Suchergebnisse

ABSTRACTObjectivesUnintentional house fire incidents, injuries and deaths are a serious public health concern in the UK, which disproportionally affect certain groups in the population. Whilst house fires have decreased in recent years; growing financial pressures in the Fire and Rescue Services (FRSs) have resulted in funds dedicated to fire preventative activities becoming increasingly limited. To ensure ever limiting resources are targeted towards those households at greatest risk, it is essential the FRSs' are accurately informed about the types of household at increased risk. The aim of this project is to undertake a large-scale case-control study, to identify the distinguishing household level risk factors associated with unintentional house fire incidents, injuries and deaths.
ApproachUnintentional house fire incidents reported to the Welsh FRS between the years 2003-2008, were anonymised and incorporated into the Secure Anonymised Information Linkage (SAIL) Databank at the Farr Institute, Swansea University. 6943 case households (households which reported a fire to the FRS) were time-matched to 347,150 control households (case:control ratio 1:50). Individuals registered as living at these properties on the date of the fire were established using the Welsh Demographic Service (WDS) dataset. Household level variables will be created by linking case and control households to other demographic, health, educational and environmental datasets in SAIL. Conditional Logistic Regression will be used to estimate matched odds ratios and 95% confidence intervals.
ResultsPotential risk factor variables were selected on the basis of a systematic review and theoretically plausible variables. Covariates include: household composition (e.g. age and gender of residents), socioeconomic status, educational attainment, smoking, alcohol consumption, mental health conditions, other health related conditions, mobility and sensory impairments and property related characteristics. Fire related circumstances (e.g. fire ignition source, presence of a smoke alarm) will also be investigated in logistic regression models exploring risk factors for injury and death. Results will be presented at the conference.
ConclusionThis is the first large-scale analysis of risk factors for unintentional house fire incidents, injuries and deaths. The findings from this project will be translated into comprehensible infographics, designed to support the FRSs, other partner organisations and the general public, recognise high risk households in need of preventative interventions.

Background with rationaleInjury is a leading cause of premature mortality, morbidity and disability worldwide. In Wales, injuries result in approximately 3 deaths, 107 hospital admissions and 868 emergency department (ED) attendances every day. Health indicators are quantifiable measures designed to summarise a population's health, and ultimately inform policy and practice. In 2017, several experts identified a need for injury indicators in Wales, to support injury surveillance and prevention efforts.
Main AimTo develop a suite of injury indicators, accessible to the public via an online tool, to inform policy and practice across Wales.
Methods/ApproachInjury specialists in the All Wales Injury Surveillance System (AWISS) proposed a list of measurable injury indicators, based on: data availability, major areas of interest from a Welsh Government strategy perspective, injuries contributing the greatest burden, and injuries for which effective interventions existed. A consultation process with experts and stakeholders resulted in 25 agreed indicators covering all-cause injuries, falls in older adults, hip fractures, road traffic injuries, injuries in the home and at leisure, burns and scalds, poisonings and intentional injuries. Anonymised, routinely collected ED data, inpatient and mortality data in the Secure Anonymised Information Linkage (SAIL) databank at Swansea University were used to generate indicator estimates. Estimates will be updated annually and are accessible via an online interactive tool.
ResultsThe development of a free, online injury indicator tool provides practitioners and policy makers across Wales, with the information required to make informed decisions. However, data quality issues hamper the extent to which conclusions can be made.
ConclusionsInjury indicators have the potential to inform policy and practice across Wales. The adoption of a simplified, standardised data collection system in EDs across Wales is recommended to improve data validity and reliability.

