bluuebunny/crossref_metadata_2025_split huggingface dataset:
Partial Agreements in Probabilistic Linkages
Authors: Brown Adrian , Randall Sean , Ferrante Anna , Boyd James 
Published: September 2018

IntroductionRecord linkage units around the world use probabilistic linkage techniques for routine linkage of large datasets. It is widely known how probabilities are converted to agreement and disagreement weights for each field, yet there has been little exploration of the methodology to optimally convert field similarity scores into partial weights.
Objectives and ApproachString similarity comparators such as Jaro-Winkler are commonly used in traditional linkage, other comparators such as the Sorenson Dice coefficient, Jaccard similarity and Hamming distance are used in alternative privacy-preserving record linkage techniques. Determining partial weights to apply at each level of similarity is a non-trivial task. However, both types of linkages would greatly benefit from similarity to weight functions for each field that maximises the accuracy of the linkage.
We evaluated several methods for computing partial agreement weights and applied these to synthetic datasets with varying levels of corruption. We then evaluated the methods on real administrative datasets.
ResultsExact comparisons can miss matches where typographical errors or misspellings produce small changes in value. Similarity comparisons can reduce the number of missed matches, but may also increase the number of incorrect matches.
Various results of the partial agreement methods on Jaro-Winkler, Sorenson Dice coefficient, Jaccard similarity and Hamming distance comparators will be presented. A generic function to convert similarity values to weights, created from synthetic data, can be used on most datasets with a greatly improved result in linkage quality. However, maximising the linkage quality requires the creation of similarity-to-weight functions that are optimised for each dataset.
Conclusion/ImplicationsAccuracy in record linkage is vital for the correct analysis of linked data. It is even more critical in privacy-preserving record linkage where the ability for clerical review is limited. Optimised functions for converting similarities to partial weights can significantly improve the quality of linkage and should not be overlooked.
Public Cloud: The Future of Record Linkage?
Authors: Brown Adrian , Randall Sean , Ferrante Anna , Boyd James 
Published: September 2018

IntroductionBusinesses worldwide are increasingly adopting the storage, compute and analytical services provided by cloud computing. Yet, few operational linkage units are keeping pace with this world of technological change - most use legacy systems approaching their limits with the rapidly increasing size and range of datasets now required for linkage.
Objectives and ApproachTo meet the demands of linkage for the near future, it is important that new solutions for linkage consider the services provided by public cloud infrastructure for compute, storage and analytics. We examined Platform as a Service (PaaS) offerings for use in the development of a cost-effective cloud model for scalable, privacy-preserving record linkage (PPRL). PPRL techniques were adapted to maximise the quality of linkage and to automate as much of the process as possible. Finally, a prototype was created to demonstrate the capabilities and potential of the model.
ResultsWe present our cloud model for PPRL, a platform for record linkage that provides rapid scaling of resources to meet demand, and the results of how our prototype performed on massive datasets.
Conclusion/ImplicationsThe application of record linkage using relatively inexpensive cloud infrastructure represents a significant step towards providing an efficient and scalable record linkage service to researchers and government. Larger datasets can be linked efficiently, including national or cross-jurisdictional datasets, with little investment in private infrastructure, and improved turnaround times for researchers.
Linking Antimicrobial Resistance Surveillance Data to Provincial Hospital Records: A Descriptive Study of Patient and Facility-level Characteristics
Authors: Lee Seungwon , Ronksley Paul E , Garies Stephanie , Quan Hude , Faris Peter , Li Bing , Henderson Elizabeth 
Published: September 2018

