bluuebunny/crossref_metadata_2025_split huggingface dataset:
Epidemiology and Clinical Course of Chronic Autoimmune Neuropathies During the SARS-CoV-2 Pandemic in Latvia
Authors: Glāzere Ieva , Kalniņa Marija Luīze , Rots Dmitrijs , Roddate Marija , Gailīte Linda , Kurjāne Nataļja , Ķēniņa Viktorija 
Published: September 2023

Background:
Chronic autoimmune neuropathies are a group of rare neurological disorders caused by the immune response to autoantigens in the peripheral nervous system.

Objective:
This study aimed to report epidemiological data in Latvia on the most common chronic autoimmune neuropathies and evaluate SARS-CoV-2 infections or vaccinations’ impact on our patient’s clinical course of the disease.

Methods:
A single-center observational study was performed, which included all patients diagnosed with chronic inflammatory demyelinating polyneuropathy (CIDP) and multifocal motor neuropathy (MMN) in Latvia since 2015. Prevalence and incidence were calculated by using population data from the corresponding time of the Central Statistics Bureau of Latvia. Detailed clinical evaluation and re-assessment of diagnostic criteria were performed in the whole study group. All patients underwent telephone interviews regarding their SARS-CoV-2 vaccination or infection status.

Results:
This study included 23 CIDP and 8 MMN patients. The point prevalence and incidence of CIDP were 1.21 per 100 000 people and 1.16 per 100 000 people, respectively. The point prevalence and incidence of MMN were 0.42 per 100 000 individuals and 0.40 per 100 000 individuals, respectively. Most of the patients involved in this study (90%, n=28) with CIDP or MMN were vaccinated against SARS-CoV-2 infection, and none of the patients experienced fluctuations or relapse regarding autoimmune polyneuropathy.

Conclusion:
The incidence and prevalence of CIDP and MMN in Latvia are similar to previously reported European studies. In our study group, vaccination against SARS-CoV-2 was safe and did not negatively affect the clinical course of CIDP and MMN patients.
Mechanistic Insights on Viral Factors Involved in Asymptomatic COVID-19
Pathogenesis
Authors: Kaur Satinder , Bala Renu , Mishra Prakash Chandra , Singh Vineeta , Hora Rachna 
Published: September 2023

Abstract:
SARS-CoV-2 infection may cause asymptomatic, pre-symptomatic or symptomatic COVID-19 disease. While symptomatic infections are at the centre stage for disease diagnosis and treatment, asymptomatic and pre-symptomatic cases heighten the challenge of transmission tracking ultimately leading to failure of control interventions. Asymptomatic cases appear due to a variety of host and viral factors and contribute substantially to the total number of infections. Through this article, we have tried to assemble existing information about the role of viral factors and mechanisms involved in the development of asymptomatic COVID-19. The significance of ‘PLpro’- a protease of Nidovirales order that removes ubiquitin and ISG15 from host proteins to regulate immune responses against the virus and hence disease presentation has been highlighted. PL-pro dampens inflammatory and antiviral responses, leading to asymptomatic infection. 11083G>T-(L37F) mutation in ‘Nsp6’ of SARS-CoV-2 also diminishes the innate immune response leading to asymptomatic infections. It is, therefore, pertinent to understand the role of proteins like PLpro and Nsp6 in SARS-CoV-2 biology for the development of transmission control measures against COVID-19. This review focuses on viral molecular mechanisms that alter disease severity and highlights compounds that work against such regulatory SARS-CoV-2 proteins.
A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study
Authors: Matsuzaki Keiichi , Kitayama Megumi , Yamamoto Keiichi , Aida Rei , Imai Takumi , Ishida Mami , Katafuchi Ritsuko , Kawamura Tetsuya , Yokoo Takashi , Narita Ichiei , Suzuki Yusuke 
Published: September 2023

Abstract

Background
In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM.

Objective
This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM.

Methods
Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2.

Results
SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format.

