bluuebunny/crossref_metadata_2025 huggingface dataset:
An innovative linked Electronic Health Record derived data ecosystem for understanding the complexities of health and ageing.
Authors: Andrew Nadine , Beare Richard , Carver Alison , Ravipati Tanya , Parker Emily , Collyer Taya , Ung David , Srikanth Velandai
Published: September 2024

ObjectiveWe describe the creation of an Electronic Health Record (EHR) derived linked data platform within the Australian National Centre for Healthy Ageing (NCHA).
ApproachOur approach involved establishing a core set of EHR data suitable for research from the sole public health provider within a defined geographic region. Relevant items were selected based on published literature and consensus processes and curated within a specialised research data warehouse. Curation involved: data validation, quality assessment, internal linkage between episodes and data harmonisation/merging. An AI supported natural language processing pipeline was implemented to extract text-based data. Approvals were obtained from government agencies to link commonwealth claims data (primary care, medication, aged care, death) and state administrative hospital data, for all residents aged ≥60 years. Publicly available datasets were scoped for inclusion and collaborations established to incorporate local council data. 
ResultsA core of 113 curated items for >900,000 people over 10 years have been established across 4 hospitals and 10 community/outpatient services. Linked state and commonwealth data were obtained for 179,089 residents aged >60 years (Jan 2010-May 2021, linkage accuracy 98.4%). Environmental (greenery, air pollution, walkability) and Census data have been incorporated for each neighbourhood (Statistical Area 1).  Data access, extraction and release has been tested through over 30 use-case projects in: dementia, residential aged care, medication use, homelessness, environmental impacts on health and ageing and health service redesign. 
ImplicationsThe NCHA Data Platform provides an international exemplar for the use of linked EHR data in advancing population health research.
The occupational roles of nurses and midwives in the UK: an analysis of the Nursing and Midwifery Council-census England and Wales 2021 data linkage study
Authors: Jamieson Michelle , Savinc Jan , Atherton Iain
Published: September 2024

ObjectiveThe nursing and midwifery professions are facing challenges to recruitment and retention. Understanding the occupational choices of people registered as nurses and midwives can inform policy as to the potential of initiatives to encourage registrants back to practice. This study thus aims to ascertain the occupational roles taken by nurses and midwives in the UK in 2021 and the drivers behind choosing to work outside of direct clinical practice.
ApproachThe Nursing and Midwifery Council (NMC) register contains information on everyone meeting required standards to practice in those professions in England and Wales (currently around 800 thousand people). Registrants must not only complete a course of study for registration but must revalidated every three years. The register has been linked to the 2021 census for England and Wales. The census includes questions ascertaining occupation. Information provided is categorised to provide indication of individuals’ main occupation. These include codes for nursing and for midwifery which provides indication not only to occupation but also to engagement in clinical practice. Analysis uses logistic regression to ascertain how socio-demographics influence engagement in clinical practice or otherwise.
Conclusions and ImplicationsData linkage for this study is now completed with the conference being the first time results will be shared. This will be the first time there has been concrete evidence as to nurses and midwives actual occupational roles. A stakeholder group supporting this work includes individuals from Chief Nurses’ Offices to enable findings to feed into policy.
Roadmap for Linking Registry Data with Health Services Data to Support Evidence-Informed Decision-Making.
Authors: Nunes de Melo Magda , Hayes Anne , Victor Charles , Lambert Laurie , Caetano Trish , Mittmann Nicole
Published: September 2024

ApproachAs part of a learning period to optimize the use of RWE for decision-making for drugs for rare diseases, the [organization name removed to allow for blind review] conducted an environmental scan to map real-world data in patient registries. Over 400 patient registries were identified, signaling the potential wealth of untapped information to support decision-making by linking registry data with health services data. To better understand the challenges faced by registry holders hoping to link registry data with health services data sources available in Canada, a series of interviews were conducted with several Canadian rare disease registries. In addition, a literature review was completed, and Canadian experts in epidemiology, privacy, record linkage, registry science, and health services research were consulted to inform the development of a roadmap to meet various stakeholder needs.
ResultsThe resulting roadmap consists of 8 specific steps covering topics related to registry purpose, informed consent, ethical approval, participant privacy, governance, data linkability, participant identifiability and jurisdictional requirements.
ConclusionThe roadmap is currently undergoing pilot testing by a pan-Canadian rare disease registry. A final [organization name] report and an accompanying roadmap in a checklist format to facilitate implementation will be finalized, disseminated across key stakeholders, and made publicly available.
ImplicationsWhile developed for registries, the roadmap applies to the linkage of clinical trial or cohort study data, or other systematically gathered patient-level data to health services data.
Data Quality Implications in Research when Transitioning to a New Electronic Medical Record
Authors: Hsu Zoe , Chisholm Cassandra , Lategan Conné , Lang Eddy , Wang Xiaoming , Youngson Erik
Published: September 2024

