bluuebunny/crossref_metadata_2025_split huggingface dataset:
The use of linked data to develop and validate measures of variation in mortality and readmissions for public reporting purposes in New South Wales, Australia
Authors: Sutherland Kim , Marashi-Pour Sadaf , Chen Huei-Yang , Lévesque Jean-Frédéric 
Published: April 2017

ABSTRACTObjectivesTo investigate variation across 78 New South Wales public hospitals, in mortality in the 30 days following admission and in returns to acute care (readmissions) in the 30 days following discharge for acute myocardial infarction, ischaemic stroke, heart failure, pneumonia, hip fracture surgery.
ApproachLinked data were used to (1) construct an analytic unit – an index period of care that comprised concatenated acute, contiguous hospitalisations with the principal diagnosis of interest; (2) to capture outcomes both within the index hospital and following discharge, wherever they occurred; (3) to enhance risk adjustment with one year look back for relevant comorbidities; (4) to assess fair attribution of outcomes. A risk-standardised mortality ratio (RSMR) and a risk standardised readmission ratio (RSRR) were calculated as the ratio of the observed to the expected number events at a given hospital, by developing and validating condition specific system-level prediction models. Funnel plots identified outliers. For the RSRR, the competing risk of death was considered.
ResultsFor both outcome indicators, sensitivity was enhanced by the use of linked data (33%-100% more deaths; 23%–32% more returns to acute care or readmissions). For mortality, RSMRs that only capture deaths in hospital, as opposed to deaths within 30 days of admission, were shown to be biased and change the outlier status of about 20% of hospitals. Including socioeconomic status in risk adjustment models altered the outlier status of about 10% of hospitals on the cusp of statistical significance but did not significantly alter the RSMRs. For returns to acute care, sensitivity analyses that included socioeconomic status in the models found there was no significant improvement in discriminatory power. For example, in the case of ischaemic stroke, the c-statistic for the model without inclusion of SES was 0.593 (0.578-0.610); inclusion of SES resulted in a c-statistic of 0.600 (0.583-0.616). There were some changes in hospital-level results but there was no clear evidence of a systematic effect on results.
ConclusionThe risk-standardised ratio method, based on linked data, compares a hospital’s results given its case mix with an average New South Wales hospital with the same case mix. Ratio-based indicators have been reported publicly and have proven to be a valuable screening tool to identify hospitals where further investigation may be required locally.
Data linkage of Psychiatric and Maternity data to investigate the pregnancy outcomes of women with Non-affective Psychosis in Scotland
Authors: Mcskimming Paula , Barry Sarah , Park John , Bhattacharya Sohinee , MacBeth Angus 
Published: April 2017

