bluuebunny/crossref_metadata_2025_split huggingface dataset:
Healthcare costs at the end-of-life among immigrant and non-immigrant groups in Manitoba, Canada
Authors: Debbarman Shantanu , Witt Julia , Oguzoglu Umut , Urquia Marcelo L. 
Published: September 2024

ObjectivesIt is known that healthcare costs tend to increase during the last year of life. Recognizing the importance of efficient resource distribution for end-of-life care, this study compares healthcare costs incurred for migrants and long-term Manitobans and identifies the factors that impact healthcare costs during the last year of life.
MethodsThis retrospective matched-cohort study used 15 databases linked at the individual-level, including immigration records, medical claims, hospital abstracts, drug prescriptions, emergency department visits, home care, long-term care, vital statistics mortality, housing and employment/income assistance, for those who died between January 2005 and December 2022 in Manitoba. Conditional zero-inflated gamma hurdle (ZIG) and quantile regression models were used.
ResultsThe average end-of-life healthcare costs for international migrants (2469) and Long-term Manitobans (2362) were CA$44,909 and CA$16,593, respectively. According to the adjusted ZIG model, international migrants had 17% higher costs. Among international migrants, Government Assisted and Blended Visa Office-Referred Refugees (GAR/BVOR) had 36% higher costs than Long-term Manitobans. Additionally, costs were higher for those without a partner (13%), receiving employment/income assistance (30%), having higher comorbidity (309% for 4+ comorbidities vs. 0 comorbidities), death at the hospital (171%), and long-term care (169%). Adjusted Quantile regression analyses revealed that only GAR/BVOR had higher costs across all levels of the cost distribution than long-term Manitobans.
ConclusionIn the last year of life, international migrants incurred greater healthcare costs than non-immigrants. However, the differences with non-immigrants varied depending on international migrants’ characteristics.
Individual, household, and socio-economic predictors of COVID-19 testing and vaccination outcomes: a whole population linked data analysis
Authors: Satherley Nicole , Sporle Andrew 
Published: September 2024

ObjectiveThe COVID-19 pandemic has exposed numerous social inequities in health outcomes across nations, despite early warnings of their potential. Poorer outcomes for ethnic minorities, and to a lesser extent Indigenous peoples, have been widely reported, and may be explained in part by household conditions and poorer socio-economic circumstances. This study aimed to identify the effects of diverse individual and social conditions on COVID-19 outcomes within the New Zealand population.
MethodWe conducted a whole population analysis of the association between individual (e.g. ethnicity, disability status), household (e.g. household composition), and socio-economic (e.g. crowding, housing quality, income) factors and four COVID-19 health outcomes – infection, hospitalisation, mortality, and vaccination status. We constructed variables of interest from linked administrative data in New Zealand’s Integrated Data Infrastructure, and examined associations within the 2018 Census usually resident population (analysis Ns = 518,571 - 3,216,696).
ResultsAnalyses showed that Māori (the Indigenous people of New Zealand) and Pacific peoples, an ethnic minority, experienced worse outcomes than other New Zealanders across all COVID-19 outcomes. These ethnic group differences were persistent, yet reduced, when accounting for household and socio-economic variables. Factors including disability status, high housing mobility, poor quality housing, and household crowding were also widely predictive of worse outcomes.
ConclusionEthnic inequity in a range of COVID-19 outcomes is present in a country where the overall impacts of the pandemic were relatively limited. Individual, household, and socio-economic factors are associated with diverse COVID-19 outcomes, and policy amenable factors may help explain the presence of ethnic inequities.
Early life child protection contacts and developmental risk at age five: a whole-of-population cohort study of 479,413 children in two Australian states.
Authors: Falster Kathleen , Pilkington Rhiannon , Ahmed Tasnia , Montgomerie Alicia , Hanly Mark , Newton BJ , Brownell Marni , Edwards Ben , Lingam Raghu , Shakeshaft Anthony , Cretikos Michelle , Stewart Jessica , Hawkins Katherine , McLean Kitty , Lynch John 
Published: September 2024