The province of Alberta has been implementing a new hospital based electronic medical record (EMR) over the past several years based on the Epic system.  With this implementation comes challenges related to consistent mapping of data to ensure consistency of data over time before and after implementation, even for standardized and routinely used elements.
In a research study evaluating potential disparities in access to Emergency Department (ED) care, particularly in wait times and patient boarding (the time spent in the ED awaiting transfer to an inpatient bed), between mental health (MH) and non-MH patients during the COVID-19 pandemic. An interrupted time series analysis was used to evaluate the impact of the pandemic on boarding time. While there initially appeared to be a large decrease in boarding time near the start of the pandemic, it was later determined to be a data quality issue corresponding to the rollout of the new EMR at one hospital, despite the data coming from a secondary standardized dataset that is used for provincial and national reporting and expected to be reliable.
This presentation will highlight how the issue was identified, what steps were taken to confirm the underlying cause, and how it was corrected to ensure accurate results in this research study.
New Zealanders living with a family member who has a long-term health condition: cross sectional analysis of integrated Census and administrative data
Authors: Underwood Lisa , Bowden Nick , Teng Andrea , Dewes Ofa , Marek Lukas , Milne Barry
Published: September 2024

BackgroundLiving with a family member who has a long-term health condition (HC) is associated with poorer health and well-being outcomes, but the number and socio-demographics of people and families impacted by a family member who has an HC is unknown. 
MethodsUsing the Integrated Data Infrastructure (IDI), a collection of linked administrative datasets for the full New Zealand (NZ) population, we identified n= 1,043,172 families using 2013 NZ Census data, and used health data over the previous 5-years to ascertain whether people in these families (n=3,137,517) had cancer, chronic obstructive pulmonary disease, coronary health disease, diabetes, dementia, gout, stroke, traumatic brain injury (TBI) or a mental health/behavioural condition (MHBC). 
ResultsOver 60% of families included at least one person with a HC. The most common HCs were MHBCs (39.4% of families), diabetes (16.0%), and TBI (13.9%). A high proportion of multi-generation families (73.9%) included a member with a HC. Two-thirds (67.7%) of Pacific Peoples either had a HC themselves or were living with a family member who had a HC, compared with 62.3% of Europeans and 62.5% of Māori (the indigenous peoples of NZ). At the highest level of socioeconomic deprivation, 57.6% of children lived with a family member who had a HC. 
ConclusionsThree in five NZ families were living with a HC, with differences in the proportion affected according to family composition, socio-economic status and an individuals’ ethnicity. This suggests that there are a substantial number of people at risk of the associated poor outcomes.
Designing a utility evaluation framework for synthetic health data
Authors: Marashi Amir , Dohadwalla Sadiq , Browne Patrick , Hachey Ben
Published: September 2024

ObjectivesSynthetic data (SD) promises to unlock health data for training, research, and innovation. However, where utility evaluation is performed, it is applied ad-hoc for a single task of interest. We produce an initial design for a robust benchmark across a range of tasks.
ApproachWe undertook several projects as a prototyping experiment to gather requirements. These projects replicate previous studies performed on the Medical Information Mart for Intensive Care — a dataset used in more than 4,000 studies. We refine definitions, identify personas, draft a user statement, and collect requirements.
ResultsDefinitions: We define utility as an extrinsic measure of SD on a larger system, most often through comparison to system performance on real data. This contrasts with fidelity, which measures the accuracy of SD through direct comparison to real data.
Personas: Data custodian, User of SD, SD researcher.
User statement: As a technical stakeholder, I need a reliable way to measure the utility of datasets and a benchmark to compare generation techniques.
Requirements: SD researchers can focus on generation not evaluation; Supports comparison and leaderboards; Based on relevant and applications; Comprehensive across study types and applications; Future proof for population research requiring linking.
ConclusionWe propose the following design:

Data pipelines follow an extract-generate-evaluate workflow.
Study types include cross-sectional and longitudinal.
Applications include predictive modelling and clinical research.

This results in a comprehensive utility benchmarking suite that complements current frameworks for fidelity and privacy of SD.
Improving Electronic Health Record (EHR) data linkages for identifying Aged Care Residents through publicly available data
Authors: Ravipati Tanya , Beare Richard , Srikanth Velandai , Carver Alison , Huang Lei , Dhillon Rashwinder , Andrew Nadine E
Published: September 2024

IntroductionAccurate identification of Aged Care residents accessing hospital services is challenging due to multiple hospital Electronic Health Record (EHR) systems and entry points resulting in inconsistent addresses and coding. Improving accuracy and completeness of addresses is crucial for cohort identification and data linkage. 
ObjectiveDevelop methods to validate cross-sectoral data linkages to improve identification of aged care residents within EHR systems.
ApproachAmong individuals aged ≥65 years (or ≥50 identifying as Aboriginal or Torres Strait Islander) between January 2013 to December 2023 within our Data Platform, we first used data items mandated by the Department of Health to identify aged care residency. Data quality was enhanced using a residential address index derived from Australian Geocoded National Address File (G-NAF). We evaluated sensitivity, specificity, and Positive Predictive Values (PPVs) of recorded addresses against the Australian Institute of Health and Welfare (AIHW) aged care services list. 
ResultsAmong 409,677 addresses extracted, 55.4% (raw) had sufficient data for validation. This increased to 89.9% (enhanced) with G-NAF. Among 79,028 admitted and 88,876 emergency patients, 22.7% and 18.4% respectively were aged care residents. We compared performance of raw vs enhanced comparisons were: sensitivity (admission: 34.1% vs 73.8%), (emergency: 42.6% vs 66.2%); specificity (admission: 96.2% vs 90.9%), emergency (96.8% vs 95.2%); Positive Predictive Value (admission: 61.3% vs 58.8%), (emergency:  64.2% vs 65.3%).
Conclusions and ImplicationsOur approach significantly increased sensitivity without sacrificing specificity, enhancing Aged Care resident identification in EHR data. It also improved linkage with external datasets and Statistical Area Level 1 (SA1) mapping.
Impact of the Welsh Government Flying Start programme on children’s BMI and immunisation uptake
Authors: Colonna Silvia , Helliwell Kathryn
Published: September 2024

