A critical review of studies of the association between demands for hospital services and air pollution.

Abstract

Studies of the associations between air pollution and hospital admissions and emergency room use are reviewed, including studies of air pollution episodes, time-series analyses, and cross-sectional analyses. These studies encompass a variety of methods of analysis and levels of air quality. Findings from all three types of studies were generally consistent in that almost all of the studies reviewed found statistically significant associations between hospital use and air pollution; this unanimity may have resulted in part from publication bias. These associations were characterized by elasticities of the order of 0.20; i.e., a 100% change in air pollution was associated with a change in hospital use of about 20%, for specific diagnoses. Respiratory diagnoses were emphasized by most studies; cardiac diagnoses were included in five of them. The air pollutants most often associated with changes in hospital use were particulate matter, sulfur oxides, and oxidants. Apart from the major air pollution episodes, there was no obvious link between air pollution level and the significance or magnitudes of the associations. Long-term indicators of hospitalization appeared to also be influenced by medical care supply factors, including the numbers of beds and physicians per capita. These nonpathological causal factors could also have influenced the findings of the time-series studies by introducing extraneous factors in the patterns of admissions. Although consistent associations have been shown between hospital use and air pollution, further research is required to distinguish among potentially responsible pollutants and to deduce specific dose-response relationships of general utility.

Association between atmospheric pollutants and hospital admissions in Lisbon

“…According to Lipfert (1993Lipfert ( , 1997, the semi-elasticity at the average of both atmospheric pollutant and hospital admission was calculated from the risk difference provided by the slope of the linear regression. The use of the semi-elasticity makes it easier to compare our results, obtained from a linear model, with those obtained from time-series studies using Poisson (Cameron and Trivedi 1998) and log-linear models, which use the Risk Ratio as the measure of effect.…”

“…The daily data may consist of the actual daily values of health and pollution or some form of average over a number of days. Some studies have modelled single day health events with exposure averages over multiple days (Lipfert 1993;Lumley and Sheppard 2000;Roberts 2005;Sarmento et al 2009;Smith et al 2000) and a minority has modelled the health events also as averages over multiple days, while preserving the days as units of comparison (Schwartz 2000a, b).…”

Air pollution and emergency admissions for cardiorespiratory diseases in Lisbon (Portugal)

“…30,[45][46][47][48][49] Here, Y i will signify the number of hospital admissions for day i = 1, 2, …, n. X = [x 1 ,…, x p ] denotes the (n x p) design matrix based on all covariates under consideration being x j = [x j;1 ,…, x j;i ; …; x j;n ] T . The design matrix X can include basic terms for smoothing splines to model nonlinear trends, meteorological and pollution variables, seasonal and weekly indicators, among others.…”

Effects of Exposure to Particles and Ozone on Hospital Admissions for Cardiorespiratory Diseases in SetúBal, Portugal

“…The significant ones were selected by considering those relationships where the p value of the slope of AP was less than 5% for at least one of the models and when the slope of the AP had the same sign for all 4 models. According to Lipfert (1993Lipfert ( , 1997, and in order to express the effect of AP on HA, the semielasticity at the mean of both AP and HA was calculated from the risk difference provided by the slope of the linear regression. The use of the semi-elasticity makes it easier to compare our results, obtained from a linear model, with those obtained from time-series studies using Poisson and log-linear models, which use the Risk Ratio as the measure of effect.…”