1 Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD.
2 Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
3 Center for Injury Research and Policy, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
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ABSTRACT |
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accidents; aviation; geography; information systems; Monte Carlo method
Abbreviations: NTSB, National Transportation Safety Board; SD, standard deviation
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INTRODUCTION |
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In recent years, four studies have delineated the association between aviation crashes and geography but were limited in several respects (25
). First, analyses were based on state populations (4
, 5
) or on pilot flight hours (2
). To our knowledge, the severity of crashes, as reflected in crash fatality rates, has not been studied in relation to their geographic occurrence except in a single state, Colorado, where research found that 32 percent of the crashes in mountainous terrain within 50 nautical miles (1 mile = 1.85 km) of Aspen, Colorado, were fatal compared with 19 percent in the rest of Colorado (3
). Second, conclusions were aggregated to the state level, a political classification of geography that does not, for example, allow comparison of specific mountainous regions, thus increasing the possibility of overlooking extremely localized areas of higher-than-average rates within individual states or that cross state lines. Third, variation in the spatial distribution of aviation fatalities was not studied by using geographic information systems and spatial statistics.
The aim of this study was to examine the geographic distribution of crash fatalities of general aviation pilots. In this paper, spatially referenced crash data are displayed in detailed contour maps, and geographic variations in pilot fatality rates are assessed statistically on the basis of Monte Carlo simulations.
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MATERIALS AND METHODS |
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The NTSB recorded 35,570 general aviation crashes between 1983 and 1998. Excluded from our study were 1,335 crashes involving gliders, balloons, blimps, ultralights, and gyroplanes; 2,931 crashes occurring in international waters, Puerto Rico, Alaska, or Hawaii; and 3,160 crashes for which longitude and/or latitude data were missing. Consequently, crash data were analyzed for only flights of airplanes and helicopters conducted under Title 14 of the Code of Federal Regulations Part 91 in the continental United States (refer to the following Internet Web site for more information: http://www.access.gpo.gov/nara/cfr/index.html). Because of lower speeds and more controlled descents and impacts during taxiing, take-off, and landings, crashes on airport property are one sixth as likely to result in a pilot fatality as a crash occurring away from airport property (68
). Therefore, 14,093 crashes that occurred on airport property were also excluded from our analysis.
Crashes were coded as fatal or nonfatal according to pilot-in-command casualty and were then plotted on an electronic map of the continental United States by using the crash-site longitudes and latitudes provided by the NTSB. To estimate the crash fatality rate for any one location, a grid, with intersection points 50 miles apart, was created over the crash-site map. For each grid intersection, a circular spatial filter with a 50-mile radius from the grid intersection was drawn, and the numbers of fatal crashes and nonfatal crashes within the circle were counted. Pilot crash fatality rates were calculated for each grid intersection by dividing the number of fatal crashes within a filter by the total number of crashes within the same filter. By repeatedly testing various radii, we determined that the majority of filters with a 50-mile radius contained at least 10 crashes. If a filter had nine or fewer observed crashes, rates were not calculated directly but were interpolated from neighboring grid intersections that had 10 or more observations (9). Interpolation was possible because of the overlap of approximately 25 percent of the area of one circular filter with its neighboring filter. The 50-mile-radius filter was sufficient to include enough crashes so that a fatality rate could be estimated while also capturing the variability in the spatial pattern of these rates. For comparison, pilot crash fatality rates per state were also calculated, and results are presented in table 1.
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Once fatality rates were calculated, all crash sites were categorized as occurring within low-fatality-rate areas, medium-fatality-rate areas, or high-fatality-rate areas. Since the range of fatality rates among all states was observed to be 1939 percent, low-fatality-rate areas were defined as having fatality rates of less than 19 percent. High-fatality-rate areas were defined as having fatality rates higher than 39 percent. Chi-square tests and one-way analysis of variance tests were used to determine whether factors related to pilot characteristics, aircraft characteristics, and crash circumstance were disproportionately distributed among the low-, medium-, and high-rate areas. Significance was achieved if p values were 0.05 or less. All p values presented in the study were based on two-sided tests. Chi-square tests were also used to determine whether factors associated with fatal crashes were disproportionately distributed among the low-, medium-, and high-rate areas.
The data were analyzed by using SAS software (12). The Distance Mapping and Analysis Program (DMAP) was applied to estimate fatality rates by creating grids and variable spatial filters; it was also used to test for statistical significance with Monte Carlo simulations (13
). Interpolated geographic data were displayed by using ArcView GIS software (14
).
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RESULTS |
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The contour map of pilot crash fatality rates showed that rates within the continental United States varied noticeably, even within individual states (figure 1). Seventy-four geographic areas within the continental United States were categorized as having low fatality rates. Fifty-three geographic areas had higher-than-average rates (4090 fatalities/100 crashes). One such area in northeast Tennessee included portions of six states (figure 2).
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A total of 1,077 crashes occurred in the high-fatality-rate areas, of which 533 resulted in pilot death, a pilot fatality rate of 49.5 per 100 crashes. In the low-fatality areas, 736 crashes resulted in 93 deaths, a pilot fatality rate of 12.6 per 100 crashes.
