bluuebunny/crossref_metadata_2025 huggingface dataset:
Is there equity of access to revascularisation in Wales by socioeconomic deprivation?
Authors: Evans Lloyd
Published: November 2019

Background Long-standing evidence of inequity has been shown to exist in the management of patients with heart disease. The majority of UK studies during the last 20 years have concluded that patients living in the most deprived areas were disadvantaged by lower revascularisation rates, despite having a higher level of need.

Over the last 10 years, a systematic approach at tackling health inequity has played an important role in public health policy and practice throughout the UK. National service frameworks for cardiac disease have contributed to significant changes to the way coronary revascularisation is provided. Their aim is to provide access to the best possible treatment in timely fashion for all with heart disease.

Aim To investigate the impact of service redesign in the provision of revascularisation procedures on the historical socioeconomic inequity in revascularisation rates for patients with heart disease.

Methods A retrospective cohort design was created through the linkage of population administrative datasets, with cohorts created in both the pre- and post-service redesign periods.

Cox Proportional Hazards models were used to estimate the likelihood of receiving revascularisation to see did these differ between periods by socioeconomic status.

Results Revascularisation was provided equitably by socioeconomic deprivation after the service redesign, whereas pre-service redesign, subjects resident in the most deprived quintile were 20% less likely to receive revascularisation.

Measures to improve access to services are working, with previous signs of socioeconomic inequity of access to revascularisation not apparent, but rather increased access for all and in a timelier manner regardless of socioeconomic deprivation.

Conclusion While inequities and inequalities remain throughout healthcare today, this research provides evidence that measures can be successful in hard to reach groups such as those living in the most deprived areas, and these unnecessary variations can be reduced or eradicated in the future.
Early-life cognitive ability and recovery from stroke
Authors: Altschul Drew , Iveson Matthew , Deary Ian
Published: November 2019

Background In ageing populations it is increasingly important to understand what contributes to health and wellbeing in later life. Older adults in particular are prone to function-limiting health issues which impair their ability to be productive and live independently. The incidence of stroke – one of the most common morbidities among older adults – has been shown to be predicted by early-life cognitive ability, such that those with higher cognitive ability are at lower risk. However, less is known about the role that early-life cognitive ability plays in recovery from stroke.

Aim Investigate the association between early-life circumstances, particularly cognitive ability, and later-life recovery from stroke.

Methods Using a large sample of individuals born in Scotland in 1936, historic data from the Scottish Mental Survey 1947 will be linked to administrative and healthcare records from across the life course. Incidence of stroke can be identified using healthcare records prior to 2011, and later functional outcomes will be extracted from the Scottish Stroke Care Audit and from Scottish Censuses 2001 and 2011.

Analysis Multiple regression models will be used to examine associations between early-life variables and changes in post-morbid general health and physical function among those experiencing stroke prior to 2011. Time since morbidity, number of morbidity events prior to follow-up, and severity of morbidity will be included as covariates. Analyses will be stratified by type of stroke to account for heterogeneity in aetiology, and recovery profiles will be compared between different types of stroke where possible.
Enough with the tables, we need ideas.
Authors: Stevens Antony
Published: November 2019

One of the consequences of owning a farm in Central Brazil is that I frequently meet people who do things and have life narratives that we are unlikely to come across in texts on sociology or epidemiology. In the nearby town they live in streets that have postal codes that would place all the residents in the same cell of a contingency table.

But in each residence lives a family with a separate and unique story. I believe that it is worth asking whether it is really the perceived similarities that determine their health outcomes. Yes, perhaps when sanitation is involved, but there are other health outcomes that would not be centered on the postcode.

I have spent the last two years helping to link notifications of interpersonal violence with birth and mortality records. The idea is to find some way to stop men harming their partners. If this can be achieved by changing the way the law reacts to the violence then these linkages may prove useful, especially when legal and penal records are included in the studies.

But what if what needs to be changed is the way a boy is treated in his first year of life? It is unlikely that information collected at the time of the violence would be accurate about events in early childhood. How could record linkages tell us that we should be looking elsewhere? I have no idea. But I believe it to be the most important question in population studies today.

