bluuebunny/crossref_metadata_2025 huggingface dataset:
A discovery platform to improve visibility of linked data across Australia.
Authors: Miller Kate , Flack Felicity , Bennett Vicki , Smith Merran
Published: September 2024

BackgroundMetadata plays a crucial role in the health research infrastructure ecosystem. Despite the abundance of metadata for data collections in Australia, there is no simple way for researchers to search for standardised metadata about linked and linkable data collections across sectors or jurisdictions, making it an arduous and time-intensive task.
MethodsThe project comprised three phases: an initial scoping exercise to understand the current state of metadata and best practice; a national consultation involving researchers data linkage staff and data custodians to develop a high-fidelity prototype; and a final build and implementation phase. The platform underwent several prototyping and testing cycles to refine the digital experience.
ResultsKey expert interviews confirmed that there is a wealth of metadata available in Australia, but it is difficult for researchers to access and evaluate. Consultations with researchers identified opportunities to standardise metadata and provide a centralised platform to enhance the discoverability of linkable data collections for research. High value platform features included searching, browsing and filtering capabilities, variable list metadata, standardised formats, sample data, and frequently asked questions. The final design and functionality reflected user consultations and data custodian input on feasibility and resulted in the standardisation of metadata for linked data collections across sectors and jurisdictions.  
ConclusionThe Population Health Research Network developed and implemented a metadata platform to enable researchers to assess and evaluate the suitability of data collections for data linkage projects more readily. Improved metadata accessibility will save time and enhance the quality of linked data applications.
Methodological Challenges when Using Routinely Collected Health Data for Research: A scoping review.
Authors: Wei Lili , Kuenzig Ellen , Im James , Zheng Yan , McLinden Taylor , Emerson Scott , Eissa Azza , Halder Henry , Shaw Richard , Chan An-Wen , Dixon William , Ehrenstein Vera , Guttmann Astrid , Harron Katie , Hemkens Lars G. , Hróbjartsson Asbjørn , Lyons Ronan , MacDonald Shannon E. , Maniate Jerry , Moher David , Petersen Irene , Quan Hude , Schmidt Sigrún , Sørensen Henrik , Wang Shirley , McAllister David A. , Langan Sinéad M. , Benchimol Eric I.
Published: September 2024

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. 
This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. 
We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. 
Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.
Identifying patterns of Long-COVID diagnosis pathways: a Latent Class Analysis of Welsh clinical data
Authors: Abbasizanjani Hoda , Bedston Stuart , Robinson Lucy , Curds Matthew , Akbari Ashley
Published: September 2024

ObjectivesLong-COVID (LC) diagnosis remains challenging due to its variable, multisystem, episodic symptoms, and lack of uniformity in the critical time points. We aim to classify diagnosis pathways and associated demographic and health determinants for clinically diagnosed LC patients in Wales.
ApproachWe created a cohort of LC-diagnosed adults in Wales from 2020 to 2023 using population-scale data within the SAIL Databank. To classify diagnosis pathways, we extracted a set of indicators suggestive of the general diagnosis process within 6-months before the diagnosis, including LC symptoms, clinical investigations, and management. We conducted Latent Class Analysis (LCA) to examine how these indicators cluster, adjusting the best-fit model for various covariates such as COVID-19 infection history and sociodemographic characteristics.
ResultsOf the 7,973 identified LC patients, 59% had clinical investigations, 59% had a clinical management record, and 39% had at least one related symptom. Using LCA, we identified 7 distinct patterns of diagnosis pathways, and how background characteristics associated with each. 
ConclusionDespite existing guidelines, LC diagnosis is complex due to LC’s diverse manifestations and the absence of exclusive symptoms/tests. Late or missed LC diagnosis can have a detrimental effect at individual, community, and national levels. Previous UK studies have indicated low recording rates of LC compared with self-reported surveys. Understanding current diagnosis pathways can shed light on this discrepancy and identify potential confounding factors that delay/impact LC diagnosis. This insight is crucial for standardising diagnosis pathways, enabling earlier and more accurate detection of LC and thereby mitigating its impact.
Integrating Gender Identity and Sexual Orientation in Population-Based Administrative Data
Authors: Hunter Mikayla , Nickel Nathan , The SPECTRUM Partnership
Published: September 2024

