bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Social and Locality Variations of Dog Bites in Scotland Using Administrative Data Sources.
Authors: Hooper Jade , Lambert Paul , Buchanan-Smith Hannah , Robertson Tony
Published: September 2022

ObjectivesPrevious research has shown that hospital admissions for dog-bites are highest in the most deprived areas across England and Wales. In Scotland, thus far there has been no rigorous empirical investigation into social inequalities in dog-related injuries. This study aims to address this gap through analysis of linked administrative data.
ApproachThe study uses administrative health data from NHS24 calls, A&E and SMR01 records involving dog-related injuries linked to Scotland Census micro-data. Area-based measures of social circumstance are considered through the SIMD, along with an exploration of novel, area-level characteristics including measures of local greenspace, average garden size and dog populations. Individual/household level measures of social circumstance taken from the Scottish Census are used to compare the characteristics of individuals with health records involving dog-related injuries to a random sample of individuals not appearing in the health data sets.
ResultsWhen looking at individual level records and during modelling at aggregate area level, SIMD was an important factor in all models. Whilst some variation was observed across the different types of health data, the number of records, incident risk ratios and odds ratios were all consistently at least 2-3 higher when comparing the most to least deprived areas. Accounting for dog populations and introducing interaction terms for SIMD decile by dog population increased the main effect of SIMD.
When comparing individual/household level measures of social circumstance taken from the Scottish Census, occupation-based measures such as NS-SEC appeared to be relatively important predictors of risk, alongside ethnicity, household composition and age, with children disproportionately represented.
ConclusionIncident rates of dog-related injuries were higher in more deprived areas and circumstances where individuals may be seen as more socially disadvantaged at the individual or household level. Social/legal policies related to dog-bites typically don’t consider social disadvantage in any meaningful way. These results show the importance of doing so.
Administrative Data Based Population Estimates for Scotland.
Authors: Rowley David , Ellis Caroline
Published: September 2022

In December 2021, we published statistical research on Administrative Data Based Population Estimates (ABPEs) for Scotland’s population in 2016, 2017 and 2018.  This work was developed as part of a project to consider how administrative data could be used to support Scotland’s Census.
Following the governance process, administrative datasets were processed and de-identified, before being transferred to Scotland’s National Safe Haven for linking and analysis.  The datasets used include data from health, the electoral register, vital events registrations, and education.  The methodology used several linking variables so data could be linked, even without exact agreement between records.  Records from across the data sources were resolved into individuals using these links.  Business rules then indicated which individuals to include in Scotland’s Integrated Demographic Dataset (SIDD).  The ABPEs were then produced from this and compared with the official mid-year population estimates (MYEs) to determine success.
On aggregate, the population estimates from the ABPEs are very similar to the MYEs, differing by less than 0.5 per cent in each year.  When broken down further, larger differences occur with ABPEs having more males and fewer people aged over 65 when compared with the official statistics. A notable difference between the two is for males aged between 30 and 65 in deprived areas, with ABPEs up to 20% higher than the MYEs. These differences by deprivation are smaller for other age ranges and for females. The ABPEs tend to be higher than official estimates for urban areas, and lower for rural areas.  Differences for each local authority area range from 5 per cent below to 4 per cent above official estimates.
It is therefore possible to produce Scottish population estimates purely from administrative sources that roughly agree with MYEs. Further investigation will help understand the differences for particular groups, and will be explored in future years by comparing ABPEs with the 2022 Census.
Anticholinergic burden in middle and older age is associated with reduced cognitive function, but not with brain volume.
Authors: Mur Jure , Marioni Riccardo , Russ Tom , Muniz-Terrera Graciela , Cox Simon
Published: September 2022

BackgroundAnticholinergic drugs block muscarinic receptors in the body. They are commonly prescribed for a variety of indications and their use has previously been associated with dementia and cognitive decline.
MethodsIn UK Biobank participants with linked health-care records (n=171,266, aged 40-71 at baseline), we calculated total anticholinergic drug burden according to 15 different anticholinergic scales and due to different classes of drugs. We then used linear regression to explore the associations between anticholinergic burden and various measures of cognition and structural MRI, including general intelligence, 9 separate cognitive domains, total brain volume, volumes of 68 cortical and 16 subcortical areas, and fractional anisotropy and median diffusivity of 25 white-matter tracts.
ResultsAnticholinergic burden was modestly associated with poorer cognition across most anticholinergic scales and cognitive tests (6/9 FDR-adjusted significant associations, std. betas range: -0.033, -0.006). The association was mostly driven by antibiotics (std. beta=-0.029, 95% p<0.001) and drugs to treat disorders of the nervous system (std. beta=-0.017, p<0.001). Anticholinergic burden due to the pharmacological subclass of glucose-lowering drugs (beta=-0.038, p<0.001) and the anatomical class of respiratory drugs (beta=0.016, p=0.03) was associated with total brain volume. However, anticholinergic burden was not associated with any other measure of brain macro- or microstructure (p>0.07).
DiscussionAnticholinergic burden is mildly associated with poorer cognition, but there is little evidence for an effect for measures of brain structure. Future studies might focus more broadly on polypharmacy or more narrowly on distinct drug classes, instead of using purported anticholinergic action to study the effects of drugs on cognitive ability.
Changes in healthcare costs and survival in the era of immunotherapy and targeted systemic therapy for melanoma.
Authors: Nguyen Paul , Bateni Sarah , Eskander Antoine , Seung Soo Jin , Mittmann Nicole , Chan Kelvin , Look-Hong Nicole , Hanna Timothy
Published: September 2022