Statisticians are always pleased to tell us that we have failed to prove something. We need a methodology that tells us where to look. Also it must be based on something with more possibilities than those currently offered by diluted Marxism.
Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications
Authors: Tibble Holly , Sheikh Aziz , Tsanas Athanasios , Horne Robert , Mizani Mehrdad , Simpson Colin , Lay-Flurrie James
Published: November 2019

Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage.

Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records.

Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined.

Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records.

Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.
Depression and changes in educational attainment using administrative data: The role of socio-demographic characteristics
Authors: Wickersham Alice , Dickson Hannah , Stewart Robert , Ford Tamsin , Downs Johnny
Published: November 2019

Background Research has shown depression to result in poorer educational attainment among children. However, comparatively little is known about the relationship between depression and attainment over time, and the role that children’s socio-demographic characteristics might play.

Aim To describe the educational attainment trajectory profiles of children with a clinical diagnosis of depression, and to explore whether these trajectory profiles vary by gender, ethnicity, area-level deprivation and age at diagnosis.

Methods This is a historical, longitudinal cohort study, making use of two linked administrative datasets. First, a cohort of children (<18 years) were identified who had received a first diagnosis of depression from the South London and Maudsley NHS Foundation Trust between 2007 and 2013 (n=1490). Their pseudonymised secondary mental healthcare records were extracted via the Clinical Record Interactive Search application at the Trust’s Biomedical Research Centre. These records were then linked to England’s National Pupil Database, which contains attainment data for these children at three timepoints of interest: Key Stage 1 (ages 5 to 7 years), Key Stage 2 (7 to 11 years) and Key Stage 4 (14 to 16 years). Longitudinal mixed models for repeated measures will be conducted to model changes in attainment over these three timepoints according to gender, ethnicity, area-level deprivation and age at diagnosis.

Results Preliminary results are suggestive of attainment trajectories in this cohort varying according to their socio-demographic characteristics. Analysis will be completed August 2019.

Conclusion The findings from this study will identify whether some groups of depressed children experience better or worse patterns of educational attainment over time. This could highlight priorities for intervention in England’s ongoing efforts to improve child mental health provision in schools.
The Office for National Statistics Longitudinal Study
Authors: Sizer Alison , Duke-Williams Oliver
Published: November 2019

Background and Rationale The ONS Longitudinal Study (‘the LS’) covers England and Wales and includes individual data from the 1971 – 2011 decennial censuses and linked information on births, deaths and cancer registrations. It is representative of the population of England and Wales.

Aim This presentation describes the LS and the linked administrative data, and showcases recent/ prominent examples of research.

Methods and Approach The LS is built around samples drawn from decennial censuses, with its initial sample drawn from the 1971 Census. It also contains information about other people living in a sample-member’s household. Substantial emphasis is placed on security of access to the data and its responsible use. All research outputs are checked and are only released to users once disclosure control requirements are met. Linkage of study members from one census to another and vital events is carried out by ONS.

Results The LS has been used for a variety of research. Using linked census and death records occupational differences in mortality rates have been researched. Individual records from all five censuses have been used to contribute to research social mobility, and research has also investigated the effects of long-term exposure to air pollution. Research has provided evidence of impact for social policy issues, e.g. health inequalities and the State Pension Age Review.

Discussion The main strength of the LS is its large sample size (>1 million), making it the largest nationally representative longitudinal dataset in the UK. This allows analysis of small areas and specific population groups. Sampling bias is almost nil, and response rates are very high relative to other cohort and panel studies.

Conclusion The ONS Longitudinal Study is a vital UK research asset, providing access to a large sample of census data linked across five censuses. It is strengthened through linkage to events data.
Data linkage for public health research – the Fforestfach tyre fire
Authors: May Leon , Evans Lloyd
Published: November 2019

Background The Fforestfach tyre fire started on the 16th of June 2011 and continued to burn for 22 days.

Smoke from tyre fires contain a number of chemicals that might cause health problems, especially for people who already have long-term health conditions.

This research investigated whether people living close to the Fforestfach fire contacted their General Practice (GP) more often during the fire than they might have done otherwise. This is important both for the people living in the Fforestfach area and also for those living near similar fires in the future.

Aim To use advances in mapping and data linkage techniques to assess associations between the Fforestfach fire incident and respiratory and cardiovascular health outcomes. The report focusses on the occurrence of respiratory and cardiovascular Read codes in patient’s GP records.

