bluuebunny/crossref_metadata_2025_split huggingface dataset:
High quality linkage using Multibit Trees for privacy-preserving blocking
Authors: Brown Adrian , Borgs Christian , Randall Sean , Schnell Rainer 
Published: April 2017

ABSTRACT
ObjectivesAs privacy-preserving record linkage (PPRL) emerges as a method for linking sensitive data, efficient blocking techniques that help maintain high levels of linkage quality are required. This research looks at the use of a Q-gram Fingerprinting blocking technique, with Multibit Trees, and applies this method to real-world datasets.
ApproachData comprised ten years of hospital and mortality records from several Australian states, totalling over 25 million records. Each record contained a linkage key, as defined by the jurisdiction, which was used to assess quality (i.e. used as a ‘gold standard’). Different parameter sets were defined for the linkage tests with a privacy-preserved file created for each parameter set. The files contained jurisdictional linkage key and a Cryptographic Long-term Key (the CLK is a Bloom filter comprising all fields in the parameter set).
Each file was run through an implementation of the Q-gram Fingerprinting blocking algorithm as a deduplication technique, using different similarity thresholds. The quality metrics of precision, recall and f-measure were calculated.
ResultsResultant quality varied for each parameter set. Adding suburb and postcode reduced the linkage quality. The best parameter set returned an F-measure of 0.951. In general, precision was high in all settings, but recall fell as more fields were added to the CLK. We will report details for all parameter settings and their corresponding results.
ConclusionThe Q-gram Fingerprinting blocking technique shows promise for maintaining high quality linkage in reasonable time. Determining which fields to include in the CLK for the linkage of specific datasets is important to maximise linkage quality, as well as selecting optimal similarity thresholds. Developing new technology is important for progressing the implementation of PPRL in real-world settings.
How do you measure up? Methods to assess linkage quality
Authors: Ferrante Anna , Boyd James , Randall Sean , Brown Adrian , Semmens James 
Published: April 2017

ABSTRACT
ObjectivesRecord linkage is a powerful technique which transforms discrete episode data into longitudinal person-based records. These records enable the construction and analysis of complex pathways of health and disease progression, and service use. Achieving high linkage quality is essential for ensuring the quality and integrity of research based on linked data. The methods used to assess linkage quality will depend on the volume and characteristics of the datasets involved, the processes used for linkage and the additional information available for quality assessment. This paper proposes and evaluates two methods to routinely assess linkage quality.
ApproachLinkage units currently use a range of methods to measure, monitor and improve linkage quality; however, no common approach or standards exist. There is an urgent need to develop “best practices” in evaluating, reporting and benchmarking linkage quality. In assessing linkage quality, of primary interest is in knowing the number of true matches and non-matches identified as links and non-links. Any misclassification of matches within these groups introduces linkage errors. We present efforts to develop sharable methods to measure linkage quality in Australia. This includes a sampling-based method to estimate both precision (accuracy) and recall (sensitivity) following record linkage and a benchmarking method - a transparent and transportable methodology to benchmark the quality of linkages across different operational environments.
ResultsThe sampling-based method achieved estimates of linkage quality that were very close to actual linkage quality metrics. This method presents as a feasible means of accurately estimating matching quality and refining linkages in population level linkage studies. The benchmarking method provides a systematic approach to estimating linkage quality with a set of open and shareable datasets and a set of well-defined, established performance metrics. The method provides an opportunity to benchmark the linkage quality of different record linkage operations. Both methods have the potential to assess the inter-rater reliability of clerical reviews.
ConclusionsBoth methods produce reliable estimates of linkage quality enabling the exchange of information within and between linkage communities. It is important that researchers can assess risk in studies using record linkage techniques. Understanding the impact of linkage quality on research outputs highlights a need for standard methods to routinely measure linkage quality. These two methods provide a good start to the quality process, but it is important to identify standards and good practices in all parts of the linkage process (pre-processing, standardising activities, linkage, grouping and extracting).
Born a bit too early: A study of early planned birth and child development at school age
Authors: Bentley Jason , Roberts Christine , Bowen Jenny , Martin Andrew , Morris Jonathan , Nassar Natasha 
Published: April 2017