Flying Start (FS) is the Welsh Government’s targeted Early Years programme for families with children under 4 years of age who live in some of the most disadvantaged areas of Wales.
The programme aims to mitigate the impact of poverty which is linked to poor life outcomes in early childhood. In particular, this study aims to investigate the impact of FS programme on FS children’s health outcomes in the form of Body Mass Index (BMI) and immunisation uptake, using linking analysis. The datasets linked to the FS cohort are respectively the Child Measurement Programme (CMP) and the National Community Child Health (NCCH). CMP consists of routinely taking height and weight measures of children in reception year to monitor BMI, whereas the NCCH is used to monitor health surveillance programmes, including immunisation. This project will use linked data to investigate the prevalence of fully immunised children across different levels of engagement with FS programme (normal and under-engagement). It will also look at prevalence of different BMI thresholds (underweight, overweight, and normal weight) and differences in mean BMI scores by FS programme engagement levels. In particular, chi-square will be used to explore differences in the proportion of fully immunised children and BMI thresholds, and t-test will be used to identify between-group differences in mean BMI scores.
Results from this programme will inform of long-term health outcomes in relation to Early Years programmes. It will also help policy target resources and inform relevant interventions to support the healthy growth of children.
Comparing the linkage performance of fastLink, Match*Pro and Splink at the Florida Cancer Registry using simulated pseudopeople data
Authors: Alexandersson Anders
Published: September 2024

ObjectivesThe objective was to compare the record linkage performance of fastLink, Match*Pro, and Splink using realistic simulated "Rhode Island" data from the Python package pseudopeople. A requirement was no expected wrong matches (false positives, FP) after a limited manual review.
ApproachWe incorporated custom noise on (a) Social Security Number (SSN) and (b) address. We first used high-quality data where SSN was available and then low-quality data where SSN was unavailable. Missed matches (false negatives, FN) were acceptable. We used an iterative process of determining the “appropriate” match threshold and the correct matches (true positives, TP).
ResultsThe two analysis datasets, df1 and df2, resulted in 660,227 actual matches and 9,987 actual non-matches. The specified match threshold probability was 0.995 for fastLink, 0.999999999 for Splink, and about 0.99 for Match*Pro. When SSN was available, the (rounded) results were: Splink 627,000 TP (4 FP), fastLink  577,000 TP (15 FP), and Match*Pro 436,000 TP (4 FP) in the default frequency-based EpiLink-like option; the EM option crashed. When SSN was unavailable, the results were: Splink 579,000 TP (3 FP), fastLink  582,000 TP (137 FP), and Match*Pro 442,000 TP (3 FP); the EM option had 516,000 TP (442 FP). The fastLink developers are working on adding Active Learning.
ConclusionOverall, Splink and fastLink performed better than Match*Pro. A likely explanation is that only Splink and fastLink provide match probability and are open source. Splink (Python) and fastLink (R) require basic user programming skills. The results will be updated once the final report has been approved.
Health selection among outmigrants, return migrants and non-migrants in three subcohorts of international, interprovincial migrants and non-migrants in Manitoba, Canada
Authors: Urquia Marcelo , Detillieux Gilles , Debbarman Shantanu
Published: September 2024

Objective and ApproachLinking national and provincial immigration registers with health care utilization datasets at the Manitoba Centre for Health Policy, we assembled a cohort of 816,185 adults who resided in Manitoba, Canada and were followed up for at least one year between 1985 to 2023 to outmigration, return migration or death. The cohort included three subcohorts of international immigrants (16.4%), interprovincial migrants (10.8%) and all other Manitobans (AOM) (72.8%). Within each subcohort, we matched ‘stayers’ who never migrated, outmigrants and returnees on sex, birth year and place of residence and compared their hospitalization rates and Charlson and Elixhauser comorbidity scores 1-year before outmigration and 1-year after return migration.
ResultsOutmigrants had lower hospitalization rates than stayers among AOM [Adjusted Relative Rate (RR): 0.80; 95% confidence interval (CI): 0.78, 0.82] and in the other two subcohorts. Comorbidity scores were also consistently lower among outmigrants compared to stayers in all three subcohorts, even after restricting to hospitalized cases. Outmigrants whose destination was another country were healthier than those who migrated to other provinces. Returnees had better health status than stayers upon return in the AOM subcohort only but lower than those who did not return at the time of outmigration in all three subcohorts.
ConclusionsMigration is associated with positive health selection among international immigrants, interprovincial migrants and the local population.
ImplicationsContrary to common belief, health selection is not restricted to international immigrants. Selective migration may represent a source of bias in health-related population-based studies (e.g., sampling and informative censoring).
Transparent reporting on multi-site studies about algorithms for linked health data: A systematic review of Health Data Research Network Canada’s Algorithms Inventory
Authors: Lix Lisa M , Ahmed Nasiba , Fung Daryl , Al-Azazi Saeed , Hamm Naomi C
Published: September 2024