Information regarding biological sex is collected for administrative and clinical purposes, and often is conflated with gender in clinical encounters as well as in population-based data studies. Sex is collected as either ‘male’ or ‘female’, adhering to the colonial notion of binary sex and gender identities. Meaningful information on sexual/romantic orientation is also absent from these datasets. Sex, gender, and sexual/romantic (SR) orientation are key aspects informing how people experience the world and how we interpret administrative data.
SOGI-HE (Sexual Orientation and Gender Identity – Health Equity) will be the first 2SLGBTQIA+ dataset at the Manitoba Centre for Health Policy by using a novel survey tool to collect information on gender identity, and SR orientation. The data from this survey will be linked into the Population Health Data Repository housed at the Manitoba Centre for Health Policy, enabling analyses that incorporate constructs of sex, gender, and SR orientation. These data will make visible the experiences of the 2SLGBTQIA+ community that have been historically absent from population-based data studies. Data equity and 2SLGBTQIA+ visibility within data play an important role in advancing the field and lays the groundwork for future data linkage projects.
In this presentation we will discuss the development of SOGI-HE through community-based research principles and the implications it has on future 2SLGBTQIA+ research conducted in the province of Manitoba (Canada). Further, we will discuss the ethical implications associated with queer data sovereignty in a precarious political climate for 2SLGBTQIA+ people and their rights.
From Probabilistic to Fuzzy Matching Record Linkage: A promising Transition
Authors: Azimaee Mahmoud , Kalappa Gangamma , Ma Charlotte , Wang Nan , Shen Winnie
Published: September 2024

ObjectiveProbabilistic Record Linkage (PRL) heavily relies on manual intervention for gray area resolutions. This makes PRL extremely time and resource intensive. No matter how scientifically sound and robust PRL method is, it didn’t meet close-to-real-time data availability requirements at ICES.  
ApproachDuring a thorough evaluation and comparison process of three software and two methods of Record Linkage, ICES initiated a semi-design of experiment to select an optimal record linkage approach. For this experiment, a large Ontario data on 12 million individuals with required linkage variables plus valid Ontario health card numbers was selected. While the availability of the health card number enabled assessment of the accuracy of different approaches, the analysts were blinded to the correct health card numbers during the process. If manual intervention was required, it was repeated by two analysts to allow capturing human error.
ResultsPRL-based software needed the most personnel time to complete the process. Human errors were identified during the manual intervention due to subjective decisions by the analysts. Fuzzy Matching approach eliminated manual intervention but achieved comparable linkage rate to PRL while maintaining the same accuracy. The Fuzzy Matching software costs were higher; however, the data timeliness was significantly improved, and the clerical review costs and human error were eliminated.
ConclusionsThe Modernization of Record Linkage (MORL) project was a successful demonstration of the advantages of Fuzzy Matching over PRL method. However, the implementation of new approach at the organization level was a challenging change management.
New and Pre-existing Eating Disorders Among Adolescents and Young Adults During the COVID-19 Pandemic: A Population-Based Cohort Study
Authors: Babujee Amreen , Lu Hong , Stukel Therese , Saunders Natasha , Guttmann Astrid , Kurdyak Paul , Vigod Simone , Fu Longdi , Toulany Alene
Published: September 2024

ObjectivesOur understanding of the contribution of new presentations versus pre-existing eating disorders during the COVID-19 pandemic is limited. This study aims to evaluate rates of emergency department (ED) visits and hospitalizations for eating disorders among adolescents and young adults (YA) new to care and those with pre-existing eating disorders during the pandemic.
ApproachWe conducted a population-based cross-sectional study using linked health administrative data for Ontario residents aged 10-26 during the pre-pandemic (Jan. 1, 2017-Feb. 29, 2020) and pandemic periods (Mar. 1, 2020-Jun. 30, 2022). We used Poisson generalized estimating equations models to predict expected overall and monthly rates of eating disorder-related ED visits and hospitalizations among those with a new and pre-existing eating disorder.
ResultsCompared with expected rates, ED visits increased during the pandemic among only adolescents with new eating disorders (adolescent RR 2.12, 95% CI [1.84,2.45]). Additionally, both adolescents and YA with pre-existing eating disorders experienced an increase in ED visits (RR 2.78, 95% CI [2.28, 3.38] and RR 1.52, 95% CI [1.25, 1.85], respectively). Similarly, hospitalizations for new presentations increased solely for adolescents (RR 1.48, 95% CI [1.34,1.64]), while hospitalizations for pre-existing eating disorders increased for both adolescents (RR 1.82, 95% CI [1.43,2.32]) and YA (RR 1.12, 95% CI [1.01,1.23]).
ConclusionsThere was an increase in acute care visits for eating disorders during the pandemic, especially among adolescents and YA with pre-existing conditions. This differentiation is important in advancing our understanding of the pandemic's effects on adolescents and YA and the healthcare system receiving them.
A population-based repeated cross-sectional study using administrative health data to examine the impact of the COVID-19 pandemic on mental wellness in citizens of the Métis Nation of Ontario
Authors: Simms Abigail , Beaulieu Tera , Michaud Benny , Tsui Noel , Zagorski Brandon , Edwards Sarah
Published: September 2024

