bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysing Siamese Neural Network Architectures for Computing Name Similarity.
Authors: Vinden Nicholas , Foxcroft Jeremy , Antonie Luiza 
Published: August 2022

IntroductionA wide assortment of string similarity measures can be used to determine how similar two names are. A diverse set of discriminating and independent features for name similarity are important for classification during record linkage. A Siamese neural network could surpass traditional string similarity measures for the name similarity problem.
Objectives and ApproachThis research aims to compare a classifier based on the Siamese network architecture with a Random Forest classifier. In addition to comparing overall performance, we seek to answer whether there are any special properties of certain matching name pairs where the complexity of the Siamese network offers particular benefit.
Our data consists of 25,000 last name pairings, with each pair being two variants of a family name. Name similarity predictions from the Siamese network are compared to a Random Forest model that serves as an ensemble of existing string similarity measures.
ResultsWe compare the similarity scores yielded by the two methods and discuss the results. We describe the representation of names to each method; name representation is computed formulaically for the traditional measures but is learned by the Siamese network during training. The comparison of different methods is made both in terms of their similarity prediction quality, and the computational cost to generate the predictions.
As expected, the Siamese network necessitates a significant computational cost to train. Unexpectedly, the ensemble of traditional measures yields almost identical overall classification performance. However, we expect that further analysis of false positives and false negatives will yield some insight into when practitioners should consider one method over the other.
Conclusions/ImplicationsResults suggest that there may be instances where a Siamese network outperforms other similarity measures, although training a Siamese network comes at a considerable computational cost. It is worth considering this approach to name similarity as an additional similarity feature when performing record linkage tasks.
Evidence from an Applied Health Research Question (AHRQ): Health care utilization of publicly funded rehab services for patients post COVID-19 diagnosis.
Authors: Golding Haley , Churchill Katie , Levy Charissa , An Diana , Hall Ruth , Plumptre Lesley 
Published: August 2022

ObjectivesThe Rehabilitative Care Alliance issued an Applied Health Research Question request to collect information regarding healthcare and rehabilitation use among COVID-19 positive individuals. The objective of this project is to determine the association between length of stay (LOS) in acute care and the number of rehabilitation services used post COVID-19 diagnosis.
ApproachHospital and rehabilitation service use was identified among individuals diagnosed with COVID-19, using administrative health data. Admission into acute care within 30 days post COVID-19 diagnosis was recorded. Use of inpatient, physiatry and home-care rehabilitative services were collected until March 31st 2021. Outpatient rehabilitation reporting is not mandatory and was not included. Marginalization was evaluated using the Ontario Marginalization Index factor scores. The association between LOS in acute care and number of rehabilitation categories used was assessed using a negative binomial model, stratified by with or without a stay in the ICU and controlling for age, sex, comorbidities and long-term care residence.
ResultsOf 181,139 individuals diagnosed with COVID-19 prior to December 31st 2020, 5% were hospitalized. Of those hospitalized 2.3% then entered rehabilitation compared to 0.06% who were not hospitalized post COVID-19 infection. Rehabilitation users had higher residential instability (mean=0.45 vs -0.01 in the overall cohort), dependency (mean=-0.02 vs -0.27) and material deprivation (mean=0.37 vs 0.19) but similar ethnic diversity (mean=0.87 vs 0.90) compared to the full cohort. LOS in acute care was associated with a 3.3% increased risk of using additional rehabilitation services for individuals without a stay in the ICU (RR 1.033, 95% CI: 1.011 to 1.055; p=0.0036), and a 3.7% increased risk for individuals with a stay in the ICU (RR 1.037, 95% CI: 1.025 to 1.048; p<.0001).
ConclusionsPost COVID-19 diagnosis, a larger proportion of rehabilitation service users were hospitalized compared to all COVID-19+ individuals. Additionally, LOS in acute care was associated with the use of more rehabilitation care categories following a COVID-19 diagnosis, and the association was stronger for more severe cases requiring an ICU stay.
Ancillary Data Record Linkage to characterize the completeness of data for the All of Us Research Program.
Authors: Yang Yuyang , Rodriguez Kelsey , Basford Melissa , Nambiar Sidd , Berman Lew , Kho Abel 
Published: August 2022