IntroductionChildren who experience maltreatment have worse health and development outcomes than other children. Early prevention relies on opportunities to respond to system contacts that reliably indicate the population burden of children’s future developmental risk.
Objectives and ApproachWe quantified the population burden of developmental vulnerability at age five by the type, timing, and frequency of child protection contacts before school, among children in two Australian states. We used linked whole-population births, child protection and Australian Early Development Census (AEDC) data (2009-2018 cycles) in New South Wales (NSW) and South Australia (SA).
Results56,650/398,702 (14%) NSW and 12,617/80,731 (16%) SA children had ≥1 child protection contacts before school. The risk of developmental vulnerability on ≥1 domains was lowest in the no child protection group (NSW, 17-18%; SA, 19%), with higher risks in the child protection report (NSW, 28-29%; SA, 32-35%) through to the OOHC (NSW, 35-38%; SA, 39-50%) groups, with a similar pattern for the risk of medically diagnosed conditions. Children with only one child protection report before school had a higher developmental risk than the no child protection group (NSW, 34% versus 21%; SA, 42% versus 24%).
Conclusions/ImplicationsEven a single child protection report in the first 2000 days of children’s lives was a robust indicator of developmental risk at age five, with higher developmental risks among children with more serious child protection contacts before school. Child protection reports represent an under-utilised asset to inform early universal and targeted support from health, human and early education services.
Understanding health data social licence: An international comparison of community attitudes towards health data use across Canada and Australia
Authors: Burt Julia , Miller Kate , Flack Felicity 
Published: September 2024

ObjectiveResearch has found general but conditional support for health data being used for public benefit. The term “social licence” describes which uses of health data the public supports and under what conditions. Here, we aim to compare two approaches to understanding community attitudes towards health data use, and how social licence may differ, between Canadian and Australian populations.
ApproachFactors that affect community support for health data use include the specific population, the type(s) of data, and the engagement approach used. In Canada, facilitated dialogues were held to explore whether (i) there were uses of health data that diverse members of the public all supported and (ii) there was consensus on essential requirements for health data social licence.
In Australia, national surveys and citizens’ juries were conducted to better understand (i) attitudes towards private sector data use and (ii) the ethical, legal and social implications of using general practice data in research.
ResultsDespite the different approaches taken, many conditions for social licence were similar across Canadian and Australian participants. Both groups agreed on conditions for health data social licence related to equity, governance, privacy and transparency. However, there was a stark contrast between levels of support for private sector data use, personal control and consent.
ConclusionThis comparative exercise contributes valuable insights into the ongoing dialogue surrounding community attitudes towards health data use. Continued research monitoring health data social licence across populations is imperative for public trust while gaining full benefits from health data use in research.
Using newly linked hospital and population data to identify opportunities to reduce harms from sedative-hypnotic prescribing at population scale
Authors: Roberts Surain B , Imrit Mohammad A , Ismail Marwa F , Shen Trong , Soong Christine , Liu Barbara , Mak Denise YF , Kushnir Vladyslav , Rosella Laura C , Razak Fahad , Verma Amol A 
Published: September 2024

