Time series regression studies in environmental epidemiology
By K. Bhaskaran, Antonio Gasparrini, S. Hajat, L. Smeeth, Ben Armstrong. International Journal of Epidemiology, 2013
1,091 citations recorded by the source corpus.
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.
Subfields: Environmental Engineering, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/1949e9b4703ea20ece3f08c91f62b723a65f77d8
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In Environmental Engineering
In Health, Toxicology and Mutagenesis
Citations in the corpus
- Interrupted time series regression for the evaluation of public health interventions: a tutorial (2016, methodology): “(2013) for environmental time series, and in more detail elsewhere, can also be applied to ITS studies, including: stratified analyses according to potential effect-modifying variables; increasing power by allowing different locations to have trends modelled individually rather than relying on the a”
- The use of controls in interrupted time series studies of public health interventions. (2018, methodology): “These analytical issues are beyond the scope of this paper but have been described in more detail elsewhere.(2, 38-40)”
- Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis (2014, neutral): “To aid understanding the models we describe their application to a study of daily air ozone 7 pollution in relation to counts of deaths from London from 2002-6, using data previously 8 published.[2] Primary confounder control is by stratifying time by month and day-of-week, a 9 typical case crossove”