bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the coverage of Statistics New Zealand's Integrated Data Infrastructure
Authors: Minshall Gareth , Gomez Antony , Christine Bycroft 
Published: April 2017

ABSTRACTObjectivesStatistics New Zealand’s Integrated Data Infrastructure (IDI) combines information from a range of government agencies (such as tax, health and education data) in order to provide the insights government needs to improve social and economic outcomes for New Zealanders. New Zealand has no national population register or unique identifier used in common across these multiple data sources, and probabilistic linkages are a feature of the IDI. A challenge for researchers is to understand the impact of linkage errors and coverage issues present in the linked data, and to develop the rules necessary to define their target population. We outline the statistical infrastructure Statistics New Zealand is developing to help researchers navigate these issues.
ApproachA method has been developed to identify NZ residents at a given time from the much larger number of individuals present in the IDI. Census data linked to the IDI offers insight into the coverage of key population groups and the quality of the attribute information held in the IDI (e.g. location and ethnicity). We are assessing ways that Statistics New Zealand could use these findings to assist researchers in forming their population of interest and assess the potential for bias.
ResultsThe derived administrative resident population is compared with the official population figures and patterns of under- and over-coverage are identified at an aggregate, and individual level. Some coverage discrepancies may be improved through reducing linkage errors. Comparison with census data reveals some significant quality issues with location and ethnicity variables in administrative collections. Work is underway to improve methods for combining information from multiple sources of varying quality.
ConclusionIdentifying NZ residents at a given time, and quantifying errors in administrative data sources will assist researchers ability to recognise and adjust for these errors in their analysis. Simply quantifying (often for the first time) the limitations of administrative sources also provides impetus to improving the collection of these variables at source.
Using family relationships to improve consistency of identification of Aboriginal people in linked administrative data
Authors: Gibberd Alison , Simpson Judy , Eades Sandra 
Published: April 2017

ABSTRACTObjectivesWith the life expectancy of Aboriginal Australians 10 years less than non-Aboriginal Australians, administrative health datasets are essential tools to identify disparities and track changes over time. However, the Aboriginal status of individuals is often inconsistently recorded within, and across, datasets and Aboriginal people are frequently undercounted. Therefore, information from multiple linked records is often used resolve inconsistencies and undercount. However, very different algorithms are used and their performance is poorly understood.
We aimed to (i) assess the relative accuracy of three common algorithms by comparing how consistently family members were categorised; (ii) develop a new algorithm that incorporates information about the Aboriginal status of family members; and (iii) investigate the impact of these algorithms on estimates of health outcomes.
ApproachOur sample was people born from 1980 onwards recorded as Aboriginal at least once in 6 Western Australian datasets, as well as their Aboriginal and non-Aboriginal family members.
We applied the most inclusive algorithm, ‘ever-Aboriginal’ (EA, any record of being Aboriginal), and two more specific algorithms, ‘multi-stage median’ (MSM) and ‘last record’ (LR), to linked birth, birth registration, birth defects and hospital inpatient records. We also supplemented these algorithms with information from any children’s birth records (EA+, MSM+ and LR+). Finally, we used records from all family members to derive a final Aboriginal status (MSM+ with family), using a range of decision rules, such as classifying someone as Aboriginal if they had two full siblings categorised as Aboriginal.
ResultsEA+ classified 61% of our sample of 156,426 people as Aboriginal, compared with only 51% for LR.
MSM+ resulted in the most consistent classification of full siblings; only 8% of pairs had one sibling identified as non-Aboriginal and the other as Aboriginal, compared with 14% using EA+. MSM+ also performed best with parent-child triads; the classifications of the child and parents were inconsistent in only 8% of cases, compared with 27% using EA.
The proportions of low birthweight babies were similar for EA+, MSM+ and MSM+ with family, (10.6%, 10.8% and 10.8%, respectively). Stillbirth rates were also similar. However, an estimated 46% of mothers smoked during pregnancy for EA+, compared with 50% for both MSM+ and MSM+ with family.
ConclusionOf the 6 algorithms examined, MSM+ assigned Aboriginal status most consistently and EA was the least reliable. Estimates of health outcomes were worse for Aboriginal people when the more consistent algorithms were used. However, the differences were generally small.
A UKSeRP for SAIL: striking a balance
Authors: Jones Kerina , Ford David , Brooks Caroline 
Published: April 2017

