Methodological Issues in the 

Study of Airplane Accident 

Rates by Pilot Age: Effects of 

Accident and Pilot Inclusion 

Criteria and Analytic Strategy

Dana Broach

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

May 2004

Final Report

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Office of Aerospace Medicine

Washington, DC 20591

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Technical Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-04/8

5. Report Date 

May 2004 

4. Title and Subtitle 

Methodological Issues in the Study of Airplane accident Rates by Pilot 

Age: Effects of Accident and Pilot Inclusion Criteria and Analytic 

Strategy

6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. 

Broach D 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Aerospace Human Factors Research Division (AAM-500) 

Civil Aerospace Medical Institute 

P.O. Box 25082 

Oklahoma City, OK 73125 

11. Contract or Grant No.

12. Sponsoring Agency Name and Address 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Avenue, S.W. 

Washington, DC 20591 

13. Type of Report and Period Covered 

15. Supplementary Notes 

This work was performed under approved research task AAM-00-A-HRR-520. 

16. Abstract 

At the direction of the U.S. Senate, Broach, Schroeder, and Joseph (2000a, b) examined accident rates by age 

for professional air transport and commercial pilots using an analysis of variance (ANOVA) approach. As an 

extension of that work, this report focuses on methodological issues requiring careful consideration and 

definition in any analysis of aviation accident rates by pilot age. Three methodological issues are considered: 

(a) accident inclusion criteria; (b) pilot inclusion criteria; and (c) analytic strategy. Previous studies are 

interpreted with respect to these issues, and an additional analysis is presented to illustrate the impact of 

methodological choices on study outcomes. Overall, the comparisons and additional analysis indicate that 

accident and pilot inclusion criteria and analytic strategy have substantial impact on study outcomes. 

Recommendations are presented for future studies of the relationship of pilot age to aviation accidents. 

17. Key Words 

Pilot Age, Aviation Accidents, Accident Rates,

Age 60 Rule, Mandatory Pilot Retirement

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Document is available to the public through the 

National Technical Information Service, 

Springfield, VA 22161

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Unclassified 

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26

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1

METHODOLOGICAL ISSUES IN THE STUDY OF AIRPLANE ACCIDENT RATES

BY PILOT AGE: EFFECTS OF ACCIDENT AND PILOT

INCLUSION CRITERIA AND ANALYTIC STRATEGY

INTRODUCTION

In 1959, the Federal Aviation Administration (FAA) 

adopted what has come to be known as the “Age 60 Rule” 

(24 Fed. Reg. 9,767, December 5, 1959). This regulation 

prohibits any air carrier from using the services of any 

person as a pilot or co-pilot, and prohibits any person from 

serving as a pilot or co-pilot, on an airplane engaged in 

operations under Part 121 of the Federal Aviation Regulations (FARs) if that person has reached his or her 60th 

birthday [14 C.F.R. §121.383(c)]. The “Age 60 Rule” has 

been, and continues to be controversial. The rule has been 

the subject of commentary, research, and legal challenge 

since its inception. Most recently, the rule has been the 

subject of Congressional interest. For example, three bills 

(Senate 361 and House Resolutions 481 and 1063) were 

introduced in the 106th Congress to change the rule. The 

U.S. Senate directed the FAA in 1999 to conduct a study 

of pilot age and accident rates (U.S. Senate, 1999). The 

Senate language provided very specific directions as to 

how to conduct the desired study. In response, the FAA 

completed four studies. The first study (“Study 1”) was 

an annotated bibliography of the research literature from 

1991 to 1999 (Schroeder, Harris, and Broach, 1999). 

The second study (“Study 2”) presented a re-analysis 

of data included in a study of pilot age, accidents, and 

incidents reported by the Chicago Tribune in 1999 and a 

discussion of methodological issues in the study of pilot 

age and accident rates (Broach, 1999).

The third and fourth reports documented two empirical 

studies of pilot age and accident rate. Study 3 (Broach, 

Schroeder, & Joseph, 2000a) examined accident rates by 

age for professional pilots holding Air Transport Pilot 

(ATP) and Class 1 medical certificates. The fourth and 

final study (“Study 4”) in the set investigated accident 

rates by age for professional pilots holding ATP or Commercial Pilot (CP) and Class1 or 2 medical certificates 

(Broach, Schroeder, & Joseph, 2000b). The results of the 

analyses of accident rate, with pre-planned comparisons 

between age groups on either side of the “Age 60 Rule” 

in the two empirical studies were mixed. On one hand, 

a significant relationship between age and accident rate 

was observed in both studies. Specifically, a statistically 

significant quadratic trend between age and accident 

rate was reported in both of the empirical studies. That 

is, accident rates were higher at younger ages (less than 

40), lower through the forties and early fifties, then rose 

again in the late fifties. A linear trend also fit the age-accident rate data, but less well than the quadratic trend. 

On the other hand, the a priori planned comparison of 

the accident rates for pilots age 55-59 with that for pilots 

60-63 was significant in one study but not the other. 

