Suchergebnisse

IntroductionThe electronic Frailty Index (eFI) and the Hospital Frailty Risk Score (HFRS) have been developed in primary and secondary care respectively.
Objectives and ApproachOur objective was to investigate how frailty progresses over time, and to include the progression of frailty in a survival analysis.To do this, we performed a retrospective cohort study using linked data from the Secure Anonymised Information Linkage Databank, comprising 445,771 people aged 65-95 living in Wales (United Kingdom) on 1st January 2010. We calculated frailty, using both the eFI and HFRS, for individuals at quarterly intervals for 8 years with a total of 11,702,242 observations.
ResultsWe created a transition matrix for frailty states determined by the eFI (states: fit, mild, moderate, severe) and HFRS (states: no score, low, intermediate, high), with death as an absorbing state. The matrix revealed that frailty progressed over time, but that on a quarterly basis it was most likely that an individual remained in the same state.
We calculated Hazard Ratios (HRs) using time dependent Cox models for mortality, with adjustments for age, gender and deprivation. Independent eFI and HFRS models showed increased risk of mortality as frailty severity increased. A combined eFI and HFRS revealed the highest risk was primarily determined by the HFRS and revealed further subgroups of individuals at increased risk of an adverse outcome. For example, the HRs (95% Confidence Interval) for individuals with an eFI as fit, mild, moderate and severe with a high HFRS were 18.11 [17.25,19.02], 20.58 [19.93,21.24], 21.45 [20.85,22.07] and 23.04 [22.34,23.76] respectively with eFI fit and no HFRS score as the reference category.
ConclusionFrailty was found to vary over time, with progression likely in the 8-year time-frame analysed. We refined HR estimates of the eFI and HFRS for mortality by including time dependent covariates.

IntroductionDemographic profiling is an important aspect of anonymised healthcare research to identify the population of interest. Typically, administrative data is used in conjunction with patient registers to create cohorts, but it can be a time consuming process. We describe a method using routinely collected health data to identify vulnerable populations.
Objectives and ApproachUsing existing longitudinal data and the Residential Anonymised Linking Field (RALF) we aim to identify institutions linked to vulnerable populations. We search for specific characteristics of these institutions including the age of occupants, number of current residents, and rate of change of occupants. We also aim to compare our method to a pseudonymised national registry for care homes to ensure it is accurate. This can effectively reduce the need for repeat pseudonymisation of institutions, which is both expensive and time consuming.
ResultsTo implement our method we found the most recent address for living individuals aged 65-95. This produced 202,640 residences from 1,330,335. Of the 202,640 residences, 1347 had four or more cohabitants aged 65-95, and 172 had exactly three residents with ten or more distinct individuals registered over a 10-year period. Our final synthetic dataset therefore had 1519 unique potential care homes to compare to the national registry, which contains 1525 registered care homes.
We can now link the synthetic dataset to individuals to flag their residential status, which may be a defining factor in their level of care. Furthermore, we can answer specific research questions relating to their residency, such as the time it takes to move to a care home following a hospital admission.
Conclusion/ImplicationsBy using quantifiable characteristics of care homes we were able to create a synthetic care home register by searching existing data. This is a reproducible process that would be of particular benefit for projects where a registry is not available, or where time or cost would limit the availability.

IntroductionAging populations with increasing frailty have major implications for health services internationally, and evidence-based treatment becomes increasingly important. The development of an electronic Frailty Index (eFI) using routine primary care data facilitates implementation of evidence-based interventions. However, the eFI does not account for time restrictions regarding when information was recorded.
Objectives and ApproachOur aim is to implement and further validate the eFI using the Secure Anonymised Information Linkage (SAIL) databank, introducing refinements based on time restrictions.
Our implementation of the eFI identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on the internationally established cumulative deficit model, and each deficit contributes equally to the eFI value.
However, although each deficit is equally weighted, only one of them is currently time dependent. We therefore analyse the time at which each deficit is identified, and propose time dependent cut-points based on our findings.
ResultsWe were able to successfully implement the eFI using data from over 400,000 individuals from the Welsh population using data held in the SAIL databank. Our results agree with the baseline characteristics and distributions of frailty found in the original development of the eFI.
We also found that the percentage of individuals identified as frail increased as the number of years of records included was increased. Furthermore, the increase in percentage year by year was almost linear for a number of the deficits. This led to the identification of time bounds for particular deficits, which could help to refine future implementations of the eFI.
Conclusion/ImplicationsOur work validates and refines the eFI, which is a particularly useful resource as it uses existing primary care data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily reproducible and achievable by others.

BackgroundAging populations with increasing frailty have major implications for health services, and evidence-based treatment becomes increasingly important. The development of the electronic Frailty Index (eFI) using routine primary care data facilitatesthe implementation of evidence-based interventions and care.
MethodOur implementation of the eFI in the Secure Anonymised Information Linkage (SAIL) databank identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on a cumulative deficitmodel, and each deficit contributes equally to the eFI value.
FindingsAlthough each deficit is equally weighted, only one is currently time dependent. We therefore analysed the cumulative prevalence of each deficit on a year-by-year basis. This led to the identification of time bounds for particular deficits, which willhelp to refine future implementations of the eFI. We also further validated the eFI using data from over 400,000 individuals held in SAIL.
ConclusionThe eFI is particularly useful as it uses existing data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily achievable by others.

BackgroundDemographic profiling is an important aspect of anonymised healthcare research used to identify populations of interest. Typically, administrative data is used in conjunction with patient registers to create cohorts, but it can be a time consuming process.
ObjectivesWe aim to create and apply a method of identifying care homes using existing administrative data. We also aim to test the accuracy of our method by comparing the results to a pseudonymised national care home registry. This will allow us to prove whether proxy methods may be of sufficient accuracy for data linkage research in the future.
Methods (including data)Our method uses quantifiable characteristics from longitudinal data to identify potential care homes. This includes the number and age of occupants, current residence and rate of change of occupancy.
ConclusionsThis method is a reproducible process that would be of particular benefit for projects where a registry is not available, or where time or cost would limit the availability. This method can also be generalised to any communal establishment, where often the identification of vulnerable populations (antibiotic resistance, infectious disease etc.) is particularly beneficial.

Reducing the burden of falls and fall-related admissions to hospital and care homes is an important policy area. Falls cause significant injury leading to a reduced quality of life. We wanted to know if the environment around people's homes changes the risk of falls for older people in Wales.
We linked routinely collected, anonymised health data on frailty, dementia, hospital admissions, care home admission, and A&E attendance in the Secure Anonymised Information Linkage Databank. We also linked individual level demographic data and household level neighbourhood walkability and accessibility metrics detailing the built environment (e.g. access to services, greenspace). We capture change in the built environment and house moves between January 2010 and December 2017. Using unadjusted and adjusted cox regression models we will assess how the risk and severity of a fall changes in relation to the built environment.
We have created a dynamic national e-cohort linking data on frailty, dementia, hospital admissions, care home admission, A&E attendance and built environment measures for people living in Wales aged 60 and above between January 2010 and December 2017. At the conference we will report summary statistics for each quarter as well as the overall population. We will also report unadjusted cox model odds ratios, as well as fully adjusted models. We will adjust for age, sex, urbanicity (urban/rural), deprivation (WIMD), dementia diagnosis, and seasonal weather trends. We will also report odds ratios for our stratification analysis where we will investigate whether associations vary by urbanicity and deprivation.
With an aging population, it is becoming more important to help the current and future older population age healthily, preventing adverse health outcomes before they happen. This research will help guide policy and resource allocation to support people staying at home and have a more fulfilling life for longer.

