bluuebunny/crossref_metadata_2025_split huggingface dataset:
K-Anonymity Based Privacy Risk Budgeting System for Interactive Record Linkage
Authors: Kum Hye-Chung , Jain Prannay 
Published: April 2017

ABSTRACTObjectiveInformation privacy theory demonstrates mathematically that privacy is a budget constrained problem and that privacy preserving algorithms(e.g., differential privacy) must rely on a budgeting system. Thus, we design a privacy measure as a function of information disclosed to support incremental information disclosure required for safe interactive record linkage. The privacy measure will determine the increase in the privacy risk for any given information disclosed during record linkage.
ApproachMathematically, the identity disclosure risk is inversely proportional to the number of entities in the population that share the information disclosed. If the information refers to one and only one person in the population, then the identity of the person has been fully disclosed by the information revealed. On the other hand, if the information disclosed is identical for multiple people(say n), then the information is less revealing as it could refer to any one of the n people. The larger the n, the lower the privacy risk. Thus, the anonymity-set size is defined as the number of people in the population that share the same identifying information. The privacy risk measure has one prespecified parameter k, which represents the minimum anonymity-set size to guarantee no privacy risk. That is, for any disclosed information, if the anonymity-set size is less than k, then a privacy risk is present and the risk score will be calculated. A commonly accepted threshold for k is 5 or 10. On the one hand, when all entities have anonymity-set size less than k, the privacy risk would be 100%. On the other hand, if all entities have anonymity-set size greater than or equal to k, the privacy risk would be 0%.
ResultsThe budgeting system contributes to the much-needed methods for protecting privacy while still supporting high quality interactive record linkage by allowing safer manual resolution of uncertain linkages. The budgeting system supports refining effective visual encoding techniques for incrementally revealing only the required information on an as-needed basis during manual resolution of uncertain linkages as well as refining the design for a visual interface to facilitate privacy preserving data standardization, cleaning, and conflict resolution for interactive record linkage. We evaluate the budgeting system with the NC voter registry data.
ConclusionThe k-anonymity based privacy risk budgeting system provides a mechanism where we can concretely reason about the tradeoff between the privacy risks due to information disclosed, accuracy gained, and biases reduced during interactive record linkage.
It’s a big, BAD World- the UK picture of Big and Administrative Data
Authors: Webb Joanne 
Published: April 2017

ABSTRACT
Background & AimsThe Economic and Social Research Council (ESRC) have established a three strand programme to encourage the use of Administrative and Big Data in research. The first strand funded the Administrative Data Network, a UK-wide partnership between universities, government bodies, national statistics authorities and the wider research community. www.adrn.ac.uk. The Network facilitates secure research access to linked, de-identified administrative data to enable real-world analysis that can benefit society.
The second strand covers the Big Data Centres: the Business and Local Government Data Centre, the Urban Big Data Centre and the Consumer Data Research Centre. These concentrate on making data routinely collected by business and local government organisations accessible for academics in order to undertake research in the social sciences.
The third strand enables partnerships between academic institutions and citizen and voluntary sector organisations, to establish or build on relationships between academic researchers and civil society organisations. The aim is to demonstrate the value of improved data infrastructure, enabling collection and analysis of data which is of interest to civil society organisations and empowering the sector to better use its own data.
All three strands aim to enable research in social sciences while safeguarding individuals’ identities. Working in a diverse changing landscape brings its own challenges and possibilities. The University of Essex hosts the Administrative Data Service of the ADRN, the Business and Local Government Data Centre and the Human Rights, Big Data and Technology Project. The challenge is to describe the boundaries, overlaps and synergies in the landscape.
This paper will review the UK network of these partnerships, a description of some of the challenges of taking part in such a diverse growing landscape and some of the benefits to researchers and society.
Managing Emerging Data Types in the NGLMS
Authors: Farrow James 
Published: April 2017