ObjectivesMelanoma treatment has evolved over the past decade with the adoption of adjuvant and palliative immunotherapies and targeted therapies and changes in use of sentinel node biopsy. The impact on real-world healthcare costs and outcomes is uncertain. Here we examine changes in healthcare costs and survival using administrative data.
ApproachUsing data from the universal healthcare system in Ontario, Canada, we examine a propensity-matched, retrospective cohort of patients aged 20+ years with Stage I-IV invasive cutaneous melanoma from 2018-2019 compared with those from 2007-2012. The primary outcomes were public payer’s mean healthcare per-person costs, and overall survival (OS). Costs were estimated with an established case-mix and claim-based costing algorithm for Ontario, in which person-level costs are allocated for the various healthcare utilizations over time. Standardized mean differences were used to compare costs, and the log-rank test and Cox regression were used to compare survival among stage-stratified, propensity-score matched cohorts.
ResultsWe identified 1,138 patients with melanoma from 2018-2019 and 7,654 from 2007-2012. After stage stratification and propensity-matching (N=1,101 per cohort), sentinel lymph node biopsy (62.3% vs. 43.4%) and systemic therapy use (27.3% vs. 12.5%) were more frequent in 2018-2019 compared to 2007-2012. 2018-2019 patients had greater mean healthcare (including systemic therapy) costs compared to 2007-2012 with Stage II ($27,835 vs. $21,179), III ($90,508 vs. $46,242) and IV disease ($118,398 vs. $46,500). There was a seven-to-twelve-fold increase in mean systemic therapy costs for treated patients with Stage III ($68,207 vs. $9,832) and IV disease ($80,905 vs. $6,883). OS was greater in 2018-2019 versus 2007-2012 (2-year OS: 87.8% [95% Confidence Interval {CI}: 85.8-89.6%] vs. 83.7% [95% CI: 81.3-85.7%]; Hazard Ratio {HR}: 0.72 [95% CI: 0.59-0.89]; p<0.05).
ConclusionThese real-world data highlight trade-offs with adoption of new effective systemic therapies for melanoma, with a greater economic burden to the healthcare system but an associated improvement in survival. Such evolving paradigm changes may prompt dynamic evaluations of healthcare resources and policies to ensure cancer care is sustainable.
Challenges and lessons learned from two countries using linked administrative data to evaluate the Family Nurse Partnership.
Authors: Cavallaro Francesca , Cannings-John Rebecca , Lugg-Widger Fiona , Van der Meulen Jan , Gilbert Ruth , Kennedy Eilis , Robling Michael , Harron Katie
Published: September 2022

ObjectivesWe describe the challenges and lessons learned from two studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP), an intervention supporting adolescent mothers in England(E) and Scotland(S). We present recommendations for studies using linked administrative data to evaluate complex interventions.
ApproachWe constructed two cohorts of all mothers aged 13-19 giving birth in NHS hospitals in England and Scotland between 2010-2016/17 using linkage of mothers and babies in hospital admissions data (E:Hospital Episode Statistics/S:Maternity Inpatient and Day Case), and identified FNP participation through linkage to FNP programme data. We additionally linked to health, educational and social care data for mothers and their babies (E:National Pupil Database/S:eDRIS). We used these data to identify key risk factors for enrolment in the FNP, assess the effect of the FNP on maternal and child outcomes, and determine programme characteristics modifying the effect of the FNP.
ResultsKey challenges: characterising the intervention and usual care, understanding quality of multi-sector data linkage, data access delays, constructing appropriate comparator groups and interpreting outcomes captured in administrative data. Lessons learned: evaluations require detailed data on intervention activity (dates/geography), and assessment of usual care, which are rarely readily available and are time-consuming to gather; data linkage quality is variable/not available, making defining denominators challenging; data access delays impeded on data analysis time; unmeasured confounders not captured in administrative data may prevent generation of an appropriate comparator group. Recommendations: Characteristics informing targeting should be explicitly documented, and could be enhanced using linked primary care data and information on household members (e.g. fathers). Process evaluation and qualitative research could help to provide better understanding of mechanisms of effect.
ConclusionLinkage of administrative data presents exciting opportunities for efficient evaluation of large-scale, complex public health interventions. However, sufficient information is needed on programme meta-data, targeting and important confounders in order to generate meaningful results. Study findings should help stimulate exploration with practitioners about how programmes can be improved.
Structural health determinants and comorbidity risk factors for COVID-19 outcomes among participants in the UK Biobank.
Authors: Allen Deborah , Vergara-Lope Alejandra , Wilson Daniel
Published: September 2022

ObjectivesCOVID-19 disproportionately affects older patients with pre-existing health conditions. However at the time of writing, June 2021, the relationship between COVID-19, ethnicity and deprivation is unclear. We estimated associations between individual comorbidities and local deprivation with a range of COVID-19 outcomes, in a large national cohort: UK Biobank.
ApproachData linkage between COVID-19 records in England with baseline participant information and secondary care diagnoses from the UK Biobank allowed estimation of associations between deprivation, comorbidities and COVID-19 outcomes.
Multivariable logistic regression models were constructed for COVID-19 test results, hospitalisations and deaths recorded between 1st January 2020 to 31st March 2021. High-risk clusters for COVID-19 were mapped using principal components analysis and minimum variance stratification, according to age, comorbidities and deprivation.
27,306 participants tested for SARS-CoV-2 were eligible for inclusion. Of these, 5,196 tested positive, 2,724 were hospitalised and 475 died.
ResultsPositive linear trends were observed between higher local deprivation and higher odds of SARS-CoV-2 infection, COVID-19 hospitalisations and deaths (p<0.001).
The odds of testing positive was 19% lower for participants with cancer (adjusted odds ratio, aOR=0.81, 0.74-0.88). Higher odds of testing positive were associated with younger age, male sex, cerebrovascular disease and more severe deprivation.
Participants with diabetes were at 42% higher odds of hospitalisation (aOR=1.42, 1.30-1.56). In addition to older age and male sex, strongest comorbidity risk factors for COVID-19 death were cerebrovascular disease, diabetes and mild liver disease with aOR of 1.78 (1.34-2.36), 1.71 (1.41-2.08) and 1.85 (1.31-2.61) respectively, after full adjustment.
Participants recruited from North-West England were in highest clusters of COVID-19 risk, whilst participants from South England were at lowest risk.
ConclusionThe burden of COVID-19 increased at higher levels of deprivation, and the presence of specific comorbidities. Individuals in more deprived areas were at significantly higher odds of infection, hospitalisation and death from COVID-19. Several cardiovascular comorbidities and associated risk factors were associated with COVID-19 hospitalisation and death.
The use of healthcare datasets to investigate the impact of dose intensity in the adolescent and young adult cancer population.
Authors: Hughes Nicola , Feltbower Richard , Stark Dan
Published: September 2022

Objectives

To investigate whether the dose intensity of chemotherapy received by Adolescent and Young Adult (AYA) cancer patients impacts on their survival.
To assess and compare the utility of existing healthcare data available at a regional, national and international level to answer this question.

ApproachA regional dataset has been created through linkage of the Yorkshire Specialist Register of Cancer in Children and Young People to electronic chemotherapy prescribing data from Leeds Teaching Hospitals NHS Trust, providing a detailed dataset.  The national dataset comprises of data from the National Cancer Registry and Analysis Service (NCRAS) linked to the Systemic Anti-Cancer Therapy (SACT) dataset which collects chemotherapy prescribing data from all hospitals in England. This dataset provides bigger patient numbers but data at a pseudonymised level.  The international dataset comprises of anonymised data from clinical trials.
ResultsThe data has being linked, cleaned and validated.  Survival analysis is being carried out using a causal inference framework.
ConclusionThis study will describe the value of existing healthcare care sets in the AYA cancer population.  Identification of areas in which the datasets are lacking will help inform data controllers regarding ways in which data collection can be optimised for this important patient group.
Care trajectory in homes care users across mortality-risk profiles: an observational study.
Authors: Murmann Maya , Manuel Douglas , Tanuseputro Peter , Bennett Carol , Pugliese Michael , Roberts Rhiannon , Li Wenshan , Hsu Amy T.
Published: September 2022

