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 ofthem. 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.

Methodology to Assess Air Pollution Impact on Human Health Using the Generalized Linear Model with Poisson Regression

“…Important potential confounding factors that may bias time series studies of air pollution and mortality (or morbidity) are factors which vary on shorter timescales like calendar specific days, such as day of the week and holiday indicator (Lipfert, 1993). These trends are not necessarily present, but they occur often enough that they should be checked (Samoli et al, 2011;Schwartz et al, 1996).…”

“…The GLM with Poisson regression has been widely applied in epidemiological studies of air pollution (Dockery & Pope III, 1994;Dominici et al, 2002;Lipfert, 1993;Metzger et al, 2004;Tadano et al, 2009) but it needs caution, as sometimes this model may not fit well to the database. There are two statistical methods that can be used to evaluate goodness of fit in GLMs, as follows.…”

“…In studies of air pollution impact on human health with non-negative count data as response variable, the GLM with Poisson regression is broadly applied (Dockery & Pope III, 1994;Dominici et al, 2002;Lipfert, 1993;Metzger et al, 2004).…”

“…As a consequence, there was a vehicular and industrial growth that combined with unfavorable meteorological conditions caused several worldwide episodes of excessive air pollution with life losses and health damage. Some examples were the well known Donora disaster in October, 1948 and fog episodes occurred after December, 1952 in London (Lipfert, 1993).…”

Asthma and Air Pollution. Where Are We?

“…In the 1980s, many studies showed an increase in hospital admissions due to asthma associated with O 3 and particles exposure in adults, children, and the elderly. Most of these studies were conducted in the USA 19 . They all described the health effects at concentrations below the North American air quality standards.…”