Methods Using data linkage, information provided by the Met office was used to identify households likely to have been exposed to above threshold levels of pollution. Residents from these households were linked to their GP records via the Secure Anonymised Information Linkage (SAIL) databank. Logistic regression models tested associations between above-threshold exposure to a specific type of pollution (PM10) and an increase in GP contact.

Results Regression modelling demonstrated a small but significant increase in GP contact for respiratory conditions in patients with pre-existing asthma. The models did not demonstrate any affect in the general population.

Conclusion The study demonstrated the value of linking health and environmental data using advanced data linkage techniques.

Findings support current health advice used in environmental incidents such as this, that individuals with certain chronic conditions may be more likely to experience symptoms when exposed to 24-hour mean concentrations of PM10 exceeding 50µg/m3; but the risk of significant symptoms as a result of such exposure in the general population is likely to be minimal.
Building a research partnership between computer scientists and health service researchers for access and analysis of population-level health datasets: what are we learning?
Authors: Schull Michael , Brudno Michael , Ghassemi Marzyeh , Gibson Garth , Goldenbrg Anna , Paprica Alison , Rosella Laura , Stukel Therese , Victor Charles , Virtanen Carl
Published: November 2019

Background and rationale There is widespread enthusiasm to improve health through the application of artificial intelligence and machine learning (AI/ML) methods to large population-level health datasets. Achieving this may require successful collaboration between institutions as well as between computer scientists (CS), machine learning researchers (MLR) and health service researchers (HSR).

Main Aim Describe lessons learned in creating the Health Artificial Data and Analysis Platform (HAIDAP) in Ontario, Canada.

Methods/Approach A partnership between a HSR institute (ICES), an AI/ML institute (Vector) and a high-performance computing center (HPC4H) was initiated in 2017 to enable the application of AI/ML methods to population-level health data for the province of Ontario (population 14M). We describe lessons learned (and being learned) following the HAIDAP’s launch.

Results The HAIDAP was launched in 2019. Major learnings include: 1) importance of institutional partnerships and alignment with institutional strategies; 2) potential of joint institutional risk-sharing models; 3) need for scientific collaborations bridging disciplines around joint research projects; 4) sensitivity to different scientific cultures (e.g., academic prestige of conference proceedings for MLR vs journal publications for HSR; traditional statistical vs. ML model assumptions); 5) differences in research timeline expectations; 6) different experience with and expectations for access to de-identified routinely collected data (e.g., need for research ethics committee project approvals and privacy impact assessments); 7) developing data access models that enable greater flexibility (e.g., importing code or using open source tools); 8) broadening data access models to allow modern high-dimensional exploratory data analysis; 9) obtaining support of information/privacy regulator; 10) the hardware is the (relatively) easy part compared to other success factors.

Conclusion The HAIDAP has enabled multi-disciplinary collaborations and novel AI/ML research of Ontario’s population-level health data. Collectively we have learned that additional effort is required to develop systems and processes enabling more efficient access to data and analytic tools for the analysis of administrative health data.
Thriving, catching up or falling behind: Immigrant and refugee children’s kindergarten competencies and later academic achievement
Authors: Gagné Monique , Guhn Martin , Emerson Scott , Magee Carly , Milbrath Constance , Gadermann Anne
Published: November 2019

Background with rationale Immigrant and refugee children and adolescents form a growing socially, culturally, and economically diverse group with varying adaptation outcomes.

Main Aim In this Canadian, population-based study, we wanted to identify the varying academic achievement trajectories that immigrant and refugee children followed from childhood to adolescence (e.g., thriving, catching up, or falling behind) and whether these differences could be predicted at school entry, based upon select social/migration factors and teacher-assessed literacy, numeracy, and social-emotional competencies in kindergarten.

Methods The study used a retrospective, longitudinal, population-based design and leveraged linked, individual-level administrative data from four sources (Immigration, Refugees, and Citizenship Canada, Ministry of Education, Ministry of Health, Human Early Learning Partnership) to identify a cohort of immigrant and refugee children in British Columbia, Canada (N = 9,216). We utilized a novel analytical approach (Group-based Trajectory Modeling) that allowed us to capture heterogeneity in the Grade 4 to Grade 10 academic (literacy and numeracy) trajectories.

