Case-Crossover Analysis of Air Pollution Health Effects: A Systematic Review of Methodology and Application

By Carracedo-Martinez, Eduardo; Taracido, Margarita et al. | Environmental Health Perspectives, August 2010 | Go to article overview

Case-Crossover Analysis of Air Pollution Health Effects: A Systematic Review of Methodology and Application


Carracedo-Martinez, Eduardo, Taracido, Margarita, Tobias, Aurelio, Saez, Marc, Figueiras, Adolfo, Environmental Health Perspectives


BACKGROUND: Case-crossover is one of the most used designs for analyzing the health-related effects of air pollution. Nevertheless, no one has reviewed its application and methodology in this context.

OBJECTIVE: We conducted a systematic review of case-crossover (CCO) designs used to study the relationship between air pollution and morbidity and mortality, from the standpoint of methodology and application.

DATA SOURCES AND EXTRACTION: A search was made of the MEDLINE and EMBASE databases. Reports were classified as methodologic or applied. From the latter, the following information was extracted: author, study location, year, type of population (general or patients), dependent variable(s), independent variable(s), type of CCO design, and whether effect modification was analyzed for variables at the individual level.

DATA SYNTHESIS: The review covered 105 reports that fulfilled the inclusion criteria. Of these, 24 addressed methodological aspects, and the remainder involved the design's application. In the methodological reports, the designs that yielded the best results in simulation were symmetric bidirectional CCO and time-stratified CCO. Furthermore, we observed an increase across time in the use of certain CCO designs, mainly symmetric bidirectional and time-stratified CCO. The dependent variables most frequently analyzed were those relating to hospital morbidity; the pollutants most often studied were those linked to particulate matter. Among the CCO-application reports, 13.6% studied effect modification for variables at the individual level.

CONCLUSIONS: The use of CCO designs has undergone considerable growth; the most widely used designs were those that yielded better results in simulation studies: symmetric bidirectional and time-stratified CCO. However, the advantages of CCO as a method of analysis of variables at the individual level are put to little use.

KEY WORDS: air pollution, crossover studies, epidemiologic methods, health, systematic review. Environ Health Perspect 118:1173-1182 (2010). doi:10.1289/ehp.0901485 [Online 31 March 2010]

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The first epidemiologic studies on the impact of air pollution on health were undertaken as a consequence of the extreme pollution episodes that took place in the decades from 1930 to 1960. The association between air pollution and certain health variables was made clear by simple graphic representations or by comparisons of mortality rates for these time periods (Firket 1931; Logan 1953). Since that time, air pollution levels have fallen substantially, such that, to evaluate their effects on health, longer time series are requited. To this end, epidemiologists began to use dynamic regression models in the 1970s that consisted of models in which the relationship between the dependent and explanatory variables were distributed over time, rather than being expected to occur simultaneously. Moreover, investigators were able to control for residual autocorrelation, with the error being specified by means of autoregressive integrated moving-average models (ARIMA). The problem with these types of models is that they assume that the dependent variable is distributed normally, which, in fact, is extremely rare in the daily outcome count variables of morbidity and mortality events (Saez et al. 1999).

The early 1990s saw the appearance of linear models based on Poisson regression, in which a parametric approach was used to control for trend and seasonality because the event counts more typically have a Poisson distribution. These models use the variable "time" and its transforms, quadratic and sinusoidal functions (sine or cosine) of different frequency and amplitude, to control for the effect on the dependent variable (mortality or morbidity) of unmeasured variables that may vary seasonally, such as in pollen concentration, meteorological variables, and influenza outbreaks, or that may have a trend, such as changes in a city's population distribution, in order to ascertain the effect of such variables on the dependent variable (Saez et al.

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