ABSTRACT
ObjectivesWe describe the management system used by the Next Generation Linkage Management System (NGLMS) built for SA.NT DataLink in Adelaide, Australia. The NGLMS is a bespoke system built on freely available open source components where a graph (in the computer science sense) structure is used to store a ‘more natural’ representation of linked records explicitly in a graph database: records as vertices and relationships as edges between vertices.
ApproachThe NGLMS is designed to manage linked data effectively and permit fast individual cluster extraction while retaining rich relationship information. It holds probabilistic and statically-linked data by storing all significant pair-wise relationships between records as edges in a graph, allowing clustering with different parameters to be performed dynamically. Records are heterogeneous and may contain different data types: birth records, hospital separations, census data, pharmaceutical prescriptions, educational data. The relationships between records are also heterogeneous and may represent arbitrary relationships not just a probabilistic record similarity. For example, familial (parent/child), tribal kinship structures, genomic (and other omic) information, employer/employee relationships, educational information, living arrangements, census information, and so on. Storing this information allows for richer queries than just ‘do these records represent the same entity’. For example a single rich query to the database could be ‘find all records of all siblings’, ‘create genealogies based on birth information’, ’create household groups based on census/cohabitation information’, or ‘find employees working in areas affected by recent floods with hospitalisations during that time period.’
ResultsWe present details of the loading of birth and perinatal data incorporating parent (mother and father) relationships for some South Australian datasets and the technical configuration of the NGLMS to support this. We discuss the queries made possible as a result. Rich non-traditional data is stored in the same manner as probabilistic record similarities and has allowed clustering queries which mix explicit deterministic statements about the data and probabilistic statements concerning record relationships.
ConclusionRich queries over data may be expressed by storing rich heterogeneous information about records and relationships explicitly as a graph and by determining clusters late in the extraction process. Modern graph database technologies make this effective even in the face of datasets containing 10’s to 100’s of million records and billions of edge relationships.
Can censoring of research outputs be automated to ensure robust data protection?
Authors: Nicholas Michael , Davies Chris , Nock Kelly , Bailey Kerry , Barker Craig , Player Luke , Thomas Helen 
Published: April 2017

ABSTRACT
BackgroundGuidance regarding research outputs recommends censoring so that even when aggregating anonymised linked data no cell should have less than 5 -10 units. This is recommended to decrease the likelihood of re- identification. Leaving those cells empty is not adequate if other cells can be used to identify the numerical value of that cell. Some outputs necessitate a large number of tables to be exported this will become more common. This was the case where the outputs from a research study involved several large tables which drove a front end interactive visualisation. As linked data outputs are used to make operational decisions which necessitates timely data outputs of large amount of aggregated data this issue will be more common. Human scanning of all tables may not be time or cost effective and can be subject to human error.
ApproachMany methods of censoring were considered including Barnardisation (adding or subtracting 1 randomly to small numbers) suppression and a combination of methods. It was then necessary to code the methods to ensure that censoring was implemented in all cells in the output and that the output was still meaningful. It was then necessary to check the outputs for quality and introduce an ‘audit’ system to ensure that the quality was maintained but did not impact on the outputs of the findings.
DiscussionSoftware engineers were able to develop an algorithm that performed safe censoring using a level of ‘10 or under’. It also ensured that the statistical tables were still functional. The presentation will describe how this was done and demonstrate some examples of the impact on the output. Some stakeholders felt that the censoring of the anonymised aggregated data went beyond the ‘reasonable effort’ required to re- identify individuals. Some expressed the opinion that the lack of detail and missing data that this method results in is excessive and has been sacrificed for the sake of minimal risk. Some stakeholders felt the risks had been allowed to outweigh the societal benefits. The team were assured that although the censoring may be considered excessive by some it did ensure safe censoring and offered as low a risk a possible for re-identification. However routine implementation of this method has not been agreed.
Involving People with Lived Experience of Homelessness in Electronic Health Records Research
Authors: Luchenski Serena , Clint Sharon , Aldridge Rob , Hayward Andrew , Maguire Nick , Story Alistair , Hewett Nigel 
Published: April 2017

ABSTRACTObjectivesWe held a public engagement workshop to involve people with lived experiences of homelessness and social exclusion, experts by experience (EBE), in our research. The purpose of the workshop was to provide context to the findings of a review series on Inclusion Health and to inform future research using electronic health records (EHRs) of homeless and socially excluded people.
ApproachParticipants included 16 volunteers and one staff member from Groundswell (a registered charity that promotes inclusive solutions to homelessness), four academics, one clinical provider, and two note-takers/photographers. The full-day workshop was held at University College London. We used innovative participatory activities specifically designed for use with hidden and marginalised populations to stimulate open dialogue. Methods included general discussion, conceptual mapping, modelling, electronic voting, brainstorming, ranking, graphing, stakeholder analysis, and evaluation surveys. Activities were fast-paced and conducted in small groups, as the whole group, individually, or in pairs. The meaning of Inclusion Health, barriers that lead to exclusion, and values and actions that promote inclusion were discussed. Health statistics on homeless and other socially excluded groups were presented and informed conversations about EHR research, including data collection (new data versus collation of administrative data), data linkage, consent, anonymisation, data security, and ‘the surveillance society.’
ResultsNotes from the workshop and the evaluation surveys indicated that the workshop was engaging, inclusive, and successful. When clearly explained, EBE were positive towards research using EHRs to improve Inclusion Health, including data linkage of sensitive health and social data. EBE expressed that housing, advocacy, and psychosocial therapies were the most important interventions for improving health of people who are homeless; however, we found limited evidence for these interventions in our review. Overall, findings demonstrate the value of involving the people who have been socially excluded to develop research priorities and to interpret findings.
ConclusionUsing a participatory and dynamic approach to involve people with lived experience of homelessness and exclusion is an effective public engagement methodology for complex topics such as EHR research and data linkage. Information provided in the workshop was useful for interpreting findings, identifying strengths and gaps in health and social services, and developing research and practice recommendations.
Identifying Household Level Risk Factors for Unintentional House Fire Incidents, Injuries and Fatalities
Authors: Turner Samantha , Rodgers Sarah , Lyons Ronan 
Published: April 2017

