Assessment of short‐term association between health outcomes and ozone concentrations using a Markov regression model

Longitudinal binary data are often used in panel studies where short‐term associations between air pollutants and respiratory health outcomes are investigated. A Markov regression model in which the transition probabilities depend on the covariates, as well as the past responses, was used to study the short‐term association between daily ozone (O 3 ) concentrations and respiratory health outcomes in a panel of schoolchildren in Armentières, Northern France. The results suggest that there was a small but statistically significant association between O 3 and children's cough episodes. A 10 μg/m 3 increase in O 3 concentrations was associated with a 13.9 % increase in cough symptoms (CI 95% = 1.2–28.1%). The use of a Markov regression model can be useful as it permits one to address easily both the regression objective and the stochastic dependence between successive observations. However, it is important to verify the sensitivity of the Markov regression parameters to the time‐dependence structure. In this study, it was found that, although what happened on the previous day was a strong predictor of what happened on the current day, this did not contradict the O 3 ‐respiratory symptom associations. Compared to the Markov regression model, the signs of the parameter estimates of marginal and random‐intercept models remain the same. The magnitudes of the O 3 effects were also essentially the same in the three models, whose confidence intervals overlapped.