ABSTRACT
ObjectivesFetal growth and development is a continuum with the optimal time of birth at 39-40 weeks gestation. Internationally, significant changes in clinical practice have seen planned birth (labour induction or pre­labour caesarean section) before 40 completed weeks gestation increase. Fetal brain development accelerates rapidly in the later stages of pregnancy from 32 weeks gestation, making it vulnerable to disruption from even slightly shortened gestation. This study aimed to investigate the association of gestational age and mode of birth with early childhood development.
ApproachThis study utilised a population-based record-linkage of administrative birth, hospital and development data to obtain a cohort of 153,730 live born infants in New South Wales, Australia. These infants were born between 2002 and 2007, with a gestational age of at least 32 weeks and had an early development assessment in their first year of school in 2009 or 2012. Childhood development was assessed in five main domains (physical health and wellbeing, language and cognitive skills, social competence, emotional maturity, and communication skills and general knowledge) with each comprised of a number of sub-domains. Children with a score below the 10th percentile nationally are considered developmentally vulnerable (DV) in that domain. Children who were DV for two or more of the five main domains are classified as developmentally high risk (DHR) and this was the primary study outcome. Robust multivariable Poisson models were used to obtain individual and combined relative risks for gestational age and mode of birth with DHR, adjusted for maternal demographic, socio-economic, perinatal, and child characteristics.
ResultsOverall, 9.6% of children were DHR. The adjusted relative risk (aRR) (95% confidence interval) of being DHR decreased with increasing gestational age (referent: 40 weeks); 32-33 weeks 1.25 (1.08-1.44), 34­-36 weeks 1.26 (1.18-1.34), 37 weeks 1.17 (1.10-1.25), 38 weeks 1.06 (1.01-­1.10), 39 weeks 0.98 (0.94-1.02), 41+ weeks 0.99 (0.94-­1.03) and for planned birth (referent: vaginal birth following spontaneous labour), 1.07 (1.04­-1.11). The aRR for planned birth at 37 weeks was 1.26 (1.18-­1.34) and at 38 weeks 1.13 (1.08-­1.19).
ConclusionEarly (<39 weeks gestation) planned birth is associated with an increased risk of poor development in children starting school. Given the timing of planned birth is modifiable, delaying birth for an additional week or more may improve child development. Strategies and interventions to inform more judicious decision making, weighing all the risks and benefits for early planned birth are required to ensure optimal child health and development.
The relationship between maternal race-ethnicity, immigrant status and country of birth and the risk of having a child with autism spectrum disorder in Western Australia
Authors: Fairthorne Jenny , De Klerk Nick , Leonard Helen 
Published: April 2017

ABSTRACTBackgroundThe risk of autism spectrum disorder (ASD) varies by maternal race-ethnicity, immigrant status and birth region. Women from Asia and East Africa have had higher rates of ASD with ID in their children. We aimed to investigate the odds of ASD with ID in children born in Western Australia (WA) according to maternal race-ethnicity,country of birth and immigrant status.MethodsWe linked state registries and examined the odds of ASD with ID in children born in WA from 1994-2005, by maternal race-ethnicity, country of birth and immigrant status.ResultsCompared to Caucasian non-immigrant women immigrant women were 40% less likely, to have a child with ASD with ID. Women of Asian race-ethnicity from Central and South Asia were slightly more likely than Caucasian non-immigrant women to have a child with ASD with ID but children of women of Asian race-ethnicity from other parts of Asia had about half the odds of having ASD with ID. Black women from East Africa had more than three and a half times the odds of ASD with ID in their children.Conclusions Our results suggest an interaction effect between race-ethnicity, immigrant status and birth region, with Black East African women having highest odds of a child with ASD with ID. Research is implicated on specific risk and protective factors for ASD with ID in the children of immigrant women.
Diseases and Geographic Variation: Are we missing the whole picture?
Authors: John Gareth 
Published: April 2017

ABSTRACT
BackgroundEpidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations. However, where an epidemiological analysis relies solely on data from large-scale anonymous administrative data sources, the available geographic information for the individuals concerned relates in general to just one single point in time; for example the places of residence of individual on the day that they were registered with a particular disease, or their places of residence on the day that they were admitted to hospital.
This information may not be sufficient however, especially when considering diseases where there may be a long period of time between an exposure to a particular hazard and the subsequent onset of disease.
MethodA solution to this problem possibly lies within administrative sources of data such as the Welsh Demographic Service (WDS), which is accessible to NHS Wales analysts and users of the SAIL databank in a pseudonymised format. The WDS contains the details of all Welsh residents who have been registered with a GP Practice since 1992, including a full history of changes to their addresses and GP practices. This data can be used to easily ascertain an individual’s address at any point in time, for example on a particular census date, or within a time period that is relative to a particular event, e.g. 10 years prior to disease registration. However, this research will look at how to incorporate all of an individual’s available address information into an epidemiological analysis.
ResultsTwo main approaches will be demonstrated; the first using a “Person Years at Risk” approach which attempts to apportion numerator and denominator according to the number of previous residences, and the second using a Case Control approach, comparing the geographic spread of addresses in the diseased group of patients versus the non-diseased (control) group, with age and gender matched controls also drawn from the WDS.
Dementias Platform UK (DPUK) Portal
Authors: Thompson Simon 
Published: April 2017