Objective and ApproachSedative-hypnotic medications are associated with harms such as delirium and falls, and may be overprescribed in hospitals. Measuring potentially inappropriate in-hospital sedative-hypnotic prescriptions requires distinguishing new initiations from pre-hospital prescriptions, but pre-hospital medications may not be captured in hospital data. To quantify physician- and hospital-level variation in sedative-hypnotic prescribing, we use linked data between GEMINI (which includes EMR data from >1.8 million admissions across 30 hospitals, approximately 60% of medical inpatient beds in Ontario) and ICES (which houses administrative healthcare databases, including outpatient prescriptions for adults ≥65 years). We will validate whether data in hospital clinical notes or pharmacy records can reliably identify pre-hospital prescriptions.
ResultsUsing provincial health insurance number, 98.1% of GEMINI records could successfully be linked deterministically to ICES. Our exploratory sedative-hypnotic cohort included 90,806 general medicine patients treated by 437 physicians from 17 hospitals across Ontario from 2021-2022. Median age was 72 years and 48.7% were female. The proportion of patients receiving a new sedative-hypnotic prescription varied markedly across hospitals (median 0.21, range 0.04-0.41) and across physicians within hospitals (median within-hospital difference between lowest and highest prescriber: 0.18, range of within-hospital differences 0.07-0.67). Validation of new rather than continued prescriptions is underway. Full results will be ready for presentation at the conference.
ConclusionSedative-hypnotic prescribing is common and highly variable in hospitals across Ontario, indicating an opportunity to improve patient safety.
Implications Based on these analyses, the province of Ontario has made reducing in-hospital sedative-hypnotic prescribing a provincial priority for 2023-2024.
Recommended Minimum Elements for Transparent Reporting of Multi-Jurisdiction Algorithm Feasibility Studies
Authors: Hamm Naomi , Bartholomew Sharon , Zhao Yinshan , Peterson Sandra , Al-Azazi Saeed , McGrail Kimberlyn , Lix Lisa 
Published: September 2024

BackgroundResearch and surveillance using routinely collected health data rely on algorithms to ascertain disease cases or health measures. Where algorithm validation studies are not possible due to lack of a reference standard, algorithm feasibility studies can be used to create and assess algorithms for use in more than one population or jurisdiction. Publication of the methods used to conduct feasibility studies is critical for transparency and reproducibility. Existing guidelines applicable to feasibility studies, including the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statements, may benefit from additional elements to capture aspects particular to multi-jurisdiction algorithm feasibility studies and ensure full reproducibility.
MethodsA subcommittee of members from Health Data Research Network (HDRN) Canada’s Algorithms and Harmonized Data Working Group (AHD-WG) reviewed items within the STROBE and RECORD guidelines and compared these to published feasibility studies. Items not contained within STROBE or RECORD but recommended to ensure transparent reporting of feasibility studies were identified. The AHD-WG reviewed and approved these additional recommended elements.
ResultsEleven additional recommended elements were identified: one element for the title and abstract, one in the introduction, five in the methods, and four in the results sections. Elements primarily addressed reporting jurisdictional data variabilities, data harmonization methods, and algorithm implementation.
SignificanceImplementation of these recommended elements, alongside the RECORD guidelines, is intended to encourage consistent publication of methods that support reproducibility, as well as increase comparability and international collaborations.
Injury Assessment of Pedestrians, Bicyclists, and Motorcyclists Utilizing Linkage of Police-Reported Crash Data to EMS Data in Michigan
Authors: Bowman Patrick , Flannagan Carol , Leslie Andrew , Spradlin Helen 
Published: September 2024

In Michigan, police crash reports are the primary source of data on traffic crashes. Though many aspects are very reliable, some details are difficult to observe or require subjective judgement. Injury severity, which is central to the road safety issue, unfortunately falls into the second category. The KABCO injury scale used by law enforcement classifies injuries as “possible,” “suspected minor,” and “suspected serious” which may not correspond to determinations made by medical professionals. EMS records can provide a means to improve crash injury data using medical evaluations, but these records are not linked to the police reports.
This project explores the linkage of police crash reports and EMS records using both Michigan data sources from 2018 to 2021. Given the association between severity of injuries and person type, three crash subsets with high injury risk were the focus: pedestrians, bicyclists, and motorcyclists. Collectively, these person types represent 25.1% of the police reported fatalities and suspected serious injuries in Michigan in 2022 despite being only 1.2% of the crashing population.
Person-level records for pedestrians, bicyclists, and motorcyclists were identified and divided into three separate datasets for each data source. EMS data events were restricted cases believed to be associated with traffic crashes using ICD-10 injury codes. Personal identifiers were not provided, so probabilistic linkage was used to link person crash records to EMS records. Event date, location, ambulance ID, and person demographic variables were utilized. Linkage rates and injury severity agreement was computed to assess differences between the two datasets.
Do children receiving care and support have higher rates of school exclusion than their peers?
Authors: Hodges Helen 
Published: September 2024