IntroductionResearch involving care homes is often difficult due to a lack of data and ethical issues. Wales (United Kingdom) contains approximately 1.3million residences, of these 717 are officially recorded as care homes for older people.
Objectives and ApproachOur objective was to develop a predictive methodology for identifying care homes in administrative data.
We used two data sources within the Secure Anonymised Information Linkage Databank to conduct our study. The Welsh Demographic Service Dataset (WDSD) contains all residences in Wales and demographic details of their occupants. An anonymised dataset of deterministically matched care home addresses was used to determine which of the residences in the WDSD were care homes.
We used details in the WDSD to determine the average age of the occupants, the number of people who moved into the residence in a year, and the number of people who died in a year. We were interested in care homes for older people and restricted all the residences in the WDSD to only those with an average age of 50+ years. We applied logistic regression to determine a probabilistic match for care homes based on the above characteristics. We determined an optimal cut-point for the probability of a residence being a care home based on the sensitivity and specificity.
ResultsRestricting the WDSD to have an average age of occupants of 50+ created a dataset of 3,939 residences, containing 562 care homes. After applying the logistic model to predict the care homes, we found an optimal probability cut-point which resulted in 548 true positives, 105 false positives, 14 false negatives, and 3,272 true negatives.
ImplicationsIdentification of care homes in an anonymised databank using only demographic data allows research into healthcare pathways for this hard to reach and under-researched population.

ObjectivesA defining feature of the COVID-19 pandemic in many countries were the tragic extent to which care home residents were affected and the difficulty in preventing the introduction and subsequent spread of infection.
ApproachUtilising linked data in the SAIL Databank we set out to develop a linked data platform as part of the 'One Wales' approach to generate evidence to inform policy makers on the key areas of transmission pathways, care home characteristics, excess mortality, and the impacts of vaccination. We used multi-sectoral linked data including routinely collected health data, administrative data and GIS generated metrics on care home characteristics and community infection rates to better understand how multiple factors impacted on care home residents.
ResultsWe created a care home index with enhanced care home characteristics for all care homes in Wales and were able to link 15,773 care home residents in the SAIL Databank to 923 care homes. We were able to generate early evidence demonstrating an increased risk of mortality for care home residents during Wave 1 (adjusted HR 1.72 compared to 2016). We were able to show that hospital discharge in Wales during the initial stages of the pandemic, although significant, had a much smaller impact on subsequent infections than care home size and accounted for 1.8% of infected discharge events. We also showed that community prevalence, inpatient appointments and people living with dementia all contributed to increased risks of catching COVID in a care home.
ConclusionThe response of the 'One Wales' team and the SAIL linked data platform facilitated meaningful insight on the impacts of COVID in social care settings in Wales. The evidence generated was used by policy makers from Welsh and UK Governments to inform policy direction as the pandemic progressed.

IntroductionCritical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.
A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research.
ObjectiveTo describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care.
MethodTo demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales.
ResultsWhen applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) hadan emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission.
ConclusionThis methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.

IntroductionAround a third of people aged 65+, and around half of people aged 80+ fall at least once a year in the United Kingdom. Homes may be adapted to try to prevent falls, but evidence on the effectiveness of home adaptations is limited.
Objectives and ApproachOur objective was to determine if proactive (Care&Repair service) and reactive (rapid response) home adaptations provided by Care&Repair Cymru resulted in a reduced risk of a fall for older people aged 60+ in Wales.
We constructed a longitudinal dataset from the Secure Anonymised Information Linkage Databank. We created quarterly intervals for 5-years pre and post the date a home adaptation was received, or a randomly assigned date for the comparator. Per quarter, we created a binary indicator of whether someone had a fall at home that was recorded in either the emergency department or hospital admission data sources for Wales. We included key demographic variables as covariates. We analysed the data using logistic regression and a difference-in-difference approach.
ResultsWe analysed 634,046 individuals, of whom 60,794 received the proactive Care&Repair service, and 47,244 received the reactive rapid response service. People receiving proactive or reactive home adaptations from Care & Repair were at around a two-fold increased risk of falling, compared to those who did not receive home adaptation with Odds Ratios (ORs) of 2.13 [95%CI: 2.07, 2.20] and 2.73 [2.64,2.81] respectively. Falls risk increased per quarter for all individuals, but after intervention delivery, the rate of increase fell in the intervention groups.
ConclusionPeople receiving home adaptations from Care&Repair had a higher chance of a fall, indicating the service was successfully identifying those in need. Falls risk increased for everyone over time, but this was counteracted for people receiving an intervention.