Conclusions
Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.
Effectiveness of ivermectin and moxidectin against cyathostomins in four horse breeding farms in Mexico
Authors: Martínez Ortiz de Montellano Cintli , Rodriguez-Vivas Roger Ivan , Rosado-Aguilar José Alberto , Galicia-Velázquez Guadalupe 
Published: September 2023

This study aimed to evaluate the effectiveness of oral ivermectin and moxidectin against natural cyathostomin infection in four horse farms located in the central regions of Mexico. 445 horses of the Warmblood (145), Thoroughbreds (100), and Quarter Horses (200) breeds, aged between 6 months and 27 years, were used. Data on horses and parasite control methods were collected through interviews with farm owners and veterinarians. Using the McMaster technique, fecal samples were processed from all 445 horses, 180 of which were positive for cyathostomins. On each farm, a selection was made of 45 animals meeting the criteria of a Fecal Egg Count Reduction Test yielding results exceeding 150 eggs per gram of strongylid-type nematodes. Subsequently, three separate experimental groups were formed for each farm, each consisting of 15 horses The first group was treated with oral ivermectin 1.87 %; the second group with oral moxidectin 2 %; and the third was the non-treatment control group. Coprocultures were also performed to identify the presence of nematode species. The data obtained were analyzed with RESO.exe©️. Three of the four farms achieved a 100 % reduction in eggs per gram with both macrocyclic lactones. One farm achieved 93 % reduction with ivermectin and 87 % with moxidectin. This study demonstrates that macrocyclic lactones effectively reduce cyathostomins in three of the four farms studied. The results suggest potential cyathostomin resistance to macrocyclic lactones, particularly moxidectin, on one farm. Given these findings, sustainable parasite management is required on horse breeding farms in Mexico.
Measuring irregular migration in Chile: The use of data linkage methods
Authors: Harnecker Belén , Mallea Felipe 
Published: September 2023

Foreign population in Chile almost doubled between 2017 and 2021. In recent years, the sources to measure the phenomenon has been the 2017 Census, plus administrative data from the migratory institutions. Yet, traditional estimation methods have not included irregular migration in all its forms, despite the fact that between 2018 and 2022 illegal entries grew by 754\%. Official numbers had only included (1) people registered by the day of the census, and (2) people that, from that day at least applied for one residence permit. However, records from other public institutions (education, health and police) suggests there are a considerable number of migrants that meets neither of the two criteria, that don’t have and official Chilean ID (RUN), but have registered in different services with provisional identifiers.
In order to properly account for irregular migration flows, this exploratory study presents a method of deterministic integration of personal administrative records from different public institutions (police, health, education and migrations), of foreign people that don’t have a unique identifier (RUN), using personal variables: names, surnames and date of birth. The proposed methodology can be summarized in four steps: (1) cleaning and standardizing records and variables (2) building an identification key and removing exact duplicated records in each data set (3) removing partial duplicated records in each data set using fuzzy matching techniques, and (4) matching the data sets using fuzzing matching techniques. The result of this procedure is the number of foreign people that showed signs of being residents by registering in the public services, but don’t have any registration of visa applications with the National Migration Service. These individuals, according with the existing law (Law N° 21.325), live in a (probable) irregular status.
This study not only represents and innovation in estimating foreign population in Chile as it accounts for inhabitants in irregular situation, but it also proposes a novel standard of registration and integration of personal administrative records of different public services.
Refugee Integration Outcomes cohort study: Evidence for policy and planning
Authors: Frowen Sam , Rogers Nicky , Evans Alan , Toft Hannah , Black Angela , Skipper Tilly , Peate Caroline , Hodgson Raoul 
Published: September 2023

Evidence on refugee integration outcomes in the UK is lacking, partly due to an absence of datasets which permit refugees to be identified. The RIO longitudinal cohort study designed in collaboration with the Home Office aims to address this by linking administrative data longitudinally.
RIO covers cohorts granted asylum and refugees resettled in England & Wales via the Vulnerable Persons and Vulnerable Children’s resettlement Schemes between 2015 and 2020. Linked data include the Personal Demographics Service from the NHS and Exit Checks from the Home Office. Census 2021 data have also been linked to the study. Deterministic linkage algorithms addressed different naming conventions across a wide set of cultures and administrative data quality. Associative linkage methods were developed to match residuals from the deterministic stage to their corresponding household if present. We conducted our own internal quality analysis to assess the quality of our linkage algorithms to improve our methodology ahead of incorporating new administrative data such as HMRC and DWP data.
Experimental analysis has looked at social and economic outcomes for these refugee cohorts. Linkage to NHS data helps us understand access to health services and time taken to access these services once resettled. Census 2021 data provide a rich understanding of integration outcomes up to 6 years after arrival or grant of asylum. We demonstrate the potential of linked census data to provide evidence on housing, education, health, access to the labour market, education, households and secondary migration but also how this varies for asylum and refugee cohorts but also by age, sex and geographical region.
RIO is aligned to the ONS strategic objective of inclusivity and recommendations made by the UK National Statistician’s Inclusive Data Task Force. RIO will ultimately help inform local authorities, government, charities and other organisations with resource allocation for these vulnerable populations. We are planning to make this dataset available to Accredited Researchers via the ONS Secure Research Service and the Integrated Data Service (IDS).
Education and social care predictors of offending trajectories: A UK administrative data linkage study
Authors: Dickson Hannah , Vamvakas George , Short Roxanna , Blackwood Nigel 
Published: September 2023