ABSTRACT
ObjectivesWhilst the current expansion of health-related big data and data linkage research are exciting developments with great potential, they bring a major challenge. This is how to strike an appropriate balance between making the data accessible for beneficial uses, whilst respecting the rights of individuals, the duty of confidentiality and protecting the privacy of person-level data, without undue burden to research.
ApproachUsing a case study approach, we describe how the UK Secure Research Platform (UKSeRP) for the Secure Anonymised Information Linkage (SAIL) databank addresses this challenge. We outline the principles, features and operating model of the SAIL UKSeRP, and how we are addressing the challenges of making health-related data safely accessible to increasing numbers of research users within a secure environment.
ResultsThe SAIL UKSeRP has four basic principles to ensure that it is able to meet the needs of the growing data user community, and these are to: A) operate a remote access system that provides secure data access to approved data users; B) host an environment that provides a powerful platform for data analysis activities; (C) have a robust mechanism for the safe transfer of approved files in and out of the system; and (D) ensure that the system is efficient and scalable to accommodate a growing data user base. Subject to independent Information Governance approval and within a robust, proportionate Governance framework, the SAIL UKSeRP provides data users with a familiar Windows interface and their usual toolsets to access anonymously-linked datasets for research and evaluation.
ConclusionThe SAIL UKSeRP represents a powerful analytical environment within a privacy-protecting safe haven and secure remote access system which has been designed to be scalable and adaptable to meet the needs of the rapidly growing data linkage community. Further challenges lie ahead as the landscape develops and emerging data types become more available. UKSeRP technology is available and customisable for other use cases within the UK and international jurisdictions, to operate within their respective governance frameworks.
The ELAStiC (Electronic Longitudinal Alcohol Study in Communities) project
Authors: Akbari Ashley , Lyons Ronan , Berridge Damon , Gallacher John , MacLeod John , Heron Jon , Hickman Matthew , Mahedy Liam , Bellis Mark , Fone David , Paranjothy Shantini , Trefan Laszlo , Evans Annette , Dunstan Frank , Tingay Karen , Bandyopadhyay Amrita , Gross Vanessa , Wang Yu-Chiao , Moore Simon 
Published: April 2017

ABSTRACT
ObjectivesThe ELAStiC (Electronic Longitudinal Alcohol Study in Communities) project was established to determine factors that predict pathways into alcohol misuse and the life-course effects of alcohol use and misuse on health and well-being. This is achieved through accessing existing longitudinal data that are key sources of evidence for social and health policy, developing statistical methods and modelling techniques from a diverse range of disciplines, working with stakeholders in both policy, practice and the third sector to bring relevance to the work, and to bring together a diverse team of experts to collaborate and facilitate learning across diverse fields.
ApproachThe project will link data that include cohort studies such as; UK Biobank, ALSPAC (Avon Longitudinal Study of Parents and Children), Millennium Cohort Study, British Household Panel Survey, Understanding Society, E_CATALyST (Caerphilly Health and Social Needs Electronic Cohort Study) and WECC (Wales Electronic Cohort for Children). These data will be linked with routine data from primary and secondary healthcare in England, Scotland and Wales. Additional data from education and police data source will also be linked as part of the project.
The main work packages for the project are:
Methodological InnovationsMethodological developments in mechanisms for correcting bias in reporting alcohol consumption and for combining routine data with cohort data; the application of Markov models for examining the extent to which past behaviour influences future behaviour, and econometric hedonic pricing methods for providing insights into the costs of alcohol-related harm.
Pathways into HarmDo family structure, household composition, youngsters’ previous ill-health and educational attainment predict their use of alcohol and what socio-economic factors and household transitions contribute to hazardous alcohol consumption in adults?
Secondary HarmsWhat is the effect on children’s health and educational achievement of living in households in which one or more adults has experienced alcohol-related harm?
Mental Health & Well-BeingWhat is the relationship between alcohol consumption, hospital admission and mental health in adults and children?
ResultsThe results of the data linkage between the multiple cohorts and health, education and police data will be reported. The challenges of linking cohort and other data types from different nations will be discussed.
ConclusionsOur project will aim to provide evidence that informs the UK Government’s commitment to “radically reshape the approach to alcohol and reduce the number of people drinking to excess”, by working with existing longitudinal data collected in the UK to inform policy and practice.
Privacy protected text analysis in DataSHIELD
Authors: Wilson Rebecca , Butters Oliver , Avraam Demetris , Turner Andrew , Burton Paul 
Published: April 2017