ObjectiveMulti-site studies that use routinely-collected, linked health data often rely on validated algorithms to harmonize definitions of health conditions or other population characteristics across sites. If algorithm validation is not practicable, feasibility studies that assess whether an algorithm can be consistently and accurately implemented across sites, are recommended. We investigated the transparency of reporting on multi-site algorithm feasibility studies found in Health Data Research Network (HDRN) Canada’s Algorithms Inventory.
ApproachPublished, multi-site Canadian feasibility studies about chronic or infectious disease algorithms were assessed using an adaptation of the RECORD (REporting of studies Conducted using Observational Routinely collected health Data) and STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines. Reviewers were trained on a subset of studies. The remainder were independently assessed by two reviewers and quality checks were conducted. Results were descriptively summarized.  
ResultsThirty published multi-site algorithm feasibility studies were reviewed; the majority were conducted in four or more sites, and 83% assessed chronic disease algorithms. Almost one-fifth (17%) of the studies did not justify the methods used to assess algorithm feasibility. However, the majority (>95%) addressed potential sources of bias and discussed algorithm generalizability. 
ConclusionsWe observed accurate and complete reporting of most elements of multi-site algorithm feasibility studies conducted in Canada. These studies provided important information about availability of data elements, generalizability, and potential algorithm uses. 
ImplicationsTransparent reporting of algorithm feasibility studies facilitates algorithm reuse, enhances the credibility and reproducibility of study findings, and promotes collaboration within the data linkage community.
Establishing Australia’s national linked COVID Register
Authors: Gates Louise , Slimings Claudia , O’Donnell Bronte , Reynolds Anna , Sparke Claire , Luong Vivien , Palamara Daniel , Foo Renee , Bakshi Mariam , D'Souza Vanessa
Published: September 2024

Objective and ApproachThe Australian Institute of Health and Welfare (AIHW) is an independent statutory Australian Government agency tasked with collating health data across federal and state/territory governments, and is accredited to provide complex integration, de-identification and secure access to linked data.
To support research into the COVID pandemic, the AIHW has created a national-level COVID Register, which integrates COVID case data from infectious disease registers, and data from hospitals and intensive care, immunisation, deaths, health system usage data, aged care and disability support.
ResultsData were probabilistically linked using the AIHW’s linkage spine, matching names, addresses, date of birth and sex. The AIHW data linkage protocols are based on the Five Safes framework which reinforce management of the privacy and confidentiality of data.
Linkage rates for COVID cases across participating jurisdictions generally exceeded 95%. Ongoing work aims to increase the coverage of COVID cases, both in time and to include case data from one remaining jurisdiction.
ConclusionsLinking disparate datasets from multiple sources has created a critical resource for exploring COVID and patient journeys in Australia. Currently, the Register supports projects to estimate expenditure associated with COVID, describe the impact of COVID for subgroups such as people living rural and remote areas, or those with dementia.
ImplicationsUltimately, the Register will be integrated into the National Health Data Hub to enable ongoing health research and analysis through enduring national linked health data. The value of the Register extends beyond COVID and into other infectious diseases and chronic conditions.
CURL Extensions – Weaving a Unique Property Reference Number into Address Histories in the Scottish Community Health Index
Authors: Clark David
Published: September 2024

The Community Health Index (CHI) is the patient registration system for the National Health Service (NHS) in Scotland, covering the entire population. CHI has long been used as a population Indexing Spine to facilitate health-based data linkage for both operational and research purposes in Scotland.
Changes in patient’s residential address since 2000 have been captured in a CHI Address History File.
Addresses in CHI are recorded in unstructured free-text format, so property-based analyses and linkage is problematic. In August 2020, 90% of CHI addresses for currently registered patients, and people who had died that year, were seeded with a Unique Property Reference Number (UPRN). This product, called CHI-UPRN Residential Linkage (CURL), enabled NHS Scotland to harness important information on the patient population (5.8 million) and their place of residence, facilitating several data products related to COVID-19.
The current project is extending CURL to UPRN-seed the 31 million address histories of everyone registered with a family doctor in Scotland since January 2000. 
The project, due for completion in Summer 2024, will look at alternative address-matching algorithms with the aim of achieving a 95% UPRN-match rate across the longitudinal CURL file.
Quality assurance exercises help us to understand the precision and sensitivity of matching across time and geography. Analysis of how the longitudinal file is constructed across the person and property-level indices permits a deeper understanding of the capabilities and limitations of the CURL product when used to deliver innovative analyses or modelling that link people and places over time.
Implementing a streamlined data access governance process for the release of routinely collected electronic health record data for research
Authors: Parker Emily , Kim Naude , Ravipati Tanya , Beare Richard , Clavarino Lee-Anne , Srikanth Velandai , Andrew Nadine
Published: September 2024

