An approach for estimating the health effects of changes over time in air pollution: an illustration using cardio-respiratory hospital admissions in London.

Objectives: First, we present a general analytical approach to estimating the association between medium-term changes in air pollution and health across small areas. As a specific illustration, we then applied the approach to data on London residents from a 4-year period to test whether reductions in traffic-related air pollution were associated with reductions in cardio-respiratory hospital admissions.

Methods: A binomial distribution was used to model change in admissions over time in each small area, which was measured as the proportion of admissions in 2003-2004 out of admissions over all study years (2001-2004). Annual average concentrations of nitrogen oxides (NO(x)) were modelled using an emissions-dispersion model. The association between change in NO(x) and change in hospital admissions was estimated using logistic regression and an instrumental variable approach.

Results: For some diagnostic groups, suggestive associations between reductions in NO(x) and reductions in admissions were observed, for example, OR=0.97 (95% CI 0.96 to 0.99) for an IQR decrease in NO(x) (3 microg/m(3)) and all respiratory admissions. Accounting for spatial dependence attenuated several of the associations; for respiratory admissions, the OR was 1.00 (95% CI 0.98 to 1.02), leaving only that for bronchiolitis significant (OR=0.91; 95% CI 0.84 to 0.99). In this particular illustration, the instrumental variable approach did not appear to add information.

Conclusions: In this illustration, there was relatively limited power to detect an association between changes in air pollution and hospital admissions over time. However, the analytical approach could deliver more robust estimates of the health effects of changes in air pollution in settings with greater spatial contrast in changes in air pollution over time.