IntroductionAntimicrobial resistance (AMR) is an emerging phenomenon where microorganisms develop resistance against treatment antimicrobials, resulting in ineffective clinical interventions. The recent development of AMR surveillance systems at global and national stages highlights the growing importance of this topic from a public health perspective.
Objectives and ApproachThe objective was to link standardized population-based hospital AMR surveillance data with hospitalizationrecords to inform patient safety practices in Alberta, Canada. Incident inpatient cases of Methicillin-Resistant Staphylococcus aureus (MRSA),identified by Alberta Health Services Provincial Infection Prevention and Control(IPC) Surveillance from five acute care facilities in the Calgary zone (April 2011 to March 2016),were deterministically linked to the Discharge Abstract Database using Provincial Healthcare Number and gender. The incident cohort was stratified into hospital-acquired (HA-MRSA) and community-acquired MRSA (CA-MRSA) cases. Descriptive statistics were used to describe the patient outcomes and facility characteristics of these two groups.
ResultsA total of 2550 unique patients, representing 93.5% of the surveillance cohort, were successfully linked to hospitalization records. A total of 1259 patients belonged to HA-MRSA categories and 1291 patients belonged to CA-MRSA categories. Patients with HA-MRSA had longer hospital stays, were older, were more likely to have prior hospitalizations, had higher Charlson Comorbidity Scores, and were more likely to die in hospital when compared to patients with CA-MRSA. HA-MRSA results emphasized the important roles of in-hospital patient safety practices whereas CA-MRSA results alluded to the impact of community public health and primary care services onthe risk of hospitalization, although detected CA-MRSA numbers were likely underestimated due to selection bias within our linked cohort.
Conclusion/ImplicationsThis is first Canadian study describing HA-MRSA and CA-MRSA using linked population databases. It offers a glimpse into the intricate relationship between patient health and our healthcare system. This knowledge represents an important step forwarding building IPC strategies for managing AMR and improving outcomes in Alberta and in Canada.
Using linked data and family studies to understand multigenerational causes of low birthweight among Australian Aboriginal infants
Authors: Gibberd Alison , Simpson Judy , McNamara Bridgette , Eades Sandra 
Published: September 2018

IntroductionLow birthweight is common among Aboriginal infants. Birthweight is correlated across generations due to the transmission of genetic and environmental factors. Another cause may be fetal programming, where a fetus’ response to a hostile environment leads to poor adult health and, in turn, a poor uterine environment for her offspring.
Objectives and ApproachIdentifying a causal relationship between maternal birthweight and offspring birthweight is difficult. However, we can gain insights by approaching the question from several different angles, including family studies involving both parents and cousins who share maternal grandparents. Family studies using linked data are possible with data from Western Australia (WA), the only Australian state with a database of family relationships.
We used linked birth, hospital, mental health, and family relationship records of 12,865 Aboriginal singletons born 1998 to 2011 in WA whose mother also linked to a WA birth record from 1980 onwards, and their parents’ records.
Results17% of births were small for gestational age. Using a linear regression model with a generalised estimating equation approach for offspring birthweight z-score (BWZ), the coefficient for maternal BWZ was 0.17 (95% CI: 0.14, 0.20), compared to 0.13 (95% CI: 0.10, 0.16) for paternal BWZ. The difference in coefficients (0.03 [95% CI: -0.01, 0.08]) provides only limited support for the fetal programming hypothesis. Other associations with offspring BWZ were much larger, including maternal smoking (-0.39 [95% CI: -0.45, -0.34]). After restricting the sample to cousins with shared maternal grandparents (fixed-effects model), the mother-offspring association was fully attenuated (-0.01 [95% CI: -0.07, 0.05]), suggesting transmission of maternal genetic and environmental factors alone can explain the association, though the 95% confidence interval was wide.
Conclusion/ImplicationsIf fetal programming is an important cause of low birthweight, Aboriginal people would be disproportionately affected, following generations of low birthweight and chronic disease. However, the family studies indicate fetal programming has a limited role compared to other risk factors in the current pregnancy.
An effective privacy enhanced interface to support record linkage decisions
Authors: Kum Hye-Chung , Ilangovan Gurudev , Li Qinbo , Li Yumei , Ragan Eric 
Published: September 2018