ObjectiveThe All of Us Research Program (AoURP) is an ambitious effort to gather health data from one million Americans to accelerate research. We linked Electronic Health Records (EHR) and insurance claims data to characterize the degree to which ancillary datasets can improve data completeness for care received by AoURP participants.
ApproachWe sought to link EHR data for 400,000 consented AoURP participants with insurance claims data provided by IPM.AI (Swoop Analytics), a commercial analytics company who have insurance claims data for 300M (over 90% of) Americans. We utilized a HIPAA-compliant privacy-preserving record linkage method (tokenization, provided by Datavant) to match patients between datasets. We evaluated match fidelity and the degree of overlap between AoURP EHRs and IPM.AI claims data. We characterized the association of patient and organizational level factors (demographics, healthcare provider organization, reporting site) with match performance.
ResultsAs of submission of this abstract, 41% of AoURP EHRs matched with IPM.AI claims. We compared patient healthcare encounters, diagnosis codes (DX), procedure codes (PX), and national drug codes (NDC) for matched patients by month. The union of AoU and IPM.AI data greatly increased data completeness in matched patients. Only 20% of healthcare encounters were seen by AoURP and IPM.AI concurrently while 25% were unique to AoU EHRs and 55% to IPM.AI claims on a monthly level. The number of diagnosis events compared between AoURP and IPM.AI is roughly equal (AoU +6%) while procedure events are elevated in claims data (23%) and drug counts are greatly elevated in AoURP EHR data (71%). We found that matched patients had more healthcare encounters compared to unmatched patients.
ConclusionTo our knowledge this is the first effort to address challenges in AoURP data completeness through complementary data linkage. Our results suggest that supplementary data linkage can improve data completeness in a large national research initiative. We identified several patient factors that require further investigation in improving match fidelity.
Brazilian governmental database linkage to correct the municipalities underreported cases in a time-dependent cluster analysis on COVID-19.
Authors: Ara Anderson , Santo Jonatas Espirito , Conceição Jackson , Barreto Marcos Ennes , Costa Lilia Carolina Carneiro , Souza Rafael Felipe da Silva , Fiaccone Rosemeire Leovigildo , Barreto Mauricio Lima , Ichihara Maria Yury Travassos 
Published: August 2022

ObjectivesCovid-19 databases have detailed information about each affected person in Brazil, but it has flaws in counting the number of cases, which are underreported. We aimed to construct and correct the cases dataset by linking different sources of data observations to study the pandemic evolution in Brazilian municipalities.
ApproachUsing the electronic Unified Health System (e-SUS), a public and governmental database, we calculated the pandemic curves of COVID-19 cases. We applied the following approaches to investigate data anomalies a) to perform a descriptive analysis and compare these results with a non-governmental database using Dynamic Time Warping distance; b) to verify and correct municipalities data anomalies linking to other public governmental database namely National Council of Health Secretaries (CONASS) with e-SUS. c) To apply a K-means DTW Barycenter Averaging in clustering analysis to describe the general behaviors of pandemic in Brazilian Municipalities.
ResultsAround 10% records of cases in the e-SUS public governmental database were underreported. After the linkage and the data updating procedure, the time-dependent clustering analysis presents no anomalies and more interpretable results. The clustering analysis provided eight different behaviors of COVID-19 curves of cases. The degree of intensity for prevalence and incidence rates were identified according to eight clusters from the lowest to highest.
ConclusionsUsing the matching procedure based on Dynamic Time Warping distance to correct the municipalities unreported cases, we provided a richer dataset to support a clustering time dependent analysis to characterize the Pandemic evolution in Brazil. These results may be explored in future deprivation social studies.
Addressing multi-region data linkage needs through data sharing agreements – Three Canadian initiatives.
Authors: Maillet Donna Curtis , McDonald Ted 
Published: August 2022

ObjectivesData sharing and administrative data access for multi-jurisdictional research must accommodate all local requirements for the protection of personal information and personal health data. Navigating the different safeguards for data sharing found in all relevant Canadian provincial and territorial legislation requires exploring innovative, privacy compliant solutions.
ApproachHealth Data Research Network Canada (HDRN Canada) and one of its provincial data centre partners, New Brunswick Institute for Research, Data and Training (NB-IRDT), are undertaking unconventional approaches to data sharing that balance legislative compliance and data access needs. Three approaches are being pursued: harmonizing data sharing agreements between a national longitudinal study and 10 provincial data centres; facilitating 4 individual data sharing agreements with one regional cohort of a nationwide study; and coordinating data sharing between 4 provincial government departments and one national data centre. Each approach has unique features that presented problems needing innovative resolutions.
ResultsThe experiences and challenges of these three approaches to data sharing for multi-regional research have been perplexing but ultimately are leading to productive outcomes. Learnings acquired enable partners to move beyond anecdotal perceptions of data sharing limitations to practical solutions that can be applied in future data sharing partnerships.
The ongoing project management approach of these initiatives identified a core set of somewhat predictable devices to ensure the advancement of simultaneous multiple regional data sharing agreements. These include the assignment of a project coordinator role, clear and ongoing communication, and the ability to negotiate and manage expectations. More innovative, however, is the roadmap that evolved, laying out the necessary (previously unanticipated) measures and sequence in which they should occur for the most expedient outcome.
ConclusionTo harness the potential of multi-regional research in Canada, provinces and territories must identify feasible and efficient data sharing solutions. The identification of adoptable and replicable legislatively compliant data sharing practices will increase potential data accessibility. This will increase available data and data platforms for research informed policy decisions.
COVID-19 transmission and infection: linkage of COVID-19 Infection Survey, Test and Trace, and Patient Demographics Survey.
Authors: Maizey Leah , Plachta Josie , Clarke Holly , Collyer Sarah , Cummins Sarah , De La Serna Gabriela , Gammon Shelley , Harries Ben , Pereira Elizabeth , Youell Caroline 
Published: August 2022

