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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.

IntroductionMulti-morbidity and polypharmacy are increasing but are under investigated. Data linkage has much to offer in understanding trends in prevalence, inter-relationships between variables and impact on healthcare activity. We created Welsh population e-cohorts in 2000 and 2014 to study these issues, using the Secure Anonymised Information Linkage (SAIL) Databank.
Objectives and ApproachThe aim of this study was to measure changing prevalences of multimorbidity, initially through disease chapter prescribing and then to explore the relationship between the number of morbidities recorded in primary care and use of different hospital based outpatient services. Data linkage was used to create cohorts of Welsh residents registered to SAIL providing General Practices (GPs) for at least 360 days in 2000 and 2014. The 13 Read code drug chapters were used to calculate morbidity scores between 0 and 13. Proportional odds or cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns.
ResultsThe GP cohorts included 1.6 million and 2.1 million population with 56.6% and 73.4% having at least one recorded morbidity for 2000 and 2014 respectively. In 2014, 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least common (2.7%) in 5-9 year olds. Some 35% of individuals attended at least one outpatient specialty in 2014, varying from 22.4% for 5-9 year olds and 63.2% for 80-84 year olds.
Preliminary modelling results show the number of GP recorded morbidity chapters was strongly related to increasing outpatient attendances at different specialties, e.g. OR 15.3 (95%CI: 15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group. Increasing age and female gender were associated with increased numbers of specialists attended whilst deprivation had a more modest impact.
Conclusion/ImplicationsThere has been a large increase in recorded multimorbidity across all age groups in Wales. In this exploratory cross-sectional design, multimorbidity was strongly related to increasing use of outpatient services. Further work is ongoing to define and utilise more refined multimorbidity metrics and incorporate longitudinal designs in analysis.

ObjectivesMulti-morbidity and polypharmacy are increasing and interrelated phenomena but are poorly understood. The aim of this study is to contribute to the understanding of these issues, measure the changing prevalence's of multimorbidity/ polypharmacy and explore the relationship between multimorbidity as recorded in primary care and the use of outpatient services.
MethodsThe Secure Anonymised Information Linkage (SAIL) Databank facilitated linkage techniques to create population based e-cohorts of de-identified Welsh residents. Individuals were registered to a SAIL providing General Practice (GP) for at least 360 days in 2000 and 2014. Categories of morbidity were created using the 13 Read drug code chapters. In an initial cross sectional exploratory analysis proportional odds and cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns in the same year.
FindingsThe GP e-cohorts included 1.6 million (2000) and 2.1 million (2001) people, with 56.6% and 73.4% having ≥1 recorded morbidity for 2000 and 2014, respectively. In 2014, groups with 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least (2.7%) in 5-9 year olds. Some 35% of individuals attended ≥1 outpatient specialty in 2014; 22.4% in 5-9 year olds and 63.2% for 80-84 year olds.
Results from preliminary models showed the number of GP recorded morbidities was strongly related to increasing outpatient attendances at different specialties, OR=15.3 (95%CI:15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group.
ConclusionPreliminary analysis has shown large increases in GP recorded multimorbidity across Wales over fifteen years and strong relationships and NHS service utilisation in cross-sectional analyses. Further work will include creating more refined definitions for multimorbidity metrics, linkage to hospital admission data, comparisons across healthcare settings and the development of longitudinal models.

ABSTRACT
ObjectivesEMRTS launched in April 2015 which saw consultants join critical care practitioners on air ambulance missions and emergency response vehicles. EMRTS delivers pre hospital, Emergency Department equivalent critical care for people in Wales with life or limb threatening injuries or illness from scene to definitive care. The evaluation will compare patients treated by EMRTS to cases both pre-EMRTs and EMRTS-offline (at night or bad weather) in improving survival and functional and quality of life outcomes.
ApproachThe evaluation will require the use of multiple datasets to follow patients from time of incident, throughout hospital care and to calculate post care health status. Electronic reporting forms will be used to measure incident details, patient vitals and all treatments/procedures pre hospital, creating the EMRTS dataset and cohort. Patients' data will be linked to the Emergency Department Dataset (EDDS), Patient Episode Database for Wales (PEDW), Critical Care dataset, Trauma Audit and Research Network (TARN) and Intensive Care National Audit and Research Centre (ICNARC). TARN and ICNARC will provide data on patients transferred to English specialist care facilities.
ONS (Office of National Statistics) and WDS (Welsh Demographic Service) data will be used for demographic and survival analysis.
GIS techniques will be used on WAST (Welsh Ambulance Service Trust) data for time saved analysis in transferring patients to definitive care.
Following discharge from hospital patients will be interviewed six months and one-year post incident for longer-term functional and quality of life outcomes.
ResultsDatasets are currently being assembled for the service evaluation. Data will be encrypted and anonymized through NWIS (NHS Wales Informatics Service) to enable research through the Secure Anonymised Information Linkage (SAIL) facility. Results will include rate of survival, effect on length of hospital stay, time to definitive care, the use and appropriateness of EMRTs specific interventions and functional and quality of life outcomes.
ConclusionsTo allow for data collection the service evaluation is still in the early stages and will be compared with local and international data.