When the study population was defined as professional 

pilots holding ATP and Class 1 medical certificates in 

Study 3, no significant difference was found between 

accident rates for pilots in the 55-59 and 60-63 year old 

age groups. But when the study population was defined 

as professional pilots holding ATP or CP and Class 1 or 

2 medical certificates in Study 4, the accident rate for 

pilots in the 60-63 group (0.52 accidents per 100,000 

annualized flight hours) was significantly greater than 

the rate for pilots in the 55-59 age group (0.27 accidents 

per 100,000 annualized flight hours; unequal variances, 

t(11.18) = 2.34, p < .05).

METHODOLOGICAL ISSUES

The difference between the results reported in Studies 

3 and 4 suggest that methodological choices may have 

substantial influence on study outcomes. As noted by 

Li in a 1994 review, two key choices in the study of 

aviation accident rates are (a) the selection of events 

to include in the numerator and (b) the estimate of 

exposure in the denominator. Each of these choices 

entails decisions such as selection of a source database 

and record inclusion and exclusion criteria. However, 

another key choice is analytic strategy, as discussed in 

the previous empirical studies. Particular considerations 

are data aggregation, data grouping, rates versus counts 

of events as the dependent variable, and the analytic 

technique used. The purpose of the present study was 

to assess the effects of these methodological choices on 

study outcomes. The previous studies of age and accident rates are compared in terms of events included, 

exposure estimates, and analytic strategies. An additional 

analysis of the data set developed by Broach, Schroeder 

and Joseph in 2000 (“FAA Age 60 data set”) is presented 

to illustrate the sensitivity of results to the methodology. 

Implications for future studies of pilot age and accident 

rates are discussed in closing.
2 

Accident Inclusion Criteria

The first methodological choice is the selection of 

events to analyze. Studies 3 and 4 were constrained by 

language in the U.S. Senate (1999) report directing those 

efforts. The critical passage reads:

The Committee directs the FAA to conduct a survey of all 

available non-scheduled commercial (and non-commercial, 

if available) data concerning the relative accident data correlated with the amount of flying by pilots as a function of 

their age for pilots of age 60–63 and comparing it with all 

four year groupings of scheduled commercial pilots (and 

non-commercial pilots, if available) declining from age 60, 

i.e., 56–59, 55–58, 54–57, * * * to 21–24. etc. In addition, 

compare the discernable groups in their entirety and track 

accident frequency as a function of age. (p. 80).

The Senate language focused on accidents. The National Transportation Safety Board (NTSB) maintains the 

official system of records for aviation accidents. Therefore, 

the first methodological choice made in both Studies 3 

and 4 was to select the NTSB system of accident records 

(“NTSB database”) as the source for event data. The 

next step was to define event inclusion criteria. Given 

the Senate language and the fact that the rule applied 

only to pilots of aircraft engaged in common carriage 

(e.g. commercial operations), Broach, Schroeder and 

Joseph focused on accidents in commercial operations. 

As shown in Figure 1, criteria for extraction of accident 

records from the NTSB database were: (a) the regulation under which the flight was conducted (Parts 121 

or 135); (b) the date of the accident (January 1, 1988 

through December 31, 1997); and (c) availability of a 

final report. Using these criteria, the NTSB extracted 

1,359 aviation accident records for analysis by the FAA. 

Records with incomplete or missing pilot identifiers (24 

records) or for events caused by terrorism (one record) 

were excluded from the data set as shown in Figure 1, 

resulting in a pool of 1,334 aviation accident records for 

the studies. The records were further reduced in Study 3 

to 680 by selecting those records for accidents where the 

pilot held an ATP and Class 1 medical certificate at the 

time of the accident. In Study 4, all 1,334 records were 

included in the analysis.

Differences in the characteristics of the accidents included in each study were examined as the first step in 

the investigation of methodological influences on study 

results. One difference between Studies 3 and 4 was in 

the proportion of flights conducted under Part 135. 

Study 3 included 393 flights conducted under Part 135 

(58% of accidents), while Study 4 included 1,047 flights 

conducted under Part 135 (78% of included accidents). 

Similarly, 391, or just over half (57%) of the flights in 

Study 3 were scheduled flights, compared with 424, or 

just under a third (32%), in Study 4. One-third (221, or 

32.5%) of operators of accident aircraft were certificated 

for on-demand/air taxi operations only in Study 3, compared with over half (768, or 57.5%) in Study 4.

There were differences also in the aircraft characteristics 

between the two studies. For example, 91 (or 13%) of 

the accidents included in Study 3 involved fixed-wing, 

single-engine aircraft, compared with 439 (33%) in Study 

4. Aircraft with 9 or fewer seats were involved in over a 

third (258, or 38%) of the accidents in Study 3, compared with two-thirds (864, or 65%) of the accidents in 

Study 4. Aircraft with reciprocating engines accounted 

for a third (34% or 229) of the accidents included in 

Study 3, compared with over half (53%, or 707) of the 

Study 4 accidents.

This review of the previous studies by Broach et al. 

indicated that the numerator in Study 4 was dominated by 

accidents involving smaller aircraft operating under Part 

135. Historically, operations with smaller aircraft under 

Part 135 have had higher accident rates than operations 

with larger aircraft under Part 121 (Federal Aviation 

Administration, 1999; National Transportation Safety 

Board, 2002). It is unclear, therefore, if the analysis of 

accident rate by pilot age reflected the influence of age, 

the inherent risk of small aircraft operations characterizing Part 135 operations, or both.

