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      Mortality risk attributable to high and low ambient temperature: a multicountry observational study


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          Although studies have provided estimates of premature deaths attributable to either heat or cold in selected countries, none has so far offered a systematic assessment across the whole temperature range in populations exposed to different climates. We aimed to quantify the total mortality burden attributable to non-optimum ambient temperature, and the relative contributions from heat and cold and from moderate and extreme temperatures.


          We collected data for 384 locations in Australia, Brazil, Canada, China, Italy, Japan, South Korea, Spain, Sweden, Taiwan, Thailand, UK, and USA. We fitted a standard time-series Poisson model for each location, controlling for trends and day of the week. We estimated temperature–mortality associations with a distributed lag non-linear model with 21 days of lag, and then pooled them in a multivariate metaregression that included country indicators and temperature average and range. We calculated attributable deaths for heat and cold, defined as temperatures above and below the optimum temperature, which corresponded to the point of minimum mortality, and for moderate and extreme temperatures, defined using cutoffs at the 2·5th and 97·5th temperature percentiles.


          We analysed 74 225 200 deaths in various periods between 1985 and 2012. In total, 7·71% (95% empirical CI 7·43–7·91) of mortality was attributable to non-optimum temperature in the selected countries within the study period, with substantial differences between countries, ranging from 3·37% (3·06 to 3·63) in Thailand to 11·00% (9·29 to 12·47) in China. The temperature percentile of minimum mortality varied from roughly the 60th percentile in tropical areas to about the 80–90th percentile in temperate regions. More temperature-attributable deaths were caused by cold (7·29%, 7·02–7·49) than by heat (0·42%, 0·39–0·44). Extreme cold and hot temperatures were responsible for 0·86% (0·84–0·87) of total mortality.


          Most of the temperature-related mortality burden was attributable to the contribution of cold. The effect of days of extreme temperature was substantially less than that attributable to milder but non-optimum weather. This evidence has important implications for the planning of public-health interventions to minimise the health consequences of adverse temperatures, and for predictions of future effect in climate-change scenarios.


          UK Medical Research Council.

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            Is Open Access

            Time series regression studies in environmental epidemiology

            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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              Reducing and meta-analysing estimates from distributed lag non-linear models

              Background The two-stage time series design represents a powerful analytical tool in environmental epidemiology. Recently, models for both stages have been extended with the development of distributed lag non-linear models (DLNMs), a methodology for investigating simultaneously non-linear and lagged relationships, and multivariate meta-analysis, a methodology to pool estimates of multi-parameter associations. However, the application of both methods in two-stage analyses is prevented by the high-dimensional definition of DLNMs. Methods In this contribution we propose a method to synthesize DLNMs to simpler summaries, expressed by a reduced set of parameters of one-dimensional functions, which are compatible with current multivariate meta-analytical techniques. The methodology and modelling framework are implemented in R through the packages dlnm and mvmeta. Results As an illustrative application, the method is adopted for the two-stage time series analysis of temperature-mortality associations using data from 10 regions in England and Wales. R code and data are available as supplementary online material. Discussion and Conclusions The methodology proposed here extends the use of DLNMs in two-stage analyses, obtaining meta-analytical estimates of easily interpretable summaries from complex non-linear and delayed associations. The approach relaxes the assumptions and avoids simplifications required by simpler modelling approaches.

                Author and article information

                Lancet (London, England)
                25 July 2015
                25 July 2015
                : 386
                : 9991
                : 369-375
                [a ]Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
                [b ]Department of Social and Environmental Health Research, London School of Hygiene & Tropical Medicine, London, UK
                [c ]Department of Epidemiology and Biostatistics, School of Population Health, University of Queensland, Brisbane, QLD, Australia
                [d ]Department of Pediatric Infectious Diseases, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
                [e ]Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, ON, Canada
                [f ]Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
                [g ]Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain
                [h ]School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
                [i ]Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
                [j ]Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
                [k ]School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA
                [l ]Department of Environmental and Occupational Medicine, National Taiwan University, Taipei, Taiwan
                [m ]Department of Public Health, National Taiwan University, Taipei, Taiwan
                [n ]Department of Environmental Health, Fudan University, Shanghai, China
                [o ]Graduate School of Public Health & Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
                [p ]Department of Pathology, School of Medicine, University of São Paulo, São Paulo, Brazil
                [q ]Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
                Author notes
                [* ]Correspondence to: Dr Antonio Gasparrini, Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK antonio.gasparrini@ 123456lshtm.ac.uk
                © 2015 Gasparrini et al. Open Access article distributed under the terms of CC BY

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