IntroductionMonitoring social wellbeing and its relationship to health service utilisation by means of appropriate measurement tools can potentially provide a complementary view for influencing service development. Aspects of wellbeing have been collected in the Welsh Health Survey (WHS) while routine health data captures health service utilisation.
Objectives and ApproachWHS was used to link self-reported wellbeing to health outcomes, prior to linking to routinely collected data. Initially, a measure for personal wellbeing was developed using the four personal wellbeing questions defined by The Office of National Statistics (ONS), included in national surveys from 2011 onward. We conducted regression analysis to identify potential predictors of personal wellbeing scores our model included self-reported lifestyle behaviour, self-reported health, education, work, household and general demographics. Links to primary care, hospital and emergency department datasets are being developed to provide insight into the relationship between wellbeing, multi-morbidity and health service utilisation.
ResultsFour wellbeing questions had similar scoring patterns across age groups which is different to most health indicators that tend to show a marked health decline with increasing age. There is a difference between mean wellbeing score for males and females. Our finding showed that self-reported of 'excellent' or 'very good' general health has the largest positive effect on wellbeing while positive viewpoint on self-health has the second largest effect on our model. In addition, being retired from a paid job, eating at least one or 5+ portion of fruit and vegetables and giving up smoking have positive impact on population wellbeing. In contrast, not being able to work, intermediate household occupancy, consuming alcohol in last 12-months, having long-standing illness, showed a negative impact on wellbeing.
Conclusion/ImplicationsThis project established robust methodology on utilizing survey and routine health data for monitoring and evaluation purposes. We also evaluated the linkability of survey data The latest release of National Survey for Wales (NSW) will cover a combination of self-reported health measures and aims for a higher linkage consent rate.

BackgroundMonitoring social wellbeing and its relationship to health service utilisation by means of appropriate measurement tools can provide a complementary view towards service development. Welsh Health Survey (WHS) collects aspects of wellbeing while routine health data captures details around health service utilisation.
ObjectiveThe aim of this project was to evaluate the linkage ability of routine health data with survey data and establish a methodology for utilizing survey data as a measure for self-reported health outcomes.
MethodWe used WHS data from UK data archive to link self-reported wellbeing to health outcomes, a measure for personal wellbeing was developed using the personal wellbeing questions defined by Office of National Statistics (ONS), included in national surveys from 2011 onward. WHS was then linked to routine health data using SAIL Databank. We conducted regression analysis to identify potential predictors of personal wellbeing by linking primary care, hospital and emergency department datasets, to develop and provide insight into the relationship between wellbeing, multi-morbidity and health service utilisation.
FindingsWellbeing questions had similar scoring patterns across age groups which is different to most health indicators that tend to show a marked health decline with increasing age. Our findings showed that self-reported of 'excellent' or 'very good'general health has the largest positive effect on wellbeing while positive viewpoint on self-health has the second largest effect.
ConclusionsCombining and harmonising data from multiple sources and linking them to information from a longitudinal cohort create useful resources for population health research. These methods are reproducible and can be utilised by other researchersand projects.

Abstract
Air pollution (AP) is a significant environmental risk to human health. Historically, the impact of AP exposure has focused upon the physical health effects, yet the implications of AP on mental health have received limited attention. Despite this, recent research has highlighted emerging evidence supporting a possible aetiological link. The purpose of this study, therefore, was to investigate the potential consequences of AP upon mental health and well-being. In a PRISMA based systematic review multiple databases were searched from January 2012 to 2022 for peer-reviewed, English-language, human based primary research. Of the 2,224 studies identified in the literature search, 94 met the inclusion criteria. The mental health and wellbeing issues explored were psychosis, anxiety, suicide, mania, overall diagnosed conditions, and self-reported wellbeing symptoms. Depression was omitted due to the volume of attention the condition has received in the literature. Key air pollutants, particulate matter (PM10 and PM2.5), sulphur oxides (SOx), carbon monoxide (CO) and nitrogen oxides (NOx) were investigated. Data revealed a positive association between psychotic disorders and NOx exposure in ten studies with only one contradictory finding. Whereas the other conditions received multiple contradictory findings. To compare these results to real-life situations, currently a pilot study is exploring associations between psychotic disorder diagnoses and AP, specifically NOx, in Wales using the Secure Anonymised Information Linkage (SAIL) database. There is a large body of evidence showing associations between mental health and AP. However, due to research complexities and some contradictory findings more high-quality research is required to elucidate these relationships.