ABSTRACTObjectiveWomen with a diagnosis of non-affective psychosis have a lower fertility rate than the general population. However, perinatal outcomes in mothers with non-affective psychosis are under-researched. What is the general fertility rate (GFR) of women with a lifetime diagnosis of non-affective psychosis in Scotland? Does such a diagnosis affect the outcome of pregnancy?
ApproachAn ‘exposed’ cohort with non-affective psychosis and at least one pregnancy was established using a combined dataset derived via data linkage in local safe havens of routine psychiatric and maternity data from two Scottish regions:•NHS Grampian (NHSGr): Aberdeen Maternity and Neonatal Databank (AMND); psychiatric casenotes•NHS Greater Glasgow and Clyde (NHSGG&C): SMR02; PsyCIS bespoke psychiatric databaseExposed women were matched to women without a diagnosis of non-affective psychosis, by maternal age (NHSGG&C only), year of first birth, parity and deprivation, sourced from AMND/SMR02 in a 3:1 unexposed:exposed ratio.
Demographics and pregnancy outcomes of exposed versus unexposed women were analysed to describe effect of psychosis on pregnancy.
ResultsMany challenges were encountered in terms of having legal agreements in place between institutions and safe havens, constructing cohorts and datasets within each study site and joining the data to analyse the overall cohort. Challenges with the data itself included discrepancies between the variables measured in datasets in different sites and missing information within patient records, particularly in earlier years. Preliminary results for the NHSGG&C region show that the average GFR for exposed women aged 15-44 from 2005 to 2014 was 14.38 compared to the general population rate of 55.26. The number of women ever having a miscarriage was significantly higher in the exposed group (23.4% vs 9.9%; p-value <0.001). However during the study period (1996 to 2014), more unexposed women had miscarriages (0.5% vs 4.7%; p-value = 0.002). There were no significant differences in pregnancy complications for the study period. The mean birthweight of babies was lower (3.23kg vs 3.35kg; p-value = 0.029) and more babies were admitted to neonatal units (17.5% vs 9.8%; p-value = 0.004) in the exposed group. Results for the NHSGr region and the combined dataset will also be reported.
ConclusionThis work highlights that there remain hurdles to linking data across sites, despite availability of rich datasets within Scotland. Women with non-affective psychosis within NHSGG&C region had a lower fertility rate on average than the general population and some poorer outcomes, such as birthweight and rate of admission to neonatal units.
Looked after children and access to dental services and oral health in Scotland: a national data-linkage study (LAC-DENTAL)
Authors: McMahon Alex , Sharpe Katharine , Macpherson Lorna , Wood Rachael , Connelly Graham , Milligan Ian , Wilson Philip , King Albert , Elliott Lawrie , Conway David 
Published: April 2017

ABSTRACTObjectivesChildren that are ‘looked after’ include those that are accommodated in foster, kinship and residential care placements, as well as those at home on compulsory supervision. They have poorer physical and mental health than their peers and there are concerns about the relatively high levels of untreated morbidity. Oral health and access to dental services among Looked After Children (LAC) has received limited attention to date. The objective of this study was to compare the oral health and access to dental services of children who are looked after by the state, with comparable children in the general population.
ApproachSchool and Social Work datasets were able to be linked using the Scottish Exchange of Data (ScotXed) Unit. This in turn was linked with health data making use of the Scottish national record-linkage system provided by the NHS eDRIS team for the FARR Institute Scotland. All of the following datasets used in this study are complete national datasets for the time periods noted. School Pupil Census 2012: a census of children in local authority primary and secondary schools that provides each child’s age, sex and socioeconomic status as measured by the Scottish Index of Multiple Deprivation (SIMD). The other datasets cover the period 2008-2012: LAC- all children with social work referrals for various types of placement; registration with dentists; hospital discharge data for all episodes of tooth extraction; National Dental Inspection Programme (NDIP) data from Primary 1 and Primary 7 school years (dental decay). The LAC group were compared to their peers by logistic regression adjusted by SIMD, using remote access to the National Safe Haven.
ResultsThere were 633204 subjects in the study group (10927 LAC, 622280 nonLAC). Ages ranged from four to 17 years (mean 12 LAC and 10 nonLAC); with 53% male for LAC and 51% nonLAC; and 42% in the most deprived SIMD level for LAC and 21% for non-LAC. The subjects in the LAC group were more likely to have dental decay at Primary 1, odds-ratio (OR) 2.93 (2.55, 3.38), and Primary 7, OR 1.81 (1.68, 1.94). LAC subjects were less likely to be registered with a dentist, OR 0.50 (0.43, 0.59), and more likely to have teeth extracted, OR 1.50 (1.40, 1.60). All tests p<0.001.
ConclusionLooked after children are more likely to have dental problems and less likely to use dental services than their peers, after adjustment for socioeconomic status.
Hospital admissions in children who had pelvicalyceal diltation detected on ultrasound scan during pregnancy: an e-cohort study
Authors: Wright Melissa , Paranjothy Shantini , Fone David , Brophy Sinead , Demmler Joanne 
Published: April 2017