Results We found that immigrant and refugee children followed a range of academic achievement trajectories from Grade 4 to Grade 10 – some children thriving, some catching up, and others falling behind over time. A number of social/migration factors (e.g., sex and refugee status) as well as literacy, numeracy, and social-emotional competencies in kindergarten predicted these later academic trajectories in unique and sometimes powerful ways.

Conclusion In all, we found that not all immigrant and refugee children start school on equal footing and this was associated with long-term outcomes. The implications for the importance of early, tailored interventions to set immigrant and refugee children onto paths of positive adaptation will be discussed.
Ambient Air Pollution and Health in Northern Ireland
Authors: Rowland Neil , McGovern Mark , McVicar Duncan , O'Reilly Dermot
Published: November 2019

The breathing of polluted air is associated with the development and exacerbation of various health conditions, and the cost of treating such conditions is substantial. However, while the correlation with respiratory-related health conditions is well documented, less is known about this relationship from a longitudinal perspective and how pollution affects other health problems, such as neurodegenerative diseases (e.g. dementia) or diseases of the endocrine system (e.g. diabetes). More research is therefore needed to better identify the health risks and costs of pollution.

The aim of this study is to investigate the relationship between exposure to ambient air pollution and health outcomes at the individual level. The analysis is based on data from the Northern Ireland Longitudinal Study (NILS) - a representative 28% sample of the Northern Ireland population drawn from an administrative health database and linked to Census returns and other administrative databases - linked with routinely collected prescriptions data. Exposure to air pollution is assessed by linking modelled background pollution data at a 1x1 km grid square resolution to residential address. Annual measures of eight separate pollutants will allow us to examine cumulative exposure between 2001 and 2016.

Regression analysis will be used to estimate the health effects of exposure to air pollution. Preliminary analysis currently underway will focus on outcomes such as self-reported health at the 2011 Census (e.g. the presence of breathing difficulties and memory loss) and all-cause and respiratory-related mortality since 2001. Stage two of the analysis will utilise the prescriptions linkage to define illness-specific health outcomes using receipt of medicines prescribed by General Practitioners (GPs) to treat cardiovascular- and respiratory-related health conditions as well as diabetes, dementia and Parkinsonism. Longitudinal analysis will enable us to assess whether these relationships are affected by unobserved confounders. Preliminary results for both stages of the analysis are expected by autumn 2019.
InFORM: Improving care for people who Frequently call 999: co-production of guidance through an Observational study using Routine linked data and Mixed methods
Authors: Khanom Ashrafunessa , Edwards Adrian , Edwards Bethan , Hughes Heather , John Ann , Petterson Robin , Phillips Ceri , Porter Alison , Rees Nigel , Scott Jason , Watkins Alan , Snooks Helen
Published: November 2019

BackgroundPeople who frequently call the 999 ambulance service present an operational challenge to providers and their needs are inadequately met by current service provision.

Aim of researchTo understand patterns and health outcomes of frequent calling and to work with stakeholders to co-produce guidance for formal testing in a future evaluation.

MethodsThis mixed methods study will include a scoping review of national and international literature followed by an epidemiological study of callers at the all Wales level exploring health outcomes through anonymised linked data. We will also explore the views of patients using qualitative Bio-photographic interview method with a follow up interview at six months and use focus groups with care providers from across primary and emergency care and the third sector. We will use generalised linear model to analyse quantitative data and qualitative data will be analysed thematically.

ResultsFindings will include follow up of eligible patients; successful data matching and data linkage; retrieval of outcomes within 12 months. Outcomes will include: adverse events, deaths, emergency admissions; 999 attendances. Qualitative results will include Bio-photographic interviews with completed scrap books and interviews based on the books (n=34). Care provider focus groups (n= 22).

Output Co-produced guidance developed with stakeholders.

ConclusionHigh users of the 999-ambulance represent a significant policy challenge to emergency ambulance services and often present with complex needs. This study will inform on the characteristics of callers and how to address their care supported with a co-produced guidance for care providers.
National Therapeutic Indicators in Scotland and Financial Incentives
Authors: MacBride-Stewart Sean , Guthrie Bruce , Marwick Charis , Hurding Simon
Published: November 2019

Background with rationale National Therapeutic Indicators (NTI) in Scotland use national administrative data to identify variation and drive quality improvement in GP practice prescribing. Twelve indicators were developed in 2011 and in 2012 practices were offered financial incentives to review their prescribing in two where there was greatest potential for improvement.