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.

ObjectivesThe COVID-19 pandemic has had a detrimental impact on healthcare utilisation, resulting in increased mortality both directly and indirectly associated with COVID-19. We aimed to assess the impact of the COVID-19 pandemic on all-cause and disease-specific mortality and further explore the impact of potential inequalities, deprivation status and ethnicity.
ApproachPopulation-scale, individual-level, anonymised linked, routinely-collected electronic health records from demographic and administrative sources were used for two cohorts: i) C19-COHORT16 included individuals alive and resident in Wales on the 1st January 2016 with follow-up until death, break-in Welsh residency, or 31st December 2019; ii) C19-COHORT20 included individuals alive and resident in Wales on 1st January 2020 with follow-up until death, break-in Welsh residency, or study end. We used time-series analysis to investigate trends in mortality over time. We fitted negative binomial models to estimate expected all-cause and disease-specific mortality and compared these estimates to observed mortality in C19-COHORT20.
ResultsExcess all-cause and COVID19-related deaths were higher during the period where the alpha variant was dominant. The trend in deaths decreased during the omicron dominant period. The Asian population had increased mortality during the period where the delta variant was dominant. Mortality was increased for most deprived groups compared to least deprived groups, however, the magnitude of this effect remained unchanged during the pandemic. COVID-19 indirectly affected cancer, circulatory, trauma, digestive and mental health related deaths, with a higher than expected mortality. The majority of trauma related deaths occurred early on in the pandemic, where a higher than expected number of deaths occurred outside of an NHS establishment. Mortality associated with respiratory disease (unrelated to COVID-19) was significantly lower than expected during the COVID-19 pandemic.
ConclusionIncreased all-cause and disease-specific mortality was observed during the COVID-19 pandemic. Excess deaths may be a result of reduced healthcare utilisation, delayed investigation and/or treatment of chronic diseases. As healthcare systems recover from COVID-19, investigation of mortality trends will play a central role in healthcare planning, utilisation and resource use.

Background with rationaleNew insights into the wider demographic context of multimorbidity has been prioritised, notably among disadvantaged and ethnically-diverse populations with a high disease burden. We propose an innovative approach to quantify health burden and disease clustering at household level, to enable predictors of household multimorbidity to be investigated and understood.
Main AimTo quantify multi-morbidity at the household level using general practitioner (GP) electronic health records (EHRs) in a geographically-defined ethnically-diverse inner city population.
MethodsWe extracted clinical and patient address data from GP EHRs from four east London boroughs (Tower Hamlets, Newham, Waltham Forest and City & Hackney). We included currently registered patients aged ≥18 years as at July 2018, and excluded those with duplicate or complex registrations, new registrations in the previous 12 months, or registrations without historical clinical data or occurring prior to 1948, as well as inactive patients with no recorded EHR activity in varying years depending on age and gender.
We defined multimorbidity using 16 long-term condition Read codesets defined in the Quality and Outcomes Framework. We grouped patients into households, defined as those sharing the same home address on their GP registration, represented by a pseudonymised Unique Property Reference Number.
ResultsProvisional data are presented. We identified 737,920 patients (51% female) eligible for this study out of a total population of 1,171,483 currently registered patients. Of these, 23% aged <20, 69% aged 20-64, 8% aged >=65, 38% White ethnicity, 3% Mixed, 30% Asian, 14% Black, 5% Other and 12% Not Stated/Null. We identified 312,582 shared households among 737,920 patients. Analyses to derive household-level summary characteristics and relate these to multimorbidity burden are in progress and will be presented.
ConclusionHousehold-level multi-morbidity can be quantified using clinical and patient address data in GP electronic health records.