ObjectivesThe age-crime curve indicates that criminal behaviour peaks in adolescence and decreases in adulthood, but longitudinal studies suggest that this curve conceals distinct patterns of (re)-offending or trajectories. Some trajectories (e.g., life course persistent offenders) are reported to have distinct risk factors and more negative outcomes than others (e.g., adolescent limited offenders).
MethodsThe current study had two main objectives: (1) To use UK administrative crime data to identify trajectories of (re)-offending; and (2) To prospectively identify (re)-offending trajectories using longitudinal administrative education and social care data. This project uses linked UK administrative data containing the anonymised education and social care records for individuals born between September 1985 and August 1999, which have been linked to later official crime records up to the end of 2017. To identify offending trajectories, we used information on offence type, age of first conviction/caution, age of last recorded conviction/caution and offending history at three age points (Juvenile: 10-17 years; Young adult: 18-20 years; Adult: 21-32 years).
ResultsLatent Class Analyses with and without ‘Gender’ and ‘Ever served a custodial sentence’ as covariates was conducted to identify trajectories of (re)-offending. We are currently developing statistical models to see if we can use prospective longitudinal education and social care factors to discriminate between these trajectories. In my talk, I will share findings on the offending trajectories identified and present some early results on the key education and social care drivers of the offending trajectories.
ConclusionFindings from this study has the potential to provide deeper insights into how these education and social care factors might affect (re)-offending patterns. This could inform education, social care and criminal justice system responses to offending behaviours which seek to reduce offending and its associated social and economic costs.
Healthcare in prison: Does it matter how it is provided?
Authors: Bowstead Janet 
Published: September 2023

ObjectivesFrom April 2013, responsibility for healthcare services for prisoners was shifted to NHS England. Administrative survey data from English prisons covering 2000-2021 was used to identify if this change affected detainees’ experiences of healthcare quality and/or access; as well as the association with other characteristics of prison or prisoner.
MethodsSince 2000, HM Inspectorate of Prisons (HMIP) has carried out surveys of detainees as part of its inspections. This presentation will highlight the potential of these datasets by presenting substantive results of analysis on detainees’ experience of healthcare in prison. Merging datasets over time provides continuity of some variables over the whole period, with responses from up to 95,000 individuals. Variables of detainees’ assessment of the ease of access to different healthcare professionals, as well as the quality of services provided, were analysed over time and in terms of association with different types of prison and demographics of prisoner.
ResultsThe HMIP data are used to inform inspections and reports, and an ESRC-funded project has now developed these administrative datasets for wider research use. With a timeframe of over 20 years, the data can be analysed on a range of policy-relevant prison issues, such as safety, preparation for release, support within prison, treatment of prisoners, and access to information, legal rights, education, exercise and healthcare. These can be associated with demographic characteristics of detainees, and functional types of prison; as well as the analysis presented here of trends over time and whether these can be aligned to distinct policy or practice changes. The policy change in healthcare provision in April 2013 is contextualised within trends of greater health needs of prisoners and differentials between prisoners.
ConclusionHMIP detainee survey data are now archived with the UK Data Service for research use. Cross-sectional analysis on a range of demographic factors shows differential healthcare needs and experiences, and analysis over time indicates both trends and the impact, or not, of policy changes on detainees’ experiences.
Which people are most affected by changes to data linkage methodology? An exploration of patient, organisational and spatiotemporal characteristics in administrative hospital data in England
Authors: Stone Tony , Harron Katie 
Published: September 2023