ABSTRACTBackgroundEstablished by the Medical Research Council (MRC) in June 2014, it is a £53 million collaboration between universities and drug companies to transform the best dementia research into the best treatments as quickly as possible.
DPUK will bring together 33 different dementia cohorts as well as imaging, brain bank, genomics and NLP.
MethodTo achieve this DPUK has a large number of work packages developing knowledge, capability and science. Work package 2 - DPUK Data portal has many cross cutting themes but its main aim is to provide a central access mechanism for the researchers to access the diverse datasets in a secure research platform which is delivered using the UK Secure e-Research Platform (UKSeRP).
The Portal supports the work of WP1 Cohort Profiling by providing information capture systems as well as automated publicly accessible summaries and more detailed reporting for DPUK members.
There are obvious sensitives around the sharing of the cohort datasets, especially given the many years of investment and commitment by the cohorts PI. However, many of these cohorts are no longer funded and risk of becoming obsolete and possibly being lost unless they become adopted by a programme such as DPUK. UKSeRP will provide a technical platform far more advanced than is often available to the cohorts and therefore the cohorts will benefit from these additional capabilities. The system leaves data ownership with the cohort PI and the governance structure puts them in the centre of the process and ensures that the cohort data is only used in projects that the cohorts are comfortable with.
The Portal supports the project application and approval process, and supports the governance structure and cohort engagement process. The application process is common regardless of the type of dataset required (phenotypic, genomic, imaging, physical samples)
ConclusionDPUK has extended UKSeRP to add the technical capabilities developed jointly with other work packages, most notably the addition of a fully federated image retrieval and analysis system and the genomics analysis system.
The platform will be fully operational by the end of the year.
The Australian Census Longitudinal Dataset 2006-2016: A Multi-Wave Approach to Maintaining a Longitudinal Dataset
Authors: Kindermann Bindi , Nunan Theresa , Hinde Sarah , Chipperfield James 
Published: April 2017

ABSTRACT
ObjectivesThe Australian Census Longitudinal Dataset (ACLD) is a nationally representative longitudinal dataset increasingly used to study Australian social and economic transitions. The ACLD will become an even more powerful resource when it is extended to include linked records from the 2016 Australian Census of Population and Housing.
The ACLD 2006-2011 dataset consists of a 5% representative sample of 2006 Census records and the linked 2011 Census records, and has been a well-received product in Australia as our first longitudinal Census dataset. In the process of extending the dataset to include linked records from the 2016 Census, we are aiming to improve the dataset by addressing issues such as linkage error and the under representation of specific population groups, while also taking advantage of recent changes in data linking methodology and policy that enable us to conduct better quality linking.
ApproachA multi-wave framework will be implemented for maintaining and improving the ACLD sample (as an alternative to the popular top-up sampling approach). Under the multi-wave framework, a sample is selected from each Census and is the beginning of a new wave of the ACLD, to be linked to each following Census. Allowing the sample selection method to be different for each wave enables us to apply learnings from previous waves to selecting a better sample for the next. For example, we could design the 2011 Wave sample to over sample certain population groups which were under represented in the 2006-2011 linked sample. We could also adjust the sample size, or change the method of sample selection in reaction to changing data item response rates.
ResultsInitial analysis suggests the multi-wave approach will enable high quality analysis of transitions in hard to link and previously under represented population groups between 2011 and 2016. While these improvements cannot be retrospectively applied to the sample selected for the 2006 Wave, the linking strategy will also enable unbiased analysis of the Australian longitudinal population from 2006 to 2016.
ConclusionThe application of the multi-wave framework will enable the production of a high quality ACLD 2006-2016 product with minimal bias and improved sample representativeness, which can be used to explore changes in Australian society between the 2006, 2011 and 2016 Censuses. The flexibility of the multi-wave framework will allow continued improvement to the form and function of the ACLD in future waves.
Estimating the population effect of maternal alcohol use disorders on the educational achievement of children
Authors: Hafekost Katherine , Johnson Sarah , Bower Carol , Semmens James , O'Leary Colleen 
Published: April 2017

