ABSTRACT
Analyses involving data from many locations throughout the world have now been conducted to assess the association between air pollution and mortality. To date, six independent analyses of mortality data for Philadelphia, Pennsylvania, have been reported. In this new analysis of Philadelphia data for 1974-1988, Poisson regression models were developed to estimate the increased risk of daily mortality associated with air pollution while controlling for longer-term time trends and season and for weather. Model development was based on prior understanding of the effects of these factors on mortality and on consideration of model fit. The authors found moderate correlations of daily concentrations of total suspended particles (TSP), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO), and only slight correlations of ozone (O3) with other pollutants. When included individually in the model, the means of current and previous days' levels of TSP, SO2, and O3 had statistically significant effects on total mortality; pollutant increases of an interquartile range (34.5 micrograms/m3, 12.9 ppb, and 20.2 ppb, respectively) were associated with increases in mortality of around 1% for TSP and SO2, and of around 2% for O3. The effects of TSP and SO2 were diminished when both pollutants were simultaneously included in the model, whether pairwise or in the full multi-pollutant model. These analyses confirm the association between TSP and mortality found in previous studies in Philadelphia and show that the association is robust to consideration of other pollutants in the model.
PIP: Data from various studies on the association between air pollution and mortality in Philadelphia, Pennsylvania, undertaken in the period 1974-1988 are used in a new analysis in which the authors attempt to control for the effects of temporal factors, weather, and other pollutants in order to estimate the independent effect of total suspended particles (TSP) on mortality. The results confirm the association between TSP and mortality found in previous studies in Philadelphia and show that the association is robust to consideration of other pollutants.
Subject(s)
Air Pollution/statistics & numerical data , Mortality , Aged , Air Pollutants/analysis , Air Pollution/adverse effects , Algorithms , Cause of Death , Confidence Intervals , Female , Humans , Male , Middle Aged , Models, Statistical , Philadelphia/epidemiology , Poisson Distribution , WeatherABSTRACT
We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.
Subject(s)
Cluster Analysis , Models, Statistical , Risk , Statistics, Nonparametric , Air Pollution/adverse effects , Bias , England/epidemiology , Female , Humans , Infant, Newborn , Laryngeal Neoplasms/epidemiology , Lung Neoplasms/epidemiology , Male , Monte Carlo Method , Poisson Distribution , Population Density , Reproducibility of Results , Risk Assessment , Sex Ratio , Space-Time ClusteringABSTRACT
The inclusion of blood pressure measurements in the routine health appraisal of junior school children was acceptable to children and staff and did not create undue anxiety. Of 677 children only 9 (1.33%) required follow up, 2 being referred to hospital out patients. With appropriate support school nurses should be encouraged to measure blood pressure.