Pilot Inclusion Criteria

The next methodological choice focuses on pilot inclusion criteria. As noted by Li (1994), the denominator 

in epidemiological studies of aviation accidents typically 

consists of some measure of exposure (to the risks of 

flight). Sources for exposure estimates include (a) flight 

hours documented by NTSB in the accident investigation and (b) self-reported flight hours from the medical 

examination. Estimates of flight hours based on NTSB 

accident investigations are, by definition, restricted to 

accident pilots only, and thus do not provide a proper 

estimate for the population at risk (e.g., all pilots or some 

subset, including those not involved in accidents). Selfreported flight hours at the time of the medical examination have been used in several investigations, including 

Studies 3 and 4. The official system of records for these 

data is the FAA’s Comprehensive Airman Information 

System (CAIS).

The researcher again faces the problem of defining 

criteria by which to extract records for a subset of airman of interest from a large, complex database. The pilot 

inclusion criteria for Studies 3 and 4 are illustrated in 

Figure 2. The inclusion criteria were: date of medical 

examination; self-reported occupation; age at the time 

of the medical examination; type of pilot certificate; and 

class of medical certificate issued. Employer type was used 

as an exclusion criterion in both studies after matching 

the exposure records to NTSB accident records by pilot 
3

NOT air taxi or

cargo only?

NTSB Accident

Records

(N=1,332)

Multi-engine?

Passenger

operation?

Seats > 9?

Fixed-wing,

land airplane?

Review for

Missing Data

Usable NTSB

Records

(N=1,334)

NTSB Accidents

Database

Extraction

(FAR = 121, 135;

Dates = 01/01/88

to 12/31/97)

NTSB Accidents

(N=1,359)

ATP

Certificate?

Study 3 Accidents

(n=680)

ATP or CP

Certificate?

Class 1

Medical?

Class 1 or 2

Medical?

Study 4 Accidents

(n=1,334)

2nd Review for

Missing Data

Study 5 Accidents

(n=213)

Yes

Yes

Yes

Yes

Study 4

Yes

Yes

Study 3

Yes

Yes

ATP

Certificate?

Class 1

Medical?

Yes

Yes

Yes

Figure 1 

Accident inclusion logic for Study 5 compared with Studies 3 and 4
4 

Study 4

Study 3

FAA CAIS

ExamDate =

(1988,1997)

Occupation=

Prof Pilot

Certificate =

ATP

Medical =

Class 1

ExamDate =

(1988,1997)

Occupation=

Prof Pilot

Medical =

Class 1 or 2

Employer NOT

GOVT, MIL

Employer NOT

GOVT, MIL

Certificate =

ATP or COM

Study 3 Records

(n=891,183)

Study 4 Records

(n=939,769)

ExamDate =

(1988,1997)

Occupation=

Prof Pilot

Medical =

Class 1

Employer =

Same as

Accident pilots

Certificate =

ATP

Study 5 Records

(n=753,842)

Study 5

Recent Hours

>= 200

Total Hours >=

1,500

Figure 2 

Pilot inclusion logic for Study 5 compared with Studies 3 and 4 
5

identifiers and year. Matched exposure-accident records 

for pilots age 64 or older were also excluded in view of 

the Senate direction to compare accident rates for pilots 

age 60-63 with accident rates for younger pilots.

In both studies, records for examinations conducted 

between January 1, 1988 and December 31, 1997, were 

extracted from the CAIS medical database. Only records 

for pilots reporting “professional pilot” as the occupation were retained in both studies. In Study 3, records 

for pilots age 23 or greater at the time of their medical 

and holding ATP certificates were included. In Study 4, 

records for pilots age 18 or greater at the time of their 

medical examination and with either ATP or CP certificates were included. Study 3 included pilots with Class 

1 medical certificates, while Study 4 included pilots with 

either Class 1 or 2 medical certificates. The inclusion 

criteria resulted in a pool of 891,183 records of Class 1 

medical examinations for ATPs in Study 3. There were 

939,767 records of Class 1 or 2 medical examinations 

for ATPs or Commercial Pilots available for Study 4. 

The CAIS medical records were aggregated by year and 

pilot identifiers and exposure hours annualized and then 

matched to NTSB accident records as described in the 

previous reports. Matched exposure-accident records for 

pilots employed by the government or military and pilots 

age 64 or older were excluded in both analyses in view 

of the specific Senate language focusing on pilots age 

60 to 63. The characteristics of the resulting samples of 

pilots by age group and type of employer in each study 

are presented in Table 1.

Records for pilots indicating employment by an identifiable Domestic, Flag, or Supplemental (Part 121) carrier 

dominated both matched accident-exposure data sets. In 

Study 3, records from pilots reporting employment by 

an identifiable Part 121 carrier accounted for 75% of all 

matched accident-exposure records from which accident 

rates were calculated. Even among the oldest pilots (ages 60 

to 63), identifiable Part 121 carriers dominated, accounting for 71% of those 7,258 matched accident-exposure 

records. In Study 4, records for pilots reporting employment by Part 121 carriers accounted for 73% of the total 

matched accident-exposure records. Even among the oldest pilots age 60 to 63, 67% of the records were for pilots 

reporting employment by identifiable Part 121 carriers. 

