bluuebunny/crossref_metadata_2025 huggingface dataset:
A Bayesian Multi-Layered Record Linkage Procedure to Analyze Functional Status of Medicare Patients with Traumatic Brain Injury
Authors: Gutman Roee , Thomas Kali , Shan Mingyang
Published: September 2024

Understanding associations between injury severity and post-acute care recovery for older adults with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients’ injury, demographics, and long-term health outcomes. This data is despersed across two different datasets: Medicare claims data and the National Trauma Data Bank (NTDB). Because of privacy regulations, unique identifiers are not available to link records across these two data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We implement the proposed method to examine the associations between functional status assessments and TBI patients' ability to be independent following their injury by linking records from Medicare enrollment records and the NTDB.
Harnessing Machine Learning to Predict Methadone Overdose Risk: Insights from Illinois's SUDORS Data
Authors: Liu Yingxuan , Mason Maryann
Published: September 2024

Knowledge of decedent characteristics associated with methadone involvement in unintentional drug overdose deaths can help to inform efforts to intervene in these deaths.  ML models with existing data, offer a cost-effective means to help create effective interventions. In this study, we aim to develop an optimized ML algorithm for predicting overdose death patterns due to methadone using data from Illinois's State Unintentional Drug Overdose Reporting System (SUDORS).
We utilized IL SUDORS 2019-2022 data for training (n = 11931) with selected indicators (n = 28). To address the imbalance in non-methadone-related deaths, we applied the Synthetic Minority Oversampling Technique. Next, we assessed various machine learning models, including logistic regression, support vector machine, random forest, neural network, and ridge regression. Model performance was evaluated using metrics such as accuracy, precision, area under the precision-recall curve (AUPRC), etc.
Utilizing separate training (n = 15813), validation (n = 3388), and test (n = 3389) datasets, the Random Forest Model outperforms all other models with 92% accuracy, 91% precision, 93% recall, and 0.97 AUPRC. Notably, these outcomes were achieved using primarily demographic indicators. Through analyzing partial dependence plots, we were able to see how changes in each indicator dynamically influence methadone-related fatalities.
This study demonstrates the potential of ML models in identification of methadone involvement in unintentional drug overdose deaths and contributes to the knowledge base for the prevention of these deaths. Leveraging SUDORS data, the Random Forest Model exhibited exceptional performance, highlighting its value for healthcare professionals, prevention and harm reduction specialists, and policymakers.
10 year risk of death in adolescents with learning disability or autism in England: a national cohort study using linked health and education data from ECHILD
Authors: Zylbersztejn Ania , Ward Isobel , Harron Katie , Gilbert Ruth
Published: September 2024

BackgroundTransition from adolescent to adult health and education services is a vulnerable period for young people with learning disability (LD) or autism spectrum disorders (ASD), yet there is a lack of information on their risk of death in that period.
MethodsWe developed an English national cohort of 11-year-olds enrolled in state schools between 2009/10-20014/15, followed-up until their 22nd birthday, death or 31st August 2020 using linked health, mortality and education data from the ECHILD database. LD and ASD were indicated using diagnoses from hospital admissions or if LD or ASD were a reason for special educational needs provision in education records before age 11. We estimated crude mortality rates and compared with unaffected peers using rate ratios (RR).
ResultsThe cohort comprised 3,080,516 young people (52% boys), of whom 4,589 (0.15%) died (crude mortality rate: 0.17/1000 person-years, 95% CI: 0.16-0.17/1000).
45,174 (1.5%) young people were autistic (85% boys; 15% with co-existing LD), of whom 113 died (mortality rate: 0.29/1000 person-years, 95%CI: 0.24-0.35/1000). Mortality was 1.7 times higher for autistic young people compared to their peers (RR: 1.7, 95% CI: 1.4-2.1). Among 40,264 (1.3% of all) young people with LD (63% boys; 17% with co-existing ASD) there were 812 deaths (mortality rate: 2.31/1000, 95%CI: 2.15-2.47/1000PY). They had 17 times higher risk of death (RR: 16.4, 95%CI: 15.2-17.7).
ConclusionsYoung people with LD/ASD are more likely to die than their peers. Findings on disparities within these groups according to co-existing comorbidities, sex and socioeconomic factors will be reported.
Using linkage to assess coverage of population estimates
Authors: Plachta Josie
Published: September 2024

