bluuebunny/crossref_metadata_2025_split huggingface dataset:
Equity in practice: Lessons from a cohort-based model for community voice and input in data linkage and use
Authors: Hawn Nelson Amy , Jenkins Adelia , Kemp Deja , Berkowitz Emily , Burnett TC , Rios Benitez Jessie , Zanti Sharon 
Published: September 2024

Objective and ApproachCross-sector data sharing and linkage can transform information about individuals into actionable intelligence to build stronger, healthier, and more just communities. Yet, the use of cross-sector data can also reinforce legacies of racist policies and produce inequitable resource allocation, access, and outcomes. To avoid this, we must embed data equity practices throughout the data life cycle and provide mechanisms for community voice and input. However, few integrated data systems are doing this well.
To combat this challenge, AISP designed a 30-month technical assistance program–the Equity in Practice Learning Community (EiPLC)--to collaboratively develop guidance and models for centering racial equity in data integration. This session provides an overview of our EiPLC Scope & Sequence, a curriculum we designed to guide this work. Currently, 10 jurisdictions in the U.S. are receiving coaching around this curriculum and shifting their data integration practices to advance equity.
ResultsTo date, progress has been nonlinear, with positive results. Work in action across the cohorts includes changes to cross-agency data governance, collaborative research agendas, legal agreements, staffing, and community participation practices including.
ConclusionsWhile these efforts are nascent, progress is evident and site team members indicate growth as individuals and as site teams. Key features of success include a focus on building relationships, establishing working norms, grappling with racialized histories, and interrogating the role of structural racism and systems of power.
ImplicationsThe EiPLC provides a successful model for supporting sites to embed data equity principles throughout the data life cycle.
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.
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.
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.
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.