Main AimTo quantify the impact of 2012 NTI alone and in combination with financial incentives.

Methods/ApproachSix NTI were selected for analysis, aiming for heterogeneity in terms of whether the focus was quality of care (clinical) or cost effectiveness (cost). Impact was evaluated using segmented linear regression of time-series prescribing data, and changes in prescribing at 12 and 24 months after NTI introduction were estimated. Other national interventions expected to influence GP prescribing were also modelled.

ResultsOne selected NTI could not be modelled because of non-linear trends in the baseline period. In practices not financially incentivised to review prescribing, improvements were variable. At 12 months relative changes were dipyridamole −31.9% (95%CI; −43.1 to −20.7), quinine −3.6% (95%CI; −7.2 to −0.1), non-preferred statins 6.2% (95%CI; 1.0 to 11.5), hypnotics and anxiolytics −2.9% (95%CI; −7.3 to 1.6) and high strength inhaled corticosteroids −1.0% (95%CI; −1.7 to −0.3).

In comparison financially incentivising practices resulted in larger improvements and improvements in all NTIs modelled. At 12 months relative changes were dipyridamole −65.3% (95%CI; −68.4 to −62.2), quinine −52.3% (95%CI; −54.7 to −49.9), non-preferred statins −17.8% (95%CI; −21.9 to −13.6), hypnotics and anxiolytics −9.6% (95%CI; −12.6 to −6.7) and high strength inhaled corticosteroids −6.0% (95%CI; −7.3 to −4.8).

ConclusionGP prescribing can be improved through feedback of national administrative data prescribing indicators. Impact on prescribing is larger when financial incentives to focus on particular areas of prescribing are additionally used.
Linking Pathology Datasets – Trials and Triumphs
Authors: Stokes Brian , Jose Matthew , Wiggins Nadine , Albion Tim
Published: November 2019

Background with rationaleThe burden of chronic kidney disease (CKD) has increased rapidly in Australia over recent years. The financial cost of treating people with end-stage kidney failure by conventional models of dialysis or transplantation represents a substantial healthcare cost and is likely to continue to increase in coming years. Tasmania, the smallest state of Australia and its only island state, has the highest burden of chronic disease nationally, including kidney disease. The aim of this study was to use data-linkage to develop a state-wide dataset to quantify the burden and distribution of CKD, including identifying barriers to dialysis treatment services.

Methods/ApproachThe Tasmanian Data Linkage Unit (TDLU) used a complex data linkage design comprising seven disparate datasets representing public and private pathology, public hospital admitted patient and emergency department data, cancer records, dialysis and transplant records and death notifications. A cohort was selected from public and private providers of pathology services in the state to support the establishment of a comprehensive researchable dataset. The datasets spanned the period 2004-2017 and included linkage of both state and national data.

ResultsThe study cohort comprised just under 490,000 individuals in the Tasmanian population from the two pathology datasets, with a combined total of 1,347,00 total links made across all datasets. Individual unit records were geocoded according to the Australian Statistical Geographic Standard (2011) with over 92% of the 374,000 unique addresses identified in the public pathology dataset geocoded to address level.

ConclusionThe final researchable dataset compiled by the research team following linkage is providing an enormously powerful asset to help answer questions specific to CKD in Tasmania, and that in turn is anticipated to result in greater access to services, improved care and better patient outcomes.
Unleashing The Power of Your Master Linkage Map – Is There A Role For Business Intelligence Tools In Supporting Data Linkage?
Authors: Stokes Brian
Published: November 2019

Background with rationaleBusiness Intelligence (BI) software applications collect and process large amounts of data from one or more sources, and for a variety of purposes. These can include generating operational or sales reports, developing dashboards and data visualisations, and for ad-hoc analysis and querying of enterprise databases.

Main AimBusiness Intelligence (BI) software applications collect and process large amounts of data from one or more sources, and for a variety of purposes. These can include generating operational or sales reports, developing dashboards and data visualisations, and for ad-hoc analysis and querying of enterprise databases.

Methods/ApproachIn deciding to develop a series of dashboards to visually represent data stored in its MLM, the TDLU identified routine requests for these data and critically examined existing techniques for extracting data from its MLM. Traditionally Structured Query Language (SQL) queries were developed and used for a single purpose. By critically analysing limitations with this approach, the TDLU identified the power of BI tools and ease of use for both technical and non-technical staff.