In other words, the FAA Age 60 data set was dominated 

by records from pilots of all ages reporting employment 

by identifiable Part 121 operators, that is, the population most likely impacted by the “Age 60 Rule.” At first, 

this seemed puzzling given the common description of 

the rule as forcing retirement at age 60 on airline pilots. 

There are two possible explanations. First, the “Age 60 

Rule” prohibits a person who has reached age 60 from 

serving as the pilot-in-command or co-pilot of aircraft 

engaged in operations under Part 121; the rule does not

require mandatory retirement of a pilot or termination 

of the pilot’s employment upon reaching age 60. Thus, it 

is possible that the older pilots continued working for an 

air carrier after reaching age 60 in roles other than pilot 

for flights operated under Part 121. Second, many airline 

operators are certified for multiple classes of operations. 

It may be the case that older pilots remained with the 

reported carrier and served as the pilot for flights conducted under regulations other than Part 121.

While some pilots stayed on, it appears that most had 

sought other opportunities as evidenced by the ten-fold 

reduction in the number of records for pilots age 60-63 

compared with age 55-59. As shown in Table 1, pilots age 

60 to 63 at the time of medical examination represented 

1.2% of matched accident-exposure records in both studies. As a result, estimates of exposure hours for this age 

group are based on far fewer records than for pilots in 

other age ranges. It is also worth noting that the number 

of matched accident-exposure records declines with age 

starting with the 40-44 age group in both studies. Additional research into the “career history” of professional 

pilots might be warranted. Such research might investigate 

the reasons for attrition, including health and retirement, 

from the occupation at different ages.

Inspection of Table 1 also indicates that the distribution of pilots across employer types changes with pilot 

age in both studies. For example, 86% of the 61,663 

accident-exposure records for pilots in the 55-59 age 

group indicated employment by an identifiable Part 

121-certificated operator in Study 3, compared with 

just 71% of the 7,258 accident-exposure records for 

pilots in the 60-63 age group who indicated employment by Part 121 operators. The proportion of pilots age 

55-59 employed by Part 121 operators was significantly 

greater than the proportion of Part 121 employee pilots 

age 60-63 in Study 3 (Z = 27.27, p < .001). Moreover, 

the proportion of matched accident-exposure records 

indicating employment by “Other” for pilots in the 60-

63 age group was nearly double the proportion of pilots 

in the 55-59 age group. A similar pattern of change in 

employer types with age was also observed in Study 4. It 

is unclear from review of the CAIS data dictionaries what 

types of operators are included in the “Other” employer 

category. Moreover, as shown in Table 1, records for pilots 

employed by entities other than Part 121 and 135 carriers 

were included in the matched accident-exposure records 

in Studies 3 and 4.

The effect of this sampling error (e.g., inclusion 

of pilots employed by entities other than Part 121 or 

135 carriers) was to inflate the exposure estimate in the 
6

Table 1 Number of matched exposure-accident records by employer type and age group for Studies 3 and 4 

Employer Type 

Age Group Part 135 Part 121 Foreign Government Military Industry Other Self Missing Total 

Study 31

LE 29 1,648 19,098 334 85 22,957 52 451 44,62530-34 2,161 61,415 1,157 71 27,802 75 674 93,35535-39 1,818 86,549 1,656 76 23,716 71 581 114,46740-44 1,397 85,488 1,249 100 20,614 64 529 109,44145-49 1,255 84,994 1,039 101 17,641 77 507 105,61450-54 681 77,466 915 114 11,953 46 445 91,620

55-59 362 53,054 759 61 7,104 58 265 61,663

60-63 47 5,155 149 31 1,746 24 106 7,258

Total 9,369 473,219 7,258 639 133,533 467 3,558 628,043

Study 42

LE 24 199 1,242 449 34 3,813 26 206 5,96925-29 1,587 19,406 1,526 86 23,488 64 580 46,73730-34 2,245 64,339 2,409 90 30,837 112 791 100,82335-39 1,885 90,258 2,231 98 26,565 106 734 121,87740-44 1,444 87,881 1,807 126 23,168 85 736 115,24745-49 1,295 87,494 1,567 123 19,884 105 658 111,12650-54 715 79,689 1,494 134 13,698 79 630 96,43955-59 378 54,175 1,375 76 8,103 75 374 64,556

60-63 50 5,396 279 33 2,028 39 206 8,031

Total 9,798 489,880 13,137 800 151,584 691 4,915 670,805

Notes: 1Study 3 total excluded 1,396 accident-exposure records for pilots age 64 or older. 2Study 4 total excluded 1,803 accident-exposure records for pilots age 64 or older. 
7

denominator, leading to an under-estimate of accident 

rates. For example, if the number of Part 121 or 135 

accidents for a given age group is 100, and the number 

of exposure hours is 25,000,000, then the accident rate 

is 100/25,000,000 or .400 accidents per 100,000 flight 

hours. If the exposure hours are inflated by hours contributed by pilots other than those employed by certificated 

Part 121 and 135 operators by a factor of 1% (250,000 

hours), then the accident rate is 100/25,250,000 (.396 

accidents per 100,000 flight hours). If exposure hours 

are inflated by 10%, then the accident rate decreases to 

.363 per 100,000 flight hours. Overall, it appears that the 

“true” accident rate for pilots covered by the rule across 

age groups may have been underestimated in Studies 3 

and 4 as a result of inclusion of exposure hours from 

pilots not in the target population of certificated Part 

121 and 135 operators.

