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In environmental epidemiology studies, health response data (e.g.hospitalization or mortality) are often noisy because of hospital organizationand other social factors. The noise in the data can hide the true signalrelated to the exposure. The signal can be unveiled by performing a temporalaggregation on health data and then using it as the response in regressionanalysis. From aggregated series, a general methodology is introduced toaccount for the particularities of an aggregated response in a regressionsetting. This methodology can be used with usually applied regression models inweather-related health studies, such as generalized additive models (GAM) anddistributed lag nonlinear models (DLNM). In particular, the residuals aremodelled using an autoregressive-moving average (ARMA) model to account for thetemporal dependence. The proposed methodology is illustrated by modelling theinfluence of temperature on cardiovascular mortality in Canada. A comparisonwith classical DLNMs is provided and several aggregation methods are compared.Results show that there is an increase in the fit quality when the response isaggregated, and that the estimated relationship focuses more on the outcomeover several days than the classical DLNM. More precisely, among variousinvestigated aggregation schemes, it was found that an aggregation with anasymmetric Epanechnikov kernel is more suited for studying thetemperature-mortality relationship.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality