Study design
We conducted a population-based retrospective cohort study by analyzing administrative ED records from the National Ambulatory Care Reporting System (NACRS) database. The STROBE statement was followed for reporting of results (Additional file 1) [19].
Population
All adult patients aged (≥18 years) triaged in an Ontario ED and arriving by either paramedic transport or self-referral were included. We excluded patients who were not triaged by hospital staff (registered but left prior to triage), as hospital admission is not possible in this cohort and represent a very small and distinct cohort of patients. Furthermore, patients were excluded if their mode of transportation included any air ambulance transportation. ED visits that did not result in a discharge or admission from the ED were excluded (i.e. dead on arrival, triaged but left prior to physician assessment), as these outcomes are not relevant to the studies objective. Data for this study represents a population-based view of ED use and paramedic transport. No sampling methods were required, all records meeting eligibility criteria were included to minimize bias.
Data sources
Data were extracted from the NACRS dataset, housed in the Institute for Clinical Evaluative Sciences (IC/ES), on eligible patients who visited an ED in Ontario between April 1, 2015 to March 31, 2020. This timeframe represents the most recently available five-year period prior to the COVID-19 pandemic, when paramedic and hospital utilization may have changed [20]. NACRS is a hospital and community-based ambulatory care administrative database that collects all patient visit data at the time of service [21, 22]. IC/ES is a non-profit, independent corporation that supports the study of health service and population-wide outcomes in Ontario using administrative databases.
Variables and measurement
All patient characteristics included in this study were measured and recorded at the time of ED registration and selected based on the prior literature, clinical judgement and data availability. Characterises included sex, age, access to primary care, triage acuity, comorbidities, primary diagnostic category, ED geographic location, ED visit outcome, and repeat ED visits within 30 days. Variables were collapsed into ordinal and nominal categories to facilitate model stability when data were non-continuous and truncated (i.e., < 5% per cell of cohort).
Patient age was originally extracted as a twenty-level categorical variable, due to personal health information privacy restrictions. Age was further collapsed into three categories to parallel major age progressions (18–39, 40–64, 65–105 years). Access to primary care was recorded at the time of visit as the identifying physician overseeing the majority of primary healthcare. Data were also collected on population density, and classified as urban or rural.
Triage acuity was assigned by the ED triage nurse following ED registration, not by a paramedic, using the Canadian Triage and Acuity Scale (CTAS). CTAS is an ordinal scale that ranges from one to five, with a score of one indicating the most emergent (resuscitation) and five the least urgent (non-urgent) [23]. Triage acuity was condensed into three categories, similar to prior ED studies in Canada, [24] as CTAS score one (0.8%) and five (4.9%) are relatively infrequent: scores of one and two were grouped as ‘emergent’, scores of three as ‘urgent’, and scores four and five grouped as ‘non-urgent’.
Main diagnoses were assigned by an attending ED physician and recorded using the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10). Comorbidities were recorded as pre-existing diagnoses at time of ED visit and included hypertension, diabetes, chronic obstructive pulmonary disease, asthma, rheumatoid arthritis, congestive heart failure, bowel disease, and cancer.
Statistical analysis
Descriptive statistics were reported using measures of frequency and proportions and stratified between modes of transportation (paramedic and self-referral). Multivariable binary logistic regression was used to calculate the association between the mode of transportation and hospital admission status, after adjusting for age, sex, triage acuity, comorbidity count, population density, repeat ED use, and the presence of system-specific disease condition. Results were reported as crude and adjusted odds ratios to show independent associations of each characteristic, alongside corresponding 95% confidence intervals (CI). Data were managed and analyzed in R software (v.3.6). Missing data was scant (< 1%) and handled using pairwise deletion.