Abstract：
:Time-series studies have linked daily variations in nonaccidental deaths with daily variations in ambient particulate matter air pollution, while controlling for qualitatively larger influences of weather and season. Although time-series analyses typically include nonlinear terms for weather and season, questions remain as to whether models to date have completely controlled for these important predictors. In this paper, the authors use two flexible versions of distributed lag models to control extensively for the confounding effects of weather and season. One version builds on the current approach to controlling for weather, while the other version offers a new approach. The authors conduct a comprehensive sensitivity analysis of the particulate matter-mortality relation by applying these methods to the recently updated National Morbidity, Mortality, and Air Pollution Study database that comprises air pollution, weather, and mortality time series from 1987 to 2000 for 100 US cities. They combine city-specific estimates of the short-term effects of particulate matter on mortality using a Bayesian hierarchical model. They conclude that, within the broad classes of models considered, national average estimates of particulate matter relative risk are consistent with previous estimates from this study and are robust to model specification for weather and seasonal confounding.
journal_name
Am J Epidemioljournal_title
American journal of epidemiologyauthors
Welty LJ,Zeger SLdoi
10.1093/aje/kwi157subject
Has Abstractpub_date
2005-07-01 00:00:00pages
80-8issue
1eissn
0002-9262issn
1476-6256pii
162/1/80journal_volume
162pub_type
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journal_title：American journal of epidemiology
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pub_type： 杂志文章
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