ABSTRACTObjectiveTo explore whether children with pelvicalyceal dilatation (PCD, a marker detected during the 18-20 week gestation ultrasound scan in which there is enlargement of tubes that collect urine in the kidney) have more hospital admissions for kidney problems in childhood compared to children without the marker.
Approach We were funded by NISCHR to study outcomes associated with markers of uncertain significance at the second trimester anomaly scan (Welsh Study of Mothers and Babies). Data collected in the WSMB was uploaded to the Secure Anonymised Information Linkage (SAIL) databank and record linked to hospital activity data. Patterns of hospital admissions for renal causes were described and compared between those with no markers and those with PCD. Children were followed up from birth until 31st December 2014 or until the age of 5. A Cox Proportional Hazard Model was used to investigate the impact of PCD on time to first presentation.
Results (Preliminary)Of the WSMB cohort, 20,834 children were eligible for inclusion in analyses. Those with PCD had 6.29 times the hazard of a renal admission compared to those without the marker (95% CI: 3.69 to 10.72). Children with PCD were more likely to have multiple renal admissions to hospital - median (interquartile range) number of renal admissions, 2.5 (1 to 5) compared to 1 (1 , 1) in children without markers.
ConclusionPreliminary analysis suggests there is increased childhood renal morbidity associated with the presence of a PCD marker detected on the 18-20 week gestation ultrasound scan. These findings will inform the discussions clinicians have with parents when discussing the implications of this marker for the health of the chid.
Using linked health survey and Census data to understand transitions to instutional care among Canadian seniors
Authors: Sanmartin Claudia , Garner Rochelle 
Published: April 2017

ABSTRACT
ObjectivesWhile existing data sources, such as the 2011 Census, provide an accurate count of who is currently institutionalized, there is a significant gap in terms of our understanding of who is at risk for institutionalization and what the future demand for care will be. The objective of this study is to use linked national health survey, the 2005 Canadian Community health Survey (CCHS), to the 2011 Census to identify factors associated with transitions from private households to alternative living arrangements, specifically long-term care (nursing homes), and retirement homes among Canadians 55 years of age and older.
ApproachHierarchical deterministic methods were used to link the 2005 CCHS (n=114,000) to the 2011 Census (n=35 million) using identifying variables common to both data sets (i.e. name, birthdate, sex, postal code, social insurance number). Sex specific multivariate regression models with multiple outcomes were used to assess the impact of a comprehensive set of factors (i.e. demographic, socio-economic, health status, chronic conditions and marital status) available in the CCHS on the likelihood of residing in three possible home environments, relative to living in a private dwelling (PD): long-term care (LTC), retirement homes (RH) or private dwelling with support (PDS) as identified in the 2011 Census. Analyses were adjusted for mortality.
ResultsOver 85% of CCHS records were linked to the 2011 Census (n=92,849). Among those 55 years of age and older (n=29,934), approximately 2.0% and 1.6% were living in LTC and RH respectively: an additional 7.2% were living in PDS. Results of the regression analyses, revealed that those with Alzheimer’s disease were at highest risk of transition to LTC (OR=11.7 females; 6.8 males). Losing a spouse was significantly associated with transitions to LTC, RH and PDS for both men and women. Being an immigrant was protective, with immigrant seniors less likely to transition to LTC and RH. Other factors significantly associated with transitions to LTC included low income, poor mental health (women only), and assistance with activities of daily living (meal preparation for women, finances for men). Regional variations were also noted.
ConclusionNewly linked health survey and census data provide a unique opportunity to take a comprehensive look at those most at risk for institutionalization.
Automated de-identification of clinical free-text
Authors: Dehghan Azad , Liptrot Tom , O’hara Catherine , Barker-Hewitt Matthew , Tibble Daniel , Nenadic Goran 
Published: April 2017

