 cities and hospital admission data from 12 cities were available from 1990 to 2000. The short-term effect on mortality/morbidity was evaluated through city-specific and pooled time series analysis. The interaction between weather and air pollutants was evaluated and health impact assessments were performed to quantify the effect on the different populations. A heat/health watch warning system to predict oppressive weather conditions and alert the population was developed in a subgroup of cities and information on existing prevention policies and of adaptive strategies was gathered.</p> <p>Results</p> <p>Main results were presented in a symposium at the conference of the International Society of Environmental Epidemiology in Paris on September 6^th2006 and will be published as scientific articles. The present article introduces the project and includes a description of the database and the framework of the applied methodology.</p> <p>Conclusion</p> <p>The PHEWE project offers the opportunity to investigate the relationship between temperature and mortality in 16 European cities, representing a wide range of climatic, socio-demographic and cultural characteristics; the use of a standardized methodology allows for direct comparison between cities.</p

Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study

Background: Missing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal with them, and, in particular, if the missing at random (MAR) assumption is valid or missing not at random (MNAR) mechanisms should be assumed. In this paper we investigated this issue through a sensitivity analysis within the ACTION study, a multicenter cluster randomized controlled trial testing advance care planning in patients with advanced lung or colorectal cancer. Methods: Multiple imputation procedures under MAR and MNAR assumptions were implemented. Possible violation of the MAR assumption was addressed with reference to variables measuring quality of life and symptoms. The MNAR model assumed that patients with worse health were more likely to have missing questionnaires, making a distinction between single missing items, which were assumed to satisfy the MAR assumption, and missing values due to completely missing questionnaire for which a MNAR mechanism was hypothesized. We explored the sensitivity to possible departures from MAR on gender differences between

Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health

Since mid-1990s, Generalised Additive Models (GAM) became very popular for the analysis of short-term effects of air pollution on health. Such approach involves specification of non parametric functions to adjust for confounding effect of unobserved variables with a systematic temporal behaviour and to model weather variables and influenza epidemics. Recently critical points in using commercial statistical software for fitting GAMs were stressed (Dominici et al., 2002; Ramsey et al., 2003) and some reanalyses of time series data on air pollution and health were performed. This new attention to semi-parametric models has led researchers to consider alternative estimation methods for GAMs and to wonder whether simpler parametric models can be a better choice than GAMs (Lumley and Sheppard, 2003). The purpose of this work is to show by simulation analyses some of the problems which we could find using GAMs, and to discuss real advantages of semi-parametric approach with respect to a fully parametric alternative, based on specification of Generalized Linear Models with natural cubic splines (GLM + NS). Here we considered the situation in which only the smooth function for time trend is included in the model. Generalized Additive Models were fitted by the direct methods implemented in R software (Wood, 2000). Different simulation analyses were performed, varying the "true" number of degrees of freedom for the smooth function, the concurvity amount in data and the "true" size of air pollutant effect. Our simulations show that GAM provide biased estimates of air pollutant effect, the bias being not negligible for moderate concurvity amount and small effect size. We found also that using semi-parametric approach a certain amount of undersmoothing is needed to obtain appropriated estimation of risk. Good performance was obtained selecting the smoothing parameter by Generalized Cross Validation. On the contrary analysis of partial autocorrelation of residuals from GAM brings to inappropriate model selection. GLM+NS is a good alternative to semi-parametric approach, resulting robust to misspecification of degrees of freedom for the spline. However the applicability of such approach should be considered carefully in presence of particular local variations of seasonality or in presence of outliers, because results could be sensitive to knots placement. Moreover the choice of knots positions could be a very important problem in smoothing other covariates like temperature

Fine airborne particles: when alarming levels are the standard

Objectives To quantify the contribution of each individual month to the annual mortality burden attributable to particulate matter (PM) in 2015 in Milan, Italy, after authorities and media considered December 2015 as an outlying month carrying an exceptional population exposure to PM. Study design We used routinely available daily time series of air pollution and mortality to perform an assessment of the impact of PM exposure on population health. Methods By combining daily death counts with daily PM levels, as well as the yearly average of the number of deaths with the yearly average of PM concentrations, impact estimates were calculated in terms of deaths attributable (AD) to levels of PM10 and PM2.5 exceeding the daily or the annual European Union (EU) exposure limits. Results On a monthly basis, the estimated AD for exceeding the daily EU limits for more than 35 days were 18.4 (PM10) and 33.2 (PM2.5) between January and March, and 20.0 and 31.9 between October and December, respectively. On an annual basis, the EU limit for PM10 was almost met and, therefore, the estimated impact in terms of AD was practically null. Conclusions Impact results should be interpreted in the light of the skewness of the daily PM concentration distribution. The number of days above the limits is more important than the average annual concentration in determining the number of attributable deaths. The impact of PM on mortality is substantial during the whole winter season irrespective of its annual average concentration. Our estimates further stress the need for a revision of the current European air quality standards

Impact of heat on mortality in 15 European cities: attributabledeaths under different weather scenarios

Background High ambient summer temperatures have been shown to influence daily mortality in cities across Europe. Quantification of the population mortality burden attributable to heat is crucial to the development of adaptive approaches. The impact of summer heat on mortality for 15 European cities during the 1990s was evaluated, under hypothetical temperature scenarios warmer and cooler than the mean and under future scenarios derived from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (SRES). Methods A Monte Carlo approach was used to estimate the number of deaths attributable to heat for each city. These estimates rely on the results of a Bayesian random-effects meta-analysis that combines city-specific heat-mortality functions. Results The number of heat-attributable deaths per summer ranged from 0 in Dublin to 423 in Paris. The mean attributable fraction of deaths was around 2%. The highest impact was in three Mediterranean cities (Barcelona, Rome and Valencia) and in two continental cities (Paris and Budapest). The largest impact was on persons over 75 years; however, in some cities, important proportions of heat-attributable deaths were also found for younger adults. Heat-attributable deaths markedly increased under warming scenarios. The impact under SRES scenarios was slightly lower or comparable to the impact during the observed hottest year. Conclusions Current high summer ambient temperatures have an important impact on European population health. This impact is expected to increase in the future, according to the projected increase of mean ambient temperatures and frequency, intensity and duration of heat waves