ObjectivesVarious analgesics are frequently prescribed to cancer patients for whom pain contributes to poor physical and emotional health and well-being. We examined changes in trends of analgesic prescribing in over 35,000 cancer patients diagnosed in the Welsh population before and during the COVID-19 pandemic in order to gain insight into the COVID-19 pandemic effects on cancer patients' ability to receive analgesia and their potential ability to control their pain via medications.
ApproachWithin the Secure Anonymised Information Linkage (SAIL) Databank trusted research environment (TRE), patients diagnosed with incident primary breast, lung, colorectal or prostate cancers during 2017–2021 were obtained from Cancer Network Information System Cymru (CaNISC) dataset and patients' prescription records were identified from Welsh Longitudinal General Practitioner (WLGP) dataset before being linked to their oncology e-record. We calculated opioid and non-opioid analgesic items prescribed per patient per year (PPPY) since cancer by clinical and demographic factors including cancer type, stage at diagnosis, diagnosis year, age at diagnosis, sex, comorbidities and patients' socioeconomic status. These factors were included to model the effects of the COVID-19 pandemic on trends in analgesic prescribing for each cancer group.
ResultsWe detected significant differences in the number of analgesic items prescribed PPPY in patients diagnosed before the COVID-19 pandemic (2017–2019) and those during the pandemic (2020–2021), with 1.3 more items PPPY prescribed for the latter group (p<0.001). Differences were accounted for largely by prescriptions for lung cancer patients, having 2.74 more items PPPY prescribed (p<0.001), the highest among the four cancer types evaluated. Patients diagnosed with a late-stage cancer had significantly more items prescribed than patients diagnosed at an early stage (p<0.001), with stage IV patients having 15.7 opioid items PPPY prescribed. For patients diagnosed at stage I, this rate PPPY was 6.7. Significant differences were also identified between patients from different socioeconomic backgrounds (p<0.001), with patients from the most deprived areas prescribed 11.3 items PPPY, 5.8 more than those from the least deprived areas.
ConclusionsThe significant impact of COVID-19 pandemic on pain medication prescribing for cancer patients could be partly related to the impact of COVID-19 lockdowns on presentation, waiting lists and diagnosis timings, and access to healthcare for prescriptions after diagnosis. Explanatory factors revealed by this study can help inform policymakers and provide guidance in improving pain relief for cancer services.

The COVID-19 pandemic has placed a spotlight on existing and enduring health inequalities experienced by different ethnic groups. There has been a longstanding call to generate and improve the use of ethnicity data available across different data sources, in order to improve our understanding of health risks, behaviours and outcomes.
We used multiple anonymised individual-level population-scale data sources available within the Secure Anonymised Information Linkage (SAIL) Databank to develop a harmonised ethnicity spine for the population of Wales. We documented ethnicity information in multiple longitudinal records from January 2000 onwards. Data sources included: health and social care, birth and mortality records, national census records, specialist clinical audits and registers, surveys and other routine electronic data. To enable multi-source harmonisation, we explored the ethnicity categorisation as well as temporal changes in recording and classifications by obtaining distribution of records for population, which informed our harmonisation algorithm for standardisation of ethnicity records.
We used over 20-data sources on ~5-million individuals, spanning varying time-periods starting from January 2000 upto a maximum of 22-years. We harmonised available recorded ethnicity values into standardised ethnic classification groups within a national ethnicity-spine. Furthermore, we investigated the impact of different harmonisation methods, including composite, latest date of recording, modal and weighted modal results. With the main focus of the methodological development being in response to the COVID-19 pandemic, when linked to the ~3.1 million individuals alive and resident in Wales from January 2020, we generated harmonised ethnic groups towards ~95% completeness in data coverage for the whole population of Wales. The predominant ethnic group in Wales observed was White, accounting for 89% of the population when using the latest date of recording method.
This research highlights challenges in using longitudinal ethnicity data across different sources. Further work is needed to understand the basis on which individuals / organisations record ethnicity overtime. We recommend improvements recognising differences between ethnicity and other social constructs (e.g. ancestry, nationality, country of origin) are better documented / understood.

IntroductionThere is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality related health conditions. This study explored the effects of air quality and respiratory health conditions on educational attainment for 18,241 pupils across the city of Cardiff, United Kingdom.
Objectives and ApproachAnonymised, routinely collected health and education data were linked at the household and school level with modelled high spatial resolution pollution data, and daily pollen measurements using the Secure Anonymised Information Linkage (SAIL) databank. This created 7 repeated cross-sectional cohorts (2009-2015). Multilevel linear regression analysis examined whether exam performance was associated with health status and/or air quality levels averaged at school and home locations during revision and examination periods. We also investigated the combined effects of air quality and associations with educational attainment for pupils who were treated for asthma and/or Severe Allergic Rhinitis (SAR), and those who were not.
ResultsThe cohort contained 9337 males and 8904 female pupils. There were 871 treated for asthma, 2091 for SAR, and 634 treated for both. Asthma was not associated with exam performance (p=0.700). However, SAR was positively associated with exam performance (p 2) was negatively associated with educational attainment (p = 0.002). Other indicators of air quality (pollutants: Ozone, Particulate Matter - PM2.5, and pollen) were not associated with educational attainment (p> 0.05). Exposure to NO2 was negatively associated with educational attainment irrespective of treatment for asthma or SAR. There was no combined effect of air quality on the variation in educational attainment between those who are treated for asthma and/or SAR and those who were not.
Conclusion/ImplicationsIrrespective of health status, exposure to NO2 was negatively associated with educational attainment. Treatment seeking behaviour may be a possible explanation for the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviour should be investigated.

Introduction The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm. Methods and analysis We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell's C, Brier Score, R 2 and Royston's D. Ethics and dissemination Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal.