The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Time series regression studies in environmental epidemiology
Open accessAbstract
Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.
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Cites
Investigating causal relations by econometric models and cross-spectral methods
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Cited by
Interrupted time series regression for the evaluation of public health interventions: a tutorial
Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw098 · Cited by 3,049
Self-citationMortality risk attributable to high and low ambient temperature: a multicountry observational study
Gasparrini, A., Guo, Y., Hashizume, M., Lavigne, E., Zanobetti, A., Schwartz, J., Tobias, A., Tong, S., Rocklöv, J., Forsberg, B., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(14)62114-0 · Cited by 2,965
Self-citationTemporal Variation in Heat–Mortality Associations: A Multicountry Study
Gasparrini, A., Guo, Y., Hashizume, M., Kinney, P. L., Petkova, E. P., Lavigne, E., Zanobetti, A., Schwartz, J. D., Tobias, A., Leone, M., et al. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1409070 · Cited by 470
Self-citationConditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis
Armstrong, B. G., Gasparrini, A., & Tobias, A. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-122 · Cited by 435
Self-citationLiu, J., Varghese, B. M., Hansen, A., Xiang, J., Zhang, Y., Dear, K., Gourley, M., Driscoll, T., Morgan, G., Capon, A., et al. (2021) · Environment International · doi:10.1016/j.envint.2021.106533 · Cited by 406
Metadata
- Authors
- Bhaskaran, K., Gasparrini, A., Hajat, S., Smeeth, L., & Armstrong, B.
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-12
- Volume
- 42
- Issue
- 4
- Pages
- 1187-1195
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- OpenAlex
- W2115112857
- Cited by
- 1,150
- References
- 3
- FWCI
- 23.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
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