ObjectivesThe Demographic Index (DI) comprises of five linked administrative datasets, used for population estimation. Current linkage methods are not ideal to utilise the power of this asset. Using the 2021 England and Wales Census, we developed an innovative composite linkage method to fully utilise the power of the DI.  
ApproachUsing non-greedy deterministic and probabilistic linkage methods, we linked the DI to the Census at a composite level where we believe links exist – i.e., linking a Census record with a DI cluster (consisting of linked records from the data sources used to make the DI). Next, pairwise linkage of records within these clusters was performed to ensure that every DI record within the cluster was a link. Clerical review of a sample of the full linkage was performed using clerical resolution and search to resolve uncertain and conflicting links and to inform the quality of our linkage. 
Due to data quality errors in the administrative data, clerical techniques were not always able to determine true match status, so clerical identified ‘uncertain’ links to estimate precision and recall ranges rather than a set estimate. 
Results A quality analysis to estimate linkage accuracy was conducted and suggests that the linkage has a precision of between 99.3% and 99.7%, and recall between 99.1% and 99.7%. 
ConclusionWe have linked the ONS’ composite population-level dataset to the 2021 England and Wales Census. The presentation will showcase the methods developed and how we ensured the highest quality possible. 
*** Please note that this was subbmited in 2022 but the presenters were unable to present due to the UK Queen's death.
Unlocking data to inform public health policy and practice: A pilot study on utilising police data for linked population data research
Authors: Bandyopadhyay Amrita , Brophy Sinead , Kennedy Natasha , Jones Hope , Evans Julie , Bellis Mark A , Rowe Benjamin , Spasic Irena , Mcnerney Cynthia L. , Moore Simon
Published: September 2024

ObjectiveThe UK Government increasingly emphasises a comprehensive, multi-agency approach to tackling crime. This pilot study explores the feasibility of integrating police data with routine health data to develop a holistic understanding of the predictors of domestic abuse (DA). 
ApproachThe study encompasses three work-packages a) coding the narrative data from Domestic Abuse, Stalking and Harassment (DASH) risk assessment report from Public Protection Notifications (PPNs), which are information-sharing documents recording safeguarding concerns and shared with partner agencies b) exploring to identify what works to harmonise different software systems within police data-sharing approach, c) constructing a case study by linking PPN DASH data with routine health and administrative records. These efforts illustrate the potential of national data-sharing initiatives. 
Results
a) Text mining methods successfully identified and coded (with over 95% accuracy) 17 refereed/involved agencies from the PPN DASH data.  
b) Barriers to data sharing were attributed to a lack of clarity and consensus regarding appropriate information sharing, rather than technical obstacles. This barrier can be overcome by an unambiguous framework, endorsed at an elevated level, highlighting which data should be shared. 
c) The data-linkage study revealed that victims of DA had prior interactions with healthcare services before their initial PPN, and younger pregnant victims had higher risk of future healthcare emergency visits.  
Conclusion and ImplicationPolice and health data integration enhances evidence-based prevention and early identification of vulnerable individuals by both law enforcement and public health services.
Timely data on the dynamic drug supply is crucial in addressing the ongoing drug poisoning crisis. The team is exploring ways to share information with emergency responders, healthcare providers, government officials, and pwSUD.
Authors: Patil Tanmay , Walker Robin , Shi Congshi , Song Wanning , Seidel Judy , Oddie Scott
Published: September 2024

ObjectiveTransitioning from hospitals to primary care brings significant challenges, including increased readmission rates, mortality, and costs due to information gaps. Alberta, Canada, addressed this by integrating 1000+ health systems and implementing Connect Care (CC), a Clinical Information System (CIS), to enhance patient safety and care coordination. In 2020, the Primary Health Care Integration Network (PHCIN) released the Home to Hospital to Home (H2H2H) Transitions Guideline and metrics to improve patient outcomes and system integration. 
ApproachThis study examines provincial data on H2H2H transitions measures from acute care hospitals using CC between April 1, 2022, and October 31, 2023, to assess transitions and support improvement efforts. CC data assesses transition components, including confirming primary care providers at hospital discharge, utilizing the LACE Readmission Risk Index, and ensuring timely discharge summary (DS) signoffs. It links with administrative data to evaluate post-discharge outcomes like primary care physician follow-up, unplanned readmissions, and Emergency Department (ED) visits.
Results47 CC-implemented sites show nearly 80% discharges identified a primary care provider; less than 5% incorporated the LACE index in DS. About 90-93% of DS were signed within 24-72 hours. Approximately 62% of moderate-risk and 55% of high-risk discharges received timely follow-up. Readmission and ED visit rates within 7-30 days varied from 2-8% and 5-11% respectively.
ConclusionIn conclusion, adopting CC and H2H2H transition metrics facilitates integrated care measurement, highlighting the need for risk index inclusion and high-risk discharge follow-up for improved patient transitions. Sustained initiatives are vital for optimal outcomes and system integration in Alberta.
An Applied Research Partnership to Improve The Children’s Behavioral Health System Of Care
Authors: Zanti Sharon
Published: September 2024