Analytic Strategy

Analytic strategy includes the decision to focus on rates 

or counts as the appropriate unit of analysis. Analysis 

of rates requires computing and comparing the ratio of 

events to exposure for definable groups. An analysis of 

rates also requires that data be grouped by some rule and 

aggregated within those groups. Studies 3 and 4 analyzed 

accident rate by age group. The Senate report language 

directed that the data be grouped in overlapping, 4-year 

increments (e.g., “... declining from age 60, i.e., 56–59, 

55–58, 54–57, * * * to 21–24,” U.S. Senate, 1999, p. 

80). However, as noted in Studies 3 and 4, such a grouping violates the assumption of independence between 

groups required for statistical analysis by techniques such 

as analysis of variance (ANOVA). In both studies, an additional analysis was performed, grouping cases by age at 

the time of the medical examination into independent 

age groups, as shown in Table 1. In both studies, accident 

and exposure data were aggregated by calendar year and 

age group in order to have more cases than levels on the 

grouping variable (age group). As noted in those reports, 

this resulted in 80 year-age group records. The unit of 

analysis was the year-age group combination, not the 

individual pilot.

While there was an equal number of “cases” (e.g., the 

80 year-age group records) in each study, substantially 

different numbers of pilots contributed exposure hours 

to the year-age group combinations. For example, the 

number of pilots age 60-63 contributing exposure hours 

to the rate denominator in Study 3 ranged from 406 to 

1,057 across the 10 years encompassed by the study. 

In comparison, about 7 to 10 times as many pilots age 

55-59 contributed exposure hours (4,404 to 7,542 each 

year). The reports for Studies 3 and 4 noted the greater 

variability in the accident rate for the oldest age group 

(60-63) and attributed it to the smaller number of pilot 

records for that age group.

However, the greater variability in the accident rates 

for older pilots does not completely explain the difference 

in results reported in Studies 3 and 4. On one hand, a 

statistically significant quadratic trend across age groups 

was reported in Study 3, but the a priori comparison 

between pilots in the 55-59 and 60-63 age groups was 

non-significant. On the other hand, in Study 4, both 

the quadratic trend across age groups and the a priori

comparison of accident rates for pilots age 55-59 to that 

for pilots 60-63 were statistically significant. In reviewing these studies, Wilkening (2002; see also Woolsey, 

2003a) suggested that flight time accumulated in Part 

121 operations “dominates the denominator” up to age 

60, and the numerator includes accidents occurring 

under both Part 121 and 135. However, after age 60, 

the numerator is based on Part 135 accidents only, and 

the “much safer Part 121 flight hours” are missing from 

the denominator, leading to “artificially higher” accident 

rates for pilots over age 60 (aged 60 to 63 specifically). 

Wilkening suggested that the statistical difference in accident rates for the 55-59 and 60-63 age groups reported 

in Study 4 was an artifact, and may have reflected the 

historically higher risks associated with operations under 

Part 135 rather than the risk associated with pilot age. 

This concern has also been noted in an appellate court 

decision upholding the FAA’s rejection of a petition for 

exemption from the rule (Baker et al. v. Federal Aviation 

Administration, 1990).

Another possibility is that the difference is attributable to factors other than age, such as the type of flying 

conducted by older pilots. For example, it is impossible 

for pilots over age 60 to accumulate hours as a PilotIn-Command (PIC) or co-pilot of an aircraft operated 

under Part 121 in view of the “Age 60 Rule.” While direct 

information on type of flying is not available, the selfreported “employer” at the time of medical examination 

can serve as a proxy or indicator for the type of flying 

engaged in by a pilot. To determine if other factors such 

as type of flying might explain the observed difference in 

accident rates, the characteristics of pilots age 55-59 and 

60-63 in Study 4 were compared. Overall, the proportion of pilots age 55-59 employed by an identifiable Part 

121 operator (54,175 of 64,556 records, or 83.9%) was 

greater than the proportion of pilots age 60-63 (5,396 

of 8,031 records, or 67.2%; Z = 30.78, p < .001). Conversely, fewer pilots age 55-59 in Study 4 reported “Other” 

employment (8,103 records, or 12.6%) compared with 

pilots age 60-63 (2,028 records, or 25.3%; Z = -25.30, p

< .001). This suggests that pilots in the 55-59 and 60-63 
8 

age groups systematically varied on at least one dimension 

(self-reported “employer” as a proxy for type of flying) in 

addition to age. Consequently, the statistical difference in 

accident rates reported in Study 4 might be attributable 

in part to a factor other than pilot age.

Discussion

Review of Studies 3 and 4 in terms of accident inclusion criteria, pilot inclusion criteria, and analytic strategy 

suggest that methodological choices have a substantive 

effect on study outcomes. In particular, the observed 

statistical difference in accident rates observed for pilots 

in the 55-59 and 60-63 age groups in Study 4 might be 

attributable to at least one factor (employer type, as a 

proxy for type of flying) other than age. Moreover, the 

apparent change in accident rate at age 60 might be an 

artifact of a change in the numerator and denominator 

(e.g. removal of accidents under Part 121 in the numerator and removal of flight hours accumulated under Part 

121 in the denominator). It has been suggested that the 

apparent change in accident rate may reflect the greater 

historical risk associated with Part 135 operations rather 

than the effects of age (Wilkening, 2002; Woolsey, 2003). 

