scholarly journals Syphilis in London’s Children’s Hospitals (1852 - 1921)

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Matthew J. Lee ◽  
Thomas J. Siek ◽  
Cara S. Hirst
Keyword(s):  
Hospital Admissions ◽  
Congenital Syphilis ◽  
Venereal Disease ◽  
Large Data ◽  
Data Sets ◽  
World War One ◽  
Children's Hospitals ◽  
Working Classes ◽  
Children’S Hospitals ◽  
The Social

Establishing the palaeoepidemiology of diseases in children is a difficult task due to limited written and physical evidence. Historic admissions records from children’s hospitals can provide large data sets allowing insights into this area, rather than just case studies which are what most commonly appear within the palaeopathological literature. An oft ignored aspect of childhood illness is venereal disease due to the social taboo surrounding this topic. This study aimed to investigate the extent of syphilis within Victorian and Edwardian London’s children’s hospitals and explore the socioeconomic context this disease was occurring within. This was achieved by examining digitised hospital admissions data covering the mid-nineteenth to early twentieth centuries for three children’s hospitals. These records revealed a significant spike in admissions for congenital syphilis following World War One. This was likely due to the return of troops from the warfront who had been infected whilst in mainland Europe. It was also found that the upper levels of the working classes accounted for the majority of the admissions, despite these institutions being created to aid the children from the lowest socioeconomic groups. Finally, this paper highlights the need for researchers to also consider the possibility of children having acquired syphilis rather than congenital syphilis when examining such records

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

Social Science Data Analysis

2013 ◽  
pp. 131-147
Author(s):  
Anthony Scime ◽  
Gregg R. Murray
Keyword(s):  
Data Mining ◽  
Social Science ◽  
Large Data ◽  
Data Sets ◽  
Health And Wellness ◽  
Science Data ◽  
Social Scientists ◽  
The Social ◽  
Social Scientific ◽  
Behavioral Predictions

Social scientists address some of the most pressing issues of society such as health and wellness, government processes and citizen reactions, individual and collective knowledge, working conditions and socio-economic processes, and societal peace and violence. In an effort to understand these and many other consequential issues, social scientists invest substantial resources to collect large quantities of data, much of which are not fully explored. This chapter proffers the argument that privacy protection and responsible use are not the only ethical considerations related to data mining social data. Given (1) the substantial resources allocated and (2) the leverage these “big data” give on such weighty issues, this chapter suggests social scientists are ethically obligated to conduct comprehensive analysis of their data. Data mining techniques provide pertinent tools that are valuable for identifying attributes in large data sets that may be useful for addressing important issues in the social sciences. By using these comprehensive analytical processes, a researcher may discover a set of attributes that is useful for making behavioral predictions, validating social science theories, and creating rules for understanding behavior in social domains. Taken together, these attributes and values often present previously unknown knowledge that may have important applied and theoretical consequences for a domain, social scientific or otherwise. This chapter concludes with examples of important social problems studied using various data mining methodologies including ethical concerns.

Abstract 17235: Trends in Endocarditis Hospitalizations at US Children's Hospitals From 2003-2014: Impact of the 2007 American Heart Association Antibiotic Prophylaxis Guidelines

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Katherine E Bates ◽  
Matthew Hall ◽  
Samir S Shah ◽  
Kevin D Hill ◽  
Sara K Pasquali
Keyword(s):  
American Heart Association ◽  
Hospital Admissions ◽  
American Heart ◽  
Heart Association ◽  
Recent Analysis ◽  
Oral Streptococci ◽  
Children's Hospitals ◽  
Children’S Hospitals ◽  
Significant Difference ◽  
Over Time

