The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study.

ABSTRACT:

Background

Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date.

Methods

We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation.

Findings

We observed progressive

SUBMITTER: Ballester J 

PROVIDER: S-EPMC10769891 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature