Despite to high burden of road traffic injuries (RTIs), the RTI epidemiology has received less attention with rare investments on robust population cohorts. The PERSIAN Traffic Safety and Health Cohort (PTSHC) was designed to assess the potential causal relationships between human factors and RTI mortality, injuries, severity of the injury, hospitalised injury, violation of traffic law as well as offer the strongest scientific evidence.

The precrash cohort study is carried out in four cities of Tabriz, Jolfa, Shabestar and Osku in East Azerbaijan province located in northwest Iran. The participants were people who sampled among the general population. The cluster sampling method was used to enrol the households in this study. The PTSHC encompasses a wide and comprehensive range and types of data. These include not only the common cohort data collections such as medical examination measures, previous medical history, bio assays and behavioural assessments but also includes data obtained using advanced novel technologies, for example, electronic travel monitoring, driving simulation and neuro-psycho-physiologic laboratory assessments specifically developed for traffic health field.

A total of 7200 participants aged 14 years and above were enrolled at baseline, nearly half of them being men. The mean age of participants was 39.2 (SD=19.9) years. The majority of participants (55.4%) belonged to the age group of 30–56 years. Currently, approximately 1 200 000 person-measurements have been collected.

PSTHC will be used to determine the human-related risk factors by adjusting for the vehicle and land-use-related factors. Therefore, a lot of crashes can be prevented using effective interventions. Although this cohort provides valuable data, it is planned to increase its size to achieve the highest level of evidence with higher generalisability. Also, according to the national agreement this cohort is going to be extended to several geographical regions in second decade.

To assess the survival predictivity of baseline blood cell differential count (BCDC), discretised according to two different methods, in adults visiting an emergency room (ER) for illness or trauma over 1 year.

Retrospective cohort study of hospital records.

Tertiary care public hospital in northern Italy.

11 052 patients aged >18 years, consecutively admitted to the ER in 1 year, and for whom BCDC collection was indicated by ER medical staff at first presentation.

Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed haemoglobin, mean cell volume (MCV), red cell distribution width (RDW), platelet distribution width (PDW), platelet haematocrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils and platelets. Discretisation cut-offs were defined by benchmark and tailored methods. Benchmark cut-offs were stated based on laboratory reference values (Clinical and Laboratory Standards Institute). Tailored cut-offs for linear, sigmoid-shaped and U-shaped distributed variables were discretised by maximally selected rank statistics and by optimal-equal HR, respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges and in-hospital admission) were analysed using Cox multivariable regression. Receiver operating curves were drawn by summing the Cox-significant variables for each method.

Of 11 052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted to the hospital. After a 306-day median follow-up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73 years (HR=4.6, 95% CI=4.0 to 5.2), in-hospital admission (HR=2.2, 95% CI=1.9 to 2.4), ER admission during SARS-CoV2 surges (Wave I: HR=1.7, 95% CI=1.5 to 1.9; Wave II: HR=1.2, 95% CI=1.0 to 1.3). Gender, haemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophil counts were significant overall. Benchmark-BCDC model included basophils and platelet count (area under the ROC (AUROC) 0.74). Tailored-BCDC model included monocyte counts and PCT (AUROC 0.79).

Baseline discretised BCDC provides meaningful insight regarding ER patients’ survival.