ABSTRACTObjectivesUnintentional house fire incidents, injuries and deaths are a serious public health concern in the UK, which disproportionally affect certain groups in the population. Whilst house fires have decreased in recent years; growing financial pressures in the Fire and Rescue Services (FRSs) have resulted in funds dedicated to fire preventative activities becoming increasingly limited. To ensure ever limiting resources are targeted towards those households at greatest risk, it is essential the FRSs’ are accurately informed about the types of household at increased risk. The aim of this project is to undertake a large-scale case-control study, to identify the distinguishing household level risk factors associated with unintentional house fire incidents, injuries and deaths.
ApproachUnintentional house fire incidents reported to the Welsh FRS between the years 2003-2008, were anonymised and incorporated into the Secure Anonymised Information Linkage (SAIL) Databank at the Farr Institute, Swansea University. 6943 case households (households which reported a fire to the FRS) were time-matched to 347,150 control households (case:control ratio 1:50). Individuals registered as living at these properties on the date of the fire were established using the Welsh Demographic Service (WDS) dataset. Household level variables will be created by linking case and control households to other demographic, health, educational and environmental datasets in SAIL. Conditional Logistic Regression will be used to estimate matched odds ratios and 95% confidence intervals.
ResultsPotential risk factor variables were selected on the basis of a systematic review and theoretically plausible variables. Covariates include: household composition (e.g. age and gender of residents), socioeconomic status, educational attainment, smoking, alcohol consumption, mental health conditions, other health related conditions, mobility and sensory impairments and property related characteristics. Fire related circumstances (e.g. fire ignition source, presence of a smoke alarm) will also be investigated in logistic regression models exploring risk factors for injury and death. Results will be presented at the conference.
ConclusionThis is the first large-scale analysis of risk factors for unintentional house fire incidents, injuries and deaths. The findings from this project will be translated into comprehensible infographics, designed to support the FRSs, other partner organisations and the general public, recognise high risk households in need of preventative interventions.
Administrative, legal, and organizational hurdles in data linkage: Experiences and lessons from the recent National Center for Health Statistics (NCHS) - U.S. Department of Housing and Urban Development (HUD) data linkage project
Authors: Sperling Jon , Lloyd Patricia , Helms Veronica , Brittain James 
Published: April 2017

ABSTRACT
ObjectivesThrough a collaborative agreement between the NCHS and HUD, the first linkage of two national population health surveys to HUD administrative records of housing assistance was recently completed. Based on this multi-year effort, this paper emphasizes key requirements for making progress in and between regulatory and legal environments; confidentiality and data security.
BackgroundAs the principal health statistics agency in the United States, the National Center for Health Statistics (NCHS) collects and disseminates statistical information that guide actions and policies to improve the health of the American people. The mission of the U.S. Department of Housing and Urban Development (HUD) is to create strong, sustainable, inclusive communities and quality affordable homes for all. Prior to the release of the NCHS-HUD linked file, there was no existing data source to reliably estimate the prevalence of health conditions and outcomes for households receiving HUD housing assistance. Rather than conducting its own health survey, costing millions of dollars, HUD leveraged the existing NCHS and Census knowledge base, the data linking expertise of NCHS and its Research Data Center (RDC) infrastructure. With the NCHS-HUD linked data files, analysts and researchers can now examine health conditions, health behaviors, access to health services and use of health services in relation to housing.
ApproachWhile technical (e.g., temporal, spatial, definitional, linkage keys) challenges for linking national-level surveys and administrative data have been the subjects of many discussions, often the organizational, legal and behavioral issues are more daunting and usually determine success. To comply with regulations at NCHS and HUD, a memorandum of understanding clearly outlined the manner with which personally identifying information (PII) would be protected, and how data would be shared across agencies.
Results and ConclusionThrough this extraordinary and innovative, no-cost, collaborative and strategic research partnership with the Department of Health and Human Services/Centers for Disease Control and Prevention/NCHS, HUD is learning more about the health of tenants receiving housing assistance. The data linkage project is a model for data sharing that aligns with U.S. Office of Management and Budget (OMB) directives for sharing data across federal agencies to reduce respondent burden and improve federal programs and as well as the health of American people.
Codifying unstructured data: A Natural Language Processing approach to extract rich data from clinical letters
Authors: Lacey Arron , Lyons Jane , Akbari Ashley , Turner Samantha L , Walters Angharad M , Fonferko-Shadrach Beata , Pickrell Owen , Rees Mark I , Lyons Ronan A , Ford David V , Middleton Rod M 
Published: April 2017