Objective and ApproachThis session describes a doctoral student-led process to align research, policy, and practice partners around the use of integrated data to address a policy priority—the mounting youth mental health crisis. The project partners engaged in a five-year governance process to leverage integrated administrative data from a large health and human service agency in one U.S. state. Qualitative field notes—collected while conducting quantitative analyses and engaging partners—were analyzed to document a realistic timeline and process for ethical, actionable research using cross-sector data. 
ResultsFive key lessons for partnership-engaged research emerged, emphasizing the importance of adapting to changes in project themes, leveraging strong partnership to address data quality concerns, nurturing existing and new relationships, balancing data access and data privacy, and supplementing administrative data with contextual data.
ConclusionsThough engaging deeply with partners takes substantial time and energy, there are benefits to researchers, policymakers, practitioners, and most importantly, residents whose data are represented. Furthermore, building pathways to legally and ethically access integrated data can promote the efficient, strategic use of data for social policy research, and ultimately, be leveraged to support evidence-based policymaking in an ever-changing world.
ImplicationsThis session underscores the value of forming applied research partnerships and building legal and ethical pathways to access integrated data when tackling complex social policy issues.
A race against time – an evaluation of lifesaving treatment pathways for patients following cardiac arrest in a non-hospital setting.
Authors: Bailey Rowena , Herbert Laura , Pugh Richard , Lyons Ronan , Tszakmany Tamas
Published: September 2024

BackgroundCardiac arrest occurs when the heart ceases pumping blood around the body. Most take place outside hospital. A sequence of effective interventions (a “chain of survival”) is essential for patient survival and recovery of organ function. Out of Hospital Cardiac Arrest (OHCA) requires prompt recognition and call for help, proficient CPR, early defibrillation and conveyance by the emergency services to an appropriate hospital. If initial resuscitation is successful, effective post-resuscitation care is required, delivered usually in the intensive care unit (ICU). New patient pathways were introduced following an update to national guidelines, which recommend conveyance of patients to centres with percutaneous coronary intervention (PCI) services where possible.
ObjectiveWe have evaluated outcomes for patients, comparing those conveyed to PCI settings and those conveyed elsewhere to quantify the effect of the new patient pathway on mortality, survival and length of hospital stay.
ApproachWe have linked electronic health records from emergency departments, acute hospital settings, ICUs and the national deaths register for OHCA patients living in Wales, UK. We have undertaken temporal analysis and time to event analysis to compare patient subgroups. 
ResultsVariation in patient pathways will be described using characteristics of conveyance method, the type of treating hospital and transfers between PCI and non-PCI settings adjusted for individual patient level confounders such as age, sex and comorbidities.
ConclusionThis work is important to evaluate efficacy of operational procedures and inform national policies which aim to improve survival following OHCA and save lives.
Surveillance of violence through health service interactions in Wales: A longitudinal national data linkage study.
Authors: Lyons Jane , Doel Harri , Fry Richard , Johnson Rhodri , Lacey Arron , Strafford Huw , Turner Samantha , Lyons Ronan , Griffiths Lucy
Published: September 2024

Objective and ApproachThe study aimed to utilise multiple population-wide linked administrative, health, and survey data sources to identify victims from violence across various sociodemographic groups using healthcare service interactions. We calculated violence rates for the population of Wales to identify groups of people that experience higher rates of violence compared to the general population. 
Our approach involved creating an observational population-wide e-cohort utilising data held in the Secure Anonymised Information Linkage (SAIL) Databank. General practice records, emergency department attendances, hospital admissions, and the Office of National Statistics (ONS) deaths register were linked to the Welsh Demographic Service dataset to include individuals living in Wales, UK between 2013-2022, allowing for a 10-year analysis. 
ResultsIn total, 3.3 million individuals were included in the study, with 80,079 individuals having interacted with health services or died due to violence over the 10-year period. On average, every 4 per 1000 of the study population had experienced violence (2013-2022) with the majority of these being captured through emergency department attendances. 55% were male, of which 71% were aged under 35. The majority of individuals lived in the more deprived areas of Wales (61%).  
Conclusions and ImplicationsIt is important to understand and quantify violence over time to produce insight into which sociodemographic groups of individuals are being disproportionately impacted by violence for designing disparity-reducing policy interventions. ONS Census 2021 data will be used to examine the impact of violence by protected characteristics (e.g. disability, race, sexual orientation) under the UK Equality Act 2010.
Mental health of children with special health needs in Manitoba, Canada
Authors: Reid-Westoby Caroline , Enns Jennifer , Brownell Marni , Detillieux Gilles , Nickel Nathan , Janus Magdalena
Published: September 2024