ObjectivesIn Australia, there are no standardised processes for the release of Electronic Health Record (EHR) data for research. We describe our streamlined ethics and data access governance framework for the release of EHR data for research.
ApproachIn consultation with our academic and health service partner organisations and with reference to national and international standards, we developed a process to safely and efficiently release EHR data for research. The Five Safes Framework guided our processes ensuring: safe people; safe projects; safe settings; safe data; and safe outputs. Piloting of processes has commenced. 
ResultsStreamlined data access and release processes acceptable to all stakeholders were implemented using an online system. Independent researcher and consumer review of an Expression of Interest, followed by ethics review/approval from the health service research office ensures projects are methodologically and ethically sound. Completion of a detailed technical assessment form, implementation of a signed data use agreement and release of de-identified data to a secure e-research environment promotes safe data and settings. Constraints around publication are enforced via pre-publication review ensuring safe outputs. Nine projects are in process using our system with an average of 4 days for EOI review and 6 days for TAF review.
ConclusionWe have established a streamlined data access pathway to minimise duplication of effort and allow timely and secure access to EHR research data. We have built in efficiencies and promoted a cultural shift for clinicians who have previously not had data access processes governed in this way.
Collaborating across government agencies to combine linkage processes and algorithms to create an Australian National Linkage Spine (NLS) to facilitate cross-jurisdictional linkage
Authors: Nunan Theresa , Ougrinovski Elena , Frazer Brett , Dahl Kelly , Wilson Bronwyn , Jenkins Rian , Paine Clinton
Published: September 2024

The Australian Bureau of Statistics (ABS) and the Australian Institute of Health and Welfare (AIHW) are co-technical lead agencies for developing the National Disability Data Asset (NDDA) which will bring together data across jurisdictions to build a more complete picture of the lives of people with disability. ABS and AIHW are collaboratively building a National Linkage Spine (NLS), a high-quality high-coverage population dataset to be shared under new legislation to facilitate cross-jurisdictional linkage and build the NDDA.
ABS and AIHW each have extensive data integration experience, however our linkage methods have evolved differently in response to the needs of our respective researcher client bases and the datasets we work with. An ABS-AIHW cross-agency team was established to align terminology and share data processing methods, linkage approaches, and locally built linkage tools. The team collaborated on the design of an NLS that combines the best aspects of each of our approaches.
The NLS build methodology delivers aligned processing of input data and applies a combination of ABS’s deterministic linkage algorithm and AIHW’s probabilistic linkage and machine learning algorithms. The NLS was peer reviewed by its future jurisdictional users and is considered fit for purpose for delivering the NDDA.
The open sharing of knowledge and tools has significantly benefited both agencies and the broader NDDA project. Takeaways from this project extend beyond it, with improved data process approaches being considered by each agency in other work, and a growing common agreement of linkage best practice.
Exploring longitudinal patterns of absences, exclusions and attainment in secondary schools in Scotland
Authors: Treanor Morag , Troncoso Patricio , Williamson Lee , Macintyre Cecilia
Published: September 2024

Objective and ApproachWe seek to understand the impacts that absences and exclusions have on educational outcomes in secondary school pupils in Scotland. We adopt a Multilevel Structural Equations Modelling approach to analyse data from the newly linked administrative database created under the “Children’s Lives and Outcomes” research strand of the Scottish Centre for Administrative Data Research (SCADR), which includes data from Education Analytical Services (Scottish Government) and Public Health Scotland (NHS) from the period between 2007-2019, and the 2001 and 2011 Census.
ResultsWhile exclusions, absences and attainment are known to be associated with each other and with individual and structural factors, less is known about how these phenomena play out over time in the Scottish education system. Our analyses focus on a particular cohort of students followed up over their entire schooling period to understand how longitudinal patterns of school absences and exclusions, and other relevant individual and structural factors are interrelated over time. We show that variation across schools and local authorities in these outcomes is significant and substantial in Scotland. We also analyse the impact of these longitudinal patterns on attainment, with an emphasis on mental health wellbeing and familial socioeconomic circumstances.
Conclusions and ImplicationsBy elucidating further these relationships and interrelationships, we expect our findings to be pertinent to policymakers and practitioners, contributing to inform strategies, policies and/or interventions to reduce the inequalities in exclusions, absences and attainment, ultimately helping to improve children and young people’s school experiences and outcomes.
Developing an integrated HIV clinical and population database in rural south Africa
Authors: Gareta Dickman , Lauren Evelyn , Shumba Khumbo , Herbst Kobus , Onoya Dorina , Bor Jacob
Published: September 2024

