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Time series regression studies in environmental epidemiology

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TLDR
The analysis process is outlined, 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.
Abstract
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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Interrupted time series regression for the evaluation of public health interventions: a tutorial

TL;DR: This tutorial uses a worked example to demonstrate a robust approach to ITS analysis using segmented regression and describes the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders.
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The use of controls in interrupted time series studies of public health interventions.

TL;DR: Researchers undertaking controlled interrupted time series studies should carefully consider a priori what confounding events may exist and whether different controls can exclude these or if they could introduce new sources of bias to the study.
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Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities

TL;DR: This nationwide study provides a comprehensive picture of the non-linear associations between ambient temperature and mortality from all natural causes and main cardiorespiratory diseases, as well as the corresponding disease burden that is mainly attributable to moderate cold temperatures in China.
References
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Book

Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI

Investigating Causal Relations by Econometric Models and Cross-Spectral Methods

TL;DR: In this article, the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation, and measures of causal lag and causal strength can then be constructed.
Book ChapterDOI

Investigating causal relations by econometric models and cross-spectral methods

TL;DR: In this article, it is shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation, and measures of causal lag and causal strength can then be constructed.
Journal ArticleDOI

Generalized Linear Models

TL;DR: Generalized linear models, 2nd edn By P McCullagh and J A Nelder as mentioned in this paper, 2nd edition, New York: Manning and Hall, 1989 xx + 512 pp £30
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