Objective and ApproachThe COVID-19 pandemic led to unprecedented levels of mental unwellness and yet there are no reports to date that share Métis-specific data. Our study examined changes in patterns of mental and addictions-related (MHA) outpatient health service utilization using population-based data on Métis Nation of Ontario (MNO) citizens before and during the COVID-19 pandemic. Administrative health data in Ontario, Canada between 2017 and 2022 was linked with the MNO's Citizenship Registry (2022) under an existing Data Governance and Sharing Agreement. Monthly rates of MHA outpatient visits were compared between the pre-COVID-19 period (March 2017 to February 2020) and post-COVID-19 onset (March 2020 to December 2022), and rate ratios comparing observed and expected rates were derived using Poisson generalized estimating equations. Stratifications by age and sex were examined.   
Results and ConclusionAmong 28,400 MNO citizens (50.3% male; median age 44 years; 28.3% living rurally), the average monthly rate of MHA outpatient visits was 47.4 per 1000 population pre-COVID-19. During the COVID-19 pandemic, the observed MHA outpatient visit rates were 13% higher than expected (RR=1.13; 95% CI: 1.02-1.26). MHA outpatient visits were higher in females compared to males and MNO citizens aged 30 to 64 years versus older or younger citizens. 
Implications This study is the first to use population-level study to examine MHA health service use in Métis people. High-quality, contemporary data on the mental health outcomes of MNO citizens is crucial to inform MNO’s Mental Health and Addictions programming, resource allocation and emergency preparedness.
Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction
Authors: Pan Jie , Lee Seungwon , Cheligeer Cheligeer , Martin Elliot , Riazi Kiarash , Quan Hude , Li Na
Published: September 2024

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs.
MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard. 
ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns.
ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs.
ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.
Increased mortality among mothers involved in family court care proceedings compared with their peers in England
Authors: Ireland Georgina , Wijlaars Linda , Jay Matthew , Gilbert Ruth
Published: September 2024

ObjectiveResearch has called for Family Courts (FC) and social care services involved in removing children from parental care to consider and address the health of the mother.
ApproachWe linked England FC and healthcare data to compare the 10-year mortality risk and cause of death after a first live birth (FLB) for mothers who were involved or not in FC proceedings in England.
Results3,072,946 mothers (15-39 years) with a FLB between 2007 and 2019 were followed for up to 10 years (median:8.7 years, IQR: 5.4-10), during which 33,759 were involved in FC. Ten-year cumulative mortality was higher among the FC mothers (1.7 vs 0.3%) and the age-adjusted mortality rate ratio was 7.8 (95%CI: 7.00-8.6).
The majority of maternal deaths in both groups met criteria for avoidable death (FC 82.9% vs no-FC 66.8%). Injury was the leading cause of death (204/420; 48.5%) in mothers involved in FCs and cancer (2,823/6,208; 45.5%) in mothers in the no-FC group. Compared with no-FC mothers, more deaths were related to drug use (35.0% vs 6.0%), suicide (14.5% vs 8.4%) or alcohol (9.8% vs 3.5%).  
Among mothers involved in FCs, maternal mortality was higher in cases when children were placed into out-of-home care (1.65%, 463/28143), than when placed at home (0.94%, 53/5563) at first FC proceeding.
Conclusions and ImplicationsAddressing high rates of avoidable mortality among mothers involved in FCs is important for children and parents and could be monitored using routinely linked healthcare, social care and family court data.
Identifying disease phenotypes from linked health data: Comparison of self-report, hospital inpatient and primary care in UK Biobank.
Authors: Conroy Megan , Lacey Ben , Reeves Gillian , Allen Naomi
Published: September 2024

ObjectivesAdministrative health data is commonly used for epidemiological research, however it is not well understood how disease phenotypes replicate across different data sources.
ApproachUK Biobank is a prospective cohort study of 500,000 adults, with ascertainment of health outcomes using administrative health data.  Prevalence at recruitment for 33 diseases were calculated in each health data source: self-report, primary-care, and hospital episode statistics (HES). Consistency of disease identification between sources, and median days between first diagnosis across data sources was determined. Linear regression was used to investigate determinant of differences in the average time between first diagnosis in primary-care and HES data.
ResultsHypertension was the most commonly identified disease in both self-report and HES (26.5% and 12.1% respectively), and anxiety in primary-care (12.7%). Diseases could be grouped into: 1) those identified largely by self-report alone (e.g. migraine, constipation), with inconsistency in the date first diagnosed; 2) those identified largely by primary care alone (e.g. anxiety, depression), also with inconsistency in the date first diagnosed; and 3) those that appeared mostly in all three sources, with highly consistent date of first report (many were emergency hospital admissions [e.g. stroke]). A number of variables were associated with time between primary-care and HES diagnosis. For example, heavier smokers had a significantly shorter period between first primary-care and first HES record for asthma, diabetes and hypertension.
Conclusions and ImplicationsThese results indicate that there are inherent biases in diseases ascertained from linked health data that must be taken into account for epidemiological studies.
Pushing health data access boundaries - adapting NHS governance for 3 challenges: clinical free-text, safe commercial access and AI development
Authors: Tilbrook Amy
Published: September 2024