ABSTRACT
ObjectivesDataSHIELD (www.datashield.ac.uk) was born of the requirement in the biomedical and social sciences to co-analyse individual patient data (microdata) from different sources, without disclosing identity or sensitive information. Under DataSHIELD, raw data never leaves the data provider and no microdata or disclosive information can be seen by the researcher. The analysis is taken to the data - not the data to the analysis.
Text data can be very disclosive in the biomedical domain (patient records, GP letters etc). Similar, but different, issues are present in other domains - text could be copyrighted, or have a large IP value, making sharing impractical.
ApproachBy treating text in an analogous way to individual patient data we assessed if DataSHIELD could be adapted and implemented for text analysis, and circumvent the key obstacles that currently prevent it.
ResultsUsing open digitised text data held by the British Library, a DataSHIELD proof-of-concept infrastructure and prototype DataSHIELD functions for free text analysis were developed.
ConclusionsWhilst it is possible to analyse free text within a DataSHIELD infrastructure, the challenge is creating generalised and resilient anti-disclosure methods for free text analysis. There are a range of biomedical and health sciences applications for DataSHIELD methods of privacy protected analysis of free text including analysis of electronic health records and analysis of qualitative data e.g. from social media.
Linking child travel routes and routine health data
Authors: Mizen Amy , Rodgers Sarah , Fry Richard , Lyons Ronan 
Published: April 2017

ABSTRACTObjectivesLinking routinely collected health and environment data can allow for large scale evaluations of how our environment impacts our health. Our data linkage approach advances previous research where residence-based environmental exposures were anonymously linked in the SAIL databank using Residential Anonymous Linking Fields (RALFs). The dose-response relationship between exposure to food and dietary intake has not been widely investigated. Previous research found conflicting views on whether increased environmental exposure to unhealthy food contributes to higher BMIs. This may have been due to different methodological approaches, including imprecise exposures, small numbers, and the use of self-reported BMIs.
ApproachThis investigation calculated food exposure environments for routes from all homes to and from school. A Geographic Information System was used to calculate the environmental exposures along all potential routes up to a maximum age-appropriate walking distance from each school. Once within the SAIL databank we selected relevant routes using linked demographic and pupil datasets. To maintain privacy, the primary (doctoral) researcher generating the environmental exposures, did not have access to the final household-level exposure data in their identifiable form. The researcher automated their method so a second researcher could run the GIS analysis. Accuracy of modelled exposures will be compared with actual routes collected from GPS traces of children walking to school.
ResultsRemoving access to the final identifiable household-level route exposures enabled the primary researcher to complete analysis on the combined household and individual-level data within the secure environment. The environmental exposures were linked with routine health data from the SAIL databank; including BMI as an indicator of obesity. BMI data for 4-5 year olds, and a sample of 1300 13-14 year olds were linked to associated environmental exposures.
ConclusionDepending on modelled accuracy, a GIS and data linkage approach may allow the investigation of natural experiments and intervention evaluation at the scale of the total population. This is the first step towards anonymously modelling part of the daily exposure environment using routine data. A limitation is the lack of routinely collected BMI data for older children and teenagers an age when they are more likely to have the option to choose to buy food on the school route. This work will have many potential applications, including the delivery and evaluation of multiple school and workplace commuting interventions.
An automated tool for a uniform decentralized quality control and data analysis in multicenter studies with health care registry data
Authors: Endel Florian , Strassmayr Christa , Katschnig Heinz 
Published: April 2017