ABSTRACT
ObjectivesElectronic healthcare records (EHR) are the main data sources that facilitate epidemiology research. Routinely collected data such as primary and secondary care are now easily linked to produce novel and high impact research. There are, however, rich data locked in the free text of clinical letters that are not otherwise translated into EHRs. It is highly desirable to be able to extract this information to strengthen the body of information in existing EHRs.
The Swansea Collaborative in Analysis of NLP Research (SCANR) group at Swansea University has been established to evaluate the usage of Natural Language Processing platforms for obtaining new clinical data. To use Clix Enrich to extract SNOMED concepts from a variety of clinical free texts and produce EHRs from the extraction process.
Approach SNOMED concepts contain common items of interest such as diagnosis, medication and symptoms, as well as contextual concepts such as historical reference and negation. Clix Enrich uses the SNOMED dictionary to encode clinical free text (pre-co-ordinated) and find contextually correct SNOMED concepts (post co-ordinated). We used Clix Enrich to extract meaningful clinical terms from MS and Epilepsy consultant letters, as well as presenting complaint fields from a Welsh Emergency Department (ED).
ResultsWe tailored Clix Enrich to extract a wide variety of clinical terms from each source (fourty texts per source) and validated the extraction accuracy with clinical experts in each domain. Clix Enrich was able to accurately extract the correct diagnosis for MS, Epilepsy and ED attendance (100%, 95% and 80%), dosage and frequency of anti-epileptic medication and MS modifying therapy (90%, 100%) and EDDS score (94%). We note a probable source of discrepancy in extraction accuracy between letter sources in the frequency of abbreviated terms, particularly within the presenting complaint field of the ED sample.
ConclusionClix Enrich can be used to accurately extract SNOMED concepts from clinical letters. The resulting datasets are readily available to link to existing EHRs, and can be linked to EHRs that adopt the SNOMED coding structure, or backward compatible hierarchies. Clix Enrich comes with out-of-the-box extraction methods but the optimum way to extract the correct information would be to build in custom queries, thus requiring clinical expertise to validate extraction.
Primary care contact before and after emergency hospitalisation in children in English NHS hospitals: a linked administrative data study
Authors: Wijlaars Linda , Hardelid Pia , Gilbert Ruth 
Published: April 2017

ABSTRACT
ObjectivesEmergency admissions for ambulatory care sensitive conditions (ACSCs) are thought to be preventable through preventive or early treatment interventions in primary care. However, evidence shows up to 70% of children with ACSCs admissions have underlying chronic conditions, suggesting that these admissions could be less amenable to primary care prevention than previously thought. We determined the extent to which primary care is involved in care by assessing primary care consultations before and after an emergency admission to hospital for an ACSC.
ApproachWe used a national general practice database (Clinical Practice Research Datalink) linked to hospital admissions data (Hospital Episode Statistics) and included children aged 0-19 years with emergency admissions for ACSCs or injury between 2000-2009. ACSCs were defined as acute infections (pneumonia and lower respiratory tract infections (LRTIs), dehydration and gastroenteritis (DGE), urinary tract infections (UTIs)), and asthma. We included injuries as a control group as we did not expect to see an increase in GP consultations for this group before emergency admission.
We calculated hospital admission rates by age, gender and area-level deprivation per 1,000 child-years and determined the proportion of children consulting their GP in the week before and week after emergency admission. We examined independent risk factors using zero-inflated negative binomial regression models.
ResultsWe extracted data for 1,664,555 children with 32,442 emergency admissions for ACSCs and 39,305 for injuries. There were clear socioeconomic gradients: LRTIs, UTIs and asthma were more prevalent in more deprived children, while rates for DGE and injuries were similar between deprivation quintiles. The majority of children with emergency admissions for ACSCs consulted their GP in the week before admission (range 58.3% (asthma) - 69.5% (LRTIs)), while 24.2% of children with injuries had a GP consultation. Children with ACSCs were more likely to consult their GP after discharge, with 37% (LRTIs) - 46% (UTI) of children consulting in the week after discharge, compared to 22% after injury admission. The proportion of children consulting their GP in the week before an emergency admission decreased. For LRTIs, this decreased from 71.7% in 2000 to 55.2% in 2009.
ConclusionThe majority of children consulted a GP both before and after an ACSC emergency admission. This suggest that the degree to which primary care can prevent ACSCs conditions in children might be less than previously thought. Further research is needed to determine which community or hospital based interventions, if any, can reduce ACSC emergency admissions.