Marathons and Study Population

We identified the dates of the 11 largest U.S. urban marathons (according to the number of runners) during the period from 2002 through 2012; the 11 cities were Boston, Chicago, Honolulu, Houston, Los Angeles, Minneapolis, New York City, Orlando (Florida), Philadelphia, Seattle, and Washington, D.C.

Table 1. Table 1. Characteristics of Patients Admitted to Marathon-Affected Hospitals with Acute Myocardial Infarction or Cardiac Arrest in Cities during Major Marathons.

We sought to determine whether marathons are associated with higher mortality because of infrastructure changes that result in delays in care rather than because of race participation. We therefore focused on persons who had a relatively low likelihood of marathon participation (i.e., Medicare beneficiaries ≥65 years of age who were hospitalized for acute myocardial infarction or cardiac arrest). Many Medicare beneficiaries who are hospitalized with these nonelective conditions are of advanced age and have multiple coexisting medical conditions (Table 1).

Primary Outcome Measure and Data Sources

Our primary outcome was 30-day mortality after admission. We used several data sources. Medicare Provider Analysis and Review (MedPAR) 100% files, which contain information on all fee-for-service Medicare beneficiaries using hospital inpatient services, were used to identify hospitalizations for acute myocardial infarction (International Classification of Diseases, Ninth Revision [ICD-9] code 410.X1) or cardiac arrest (ICD-9 code 427.5) among Medicare fee-for-service beneficiaries (≥65 years of age) between January 1, 2002, and November 30, 2012. Dates of death and demographic characteristics of the beneficiaries were obtained from Medicare Beneficiary Summary files. Information on chronic medical conditions of the beneficiaries was obtained from the Chronic Conditions Data Warehouse of the Centers for Medicare and Medicaid Services.

In addition to these data, we matched the Medicare carrier file (20% sample) to the MedPAR file using the beneficiary identification number and claim date. Unlike MedPAR files, carrier files include claims for ambulance rides, which allowed us to identify whether a patient was brought to the hospital by ambulance and, if so, the miles driven. Ambulance rides were identified according to Healthcare Common Procedure Coding System code A0425. This 20% matched sample was used only for the analysis of miles driven by the ambulance on marathon dates versus nonmarathon dates.

Finally, to estimate ambulance travel times on marathon dates, we used the National Emergency Medical Services Information System (NEMSIS),14 a national repository of emergency medical services activations. NEMSIS includes ambulance travel times, pickup times, incident and destination locations, and patient characteristics. We analyzed data submitted by participating agencies in the cities we studied during 2012. The outcome of interest was elapsed travel time from scene departure to hospital arrival on a marathon date versus the same day of the week as the day of the marathon in the 5 weeks before and the 5 weeks after the marathon. We also analyzed the time of scene departure on marathon dates versus nonmarathon dates to assess delays in ambulance arrival at the scene or in patients seeking care. To ensure that we captured transports of nonrunners and to approximate characteristics of Medicare beneficiaries hospitalized for acute myocardial infarction or cardiac arrest, we restricted our NEMSIS analysis to transports involving patients 70 years of age or older (a sensitivity analysis is provided in the Supplementary Appendix, available with the full text of this article at NEJM.org). Because of confidentiality requirements, we were not provided with geographic data.

Classification of Hospitals and Dates of Hospitalizations

We identified areas within the marathon routes according to ZIP Code. For marathons that spanned areas outside city limits, we included as part of the marathon-affected area the towns that the marathon passed through. For instance, the Boston marathon passes through suburban towns such as Newton and Brookline, and so these additional ZIP Codes were included in the marathon-affected area. Hospitals located in ZIP Code areas through which the marathon route passed were defined as marathon-affected hospitals. We identified a control set of ZIP Codes that included all ZIP Code areas in the hospital referral regions that neighbored the hospital referral region in which the marathon took place.15 We defined control hospitals as all hospitals located in these surrounding hospital referral regions.

We compared 30-day mortality among patients admitted to marathon-affected hospitals on a marathon date with 30-day mortality among patients admitted to the same hospitals on the same day of week as the day of the marathon in the 5 weeks before and after the marathon. For instance, our control dates for Boston’s 2012 marathon, which occurred on Monday, April 16, included the five Mondays before and the five Mondays after that date. Dates of hospitalizations were classified as “marathon” or “nonmarathon” according to the admission date relative to the marathon date. Overall, 121 marathon dates (11 cities over 11 years) and 1210 nonmarathon dates were analyzed.