BackgroundHIV cohort data are crucial for understanding the effects of HIV infection and treatment. Linkage between HIV clinical and population records unlocks potential to understand the cause and consequences of social-economic factors on HIV-care seeking behaviors and mortality outcomes. We created HIV data infrastructure by linking HIV treatment records with routinely collected records.
MethodsWe linked HIV cohort data(n=119,434) to Health and Demographic Surveillance(n=256,425), laboratory tests(n=617,314), clinic visits and hospital admissions(n=170,325) from KwaZulu-Natal, an HIV-endemic setting in rural South Africa. We used graph-based probabilistic record linkage algorithm to link the data for HIV patients accessing care at 17 clinics within the Hlabisa sub-district. Africa Health Research Institute (AHRI) has collected health and demographic surveillance data since 2000. AHRI has set up a hospital information system in the district to capture ICD-10 code admissions and discharges.
ResultsFrom the 5% sample of 462,524 linkage clusters, the computed F-score was 0.88, sensitivity was 88% and positive predictive value was 91%. 38.5% of the patient ID clusters contained records linked across multiple databases. 36% of the HIV patient ID clusters contained records linked with laboratory tests. 19.5% of the HIV patient ID clusters contained data on demographic surveillance. 12.5% of the HIV patient ID clusters contained records linked with clinic visits and hospital admissions. 4% of patient IDs contained records across all four databases.
ConclusionWe have successfully created a cross-sectorial HIV cohort through probabilistic record linkage methods.  The cohort provides a platform to answer policy-relevant questions.
Prevalence of a dual diagnosis of mental illness and substance use disorder in Ontario, Canada: a retrospective cohort study using linked administrative data
Authors: Young Jesse , Tanner Bryan , Crocker Matthew , Sharpe Isobel , Lu Hong , Kurdyak Paul
Published: September 2024

ObjectivePopulation-based prevalence estimates of co-occurring mental illness and substance use disorder (herein dual diagnosis) are scarce and derived from a single source (e.g., survey data) which may lead to underestimation of prevalence. We linked administrative data to a representative mental health survey in Ontario, Canada to estimate dual diagnosis prevalence using a data triangulation method.
Approach We retrospectively linked the 2002 and 2012 Canadian Community Health Survey on Mental Health (CCHS-MH) to emergency department, inpatient hospital, and outpatient physician records in Ontario, Canada. Mental illness and substance use disorder were ascertained though self-report, Composite International Diagnostic Interview screening, and linked administrative health records. We estimated 1-year, 5-year, and lifetime prevalence of dual diagnosis. 
ResultsOf the CCHS-MH survey participants, 14,790 (99.8%) were included in the study. The 1-year, 5-year, and lifetime prevalence of dual diagnosis was 2.3%, 4.8%, and 9.8%, respectively, which attenuated to 2.1%, 4.1%, and 8.4%, respectively, when tobacco use disorder was removed from substance use disorder ascertainment. 
ConclusionDual diagnosis is more common in the general population than previously estimated. Considering barriers to accessing care for both mental illness and substance use disorders among people with dual diagnosis annually, our 5-year prevalence estimate is likely most informative for health system planning.
ImplicationsIn the context of increased health burden, barriers to care access, and challenges to effective treatment associated with dual diagnosis, our findings present a case for increased investment in integrated models of mental healthcare and addiction medicine.
Model-based algorithms to ascertain smoking in administrative health data: a registry-based validation study
Authors: Haque Md Ashiqul , Nickel Nathan C , Turgeon Maxime , Lix Lisa M
Published: September 2024

Objectives We developed a machine-learning model-based algorithm (MBA) for smoking in Administrative Health Data (AHD). The validity of this MBA was compared to a rule-based algorithm (RBA).
Approaches  The study included adults (≥18 years) from a clinical registry containing self-reported current smoking from 2017 to 2020 in Manitoba, Canada. Clinical data were linked to up to five years of hospitalization, physician billing claims, and prescription medication records. The RBA was based on diagnosis codes for tobacco use and nicotine dependence medication. MBAs, constructed using random forest (RF) models, included these indicators in addition to comorbid condition diagnoses and sociodemographic factors. Sensitivity, specificity, positive and negative predictive values (PPV, NPV), and 95% confidence intervals (CIs) were estimated.
Results The cohort comprised 24,718 individuals (88.6% female); prevalence of current smokers was 10.0%. The RBA had sensitivity of 27.3% (95% CI: 24.2-30.7), specificity of 96.6% (95% CI: 96.1-97.0), and PPV of 47.2% (95% CI: 42.9-51.5). The MBA had sensitivity of 68.6% (95% CI: 65.1-71.9), specificity of 76.3% (95% CI: 75.2-77.3), and PPV of 24.3% (95% CI: 23.2-25.6). NPV was high irrespective of algorithms. Stratified analyses revealed similar estimates for males and females, and the number of years of AHD did not affect the MBA results.
ConclusionsAn RF-based MBA for smoking ascertainment in linked AHD sources improved sensitivity compared to the RBA. However, the RBA excelled in specificity and PPV.
ImplicationBalancing accurate smoker identification with the risk of false positives is crucial when choosing an algorithm to ascertain current smokers using AHD.
Identifying patterns of co-occurring chronic conditions preceding dementia: An unsupervised machine learning approach using health administrative data
Authors: Maclagan Laura C. , Harris Daniel A. , Wang Xuesong , Abdalla Mohamed , Odugbemi Tomi , Marrie Ruth Ann , Austin Peter C. , Swartz Richard H. , Black Sandra E. , Ruthirakuhan Myuri , Maxwell Colleen J. , Bronskill Susan E.
Published: September 2024