ObjectivesRESPECT is a prognostic tool, developed using linked population-based data, to predict 6-month mortality in community-dwelling older adults. RESPECT is implemented and openly accessible as a web-based tool on ProjectBigLife.ca, where over 700,000 calculations have been performed to date. Our objective was to describe healthcare utilization patterns among home care (HC) users across mortality risk profiles generated from RESPECT to inform care planning for older persons who have varying mortality risks and levels of care needs as they decline.
ApproachWe conducted a retrospective cohort study examining healthcare use among HC users in Ontario, Canada, who received at least one interRAI HC assessment between April 2018 and September 2019.  Using linked health administrative data at the individual level, we examined the use of acute care (hospitalizations and emergency department (ED) visits), long-term care (LTC), and palliative home care within 6-months of each assessment and prognostication using RESPECT. Mortality risk profiles from RESPECT were created based on the median survival.
ResultsThe cohort comprised 247,377 community-dwelling older adults; 14.3% died within 6-months of an assessment. Among decedents, half (51.51%) of HC users with a predicted median survival of less than 3-months received at least one palliative care home visit; 39.17%, 34.82% and 13.84% visited the ED, were hospitalized, or were admitted to LTC, respectively. The proportion of assessments that received at least one palliative HC visit declined to 43.11% and 30.28% of assessments with a median survival between 3- and 6-months and those between 6-months and 12-months, respectively.  The proportion of assessments with an acute care use increases with increasing median survival.
ConclusionA considerable proportion of people at the end-of-life do not receive any palliative home care and continued to be institutionalized. This may be indication that the reduced life expectancies and palliative care needs of many older adults are not being recognized, thus demonstrating the value of prognostic models like RESPECT to inform care planning  for individuals in their final years of life.
Developing Machine Learning Algorithms on Routinely Collected Administrative Health Data - Lessons from Ontario, Canada.
Authors: Harish Vinyas , Ravaut Mathieu , Yi Seung Eun , Gutierrez Jahir , Sadeghi Hamed , Leung Kin Kwan , Watson Tristan , Kornas Kathy , Poutanen Tomi , Volkovs Maksims , Rosella Laura
Published: September 2022

There has been considerable growth in the development of machine learning models for clinical applications; however, less attention has been paid to applications at the health systems level. Here, we survey recent models developed using provincial administrative health data holdings in Ontario, Canada to synthesize key learnings across use cases.
We have developed four models in the areas of diabetes incidence and complications, hospitalization due to ambulatory care sensitive conditions, and hospitalization due to SARS-CoV-2 infection. Our team was highly multidisciplinary with expertise across clinical medicine, administrative health data, epidemiology, and computer science. We used a “sliding window” approach to aggregate healthcare events across multiple health administrative data sets chronologically and map them dynamically onto a patient timeline. Tree-based algorithms, specifically gradient boosted decision trees, are well suited for the underlying tabular structure of administrative data and were used for each prediction task.
Our models achieved excellent discrimination, measured by the area under the receiver operating characteristic curve, between 0.77-0.85 at prediction windows between 30 days and 3 years in advance. They were also well-calibrated, both in-the-large and in population subgroups such as older adults, those living in rural areas, and the materially deprived. Measures of feature importance revealed that our models were leveraging predictors across administrative datasets (e.g. demographics, interactions with the healthcare system, medications) in intuitive and defensible ways. Finally, we demonstrated the utility of our models with “recall at top k” metrics - for example, the top 1% of patients predicted at risk of diabetes complications represented a cost of over $400 million to the healthcare system.
We identify three key learnings needed for the successful application of machine learning methods to health administrative data: synergy between nature of training data and intended algorithm use, adherence to methodological best practices for rigour and transparency, and multidisciplinary teams with expertise across data provenance, methodological approach, and impact assessment.
Efficient population record linkage with temporal and spatial constraints.
Authors: Nanayakkara Charini , Christen Peter
Published: September 2022

ObjectivesPopulation databases containing birth, death, and marriage certificates or census records, are increasingly used for studies in a variety of research domains. Their large scale and complexity make linking such databases highly challenging. We present a scalable blocking and linking technique that exploits temporal and spatial constraints in personal data.
ApproachBased on a state-of-the-art blocking method using locality sensitive hashing (LSH), we incorporate (a) attribute similarities, (b) temporal constraints (for example, a mother cannot give birth to two babies less than nine months apart, besides a multiple birth), and (c) spatial constraints (two births by the same mother are more likely to happen in the same location than far apart). In an iterative fashion, we identify highly confident matches first, and use these matches to further refine our constraints. We adopt a block size and frequency-based filtering approach to further enhance the efficiency of the record linkage comparison step.
ResultsWe conducted experiments on a Scottish data set containing 17,613 birth certificates from 1861 to 1901, where the application of standard LSH blocking generated approximately 15 million candidate record pairs, with a recall of 0.999 and a precision of 0.003. With the application of our block size and frequency-based filtering approach we obtained a ten-fold and hundred-fold reduction of this candidate record pair set with a small reduction of recall to 0.984 and 0.962, respectively. The comparison of record pairs in the hundred-fold reduction using our iterative linking technique achieved up-to 0.961 precision and 0.811 recall. This means that our method can achieve a reduction in computational efforts, and improvement in precision of over 99% at the cost of a decline in recall below 19%.
ConclusionWe presented a method to reduce the computational complexity of linking large and complex population databases while ensuring high linkage quality. Our method can be generalised to population databases where temporal and spatial constraints can be defined. We plan to apply our method on a Scottish database with 24 million records.
Transition between inpatient rehabilitation and National Disability Insurance Scheme for Traumatic Brain Injury and Spinal Cord Injury.
Authors: Guthrie Simon , Alexander Tara
Published: September 2022