ObjectivesIn 2021, NHS Digital changed the process used to link records belonging to the same person across and within data collections. Our objectives were to identify patient, organisational and spatiotemporal characteristics associated with records impacted by this change and the implications for researchers using this data.
MethodsWe used an observational cohort study of patients, aged 55 or less, with a secondary care contact recorded in any of the NHS Digital (now part of NHS England) curated Hospital Episode Statistics (HES) datasets between April 1997 and March 2021. We compared clusters of records assigned to each patient using the HES ID (old methodology using a three-step deterministic algorithm) and the Person ID (new methodology using a master patient spine). We used multivariable logistic regression to identify patient, organisational and spatiotemporal (such as area-level deprivation and year of first contact) characteristics associated with patients whose cluster had changed.
ResultsOf 88 million hospital records in 2019, there were 18,968,711 distinct HES IDs and 18,717,142 distinct TPIs. Of the 12,701,169 HES IDs with more than one record, 145,948 (1.1%) were split into multiple Person IDs. Of the 12,999,671 Person IDs with more than one record, 483,091 (3.7%) were associated with two or more merged HES IDs. We will present an analysis using data covering the period April 1997 to March 2021 - 1.25 billion records - and present the characteristics associated with changes between linkage methods.
ConclusionOur findings indicate that this change consolidated clusters, resulting in fewer distinct individuals in the data. Our findings will inform researchers about which groups of individuals are most likely to be affected by changes to linkage methodology. This is vital for understanding potential sources of bias due to linkage error.
AD|ARC (Administrative Data| Agricultural Research Collection): Linking individual, household and farm business data for agricultural research – The challenges and opportunities of partnership working across ADR UK
Authors: Webster Nicholas , Hampton Jen , McFarlane Scott , Wilgar Peter 
Published: September 2023

ObjectivesADARC is a partnership of six government departments and three research institutions across ADR-UK aiming to build four Research Ready Datasets about farming households. A|ARC requires government data linking experts to work closely with agricultural statisticians and academics across traditional boundaries pooling a wide range of data and expertise.
MethodsThe ADARC partnership aims to build a model of collaborative working for administrative data research. ADARC brings together those collecting data with those linking and analysing de-identified data and eventually those using results to develop future policies. The data is from several different sources at individual, household and business levels requiring new methodologies developed by this collaboration. ADARC is also working between all four ADR centres to share learning across the ADR network building relationships for future projects beyond agriculture. The linkage process has been different in all four nations but this has offered new learning for all involved.
ResultsDue to the Covid-19 pandemic, Phase 1 of the project has been slower than hoped. However, ADARC has shown that it is possible to conduct a truly UK-wide four nation project. Data linking is underway in all four nations bringing together data on individuals, households and businesses and analysis has begun in Wales and England. The project to date has faced the usual challenges of data acquisition and data quality issues which the partnership approach has been vital for overcoming. Despite this, we’ve demonstrated that there’s a huge level of enthusiasm for working together across organisational barriers. ADARC has just been funded for Phase 2 to expand the datasets in scope and explore the feasibility of linking in spatial data.
ConclusionADARC has created a model which demonstrates that partnership working is essential for fully achieving the objective of ADR UK of unlocking the potential of administrative data. The lessons of ADARC also provides a powerful case study for future cross-UK projects far beyond agriculture.
Machine Learning for Data Linkage
Authors: Ellum Rhosanna , Lewis Alex , Xhaferaj Kristina , Shipsey Rachel , Račinskij Viktor , White Zoe 
Published: September 2023

Data linkage traditionally uses deterministic and probabilistic methods. Alternatively, machine learning methods can be applied as classification algorithms, using the data to inform decisions. This project compared the quality, in terms of precision and recall, of traditional methods with selected machine learning methods when applied to a standard linkage problem.
Two supervised methods, gradient boosted trees (GBT) and multiple layered perceptron classifier (MLPC), and one unsupervised method, maximum entropy classification (MEC), were implemented. The England and Wales 2021 Census to Census Coverage Survey (CCS) linkage was used as a gold-standard (GS) linked dataset to provide training samples for the supervised methods as well as testing samples for all methods. The F1 score (harmonic mean of precision and recall) was used to compare the performance of the models and to determine the optimal parameters and thresholds.
The Splink implementation of Fellegi-Sunter with Expectation Maximisation was used as a baseline for comparison.
The methods, trained on a sample of the GS, were used to link census and CCS data. All methods performed well with MEC achieving the highest precision (99.79%) but lowest recall (96.36%). The MLPC model achieved the highest F1 score (98.94%).
To understand the implications of not retraining supervised models for each dataset, the models were also used to link Census to a health dataset. The supervised models were not retrained using the health data; instead, the optimised GS models were applied. MEC had the lowest precision (96.51%) but the highest recall (98.48%) and highest F1 score (97.49%). With F1 scores of 96.99% and 96.14% respectively, the GBT and MLPC supervised models were not far behind in performance, despite not being trained using health data.
We have shown that machine learning methods can be used effectively for data linkage problems. Unsurprisingly, supervised models perform best when trained on and applied to the same data. Further research into generic training may allow us to use both supervised and unsupervised machine learning models for future data linkage.