ObjectivesActive travel to school (ATS), such as walking and cycling, not only reduces carbon emissions and air pollution but also contributes to a myriad of health benefits. Understanding the 'potential' for ATS across Wales is poorly understood yet vital to inform policy and practice aimed at increasing ATS.
MethodsUsing geospatial techniques, network distances have been calculated for all residences to all schools in Wales. Distances for each residence were de-identified and imported into the Secure Anonymised Information Linkage (SAIL) databank using SAIL's split file approach. Within SAIL, anonymised distances were linked to encrypted school locations and a cohort of approximately 440,000 children attending a state school in Wales as recorded in the Education Wales (EDUW) dataset. Travel distance to school was subsequently filtered for each child within our cohort.
ResultsThe incorporation of geospatial network derived origin-destination pairs (n=2,205,000,000) allows us to explore how households can engage in ATS, and how this varies by primary/secondary school, welsh-speaking, urban/rural, and socioeconomic status. Initially we will present findings based on distance alone, with future refinements to encompass other factors influencing ATS including: active travel routes, road safety, and topography. We will link our results to anonymised ATS survey responses in SAIL, to calculate specific ATS distance thresholds by age, deprivation, and urban/rural status, including social indicators such as household composition.
ConclusionCurrently there is limited local or national comparable information on the potential for ATS in Wales. This baseline information is urgently needed to inform ATS policy and planning, and to ensure appropriate ATS interventions are prioritised for schools and communities where need is greatest.

ObjectivesA Sore Throat Test and Treat (STTT) service was introduced in selected community pharmacies to screen against Streptococcus A and appropriately treat infections, relieving pressure on General Practices (GP). The long-term impact on patient and NHS outcomes is unclear. A robust evaluation is required to inform future policy and roll-out.
ApproachA matched cohort study comparing patients who received an STTT consultation in community pharmacy (exposed) compared to consultation with GP (unexposed). Individual-level data from participating STTT pharmacies were extracted from the Choose Pharmacy IT platform, anonymised and acquired into the Secure Anonymised Information Linkage (SAIL) Databank. Both cohorts were linked to other longitudinal health and administrative data within SAIL to create study outcomes (e.g. antibiotic prescribing, re-consultation, attendance/admission at Emergency Department/hospital). The impact of the STTT service was evaluated by modelling the outcomes using logistic regression to calculate odds ratios.
Results7553 patients exposed to the STTT service (8,313 consultations) in community pharmacies in Cwm Taf Morgannwg and Betsi Cadwaladr University Health Boards between 1st November 2018 and 28th February 2020, were successfully anonymised and acquired into the SAIL Databank (97.16%). 99.5% of these individuals were then linked to the Welsh Demographic Service Dataset (WDSD), enabling demographics to be generated. 6,665 distinct consultations remained in the exposed group after exclusion criteria were applied. A matched unexposed cohort will be created, and odds ratios will be presented to compare the risk of each outcome for those exposed to the STTT service compared to usual care.
ConclusionThis innovative study is the first to link national pharmacy service data to individual-level population-scale longitudinal health and administrative data in the SAIL Databank. Our findings will inform clear and actionable recommendations regarding the service design and future roll-out of STTT services across the UK and internationally.

BackgroundAccurate data on self-harm is crucial to suicide prevention efforts. It has been previously found that around twice as many people who self-harm seek help in primary care than in secondary care. Little is known about how contacts for self-harm differ across settings at a population level. This study utilised individual-level linked data across GP, Emergency Departments (ED), outpatients and hospital admissions examining contacts across settings and time by sex for self-harm in 10-24 year olds.
MethodsA whole-population based e-cohort study of routinely collected healthcare data was conducted. Rates of self-harm across settings over time by sex were examined. Individuals were categorised based on the service(s) to which they presented.
ResultsA total of 937,697 individuals aged 10-24 years contributed 5,369,794 person years of data from the 01.01.2003-30.09.2015. There were differences in self-harm contacts by demographic variables particularly with regards to sex and admissions to hospital following ED attendance.
ConclusionThis is the first study to compare self-harm in people aged 10-24 years across primary care, EDs, and hospital settings in the UK. The high rates of self-harm in primary care and for young men in EDs highlight these as important settings for intervention. Understanding patterns of presentation will inform service planning and configuration for follow-up care and could inform tailored support, for example for males in ED. Linked data provides important evidence to support the development of interventions across healthcare settings.

