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IntroductionThe Welsh ambulance service hold very detailed information about emergency calls received, incidents and associated ambulance journeys, but very little about what happens to patients after they have been conveyed to hospital.
Linkage of ambulance data to secondary care and mortality data can allow new outcome measures to be developed.
Objectives and ApproachA proof of concept was jointly initiated by the Welsh Ambulance Service, Welsh Government and the NHS Wales Informatics in order to explore how ambulance conveyance data could be linked to other routinely collected secondary care and mortality data, with the aim of developing new outcome-based measures for the evaluation of the effectiveness of the ambulance service and the unscheduled care system as a whole.
As there were very few patient demographic data items common to both the ambulance and secondary care datasets, the resulting probabilistic linkage relied largely on the use of time and location-based distributions.
ResultsThe linkage methodology proved to be highly successful for those patients conveyed by ambulance to an ED, with the various location and time-based fields from the ambulance dataset combining well with similar fields in the ED dataset. Out of all ED attendance records which had "Ambulance" as the stated mode of arrival, an associated ambulance record was found in over 90\% of cases.
Additional exact and rules-based deterministic methods were used to link the ED attendances to associated admissions, critical care and mortality records, with new ED, in-hospital and longer term outcome variables developed.
Finally, the project team analysed the impact of ambulance response times and ED handover delays on these outcome measures.
Conclusion/ImplicationsThe work demonstrated how the linked data could provide managers, commisioners and policy-makers with a more holistic view of the unscheduled care system. Work to-date has focussed mainly on conveyances to ED, however the next stage will be to develop outcome measures relating to those patients treated at the scene or advised by phone.

ABSTRACT
BackgroundEpidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations. However, where an epidemiological analysis relies solely on data from large-scale anonymous administrative data sources, the available geographic information for the individuals concerned relates in general to just one single point in time; for example the places of residence of individual on the day that they were registered with a particular disease, or their places of residence on the day that they were admitted to hospital.
This information may not be sufficient however, especially when considering diseases where there may be a long period of time between an exposure to a particular hazard and the subsequent onset of disease.
MethodA solution to this problem possibly lies within administrative sources of data such as the Welsh Demographic Service (WDS), which is accessible to NHS Wales analysts and users of the SAIL databank in a pseudonymised format. The WDS contains the details of all Welsh residents who have been registered with a GP Practice since 1992, including a full history of changes to their addresses and GP practices. This data can be used to easily ascertain an individual's address at any point in time, for example on a particular census date, or within a time period that is relative to a particular event, e.g. 10 years prior to disease registration. However, this research will look at how to incorporate all of an individual's available address information into an epidemiological analysis.
ResultsTwo main approaches will be demonstrated; the first using a "Person Years at Risk" approach which attempts to apportion numerator and denominator according to the number of previous residences, and the second using a Case Control approach, comparing the geographic spread of addresses in the diseased group of patients versus the non-diseased (control) group, with age and gender matched controls also drawn from the WDS.

ObjectivesTo design and test a method to assess whether test events were associated with an increase in risk of confirmed COVID-19, in order to inform policy on the safe re-introduction of spectator events following decreasing incidence of COVID-19 and relaxing of restrictions.
ApproachWe designed a cohort study to measure relative risk of confirmed COVID-19 in those attending two large sporting events in South Wales during May-June 2021. First, we linked ticketing information to records on the Welsh Demographic Service (WDS) and identified NHS numbers for attendees. We then linked attendees to routine SARS-CoV-2 test data to calculate incidence rates in people attending each event for a fourteen days period following the event. We selected a comparison cohort from WDS for each event, individually matched by age band, gender and locality of residence. Risk ratios were then computed for the two events.
ResultsWe successfully assigned NHS numbers to 91% and 84% of people attending the two events, respectively. Other identifiers were available for the remainder. Only a small number of attendees (<10) had a record of confirmed COVID-19 following attendance at each event (14 day cumulative incidence: 36 and 26 per 100,000, respectively). Background incidences in Wales over the same periods were 22 and 61 per 100,000, respectively. There was no evidence of significantly increased risk of COVID-19 at either event (event 1: 3.00 (0.18-47.9), p=0.50, event 2: 0.30 (0.04-2.34), p= 0.23). However, event 1, which didn't include pre-event testing in their mitigations, had a higher risk ratio (>1) than event 2 (<1), which did include pre-event testing.
ConclusionsWe demonstrate the potential for data linkage to inform COVID-19 policy regarding sporting events. At that point in the epidemic, there was no evidence that attending large sporting events increased risk of COVID-19. However, these events took place between epidemic waves when background incidence and testing rate was low.

