bluuebunny/crossref_metadata_2025_split huggingface dataset:
Child Health Clinical Outcome Review Programme: Health Care Utilisation, Care Pathways and Educational Status in Children and Young People with Adolescent Mental Health problems with a focus on Self harm, Eating Disorders and Anxiety and Depression
Authors: Wood Sophie , John Ann , Kemp Alison , Carter Bethan , Wright Melissa 
Published: April 2017

ABSTRACT
ObjectivesTo use routinely collected datasets and data linkage to identify patterns of healthcare utilisation by children and young people (CYP) with adolescent mental health (AMH) disorders across the four UK Nations, with a particular focus on self-harm, anxiety and depression and eating disorders. The analysis will focus on healthcare utilisation during transition from child to adult services and, where possible, on the interface between different care settings e.g. primary and secondary care and health and social care.
In addition we will determine the extent to which routinely collected datasets can contribute to an assessment of the health needs and the quality of care that CYP with AMH disorders receive.
ApproachData has been requested from the national data providers in each country:

The Health and Social Care Information Centre - England
The Secure Anonymised Information Linkage (SAIL) databank - Wales
Information Services Division – Scotland
The Honest Broker Service- Northern Ireland

As well as other sources:

The Clinical Practice Research Datalink
The Paediatric Intensive Care Audit Network.

A number of restrictions and information governance regulations have had to be negotiated and put in place in order to access and store the data in the SAIL Databank, Swansea University. Descriptive cross sectional analysis is underway to address the key questions.
RationalThe project has arisen from the report ‘Overview of Child Deaths in Four UK Countries’ which found that 30-40% of 13-18 year olds were affected by mental health, learning difficulties or behavioural conditions. The diagnosis, management and services available for mental disorders in CYP are of growing concern and a source of controversy in the UK. Transitional care between child and adult services and the interface between primary and secondary/ specialist services, as well as, that between the NHS and other sectors is often disjointed. Thresholds for referral to Child and Adolescent Mental Health Services (CAMHS) are high and many adolescents are treated, at least initially, in primary health care systems.
ImplicationsAnalysis of routinely collected health datasets has the potential to describe the nature and extent of health needs and health care utilisation for CYP with AMH disorders (self-harm, depression/anxiety, eating disorders). Furthermore the knowledge obtained from using routinely collected healthcare data, will inform future routine data collection across the UK for CYP with AMH disorders.
Variations in antibiotic use for respiratory tract infections
Authors: Mckay Rachel , Law Michael , McGrail Kimberlyn , Patrick David 
Published: April 2017

ABSTRACT
ObjectivesAntibiotic resistance is a significant public health issue, driven in large part by selection pressure induced by antibiotic use. Despite knowledge that a reduction in inappropriate antibiotic use is important, significant and sustained behaviour change has remained difficult to achieve. Previous studies have suggested that prescribing decisions vary with patient comorbidities and age, as well as the physician’s age, claim volume, specialty, and continuity of care with the particular patient. Regional level variation has also been reported. The goal of this study is to explore variations in antibiotic prescribing for respiratory tract infections (RTIs) at the levels of patients, physicians, and regions.
ApproachWe used data on fee-for-service physician visits from the universal Medical Services Plan (MSP) for all residents of British Columbia, Canada from 2002 to 2012. We identified a cohort of patient visits for RTI (ICD-9 codes 460-466, 480, or 487). We derived measures of healthcare use and comorbidities at the patient level, and measures of physician claim volume and frequency of respiratory tract infection management, at the level of the physician. We used data on antibiotics filled by individuals, the number of all medications filled by individuals each year, and counts of prescriptions written by physicians (and filled) by month, from the provincial drug insurance database. We linked antibiotic prescriptions to physician visits by patient and prescriber as antibiotics dispensed within 5 days after the RTI visit. We calculated measures of regional population distributions. We used data on meteorological temperature readings assigned to each region, for each day, and calculated 28-day moving averages. We linked data on patient demographics, physician demographics, and hospitalizations to our dataset. Our analysis will use hierarchical generalized linear mixed models (GLMMs) with logit link to account for the clustering effects of patients among physicians and regions, to model the odds of an antibiotic prescription being dispensed. Measures of variation will be discussed.
ResultsBetween April 1, 2005 and March 31, 2012, there were over 10.5 million visits by nearly 3 million individuals, served by over 8000 physicians in 88 regions. Antibiotics were prescribed in 37% of all visits.
ConclusionThese are preliminary results, with full analytic results available in the coming months. These results will have implications for better understanding the extent of variations in antibiotic prescribing, and some of the drivers of these variations, as well as the potential to inform ongoing efforts to improve the appropriateness of antibiotic use.
Bringing more science to the art of linkage: Using positive predictive value of weight and outcome sets to reduce subjectivity in probabilistic linkage
Authors: Hills Brent 
Published: April 2017