The Children, Young People and Education Committee recently held an inquiry to scrutinise the Welsh Government’s progress in delivering its Programme for Government Commitment to ‘explore radical reform of current services for children looked after and care leavers’. A key observation highlighted in their report was that there was a lack of robust data collected about various aspects of the care system. Notably it was highlighted that there is a lack of data collected about permanent exclusions and suspensions from school amongst children looked after in out of home care (CLA). This study was therefore designed to fill this evidence gap.
Using anonymised linked administrative data held in SAIL Databank to identify those receiving care and support (CRCS) from their local authority, rates of permanent exclusion and suspension have been determined for the different groups within the CRCS cohort (CLA, those on the child protection register (CPR) and those receiving support who are neither CLA nor CPR) which have then been compared to their peers without this experience. Factors such as phase of education, setting, free school meal entitlement, having additional learning needs, age and gender have also been considered along with the reason for the suspension or exclusion.
In drawing comparisons with peers, an intersectional approach was adopted to identify disparity. It is hoped that findings from this student study will stimulate debate and contribute towards the development of changes in Welsh policy.
Investigating associations between the natural environment and COVID-19
Authors: Thwaites Oliver , Mizen Amy , Lyons Ronan , Owen Rhiannon , Wheeler Benedict , Fry Richard 
Published: September 2024

BackgroundThe natural environment offers a plethora of health benefits and may mitigate the risk of contracting SARS-CoV-2. However, they are also sites where interpersonal contact is frequent, amplifying the risk of transmission. Previous research has typically focused on green space or a measure of greenness. Greenness is often associated with fewer COVID-19 infections, whereas associations with green space are mixed. Very few studies have investigated blue spaces. To our knowledge, no studies have investigated associations between greenness, green space, and blue space with COVID-19 for a national population.
Objectives & ApproachWe aimed to investigate the impacts of exposure to the natural environment on the likelihood of testing positive for COVID-19 in small areas across Great Britain. We derived several measures of green space, greenness and blue space using data from Ordnance Survey and the Google Earth Engine. These measures were linked to the COVID-19 Infection Survey in a Trusted Research Environment. We used mixed-effects logistic regression to account for the multiple tests taken by each participant, adding a random effect for participant ID. We fit univariate models for each predictor and multivariate models for each environmental exposure, adjusting for sex, age, ethnicity, household occupancy, rurality, population density and deprivation.
ResultsThe survey contained test results from a random and representative sample of over 500,000 participants. Approximately 11 million tests were taken by the cohort across three years.
Conclusions & Implications The results of this study will inform planning for the next pandemic.
Development of International Classification of Diseases crosswalks using text analysis methods.
Authors: Sarkar Joykrishna , Lix Lisa 
Published: September 2024

ObjectiveTo evaluate the performance of a natural language processing (NLP) method to develop an automated crosswalk between the 9th and 10th revisions of the International Classification of Diseases (ICD) for diagnosis codes in the Charlson comorbidity index (CCI).
ApproachSBERT, an advanced NLP transformer-based model, was used to produce sentence embeddings, numeric vectors that represent the semantic meaning of text, for the labels (i.e., descriptors) of 932 ICD-10-CA (Canadian Adaptation) codes in the CCI (up to six digits). Sentence embeddings were also produced for all ICD-9-CM (Clinical Modification) code labels (15,145). Cosine similarity scores (CSS) were calculated for all possible pairs of ICD-10-CA and ICD-9-CM code labels. CSSs were classified as equivalent (CSS = 1), high (0.8 ≤ CSS < 1), and low (CSS < 0.8). CSS categories for CCI diagnosis codes were compared to an ICD-9-CM to ICD-10-CA crosswalk file manually created by the Canadian Institute of Health Information.
ResultsOf the 932 CSSs for ICD-10-CA codes in CCI, 84 (9%) were classified as equivalent, 284 (30.5%) were high, and 564 (60.5%) were low. For ICD-10-CA codes with low CSSs, the median was 0.67 (interquartile range 0.14).
Conclusions and ImplicationsAn ICD-10-CA to ICD-9-CM crosswalk based on NLP had low accuracy for identifying semantically similar diagnosis code labels. The accuracy of this method might be improved by fine-tuning and training on task-specific data. Evaluation of different text analysis-based models would provide guidance for research involving ICD code labels.
Improving reproducibility and validity in measures of chronic disease incidence using large-scale linked data in Australia.
Authors: Mallitt Kylie-Ann , Stephens Jacqueline , Dal Grande Eleonora , Hiyare Amandi , Dickson Michelle , Jaure Allison , Craig Jonathan 
Published: September 2024