This presentation will demonstrate expansions to TRE governance frameworks developed to facilitate safe data access in three advanced areas: commercial access, clinical free-text, and innovation with AI/Machine Learning.  
NHS research governance frameworks - originally set up for clinical trials and expanded into the TRE and secondary data use space – are not an immediate fit for advanced techniques in data science research. Over 2 years, public, expert and user engagement approaches were utilised to support developing frameworks within DataLoch, a Health and Social Care TRE and data access service in South-East Scotland.  
Surveys, workshops and continuous user and data controller feedback allowed the testing of tools and assessments to adapt traditional NHS governance structures. These frameworks utilise the Five Safes framework to make NHS data controllers supportive of innovative research in the public and health service’s benefit.  
The presentation will summarise the challenges faced for one TRE and researchers working to support these areas, and the frameworks that are now are being tested within an NHS Scotland Health Board. The development and implementation demonstrates that governance structures can be adapted collaboratively and paves the way for more flexible TRE governance to allow the use of NHS patient data to remain legally and ethically robust while supporting advanced techniques of analysis.
Automating thresholds in probabilistic linkage
Authors: White Zoe , Plachta Josie , Shipsey Rachel
Published: September 2024

ObjectiveThe Office for National Statistics aim is to create a platform containing a variety of datasets interlinked via data spines. The resource burden linking this many datasets is high, and so research to maximise automation is a key aim of the platform’s methodology. Probabilistic linkage is a standard linkage technique that utilises clerical resolution – a very costly process – to find acceptance threshold parameters. Here, we present research into automating threshold assignment.
Every admin dataset is different, causing the probabilistically matched pairs scores to change, a set threshold cannot be used. Instead, a data-informed threshold must be set.
ApproachA gold standard linked dataset was linked through Fellegi-Sunter, stopping at the point of finding the probabilistic threshold. To find the threshold, graphs plotting the number of records at each rounded score were created and reviewed, specifically looking at the spread of gold standard links, deterministic links, unique and non-unique probabilistic links. 
This review suggested a method of threshold assignment that provides multiple quality levels: a high-precision threshold, and a high-recall threshold. This presentation will showcase these graphs and explain how the two thresholds are calculated.
The 2021 Census is one dataset linked within the gold standard linked dataset. The 2021 Census has high quality and relatively clean data, but is likely not representative of administrative data needed in the data platform. Therefore, next steps are testing this method on other datasets with a variety of quality, characteristics, and types.
The benefits of active management of coronary heart disease (CHD) in Wales: how the use of routine collected administrative data can drive efficiency and encourage best practice in condition management.
Authors: Smith Kendal , Evans Lloyd , Davies Gareth , Akbari Ashley , Bailey Rowena , Palmer Richard , Goodfellow Jonathan , Thomas Michael , Lutchman-Singh Kerryn
Published: September 2024

Objective and ApproachWe analysed the effect on subsequent costs of the setting of the first contact for patients with coronary heart disease in Wales. By categorising health events as proactive or reactive, from the event data source (proactive = primary care (general practice) interactions, elective hospital bed days and outpatient attendances. Reactive = accident and emergency (A&E) attendances and emergency hospital bed days). A standard cost was applied to obtain an annualised cost for each patient. The unit cost for each source was GP £36, bed days £398, outpatient £143, A&E £188.
ResultsWe found 42,563 people having CHD events during 2015-2019. Of these, 18,390 had a proactive event as their first event, 23,840 had a reactive event first. The ratio of proactive to reactive events was 1: 3.7 (ratio of costs were £1: £6.30). When the first event was proactive the ratio of events was 1: 0.85 (costs were £1: £1.33). When the first event was reactive the ratio was 1: 4.4 (costs were £1: £33.00). 
Conclusion and ImplicationsThe setting of the first contact appears to be key, with costs reducing by a factor of 14 if the first contact was carried out in a planned environment. Can investment in more active management of a condition reduce overall costs?
The work has highlighted the power of using anonymised population-scale linked individual-level data and indicates the health resource reductions possible from improved managed care of patients.
Characterizing school-linked and non-school linked SARS-CoV-2 cases in children and households: a retrospective cohort study using linked population-level administrative data from Manitoba, Canada
Authors: Hnatiuk Jill , Sirski Monica , Mishra Sharmistha , Baral Stefan , Gordon-Pappas Diane , Katz Alan
Published: September 2024

