Objectives: Effects of exposure to ambient temperature can affect mortality levels for days or even weeks following exposure, presenting complexities in modelling such effects in regression analysis of daily time-series data.
Methods: We propose a new approach involving a multi-lag segmented approximation to account for the nonlinear effect of temperature and the use of two different penalized spline bases to model the distributed lag of both heat and cold exposure. As opposed to standard splines, a feature of the novel penalized framework is that more flexibility is available at short lags where change in coefficients will be greatest, and the selection of the maximum lag appears substantially less important in determining the overall pattern of the effect.
Results: Applying the approach to daily mortality in the cities of Santiago (in Chile) and Palermo (Italy), we observed a heat effect in both cities 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: in Santiago we estimated an overall increase in deaths of 2.36% (95% CI 0.26, 4.51) in the 65+ age-group associated with every 1°C decrease in temperature below the cold threshold, and a total increase of 1.11% (0.09, 2.14) per degree for Palermo. Heat effects for Palermo were much larger than for Santiago, and less harvesting of heat deaths was in evidence for the Italian city. Moreover, the estimated heat thresholds were higher in Palermo than in Santiago.
Conclusions: Our approach provides a flexible and precise way to quantify health effects of both heat and cold exposure at individual lags and to model the overall pattern of the delayed effect.
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