Author
Ben Armstrong
Other affiliations: University at Albany, SUNY, University of East Anglia, Glasgow Caledonian University ...read more
Bio: Ben Armstrong is an academic researcher from University of London. The author has contributed to research in topics: Population & Environmental exposure. The author has an hindex of 89, co-authored 331 publications receiving 27055 citations. Previous affiliations of Ben Armstrong include University at Albany, SUNY & University of East Anglia.
Papers published on a yearly basis
Papers
More filters
University of London1, University of Queensland2, Nagasaki University3, RMIT University4, Harvard University5, Spanish National Research Council6, Queensland University of Technology7, Umeå University8, Yale University9, National Taiwan University10, Fudan University11, Seoul National University12, University of São Paulo13, University of Tsukuba14
TL;DR: Most of the temperature-related mortality burden was attributable to the contribution of cold, and the effect of days of extreme temperature was substantially less than that attributable to milder but non-optimum weather.
1,569 citations
TL;DR: A modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects, based on the definition of a ‘cross-basis’, which is implemented in the package dlnm within the statistical environment R.
Abstract: Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure-response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure-response dependencies and delayed effects. This methodology is based on the definition of a 'cross-basis', a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987-2000.
1,398 citations
TL;DR: Climate change mitigation in transport should benefit public health substantially and policies to increase the acceptability, appeal, and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.
1,013 citations
TL;DR: 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.
791 citations
TL;DR: 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.
Abstract: 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. 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. 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. 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.
785 citations
Cited by
More filters
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.
9,324 citations
TL;DR: The Commission on Social Determinants of Health (CSDH) as mentioned in this paper was created to marshal the evidence on what can be done to promote health equity and to foster a global movement to achieve it.
7,335 citations
Mohammad H. Forouzanfar1, Lily Alexander, H. Ross Anderson, Victoria F Bachman1 +733 more•Institutions (289)
TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as discussed by the authors provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.
5,668 citations
TL;DR: This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference.
Abstract: When more than one statistical test is performed in analysing the data from a clinical study, some statisticians and journal editors demand that a more stringent criterion be used for “statistical significance” than the conventional P<0051 Many well meaning researchers, eager for methodological rigour, comply without fully grasping what is at stake Recently, adjustments for multiple tests (or Bonferroni adjustments) have found their way into introductory texts on medical statistics, which has increased their apparent legitimacy This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference
#### Summary points
Adjusting statistical significance for the number of tests that have been performed on study data—the Bonferroni method—creates more problems than it solves
The Bonferroni method is concerned with the general null hypothesis (that all null hypotheses are true simultaneously), which is rarely of interest or use to researchers
The main weakness is that the interpretation of a finding depends on the number of other tests performed
The likelihood of type II errors is also increased, so that truly important differences are deemed non-significant
Simply describing what tests of significance have been performed, and why, is generally the best way of dealing with multiple comparisons
Bonferroni adjustments are based on the following reasoning1-3 If a null hypothesis is true (for instance, two treatment groups in a randomised trial do not differ in terms of cure rates), a significant difference (P<005) will be observed by chance once in 20 trials This is the type I error, or α When 20 independent tests are performed (for example, study groups are compared with regard to 20 unrelated variables) and the null hypothesis holds for all 20 comparisons, the chance of at least one test being significant is no longer 005, but 064 …
5,471 citations
TL;DR: It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality.
Abstract: In 2004, the first American Heart Association scientific statement on “Air Pollution and Cardiovascular Disease” concluded that exposure to particulate matter (PM) air pollution contributes to card...
5,227 citations