Children with special health needs (SHN) may be more likely to experience mental disorders compared to their peers without SHN. The aim of this study was to establish and contextualize the risk of mental disorders among children flagged as having SHN at school entry. We linked data from the Early Development Instrument (EDI), a teacher-completed questionnaire of children’s developmental health in kindergarten, collected in Manitoba between 2006 and 2015, with provincial health administrative data up to 2019. Using binary logistic regressions, we examined the odds of receiving a diagnosis of any mental disorder, by categories of SHN: special needs, impairments in physical, vision/hearing, learning, speech, behaviour, and emotions, needing further assessment, and having 2+ SHN categories. Of the 36,462 children with EDI data linked to health administrative data, 5,882 (16.1%) were identified as having a SHN in kindergarten. The odds of developing a mental disorder varied by subtype of SHN. Children needing further assessment, with special needs, 2+ SHN categories, or with a learning, behavioural, or emotional impairment had between 1.35 and 3.27 times the odds of receiving a mental health diagnosis than their peers without these issues. Having a behavioural impairment increased a child’s odds the most. Having a physical, visual, or hearing impairment was not associated with a mental disorder. Having a special needs designation and impairments in behaviour and emotions in kindergarten puts children at risk of a future mental disorder. These findings may help generate wider mental health supports in schools for children with SHN.
A pathway to improved maternity research in Northern Ireland? Novel linkage of Northern Ireland Longitudinal Study and Northern Ireland Maternity Services System
Authors: Lowry Estelle , Rowland Neil
Published: September 2024

ObjectiveOur primary objective was to determine the social and demographic determinants of interpregnancy weight change. A secondary aim is to highlight the novel linkage of two data resources in Northern Ireland (NI) and the potential for transformative research in maternal health.
ApproachWe piloted a process to link two datasets in a safe, legal and ethical manner. The NI Maternity Services System (NIMATS) provides access to biological variables collected during the gestational period. The NI Longitudinal Study (NILS) is a rich source of Census information providing a demographic, social and economic background we would not otherwise obtain from NIMATS alone.
ResultsThe linkage of these two datasets has allowed us to link pregnancies to the same mother, enabling exploration of weight changes during the interpregnancy period(s). In NI, just over half of women giving birth (52%) were overweight. This is the highest in the UK. Descriptive statistics provided further insight into the current situation together with logistic regression which identified socio-economic and demographic correlates of interpregnancy weight changes.
Conclusion & ImplicationsObservation of women during pregnancy, linked with longitudinal, administrative data has provided valuable insights into the important public health issue of maternal obesity and into which socio-economic groups are most vulnerable to weight gain between pregnancies. Further research in maternal health has the potential to be facilitated by the unique linkage of NILS and NIMATS. This is important given the rise in complicated pregnancies during recent years and changes in maternal socio-demographic profiles.
A value approach to measuring the effect on health resource usage of specialist cardiac interventions in Wales
Authors: Davies Gareth , Akbari Ashley , Bailey Rowena , Evans Lloyd , Smith Kendal , Palmer Richard , Goodfellow Jonathan , Thomas Michael , Lutchman-Singh Kerryn
Published: September 2024

Objective and ApproachThe Welsh Health Specialised Services Committee wanted to demonstrate how specialised interventions reduced subsequent pressures on the health services.
We analysed the variation in the cost of patient pathways for eight cardiac interventions in both planned and emergency settings. A standard cost was allocated to each health event (hospital admissions, general practitioner interactions, outpatient attendances and accident and emergency attendances). A comparison was made for each intervention relating to the condition before the intervention against costs afterwards. In addition, analysis was carried out measuring which patient's characteristics affected the likelihood that patients incurred higher post-intervention costs. 
Results10,480 interventions were analysed. In 9 (of the 16) intervention combinations, post-intervention costs dropped. After the implantation of a Cardiac Device, health resource usage dropped by £1,461 per annum. In addition, the likelihood of falling into a higher cost category increased depending on deprivation. For percutaneous coronary intervention (PCI) the odds ratio and 95% confidence interval was 1.34 (1.03 to 1.76) and for Cardiac Devices 2.91 (1 to 15.23).
Conclusions and ImplicationsSome interventions in Cardiac realise significant post intervention savings for the health service. Deprivation affects the risk of patients falling into high post-intervention cost categories for PCI and Cardiac Devices.
By using anonymised population-scale linked individual-level data, it is possible to highlight interventions that as well as benefiting patients successfully, reduce future health resource usage. This type of analysis can also highlight how deprivation is linked to increased post intervention costs.
Daymet and DeGAUSS: Development of a Novel Method that Links High Spatial Resolution Gridded Meteorological Data with Geocoded Healthcare Encounters
Authors: Barrett Benjamin , Graffy Peter , Horton Daniel , Kho Abel
Published: September 2024

