PropertyValue
is nif:broaderContext of
nif:broaderContext
is schema:hasPart of
schema:isPartOf
nif:isString
  • We conducted a retrospective analysis of the TARN database, a clinical registry that collects data from all “trauma-receiving” hospitals in England for patients with traumatic injuries. The best practice tariff for major trauma centres promotes timely entry of clinical data to TARN [16]. TARN has PIAG Section 60 approval for research using the data that it holds. We split the analysis into minor and major trauma (ISS of less than and greater than 15 respectively). The trauma network system in England is predominantly focused on the most severely injured patients by concentrating resources in Major Trauma Centres and caring for the highest acuity patients at these hospitals [17]. Therefore, minor and major trauma patients are treated using distinct clinical pathways, and are analysed as such. We used national data from NHS hospitals (universally free healthcare) in England from the Trauma Audit and Research Network (TARN) to examine this potential effect. We included all individuals who were admitted to “trauma-receiving” hospitals secondary to trauma and treated as an in-patient for 3 or more days, admitted to critical care units or died in hospital. Isolated closed limb fractures and neck of femur fractures in patients over 65 are excluded from TARN. Data collection period: 1st January 2015 and 31st December 2015 and limited to patient’s resident in England. Data include patient age and sex, postcode sector, mode of injury, injury severity score (ISS), comorbidity and mortality at 30, 90 and 180 days. The primary outcome was 30-day mortality. Thirty-day mortality is determined by referencing hospital records for in-patients and via linkage to Office for National Statistics data for those who have been discharged. We selected 30-day mortality as the primary outcome variable as primary mortality diagnoses at 90 and 180 days are increasingly less likely to be caused by the index trauma episode. The primary predictor variable was SES based on deprivation of area of residence. This study assigned a standardised measure of deprivation to each record in TARN, using the English indices of deprivation 2015, Index of Multiple Deprivation (IMD) [18]. The indices are derived from census and local administrative data and used to construct seven domains of deprivation: income, employment, health and disability, education skill and training, barriers to housing and other services, crime, and living environment. The IMD is a weighted score of the seven domains, and a robust and commonly used measure of deprivation in England. The IMD is available at Lower Super Output Area (LSOA) level, a small geographical boundary containing approximately 1500 persons. Within TARN each record has a geographical indicator, the postcode-sector. To assign the Indices of Deprivation to each record we calculated a synthetic domain score and IMD score for each postcode sector in England by weighting LSOA level deprivation scores based on the geographical contribution of each LSOA to each postcode sector [18]. The resulting synthetic scores were then grouped into country specific national quintiles of deprivation for the IMD, where quintile 1 is the most deprived and quintile 5 the least deprived [19]. This is an established method of IMD estimation that has been used previously [20]. Covariates included age group, sex, ISS category and comorbidity. Age was grouped into 0 to 15 years old, 16 to 24, 25 to 39, 40 to 64, 65 to 84, and 85 and older. ISS was split into four categories, two in major trauma (ISS 16 to 24 and greater than 24), and two in minor trauma (ISS less than 9 and 9 to 15). These are established cut-offs [21,22], and their usefulness derives from patients with isolated injuries of increasing severity appearing in different groups. Comorbidity is calculated by TARN using the pre-existing medical conditions (PMC) score, modified from the Charlson Comorbidity Index. Each comorbid condition is assigned a weight, based on the impact of the condition on outcome, with higher values assigned to more severe conditions. Weights are then summed for each case [23,24]. The scores were categorised as 0, 1 to 5, 6 to 10 and greater than 10 as per TARN criteria. Data were analysed using Stata V13 (StataCorp, Stata Statistical Software: Release 13, College Station, Texas, USA). All analyses were run separately for both minor and major trauma. Absolute 30-day mortality, and proportion, were determined for each age group, sex, ISS categorisation, comorbidity group and IMD quintile. Cross-tables of IMD quintile by other variables with Chi squared tests are included in the supporting information (S1 Table). Univariate logistic regression models with 30–day mortality as the outcome measure were ran for age group, sex, ISS categorisation, comorbidity and IMD quintile. A multivariable logistic regression model was run with 30–day mortality as the outcome measure and all variables included in univariate regression input. Model fitting was based on variables identified as part of a priori statistical plan and used a categorical variable for socioeconomic deprivation (reference group: quintile 5), sex (reference group: female), age group (reference group: 0–15 years), and ISS category (reference group for minor: ISS < 9; reference group for major: ISS 16–24). Additional multivariate regression models with deprivation included as a continuous variable (IMD score) are included in the supporting information (S2 Table). Sensitivity analyses were run using 90-day and 180-day mortality as the outcome variable for the multivariate logistic regression models. The results for the sensitivity analyses are included in the supplementary appendix (S3 and S4 Tables).
rdf:type