To explore these issues further, an additional analysis of 

the data set developed in the course of the previous FAA 

studies was conducted. This additional analysis illustrates 

how methodological choices influence study outcomes.

THE IMPACT OF METHODOLOGICAL 

CHOICES: STUDY 5

The review of earlier studies suggested that factors other 

than age might influence accident rates. On one hand, 

the accidents in the numerator for pilots age 60 and older 

occurred under Part 135. On the other hand, fewer pilots 

in the older age range (60-63) reported employment by 

an identifiable Part 121 operator. Moreover, pilots age 60 

and older are prohibited from serving as PIC or co-pilot 

in Part 121 operations. Therefore, the hours contributed 

by the oldest pilots (age 60-63) to the rate denominator 

must have been accumulated under other regulations, 

while hours contributed to the rate by younger pilots 

were likely to have been dominated by hours accumulated 

under Part 121. Note that, even here, an assumption is 

involved. These disparities in exposure suggest that the 

statistical difference in accident rates for pilots age 55-59 

and 60-63 reported in Study 4 might have reflected the 

historically higher risk associated with Part 135 operations 

as well as the risks associated with pilot age.

It is precisely because of the historically higher overall accident rate in Part 135 operations that the FAA 

proposed the “One Level of Safety Commuter Rule” 

(Federal Aviation Administration, 1995a). Prior to this 

rule change, scheduled passenger-carrying operations in 

airplanes with more than 30 seats or more than 7,500 

pounds payload capacity were conducted under Part 121. 

Scheduled passenger-carrying operations in airplanes less 

than 30 seats or payload capacities of 7,500 pounds or 

less were conducted under Part 135. The March 1995 

Notice of Proposed Rulemaking (NPRM) for the “One 

Level of Safety Commuter Rule” proposed to eliminate 

the differences in requirements for scheduled passenger 

operations conducted with airplanes with 10 or more seats. 

The rule also proposed that all turbojets used in scheduled 

passenger-carrying operations under Part 135 comply with 

Part 121 requirements without regard to seating capacity. 

However, single-engine airplanes were not included in the 

proposed “One Level of Safety Commuter Rule” as Part 

121 applied only to multiengine airplanes. The proposed 

rule was founded on the belief that compliance with Part 

121 requirements would reduce the accident rate for commuter operations conducted with multi-engine airplanes 

and either 10 or more seats or turbojet engines. The final 

rule was published in December 1995 (FAA, 1995b). As 

a result of the “One Level of Safety Commuter Rule,” 

the “Age 60 Rule” now applies to passenger operations 

conducted with multi-engine airplanes (a) with 10 or 

more seats, (b) with turbojet engines, or (c) with more 

than 7,500 pounds payload capacity.

To operate a multi-engine aircraft with 10 or more seats 

or one powered by turbojet engines under these rules, the 

pilot(s) must possess certain certificates, as defined in 14 

C.F.R. § 61 (Certification: Pilots, Flight Instructors, and 

Ground Instructors). Contrary to some characterizations, 

there are no “Part 121” or “Part 135” pilots per se defined 

by the Federal Aviation Regulations. Rather, there are 

pilots who possess the pilot and medical certificates and 

aircraft type ratings required for operation of aircraft under 

the various parts of the regulations. Under the “old” Part 

135 rules, the minimum qualifications for PICs of flights 

conducted under Instrument Flight Rules (IFR) were (a) 

CP certificate, (b) Class 2 medical certificate, (c) appropriate aircraft type rating, and (d) instrument rating. An 

ATP certificate could be substituted for the CP certificate 

and instrument rating. With the implementation of the 

“One Level of Safety Commuter Rule,” the regulations 

provide that the PIC must hold an ATP certificate, the 

appropriate type rating (14 C.F.R. § 437(a)), and a Class 

1 medical certificate to operate 

(i) Nontransport category turbopropeller powered airplanes 

type certificated after December 31, 1964, that have a passenger seat configuration of 10-19 seats; 

(ii) Transport category turbopropeller powered airplanes that 

have a passenger seat configuration of 20-30 seats; or 

(iii) Turbojet engine powered airplanes having a passenger 

seat configuration of 1-30 seats
9

in scheduled, passenger-carrying operations (14 C.F.R. 

§ 121.2; 14 C.F.R. § 135.2).

These two sets of rules – those defining the aircraft 

and operations covered by Part 121 under the “One 

Level of Safety Commuter Rule,” and those defining 

the certificates required of pilots in those operations 

– provide a framework for investigating accident rates 

and pilot age as if the “One Level of Safety Commuter 

Rule” had been in place from 1988 through 1997. This 

approach offers an escape from the dilemma noted by 

the 7th Circuit Court of Appeals: “Admittedly, petitioners 

in this case face a Catch-22: from one perspective they 

cannot get exemptions until they show they can fly large 

passenger aircraft[s] safely, and they cannot show they can 

fly such planes safely until they get exemptions” (Baker 

et al. v. FAA, 1990, at 322). By using this approach, the 

accident rates for pilots age 60 to 63 in complex, multiengine passenger aircraft can be examined. Comparison 

of the results obtained within this framework to results 

obtained in previous studies may provide insights into 

the impact of the analytic methodology used in aviation 

safety studies and a basis for recommendations for the 

design of future investigations.