ObjectiveDue to human-caused climate change, extreme temperature-related morbidity/mortality are increasing global public health concerns. To better understand the burdens of heat exposure, researchers must merge accurate/granular environmental data with clinical data. We developed a privacy-preserving method that links high-spatial-resolution continuous, gridded, meteorological data to geocoded healthcare encounters.
ApproachIn an R environment, we developed code that links geocoded healthcare encounters to publicly-available Daymet data via the corresponding Daymet raster cell number and healthcare encounter date. Daymet provides daily 1km x 1km surface meteorological data over North America since 1980. User specifications include the bounding box latitude/longitude of Daymet data download, which Daymet environmental variables are linked, and the day-lag of Daymet data matching to healthcare encounter date.
ResultsLinking 500,000 healthcare encounters over a 7-day lag with 7 years of Daymet data comprising minimum/maximum temperature over a bounding box encompassing Cook County, Illinois (5,670 1km raster cells) took 11.92 minutes with 4 CPUs and 8GB RAM. Thirteen years of Daymet data took 23.85 minutes. Linking 7.5 million encounters with 7 years of data took 28.49 minutes.
ConclusionsOur newly-developed method for linking Daymet data to geocoded healthcare encounters preserves patient privacy, and processes the data efficiently over a minimal timespan.
ImplicationsDaymet data linked to healthcare encounters allows for accurate assessment of the association between environmental variable exposure and any clinical outcome over North America. Packaging the code allows for public distribution within the Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) universe of geoanalytic tools.
What’s in a name field?
Authors: McCarty Frances , Rogers Ben , Parker Jessie , Golden Cordell
Published: September 2024

The National Center for Health Statistics (NCHS) has a data linkage program that combines national survey data with key sources of health outcomes and health care utilization. The overall accuracy and quality of a data linkage depends on the quality of the data fields. This applies in a variety of data linkage methods, including clear text and privacy preserving record linkage. Data pre-processing and cleaning are essential to address data quality issues in most linkage tasks. Automating data cleaning prevents time-consuming manual reviews particularly when linkages involve a large number of records. For some data fields, these activities are relatively straightforward. For example, dates typically have a limited number of plausible values that make checking and cleaning relatively easy and straightforward. Unique identifiers (e.g., social security number) often conform to some set format or have restrictions on the values that would be expected. Other data fields such as first name and last name present greater challenges with respect to automating the cleaning process. For some data sources, first name and last name fields may contain non-name text that needs to be identified and removed. In this project, we examined the use of an artificial intelligence (AI) based large language model (LLM) and a simple rule-based algorithm to identify non-name text in name fields that should be removed prior to linkage. We also investigated the impact of automated name clean-up algorithms on the quality of an example linkage.
Linkage of Hospital and Community level records for Epidemiological Research: Experiences from Kagando Health and Demographic Surveillance System in Western Uganda
Authors: Kusuna Magdaline , Mulindwa Herbert , Kapuru Asinja , Maina Daniel , Karugu Caroline , Asiki Gershim
Published: September 2024

BackgroundLinkage of census data and hospital records is crucial in conducting epidemiological research to inform public health decisions.  In this study, we share our experiences in linking community-level data collected through census with hospital records and highlight the challenges and opportunities from our engagements with stakeholders. 
MethodologyWe undertook engagements with Uganda Christian University Ethics Research Committee, Hospital Management, and Community Health Volunteers to set up a platform to integrate hospital and census records using key identifiers. Trained data clerks prospectively linked data during patients’ visits following the data protection guidelines.
ResultsFrom a population of approximately 30,000 people, we managed to link 1000 records within three months. 
Lessons learnt and opportunities: It is possible to link data with a set of identifiers, not only relying on common identifiers, especially for communities where ownership of a national identity card or birth certificate is not universal. Building stakeholder partnerships is essential, and frequent meetings with the hospital staff, epidemiologists and data programmers helped to identify problems and solutions.
ChallengesLack of local capacity at the health facilities to develop linkage platforms. However, after development they can efficiently run the program. Frequent changes in the versions of the electronic system were later resolved.
RecommendationsThere is a need to conduct regular capacity building for stakeholders to help them understand the importance of data linkage.
ConclusionPublic engagement is an ethical imperative that enhances the quality, credibility, and long-term impact of research by incorporating the perspectives of the communities being studied.
Transforming and Modernizing Population and Housing Censuses in Africa: Experiences from the 2020 Round (2015-2024)
Authors: Shipsey Rachel , Muhwava William
Published: September 2024

The objective of our organization is to assist African National Statistical Offices (NSOs) to transition from manual to digital systems, use better methods and new technologies, and increase the reliability and accessibility of their statistics.
Innovative approaches adopted to support the transformation to digital censuses include:

Use of hand-held devices at the enumeration and dissemination stages to improve data collection and usability respectively.
Use of GIS, GPS and digital remote-sensing to enable census-mapping.
Provisioning of tablets – sourcing and sharing.
Electronic operations dashboards to enable real-time monitoring of progress and quality of enumeration.
Automatic data linkage algorithms for census to post-enumeration survey matching.
Capacity building initiatives for NSO staff.

