bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing Record Linkage methods for real-world perinatal and neonatal data without unique identifiers
Authors: Schnell Rainer , Borgs Christian 
Published: November 2019

BackgroundData on newborns is regularly linked for epidemiological research. However, hospital data often suffers from incomplete data. We report on a linkage of two population-covering administrative health databases containing neonatal and perinatal data without unique personal identifiers and with incomplete information in standard patient identifiers.
GoalTo study the effects of a policy-induced change from linking a national database without standard patient identifiers to a privacy-preserving Record Linkage method, we compare the linkage system in use to clear-text and privacy-preserving Record Linkage techniques. We expected large proportions of missing identifiers since they are not needed for clinical practice. Therefore, we expected missing links caused by missing identifiers. To study the impact of these missing identifiers on these successful links, we compared several linkage methods. Furthermore, we study the variations of linkage success between hospitals.
MethodsPerinatal and neonatal data from population-covering real-world administrative databases was linked using several variants of state of the art methods, including Privacy-preserving Record Linkage (PPRL) techniques such as multiple match keys and Bloom filter methods.
Results We report on the variation of linkage results between the hospitals and give possible explanations for the differences.
The resulting linkage success is reported for each method. The impact of incomplete data on linkage success for each method is documented.
Finally, we report on the relative performance of the modified techniques compared to standard linkage procedures used in practice.
ConclusionImplementing a record linkage system based on identifiers not required for clinical practice caused a large number of missing identifiers. Since this information is essential for successful clear-text and private linkage methods, emphasizing the need for documenting patient identifiers, especially in cases where auxiliary information (such as stable addresses, date of birth or health insurance numbers) are missing, is of central importance for implementing a privacy-preserving Record Linkage system.
Geographical factors in access: investigating the impact of distance on the use of primary care extended hours, an administrative data study.
Authors: Murphy Jen , Whittaker William , Elliot Mark , Ravindrarajah Rathi 
Published: November 2019

IntroductionNHS national targets mandate extended opening hours of doctors’ surgeries as a mechanism for increasing access to primary care, based on the assumption that unmet need is caused by a lack of appointments at the right time. Research has shown that other factors impact access and it may not simply be availability that limits an individual’s ability to access healthcare.
Aims and Objectives To determine whether distance, familiarity and deprivation impact on the uptake of extended hours GP services that use a hub practice model.
MethodsWe linked an appointments dataset to publicly available population datasets. With that linked dataset, we used negative binomial regression to model count data relating to uses of the extended hours service in one care commissioning group in the Greater Manchester city region. The dataset included 32,693 appointments across 4 hubs serving 37 practices.
ResultsFamiliarity and distance are important in predicting the number of uses of the extended hours service at a GP practice level. For a theoretical pair of practices collocated at the hub location, the model predicts a use rate of 101.2 for the non hub compared with 283.7 for the hub, a 180% uplift. For a pair of non-hub practices, one located the mean distance from the hub, the other located one mile further away, the model predicts 64.8 uses for the nearer practice, and 46.5 uses for the far practice, a 28% penalty.
ConclusionThe results indicate geographical inequity in the extended hours service. There may be many patients with unmet need for whom the extension of hours via a hub model does not address barriers to access. Providers should consider whether or not this type of model actually works to facilitate access. This is particularly of importance in the context of closing health inequality gaps.
Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019
Authors: Palmer Louise , Wroth-Smith Charlie , Edwards Anthony 
Published: November 2019

Significant data linkage work is taking place across government, as highlighted in a recent review by the Office for Statistics Regulation (“Joining up Data for Better Statistics”). This review provided insights on the scale of the data linkage work in government and its potential future opportunities. Based on this we are conducting a National Statistician’s Quality Review (NSQR) on data linkage.
These reviews investigate topics of current statistical interest and innovation with contributions from subject matter experts. The aims of this NSQR are to report on state-of-the-art data linkage methods and join up government work on data linkage to break down silos and share best practice.
We have scoped key topics of interest and linkage challenges by searching the literature and engaging with stakeholders across government and wider. Areas of interest include quality assurance of linkage, linking more than two data sources, linking encrypted data and challenges with data sharing and access for linking. Following this we approached recognised data linking experts e.g. academics, the private sector, the Office for National Statistics (ONS), wider Government Statistical Service (GSS) and other National Statistical Institutes (NSIs) to identify state-of-the-art linkage methods. This NSQR will outline the challenges faced across the GSS and set out the steps that should to be taken across the statistical system, building on the state-of-the-art data linkage methods set out by the experts.
We will present our progress and provisional findings on the leading methods being discussed in the review, as well as potential next steps for facilitating a more joined up way of working across the GSS to improve data linkage.
One Size Doesn’t Fit All: Administrative Data Quality Frameworks for Production of Official Statistics
Authors: Correia Sara , Sim Jack 
Published: November 2019