ResultsImplementing a BI tool is enabling quick and accurate production of a comprehensive array of information. Such information assists with cohort size estimation, producing data for routine and ad-hoc reporting, identifying data quality issues, and to answer questions from prospective users of linked data services including instantly producing estimates of links stored across disparate datasets.

Conclusion BI tools are not traditionally considered integral to the operations of data linkage units. However, the TDLU has successfully applied the use of a BI tool to enable a rich set of data locked in its MLM to be quickly made available in multiple, easy to use formats and by technical and non-technical staff.
Linking two administrative datasets about looked after children: testing feasibility and enhancing understanding
Authors: Hooper Jade , Cusworth Linda , Whincup Helen
Published: November 2019

Background with rationaleEvery year all 32 local authorities in Scotland provide information on looked after children in their area to the Scottish Government. This forms the basis for the annual Children Looked After Statistics (CLAS). Information is also collected by Scottish Children’s Reporter Administration (SCRA) on all children who are involved in the Children’s Hearings System. Until now these two data sets had never been linked.

Main Aim To test the feasibility and success of the linkage on the basis that these datasets had not previously been linked, and if linkage was possible, use this data to enhance our understanding of the child and process factors associated with pathways to permanence or lack of permanence.

Methods/ApproachVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians.

ResultsFor the first time, as part of the Permanently Progressing? Building secure futures for children in Scotland study, these two data sets were linked safely and successfully for 1,000 children who became looked after in 2012-13 when they were aged five and under.

The linkage provided important new information for practitioners and policymakers. In this presentation we will focus on the key findings, such as what it told us about previous referrals and methodological insights regarding these data sets and their linkage.

ConclusionThe data linkage process was complex and time-consuming but possible. The data we were able to link provided valuable information that enhanced our understanding of child and process factors.
Household Matching for the 2021 Census
Authors: Plachta Josie , Tomlin Charlie
Published: November 2019

Matching households between the UK census and census coverage survey is an essential requisite to estimating census overcount and undercount. Census quality requirements are extremely high. In 2021, we will aim to produce outputs within a year of census day (previously this has been within 16 months). Clerical searching is very time-consuming, and so to meet these challenging timelines, we need to increase the automatic match-rate.

Matching is done at the household as well as person level. In 2011, automatic household linkage primarily used a derived ‘head of household’ alongside other household variables but issues with assigning the same HOH to a household on the census and CCS reduced the effectiveness of the method. To combat this, we have designed a method that combines the variables from the household itself with sets of individual person data and runs deterministic match-keys to match households. Following this, associative matching was applied to find some of the remaining households. The method used the households of matched people to produce candidate household matches for clerical review, a process substantially quicker than clerical searching.

Using the 2011 census as our test data where 264,882 matches were found (60% automatically), our new methods matched 94% of households through deterministic match-keys with a precision of 99.99%. An additional 3% were linked through association, 5% were sent for clerical matching, leaving only 1,600 (<0.001%) matches to be found through resource-heavy clerical searching.

Using match keys made up of sets of person data in addition to household variables, and associative matching from person data, we have been able to successfully increase the number of matches made automatically on test data from Census 2011. This will decrease the resources needed for clerical matching and searching in 2021 enabling us to meet shorter timelines and maintain higher quality.
What happens without population data? – The case of Ukraine
Authors: Molnár-Kovács Dorottya
Published: November 2019

According to the recommendation of the United Nations, national censuses should be taken at least every 10 years, and countries across Africa, Asia, the Americas and Europe are usually fairly disciplined in following this instruction. True, some European countries, including Denmark, Finland, the Netherlands or Iceland have in the last 30 years replaced traditional census enumerations with maintaining precise administrative registers, Ukraine however, is not one of these countries. With its last census carried out in 2001, the largest entirely European country is similar in this regard to countries such as Uzbekistan, Iraq or Sri Lanka.