ObjectiveTo understand the pathway and diverse levels of functional impairment for people with traumatic brain injury (TBI) and spinal cord injury (SCI) as they transition from inpatient rehabilitation (IR) hospital setting to ongoing care and support under the National Disability Insurance Scheme (NDIS) in Australia.
ApproachThe Australasian Rehabilitation Outcomes Centre (AROC) has data on almost every inpatient rehabilitation episode of care since 2002, including TBI and SCI. AROC data is de-identified with a statistical linkage key (SLK-581). The NDIS dataset contains identified administrative data on participants of the scheme from which the SLK-581 was derived. Datasets were restricted to TBI and SCI records and the SLK with key dates used to link records together. The linkage was done in multiple passes with different levels of information with each link being validated using secondary information relating to date of injury, date of admission and geographical location.
ResultsOver the period 2012-2019, approximately 2,000 records from AROC episodes were linked to an NDIS participant following data validation and individual review of borderline matches We will compare the functional independence of the individual upon leaving rehabilitation with their need for support under the NDIS. Functional independence in rehabilitation is measured by clinicians using the Functional Independence Measure (FIM), a tool that requires clinicians to be trained and credentialed in its use as it is part of the funding model for IR in Australia. Need for support under the NDIS is measured by the funded supports available to a participant under the plan. We expect to demonstrate a correlation between FIM scores and funded supports and identify and analyse any unexpected results.
ConclusionThese results will inform resource allocation within the NDIS. This project demonstrates how de-identified research datasets can be linked with administrative datasets to draw new and powerful insights into government service delivery and population health while maintaining privacy. Challenges to accurate linkage can be overcome through iterative and non-deterministic approaches.
Increased risk of death following release from incarceration: an individual participant data meta-analysis of 1,314,568 adults in eight countries.
Authors: Borschmann Rohan , Keen Claire , Young Jesse , Kinner Stuart
Published: September 2022

ObjectivesPeople released from incarceration are at increased risk of death from diverse causes. We aimed to calculate the incidence of all-cause and cause-specific death after release from incarceration and identify individual-level risk factors for death.
ApproachWe conducted a series of individual participant data meta-analyses using data from >1.3 million adults released from incarceration in eight countries from 1980-2018. We used random effects meta-analysis to estimate the pooled all-cause and cause-specific crude mortality rates (CMRs), with 95% confidence intervals (CI) for the entire follow-up period, and for specific time periods after release from incarceration, overall and stratified by age, sex, and region.
ResultsWe included 1,395,318 people, 10,164,341 person-years of follow-up time, and 72,920 deaths in our analyses. The overall pooled CMR was 727 (95%CI: 623-840) per 100,000 person-years, with no difference between males and females. The risk of death was highest during the first week following release (all-cause CMR: 1,612, 95%CI: 1048-2,287, I2=91.5%), and the three most common causes of death across the entire follow-up period were 1) alcohol and other drug poisoning (CMR=144, 95%CI: 99-197); 2) cardiovascular disease (CMR: 102, 95%CI: 85-121); and 3) cancer and other neoplasms (CMR=74, 95%CI: 85-121). Leading causes of death varied across time periods following release from incarceration.
ConclusionOur findings indicate the need for routine monitoring of mortality following release from incarceration. The distribution of cause of death varies over time, such that clinical decision-making needs to be informed by the proximity to release from incarceration. The elevated risk of death in first 7 days following release highlights the urgent need for coordinated transitional care – including substance use and mental health treatment – and injury prevention initiatives.
The Intergenerational Transfer of Mental Disorders: A Population-based Multigenerational Linkage Study.
Authors: Hamad Amani , Monchka Barret , Roos Leslie , Bolton James , University Aarhus , Elgendi Mohamed , Lix Lisa
Published: September 2022