Method

Accident Rate Numerator

The NTSB provided an electronic data file for 1,359 

aviation accidents that occurred between January 1, 1988, 

and December 31, 1997 for flights conducted under Parts 

121 or 135. The steps taken to initially prepare these 

accident records were described in previous reports by 

Broach et al. (2000a,b). For this study, these accident 

records were subjected to an additional review, resulting 

in 1,332 usable accident records1.

The 1,332 accident records were next screened against 

the inclusion criteria. The accident inclusion criteria in 

Studies 3 and 4 were based on the PIC certificates only, as 

shown in Figure 1. Based on the critique of Study 4 and 

the “One Standard of Safety” rule, the accident inclusion 

criteria were expanded to include regulation under which 

the flight was conducted, type of aircraft, type of operation, 

and operator certificates, as well as pilot qualifications. 

The inclusion criteria based on aircraft characteristics were 

(a) fixed-wing, (b) land airplane, (c) multi-engine, and 

(d) 10 or more seats. Note that these criteria are slightly 

stricter than the “new” 121 rule, in that the rule provides 

that aircraft powered by turbojet engines with 1 to 9 seats 

are covered by the rule but are excluded from this study. 

The second inclusion criterion focused on the type of 

operation. Only records for aircraft engaged in scheduled 

passenger or combined passenger/cargo type operations 

were included; accident records for aircraft engaged in 

cargo- or mail-only types of operations, as categorized 

in the NTSB record, were excluded. The third inclusion 

criterion was the type of certificate held by the operator 

of the accident flight. Specifically, only records for flights 

operated by carriers with flag, domestic, supplemental 

(e.g., air carrier) and/or commuter certificates, as coded 

by NTSB, were included; accident records for flights for 

operators with a cargo-only or on-demand/air taxi-only 

certificate were excluded. Next, accident records were 

included if, and only if, the operator name was identifiable 

and also found in the list of employers in the records of 

medical examinations extracted from the CAIS. Finally, 

a record was included if and only if the PIC possessed 

the ATP and Class 1 medical certificates at the time of 

the event. Just 213 of the original 1,332 records for accidents that occurred under Parts 121 and 135 for the 

period 1988 through 1997 met these stringent criteria 

for inclusion in the numerator of the accident rate. The 

characteristics of the 213 accidents are presented in Table 

2 along with the characteristics of the accidents included 

in Studies 3 and 4. There were no instances of more than 

one accident per pilot per year.

Accident Rate Denominator

The aviation accident rate denominator generally 

represents exposure to the risk of flight. The rate may 

be expressed as accidents per pilot flight hour, similar to 

the epidemiological notion of illnesses per person-year (Li, 

1994). Exposure estimates in previous investigations of 

pilot age and accident rate have been based on recent (last 

6 months) and total flight hours as reported in the course 

of medical examinations (Broach et al., 2000a,b; Kay et 

al., 1994; Li & Baker, 1994; Li, Baker, Grabowski, Qiang, 

McCarthy, & Rebok, 2003). A critical methodological 

step in the present analysis was to define the inclusion 

criteria for the pilot records. Pilot inclusion criteria in 

Studies 3 and 4 as shown in Figure 2, were (a) self-reported 

occupation, (b) type of pilot certificate, and (c) class of 

medical certificate. Self-reported employer (categorized) 

was used as an exclusion criterion in Study 4. The pilot 

inclusion criteria were revised in the present study, as 

follows. First, as in previous studies, the occupation had 

to be coded as “professional pilot.” Second, as in Study 

3, only pilots holding an ATP were included; medical 

examination records for pilots with CP certificates were 

excluded. Third, only records of medical examinations 

for Class 1 medical certificates were included. Fourth, 

similar to the Kay et al. study in 1994, records for pilots 

were included if and only if at least 200 recent and 1,500 

total flight hours were reported at the time of the medical 

examination. The recent hours criterion was based on an 

examination of the distribution of flight time by employer 

type; 90% of pilots employed by an identifiable major 
10 

Table 2 

Characteristics of accidents included in Study 3, Study 4, and Study 5 

STUDY 3 

(N=680) 

Study 4 

(N=1,334) 

Study 5 

(N=213) 

FLIGHT REGULATION 

 Domestic, Flag, Supplemental (121) 286 286 185 

 Air Taxi & On-Demand (135) 393 1,047 28 

 Missing Data 1 1 

FLIGHT PLAN 

 None 36 129 2 

 VFR 66 190 1 

 IFR 454 613 203 

 VFR/IFR 4 6 

 Company (VFR) 106 379 3 

 Missing Data 14 17 4 

FLIGHT SCHEDULE 

 Scheduled 391 424 213 

 Unscheduled 288 909 

 Missing Data 1 1 

FLIGHT TYPE 

 Domestic 596 1,235 180 

 International 83 97 33 

 Missing Data 1 2 

OPERATION TYPE 

 Cargo 165 386 

 Mail 2 5 

 Passenger & Cargo 143 234 66 

 Passenger 368 706 147 

 Missing Data 2 3 

OPERATOR CERTIFICATES 

(as coded by NTSB) 