ObjectiveTo identify directionality of SARS-CoV-2 cases within households and schools, and characterize school, household, and neighbourhood factors associated with school and non-school linked cases. 
ApproachUsing linked administrative data housed at the Manitoba Centre for Health Policy, a cohort of 449,425 individuals from 126,670 households with school-aged children enrolled in Manitoba public schools between September 1, 2020-June 30, 2021 was created. Using population-based SARS-CoV-2 laboratory test results, education enrolment and household composition data, index and transmission cases were counted and categorized at an individual and household level as school or non-school linked. Logistic regression models examined school, household and neighbourhood factors associated with school- and non-school linked households. 
Results143280 individuals were tested for SARS-CoV-2 during the study period; 14712 individuals (10.3%) tested positive. Amongst positive cases, 1695 (11.5%) were school-linked; the remaining 13017 (88.5%) were non-school linked. 13.7% of SARS-CoV-2 positive households had ≥1 case linked to school; 86.3% did not. Preliminary results found higher incomes quintiles had lower odds of being a school-linked household, as did living outside the Winnipeg region. Having more children in a grade and more household members resulted in higher odds of being a school-linked transmission household. No associations were observed for having a child under age five in the household.
ConclusionsTaken together, these data suggest schools were not a large source of SARS-CoV-2 cases in Manitoba and factors associated with school-linked households largely mirrored broader community transmission patterns. 
ImplicationsThese findings may influence public health approaches to future pandemic control.
Improving surveillance of emergency department activity through natural language processing
Authors: Strafford Huw , Fonferko-Shadrach Beata , Pickrell W. Owen , Lyons Jane , Lacey Arron , Evans Jordan , John Gareth , Selby Jennifer , Lyons Ronan
Published: September 2024

Objective and ApproachManagers, clinicians and policymakers have limited information on near-real time patterns of attendance at emergency departments (ED). Our aim was to develop a surveillance system through extracting information from the presenting complaint to develop and feed activity dashboards, improve injury surveillance and support research. 
We developed a Natural Language Processing (NLP) pipeline using the General Architecture for Text Engineering (GATE) system. The pipeline produced Unified Medical Language System (UMLS) codes from ED presenting complaint records and also implemented the Joint Action on Monitoring Injuries in Europe (JAMIE) Minimum Dataset. We classified complaints into 31 common problem presentations (Coughs, Bleeds, Mental Health, Dizziness...), along with categories from JAMIE (Mechanism of Injury, Location, Activity, Intent…).
ResultsPreliminary results are from a pilot study of 86,416 presenting complaints from Morriston Hospital in 2014. 
The most common terms used for grouped symptoms/categories were “Injury” (38.6%, n=28691), “Pain” (22.8%, n=19420), “Unwell” (7.2%, n=6117), “Bleeding” (6.7%, n=5760), and “Shortness of Breath” (3.8%, n=3255).
32.3% of records contained Mechanism of Injury codes, including, falls (15.2%, n=13174), struck by/against (6.9%, n=5960), cuts (5.6%, n=4833), road traffic collisions (2.5%, n=2124) and poisoning (1%, n=900).  We are continuing to refine and validate the categories using precision and recall and with comparison with coded diagnoses.
Conclusions and Implications NLP can be used to extract valuable additional detailed information from ED data. We will further develop the algorithms for all hospitals in Wales for data from 2023 and design dashboards to produce a near-real time surveillance system.
The scale and timing of child protection system contacts from 0-18 years: a study of 1.9 million Australian children
Authors: Pilkington Rhiannon , Falster Kathleen , Hanly Mark , Montgomerie Alicia , Ahmed Tasnia , Newton BJ , Edwards Ben , Lingam Raghu , Shakeshaft Anthony , Cretikos Michelle , Stewart Jessica , Hawkins Katherine , McLean Kitty , Lynch John
Published: September 2024

