The effects of particulate air pollution on daily deaths: a multi-city case crossover analysis.
BACKGROUND
Numerous studies have reported that day-to-day changes in particulate air pollution are associated with day-to-day changes in deaths. Recently, several reports have indicated that the software used to control for season and weather in some of these studies had deficiencies.


OBJECTIVE
To investigate the use of the case-crossover design as an alternative.


METHODS
This approach compares the exposure of each case to their exposure on a nearby day, when they did not die. Hence it controls for seasonal patterns and for all slowly varying covariates (age, smoking, etc) by matching rather than complex modelling. A key feature is that temperature can also be controlled by matching. This approach was applied to a study of 14 US cities. Weather and day of the week were controlled for in the regression.


RESULTS
A 10 microg/m3 increase in PM10 was associated with a 0.36% increase in daily deaths from internal causes (95% CI 0.22% to 0.50%). Results were little changed if, instead of symmetrical sampling of control days the time stratified method was applied, when control days were matched on temperature, or when more lags of winter time temperatures were used. Similar results were found using a Poisson regression, but the case-crossover method has the advantage of simplicity in modelling, and of combining matched strata across multiple locations in a single stage analysis.


CONCLUSIONS
Despite the considerable differences in analytical design, the previously reported associations of particles with mortality persisted in this study. The association appeared quite linear. Case-crossover designs represent an attractive method to control for season and weather by matching.
Schwartz
J
J
Harvard School of Public Health, Exposure, Epidemiology, and Risk Program, 401 Park Drive, Suite 415 West, Boston, MA 02215, USA. jschwrtz@hsph.harvard.edu
D000397:Air Pollution / Q000009:adverse effects* / Q000706:statistics & numerical data; D015986:Confounding Factors, Epidemiologic; D018592:Cross-Over Studies; D004781:Environmental Exposure / Q000009:adverse effects / Q000706:statistics & numerical data; D006801:Humans; D009026:Mortality; D010316:Particle Size; D018570:Risk Assessment / Q000379:methods; D012621:Seasons; D014481:United States / Q000453:epidemiology; D014504:Urban Health / Q000706:statistics & numerical data*; D014887:Weather