1,721,049 research outputs found
Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias
No full textBenzene is classified as a group 1 human carcinogen by the International Agency for Research on Cancer, and it is now accepted that occupational exposure is associated with an increased risk of various leukaemias. However, occupational exposure accounts for less than 1% of all benzene exposures, the major sources being cigarette smoking and vehicle exhaust emissions. Whether such low level exposures to environmental benzene are also associated with the risk of leukaemia is currently not known. In this study, we investigate the relationship between benzene emissions arising from outdoor sources (predominantly road traffic and petrol stations) and the incidence of childhood leukaemia in Greater London. An ecological design was used because of the rarity of the disease, the difficulty of obtaining individual level measurements of benzene exposure and the availability of data. However, some methodological difficulties were encountered, including problems of case registration errors, the choice of geographical areas for analysis, exposure measurement errors and ecological bias. We use a Bayesian hierarchical modelling framework to address these issues, and we investigate the sensitivity of our inference to various modelling assumptions. <br/
Understanding and quantifying uncertainty due to multiple biases in meta-analyses of observational studies
Full text linkThere has been considerable interest recently in quantifying
uncertainty beyond that due to random error in meta-analyses.
This is particularly relevant to meta-analyses of observational studies,
since error in estimates from these studies cannot be attributed
to a randomization mechanism. Typically, observational studies are
also subject to error due to measurement error, non-participation,
and incomplete adjustment for confounding. Errors due to these
sources are often referred to as bias. To quantify uncertainty due
to bias, researchers have proposed using "bias models" and giving
subjectively elicited probability distributions to parameters that
are not identifiable in the models.
In a typical meta-analysis, probability distributions involving
tens of parameters will have to be elicited. At the same time, the
resulting estimate and uncertainty interval of the overall (meta-analytic) effect measure will generally be very sensitive to this
multi-dimensional subjectively-elicited distribution. To overcome
some of the problems associated with the use of such a distribution,
I propose an alternative method for eliciting and quantifying
uncertainty due to bias. In the method of this thesis, the lower and
upper bounds of bias parameters are elicited instead of probability
distributions. The most extreme Bayesian posterior inference
for the target parameter of interest within the specified bounds
is sought through an algorithm. The resulting lower and upper
bounds for the target parameter of interest have interpretation of
a Robust Bayes analysis.
In this thesis, the method is applied to a meta-analysis of
childhood leukaemia and exposure to electromagnetic fields. The
method of this thesis was found to produce uncertainty intervals
that are generally more conservative in comparison with the standard
approach. It is also proposed that the method be used as a
tool for sensitivity analysis, and some interesting insight is gained
from the childhood leukaemia data. [For supplementary files please contact author]
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Trends and forecasts in cause-specific mortality
Full text linkMortality forecasting models are typically limited in that they pertain only to national death rates, predict only all-cause mortality, or do not capture and utilize the correlation among diseases. I have developed a novel Bayesian hierarchical model that jointly forecasts cause- specific death rates for geographic subunits.
I examined the model’s effectiveness by applying it to United States vital statistics data from 1982 to 2011 that I prepared using a new cause of death reassignment algorithm. I found that the model not only generated coherent forecasts for mutually exclusive causes of death, but it also exhibited lower out-of-sample error than alternative commonly-used models for forecasting mortality.
I then used the model to produce forecasts of US cause-specific mortality through 2025 and analysed the resulting trends. I found that total death rates in the US were likely to continue their decline, but at a slower rate of improvement than has been observed for the past several decades. While death rates due to major causes of death like ischaemic heart disease, stroke, and lung cancer were projected to continue trending downward, increases in causes such as unintentional injuries and mental and neurological conditions offset many of these gains.
These findings suggest that the US health system will need to adapt to a changing cause composition of disease burden as its population ages in the coming decade. Forecasting research should continue to consider how to best incorporate and balance the many dimensions of mortality when producing projections.Open Acces
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles
Full text linkBackground: Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis. Methods: We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002-2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005. Results: The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of - 3.5% (95% CI: - 0.12%, - 5.74%). Conclusions: We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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