1,721,040 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/
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
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
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
Combining Survey and Census Data in Time and Space in a Developing World Context
No full textThesis (Ph.D.)--University of Washington, 2019Obtaining reliable estimates of health indicators at a granular level in space and time is important for informing health intervention and public policy decisions. In low and middle income countries, the data available come from a multitude of sources including vital registration systems, complex surveys, and disease registries. These data sources are often of varying quality. In particular, the information on health outcomes may be aggregated over space and time. Overall, this poses a modeling challenge as there is a mismatch between the underlying process, the observed data, and the inferential resolution desired. This work tackles three main issues that are commonly faced in this setting by developing Bayesian models tailored to the specific problem at hand. In particular, this work considers incorporating data where the outcome has been aggregated over space (common in census data), the outcome is associated with a point in space but the exact location is unknown (common in household survey data), and the outcome has been aggregated over time (common in modeling child mortality using census data)
Subnational Estimation of Period Child Mortality in a Low and Middle Income Countries Context
No full textThesis (Ph.D.)--University of Washington, 2021Child mortality is an important metric used in quantifying and monitoring the health of a population's children. Moreover, child mortality can be a key indicator of the overall health of a population, and is often used to quantify mortality at other ages. Over the past several decades, there have been huge global reductions in child mortality. However, child mortality remains large in many low and middle income countries (LMICs). The United Nations' Sustainable Development Goals (SDGs) call for a reduction of child mortality in the period 2015--2030. In particular, SDG 3.2 continues the global initiative to improve chid mortality outcomes by calling for an end to preventable child deaths and reaching a target under-five mortality rate (U5MR) of 25 deaths before age 5 per 1000 births and 12 deaths per 1000 births in the first month of life by the year 2030. Many methods exist for estimation of national child mortality measures, but there is a growing desire for more and better methods for subnational estimation of child mortality. Improved methods for subnational estimation of U5MR can allow for a better understanding of the geographic differences of trends in U5MR and more targeted intervention for child mortality reduction. Though there are many ways to think about geographic variability within a country, in this thesis we focus on the administrative divisions of a country that have political and infrastructural meaning. The national level of a country is referred to as Admin-0, the coarsest subnational administrative division is referred to as Admin-1, and the next coarsest as Admin-2. Many sources of child mortality data collect information for which Admin-1 estimates are reasonable, but this leads to small sample sizes at the Admin-2 level. The focus of this thesis is on subnational child mortality estimation at the Admin-2 level. In this thesis, we provide a review of demographic and statistical methods for age-specific period child mortality, synthesize notation across fields, and develop two methods for subnational estimation of U5MR at the Admin-2 data in LMICs. We make use of child mortality data from household surveys throughout this thesis and discuss in detail how an individual's mortality information is used in various existing demographic and statistical methods for mortality estimation. The two methods we develop for subnational child mortality estimation in LMICs at the Admin-2 level take different approaches to accounting for the method of data collection in the household surveys and one method incorporates census data. We apply the method developed to incorporate census data separately to the countries of Kenya and Malawi. We find mixed evidence of improvements in estimation with the incorporation of census data, and note the method's shortcomings when small sample sizes result in many Admin-2 areas with no observed deaths. The limitations of this method, motivate the need for a method that can be applied more broadly to countries with sparser coverage of household survey data. We develop a method and reproducible, replicable pipeline for data acquisition, cleaning, estimation, and visualization. We apply this method to Admin-1 and Admin-2 levels in 22 LMICs, fitting separate models for each country and administrative division. The country-specific modeling and steps in the pipeline allow us to address the unique context of child mortality in each country if the generic base model is inappropriate, such as in countries with generalized HIV/AIDs epidemics, the genocide events in Rwanda, and Cyclone Nargis in Myanmar. The estimates for 22 LMICs have been published in collaboration with UNICEF and the UN Inter-agency Group for Child Mortality Estimation, have undergone review in consultations with country representatives, and are available at \url{https://childmortality.org}. This thesis improves on the current understanding in the processing and use of child mortality data in the literature and develops two new methods for estimation of child mortality in LMICs at the Admin-2 level. In particular, the development of a clearly-defined and replicable pipeline that allows for easy adaptation to address the unique mortality estimation needs of individual countries fills a previous gap in existing methodology. However, there are areas for future research and methodological improvement. Throughout the thesis we make note of ongoing work to improve existing aspects of the developed methods and make clear the issues that remain unaddressed
Statistical Methods for Geospatial Modeling with Stratified Cluster Survey Data
Full text linkThesis (Ph.D.)--University of Washington, 2020The production of fine-scale, pixel level maps have become increasingly common in the current era of precision public health. This has led to the use of cluster level spatial models by major organizations such as WorldPop and the Institute for Health and Metrics Evaluation. However, many of these models were originally developed in the context of environmental applications, and, when estimating health and demographic indicators in low and middle income countries, they are frequently applied to demographic data from complex, multi-stage household surveys, leaving the potential for both biased and anticonservative estimates unless adjustment for the design is carried out. We highlight three potential problems. First, survey stratification and cluster level variation are often not accounted for. Second, the cluster level models either do not fully account for the population census frame, or completely ignore this aspect. This is made more problematic by confusion between the terms `prevalence' and `risk'. Third, even if stratification is accounted for in the cluster level spatial model, if that model is continuously indexed in space, then it often becomes necessary to infer what stratification level is associated with each spatial location or enumeration area (EA) when aggregating predictions, which is inevitably inexact. However, little work has been done to identify how stratification misclassification can impact predictions and how to produce predictions that are more robust to this problem. In this thesis, we investigate a variety of issues relevant to the use of cluster level data for estimating demographic outcomes continuously through space, and also as aggregates over administrative areas. We focus on the three problems mentioned above when estimating the neonatal mortality rate and secondary education completion for women aged 20--29 in Kenya using the 2014 Kenya Demographic Health Survey (DHS). First, we explore methods in small area estimation that can account for survey stratification, proposing models that include stratum level fixed effects for cluster-indexed spatial models. Second, we propose a general framework capable of accounting for cluster level, population denominator, and population-level variation as well as some aspects of EA location uncertainty. We call a model in this framework a combined population aggregation model (CPAM) since they are formed by combining standard cluster level risk models with an aggregation model for producing areal estimates. We propose a CPAM that, for Admin2 level areal estimates, produces estimates with substantially more uncertainty in the resulting neonatal mortality population aggregates of prevalence, total deaths, and relative prevalence between urban and rural areas, and at only moderately increased computational expense. The proposed CPAM is the first continuous spatial model accounting for the population census frame, cluster level variation, and population numerator and denominator variation when estimating prevalence, total counts, and relative prevalence in urban versus rural parts of an area. Lastly, we develop a Bayesian extension to the popular LatticeKrig model, which we call extended LatticeKrig (ELK). ELK allows for flexible, multiscale spatial dependence and non-Gaussian responses. We show this model holds particular promise for predicting areal aggregates due to its ability to flexibly model the covariance structure, and is more robust than traditional stochastic partial differential equation methods when accounting for confounding factors such as urbanicity
- …