As a result, to date 29 censuses have been carried out in the 2020 Round, with another 19 planned for 2024/25. Delays have been caused by economic, security and political constraints, as well as the Covid-19 pandemic. 
The implications of adopting new technologies are clear. Electronic data collection improves quality, reduces processing time and enabled enumeration during the pandemic by minimizing face-to-face contact. We will support documentation of experiences and lessons learnt in the current census round to contribute to the 2030 round.
In conclusion, the challenge of working during the pandemic gives NSOs an opportunity to ‘build back better’ and transition to full digital systems. There is a need to focus on the types of results that make the most difference e.g., on disaggregation, small area statistics, Sustainable Development Goals (SDGs) reporting, Agenda 2063 reporting, and monitoring of national development plans.
How is ‘shortage’ defined? Exploring Nursing Workforce Data across Canada 2015-2022: An Ecological Study
Authors: Harmon Megan , Martens Riley , Vatanpour Shabnam , Sapiro Natalie , Walker Robin , Risling Tracie , Eastwood Cathy
Published: September 2024

RationaleDetermining the ability of a country’s nursing workforce to meet the health care needs of the population is essential for optimal health outcomes. ‘Nursing shortage’ is frequently heralded as an issue, yet it is unclear how ‘shortage’ is defined and calculated. The purpose of this study was to collect and link publicly available Canadian data to describe and compare trends in nursing workforce capacity.
MethodsPrimary data sources included linking Statistics Canada and Canadian Institutes of Health Information (CIHI) data from 2015 to 2022. Statistics Canada tracks provincial population data and job vacancy rates. CIHI receives data from provincial nursing organizations on demographics, roles, and employment status. To estimate a sufficient workforce, job vacancy rates (a proxy for provincial need) were cross tabulated with the number of registered nurses (RNs) and registered psychiatric nurses (RPNs) per year.
ResultsThe number of RNs and RPNs in Canada has increased by 8.6% between 2015 and 2022, to a total of 322,226. Job vacancies, as a percent of total nursing supply, shows a rising trend (2.3% to 8.7%) between 2015 and 2022 across Canada. In 2022, 84.9% of Canadian RNs and RPNs in direct patient care across Canada and 86.1% were in urban settings.
Conclusion & LimitationsThis project examines Canadian nursing workforce data encompassing potential effects of the COVID-19 pandemic. The trends are limited to annual due to aggregated data. Data on a country’s nursing workforce measured monthly and consistently across provinces would yield clearer information.
ODISSEI Secure Supercomputer - a breakthrough in complex data linkage and applications.
Authors: Karpinska Kasia
Published: September 2024

The social reality is becoming increasingly complex and social scientists interested in studying those complexities and resolving real-world problems (such as increased inequalities or the spread of contiguous diseases) require an advanced infrastructure to advance this task. Ideally, a research infrastructure delivers access to rich and interlinked data that can be analysed with sufficient computational capacity while adhering to the highest security standards imposed by the sensitivity of this data. ODISSEI (Open Data Infrastructure for Social Sciences and Economic Innovation) offers such a unique opportunity in the Netherlands. In this talk, the ODISSEI Secure Supercomputer (OSSC) and its potential will be highlighted. This infrastructure allows researchers to to work with the extremely rich administrative data curated by Statistics Netherlands, covering different facets of life of the whole Dutch population and application of the complex models, pushing the boundaries of social sciences. The OSSC offers researchers sufficient computational power, memory, and GPUs to execute projects that include complex applications, such as Agent Based Models, network analysis, machine learning or LLM methods (such as life2vec). Moreover, high-security standards to which the environment adheres allow complex linkage with the genetic data, expanding the scope of research questions that can be studied even further. In this talk, the results of the ongoing studies will be presented, as well as the possibilities to conduct research using the OSSC infrastructure.
Health Outcomes Linkage in UK Biobank
Authors: Bramley Laura , Gay Jo , De Lacy Christopher , Callen Howard , Rose Sean , Flaxman Amy , Codner Gemma , Drake-Brockman Rachael , Lacey Ben , Allen Naomi
Published: September 2024

UK Biobank is a biomedical database containing de-identified data for over 500,000 participants within the United Kingdom, made globally available to researchers for health-related research in the public interest. To obtain comprehensive health outcome data, UK Biobank links to participants’ electronic medical record (EHR) data from the National Health Service (NHS) amongst others. This currently involves fifteen data pipelines covering death and cancer registries, inpatient records, primary care records, and COVID-19 test and vaccination data, with numerous others planned. 
Integrating diverse “real-world” datasets into the resource necessitates a complex data infrastructure and thorough quality assurance. Challenges include file format changes, linkage problems, incomplete or invalid records, problems with encoding systems and discordances between different data sources.  
Various methods can be used to integrate data into the resource, including commercial software, internally developed tools, and custom-scripted pipelines for individual feeds. To streamline and standardize data processing, we are trialing a new data integration architecture and toolset. This aims to reduce manual input, and improve transparency, quality assurance and efficiency within and between data pipelines. 
The pilot is currently ongoing with results expected in mid-2024. We anticipate these new tools will help to streamline the end-to-end process, which will allow the data analysis and linkage teams to focus on further improving data quality and providing more ‘research-ready’ summary outputs on researchers’ health outcomes of interest. The findings of the pilot will be relevant to researchers and data scientists looking to employ cutting-edge approaches to linkage of any large-scale population data.
Target Trial Emulation: Impact of C-Section on Birth Outcomes for Spontaneous Preterm Breech Presentations
Authors: Alexander Robin , Unger Holger , Bhaskar Swetha , Swallow Ben , Unger Stefan , Azcoaga-Lorenzo Amaya , Fagbamigbe Adeniyi , Stock Sarah , McCowan Colin
Published: September 2024