Because there is no reliable source of population data in Ukraine, estimations from different organizations and individuals vary considerably. In 2019 the UN estimates Ukraine’s population to be 43.8 M (the 2001 census result was 48.7 M), while according to the State Statistics Service of Ukraine (Grosstat), the number is 42.1 M (1.7 Million difference). The picture gets more complicated when we look at non-official estimates by politicians, non-governmental organizations, foreign press and further, otherwise serious sources: calculations that consider the proto-states of the Donbass region and Crimea to be part of Ukraine get results of 30-35 M people, while without these regions, Ukraine’s population is calculated to be somewhere between 20-26 million. Looking at these numbers the only apparent conclusion one can arrive at is the incredible degree of uncertainty.

In a country where most families are in one way or another affected by permanent emigration, forced migration due to the war, seasonal work migration and the emigration of military-age men to avoid getting drafted, the population changes quickly. In my poster presentation I aim to showcase a number of different “creative” methods that official institutions in Ukraine use to get a picture of the actual population of the country and its regions, focusing mainly in Transcarpathia. These resourceful but often highly unreliable calculations include measuring bread consumption regionally and keeping count of the active mobile service users.
Pros and cons of using anonymised linked routine data to improve efficiency of randomised controlled trials in healthcare: experience in primary and emergency care
Authors: Snooks Helen , Watkins Alan , Jones Matthew , Khanom Ashrafunessa , Jones Jenna , Lyons Ronan A
Published: November 2019

BackgroundThe use of anonymised routine linked data in designing and conducting randomised controlled trials (RCTs) has great potential. Sample sizes can be large, inclusion rates high and follow up periods prolonged, while the disruption to participants’ usual routines may be minimised. However, challenges and limitations in using routine linked data in RCTs remain.

Aims To describe, in primary and emergency settings, challenges and opportunities associated with designing and conducting RCTs using anonymised linked routine data to identify study participants and gather outcomes.

MethodsIn each of these trials we have used routine linked data as a key part of the research study design:

PRISMATIC (a stepped wedge trial of predictive risk stratification in primary care) utilised linked data outcomes related to emergency admissions to hospital, GP activity and outpatient appointments. Outcomes were included for 230,000 people registered to participating GP practices in the Swansea area

SAFER 2: a cluster randomised trial of referral to falls services by ambulance paramedics included linked data outcomes related to subsequent emergency episodes for 4,655 patients across three UK regions

TIME: feasibility trial of Take Home Naloxone randomised by city; routine linked data used to identify population for inclusion in follow up and outcomes

Regulatory processes - ethics, research and information governance permissions - have caused delay in each trial; inclusion rates have been much higher than is usual in RCTs (outcomes for >80% of eligible patients); large trials have been achievable at reasonable cost (each trial <£2,000,000). Questions remain about differences between self reported and routinely available outcomes; and between routine data outcomes collected prospectively and through the anonymised linked route.

ConclusionThere are clear benefits in using anonymised linked data outcomes in trials but further research is required to understand costs and limitations.
Defining Acute Kidney Injury Episodes
Authors: Davies Gareth , Scale Timothy , Akbari Ashley , Chess James , Lyons Ronan A
Published: November 2019

BackgroundAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.

Main AimTo describe four different potential definitions of an AKI episode.

MethodWe identified AKI using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person’s index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine.

ResultsThere were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,505, 33,759, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively.

ConclusionAKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis.
Developing and evaluating national severity distributions for use in Burden of Disease studies: a case study of cancers in Scotland
Authors: Wyper Grant , Grant Ian , Fletcher Eilidh , McCartney Gerry , Stockton Diane
Published: November 2019

BackgroundIncreasingly Burden of Disease (BOD) measures are being used to influence policy decisions because they summarise health loss in an equitable manner. An important part of producing non-fatal BOD estimates are severity distributions (SDs). The Global Burden of Disease (GBD) study use the same SDs across countries due to a lack of available data.

Aim To develop and assess the impact of national SDs compared with GBD worldwide severity distributions for 21 cancer types.

MethodsPatient-level records from the Scottish Cancer Registry for 21 cancers were obtained and linked to death registrations. We estimated prevalent cancer cases for 2016 and assigned each case to one of four phases (diagnosis and treatment; controlled; metastatic; and terminal) using GBD 2016 study definitions. SDs were calculated by considering relative proportions. The impact of choice of SDs was evaluated by comparing relative differences between weighted-average disability weights (DW) derived using GBD 2016 worldwide SDs with those derived from Scottish SDs.

