Bias correction in estimation of public health risk attributable to short‐term air pollution exposure

Environmetrics - Tập 26 Số 4 - Trang 298-311 - 2015
Wesley S. Burr1, Glen Takahara2, Hwashin Hyun Shin1,2
1Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
2Queen's University, KingstonCanada, Ontario

Tóm tắt

Numerous epidemiologic studies have reported associations between short‐term air pollution exposure and mortality. Such short‐term risk models include smooth functions of time to control for unmeasured confounding variables. We demonstrate bias in these short‐term Generalized Additive Model estimates because of lack of accounting for long timescale variations and propose a family of improved time smoothers to reduce and control the bias. The strengths of the proposed smoother are twofold: a clear separating of short‐term and long‐term effects and an obvious choice of smoothing parameters from pre‐determined timescales of interest. We demonstrate improvements through simulations and analysis of examples of air pollution and mortality data from Chicago, Il. from the National Morbidity, Mortality and Air Pollution Study database, showing reduced bias in the risk estimates. © 2015 The Authors. Environmetrics Published by John Wiley & Sons Ltd.

Từ khóa


Tài liệu tham khảo

10.1289/ehp.1306656

10.1080/00039899909602248

BurrWS.slp: Slepian smooth functions of time ‐ an extension package for ‘mgcv’ and ‘gam’2014. v.1.0‐3.http://cran.r-project.org/web/packages/slp/. [Accessed 1 March 2015].

BurrWS.Code and results for “bias in estimation...” Paper2015. GitHub repository https://github.com/wesleyburr/bias-in-estimation[Accessed 17 February 2015]. DOI: 10.5281/zenodo.15862.

10.1093/biostatistics/kxr002

10.1146/annurev.pu.15.050194.000543

10.1111/1467-985X.00170

10.1093/aje/kwf062

10.1198/016214504000000656

10.1001/jama.295.10.1127

10.4236/jep.2013.412A1003

Hastie TJ, 1990, Generalized Additive Models

HastieTJ.GAM: generalized additive models.2013. R package version 1.09.1.http://cran.r-project.org/web/packages/gam/[Accessed 1 March 2015].

10.1007/BF00330404

10.1093/oxfordjournals.aje.a009351

Peng RD, 2004, The NMMAPSdata package, R News, 4, 10

10.1111/j.1467-985X.2006.00410.x

10.1001/jama.299.18.2172

Peng RD, 2008, Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health

10.1007/s11869-012-0180-9

R Development Core Team, 2012, R:A Language and Environment for Statistical Computing

RahimKJ BurrWS ThomsonDJ.2014.Appendix A: Multitaper R Package in “Applications of Multitaper Spectral Analysis to Nonstationary Data” PhD diss. Queen's University pp. 149–183.http://hdl.handle.net/1974/12584.

10.1097/00001648-200301000-00009

10.1016/j.envres.2011.01.014

10.1093/oxfordjournals.aje.a115473

Schwartz J, Methodological issues in studies of air pollution and daily counts of deaths or hospital admissions, Journal of Epidemiology and Community Health, 50, Supplement 1:S3

10.1111/j.1539-6924.2011.01684.x

10.1002/j.1538-7305.1961.tb03976.x

10.1002/j.1538-7305.1978.tb02104.x

10.1109/PROC.1982.12433

Thomson DJ, 2001, Inverse‐constrained projection filters, In International Symposium on Optical Science and Technology, 4478, 172

10.1111/j.1467-9868.2010.00749.x

10.1201/9781420010404

10.1289/ehp.0800108

Thông tin
Thông tin xuất bản

Environmetrics

Tập 26 Số 4

298-311

Thông tin tác giả