IntroductionPrivacy enhanced technologies (PET) are those that measure and protect privacy by preventing unnecessary use of personal data without loss of the functionality of the information system. In practice, implementing such a system requires fine-grained access control so that access can be granted in smaller chunks of data.
Objectives and ApproachIn record linkage, PET to date has mostly meant separation of identifiers and sensitive information to allow access to only the necessary part. Moving beyond this current norm, we have designed a privacy enhanced interface to support linkage that discloses only the needed information at the sub variable level, when needed to make good decisions, to reduce exposure of personally identifiable information (PII). The system allows for access to PII both at (1) cell level (e.g., only names of needed people are released) or (2) sub-cell level (e.g., only part of a name, suffix or characters, is released).
ResultsIn a user study (N=104) where participants tried to link complicated situations (e.g. twins, Sr/Jr, change of last name) using the interface, we found that users given fully masked data, 0% of information disclosed, were still able to get 75% accuracy using supplemental visual markup. The markups depict data discrepancies such as swapped first and last names, transposed characters, different characters and missing data. More importantly, with this effective interface, we found that there were no statistical difference in accuracy of linkage (84%) or time taken between users with access to all data and those with access to only 30% of the data. We have released a tutorial where users can experience balancing between information disclosure and accuracy of results on sample data.
Conclusion/ImplicationsPrivacy is a major public concern when PII is legitimately accessed to link data. Our study demonstrates that a well-designed privacy enhanced interface can significantly reduce exposure of PII to people when resolving ambiguous linkages without compromising linkage quality. This research points to a new direction for PET in record linkage beyond encryption.
Poor birth outcomes among Aboriginal Western Australians and smoking, alcohol and substance misuse, and assault
Authors: Gibberd Alison , Simpson Judy , Jones Jocelyn , Williams Robyn , Stanley Fiona , Eades Sandra 
Published: September 2018

IntroductionOn average, Aboriginal neonates in Western Australia (WA) weigh 200g less than non-Aboriginal infants and are 2-3 times more likely to be preterm, stillborn, or die neonatally. They are also more likely to be exposed in utero to maternal behaviour risks like smoking, due to factors such as intergenerational trauma.
Objectives and ApproachWe aimed to estimate the proportion of small for gestational age (SGA) births, preterm births, and perinatal deaths of Western Australian Aboriginal infants from 1998-2010 attributable to maternal smoking, alcohol misuse, drug misuse, and assault against the mother. We used linked birth, hospital, mental health, and death records of all Aboriginal singletons and their parents. Using logistic regression with a generalized estimating equation approach, associations between birth outcomes and the four risk factors of interest were estimated after adjusting for maternal age, height and health. Using coefficients from these models, we estimated adjusted population attributable fractions (PAFs).
ResultsOf 28,119 births, 16% of infants were SGA, 13% were preterm and 2% died perinatally. 51% of infants were exposed to maternal smoking, alcohol misuse, drug misuse, and/or assault, and 37% [95% CI: 35%, 40%] of SGA births, 16% [95% CI: 14%, 19%] of preterm births and 20% [95% CI: 12%, 28%] of perinatal deaths were attributable to these factors, predominantly smoking. The PAFs for alcohol misuse (for example, for SGA, 3% [95% CI: 2%, 3%]) are likely to be underestimates as it is difficult to identify alcohol misuse using administrative data.
Conclusion/ImplicationsWhile smoking rates have dropped considerably, reduction measures have been less successful among Aboriginal women than non-Aboriginal women. Significant improvements in perinatal health are possible with identification and support of effective risk reduction approaches for Aboriginal women, as well as their communities and families.
Finding Maternal Siblings in Birth Registration Data to form a Pregnancy Spine – Data Linkage & Graph Based Methods for Unknown Cluster Sizes
Authors: Gammon Shelley , Morris Charles 
Published: September 2018

IntroductionWe have developed an innovative methodology to link maternal siblings within 2000 – 2005 England and Wales Birth Registration data, to form a Pregnancy Spine, a unification of all births to each unique mother.
Key challenges in this many-many linkage scenario:

Blocking (reduction of record pair comparisons)
Cluster resolution

Objectives and ApproachProbabilistic data linkage (Python) was followed by generation of clusters (using igraph in R) and graph theory community detection techniques.
To optimise geographical blocking and increase accuracy, we incorporated Internal Migration data to map the likely geographic movement of mothers between births.
Maternal sibling clusters were modelled as a graph and the structure of clusters was optimised using community detection methods to link, split and evaluate sibling groups.
Additionally, we incorporated additional childhood statistics data relating to child date of birth to evaluate likely accuracy of sibling pairs and remove false edges (links).
ResultsOur development has resulted in a new blocking method and cluster resolution method. In addition, we developed new ways to assess and measure the accuracy of sibling groups, beyond traditional classifier metrics, and infer error rates.
We applied our method to Registration Data used in earlier studies for QA of our methods.
Using this, and by comparing against other statistics on maternal sibling composition we will present results which show that a high degree of accuracy (precision / recall and new checks) was obtained for precision, recall, and other evaluation metrics.
Conclusion/ImplicationsThese methods will improve other linkage projects with unknown clusters sizes; for de-duplicating datasets, linkage of multiple datasets, or incorporation of data from a longer time-period through longitudinal linkage.
To this Spine, researchers can now append and link other data sources to answer questions about maternal and child health outcomes.
International comparison in walkable environments and hospital burden in type 2 diabetes patients
Authors: Mah Sarah M , Akbari Ashley , Fry Richard , Ross Nancy 
Published: September 2018

IntroductionWhile some comparative work has provided evidence for a universally positive impact of built environment features that promote physical activity, less is known about chronic diseases and hospitalization among different social contexts and health care systems. Parallel international linkage efforts present an opportunity to study health impacts of the built environment.
Objectives and ApproachThis study compares the impact of neighbourhood environments on health outcomes for patients with type 2 diabetes (T2D) in two countries. Neighborhood-level measures for walkable environments were derived for Canada and Wales using Geographic Information Systems. Hospitalization admissions from routine data sources and linked survey data responses (from the Welsh Health Survey and Canadian Community Health Survey) allow for the generation of population-based descriptive statistics on socio-demographic information, self-reported health, diagnostic patterns, and health care use. We examine the feasibility of investigating contextual differences in walkable environments, T2D, and hospitalization between Wales and Canada.
ResultsData linkage in respective privacy protecting safe havens in the Canada Research Data Centre Network (CRDCN) and the Wales Secure Anonymised Information Linkage (SAIL) Databank show promise for a comparative study, enabling parallel modelling of environmental and socio-demographic factors with hospitalization data. Both the Canadian and Welsh surveys ask respondents about their current diabetes status, allowing us to compare hospitalization rates and neighborhood effects of those who report having diabetes with the those who do not. Moreover, the linking of survey responses and similarity in geographic scale permitted consistent measurement of walkable environments across countries. Key administrative variables have been identified relating to health and behaviors, such as socio-demographic information, smoking status, and body mass index, and hospitalization metrics in both countries are commensurable.
Conclusion/ImplicationsThe generation of comparable linked datasets, built environment indicators and comparative research for T2D patients will have wider implications for international assessment of the impacts of environment on chronic diseases, and the hospital burden associated with these conditions.
Deaths in young people after contact with the youth justice system: a retrospective data linkage study
Authors: Kinner Stuart , Tibble Holly , Hail-Jares Katie , Borschmann Rohan , Spittal Matthew , Clough Alan , Sawyer Susan , Miller Adrian , Cadet-James Yvonne 
Published: September 2018