ObjectivesData linkage was conducted between the Office for National Statistics’ Covid Infection Survey (CIS), the Department of Health and Social Care’s Test and Trace (T&T) and NHS’ Personal Demographics Service (PDS) datasets. Linked data was required to provide reliable estimates of rates of COVID-19 transmission and infection used to inform policy regarding the ongoing pandemic.
ApproachThe CIS was created to track infection rates in the UK population. Linking CIS participants to positive tests in T&T helped improve these estimates. Linkage to PDS was required to attach NHS number to these datasets to facilitate further linkages that could also be used to inform Government about the spread of the virus. Multiple approaches were used to link the data. Initially, T&T was linked to itself via a series of strict matchkeys to cluster records belonging to the same individual, to create a person level identifier. Subsequent linkage of CIS-PDS, T&T-PDS and CIS-T&T involved deterministic linkages with matchkeys designed and applied independently. A probabilistic (Fellegi-Sunter scoring) method was used to link CIS-PDS and CIS-T&T. Additional, associative links were created between CIS and T&T records that had matched to the same PDS record but had not matched to each other.
ResultsThe accuracy of CIS-PDS and CIS-T&T linkages was high (recall and precision >98%; all 95% lower confidence intervals >93%). A quality assessment of T&T-PDS is underway, as are relevant bias analyses.
ConclusionAs a result of this linkage, COVID-19 analysts have access to enriched datasets linked to compare previously separated variables, with confidence that the linkage method used was to required quality standards. The linked data has been used to provide crucial evidence to Government on infection and re-infection rates. Subsequent linkages have enabled analysts to explore risk factors associated with different variants of the virus, vaccination status and hospital episodes. Improvements continue to be made.
Development of a Prediction Model for Survival Time in Esophageal Cancer Patients Treated with Resection.
Authors: Harrison Lyndsay , Mahar Alyson , Coburn Natalie , Boyes Randy , Pugliese Michael , Compton Carolyn , Darling Gail , Davis Laura , Decker Kathleen , Gupta Vaibhav , Kidane Biniam , Manuel Douglas , Ringash Jolie , Turner Donna , Hsu Amy 
Published: August 2022

ObjectiveAccurate estimates of survival guide decision-making for patients and oncologists. Advances in the capacity to measure complex tumour biology and patient factors allow for concurrent consideration of clinical, pathological, molecular, and biological markers for prognostication. Clinical prediction tools are a mechanism to combine and personalize these increasingly large amounts of complex information for prognostication.
ApproachWe describe the process of linking routinely collected health data, cancer registry, and pathology report data in two provinces to develop (Ontario, Canada) and validate (Manitoba, Canada) a clinical prediction tool in esophageal cancer. We compared the performance of a base model restricted to patient and disease characteristics available prior to surgical resection (e.g., age, sex, histology, comorbidities), and a more complex model including pathology specimen details (e.g., tumour stage). Cox proportional hazards models were fit to predict death at three years following resection. Internal and external validity was assessed using overall calibration and optimism corrected c-statistics. Equity was assessed through calibration in predefined patient subgroups.
Results2124 patients who underwent surgical resection for esophageal cancer between May 1, 2004 and June 30, 2016 for whom a pathology record was available were included in the study cohort. Median age was 66, with 80% males and 85% adenocarcinomas. Survival data were available until March 31, 2020. The model with pathology data had superior discrimination and calibration (calibration slope of 1.02 and intercept -0.01, and optimism-corrected c-statistic 0.77), compared to the base model (calibration slope of 0.95, intercept 0.02, and c-statistic 0.60). External validation is ongoing.
ConclusionOur study demonstrates that prediction models for cancer prognosis built solely on data from health administrative databases may be unreliable. The addition of high-quality pathology report data from electronic medical records or population-based cancer registries is necessary for accurate estimation. Our work provides a framework for combining administrative and clinical data which could be applied to the development of other clinical prediction models.
Associations between dietary patterns and cardiovascular disease risk in Canadian adults: a comparison of partial least squares, reduced rank regression and the simplified dietary pattern technique.
Authors: Lazarova Svilena , Jessri Mahsa 
Published: August 2022