ABSTRACT
ObjectiveCurrently, a probabilistic linkage is performed by our organization with final linkage classification established by users with expert knowledge applying rules referencing weight and comparison outcome sets. The particular classification results in a perceived comprehensive linkage. Acknowledged weaknesses are variation in expert knowledge and its application. Also, consideration of expert rules is often time-consuming.
We piloted a new approach, involving a file level summary of “positive predictive value” for weights and outcome sets. We contrast the new approach with the previous one and identify strengths and weaknesses.
ApproachWe resolve linkages using two different approaches, the existing method that expert users apply rules to weight and comparison outcomes, and a recent one using positive predictive values (PPV) that reduce resolution subjectivity.
The new method produces summary true positive, false positive and positive predictive values for each weight and outcome set within a file of candidate pairs above the cutoff. The top-weighted pair increments the true positive (TP) for the weight and outcome set on the record. All other candidate pairs increment false positive counts (FP). At the end of the file, PPV is calculated for each weight and outcome as TP/(TP+FP). Additionally, a .9 PPV weight threshold is established from the summary excluding weights with less than N occurrences. The approach presumes successful link
Accepted links include top-ranked records whose weight is greater than the .9 PPV threshold and top candidates with an outcome summary PPV >= .9, excluding outcome sets with less than N occurrences.
ResultsIn the pilot, the new method produced linkage results near par with the previously employed methods. Importantly, they establish accepted links by a consistent methodology, allowing for increased standardization. The method eases identification of a threshold weight and referencing summary comparison outcome PPVs identifies additional confident links in larger population-based linkages which a single weight threshold may exclude. Components require minimal tuning to data characteristics, error tolerance, and result expectations. The new method is unable to identify links established in the legacy method by additional processing or manual review. Further review of the approach as applied to other datasets is needed.
Multi-Agency Data Integration Project - Linking Data – Improving Lives
Authors: Kindermann Bindi , Hinde Sarah , Abbondante Michael 
Published: April 2017

ABSTRACTObjectivesThe Australian Government’s new public sector data management agenda is initiating a national system for integrating public data and opening up access for policy makers and researchers. The Multi-agency Data Integration Project (‘the project’) is central to achieving these goals by bringing together nationally significant population datasets with the aim of streamlining the safe sharing of integrated government data. The project provides policy makers and researchers with safe access to linked, longitudinal information about the delivery of the Australian tax and transfer system, health services, along with rich demographic information. The project has been an essential step towards better enabling the Australian Government and research community to develop evidence-based policy and target services effectively, within a tight fiscal environment. The project has prompted government agencies to find new and more streamlined ways to work collaboratively to share and make best use of public data.
ApproachThe first step of the project was to link a 2011 snapshot of four national administrative datasets with the 2011 Census. A cross-agency team of data analysts from five government agencies collaborated to evaluate the datasets and test whether the linked data could be used to answer policy questions. The linkage project included experimentation with different linking methodologies, linking strategies and information models for structuring the linkage. The evaluation tested whether the linked data was representative of key population groups of interest, and explored the validity of the content variables for measuring outcomes of interest.
ResultsHigh linkage rates (between 80-95%) were achieved for the two-way linkages, and many population groups of interest were well represented. The work is confirming the value of the linkage for answering policy questions that had been difficult to address using existing approaches. The project developed ways of describing linkage quality to policy users and approaches to addressing linkage bias for different policy uses.
ConclusionPublic sector data held by government has the power to improve life course outcomes for Australian people, households and businesses. The project has generated confidence and support for continued development of a central and streamlined integrated data system. It has also generated valuable insights about governance and how to scale up the linkage and dissemination system to support additional datasets and longitudinal data. This will maximise the value and utility of public data to support policy and research, in order to achieve a better understanding of, and deliver better outcomes for, the Australian community.
Real-time linkage: when is near enough good enough?
Authors: Irvine Katrina , Williamson Jennifer , Pye Victoria 
Published: April 2017