ABSTRACTObjectivesIncreasing interest to use unstructured electronic patient records for research has attracted attention to automated de-identification methods to conduct large scale removal of Personal Identifiable Information (PII). PII mainly include identifiable information such as person names, dates (e.g., date of birth), reference numbers (e.g., hospital number, NHS number), locations (e.g., hospital names, addresses), contacts (e.g., telephone, e-mail), occupation, age, and other identity information (ethnical, religion, sexual) mentioned in a private context.
De-identification of clinical free-text remains crucial to enable large-scale data access for health research while adhering to legal (Data Protection Act 1998) and ethical obligations. Here we present a computational method developed to automatically remove PII from clinical text.
ApproachIn order to automatically identify PII in clinical text, we have developed and validated a Natural Language Processing (NLP) method which combine knowledge- (lexical dictionaries and rules) and data-driven (linear-chain conditional random fields) techniques. In addition, we have designed a novel two-pass recognition approach that uses the output of the initial pass to create patient-level and run-time dictionaries used to identify PII mentions that lack specific contextual clues considered by the initial entity extraction modules. The labelled data used to model and validate our techniques were generated using six human annotators and two distinct types of free-text from The Christie NHS Foundation Trust: (1) clinical correspondence (400 documents) and (2) clinical notes (1,300 documents).
ResultsThe de-identification approach was developed and validated using a 60/40 percent split between the development and test datasets. The preliminary results show that our method achieves 97% and 93% token-level F1-measure on clinical correspondence and clinical notes respectively. In addition, the proposed two-pass recognition method was found particularly effective for longitudinal records. Notably, the performances are comparable to human benchmarks (using inter annotator agreements) of 97% and 90% F1 respectively.
ConclusionsWe have developed and validated a state-of-the-art method that matches human benchmarks in identification and removal of PII from free-text clinical records. The method has been further validated across multiple institutions and countries (United States and United Kingdom), where we have identified a notable NLP challenge of cross-dataset adaption and have proposed using active learning methods to address this problem. The algorithm, including an active learning component, will be provided as open source to the healthcare community.
Obtaining structured clinical data from unstructured data using natural language processing software
Authors: Lacey Arron S , Fonferko-Shadrach Beata , Lyons Ronan A , Kerr Mike P , Ford David V , Rees Mark I , Pickrell Owen W 
Published: April 2017

ABSTRACT
BackgroundFree text documents in healthcare settings contain a wealth of information not captured in electronic healthcare records (EHRs). Epilepsy clinic letters are an example of an unstructured data source containing a large amount of intricate disease information. Extracting meaningful and contextually correct clinical information from free text sources, to enhance EHRs, remains a significant challenge. SCANR (Swansea University Collaborative in the Analysis of NLP Research) was set up to use natural language processing (NLP) technology to extract structured data from unstructured sources.
IBM Watson Content Analytics software (ICA) uses NLP technology. It enables users to define annotations based on dictionaries and language characteristics to create parsing rules that highlight relevant items. These include clinical details such as symptoms and diagnoses, medication and test results, as well as personal identifiers.

ApproachTo use ICA to build a pipeline to accurately extract detailed epilepsy information from clinic letters.
MethodsWe used ICA to retrieve important epilepsy information from 41 pseudo-anonymized unstructured epilepsy clinic letters. The 41 letters consisted of 13 ‘new’ and 28 ‘follow-up’ letters (for 15 different patients) written by 12 different doctors in different styles. We designed dictionaries and annotators to enable ICA to extract epilepsy type (focal, generalized or unclassified), epilepsy cause, age of onset, investigation results (EEG, CT and MRI), medication, and clinic date. Epilepsy clinicians assessed the accuracy of the pipeline.
ResultsThe accuracy (sensitivity, specificity) of each concept was: epilepsy diagnosis 98% (97%, 100%), focal epilepsy 100%, generalized epilepsy 98% (93%, 100%), medication 95% (93%, 100%), age of onset 100% and clinic date 95% (95%, 100%).
Precision and recall for each concept were respectively, 98% and 97% for epilepsy diagnosis, 100% each for focal epilepsy, 100% and 93% for generalized epilepsy, 100% each for age of onset, 100% and 93% for medication, 100% and 96% for EEG results, 100% and 83% for MRI scan results, and 100% and 95% for clinic date.
Conclusions ICA is capable of extracting detailed, structured epilepsy information from unstructured clinic letters to a high degree of accuracy. This data can be used to populate relational databases and be linked to EHRs. Researchers can build in custom rules to identify concepts of interest from letters and produce structured information. We plan to extend our work to hundreds and then thousands of clinic letters, to provide phenotypically rich epilepsy data to link with other anonymised, routinely collected data.
A Federated Data Linkage Strategy to Support Population Health Research in Canada
Authors: Flanagan Tanya , Moore Céline , Sandoval Carolyn 
Published: April 2017