ObjectivesIndividual risk factors for dementia are well known, but the influence of co-occurring chronic conditions has not been considered. We identified clusters of chronic conditions using an unsupervised machine learning approach and examined associations with incident dementia.
ApproachUsing linked population-based administrative databases, we followed all community-dwelling adults aged 40-54 years in Ontario, Canada from April 2002 until March 2019 for incident dementia. We estimated the prevalence of 29 chronic conditions using validated algorithms and/or diagnosis codes. We reduced dataset dimensionality using multiple correspondence analysis and a fuzzy c-means clustering algorithm identified the optimal number of clusters (between 3-6 tested). Associations between clusters and incident dementia were examined using a cause-specific hazard model adjusted for sociodemographic characteristics and accounting for the competing risk of death.
ResultsWe identified 82,359 eligible individuals (random 3% sample of total eligible individuals; mean age 46.5 years; 50.4% female). Regression analyses were based on 5 comorbidity clusters (fuzzy silhouette index:0.69). Compared to the low comorbidity cluster, persons in the cerebrovascular disease/metabolic (HRadj=3.06, 95%CI[2.42,3.86]) and neuro-related/mental health clusters (HRadj=2.51, 95%CI[2.05,3.07]) had the highest rates of incident dementia, followed by the cardiovascular risk factor cluster (HRadj=1.66,95%CI[1.32,2.09]). Persons in the cancer cluster did not have an increased incidence of dementia (HRadj=0.96,95%CI[0.77,1.20]).
ConclusionsWe found significant associations between machine learning-derived clusters of chronic conditions and dementia.
ImplicationsUnsupervised machine learning approaches to identify clusters of chronic conditions may be a useful tool for considering the impact of multimorbidity on dementia risk.
A proof-of-concept minimum data set for older care home residents: what value does this add?
Authors: Tracey Freya , Crellin Elizabeth , De Corte Kaat , Lloyd Therese , Brine Richard
Published: September 2024

ObjectiveDeveloping resources And minimum data set for Care Homes’ Adoption (DACHA) is a four-year NIHR-funded project to establish what data need to be in place to support research, service development and innovation uptake in care homes for older people. An aspirational minimum data set (MDS) specification was identified, which included data from several sources and pilot variables.  
Approach767 care home residents (with a valid NHS number recorded) across three integrated care systems consented to linkage of their care home record with administrative health and social care records from NHS England. Data were linked using pseudonymised NHS number. Variables were derived to summarise healthcare utilisation.
Where variables are sourced from more than one data set, expert opinion on quality across these data sets informed hierarchical selection. We reported descriptive statistics including missingness across each of the >300 variables in the MDS, with information on how these were accessed and derived, where appropriate. Descriptive summaries were informed by stakeholder engagement and cover ambulance and emergency department utilisation.
An ongoing DACHA PPIE panel contributed to MDS development through sharing opinions on data access and linkage, and best sources of information across different domains.  
ResultsAnalysis is in progress. All results will be in pre-print publication by 31 April 2024.
ConclusionsThe MDS constructed gives rich insights on healthcare utilisation and resident needs, with opportunity to understand how these interrelate.
ImplicationsAs a proof-of-concept study, the findings of this research will inform national decision making on future linked care home data sets.
Building access to linked data for program evaluation: lessons and opportunities from the evaluation of a pan-Canadian skills training initiative
Authors: Palamar Max , Nkusi Sandra
Published: September 2024

In recent years, Canada’s statistical agency (Statistics Canada) has developed sophisticated linkage infrastructure through their Social Data Linkage Environment (SDLE), linking internal data sets held by Statistics Canada and external data held by researchers. While the SDLE presents an opportunity for better measurement of the effectiveness of policies and programs, to date it has not been used for this purpose outside of government, instead largely supporting academic research projects. This session presents a novel approach to program evaluation in the Canadian context, using the SDLE Statistics Canada administrative data alongside independently collected program data from multiple service providers to evaluate the outcomes and effectiveness of a portfolio of skills training programs. 
Our approach aims to both rigorously evaluate the long-term outcomes of participants in this portfolio of public skills training programs, and develop a replicable proof-of-concept for using linked data infrastructure in Canada to support program evaluations. Through this initiative, we identify the key design features needed to support this use case for linked data, including study design parameters, necessary datasets and linkage processes, and privacy and data governance policies. Finally, we identify opportunities for future replication of this approach in Canada, including strategies for expedited linkage and analysis of evaluation results that maintain privacy requirements.
We find that using the SDLE infrastructure for program evaluation is both feasible and desirable, however barriers exist to replicating this approach, including sectoral capacity for collecting linkable data, and transposing linkage and confidentiality rules from an academic research to a program evaluation context.
Creating a Data Cleaning and Pre-Processing Module for Generalisable Data Linkage
Authors: Robertson Kristin , Morton Sheena , Dasilva Lindsay , Whitten Tara , Gardiner Melissa , Seymour Sarah , Opgenorth Dawn , Rewa Oleksa
Published: September 2024

