Study Setting and Oversight

In 2008 (the midpoint of the study), Ontario had a population of 12,932,297 persons (of whom 9,042,286 were licensed drivers) and a total of 229,196 crashes resulting in death, disability, or property damage.14 Of these crashes, 17,929 involved an emergency department visit by the driver, for an overall annual rate of about 1.98 events per 1000 drivers. Patients had free access to outpatient, emergency, and hospital care under universal health insurance and could be tracked forward and backward in time through validated population-based databases.

The study was approved by the research ethics board of Sunnybrook Health Sciences Centre, including a waived requirement for individual consent.

Program for Medical Warnings

Medical warnings in Ontario were a joint program of the Ontario Ministry of Transportation, the Ontario Ministry of Health and Long-Term Care, and the Ontario Medical Association. As with programs of the American Medical Association, the intent was to encourage physicians to identify, report, caution, counsel, and find creative solutions for patients considered to be potentially medically unfit to drive.15,16 One element was a checklist of common diagnoses for the physician to complete.17 The program, which was supported by an official driver-improvement office,18 provided simplified documentation of the physician's warning (including the billing fee for the associated physician's service12) and resulted in suspension of the driver's license in about 10 to 30% of cases.8,19

Identification of Patients

We identified consecutive patients who had received a medical warning from a physician between April 1, 2006, and December 31, 2009. This period encompassed all available data for the first 4 years of the program and provided a minimum of 1 year of follow-up for all patients. We excluded children (age <18 years), persons living outside Ontario, and those who lacked a valid health-card number (85 patients). For patients with more than one warning, we included only the first warning in the analysis. Time zero for each patient was defined as the day of the first warning and also served to differentiate the first phase of the program (encompassing patients who were first warned in 2006 or 2007) from the second phase of the program (encompassing patients who were first warned in 2008 or 2009).

Characteristics of Patients

Data on the patient's age at the time of the warning were obtained from the population registry for Ontario, as were data on sex and place of residence (classified as urban or rural). We used validated linked identifiers to ascertain hospitalizations, emergency department visits, and outpatient visits for the full year before the warning.20 Diagnoses were ascertained on the basis of physicians' records in comprehensive billing data because the indication for each warning was not available.21 The databases did not contain information on driving records, distance traveled, functional status, use of medications, driver testing, roadway infractions, past suspensions, or subsequent licensing decisions.

Motor Vehicle Crashes

For each patient, we identified road crashes that involved the patient as the driver and that resulted in an emergency department visit by the patient to any Ontario hospital, representing all data available before and after the patient received the warning. We focused on visits for injuries due to crashes, according to the diagnostic codes of the International Classification of Diseases, 10th Revision (V20 through V69).22 We included emergency department visits involving crashes in which the patient was the driver of the motor vehicle and excluded emergency department visits involving crashes in which the patient was a passenger or pedestrian. Secondary analyses examined the excluded emergency department visits as well as time (time of day, day of week, and season of year), the type of vehicle (car, truck, or other), the type of crash (involving a single vehicle or multiple vehicles), and the severity of the driver's injury (arrival at the hospital by ambulance or other means and triage urgency).12

Self-Matching Crossover Design

We used an analytic design in which each patient served as his or her own control because a randomized, blinded trial of warnings would not have been ethical or practical. As in case-crossover designs, self-matching eliminates confounding due to genetics, personality, education, and other stable characteristics (measured or unmeasured).23 As in time-series analyses, an extended observational interval before and after the intervention addressed regression to the mean, protopathic bias, and other temporal confounders.24 A limitation of our design is that acute changes in health may increase the person's risk of trauma (owing to impairments associated with illness) or decrease the risk (owing to reduced driving because of illness).

Type of Prevention

The type of prevention was classified on the basis of whether the patient had an emergency department visit in the year immediately before the warning, hereafter termed the antecedent interval. Patients who had no emergency department visits for a road crash during the antecedent interval were considered to have received a preemptive warning, whereas patients who had an emergency department visit for a road crash during the antecedent interval were considered to have received a responsive warning. The purpose was to assess the effectiveness of prevention that either precedes or follows a major event.25 Separation of the antecedent interval from the baseline interval also helped reduce artifacts related to selection bias, reverse causality, and other distortions in before-and-after comparisons.

Medical Diagnoses in Patients

We examined the diagnoses that led to warnings by analyzing the entire antecedent interval for each patient. A list of the 20 most common diagnoses was compiled on the basis of billing data that provided diagnostic codes for all visits during the year.26 A patient with a stroke and a heart attack, for example, was classified as having received both diagnoses (see the Supplementary Appendix, available with the full text of this article at NEJM.org). The purpose was to examine common medical diagnoses that may have prompted the medical warning. We also collected data for exploratory purposes on the general degree of morbidity (numbers of outpatient clinic visits, emergency department visits, and hospital admissions) and the characteristics of the physician responsible for the warning (age, sex, years in practice, and specialization).

Potential Adverse Effects

Further analyses assessed the robustness of the study findings, checked for potential survivor bias, and tested for unintended consequences.27 The first set of analyses examined emergency department visits by persons involved in road crashes as pedestrians or as passengers. The second set of analyses examined emergency department visits related to depression (selected as a diagnosis that was frequent in the community, recorded in databases, clinically important, and potentially exacerbated by driving restrictions). The final set of analyses examined the possibility of a breakdown in the doctor–patient relationship by investigating discontinuities in outpatient care for any reason during the year before and the year after the warning.

Statistical Analysis

The primary analysis evaluated emergency department visits by drivers in road crashes and compared the baseline interval with the subsequent interval. Statistical testing was performed with the use of McNemar's test, adapted to evaluate departures from a ratio of 3:1 (because each patient provided 3 years of baseline observation and 1 year of subsequent observation).28 Statistical estimates were also confirmed with the use of time-series analyses and longitudinal generalized estimating equations (see the Supplementary Appendix). Prespecified subgroup analyses examined patients in the first phase separately (to provide an extended interval after the warning) and patients in the second phase separately (to provide an extended baseline before the warning). The year of recruitment and all other patient characteristics were subjected to post hoc subgroup analyses to check the robustness of the findings.