ABSTRACTObjectivesPrevious research has identified that heavy maternal alcohol use impacts on a child’s health and development including poor cognitive and educational outcomes. However, very few studies have used objective measures of heavy alcohol-use and standardised school-based measures. Further, the magnitude of the effect of heavy maternal alcohol use on the educational outcomes of children in Australia is unknown. The primary aim of the project was to examine the association between in-utero and childhood exposure to maternal alcohol use disorder, which provides a proxy for heavy alcohol use, and children’s educational outcomes.
ApproachThis Western Australian population cohort study made use of linked administrative data to compare the educational outcomes of a cohort of exposed children born between 1989 and 2007 whose mother had an alcohol related diagnosis recorded on health datasets, with children whose mother did not have a diagnosis. The exposed cohort of mothers was frequency matched on maternal age within Indigenous status, and year of child’s birth with a comparison cohort of mothers without an alcohol-related diagnosis. Records were linked with education records up to 2011, which included the results of standardised state and national testing for children (ages 8-14), and school attendance data (ages 6-18). Mixed multivariate models were used to examine the relationship between exposure, and timing of exposure in relation to pregnancy, and the risk of failure to meet educational benchmarks for reading, writing, spelling, numeracy and school attendance. Separate analyses were run for Indigenous and non-Indigenous children.
ResultsA higher proportion of both Indigenous and non-Indigenous exposed children failed to reach minimum standards for all domains of testing compared to those in the unexposed cohort. The risk of failure in the exposed cohort remained significant with adjustment for a set of known confounders and there was no consistent relationship between timing of exposure and academic performance.
ConclusionThis project provides a unique view of how maternal alcohol use disorders affect a child’s educational outcomes. The use of linked administrative data overcomes the use of retrospective recall of past behaviour, and self-reports of drinking patterns which may be considered socially unacceptable. Results of this project indicate that children whose mothers have an alcohol use disorder are academically at risk. These results suggest that routine monitoring of maternal alcohol use, early identification of at-risk children and intervention at both the school and family level may assist vulnerable children to reach academic benchmarks.
Developing population segments with different levels of complexity and primary health care needs: An analysis using health administrative data in British Columbia, Canada
Authors: Langton Julia , Wong Sabrina , Peterson Sandra , McGrail Kim 
Published: April 2017

ABSTRACTObjectivesPopulation subgroups can be been used organize health services and understand the quality of health care. Most commonly, populations are have been by specific diseases (e.g., health care received by diabetes patients), patient age (e.g., elderly populations), or life-stage (e.g., end-of-life care). However, these subgroups may not adequately capture the complexity and/or health care needs of different patient groups (e.g., multi-morbidity, frail elderly). Our objective is to use health administrative data to develop population segments based on patients’ primary health care needs.
ApproachOur development process occurred in three stages. First, we examined examples of population segmentation in the peer reviewed and grey literature to develop principles for our population segments. Second, we held a workshop with primary care patients, decision-makers, clinicians and researchers to seek their input on important considerations for the population segments. Third, we used health administrative data (physician claims, hospitalisations) to develop population segments for the British Columbia (BC, Canada) population. Segments were based on diagnosis codes over a two year period; for each segment we examined health care use and costs, overall and by service type, in 2014-15. ResultsWe designed our segments to be mutually exclusive, capture the vast majority of people who use primary care services, and range from healthy patients (fewer primary care needs) to more complex patients (more extensive needs). Stakeholders were supportive of population segmentation approach and suggested incorporating patient vulnerability and primary care involvement such that segments would range from patients whose needs could be fully met in primary care to those who require additional services such as specialists/acute care. Our first iteration includes three segments: stable (≤1 chronic condition, needs met by primary care); multi-morbid (≥2 chronic conditions, needs mostly met by primary care); and complex (≤1 chronic condition and presence of a health care event associated with the management of this condition suggesting the patients’ needs not fully met by primary care). ConclusionWe developed population segments designed to account for patient complexity and primary health care needs; as such, segments provide more information than traditional indices of morbidity burden based on counts of chronic conditions. These segments will be used to report information on the quality of primary care. We plan to include conduct validation studies using additional variables (e.g, socio-economic factors, level of vulnerability from patient surveys) so that segments more accurately represent the level of complexity and patients’ primary health care needs.