ObjectiveAcute renal replacement therapy (RRT) is an important life saving technology used in critical care. However, variation in clinical practice for initiation and maintenance of RRT can increase healthcare costs and worsen patient outcomes. In Alberta, Canada, a province-wide electronic health record provides a unique opportunity to measure and evaluate clinical practice. The present study aims to improve the quality of RRT delivery in Alberta critical care units by providing feedback on key performance indicators (KPIs) for RRT based on current evidence.
ApproachKPIs for RRT included an initiation pathway based on threshold lab values, and measures of RRT quality such as the time from order to treatment initiation, the average life of dialysis filters, and the actual vs. prescribed fluid removal. Clinical KPI definitions were mapped to data available in the electronic health record to evaluate current practice and track changes in KPIs over time.
ResultsMapping the clinical guidelines to the electronic health record took significant time and effort in a large team with expertise on both the data and clinical sides. Each KPI data definition went through several cycles of development, validation and refinement before being included in the dashboards and reports given to participating sites.
ConclusionsProviding accurate data on RRT ordering and delivery practices is an important step in aligning acute RRT delivery with current best practice, ultimately improving the quality of care and reducing unnecessary costs.
ImplicationsElectronic health records provide a powerful tool for evaluating clinical decisions and implementing best practice guidelines.
COVID-19 Policy Decisions in Manitoba and the Experiences of the Red River Métis: A partnership-based, whole-population linked administrative data study
Authors: Saj Danielle , Kloss Olena , Chartrand Francis , Ekuma Oke , Taylor Carole , Sanguins Julianne , Katz Alan , Mahar Alyson , Driedger Michelle , Nickel Nathan
Published: September 2024

Systematically marginalized populations, like Red River Métis, have been greatly affected by COVID-19. Manitoba’s Indigenous COVID-19 vaccine policy initially delayed prioritization of Métis. Our research team, which included Métis partners, examined the consequences of these decisions (COVID-19 infections, health service use, vaccine uptake) among Métis, and how earlier prioritization could have improved outcomes.
This retrospective cohort study linked data from the Métis Population Database to whole-population COVID testing and vaccination data, and administrative data on health service use. Restricted mean survival time models tested whether vaccination uptake differed between Métis and all other Manitobans (AOM), adjusting for sociodemographic characteristics and comorbidities. A Bayesian model will simulate how prioritization of Métis for vaccination two weeks earlier could have impacted infections.
Cumulative prevalence of COVID-19 infection rates were similar among Métis and AOM until May 2021 when rates became higher among Métis. Between May and December 2021, rates of first vaccination were lower among Metis than AOM, as were second vaccination rates between July and November 2021. Métis were more likely than AOM to be hospitalized due to Covid-19 between March and August 2021 and visit physicians for COVID related reasons from October 2020 to Feb 2021 and November 2021 to March 2022. Our analyses simulated what would have occurred had Métis been prioritized for vaccination two weeks before AOM, alongside other Indigenous peoples.
Understanding the experiences of Métis relative to AOM is critical to identifying public health strategies which close gaps in vaccine uptake and infections.
Validated Administrative Data-Based ICD-10 Algorithms for Chronic Conditions
Authors: Kuang Angela , Xu Claire , Southern Danielle , Sandhu Namneet , Quan Hude
Published: September 2024

ObjectiveThis systematic review aimed to identify ICD-10 based validated algorithms for chronic conditions using health administrative data.
MethodsA comprehensive systematic literature search using Ovid MEDLINE, Embase, PsycINFO, Web of Science and CINAHL was performed to identify studies, published between 1983 and May 2023, on validated algorithms for chronic conditions using administrative health data. Two reviewers independently screened titles and abstracts and reviewed full text of selected studies to complete data extraction. A third reviewer resolved conflicts arising at the screening or study selection stages. The primary outcome was validated studies of ICD-10 based algorithms with both sensitivity and PPV of ≥70%. Studies with either sensitivity or PPV <70% were included as secondary outcomes.
ResultsOverall, the search identified 1686 studies of which 55 met the inclusion criteria. Combining a previously published literature search, a total of 61 studies were included for data extraction. Of these, 30 studies demonstrated algorithms with high validity and 30 showed moderate validity. Studies with highly valid algorithms were based on administrative data from different countries including Canada, USA, Australia, Japan, France, South Korea, and Taiwan. Validated algorithms with PPV and sensitivity of ≥70% were identified for more than 40 chronic conditions that included several types of cancers, cardiovascular conditions, kidney diseases, gastrointestinal disorders, and peripheral vascular diseases, amongst others.
ConclusionWith ICD-10 prominently used across the world, this up-to-date systematic review can prove to be a helpful resource for research and surveillance initiatives using administrative health data for identifying chronic conditions.