Objective and ApproachOur study aimed to improve methods for identifying incident chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes in a young cohort from the Antecedents of Renal Disease in Aboriginal Children (ARDAC) study. We linked 20 state and federal administrative health datasets, including hospital records, Medicare claims, pharmaceutical data, and the National Diabetes Services Scheme (NDSS) data for the first time. This approach facilitated the development of new algorithms for disease detection. Notably, we will make the R code for these algorithms publicly available.
ResultsThe analysis, involving 3,758 ARDAC study participants, created clinically robust definitions of CKD, CVD, and diabetes (by subtype) in the Australian linked data context; and revealed that all other data sources inaccurately estimated the incidence of diabetes compared to the NDSS data.
ConclusionsThis key finding illustrates the discrepancy in diabetes incidence estimates and highlights the value of integrating multiple data sources for the investigation of chronic disease.
ImplicationsHundreds of published studies use data linkage to investigate CVD, CKD, and diabetes in the Australian context. Our novel robust case definitions, and the identified poor estimation of diabetes incidence among young people, underscores the need to critically evaluate linked data sources used to inform health policy and research. By sharing the developed R code, we support ongoing efforts to improve transparency and replicability in health research. Our study refines chronic disease definition methods and contributes to more effective public health strategies, with particular focus on improving outcomes for Indigenous communities.
Providing data analytic services for national knowledge users in a federated system: learnings from two Canadian use cases
Authors: Duchen Raquel , Mondor Luke , Gatley Jodi , Saskin Refik , Bakal Jeff , McDonald Ted , Knight John , Nickel Nathan , Burchill Charles , Youngson Erik , Magalhaes Sandra , Goosen Andrew , Bowen Sonya , Flaten Lisa , Troke Natalie , Warren Christine , Hsu Zoe , Chen Xueyi , Liu Lihui , Wang Yan , Somayaji Chandy , Shen Linyun , Youssef Simon , Magee Sarah , Dutton Daniel 
Published: September 2024

Objective and ApproachCanada’s federated health data system along with access pathways geared toward academic research, pose challenges for knowledge Users (KUs) requiring timely, pan-Canadian evidence to inform decisions. To understand challenges and explore solutions, we conducted two use cases for a pan-Canadian health technology assessment organization and a federal agency, using health administrative data at one federal and six provincial data centres. The population-based cohort studies described socio-demographics, comorbidities, treatment patterns, service utilization and costs. One focused on spinal muscular atrophy, a rare disease and the other on dementia, a complex chronic disease.
ResultsAdministrative challenges included aligning varied ethical review and data access policies/procedures including requiring local and/or academic principal investigators and differing definitions of “research” vs. planning, evaluation, and monitoring. Data-related challenges included differences in structure, timeliness, and completeness across regions resulting in difficulty aligning constructs such as incident cases and episodes of care. Privacy requirements prohibited pooling jurisdictional estimates resulting in “small cells” that couldn’t be shared with KUs.
ConclusionsWe provided analytic outputs from multiple regions, albeit with some differences and gaps, increasing knowledge around both diseases while developing capacity for combined analyses and gaining insight into national possibilities for data access.
ImplicationsAs decision makers must rely on best available information, some data is better than none. However, to improve data-analytic services for Pan-Canadian KUs, next steps will include improving data harmonization, expanding data assets and filling data gaps, implementing common data models, and exploring options for federated and/or pooled analyses.