ObjectivesMental disorders are a major public health concern. Genetic and environmental factors, both reflected in family health histories, jointly contribute to the onset of mental disorders. We examined the intergenerational transmission of mental disorders using objectively-measured family health histories from three generations.
ApproachA population-based cohort study was conducted using administrative healthcare databases from Manitoba, Canada. The cohort included offspring who were 18 years or older between 1977 and 2020 with linkage to 1+ parent and 1+ grandparent. Mental disorders were identified using diagnosis codes from hospitalization and outpatient physician visit records and included mood and anxiety, psychotic, and substance use disorders. Logistic regression models were mutually adjusted for mental disorder history in grandparents, parents and/or siblings in addition to offspring demographics: sex, region, decade of birth and income quintile, and comorbidity. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated.
ResultsOut of 125,070 individuals, 59.1% were females and 57.8% were urban residents. 41,552 (33.2%) had a mental disorder during study period and 108,682 (86.9%) had a family member with a mental disorder history. Individuals were more likely to have a mental disorder if they had a family history: mother (OR 1.52, 95% CI 1.48-1.56), father (OR 1.21, 95% CI 1.17-1.25), sibling (OR 1.33, 95% CI 1.28-1.39), grandparent (OR 1.06, 95% CI 1.03-1.09). Compared with other mental disorders, psychotic disorders had the strongest association with family history: mother (OR 2.37, 95% CI 2.00-2.82), father (OR 3.00, 95% CI 2.40-3.76), sibling (OR 2.34, 95% CI 1.79-3.05). However, there was no association between psychotic disorders and grandparent history (OR 1.00, 95% CI 0.90-1.11).
ConclusionsWe observed a strong association between mental disorders family history across three generations and the risk of the mental disorders in offspring. This association was observed for all the investigated mental disorders. This work highlights the value of multigenerational data linkage in understanding the intergenerational transfer of mental disorders.
Devolved jurisdictions: experience from early research users of family justice data in SAIL Databank.
Authors: Garside Ludivine , Hodges Helen
Published: September 2022

ObjectivesThis session will share user experience with hosted datasets across two jurisdictions. Family justice is a new domain on the SAIL Databank. Given the devolved policy agenda, challenges arose at different stages in the process of deriving an analytical dataset to compare how children leave care in England and Wales.
ApproachDetails of applications to family courts, and their legal outcomes, were first provisioned in SAIL in 2020 by the respective Children and Families Court Advisory and Support Services (Cafcass) in England and Wales.  The analytical challenge was the structure of the datasets, which are fully relational and require information to be summarised and restructured for analysis at case-, application- or child-level.  The added complexity of cross-jurisdictional datasets will be examined along four dimensions at planning stage, set-up and life course of the project: data owners’ approvals, data provisioning, metadata, and data linkage within the host platform.
ResultsEarly dialogue reduced project risk by aligning timelines with datasets availability and project restrictions, including ethical and GDPR constraints.  Metadata allowed the availability and quality of variables to be gauged, alongside the data linkage necessary for analysis. Once data access started, assessing data consistency in each database was the most time-intensive element. High quality metadata can shorten this, and bypass data quality queries. Further in the project life course, clear responsibilities for metadata and data quality ensured that user queries were better targeted. Loss from linkage was partially compensated by the fact that children unsuitably linked remained visible, so researchers could report on which types of families and cases were unmatched, and beyond the project inform about matching rates to be expected from similar linkages.
ConclusionAn important feature of the Cafcass provision in SAIL is the final approval by data owners for researchers to access and link to other datasets in SAIL. With further datasets expected, new linkage opportunities offer excellent prospects for refining comparisons of family justice across England and Wales.
Involvement and engagement of seldom-heard communities in big data research.
Authors: Teodorowski Piotr , Ahmed Saiqa , Tahir Naheed , Fleming Kate , Rodgers Sarah , Frith Lucy
Published: September 2022

ObjectivesPublic involvement and engagement have been growing within big data research. However, seldom heard voices such as migrant and ethnic minorities communities are often underrepresented. This study explored how Polish and South Asian communities in the United Kingdom could be better included in public involvement and engagement activities.
ApproachWe conducted semi-structured interviews with Polish (n=20) and South Asians (n=20) to elicit their views on big data research, public involvement and engagement. We focused on Polish and South Asian communities as they represent some of the United Kingdom’s largest migrant and ethnic minority groups. Data were analysed using inductive thematic analysis. Public advisors were involved in the analysis. They and one of the researchers come from ethnic minority and offered insider insight into participants' perspectives and thus allowing us to unpick the complexity of experiences and backgrounds.
ResultsThe majority of participants were willing to become involved or engaged in big data research. However, we found there were multiple barriers to involvement, these included: language (especially for those for whom English is the second language); use of jargon by researchers; time restrictions and unfamiliarity with big data or public involvement. Some participants questioned how much migrants could be involved when they were only in the United Kingdom on a temporary basis. The participants made recommendations for how researchers can mitigate these barriers. Awareness-raising activities would allow people to expand their understanding and build their confidence when speaking about big data research in a second language. Participants spoke of the need for researchers to work more closely with local communities, especially with local gatekeepers.
ConclusionsThe results indicate that there is no ‘right’ way to involve and engage seldom heard communities around big data research. Researchers need to engage with communities, establish trust and develop a long-lasting relationships. These partnerships should move beyond single projects and aim to benefit both researchers and seldom heard communities.
Socioeconomic gradient in mortality of working age and older adults with multiple long-term conditions in England and Ontario, Canada.
Authors: Alarilla Anne , Stafford Mai , Mondor Luke , Hughes Jay , Knight Hannah , Kone Anna Pefoyo , Woodchis Walter
Published: September 2022