ObjectivesWe aimed to compare two data reduction techniques, partial least squares (PLS) and reduced rank regression (RRR), in identifying dietary patterns associated with a high cardiovascular disease (CVD) risk in Canadian adults, and to construct PLS- and RRR-based simplified dietary patterns as well as evaluating associations between derived patterns and CVD risk.
ApproachData were collected from 24-hour dietary recalls of adult respondents in two cycles of the nationally representative Canadian Community Health Survey (CCHS)-Nutrition: CCHS 2004 linked to health administrative databases (n = 12,313) and CCHS 2015 (n = 14,020). Using 39 food groups, PLS and RRR were applied for the identification of an energy-dense (ED), high-saturated-fat (HSF) and low-fiber-density (LFD) dietary pattern. Associations of derived dietary patterns with lifestyle characteristics and CVD incidence and mortality were examined using weighted multivariate regression and weighted multivariable-adjusted Cox-proportional hazard models, respectively. Random and systematic measurement errors were adjusted for in all statistical analyses.
ResultsPLS and RRR identified highly similar ED, HSF, LFD dietary patterns with common high positive loadings for fast food, carbonated drinks, salty snacks and solid fats, and high negative loadings for fruit, dark green vegetables, red and orange vegetables, other vegetables, whole grains, legumes and soy (≥|0.17|). Food groups with the highest loadings were summed to form simplified pattern scores. Although the dietary patterns were not significantly associated with CVD risk, they were positively associated with 402 kcal/d higher energy intake (P-trends <0.05) and higher obesity risk [PLS (OR: 2.09; 95% CI: 1.62, 2.7) and RRR (OR: 1.76; 95% CI: 1.44, 2.17)] (P-trends <0.0001) in the fourth quartiles as compared to the first.
ConclusionPLS and RRR were shown to be equally effective for derivation of a high-CVD-risk dietary pattern among Canadian adults. This research highlights the importance of leveraging linked data to inform public health nutrition policies. Further research is warranted on the role of major dietary components in cardiovascular health.
Photonic hook switch: two photon polymerisation experiment protocol
Authors: Yue Liyang , Minin Oleg , Minin Igor 
Published: August 2022

Recently, a new concept for photonic switching based on photonic hook phenomena was
introduced [1,2]. When two-wavelengths beams, 1310 nm and 1550 nm, pass through a
dielectric Janus particle, they were separated and guided to different routes because of different
bending strengths, thus permitting effective photonic switching on microscale through a simple
dielectric particle. Potential applications in photonic integrated circuit are envisaged.
Experimental verification of the switch concept requires the development of an appropriate
protocol.
Corporate Governance Toward Sustainability Disclosure: Recent Development and Future Research Agenda
Authors: Dewi Luh Gede Krisna , Wiagustini Ni Luh Putu , Rahyuda Henny , Sudana I Putu 
Published: August 2022

The concept of sustainability is important in global business practices. Companies should manage business practices that are in line with the achievement of the SDGs agenda. This study aims to review previous research and fill gaps using a literature review approach. It critically reviews the theoretical perspectives and issues based on the relationship between corporate governance mechanisms and the level of corporate sustainability disclosure. This study adopts a systematic literature review approach with bibliometric analysis methods and content analysis using 44 articles from the Scopus database. The results of this study describe three clusters in the latest research developments. The first cluster is with regard to corporate social responsibility, the second is board composition; and the third is practical implications. Another finding is female director and independent director variables appear to be predictors of the sustainability report disclosure. The implication of the research is to provide comprehensive future research agenda.
Keywords: corporate governance, sustainability disclosure, board composition, bibliometric analysis
Tax Audit Signals Contribute to the Diminishing of Tax Haven Beneficiary Firm Value
Authors: Tjondro Elisa , Valentina Gracia Pulcheria , Gunawan Sianne Ivana , Dewantoro Yohannes 
Published: August 2022

The purpose of this study is to obtain an overview of the interaction of the tax havens' use and disclosure of tax audits and the impact of both on the firm value before the tax rate reduction period. The sample is Indonesia-listed firms in agriculture, basic industry and chemical, miscellaneous industry, and consumer goods sectors for 2015 – 2019. This research uses panel data and weighted least square regression. The findings indicate that using tax havens through subsidiaries is associated with increased firm value. In contrast, firms that have subsidiaries in a tax haven and disclose the result of tax audits are associated with a decrease in firm value. This research, to our knowledge, is the first research that combines the impact of tax haven utilization and tax audit disclosure on firm value in Indonesia.
Keywords: tax haven utilization, tax audit disclosure, firm value, tax haven subsidiaries, signalling theory