ABSTRACTObjectivesTimely data on the health and vital status of study participants is important for cohort management and patient recruitment processes in addition to traditional observational research. We have investigated the potential to modify data sourcing and linkage processes so that data is linked daily, in near real-time. Such modifications will improve the currency of linked data for end users but impact on the quality of the data for recent periods. This work aims to provide a conceptual framework for understanding drivers of quality and empirical evidence on the type and extent of quality impacts associated with linking death and recent hospitalisation data in near real-time for a large population.
ApproachWe compared different linkage strategies including real-time linkage to study the type and extent of quality impacts observed using a large population cohort linked to jurisdictional level hospitalisation and death data.
Results Changes in data quality arise from several sources: incomplete enumeration of the most recently occurring events (known as delayed notifications), differences in the availability of up-to-date personal information to assist in data linkage, and the adoption of faster probabilistic linkage techniques that skip some computational and/or manual steps (e.g. clustering and clerical review) that are performed in conventional gold-standard probabilistic linkage processes.
Compared to a gold standard process faster linkage techniques with reduced personal information produced quality metrics of precision, recall and F measure in excess of 98.0 for linkage of a population cohort with death registrations. While precision was maintained at 98.0 for linkage to hospitalisation data recall was reduced to 91.1.
Delayed notifications have a pronounced effect on quality. If incomplete enumeration is factored into quality metrics, the effective recall for the last quarter of data drops to 0.82 and 0.33 for deaths and most recent hospitalisation respectively.
ConclusionsHigh quality and current information about health and vital status can be obtained through data linkage, but there are tradeoffs between timeliness and ascertainment. Delayed notifications in jurisdictional level administrative data systems appear more problematic than subtle changes in linkage technique. The quality impacts of near-real time linkage vary significantly by data collection and so its usefulness is highly dependent on its desired purpose and quality requirements.
In Utero Exposure to Antihypertensive Medication During Pregnancy and Neonatal and Child Health Outcomes
Authors: Fitton Catherine , Steiner Markus , Aucott Lorna , Pell Jill , Fleming Michael , McLay James 
Published: April 2017

ABSTRACTBackgroundAlthough pharmacotherapy is to be avoided wherever possible during pregnancy, aggressive pharmacotherapy is required for the treatment of pregnancy associated hypertension, which remains a leading cause of morbidity and mortality in the UK. While the teratogenic effects of angiotensin-converting enzyme inhibitors are well documented, the possible long term effects, on the child, following in utero exposure to other antihypertensive agents remains unknown.
ApproachThe aim of this study was to systematically review all published literature relevant to possible adverse outcomes on the child associated with in utero exposure to antihypertensive medications. OVID (Medline, Embase), Scopus, EBSCO Collections (PsycINFO, CINAHL), The Cochrane Library and Web of Science databases were searched from January 1950 to January 2016 and a total of 688 papers were identified. Following review 43 primary studies and 4 Meta-analyses were eligible for inclusion. The Critical Appraisal Skills Programme (CASP) checklists were used to assess study quality. ResultsThree studies were of excellent quality the remainder were either mediocre or poor. Increased risk of low birth weight, low size for gestational age, preterm birth and congenital defects following in utero exposure to all antihypertensive agents were identified. The clinical importance of these reported risks is unclear, as many study findings were based on small case numbers. Four studies of mediocre quality reported on the relationship between in utero exposure and neurological adverse effects in offspring. Two studies reported an increased risk of attention deficit hyperactivity disorder following exposure to labetalol, and an increased risk of sleep disorders following exposure to methyldopa and clonidine. The remaining two studies identified no such associations. ConclusionsThis systematic review demonstrates a lack of published high quality studies. Available published studies indicate an increased risk of adverse child health outcomes, although it is unclear whether these outcomes are clinically significant. This review is the first step in a larger project, which is exploring child health outcomes in Scotland following in utero exposure to antihypertensive and psychotropic medications. Dispensed drug data will be used to identify mothers who have been prescribed antihypertensive or psychotropic medication during pregnancy. National databases (PIS, SMR02, SMR01, etc.) will be used to cross-link mother and child data to identify in utero exposure to the drugs of interest, and the resulting child outcomes. All aspects of child health outcomes will be assessed to identify possible adverse effects from in utero exposure to medications.
New Zealand Longitudinal Census development
Authors: Kang Inny 
Published: April 2017