IntroductionTo prevent abuse, neglect and poor outcomes for children and families, including child protection intervention, maltreatment, and removals, it is critical to understand the scale and timing of child protection system contacts to identify opportunities for early prevention. 
Objectives and ApproachWe quantified the cumulative incidence of child protection contacts during childhood, including initial reports, screened-in reports, investigations, substantiations and removal into out-of-home-care, among all children in two Australian states. We used child protection system data linked for the South Australian (SA) BEBOLD and NSW Child E-Cohort data platforms as the numerator, and census data for the denominator, for 571,497 SA children (birth-years 1991-2019) and 1,362,505 NSW children (birth-years 2005-2018). 
ResultsOf children born in 2005 (NSW SA): Two in five children were reported to child protection by age 14; one in ten children were the subject of a child protection investigation in NSW and SA; one in twelve children in NSW and one in 17 in SA, were substantiated at least once; and one in 35 children in NSW and one in 40 in SA were removed into out-of-home care at least once by age 14. Approximately half of all types of child protection contact first occurred by the child’s fourth birthday.    
Conclusions/implicationsThe scale of lifecourse child protection contact underscores the opportunities for early health and social supports to prevent child maltreatment and escalating child protection intervention. Child protection reports also represent an asset to generate public health intelligence to inform early prevention.
Evaluating the effectiveness of the HPV vaccination programme in England, using regression discontinuity design
Authors: Ward Isobel , Bermingham Charlotte , Soldan Kate , Nafilyan Vahe
Published: September 2024

Objective and ApproachIn England, the National human papillomavirus (HPV) HPV Immunisation Programme was introduced for young girls in 2008. Whilst clinical trials have shown high vaccine effectiveness, real-world evidence is still accruing. Estimating vaccine effectiveness based on real-world data without randomised control trials is challenging, but critical for policy makers. Using Census 2011 data and a population level linked dataset, we exploit a regression discontinuity design (RDD) to assess the impact of the catch-up vaccination programme on cervical dysplasia and cancer diagnoses as recorded in hospital and mortality data in girls born between 1987 and 1992. 
ResultsWe estimate that vaccination reduces the incidence of cervical dysplasia diagnoses by 27% and cervical cancer diagnoses by 75% respectively. Girls were followed for a 7-year period between ages 23 and 30 years in girls who were offered vaccination at ages 17 to 18 years as part of the catch-up cohort compared to those who were not eligible for HPV vaccination. 
Conclusions and ImplicationsOur work demonstrates how pivotal the HPV vaccination programme has been for reducing adverse cervical outcomes in young women. Additionally, it shows that the estimates of the effectiveness of HPV vaccination obtained using a RDD are comparable to those based on more classic observational studies. This work shows real world vaccination effectiveness and demonstrates how pivotal the national HPV campaign has been in young girls.
Using linked administrative data to describe a cohort of young people who have a parent with a history of homelessness (Study 1)
Authors: Distasio Jino , Hinds Aynslie , Isaak Corinne , Cidro Jaime , Nickel Nathan , Zell Sarah , Sareen Jitender
Published: September 2024

ObjectiveLittle is known about the children of parents who experience homelessness. The At Home/Chez Soi (AHCS) Research Demonstration Project, launched in 2009, was a Canadian multi-site, multi-year study that tested the effectiveness of Housing First. In Winnipeg, 513 people were enrolled, of whom 47% were parents. Our objective was to determine how the children were faring with respect to their health and social situations at a time their parent was experiencing homelessness.
ApproachThe cohort, created using the Manitoba Centre for Health Policy’s familial linkage methodology, consisted of 405 individuals (< 30 years old) who were the offspring of the Winnipeg AHCS cohort. We descriptively summarized the socioeconomic characteristics and indicators of their involvement (or lack thereof) in various government systems at or in a period up to their parent’s study enrolment date. 
ResultsApproximately half of the cohort were female (51%) and under 13 years (52%). Most (76%) resided in a household that had received income assistance. At least 50% had been placed into care at or near birth and only 17% had not been involved with the child welfare system. Of those school aged, 81% were enrolled in school and of those older than school aged, 53% had graduated. Nearly half (49%) had been diagnosed with a mental health disorder and/or with asthma (47%).
Conclusions and ImplicationsThis study is the first in a series seeking to identify ways to prevent multigenerational experiences of homelessness. Findings will generate valuable insights for upstream intervention across various systems.
The Impact of COVID-19 on Mental Health Outcomes among Recipients of Ontario Social Assistance Benefits
Authors: Campitelli Michael , Kim Eliane , Plumptre Lesley , Guttmann Astrid , Kurdyak Paul
Published: September 2024