Breech presentation is a risk factor for poor birth outcomes. Guidance for safest mode of delivery in breech presenting full term pregnancies is available but there is a lack of evidence on the optimal mode of delivery in breech presenting preterm spontaneous births. The aim of this study is to evaluate the causal effect of mode of delivery on extended perinatal death (EPD) in this population using a trial emulation.
Randomized experiments are the gold standard for estimating causal effects; however, previous attempts to conduct them in this context has resulted in recruitment challenges. Causal inference methods provide a framework for drawing causal conclusions from observational data.
A trial emulation aims to overcome limitations common to observational studies by specifying core components of a randomized trial using observational data. Our trial emulation uses national Scottish health data to identify an eligible study sample and adjust for confounding characteristics between groups undergoing caesarean and vaginal deliveries to simulate randomized assignment of mode of delivery. We were able to estimate the per-protocol impact of caesarean delivery on EPD.
Our data, containing all recorded potentially eligible births in Scotland between 1997 and 2019, was provided by eDRIS in a trusted research environment. After applying our eligibility criteria, our data contained 3,457 birth records, (2,361 c-section and 1,096 vaginal deliveries). There were 212 EPDs (1.7% (39) of c-sections and 16% (173) of vaginal deliveries).
Our preliminary results suggest that caesarean delivery reduces the odds of EPD by 73% among breech presenting spontaneous preterm births.
Involving patients and the public in research using linked confidential patient information without consent: a case study on the COloRECTal cancer data Repository (CORECT-R)
Authors: Kwok Aden Chun Hei , Downing Amy , Affleck Paul , Rossington Hannah , Howard-Murray Matt , Lwiindi Julliet , Pai Chen-Chun , Gilmour Hamilton Catriona
Published: September 2024

Objective and ApproachIn 2017, the charity Cancer Research UK funded a UK Colorectal Cancer Intelligence Hub to create the COloRECTal cancer data Repository (CORECT-R).  This was intended to be a single research data system that links all datasets across the UK relevant to colorectal cancer and makes them available for research.  As the four home nations (i.e. England, Scotland, Wales and Northern Ireland) have distinct data governance policies, data access and linkage has proven challenging.   It has not yet been possible to create a UK-wide resource, hence limiting the potential of the data available.  To address this, the Hub has recently sought to gain new governance approvals to link data from the four home nations.  As part of this process, we aimed to investigate public acceptability of using confidential patient information (CPI) without consent in CORECT-R.
ResultsSeventy-five participants from a diverse background attended one of four online consultation sessions.   Participants raised a number of issues and concerns, including ensuring transparency in the access and use of CPI by academic and commercial partners, and being well-informed of how to opt-out.  With open communication and explanation of our approach, participants gave almost unanimous support on the intended CPI use.  
ImplicationsInnovation and collaboration between researchers, data custodians and the public are essential to drive responsible and meaningful uses of population data.  We showed an exemplar of embedding patient and public involvement in designing data linkage research.  Key areas identified from the sessions will form the basis of future consultations.
Data Quality and Legal Barriers to Linking US Government Social Program Data
Authors: Hall Chapin
Published: September 2024

To link social program data, such as employment, welfare, education, and human services data, a set of high-quality variables or fields are needed to facilitate rigorous linkage.   The siloed nature of data collection in the US leads to a lack of consistency in the data that agencies collect.  The data that one needs to make high quality linkages are often exactly those that government agencies are unwilling and unable to collect, or those that have poor quality.  This has significant implications for privacy-protecting record-linkage and other techniques whereby privacy and confidentiality can be appropriately addressed.   There are some obvious examples.  For example, in the US, many agencies are unlikely to collect or share Social Security Number because of privacy concerns.  A different type of example many agencies collect race or ethnicity poorly, so that the data is not consistent over time or simply inaccurate at the point of collection.  Some programs may only collect age or month and year of birthdate. This presentation will discuss strategies to address these barriers short of changing agency practice around data sharing.  It will also address how policies are a barrier to better linkage and data use.  Also, how the proposed National Secure Data Service may affect state capacity to conduct linkage will also be discussed.   This presentation is based on 40 years of experience working with the US federal government, states, and local agencies.
A game of snakes and ladders: the world of complex health and social care data linkage
Authors: De Corte Kaat , Crellin Elizabeth , Tracey Freya , Hardy Sarah , Brine Richard , Lloyd Therese
Published: September 2024