Background with rationaleThe use of administrative data is key to achieving the UK Statistics Authority’s strategy of Better Statistics, Better Decisions. Integrating administrative data into official statistics can benefit policy decisions by allowing the possibility of greater granularity and improved timeliness in outputs, while delivering efficiency gains and reducing respondent burden. Quality assessment and communicating uncertainty of administrative data sources is critical to their effective integration into official statistical outputs.
Main AimThis presentation will discuss the main challenges of quality assuring statistical outputs containing administrative data. The differences in existing quality frameworks and identified quality metrics will be discussed. In addition, the presentation will cover the need to tailor quality assessment to answer a specific research question that an identified source is being used for and the considerations required.
Methods/ApproachA comprehensive literature review was carried out, bringing together existing quality frameworks and metrics from National Statistical Institutes (NSIs) and academia for production of statistics using administrative data sources.
ResultsThe main challenges and considerations faced when quality assuring outputs produced using administrative sources have been identified. The quality requirements for different outputs across social, business and census statistics were summarised and a general quality framework for admin data developed. This framework draws on international best practices for use in the UK statistical system.
ConclusionIntegrating administrative data presents challenges can’t be solved by a one-size fits all framework. Through unifying available guidance, an adaptable quality assurance methodology has been created, enabling the use of public data for the public good.
Quantifying multi-morbidity in an ethnically-diverse inner city population: the health burden of households
Authors: Harper Gill , Lyons Jane , Fry Richard , Akbari Ashley , Ahmed Zaheer , Lyons Ronan , Dezateux Carol , Robson John 
Published: November 2019

Background with rationaleNew insights into the wider demographic context of multimorbidity has been prioritised, notably among disadvantaged and ethnically-diverse populations with a high disease burden. We propose an innovative approach to quantify health burden and disease clustering at household level, to enable predictors of household multimorbidity to be investigated and understood.
Main AimTo quantify multi-morbidity at the household level using general practitioner (GP) electronic health records (EHRs) in a geographically-defined ethnically-diverse inner city population.
MethodsWe extracted clinical and patient address data from GP EHRs from four east London boroughs (Tower Hamlets, Newham, Waltham Forest and City & Hackney). We included currently registered patients aged ≥18 years as at July 2018, and excluded those with duplicate or complex registrations, new registrations in the previous 12 months, or registrations without historical clinical data or occurring prior to 1948, as well as inactive patients with no recorded EHR activity in varying years depending on age and gender.
We defined multimorbidity using 16 long-term condition Read codesets defined in the Quality and Outcomes Framework. We grouped patients into households, defined as those sharing the same home address on their GP registration, represented by a pseudonymised Unique Property Reference Number.
ResultsProvisional data are presented. We identified 737,920 patients (51% female) eligible for this study out of a total population of 1,171,483 currently registered patients. Of these, 23% aged <20, 69% aged 20-64, 8% aged >=65, 38% White ethnicity, 3% Mixed, 30% Asian, 14% Black, 5% Other and 12% Not Stated/Null. We identified 312,582 shared households among 737,920 patients. Analyses to derive household-level summary characteristics and relate these to multimorbidity burden are in progress and will be presented.
ConclusionHousehold-level multi-morbidity can be quantified using clinical and patient address data in GP electronic health records.
GRAPHITE: Geographic Information UK Secure E-Research Platform
Authors: Fry Richard , Mizen Amy , Akbari Ashley , Thompson Simon 
Published: November 2019