ABSTRACTObjectivesThe EU FP7 funded project CEPHOS-LINK investigates hospital re-admissions of patients with a psychiatric disorder in 6 European countries by using linked health care registry data. In addition to the problems of different healthcare, payment and data collection systems, coordinating quality control, data analysis, and statistical modeling of sensitive data with six partners is challenging. For this purpose we have designed a secure online data analysis tool to diminish the time necessary to get results and incremental adaptions of reports as well as decreasing the chance and effects of misunderstandings between national and linguistic boundaries.
ApproachA comprehensive study protocol clearly defining variables to be obtained and methods to be applied has been put together. The protocol is based on a thorough investigation of the different healthcare systems and related registries. It became clear that nonetheless misconceptions occur and the incremental improvements consumed vast amounts of available resources. Therefore a system which automatically creates the required reports including all tables, graphics and statistical models including data preparation based on a defined data structure has been developed. The report system is based on the statistical environment R and the document markup language LaTeX, tightly integrated with R's package “knitr”.As this highly flexible solution is not straight forward to apply and implies various technical dependencies, a secure online platform hiding all technical details from the users has been developed. Utilizing state of the art software containers based on Linux and docker, a customized VPN solution, authentication and SSL encryption were put together. The web application itself is developed with R's “shiny” package and allows users to simply upload a dataset in the predefined format, interactively explore the contents, apply filters and generate the customizable, standardized report. Additionally, an offline version of the application is available for all major (desktop) operating systems.
ResultsThe new platform advances data analysis and reporting in a situation where several partners are involved in analyzing local datasets, as is the case of the CEPHOS-LINK project. Integrating new features, graphics and research topics can be managed centrally while users can update their results and reports in nearly no time.
ConclusionThe additional effort spent on developing a customized platform for quality control, data analysis and reporting has been worth the effort. Benefits include quick detection of implausible results, unifying the layout and graphics often depending on the software utilized and an established common data structure.
Cancer recording in patients with type 2 diabetes in primary care and hospital admission data
Authors: Williams Rachael , Gallagher Arlene , Van Staa Tjeerd , Hammad Tarek , Leufkens Bert , De Vries Frank 
Published: April 2017

ABSTRACTObjectiveElectronic health records are increasingly used to investigate associations between antidiabetic therapy and cancer. Misclassification can impact results, especially if differential between comparators. The objective of this study was to estimate cancer misclassification when using primary care or hospital data alone.
ApproachAdults aged ≥40 years with an insulin or oral antidiabetic prescription in Clinical Practice Research Datalink (CPRD) primary care data at least a year after start of data collection, and no record of type 1 diabetes, were included. Patients were matched by year of birth (stepwise within 5 years), sex and GP practice to up to 1 non-diabetic patient. The cohort was restricted to those eligible for Hospital Episode Statistics (HES) linkage with follow-up during the study period (01/04/97-31/12/06). Follow-up started at the maximum of the registration date with the practice, practice up-to-standard date (a CPRD quality metric), and start of study period. Follow-up ended at the minimum of when the patient left the practice, the date CPRD last collected data from the practice, and end of study period. Cancer was identified in CPRD via Read codes and in HES via ICD10 codes. For each cancer case in CPRD, analysis evaluated whether there was a corresponding record in HES coded with same, different or unspecified site. Analysis was repeated for cancers identified in HES.
Results53,585 diabetic patients were matched to 47,435 non-diabetic patients. 83% of cancer cases in CPRD had a corresponding record in HES (78% with the same type). Misclassification varied by cancer site, ranging from 3% (stomach cancer) to 57% (nonmelanoma skin cancer). 83% of cancer cases in HES had a corresponding record in CPRD, with all misclassification rates <20%.
ConclusionA good level of concordance and low level of misclassification of cancer exist between CPRD primary care data and HES. The value of linking these data for establishing cancer outcomes lies more in the complimentary variables held than in reducing misclassification.
Using Linked Data to Explore the Relationship Between Walking-Friendliness of Neighbourhoods, Physical Activity and Body Mass
Authors: Ross Nancy , Dasgupta Kaberi , Sanmartin Claudia , Wasfi Rania , Hajna Samantha , Mah Sarah 
Published: April 2017

ABSTRACTObjectivesTo demonstrate the methodology and results for linking measures of neighbourhood walking-friendliness or "walkability" to Canadian health surveys and Canadian health surveys linked to administrative health care records.
ApproachWe linked multiple measures of neighbourhood walkability to hundreds of thousands of 6-digit postal codes of respondents to three large Canadian surveys using geographic information systems and anonymized banks of postal codes.
ResultsLong term exposure to walkable neighbourhoods was associated with increased reporting of walking for utilitarian purposes. Moving to a high walkable neighbourhood from a low walkable neighbourhood was associated with a full unit decrease in the body mass index of Canadian men over a 12-year follow-up. In a linkage of walkability measures to respondents who wore biosensors for a one-week period in several Canadian cities, we found that neighbourhood walkability was associated with increased reporting of utilitarian walking but not overall physical activity and step counts as measured by biosensors.
ConclusionThere is potential for walkable neighbourhoods to influence physical activity and body weight of Canadians which is more evident when individuals are followed for long periods of time.