ObjectivesThe Welsh Health Specialised Services Committee (WHSSC) has commissioned a study to assess if there is a standard value-based approach to measure and evaluate the effectiveness of specific interventions, and whether variation exists that may indicate differences in the equity of access to these services provided to patients in Wales.
ApproachUsing anonymised individual-level, population-scale, routinely collected electronic health record (EHR) data held within the Secure Anonymised Information Linkage (SAIL) Databank, we planned to identify amongst the population of Wales any patients experiencing percutaneous coronary intervention (PCI) and Transcatheter Aortic Valve Implantation (TAVI). We would measure associated patient outcomes 2-years before and after the intervention minus a 6-month clearance period on either side by measuring primary care attendances in general practice, and secondary care attendances in hospital, outpatient and emergency department data. To further inform the analysis, linkage to socio-demographic factors, comorbidities and lifestyle factors would be controlled for.
ResultsPreliminary results identified 5,999 PCIs between June 2014 and March 2020. We were able to identify 1,530 as elective and 4,358 as emergency secondary care procedures. Of the elective PCIs, 184 patients had a greater number of elective hospital days pre-PCI, and 382 patients had a greater number of elective hospital days post-PCI. Of the emergency PCIs, 135 patients had a greater number of emergency hospital days pre-PCI, and 893 patients had a greater number of emergency hospital days post-PCI. For TAVI intervention, we identified 74 elective and 49 emergency procedures. 35 of the elective TAVI had greater hospital days pre-intervention, and 17 of the elective TAVI had greater hospital days post-intervention. Primary care costs slightly rose for PCI and slightly reduced for TAVI.
ConclusionWe successfully identified interventions and measured and contrasted outcomes before and after the intervention using the SAIL Databank. For PCI, the elective intervention showed a reduced increase in bed days compared to emergency intervention. These initial findings allow us to plan an expanded analysis to examine other intervention types.

Objectives Using anonymised linked data across primary care general practice (GP) and local authority (LA) services to (1) identify unpaid carers in Swansea and Neath Port Talbot (NPT), (2) describe their health and health service use and, (3) compare these with a matched non-carer population.
MethodsUnpaid carers were identified using a) LA carers' assessment data and b) GP Read codes within the Secured Anonymised Information Linkage (SAIL) Databank. An age, sex and area-matched non-carers cohort was created using demographic data and assigned pseudo-index dates. Linked GP and secondary care data was used to establish GP interactions, hospital admissions, emergency department and outpatient attendances in the year prior to identification as a carer. Long-term conditions (LTCs) were identified using published Cambridge multimorbidity Read code lists. Chi-square, Mann Whitney U-test, and rate ratios were used to test differences in aforementioned factors between carers and non-carers.
Results We have identified a total of 2,950 unpaid carers (N=2,024 in NPT; N=926 in Swansea), primarily via Read codes (80% in NPT; 70% in Swansea). Overlap between LA and GP identified individuals is less than five percent, and GP identified individuals are significantly younger than LA identified (NPT: χ2=176, p<0.001; Swansea: χ2=35.0, p<0.001). Further research is currently ongoing to utilise these anonymised linked data to ascertain key differences between carers and non-carers in the two local authorities. Results will include the significance of differences in rates of GP interactions, emergency department attendances, hospital admissions, outpatient attendances, rates of multimorbidity (0, 1, 2+ conditions), and top five specific LTCs between carers and matched non-carers in NPT and Swansea.
Conclusion We demonstrate the novel use of local authority-held data linked to national anonymised data sources to provide locally informative evidence for this priority population. Results will provide novel insight into the health and health service usage of unpaid carers at a LA level, assisting evidence-informed local support for unpaid carers.