ABSTRACT
ObjectivesThe aim for the New Zealand Longitudinal Census (NZLC) is to link data from seven recent censuses (1981-2013) and develop weight methodology to reduce the potential for link bias in the NZLC.
ApproachThe most workable way to create a longitudinal census dataset was to link adjacent pairs of censuses and then to link these census pairs together creating six linked pairs covering the seven censuses from 1981 to 2013. We refined the deterministic linking in the six pairs and then added probabilistic linking, where possible, for the remaining records in each of the theoretically linkable populations. The linking of the 2006 and 2013 Censuses was methodological challenge as the interval between censuses was 7 years instead of the normal 5 years between censuses. The challenge was heightened by having a question that asked for respondent address five years ago (ie in 2008).
In the linking of six censuses from 1981 to 2006, we had included indicators that identified completely linked and partially linked families. To link 2013-2006 census pairs, more variables were added in the probabilistic linking stage including geographic variables and new derived variables which included the combination of the role types within family group, family number, and sex variables. In developing weights for the linked pairs, all variables were changed into numeric values and then categorised. The data was then divided into male and female and logistic regression was used to predict probabilities of being liked.
ResultsThe size of linked populations is relatively stable across similar time spans. We linked the majority of linked records in the deterministic stage which contributed links of for two-thirds of the theoretical populations. The subsequent stages included deterministic linking and probabilistic linking which added approximately 5 percentage points to the link.
ConclusionThe linking of census datasets from 1981 to 2013 to form a longitudinal data source allowed us to understand important aspects of population change across time which cannot be derived from simple cross. It provides a major new source of the analysis for insights into New Zealand demographic and social outcomes. Currently we are creating a set of generic weights for each individual record that may be applied in various research scenarios. Weighting of longitudinal data, as a part of NZLC, requires substantial in-depth statistical analysis of bias and coverage.
Survival to Diabetes by age, gender and BMI category
Authors: Nock Kelly , Bailey Kerry , Barker Craig , Thomas Helen 
Published: April 2017

ABSTRACT
AimTo carry out a 10 year survival analysis to provide insight to NHS partners regarding the time to Type -2 Diabetes across the population of ABM HB and by a variety of key criteria including BMI, gender, age and deprivation.
ApproachSurvival time was taken from the window start date (current data date – 10 years) to patient exit from the study (Death, Type-2 Diabetes diagnosis or window end date). Survival output was generated using the Kaplan-Meier estimate as part of the survival package (Therneau T. M., 2015) in R, with Type-2 Diabetes diagnosis as the event variable with the following control and stratification variables: Gender, Age Group (at window start), Average BMI Category and Deprivation Quintile.
To identify Diabetic and I particular type-2 patients we developed dictionaries using diagnosis codes from both ICD-10 and READ. These dictionaries were then mapped against the GP, Inpatient and Outpatient datasets to capture a diagnosis of Diabetes as early as possible. The logic to determine whether patients are Diabetic involved the use of 277 READ and 56 ICD-10 codes, incorporating and expanding upon the QOF standard rule-set.
To identify as many BMIs as possible three methods were used firstly using absolute BMI measurements. Secondly, using READ codes for pre-binned BMI categories. Thirdly, by combining separate height and weight measurements. The latter method in particular introduces data quality problems, both with erroneous values and in cases where different GPs use different units of measurement (e.g. metres, centimetre, feet, stones). To use these measurements in a reliable manner, we developed complex conditional statements to extract valid measurement records and store them uniformly, such that we can use them easily in a variety of data queries to classify patients by weight category.
ResultsCumulative probability (calculated from inverse ‘survival time’) to diabetes is greater in obese and more elderly. There was not the expected impact of deprivation. Representation of survival curves were difficult for lay people and some other stakeholders to interpret so stacked bar charts of cumulative probabilities were visualised. Stakeholders valued being able to interact with the different aggregated visualisations to display different aspects of the results.The results provided valuable insights for NHS partners and informed predictive models.