The online version of this article (doi:10.1186/1471-2288-14-55) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
AG conceived the idea of attributable risk measures for DLNMs and worked out their algebraic definitions. AG and ML developed the final version of the measures, planned the example and carried out the analysis, drafted the final version of the manuscript. AG provided the software implementation through the R scripts. Both authors read and approved the final manuscript.
Measures of attributable risk are an integral part of epidemiological analyses, particularly when aimed at the planning and evaluation of public health interventions. However, the current definition of such measures does not consider any temporal relationships between exposure and risk. In this contribution, we propose extended definitions of attributable risk within the framework of distributed lag non-linear models, an approach recently proposed for modelling delayed associations in either linear or non-linear exposure-response associations.
We classify versions of attributable number and fraction expressed using either a forward or backward perspective. The former specifies the future burden due to a given exposure event, while the latter summarizes the current burden due to the set of exposure events experienced in the past. In addition, we illustrate how the components related to sub-ranges of the exposure can be separated.
We apply these methods for estimating the mortality risk attributable to outdoor temperature in two cities, London and Rome, using time series data for the periods 1993–2006 and 1992–2010, respectively. The analysis provides estimates of the overall mortality burden attributable to temperature, and then computes the components attributable to cold and heat and then mild and extreme temperatures.
These extended definitions of attributable risk account for the additional temporal dimension which characterizes exposure-response associations, providing more appropriate attributable measures in the presence of dependencies characterized by potentially complex temporal patterns.
Additional file 1: R script implementing the function to compute attributable risk measures.(ZIP 2 KB)
Rothman KJ, Greenland S, Lash TL: Modern Epidemiology. 2008, Philadelphia: Lipcott Williams & Wilkins,
Breslow NL, Day NE: Statistical Methods in Cancer Research. Vol. II: The Desing and Analysis of Cohort Studies. Lyon: International Agency for Reasearch on Cancer (IARC); 1987:232–271. Chap. 6: Modelling the relationship between risk, dose and time,
Almon S: The distributed lag between capital appropriations and expenditures. Econometrica. 1965, 33: 178-196. 10.2307/1911894. CrossRef
Baccini M, Kosatsky T, Biggeri A: Impact of summer heat on urban population mortality in Europe during the 1990s: an evaluation of years of life lost adjusted for harvesting. PloS One. 2013, 8 (7): 69638-10.1371/journal.pone.0069638. CrossRef
Honda Y, Kondo M, McGregor G, Kim H, Guo YL, Hijioka Y, Yoshikawa M, Oka K, Takano S, Hales S, Kovats RS: Heat-related mortality risk model for climate change impact projection. Environ Health Prev Med. 2013, (Epub ahead of print. doi:10.1007/s12199-013-0354-6),
Wood SN: Generalized Additive Models: An Introduction with R. 2006, Boca Raton: Chapman & Hall/CRC,
- Attributable risk from distributed lag models
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