Mortality Analysis

We assessed whether patient characteristics (e.g., age, sex, race, and preexisting chronic medical conditions) were balanced among Medicare beneficiaries admitted to marathon-affected hospitals on marathon dates versus nonmarathon dates. We then plotted unadjusted 30-day mortality according to the week relative to the marathon date among the beneficiaries hospitalized in marathon-affected hospitals versus control hospitals (control hospitals were included to assess for regional trends in mortality that may be correlated with marathon dates). Because unmeasured patient characteristics that are correlated with mortality may vary between marathon dates and nonmarathon dates, we used a hospitalization-level multivariable logistic model to estimate the 30-day mortality among the beneficiaries hospitalized in marathon-affected hospitals as a function of hospital admission date (marathon date vs. nonmarathon date); the sex, age, or race of the patient; any one of 10 preexisting chronic medical conditions; the median household income in the ZIP Code area; the interaction between city and marathon day (to allow marathon effects to vary across cities); and the fixed effects for hospital. The fixed effects for hospital accounted for differences in mortality among beneficiaries hospitalized on marathon dates versus nonmarathon dates that may be mediated by patients being admitted to different hospitals. Our estimates therefore compared mortality on marathon dates versus nonmarathon dates among beneficiaries admitted to the same hospital. We report adjusted 30-day mortality among beneficiaries admitted to marathon-affected hospitals on marathon dates versus nonmarathon dates using the marginal standardization form of predictive margins averaged over the distribution of covariates in our sample.16,17 Using an identical model, we estimated the 30-day mortality among beneficiaries admitted to control hospitals, hypothesizing that the higher 30-day mortality among beneficiaries admitted to marathon-affected hospitals on marathon dates should not be observed in these hospitals. Both models used robust variance estimators to account for clustering of admissions within hospitals. The 95% confidence interval around the reported estimates reflects an alpha level of 0.025 in each tail (or P≤0.05).

Sensitivity Analyses

We conducted several sensitivity analyses. First, to ensure that we did not analyze hospitalizations that occurred as a result of participation in the marathon, we conducted a subgroup analysis that included only beneficiaries with five or more chronic medical conditions (the median number of conditions in our population); this group was unlikely to include marathon runners. We also performed an automated query of local newspapers to identify potential instances of deaths among marathon runners. Second, in a permutation test, we simulated how the observed difference in mortality among beneficiaries hospitalized on marathon dates versus nonmarathon dates compared with the difference that might be expected from chance alone, as calculated by assigning marathon-affected hospitals to “placebo” marathon dates chosen randomly throughout the year and then estimating the difference in unadjusted mortality among the beneficiaries hospitalized on the placebo marathon dates versus nonmarathon dates (1000 replications were performed). Additional analyses included alternative mortality end points, estimation of a hierarchical mortality model, inclusion of additional marathons, and an expanded definition of marathon-affected areas to address potential misclassification. (Further details on the automated query of local newspapers and additional analyses are provided in the Supplementary Appendix.)

Analysis of Potential Causes

We assessed several potential causes to explain differences in mortality among beneficiaries hospitalized on marathon dates versus nonmarathon dates. An influx of spectators into marathon-affected areas could lead to increases in the rate of acute myocardial infarction or cardiac arrest among visitors hospitalized on marathon dates, which could raise mortality if hospital resources are stretched. Alternatively, if patients delay seeking care during marathons, those hospitalized may have a higher unobserved risk of death. We assessed these possibilities by analyzing whether the number of hospitalizations for acute myocardial infarction or cardiac arrest varied across dates. Similarly, we used NEMSIS files to analyze whether the distribution of ambulance departure times varied between marathon and nonmarathon dates; delays in patients seeking care would be expected to shift the distribution of ambulance departure times to later in the day.

Second, we analyzed whether hospital care differed between marathon and nonmarathon dates, which may occur if hospitals are relatively short-staffed during marathons. We analyzed rates of percutaneous coronary intervention, mechanical circulatory support (defined as intraaortic balloon counterpulsation or insertion of a percutaneous ventricular assist device), and coronary-artery bypass grafting (procedure codes are provided in the Supplementary Appendix).

Third, we analyzed whether the distribution of hospitals providing care changed during marathons as a result of road closures that prompted diversions of ambulances or private vehicles. We computed each hospital’s share of total acute myocardial infarction or cardiac arrest hospitalizations on marathon dates and nonmarathon dates and used a Spearman correlation analysis to assess the correlation between these shares. A high correlation would imply that patients were not admitted to different hospitals on marathon dates than on nonmarathon dates.

Fourth, we analyzed whether the out-of-town composition of hospitalized patients changed during marathons, which could have led to higher mortality on marathon dates if unobserved risks of mortality were higher among patients residing outside marathon-affected ZIP Code areas. We compared distributions of ZIP Code of residence among the beneficiaries hospitalized in marathon-affected hospitals on marathon dates with those on nonmarathon dates. We also conducted a subgroup analysis of mortality that included only beneficiaries who lived in marathon-affected ZIP Code areas.

Fifth, we assessed whether scene-to-hospital ambulance travel was delayed on marathon dates. First, we used the 20% carrier files to compare the mean miles driven by ambulances on marathon dates versus nonmarathon dates. Next, because miles were reported in integers and road closures may lead to delays without increases in miles, we used NEMSIS files to compare scene-to-hospital transport time on marathon dates with that on nonmarathon dates, during mornings (defined as the period from 3 a.m. through noon), when roads should be closed, and evenings (defined as the period from 7 p.m. through 11:59 p.m.), when roads should be reopened. Finally, because delays in the care of acute myocardial infarction can lead to cardiac arrest, we used MedPAR files to estimate the probability of a diagnosis of concurrent cardiac arrest among patients with acute myocardial infarction on marathon and nonmarathon dates; we hypothesized that a greater proportion of admissions for acute myocardial infarction with concurrent cardiac arrest on marathon dates could indicate delayed care.