Objectives/ApproachWe linked the Ontario Social Assistance (OSA) Benefit Unit file from the Ministry of Children, Community and Social Services to provincial health administrative datasets housed at ICES to examine the impact of COVID-19 on Mental Health and Addictions (MHA) service use among OSA recipients.
Those receiving OSA benefits for February 2020 were matched to Ontario residents not receiving benefits based on age (±1 year), sex, income, expected resource utilization, and area of residence (N=771,891 matched pairs). We computed rates of MHA-related emergency department (ED) visits and hospitalizations in the 16-month period before (November 2018-February 2020) and after (March 2020-June 2021) pandemic onset.
ResultsMHA-related ED visit rates were much greater among OSA recipients (8.82 per 1,000 person-months) compared with matched controls (1.83 per 1,000 person-months) in the post-COVID period (Relative Rate [RR]=4.82; 95% Confidence Interval [95%CI] 4.75-4.89). Comparatively, MHA-related ED visit rates were also greater among OSA recipients (9.85 per 1,000 person-months) compared with controls (2.38 per 1,000 person-months) during the pre-COVID period (RR=4.13; 95%CI 4.08-4.18). The pre-COVID and post-COVID period RRs comparing OSA recipients and matched controls were significantly different (p<0.001). Results were consistent when examining MHA-related hospitalizations.
Conclusions/ImplicationsWhile MHA-related ED visits and hospitalizations rates were significantly higher in OSA recipients than matched controls in both time periods, this difference increased following the onset of COVID-19. Increased reliance on hospital-based MHA services post-COVID represents the greater ongoing need for, and/or reduced access to, MHA-related ambulatory care for OSA recipients during the pandemic.
Incidence and prevalence of asthma, COPD, and ILD across England, Wales, and South-East Scotland – a harmonised approach to data analysis
Authors: Scully Sean , Evans Hywel , Hatam Sara , Cook Sarah , Whittaker Hannah , Kallis Costantinos , Farr Ian , Orton Chris , Sheikh Aziz , Quint Jennifer
Published: September 2024

ObjectiveThis study aimed to create harmonised datasets for asthma, chronic obstructive pulmonary disease (COPD), and Interstitial Lung Disease (ILD), and subsequently use them to calculate the incidence and prevalence of these diseases across three UK nations (England, Wales, and South-East Scotland).
ApproachHarmonised research-ready datasets were created using three electronic healthcare record databases. Records were selected between 01/01/2004 and 31/12/2019, with yearly age and sex standardised incidence and prevalence rates calculated.
ResultsIn 2019, the incidence rates for asthma were: England, 0.89 (95%CI: 0.88–0.90); Wales, 0.66 (0.65–0.68); and South-East Scotland, 0.67 (0.64–0.71), which were all lower than in 2004. Incidence rates of COPD in England were 0.83 (0.82–0.85) and 0.67 (0.65–0.69) in Wales, which were both lower than in 2004, but Scotland remained similar at 1.06 (0.99–1.13). Incidence rates of ILD were: England, 3.27 (3.05–3.50); Wales, 1.39 (1.27–1.53); and Scotland, 1.63 (1.36–1.95), which were all higher than in 2004. Between 2004 and 2019, the prevalence of all three diseases increased across all three nations.
ConclusionsSolving differences in coding between CPRD, SAIL Databank, and DataLoch databases enabled comparable cohorts to be created and the first harmonised UK estimates of incidence and prevalence for asthma, ILD, and COPD.
ImplicationsHarmonised studies of asthma, COPD, and ILD across the UK can now be performed. The code produced is publicly available to facilitate future research, which can be adapted for other conditions and databases.
Family-homelessness and child absence from school: How data linkage research has identified and quantified the association in Wales, UK
Authors: Thomas Ian
Published: September 2024

BackgroundHome and school experiences are linked. Though evidence exists quantifying the association between family-homelessness and school absences, it largely originates from the United States. Understanding the nature of this association in Wales, UK, is important given differing social, economic, and homelessness policy contexts when compared to the United States.
ApproachAnalysis included pupils attending state funded schools in a single local authority in Wales, during the academic years 2012/13 to 2015/16. Pupils were flagged as experiencing family-homelessness in an academic year if their family were supported by the housing service operating in the local authority area. Outcomes of interest were number of total, authorised, and unauthorised half-day-sessions absent per academic year. Pupils were observed over multiple academic years (N = 105,355 pupil-years), enabling both cross-sectional and longitudinal (panel) analyses.
ResultsOn average, pupils in families accessing the housing service missed a total of 10 half-day-sessions more per academic year than pupils whose families were not accessing the service. Unauthorised half-day-sessions absent were double for pupils in families who had approached the housing service. After controlling for personal and area characteristics, rates of all absences were higher among pupils whose families accessed the service. 
ConclusionThis study represents the first published linkage of homelessness and education data in the UK. Given the apparent association between absenteeism and accessing housing support, changes to policy, practice, and law should be explored to facilitate co-operation between schools and housing services in Wales, and to protect children and their families from further disadvantage.
Linking community-acquired images to national data in the absence of interoperable systems and unique identifiers
Authors: Tochel Claire , Anderson Heather , Bernabeu Miguel , Dhillon Baljean , Javidi Malihe , MacGillivray Tom , McTrusty Alice , Penny Jon , Strang Niall , Tatham Andrew , Watson Jordan , Watts Sam
Published: September 2024

