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      Time series regression studies in environmental epidemiology

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          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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          Most cited references14

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          Models for the relationship between ambient temperature and daily mortality.

          Ambient temperature is an important determinant of daily mortality that is of interest both in its own right and as a confounder of other determinants investigated using time-series regressions, in particular, air pollution. The temperature-mortality relationship is often found to be substantially nonlinear and to persist (but change shape) with increasing lag. We review and extend models for such nonlinear multilag forms. Popular models for mortality by temperature at given lag include polynomial and natural cubic spline curves, and the simple but more easily interpreted linear thresholds model, comprising linear relationships for temperatures below and above thresholds and a flat middle section. Most published analyses that have allowed the relationship to persist over multiple lags have done so by assuming that spline or threshold models apply to mean temperature in several lag strata (e.g., lags 0-1, 2-6, and 7-13). However, more flexible models are possible, and a modeling framework using products of basis functions ("cross-basis" functions) suggests a wide range, some used previously and some new. These allow for stepped or smooth changes in the model coefficients as lags increase. Applying a range of models to data from London suggest evidence for relationships up to at least 2 weeks' lag, with smooth models fitting best but lag-stratified threshold models allowing the most direct interpretation. A wide range of multilag nonlinear temperature-mortality relationships can be modeled. More awareness of options should improve investigation of these relationships and help control for confounding by them.
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            Short-term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project. Air Pollution and Health: a European Approach.

            To carry out a prospective combined quantitative analysis of the associations between all cause mortality and ambient particulate matter and sulphur dioxide. Analysis of time series data on daily number of deaths from all causes and concentrations of sulphur dioxide and particulate matter (measured as black smoke or particles smaller than 10 microns in diameter (PM10)) and potential confounders. 12 European cities in the APHEA project (Air Pollution and Health: a European Approach). Relative risk of death. In western European cities it was found that an increase of 50 micrograms/m3 in sulphur dioxide or black smoke was associated with a 3% (95% confidence interval 2% to 4%) increase in daily mortality and the corresponding figure for PM10 was 2% (1% to 3%). In central eastern European cities the increase in mortality associated with a 50 micrograms/m3 change in sulphur dioxide was 0.8% (-0.1% to 2.4%) and in black smoke 0.6% (0.1% to 1.1%). Cumulative effects of prolonged (two to four days) exposure to air pollutants resulted in estimates comparable with the one day effects. The effects of both pollutants were stronger during the summer and were mutually independent. The internal consistency of the results in western European cities with wide differences in climate and environmental conditions suggest that these associations may be causal. The long term health impact of these effects is uncertain, but today's relatively low levels of sulphur dioxide and particles still have detectable short term effects on health and further reductions in air pollution are advisable.
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              Methodological issues in studies of air pollution and daily counts of deaths or hospital admissions.

              To review the issues and methodologies in epidemiologic time series studies of daily counts of mortality and hospital admissions and illustrate some of the methodologies. This is a review paper with an example drawn from hospital admissions of the elderly in Cleveland, Ohio, USA. The central issue is control for seasonality. Both over and under control are possible, and the use of diagnostics, including plots, is necessary. Weather dependence is probably non-linear, and adequate methods are necessary to adjust for this. In Cleveland, the use of categorical variables for weather and sinusoidal terms for filtering season are illustrated. After control for season, weather, and day of the week effects, hospital admission of persons aged 65 and older in Cleveland for respiratory illness was associated with ozone (RR = 1.09, 95% CI 1.02, 1.16) and particulates (PM10 (RR = 1.12, 95% CI 1.01, 1.24), and marginally associated with sulphur dioxide (SO2) (RR = 1.03, 95% CI = 0.99, 1.06). All of the relative risks are for a 100 micrograms/m3 increase in the pollutant. Several adequate methods exist to control for weather and seasonality while examining the associations between air pollution and daily counts of mortality and morbidity. In each case, care and judgement are required.
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                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                intjepid
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                August 2013
                12 June 2013
                12 June 2013
                : 42
                : 4
                : 1187-1195
                Affiliations
                1Department of Non-Communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK, 2Medical Statistics Department, London School of Hygiene and Tropical Medicine, London, UK and 3Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, UK
                Author notes
                *Corresponding author. Department of Non-Communicable Diseases Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. E-mail: krishnan.bhaskaran@ 123456lshtm.ac.uk
                Article
                dyt092
                10.1093/ije/dyt092
                3780998
                23760528
                66e23415-3162-4555-83da-13537c32e9f3
                Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2013.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 24 April 2013
                Page count
                Pages: 9
                Categories
                Education Corner

                Public health
                time series,environmental epidemiology,air pollution
                Public health
                time series, environmental epidemiology, air pollution

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