The average age of pilots increased from 42 years in low-fatality areas to 44 years in medium-fatality areas and 45 years in high-fatality areas. The average crash site elevation was 2,029 feet (standard deviation (SD), 2,108 feet) (1 foot = 30.48 cm) in low-fatality-rate areas, 1,500 feet (SD, 2,085 feet) in medium-fatality-rate areas, and 3,342 feet (SD, 3,132 feet) in high-fatality-rate areas. One-way analysis of variances was significant for age and elevation (F = 8.2, p < 0.001; and F = 140, p < 0.0001, respectively).
Compared with crashes in low-fatality areas, those in high-fatality-rate areas were twice as likely to be associated with flying during instrument meteorologic conditions (adverse weather) (p < 0.0001), to involve twin-engine aircraft (p = 0.003), to occur at an elevation of 2,000 feet or more (p < 0.0001), and to result in fire (p < 0.0001) (table 2).
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DISCUSSION |
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Unlike tabular data aggregated to a state level, this study created digital maps that instantly conveyed visual information on the spatial distribution of pilot fatality rates so that areas with subtle patterns of high rates could be identified quickly. This distinction is important because state borders did not coincide with the borders of areas with high case fatality rates. Thus, mapping rates into predefined areas, such as choropleth maps of states, would have concealed critical spatial patterns. For example, table 1 shows that Tennessee, Kentucky, Virginia, North Carolina, South Carolina, and Georgia had pilot crash fatality rates of 2636 per 100 crashes. However, certain areas within these states had pilot crash fatality rates ranging from 40 to 52 per 100 crashes (figure 2).
Although this study was an exploratory evaluation of spatially distributed measures of pilot fatality rates, it provides strong etiologic insight into crash patterns undetectable by nongeographic epidemiologic methodology, bolstering the notion that patterns of fatalities do not occur at random locations. Our results indicate that the areas with high pilot crash fatality rates directly mirror the physiographic regions of the United States. Specifically, higher rates were found in the rugged terrain of the Pacific Mountain System, Intermontane Plateaus, Rocky Mountain System, Interior Highlands, Appalachian region, and the Canadian Shield region, and low-to-medium rates were found in the flat terrain of the Interior Plains and Atlantic Coastal Plains.
In areas with high fatality rates, it was found that pilot fatality was associated with multiengine aircraft. Survivability may decrease because the more powerful multiengine planes tend to impact at higher G-forces than single-engine aircraft do (8). Rugged, mountainous areas and bad weather also may contribute to decreased pilot survivability by increasing the length of time required for each of the following phases of search-and-rescue operations: time to search-and-rescue notification, time to accurately locate the crash site, response time to the crash scene, stabilization of the crash site, time for occupant extraction to the rescue vehicle, and time of transport to the hospital (15
).
It is noteworthy that this spatial analysis is limited to the risk of pilot fatality given a crash. Geographic patterns of crash risk remain unaddressed because of the lack of exposure data. Although the Monte Carlo simulations provide an approach to understanding the statistical significance of the geographic patterns, a certain level of subjectivity is involved when the locations of grid intersections are created and the radius of the spatial filter is chosen. We evaluated this subjectivity by repeating the methods described above using grid intersections and radii of the spatial filters equal to 100 miles and then 10 miles. We observed that a larger filter size resulted in fewer areas with high rates and smaller filters resulted in many smaller areas with high rates; however, almost identical rate patterns were observed when mapped. Since many of the 147 filters without the required 10 crashes needed to calculate the fatality rate were located near the borders of Canada, Mexico, the Gulf of Mexico, the Atlantic Ocean, and the Pacific Ocean in such a way that approximately 50 percent of the filter area was outside US borders, border effect must be considered when the results are interpreted.
While it is impossible to eliminate all the danger inherent in flight, the number of fatalities and the severity of injury associated with general aviation crashes could be reduced. Protective structure and equipment modifications should enable general aviation aircraft to sustain the higher G-force impact encountered in many rough-terrain crashes. New technologies, such as automatic collision notification sensors, which detect collisions and automatically transmit information on the exact location, time, date, and force of the collision to a central monitoring station via the automatic vehicle location and global positioning systems satellite network, should be developed and regulated as mandatory equipment in general aviation aircraft. In turn, fire, police, and emergency medical response centers should be equipped with Automatic Vehicle Location and Global Positioning Systems receiver systems so they can effectively dispatch emergency vehicles closest to the crash site, thereby reducing response time. This reduction in response time could save lives. To further reduce search time in rugged and rural areas, all rescue aircraft should incorporate thermal and night vision technology as standard equipment.
Demonstration of high-fatality-rate areas supports the need for greater regional prevention efforts, beginning with pilot education. Pilots should be instructed on medical first aid, proper mountain flying techniques, emergency locator transmitters use and maintenance, and the importance of keeping a fully charged cellular phone on board. Pilots likely to fly over high-risk areas should also take advantage of survival courses that deal specifically with harsh climate and terrain.
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ACKNOWLEDGMENTS |
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The authors thank the National Transportation Safety Board for assistance in providing data.
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NOTES |
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Reprint requests to Dr. Guohua Li, Department of Emergency Medicine, The Johns Hopkins University School of Medicine, 1830 East Monument Street, Suite 6-100, Baltimore, MD 21205 (e-mail: ghli{at}jhmi.edu).
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REFERENCES |
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