Objective & ApproachRoutine optometry examinations, which may include retinal images, have been free to everyone in Scotland since 2006. Images and patient data are stored locally on non-interoperable systems, usually without unique identifiers. The Scottish Collaborative Optometry-Ophthalmology Network for e-research (SCONe) aimed to create a secure, longitudinal repository collating community-acquired images linked to national, routinely-collected healthcare data. We developed bulk export processes to retrieve images and patient data from current and archived systems and a series of deterministic and probabilistic algorithms to match images to patients.
ResultsWe successfully exported images and patient data from several commonly used systems. Exact matching of available data between image and clinical systems within practices linked 78% of images (IQR 71.2-84.1). Our algorithms increased match rate to 90% (IQR 83.5-98.6) of images, efficiently reducing risk of systemic bias in the cohort. Match method is included in the repository metadata to facilitate quality assurance checks after national linkage and pseudonymisation. To date over 600k images have been processed and delivered to the National Safe Haven for linkage to healthcare data.
Conclusions & ImplicationsBreakthrough research into early detection and pre-symptomatic disease can be facilitated by harnessing community-acquired retinal images which capture normal ageing and disease progression. Rather than await interoperability and widespread use of unique identifiers, this has been achieved through innovative solutions to real-world issues around time-pressured clinical data entry and non-standardised data storage. The SCONe repository continues to grow. It is currently supporting ocular disease prediction research and offers the potential for much more.
Validation of the Passive Surveillance Stroke Severity score in three Canadian provinces
Authors: Park Alison L. , Peterson Sandra , Zhao Yinshan , Austin Peter C. , Fang Jiming , Hill Michael D. , Kamal Noreen , Field Thalia S. , Joundi Raed A. , Kapral Moira K. , Yu Amy Y. X.
Published: September 2024

ObjectiveAdjusting for stroke severity is critical in stroke outcomes research. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based measure of stroke severity, initially derived in Ontario, Canada using data between 2002-2013. We assessed its geographical and temporal external validity in British Columbia (BC), Nova Scotia (NS), and Ontario, Canada.
MethodsIn each province, we identified adult in-patients with ischemic stroke or intracerebral hemorrhage and admitted from an emergency department between 2014-2019 and calculated their PaSSV score using linked administrative data. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models’ discriminative values using Uno’s c-statistic, comparing models with versus without PaSSV.
ResultsWe included 86,142 patients (n=18,387 in BC, n=65,082 in Ontario, n=2,673 in NS). The mean and median PaSSV were similar across provinces. Higher PaSSV score, reflecting lower stroke severity, was associated with a lower mortality (hazard ratio and 95% confidence intervals 0.70 [0.68-0.71] in BC, 0.69 [0.68-0.69] in Ontario, 0.72 [0.68-0.75] in NS) and long-term care admission (0.77 [0.76-0.79] in BC, 0.84 [0.83-0.85] in Ontario, 0.86 [0.79-0.93] in NS). Including PaSSV in the multivariable models improved model fit according to the c-statistics.
ConclusionWe showed that PaSSV has geographical and temporal validity. It is a useful tool for risk-adjustment in multi-jurisdiction stroke outcomes research, and a valuable addition to be included in the national algorithm inventory.
Developing a High Velocity Dataset Quality Checking Pipeline
Authors: Davies Hannah , Thayer Daniel S , Elmessary Muhammad A , Coldea Alex-Ioan , Jones Carys , Evans Lorna , Makovics Alexander , Howell-Wright Owen , Hughes Lee
Published: September 2024

ObjectiveThe volume and frequency of refreshed data within the [organisation] has increased significantly since the beginning of the COVID-19 pandemic. Therefore, a more efficient data quality (DQ) checking process was necessary.
Approach   Having previously developed an automated DQ checking tool, the focus was on re-engineering the process of DQ task allocation and communication of results.
Results 5 analysts were trained in DQ checking.  A JIRA workflow tracks the management of data loading.  When a dataset is ready for DQ, the Data Manager allocates a ticket to the DQ Lead who then allocates it onto one of the 5 analysts.  Via a DQ Slack channel, the analyst is informed and acknowledges receipt of the task.  On completion of DQ, the analyst updates the ticket and transfers it to the appropriate workflow stage.  Passed DQ tickets are transferred to the Data Manager for data release, whereas failed ones are placed “On Hold”.  The DQ Lead triages the issues and liaises with relevant parties for resolution, which may require data amendments.  On receipt of amended data, the DQ ticket is transferred back to the queue and the analyst is notified to re-check the data.
ConclusionsThe team now complete a high volume of DQ checks efficiently.  In 2023, 405 datasets, containing 1716 tables, were quality checked, with the initial DQ taking, on average, 2.6 days.
Implications The improved speed of DQ checking ensures projects access the latest available data whilst maintaining the expected DQ levels, integrity and reputation of the TRE.