ObjectivesThere is mixed evidence on the influence of number of conditions on inequalities in mortality, we explored the association between number of conditions and deprivation on mortality whilst assessing the difference between working age and older adults. We replicated the analysis in England and Ontario, to provide a cross-jurisdiction comparison.
ApproachWe used individual level-linked data from primary care activity, secondary care and mortality data in England and Ontario. For both jurisdictions, we took a random sample of 600,000 adults from 1 January 2015 and followed them up till 31 December 2019. We used cox proportional hazard to test the influence of deprivation (measured using area-level deprivation in deciles) and number of conditions (measured at baseline and capped at six conditions) on survival. Age and sex were also measured at baseline. Analyses were stratified by working age (18-64 years) and older adults (65+ years) and were repeated for England and Ontario.
ResultsDeprivation gradient in mortality rate was steeper in working age than older adults in both jurisdictions. Number of conditions was associated with increased mortality rate, this was stronger for working age than older adults, in England (working age HR = 1.60, 95% CI 1.56,1.64 and older adults HR= 1.26, 95% CI 1.25,1.27) and Canada (working age HR=1.69, 95% CI 1.66,1.72 and older adults HR= 1.39, 95% CI 1.38,1.40). After accounting for number of conditions, the mortality rate associated with deprivation decreased but remained significant. The interaction between number of conditions and deprivation showed that adults with more conditions have a higher mortality rate and those living in deprived areas also have a higher mortality rate but having more conditions attenuates the deprivation gradient in mortality.
ConclusionNumber of conditions contribute to higher mortality rate and inequalities in morality, this is stronger for working age than older adults in England and Ontario. The fragmented health-care system may be contributing to poorer outcomes, further research should help identify which part of the pathway is driving these inequalities further.
AD|ARC: Construction of a research ready dataset to better understand farmers and farming households.
Authors: Hampton Jennifer , Webster Nicholas , Jordan Sophie , Morrison-Rees Sian , Bateson Orla , Watson James , McFarlane Scott , McAlpine Alastair , Cavin Liam
Published: September 2022

ObjectivesThe AD|ARC Administrative Data: Agriculture Research Collection is an ambitious and original linkage project, bringing together information about farmers and farming households from several sources. When complete, this research-ready dataset will assist in addressing three broad themes: health and well-being, prosperity and resilience, and engagement with agri-environment.
ApproachThe dataset is being constructed from information drawn from survey, census, and administrative sources. Necessarily, this includes working across government departments to ensure comprehensive coverage of farm, business, education, and health data. Similarly, data owners, processors, and researchers are working closely to ensure the resultant dataset meets expectations. Alongside this cross-sectoral aspect, the work is also cross-jurisdictional, with the intention being for the data to capture information about farms, farmers and farming households from across the UK.
ResultsRather than focus on the detail of the substantive research that AD|ARC will enable, this paper discusses some of the challenges and successes of this linkage project to date. Drawing on the experience of the teams from across the UK (England, Northern Ireland, Scotland, and Wales), the first part will discuss challenges faced in linkage of this multi-faceted project, alongside how the population census is being utilised to better understand farming communities, through the identification of both farming households and workers. Secondly, a broader discussion of the challenges and sensitivities of working across government departments and administrations will be presented, alongside ways of working developed to recognise and overcome these.
ConclusionThe AD|ARC project will result in an invaluable resource to better understand the farming community, which in turn will help to better inform policy debate and decision making. Alongside this, the process of creating the dataset has offered opportunities for learning and insight across a range of issues.
Adults in private family law proceedings in Wales: characteristics and vulnerabilities.
Authors: Cusworth Linda , Hargreaves Claire , Alrouh Bachar , Broadhurst Karen , Griffiths Lucy , Akbari Ashley , John Ann , Johnson Rhodri
Published: September 2022

