Cost of managing patients with venous leg ulcers.

The aim of this study was to estimate costs associated with the management of patients with venous leg ulcers (VLUs) from the perspective of the UK National Health Service (NHS). The analysis was undertaken through the Secure Anonymised Information Linkage Databank which brings together and anonymously links a wide range of person-based data from around 75% of general practitioner (GP) practices within Wales (population coverage ~2.5 million). The data covered an 11-year period from 2007 to 2017. All patients linked to the relevant codes were tracked through primary care settings, recording the number of GP practice visits (number of days with an event recorded), and wound treatment utilisation (eg, dressings, bandages, etc.) Resources were valued in monetary terms (£ sterling) and the costs were determined from national published sources of unit costs. This is the first attempt to estimate the costs of managing of VLUs using routine data sources. The direct costs to the Welsh NHS are considerable and represent 1.2% of the annual budget. Nurse visits are the main cost driver with annual estimates of £67.8 million. At a UK level, these costs amount to £1.98 billion. Dressings and compression bandages are also major cost drivers with annual Welsh estimates of £828 790. The direct cost of managing patients with VLUs is £7706 per patient per annum, which translates to an annual cost of over £2 billion, when extrapolated to the UK population. The primary cost driver is the number of district nurse visits. Initiatives to reduce healing times through improving accuracy of initial diagnosis, and improved evidence-based treatment pathways would result in major financial savings.

Developing a standardised approach to the aggregation of inpatient episodes into person-based spells in all specialties and psychiatric specialties.

BACKGROUND: Electronic health record (EHR) data are available for research in all UK nations and cross-nation comparative studies are becoming more common. All UK inpatient EHRs are based around episodes, but episode-based analysis may not sufficiently capture the patient journey. There is no UK-wide method for aggregating episodes into standardised person-based spells. This study identifies two data quality issues affecting the creation of person-based spells, and tests four methods to create these spells, for implementation across all UK nations. METHODS: Welsh inpatient EHRs from 2013 to 2017 were analysed. Phase one described two data quality issues; transfers of care and episode sequencing. Phase two compared four methods for creating person spells. Measures were mean length of stay (LOS, expressed in days) and number of episodes per person spell for each method. RESULTS: 3.5% of total admissions were transfers-in and 3.1% of total discharges were transfers-out. 68.7% of total transfers-in and 48.7% of psychiatric transfers-in had an identifiable preceding transfer-out, and 78.2% of total transfers-out and 59.0% of psychiatric transfers-out had an identifiable subsequent transfer-in. 0.2% of total episodes and 4.0% of psychiatric episodes overlapped with at least one other episode of any specialty. Method one (no evidence of transfer required; overlapping episodes grouped together) resulted in the longest mean LOS (4.0 days for all specialties; 48.5 days for psychiatric specialties) and the fewest single episode person spells (82.4% of all specialties; 69.7% for psychiatric specialties). Method three (evidence of transfer required; overlapping episodes separated) resulted in the shortest mean LOS (3.7 days for all specialties; 45.8 days for psychiatric specialties) and the most single episode person spells; (86.9% for all specialties; 86.3% for psychiatric specialties). CONCLUSIONS: Transfers-in appear better recorded than transfers-out. Transfer coding is incomplete, particularly for psychiatric specialties. The proportion of episodes that overlap is small but psychiatric episodes are disproportionately affected. The most successful method for grouping episodes into person spells aggregated overlapping episodes and required no evidence of transfer from admission source/method or discharge destination codes. The least successful method treated overlapping episodes as distinct and required transfer coding. The impact of all four methods was greater for psychiatric specialties.

Measuring follow-up time in routinely-collected health datasets: Challenges and solutions.

A key requirement for longitudinal studies using routinely-collected health data is to be able to measure what individuals are present in the datasets used, and over what time period. Individuals can enter and leave the covered population of administrative datasets for a variety of reasons, including both life events and characteristics of the datasets themselves. An automated, customizable method of determining individuals' presence was developed for the primary care dataset in Swansea University's SAIL Databank. The primary care dataset covers only a portion of Wales, with 76% of practices participating. The start and end date of the data varies by practice. Additionally, individuals can change practices or leave Wales. To address these issues, a two step process was developed. First, the period for which each practice had data available was calculated by measuring changes in the rate of events recorded over time. Second, the registration records for each individual were simplified. Anomalies such as short gaps and overlaps were resolved by applying a set of rules. The result of these two analyses was a cleaned set of records indicating start and end dates of available primary care data for each individual. Analysis of GP records showed that 91.0% of events occurred within periods calculated as having available data by the algorithm. 98.4% of those events were observed at the same practice of registration as that computed by the algorithm. A standardized method for solving this common problem has enabled faster development of studies using this data set. Using a rigorous, tested, standardized method of verifying presence in the study population will also positively influence the quality of research.