1 resultado para full-scale testing
em Collection Of Biostatistics Research Archive
Filtro por publicador
- Repository Napier (1)
- Aberdeen University (3)
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- AMS Tesi di Laurea - Alm@DL - Università di Bologna (13)
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- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (2)
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- Aston University Research Archive (39)
- Biblioteca de Teses e Dissertações da USP (7)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (14)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (1)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (4)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (22)
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- Universidad de Alicante (5)
- Universidad del Rosario, Colombia (3)
- Universidad Politécnica de Madrid (48)
- Universidade dos Açores - Portugal (1)
- Universidade Federal do Pará (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (10)
- Université de Lausanne, Switzerland (3)
- Université de Montréal (2)
- Université de Montréal, Canada (7)
- University of Connecticut - USA (2)
- University of Michigan (37)
- University of Queensland eSpace - Australia (36)
- University of Washington (2)
- Worcester Research and Publications - Worcester Research and Publications - UK (2)
Resumo:
The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988-2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of PM10 for the full time period and PM2.5 for a subset of the period. For the earlier part of the period, 1988-1998, few PM2.5 monitors were operating, so we develop a simple extension to the model that represents PM2.5 conditionally on PM10 model predictions. In the epidemiological analysis, model predictions of PM10 are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space-time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.