IntroductionYoung people who have contact with the youth justice system are distinguished by a high prevalence of complex, co-occurring health problems, including known risk factors for preventable mortality. However, almost nothing is known about health outcomes for these young people after separation from the youth justice system.
Objectives and ApproachWe aimed to examine the incidence, timing, causes and risk factors for death in justice-involved young people. We linked youth justice records in Queensland, Australia 1993-2016 (N=48,963) with adult correctional records and the National Death Index. We split the cohort into three subgroups: those who had ever been in detention (n=7,643), those supervised in the community but never detained (n=12,953), and those charged with an offence but never convicted (n=28,367). We calculated all-cause and cause-specific crude mortality rates (CMRs), and indirectly standardised mortality ratios (SMRs). We used Cox regression to identify static and time-varying risk factors for death.
ResultsDuring a median of 13.6 years of follow-up there were 1,452 deaths (3.0%). The all-cause CMR was 2.2 (95%CI 2.1-2.3) per 1000 person-years, and the all-cause SMR was 3.1 (95%CI 3.0-3.3). The leading external causes of death were suicide (32% of all deaths), transport accidents (16%), accidental drug-related causes (13%), and violence (3%). In adjusted analyses, independent risk factors for all-cause mortality included being male (HR=1.4, 95%CI 1.2-1.6) and older (>=15 vs. vs. charge only; HR=1.6, 95%CI 1.2-2.0) and subsequent incarceration as an adult (HR=1.8, 95%CI 1.4-2.4).
Conclusion/ImplicationsYoung people who have contact with the youth justice system are at markedly increased risk of preventable death, after separation from that system. Efforts to improve long-term health outcomes for justice-involved youth have the potential to reduce preventable deaths in these highly vulnerable young people.
A machine-learning approach to identify cerebral palsy cases using primary care database
Authors: Fan Heng , Li Leah , Wijlaars Linda , Gilbert Ruth 
Published: September 2018

IntroductionCerebral palsy (CP) is a complex condition that can manifest in different ways and is likely to be under-recorded in primary care databases. As identification based only on diagnostic codes will underestimate the incidence of cerebral palsy, an approach is needed to use all available information to identify more cases.
Objectives and ApproachWe used a machine-learning approach to identify likely CP cases in the Clinical Practice Research Datalink (CPRD), a UK primary-care database: i) we selected potential codes (symptoms, therapies and managements) to identify CP cases by comparing relative frequencies of codes associated with known cases versus non-cases; ii) codes with higher discriminative ability were further selected using a Random Forest method based on a resampled, balanced population; iii) we calculated the predictive score for each patient employing the discriminative ability of selected codes; and iv) manually reviewed the full medical records of likely cases (i.e. patients with higher predictive score).
ResultsPrimary care records were available for 343,199 young people aged 2000 were identified as likely cases (n=365, 0.1% of previously non-cases). Their full medical records in CPRD were manually reviewed by experts and 85 children (23.3% of likely cases) were validated as CP cases additional to the 341 initially identified by diagnostic codes, resulting in a 20% increase of CP cases.
Conclusion/ImplicationsData-driven schemes, such as the machine learning methods applied here, have the potential of identifying the most informative predictors in a cost-effective and rapid way to identify likely CP cases or other complex medical conditions in primary care database.
Linking environment and health data to investigate the association between access to unhealthy food and child BMI
Authors: Mizen Amy , Rodgers Sarah , Fry Richard , Lyons Ronan 
Published: September 2018

IntroductionModelling the daily exposure environment provides evidence for policy and practice. However, the dose-response relationship between exposure to food environments and obesity has not been widely investigated. This study investigated whether increased retail food environment (RFE) exposure in children was associated with a larger body mass index (BMI).
Objectives and ApproachIndividually tailored environmental exposures were calculated in a GIS for home and school locations, and modelled walking routes to and from school. Exposures were linked to individual level health data in the SAIL databank for a cohort of individuals aged 11-13 years from south Wales who had BMI measurements. A fully adjusted multilevel regression model was fitted to investigate the association of RFE exposure with BMI. Based on the distance individuals lived from school, we investigated differences between children who have the potential to walk to school (“walkers” lived 4.8km).
ResultsHome exposure and exposure along the walk to school was significantly greater for children living in deprived catchments, compared with children living in affluent school catchments (t = -5.25, p
Conclusion/ImplicationsIncreased BMI was associated with greater RFE exposure along the walk home from school. The findings suggest that the walk home from school should be the focus for developing interventions and policies to discourage unhealthy eating. Research should be undertaken to better understand child purchasing habits.