Background: Application of case-crossover designs provides an alternative to time-series analysis for analyzing the health-related effects of air pollution. Although some case-crossover studies can control for trend and seasonality by design, to date they have been analyzed as matched case-control studies. Such analyses may exhibit biases and a lower statistical efficiency than traditional time series analyzed with Poisson.
Methods: In this article, case-crossover studies are treated as cohort studies in which each subject is observed for a short period of time before and/or after the event, thus making possible analyzing with Andersen-Gill and generalized linear mixed models. We conducted a simulation study to compare the behavior of these models applied to case-crossover designs with time series analyzed with Poisson and with case-crossover analyzed by conditional logistic regression. To this end, we created a random variable that follows a Poisson distribution of low (2/day) and high mean events (22/day). This variable is a function of an unobserved confounding variable (that introduces trend and seasonality) and data on small particulate matter (PM10) from Barcelona. In addition, scenarios were created to assess the effect on exposure exerted by autocorrelation and the magnitude of the pollutant coefficient.
Results: The full semisymmetric design analyzed with generalized linear mixed models yields good coverage and a high statistical power for air-pollution effect magnitudes close to the real values but shows bias for high effect magnitudes. This bias seems to be attributable to autocorrelation in the exposure variable.
Conclusions: Longitudinal approaches applied to case-crossover designs may prove useful for analyzing the acute effects of environmental exposures.