IntroductionIdiopathic Intracranial Hypertension (IIH) is a condition of unknown aetiology that is strongly associated with obesity. IIH predominantly affects women of childbearing age and causes chronic disabling headaches, visual disturbance and, in a minority of patients, permanent visual loss.
Objectives and ApproachWe characterised the IIH population, epidemiological trends and healthcare outcomes in Wales using routinely collected healthcare data. We used primary and secondary care healthcare diagnosis codes within the Secure Anonymised Information Linkage databank to ascertain IIH cases and controls in a retrospective cohort study between 2003 and 2017. We validated IIH diagnosis codes using anonymised secondary care lists of IIH cases.
ResultsWe analysed 35 million patient years of data (2003–2017). There were 1765 cases of IIH in 2017 (85% female). The prevalence and incidence of IIH in 2017 was 76/100,000 and 7.8/100,000/year, a significant increase from 2003 (corresponding figures=12/100,000 and 2.3/100,000/year). The odds ratio for developing IIH in the least deprived quintile compared to the most deprived quintile, adjusted for gender and Body Mass Index (BMI), was 0.65 (95% CI=0.55–0.76). 9% of people with IIH had CSF shunts with less than 0.2% having bariatric surgery. Unscheduled hospital admissions were significantly higher in the IIH cohort compared to controls (rate ratio=5.28, p<0.001) and in the people with IIH with CSF shunts compared to those without (rate ratio=2.02, p<0.01).
Conclusion / ImplicationsIIH incidence and prevalence is increasing significantly, corresponding to population increases in BMI, and is associated with increased deprivation. This has important implications for healthcare professionals and policy makers given the comorbidities, complications and increased healthcare utilisation associated with IIH.

Supplementary information for the main study 'Incidence, Prevalence and Healthcare Outcomes in Idiopathic Intracranial Hypertension: A population study ' ; This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. The study was not directly funded but we acknowledge support from the Brain Repair and Intracranial Neurotherapeutics (BRAIN) Unit and the Wales Gene Park which are funded by Health and Care Research Wales.

IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.
ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.
MethodsWe conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.
Results1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell's C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.
ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.

BackgroundThe COVID-19 pandemic has resulted in delayed diagnosis and treatment for cancer patients and increases in elective surgery waiting lists. The impact on other 'long-term' conditions (LTCs) is unclear. We examined the effects of the pandemic on the recorded incidence of 20 LTCs to inform decisions on treatment pathways and resource allocation.
ApproachWe included Welsh residents diagnosed with any of 20 LTCs for the first time between 2000-2021.
Data were accessed and analysed within the Secure Anonymised Information Linkage (SAIL) Databank.
The primary aim was to assess the impact of the COVID-19 pandemic on trends in recorded incidence. Secondarily we examined incidence by socio-demographic and clinical subgroups: age, sex, deprivation quintile, ethnicity, frailty score and learning disability.
Incidence were presented as monthly rates for each LTC. We performed interrupted time series analyses to estimate; the immediate and long-term change in rates following the pandemic; and the size of the undiagnosed population.
ResultsWe included 2,206,070 individuals diagnosed with at least one LTC.
An immediate reduction in recording of new diagnoses was observed in April 2020 across all 20 LTCs, followed by a gradual recovery towards pre-pandemic levels over the next 18 months, though at different rates across conditions. The largest difference between observed and expected (as predicted using pre-pandemic trends) incidence between January 2020 and June 2021 were in the diagnoses of COPD (-43%, 95% CI (-50%, -34%)), Asthma, Hypertension and Depression and the smallest difference was in Type 1 diabetes, dementia, stroke and TIA (-8%, 95% CI (-19% ,5%)).
Differences in the proportions of incidence by socio-demographic and clinical subgroups in the years preceding and following the pandemic have also been analysed (results to be finalised).
ConclusionThere was an abrupt reduction in the observed incidence of all 20 LTCs after March 2020 followed by a gradual recovery over consequent months towards pre-pandemic levels. Of 20 LTCs, 15 strongly indicate a reservoir of yet undiagnosed patients. The results from this study will have implications in resource allocation.