Objective and ApproachResearch on social care services requires large, comprehensive, routine datasets. Launched in November 2019, Developing resources And minimum data set for Care Homes’ Adoption (DACHA) study aims to develop a prototype minimum dataset as proof of concept and to propose implementation.
We describe our experience since 2021 of identifying, applying for, and linking care home, local, integrated care system (ICS) and national datasets, including direct-care software-provider data, GP and NHS England (NHSE) datasets.
ResultsOur key challenges:

Risk aversion and fragmented information governance within and between organisations.
Complex information governance structures must be understood before participants are recruited.
Shifts in the data landscape: data ownership moved from clinical commissioning groups to ICSs; NHSE merged with NHS Digital.
Research funding: research is required of ICSs, but it is not a system priority, so processes and funding aren’t in place, and research burdens an already strained system.

Our key lessons:

Formalising agreements early on and obtaining, and maintaining, senior, committed buy-in.
Studies should value the data sharing process through the inclusion of data controllers and information governance staff in the system in research project budgets.
The facilitation of GP and social care data collection nationally must be balanced with its administrative burden.

ImplicationsThe DACHA study overlapped with the COVID-19 pandemic and could only engage a limited number (n=3) of ICSs within project resources. Nevertheless, the complexity of the system and the plurality of actors delay or even block legitimate research interests.
The AISP Network of cross-sector data linkage efforts: Results & lessons from a national survey of 40 state and local systems in the US
Authors: Emily Berkowitz , Jenkins Della , Nelson Amy , Zanti Sharon , Kemp Deja , Burnett TC , Rios Benitez Jessie
Published: September 2024

Objectives and ApproachThis session will share findings from a national survey of 40 state and local cross-sector data linkage efforts using data to impact policy and outcomes across the United States (US). The survey, conducted in February 2023 by Actionable Intelligence for Social Policy (AISP) at the University of Pennsylvania, captured each effort’s purpose(s) for sharing data (the “why”), approach to linkage (the “how”), and the data sources being integrated (the “what”). Respondents are all current or prospective members of the AISP network, representing a cross-section of systems at different stages of development and engaged in peer exchange.
ResultsThe presentation will share results that describe the core purposes of system development and how systems operate; this includes governance structures to institutionalize sharing relationships; staffing, funding sources, and operating budgets; legal frameworks; and technical infrastructure. The presentation will also explore what data systems share, and the role a peer-led community of practice plays in developing and honing approaches to public sector data use.
ConclusionsNetwork members have demonstrated widespread impact in their communities. In the last two years, respondents collectively reported that their integrated data enabled 365 reports and briefings for policymakers to affect policy change and 160 research articles to advance knowledge for impact. They also widely reported improved relationships and trust across agencies and improved ability to evaluate what works.
ImplicationsThis presentation showcases the impact of the US community of practice in supporting diverse models for impact-oriented data systems.
An update on ICES’ Applied Health Research Question (AHRQ) program: Informing public health policy and practice using linked, population-based administrative health data in a collaborative model.
Authors: An Diana , Ho Minnie , Paterson J. Michael , Saskin Refik , Mondor Luke , Atzema Clare , Plumptre Lesley , Retnakanthan Jeruby , Skvirsky Dina , Victor Charles , Bronskill Susan E.
Published: September 2024

ObjectiveTo provide an update on the key characteristics, refinements to project flow, and future opportunities for a program providing customized research evidence to health system policymakers and providers. 
ApproachWith a goal to inform health system decision making, the program answers research questions posed by health system requestors that can be answered with linked, population-based administrative data. Now in its 10th year of operation, the program has been refined over time to improve efficiency, usefulness of research products, and requestor satisfaction. Satisfaction surveys and individual-level engagements with requestors are used to collect feedback on the needs of an increasingly diverse group of requests. 
ResultsWith 508 requests, 82 Data Sharing Agreements, and 257 unique requestors since its inception, the program informs governmental decision making, evaluates intervention effectiveness, and aids grassroots organizations’ planning for services. Notable updates to the program include, inserting multiple opportunities for connection between the program and requestors through the project life cycle to understand goals and needs; assigning staff scientists to shepherd projects and promote efficiency; training coordinators to review privacy impact assessments; and (soon) accepting a broader range of projects to include data beyond the health sector e.g. education. 
ConclusionThis program continues to improve and is an exemplar of sustainable approaches to supporting health system requestors with evidence from administrative data in program evaluation, planning and policy change. 
ImplicationsBeing responsive to requestor needs has allowed the program to attract an increasingly diverse range of requesters who can obtain impactful and timely research evidence.