ABSTRACT
ObjectivesCanada has established a pan-Canadian cohort with over 300,000 volunteer participants aged 35-69, to support research on cancer and chronic disease. A key feature of the cohort is that participants have consented to link their cohort data with administrative datasets. This prospective cohort, representing nearly 1 in 50 Canadians in this age range will be followed for multiple decades, building a platform that supports access to timely, high-quality, data related to cancer and other chronic diseases, which will enable researchers to answer complex system questions and achieve better health outcomes for Canadians.
ApproachA baseline “core” questionnaire was administered to participants capturing information on socio-demographics, economic characteristics, personal and familial history of diseases and lifestyle and health behaviours. A re-contact questionnaire is planned for 2016 to update baseline information and add depth to specific areas and capture changes over time. To realize the full potential for this cohort to support transformative research it is crucial to be able to link this data with other provincial datasets, such as cancer registries, hospital records and mortality data.
For the most part, health data in Canada resides under the purview of health providers and, or government custodians in each of the provinces and territories. As such, an innovative federated data linkage strategy is required to link cohort data with health administrative data in each regions, adhering to existing privacy and regulatory requirements, while providing central access for researchers.
ResultsTo date, 40% of all cohort participants have been linked with priority provincial administrative data. Each province has its own unique data linkage challenges, requiring unique customized solutions. By the end of 2017 we anticipate that the number of participants who will have had their data linked will increase to nearly 70%. The federated data linkage strategy and infrastructure offer an innovative approach that others can learn from; however to realize the full potential of the cohort and support transformative research partnerships and collaborations are required.
ConclusionEfforts to organize resources and establish systems for data linkage and optimize data sharing and utilization in Canada are underway and include discussions with the provincial privacy commissioners, national and provincial and territorial data custodians and other thought leaders in the field.
Barriers to high quality coding of hospital chart information to administrative data: A qualitative study
Authors: Tang Karen , Lucyk Kelsey , Quan Hude 
Published: April 2017

ABSTRACTObjectives Administrative data are widely used in research, health policy, and the evaluation of health service delivery. We undertook a qualitative study to explore the barriers to high quality coding of chart information to administrative data, at the level of coders in Canada.
ApproachOur study design is qualitative. We recruited professional medical chart coders and data users working across Alberta, Canada, using a multimodal recruitment strategy. We conducted an in-depth, semi-structured interview with each participant. All interviews were audio-recorded and transcribed. We conducted thematic analysis (e.g., line-by-line open coding) of interview transcripts. Codes were then collated into themes and compared across our dataset to ensure accurate interpretations of the data. The study team met to discuss, modify, and interpret emergent themes in the context of the barriers to coding administrative data.
ResultsWe recruited 28 coding specialists. In general, coders had high job satisfaction and sense of collegiality, as well as sufficient resources to address their coding questions. They believed themselves to be adequately trained and consistently put in the extra effort when searching charts to find additional information that accurately reflected the patient journey. Barriers to high quality coding from the coder perspective included: 1) Incomplete and inaccurate information in physician progress notes and discharge summaries; 2) Difficulty navigating a complex hybrid of paper and electronic medical records; 3) Focus on productivity rather than quality by the employer, which at times resulted in inconsistent instructions for coding secondary diagnoses and discordant expectations between the employer and the coders’ professional standards.
ConclusionFuture interventions to improve the quality of administrative data should focus on physician education of necessary components in charting, evaluation of electronic medical records from the perspectives of those who play a key role in abstracting data, and evaluation of productivity guidelines for coders and their effects on data quality.