BackgroundThe use of Geographic Information Science in administrative and health data research provides researchers and policymakers with powerful modelling capabilities when exploring spatial variations in health and social outcomes. However, there are challenges around using spatial data and associated modelling techniques in terms of computing power, skills, data quality and disclosure controls.
Main AimIn this case study, we describe our approach to overcoming some of these problems in conjunction with the Secure Anonymised Information Linkage Databank (SAIL).
ApproachThe UK Secure E-Research Platform (UKSeRP) is a suite of technologies which can be linked together to provide a secure system to support a particular use case or data resource. We have taken this foundation and built a Geographic Information UKSeRP which is solely focused on the storage, manipulation and generation of spatial data and models to support administrative and health research. The GIS system is built around the Unique Property Reference Number (UPRN) which can be anonymised into a Residential Anonymised Linking Field (RALF) and provides a precise location of households across the UK. This system allows the user to generate high-resolution spatiotemporal models of geographically varying phenomena (e.g. access to services, fast food, alcohol, greenspace and pollution) in a secure managed environment. To support the geographic data stored in the platform there is a range of GIS tools (Python, R, QGIS, PostGIS, OSRM) and standardised methods available to users which sit alongside a data catalogue. Access to the platform is managed via an information governance review process on a project by project basis to ensure best practice in disclosure control and integrity.
ConclusionSpatial data and GIS techniques are an important resource for academic and policy researchers. The GRAPHITE platform demonstrates best practice for using spatial data when used in conjunction with sensitive data.
Evaluating ATC-ICD: Assessing the relationship between selected medication and diseases with machine learning
Authors: Weibrecht Nadine , Endel Florian , Zechmeister Melanie 
Published: November 2019

IntroductionCoded diagnoses (ICD-9, ICD-10) are only available in routine data of the Austrian Health-Care system in connection with sick leave or inpatient hospital stays. Therefore, they only cover a small part of the population. Coded diagnoses from the outpatient sector are not documented.
The aim of the project is to estimate diagnoses based on filled prescriptions reimbursed by a public health insurance institution. The result is a model that can provide probable diagnoses (ICD-10 coding) based on individual medication (ATC coding).
MethodsBeginning in 2008 / 2009, the project ATC->ICD-9 has been developed by means of a statistical procedure. Here, hospital and sick leave diagnoses, as well as data on received medication are used to determine assignment probabilities.
In this project, we developed a new method to derive diagnoses from medications. Our method is based on the word2vec-algorithm: Patient histories are used as input phrases, so that low-dimensional embeddings of medications and diseases are learned. In the learned vector space, similar medications and diseases are close to each other.
ResultsTo evaluate our model, we compute the vector representation for medications and look for nearby diseases. E.g., the closest diseases to typical diabetes medication are different kinds of diabetes and retina affections, while nearby gout medications, gout and kidney diseases are found.
ConclusionFor the given examples, our model provides reasonable results. It does not only yield typical diseases to a medication, but also common secondary symptoms. This motivates to apply the model on further use cases. For example, given an anonymized list of patients, containing their medications, disease distributions of these patients can be computed.
The impacts of pre-apprenticeship training for young people
Authors: Dorsett Richard , Stokes Lucy 
Published: November 2019

Apprenticeships are the key means by which the UK government aims to build skills and tackle the problem of youth unemployment. However, not all young people are able to secure an apprenticeship. Traineeships, a voluntary six-month programme of work placements and work preparation training, were introduced in England in 2013 to help equip young people with the skills and experience required to secure an apprenticeship or employment.
The analysis in this paper uses linked administrative data on the population of trainees and a comparison sample of non-trainees to evaluate the impact of the programme on employment and apprenticeships. It uses a local instrumental variable approach, which allows selection into a traineeship to be influenced by unobserved preferences and for impacts to vary according to these preferences. The heterogeneous impacts can be aggregated to form an estimate of the average impact of treatment for all participants.
The results show no overall impact on employment for younger trainees (16-18 year-olds) but an across-the-board positive impact on the probability of becoming an apprentice. For older trainees (19-23 year-olds), no significant impact on either employment or apprenticeships is evident among participants as a whole but the results suggest that, for those more resistant to participating, traineeships may actually reduce the probability of becoming an apprentice.
These results confirm the effectiveness of traineeships as a means of facilitating apprenticeships among younger people. As such, they support the policy target of achieving 3 million apprenticeships by 2020.
Donor-based imputation methods for admin data: How to replace the number of rooms question on the Census
Authors: Tietz Stephan , Mealor Andy , Leather Fern , Dent Ali 
Published: November 2019