ObjectivesPrivate law children cases are disputes, usually between parents after relationship breakdown, about arrangements for a child’s upbringing, such as where they should live and/or who they should see. To inform policy and practice, more information is needed about the families involved, including their characteristics, circumstances and vulnerabilities.
ApproachThis paper presents findings from research by the Family Justice Data Partnership – a collaboration between Lancaster and Swansea Universities – funded by the Nuffield Family Justice Observatory. Anonymised routinely-collected, individual-level, population-scale family justice data from Cafcass Cymru was linked with hospital and GP records, within the Secure Anonymised Information Linkage (SAIL) Databank for 18,653 adults involved in private law proceedings in Wales between 2014/15 and 2019/20 and a comparison group of 186,470 adults not involved in court proceedings, matched on age, gender, deprivation. The proportion of adults in the two groups with several health-related vulnerabilities recorded was compared.
ResultsAdults involved in private law proceedings were found to have higher prevalence of mental health problems, substance use, self-harm and exposure to domestic violence and abuse than the matched comparison group. Both men and women involved in private law proceedings were between two and three times as likely to have anxiety and depression than their peers in the comparison group. Almost 1 in 6 (15.3%) cohort women and more than 1 in 10 cohort men (10.7%) had a history of self-harm, again indicating a level of vulnerability more than double that observed in the comparison group. The research also revealed greater overall use of healthcare services by adults in private law proceedings, with the largest differences seen in the need for emergency health care.
ConclusionThis research has uncovered the heightened needs and vulnerabilities of both women and men involved in private family law applications. The findings have important implications for the family justice system, and for health and other services, and need to be considered in relation to the current programme of reform.
An architecture for building cohorts of images from real-world clinical data from the whole Scottish population supporting research and AI development.
Authors: Jefferson Emily , Krueger Susan , Macleod Ruairidh , Sutherland James , Nind Thomas , Mudie Roy , Prodan Bianca , Brooks Andrew , Wallace Robert , Morris Carole , Caldwell Jacqueline , Baxter Rob , Parsons Mark
Published: September 2022

ObjectivesTo research and develop tools and methods for building cohorts of images linked to longitudinal healthcare records from real-world clinical images from the whole Scottish population. To provide this capability for the Scottish Medical Imaging service (provided by the Scottish National Safe Haven) to support research and AI projects.
ApproachClinical images, especially when linked to routinely collected health data, are extremely useful for many types of research and AI development. However, finding and using clinical images for research data is challenging because:
1) Existing software used to search for images are designed for clinical care rather than research making it easy to find images for a particular patient. They are not designed to search for all images with particular characteristics e.g. slice thickness/scanning protocol/contrast agent/patient medication.
2) Reuse of clinical images for research requires de-identification, yet identifiable data can be present in many areas of the associated image file.
ResultsThe PICTURES (InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch) 5-year programme has developed an architecture for building cohorts of images based upon research criteria and providing these in a di-identifiable form within a Safe Haven environment. There are 3 zones:

An identifiable zone which stores the raw image data and a MongoDB database which captures the metadata
A de-identified zone which provides a database and tools for cohort building which do not require imaging data expertise
Several Project Private Zones (PPZs) where researchers can install custom software and access the de-identified images for their project

The architecture supports cohort building based upon features within pixel data, image metadata and linking to longitudinal health care records.
ConclusionPICTURES is currently enhancing the cohort building user interface used by the National Safe Haven and supporting exemplar projects. The SMI service is live and accepting requests for more information. The software is open source and we welcome the use of the platform by other Safe Havens/research groups.
Data First: Criminal Courts Linked Data research report.
Authors: Eaton Georgina , Hill Kylie , Summerfield Amy
Published: September 2022

ObjectivesThe Ministry of Justice’s pioneering data linking programme Data First, funded by Administrative Data Research UK, links administrative datasets across the justice system and with other government departments to enable research providing critical new insights on justice system users, their pathways, and outcomes across a range of public services.
ApproachThe first two datasets shared under the Data First project are magistrates’ courts and Crown Court data which have been deidentified, deduplicated and linked to provide a joined-up picture of criminal court defendant and case journeys. Accredited researchers can access this data using the ONS Secure Research Service to conduct research. Administrative Data Research UK has funded four Research Fellows to conduct analysis using this linked data. Additionally, analysts within the Ministry of Justice Data First team have published a research report showcasing the potential of the linked criminal courts data. The presentation will primarily focus on this work.
ResultsThe Data First criminal courts datasets have enabled, for the first time, the extent and nature of repeat users to be explored at scale for research. In March 2022, the Ministry of Justice published exploratory analysis of returning defendants and the potential of linked criminal courts data. The key findings of this report will be covered in the presentation. The research demonstrates more than half of defendants returned to the courts within the data period, but this was highest for specific offence groups, including theft, robbery and drug offences. Locality-based analysis on Crown Court defendants highlights important insights on the backgrounds of justice system users, showing an over-representation of defendants residing in the most deprived areas in England and Wales compared to the general population.
ConclusionThe presentation will demonstrate how linked administrative data available through the ground-breaking Data First programme can be effectively used for research. This insight improves our understanding of individuals in the justice system as well as providing a rich resource to develop the evidence base for government policy and practice.