Introduction: Over the past decade, national organizations in several countries have released more restrictive guidelines for infective endocarditis (IE) prophylaxis, including the American Heart Association (AHA) 2007 guidelines. Multiple initial studies demonstrated no change in IE rates over time following release of these guidelines, however a more recent analysis over a longer time period in the UK suggested an increase in IE. This prior study primarily included adults. Current IE trends in the pediatric population are unknown. Methods: Children (<18 years) hospitalized with IE at 29 US centers participating in the Pediatric Health Information Systems Database from 2003-2014 were eligible for inclusion. Our primary analysis focused on IE most directly related to the change in the AHA guidelines, and included community-acquired IE cases (antibiotics covering oral streptococcal species started within 7 days of admission) in those >5 years of age (most likely to be receiving dental care). Interrupted time series analysis was used to evaluate IE rates over time indexed to total hospital admissions. Results: A total of 841 IE cases were identified. Median age was 13 years (interquartile range 9-15 years). In the pre-guideline period, the IE rate increased slightly over time (+0.013 IE cases/1000 hospitalizations per 6-month period, see Figure). In the post-guideline period there was a similar trend in IE rates (+0.012 IE cases/1000 hospitalizations per 6-month period) with no significant difference in slope in the pre vs. post period (p=0.9). Additional analyses in those with congenital heart disease, and in those hospitalized with any type of IE (not limited to oral streptococci) at any age, revealed similar results. Conclusions: In contrast to a recent UK study, we found no evidence of a change in IE hospitalization rates associated with revised IE prophylaxis guidelines over an 11 year period across 29 US children’s hospitals.

scholarly journals On the Design of Social Robots Using Sheaf Theory and Smart Contracts

2021 ◽  
Vol 8 ◽  
Author(s):  
Renita Murimi
Keyword(s):  
Imperfect Information ◽  
Large Data ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Data Sets ◽  
Sheaf Theory ◽  
Social Fabric ◽  
The Social ◽  
Iot Devices ◽  
Traditional Structures

The incorporation of robots in the social fabric of our society has taken giant leaps, enabled by advances in artificial intelligence and big data. As these robots become increasingly adept at parsing through enormous datasets and making decisions where humans fall short, a significant challenge lies in the analysis of robot behavior. Capturing interactions between robots, humans and IoT devices in traditional structures such as graphs poses challenges in the storage and analysis of large data sets in dense graphs generated by frequent activities. This paper proposes a framework that uses the blockchain for the storage of robotic interactions, and the use of sheaf theory for analysis of these interactions. Applications of our framework for social robots and swarm robots incorporating imperfect information and irrationality on the blockchain sheaf are proposed. This work shows the application of such a framework for various blockchain applications on the spectrum of human-robot interaction, and identifies key challenges that arise as a result of using the blockchain for robotic applications.

A Data Mining Approach to Advance Knowledge in Public Government

2010 ◽  
pp. 106-125
Author(s):  
Paola Annoni ◽  
Pieralda Ferrari ◽  
Silvia Salini
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Data Sets ◽  
National Bureau ◽  
Service Usage ◽  
Household Expenditures ◽  
Data Mining Approach ◽  
The Social ◽  
Definition Of

Data mining is the process of ‘mining’ into large quantity of data to get useful information. It comprises a broad set of techniques originated within different applicative fields to solve various types of issues. In this chapter the data mining approach is proposed for the characterization of family consumptions in Italy. Italian expenditures are a complex system. Every year the Italian National Bureau of Statistics (ISTAT) carries out a survey on the expenditure behaviour of Italian families. The survey regards household expenditures on durable and daily goods and on various services. Here the goal is twofold: firstly it describes the most important characteristics of family behaviour with respect to expenditures on goods and usage of different services; secondly possible relationships among these behaviours are highlighted and explained by social-demographical features of families. To this purpose, a series of statistical techniques are used in sequence and different potentialities of selected methods for addressing these kinds of issues are pinpointed. This study recommends that, further investigation is needed to properly focalize on service usage for the characterization, for example, of the nature of investigated services (private or public) and, most of all, about their supply and effectiveness across the national territory. Still this study may be considered an example of operational and concrete approach of managing of large data-sets in the social-economical science, from the definition of goals to the evaluation of results.

scholarly journals Hospital Admissions for Abusive Head Trauma at Children's Hospitals During COVID-19

PEDIATRICS ◽  
2021 ◽  
pp. e2021050361
Author(s):  
Nathan L. Maassel ◽  
Andrea G. Asnes ◽  
John M. Leventhal ◽  
Daniel G. Solomon
Keyword(s):  
Head Trauma ◽  
Hospital Admissions ◽  
Abusive Head Trauma ◽  
Children's Hospitals ◽  
Children’S Hospitals

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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