The Census White Paper recommends removing the number of rooms question and to utilise administrative data instead. Previous research demonstrated that Valuation Office Agency (VOA) data can be used for this purpose in principal. However, users of Census micro-level data expect a utility dataset without missingness.
This research project explored whether VOA number of rooms variable is suitable to undergo edit and imputation (E&I) within the standard census framework (i.e. donor imputation). We examined how linked admin and survey data challenges assumptions underlying the E&I process. We linked the 2011 Census with VOA data and attempted to impute VOA number of rooms using auxiliary variables from the Census to predict the missing and inconsistent values. This includes the question of whether we should allow questionnaire data to be changed where they are inconsistent with admin data.
We demonstrated that it is possible to predict VOA number of rooms from census variables, despite some assumptions being partially violated (definitional and time-frame issues, the possibility of subpopulations in the data). This was true for missingness prior to linkage and missingness due to a data linkage failure. Additionally, we successfully tested the design principal of favouring survey data over alternative data where the two are inconsistent.
We observed that some local authorities had large percentages of imputed data, and there was an increase in the percentage of properties where VOA number of rooms was equal to census number of bedrooms. This affects end-user interpretation of the data.
The result is not a general endorsement that all linked survey-admin data can be effectively treated by standard E&I procedures. However, our research can be used as a blue print for other proof-of-concept studies on imputing admin data.
People with cancer living in deprived areas of Wales are more likely to have another serious condition at diagnosis than those in the least deprived areas
Authors: Huws Dyfed , Thomas Rebecca , Howe Julie , Oddy Adele , Smith Tomos , Egan David , Long Tamsin , Shiell-Davis Kelly 
Published: November 2019

BackgroundPeople diagnosed with cancer are living longer and whilst cancer survival is improving for many cancers, there is not the same parity for all social groups - older people and people living in more deprived areas often have more chronic health conditions. We examined the association between those other health conditions and cancer incidence, prevalence and survival for all Welsh patients, for the four most common cancers and all malignant cancer cases (excluding non-melanoma skin cancer).
MethodsWe extracted data on all malignant cancer cases from the WCISU’s population-based cancer registry for diagnosis periods 1995-2015. Cases were linked to a Cluster Network and to Patient Episode Database for Wales hospital data for the preceding year to establish pre-existing health conditions. From this, a Charlson score was calculated for each case - this is a validated score to predict risk of death and disease burden.
For incidence and prevalence, we calculated the proportion of patients with Charlson score 0, 1 and 2+, and proportions with each health condition examined. We calculated one-year net survival by Charlson score or condition. Where possible, analysis was by cancer type, age-band, area deprivation, rurality, sex and stage at diagnosis.
ResultsOne in four people were already living with another serious condition. Patients diagnosed in more deprived areas of Wales were more likely to have an existing condition at diagnosis. Survival worsened as the severity or number of existing conditions increased.
ConclusionPatients diagnosed with cancer in more deprived areas of Wales were more likely to be already living with another serious condition, showing a significant decrease in their projected survival at Charlson score 1 and 2+ compared to the least deprived areas.
This work will enable acute, primary and community care, and other organisations to understand the overall burden of ill health in the cancer population in Wales.
Data intensive science and the public good: Results of public deliberations in British Columbia, Canada
Authors: McGrail Kimberlyn , Burgess Michael , O'Doherty Kieran , Bentley Colene , Teng Jack 
Published: November 2019

IntroductionResearch using linked data sets can lead to new insights and discoveries that positively impact society. However, the use of linked data raises concerns relating to illegitimate use, privacy, and security (e.g., identity theft, marginalization of some groups). It is increasingly recognized that the public needs to be consulted to develop data access systems that consider both the potential benefits and risks of research. Indeed, there are examples of data sharing projects being derailed because of backlash in the absence of adequate consultation. (e.g., care.data in the UK).
Objectives and methodsThis talk will describe the results of public deliberations held in Vancouver, British Columbia in April 2018 and the fall of 2019. The purpose of these events was to develop informed and civic-minded public advice regarding the use and the sharing of linked data for research in the context of rapidly evolving data availability and researcher aspirations.
ResultsIn the first deliberation, participants developed and voted on 19 policy-relevant statements. Taken together, these statements provide a broad view of public support and concerns regarding the use of linked data sets for research and offer guidance on measures that can be taken to improve the trustworthiness of policies and process around data sharing and use. The second deliberation will focus on the interplay between public and private sources of data, and role of individual and collective or community consent I the future.
ConclusionGenerally, participants were supportive of research using linked data because of the value such uses can provide to society. Participants expressed a desire to see the data access request process made more efficient to facilitate more research, as long as there are adequate protections in place around security and privacy of the data. These protections include both physical and process-related safeguards as well as a high degree of transparency.