ResultsFor the majority of cancers the most prevalent phase was the controlled phase, which contributed a higher proportion than the combined proportion from the other three phases across all cancers except mesothelioma. Differences in the composition of severity meant that most point-estimates of Scottish severity proportions were out-with the 95% uncertainty intervals. These differences resulted in overestimates of weighted-average DWs based on GBD 2016 worldwide SDs (17 out of 21 cancer types). The largest relative overestimates were for gallbladder and biliary tract cancer, oesophageal cancer and pancreatic cancer (71%, 32% and 31% higher respectively).

ConclusionThese findings illustrate a systematic bias introduced by using worldwide SDs. Current non-fatal BOD estimates should not be interpreted too precisely when comparing populations when they rely on data inputs from other countries. It is essential to ensure that any estimates are based upon country-specific data as far as possible.
Developing a training curriculum for researchers working with routine data: understanding professional and lay stakeholder priorities – The CENTRIC Study
Authors: Lugg-Widger Fiona , Munnery Kim , Townson Julia , Robling Mike
Published: November 2019

BackgroundAnalysis of routine data makes an important contribution to service evaluation and research, providing cost-efficiencies, objective outcomes, burden reduction for patient and the public and is promoted by both policy and funders. Organisational, legal and ethical governance provide a framework for research but there is an onus on researchers to maintain their awareness of good practice when working with routine data.

Aim To co-produce with data provider, researcher and public stakeholders a training curriculum for researchers working with routine data.

Methods A curriculum for online and face-to-face training will be developed through consultations with three stakeholder groups. The first are researchers with existing interest /experience of using routine data. An online survey will be disseminated via UK research networks encompassing a range of disciplines and sectors. The non-probabilistic survey addresses current challenges, training experiences and learning preferences. Focus groups with three UK data provider organisations (SAIL, NHS Digital, National Pupil Database) will address provider’s experience of working with researchers, any training or support they either require, provide or sign-post to and opportunities to support best practice. Thirdly, a facilitated public consultation exercise using a deliberative enquiry process will be undertaken as a day’s workshop and include lay contributors identified via the HealthWise Wales cohort.

ResultsWe will present initial results from each stakeholder engagement activity and show how this has informed the draft training curriculum and our understanding of potential benefits that the training will deliver.

ConclusionsThe public may remain largely unaware of data captured when using public services, how it may contribute to research and the protections that apply. Involving the public in the development of researcher training informs this process and enables researchers to more effectively engage with patients and participants. Training will be promoted through data provider and research networks across sectors in the UK.
The potential of linking cohort participants to official criminal records: a pilot study using the Avon Longitudinal Study of Parents and Children (ALSPAC)
Authors: Teyhan Alison , Cornish Rosie , Boyd Andy , Macleod John
Published: November 2019

BackgroundPolicing in the UK is increasingly taking a public health approach to tackling crime. Birth cohort studies are potentially well-placed to provide evidence on the most important risk and preventative factors. Linking official police records to cohort data offers considerable potential for future research, but data privacy concerns mean achieving such linkage is challenging. As a pilot, the Ministry of Justice provided ALSPAC with an anonymous extract (cannot be linked to study data) detailing consenting participants’ criminal convictions and cautions held within the Police National Computer (PNC).

Main AimTo use anonymised PNC data to inform the potential for future research using ALSPAC and individual-level linked crime data.

MethodsWe examine criminality (type, when and where committed) in the cohort participants who were sent for linkage to the PNC in 2013 when they were aged approximately 21 years (n=7358). We consider the representativeness of these participants by comparing their self-reported crime-related behaviours during adolescence to those of participants who did not consent to linkage (n=7604). Finally, we consider these self-reported measures by present-day crime linkage consent status.

ResultsOf those sent for PNC linkage, 885 were linked to a criminal record(s) comprising 2635 convictions and 1365 cautions. A third related to serious crimes, and >90% were committed in Avon and Somerset constabulary. Those sent for linkage self-reported fewer criminal behaviours than those not sent for linkage. Regarding present-day consent status, those who have explicitly opted-in generally self-report fewer criminal behaviours and have less missing data than those who have not responded to the consent campaign.

ConclusionIf future linkage to individual police records was only achieved for explicit consenters, this would under-estimate criminality in the cohort. The geographical clustering of the crimes means local police records, which are more detailed than those of the PNC, could also be used.