Exposure to ambient temperature can affectmortality levels for days or weeks following exposure, making modelling such effects in regression analysis of daily time-series data complex. We propose a new approach involving a multilag segmented approximation to account for the nonlinear effect of temperature and the use of two different penalised spline bases to model the distributed lag of both heat and cold exposure. Compared with standard splines, the novel penalised framework is more flexible at short lags where change in coefficients is greatest, and selection of the maximum lag appears substantially less important in determining the overall pattern of the effect. Applying the approach to daily mortality in Santiago (Chile) and Palermo (Italy), we observed a heat effect that was mostly immediate and followed by negative estimates consistent with short-term mortality displacement (harvesting). Cold effects were mostly positively sustained and more evenly distributed across the 60-day analysis period.
|Numero di pagine||7|
|Rivista||Occupational and Environmental Medicine|
|Stato di pubblicazione||Published - 2009|
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