Background: Measuring the true incidence of injury or medically attended injury is challenging. Population surveys, despite problems with recall and selection bias, remain the only source of information for injury incidence calculation in many countries. Emergency department (ED) registry based data provide an alternative source. The aim of this study is to compare the yearly incidence of hospital treated Home and Leisure Injuries (HLI), and Road Traffic Injuries (RTI) estimated by survey-based and register-based methods and combine information from both sources in to a comprehensive injury burden pyramide. Methods: Data from Luxemburg's European Health Examination Survey (EHES-LUX), European Health Interview Survey (EHIS) and ED surveillance system Injury Data Base (IDB) collected in 2013, were used. EHES-LUX data on 1529 residents 25-64 years old, were collected between February 2013-January 2015. EHIS data on 4004 other residents aged 15+ years old, were collected between February and December 2014. Participants reported last year's injuries at home, leisure and traffic and treatment received. Two-sided exact binomial tests were used to compare incidences from registry with the incidences of each survey by age group and prevention domain. Data from surveys and register were combined to build an RTI and HLI burden pyramide for the 25-64 years old. This project was part of the European Union project BRIDGE-Health (BRidging Information and Data Generation for Evidence-based Health Policy and Research). Results: Among 25-64 years old the incidence of hospital treated injuries per thousand population was 60.1 (95% CI: 59.2-60.9) according to IDB, 62.1 (95% CI: 50.6-75.4) according to EHES-LUX and 53.2 (95% CI: 45.0-62.4) according to EHIS. The incidence of hospital admissions was 3.7 (95% CI: 3.5-4.0) per thousand population from IDB-Luxembourg, 12.4 (95% CI: 7.5-19.3) from EHES-LUX and 18.0 (95% CI: 13.3-23.8) from EHIS. For 15+ years-old incidence of hospital treated HLI was 62.8 (95% CI: 62.1-63.5) per thousand population according to IDB whereas the corresponding EHIS estimate was lower at 46.9 (95% CI: 40.4-54.0). About half of HLI and RTI of the 25-64 years old were treated in hospital. Conclusion: The overall incidence estimate of hospital treated injuries from both methods does not differ for the 25-64 years old. Surveys overestimate the number of hospital admissions, probably due to memory bias. For people aged 15+ years, the survey estimate is lower than the register estimate for hospital treated HLI injuries, probably due to selection and recall biases. ED based registry data is to be preferred as single source for estimating the incidence of hospital treated injuries in all age groups.

ABSTRACT
ObjectivesElectronic healthcare records (EHR) are the main data sources that facilitate epidemiology research. Routinely collected data such as primary and secondary care are now easily linked to produce novel and high impact research. There are, however, rich data locked in the free text of clinical letters that are not otherwise translated into EHRs. It is highly desirable to be able to extract this information to strengthen the body of information in existing EHRs.
The Swansea Collaborative in Analysis of NLP Research (SCANR) group at Swansea University has been established to evaluate the usage of Natural Language Processing platforms for obtaining new clinical data. To use Clix Enrich to extract SNOMED concepts from a variety of clinical free texts and produce EHRs from the extraction process.
Approach SNOMED concepts contain common items of interest such as diagnosis, medication and symptoms, as well as contextual concepts such as historical reference and negation. Clix Enrich uses the SNOMED dictionary to encode clinical free text (pre-co-ordinated) and find contextually correct SNOMED concepts (post co-ordinated). We used Clix Enrich to extract meaningful clinical terms from MS and Epilepsy consultant letters, as well as presenting complaint fields from a Welsh Emergency Department (ED).
ResultsWe tailored Clix Enrich to extract a wide variety of clinical terms from each source (fourty texts per source) and validated the extraction accuracy with clinical experts in each domain. Clix Enrich was able to accurately extract the correct diagnosis for MS, Epilepsy and ED attendance (100%, 95% and 80%), dosage and frequency of anti-epileptic medication and MS modifying therapy (90%, 100%) and EDDS score (94%). We note a probable source of discrepancy in extraction accuracy between letter sources in the frequency of abbreviated terms, particularly within the presenting complaint field of the ED sample.
ConclusionClix Enrich can be used to accurately extract SNOMED concepts from clinical letters. The resulting datasets are readily available to link to existing EHRs, and can be linked to EHRs that adopt the SNOMED coding structure, or backward compatible hierarchies. Clix Enrich comes with out-of-the-box extraction methods but the optimum way to extract the correct information would be to build in custom queries, thus requiring clinical expertise to validate extraction.