 Flag carrier domestic (121) 252 252 183 

 Supplemental 12 12 

 All cargo (418) 20 29 

 Commuter air carrier 114 149 28 

 On-demand air taxi 221 768 

 Air carrier + cargo 1 1 

 Air carrier + commuter 5 5 2 

 Supplemental + cargo 2 2 

 Supplemental + air taxi 1 

 Cargo + air taxi 1 3 

(Table 2 continues) 
11

airline such as American, Delta, and United reported 200 

or more recent flight hours at the time of the semi-annual 

Class 1 medical examination. The total hours criterion was 

based on 14 C.F.R. 61.159(a). Fifth, only records for pilots 

reporting the same employer for medical examinations 

conducted within a single year were included. This fifth 

inclusion criterion was introduced to restrict the analysis 

to the subset of pilots with more stable employment and 

thus, greater continuity of exposure.

These five inclusion criteria resulted in a pool of 

753,842 pilot medical examination records for the period 

January 1, 1988, through December 31, 1997, compared 

with the 939,769 used in Study 4. Identifiable large flag, 

domestic or supplemental carriers operating flights under 

Part 121 (such as United, Delta, American, and USAir) 

accounted for 79.3% of the medical examination records 

while 1.6% were from pilots employed by smaller Part 

135 operators. However, employer was coded as “Other 

aviation operations” for 19.1% of the pool of records.

The pilot records were then aggregated by year and 

pilot identifier as in the previous studies, resulting in a 

pool of 522,586 aggregated pilot records for matching to 

accident records. Recent flight hours were summed across 

medical examinations for the year for each pilot, and the 

number of medical examinations conducted for the pilot 

for that year was counted. The number of examinations 

ranged from 1 to 3 medical examinations per year for 

each pilot2. Next, the sum of the flight hours reported 

in the last 6 months across examinations in the year was 

annualized as follows:

1. For 1 examination in a year, 2 times the summed 

recent flight hours for that year.

2. For 2 examinations in a year, the summed recent 

flight hours for that year.

3. For 3 examinations in a year, then 2/3 of the summed 

recent flight hours for that year (2 times the average reported recent flight hours across the 3 examinations).

(Table 2 continued) 

STUDY 3 

(N=680) 

Study 4 

(N=1,334) 

Study 5 

(N=213) 

 Helicopter + air taxi 1 

 Commuter + air taxi 41 84 

 Air carrier + supplemental + cargo 1 1 

 Air carrier + supplemental + commuter 1 1 

 Air carrier + commuter + air taxi 2 2 

 Supplemental + cargo + air taxi 2 

 Missing data 7 20 

AIRCRAFT TYPE 

 Fixed wing single engine airplane 91 439 

 Fixed wing multi-engine airplane 566 732 213 

 Rotorcraft 22 162 

 Missing data 1 1 

ENGINES 

 Single engine 109 585 

 Multi-engine 570 748 213 

 Missing data 1 1 

ENGINE TYPE 

 Reciprocating 229 707 4 

 Turboprop 175 214 42 

 Turboshaft 21 155 

 Turbojet/Turbofan 254 257 167 

 Missing data 1 1 

SEATS 

 9 or less 258 864 

 10 or more 421 469 213 

 Missing data 1 1 
12 

Accident Rate and Age Groups

The 213 NTSB accident records were then matched 

with the aggregated CAIS medical records for each year 

by pilot identifiers. The resulting file contained 513,240 

exposure-accident records. Only those records for pilots 

age 23 to 63 at the time of the medical examination or 

accident were retained, reducing the pool of accident-exposure records to 512,076. Finally, only those records for 

pilots with the same employers as the accident pilots were 

selected for the present study, resulting a total of 381,413 

matched accident-exposure records. In other words, the 

non-accident pilots worked for the same employers as 

the accident pilots. Presumably, the non-accident pilots 

were likely to be flying the same types of airplanes, on 

roughly the same schedules, and in the same types of 

operations as the accident pilots working for the same 

employer. The number of matched accident-exposure 

records by employer, as reported in CAIS and coded in 

the NTSB database for accident and non-accident pilots, 

is presented in Table 3.

The number of accidents was set at zero for pilot records without a matching accident record in any given 

year. Where flight hours were not available from CAIS 

data, NTSB estimates of flight time were used (n=31). 

Exposure data were not available from CAIS or NTSB 

records in 10 instances. Rather than discarding these 

records, annualized and total flight hours were imputed 

using the SPSS® Missing Values Analysis (version 7.5) 

procedure (SPSS, 1997). The SPSS® procedure estimates 

the expected value for a missing datum on the basis of an 

iterative maximum likelihood estimation algorithm.

Finally, the 381,413 matched exposure-accident 

records were coded into non-overlapping age groups: 

less than or equal to (LE) 29; 30-34; 35-39; ... 55-59; 

and 60-63 years. As in the previous studies, the matched 

exposure-accident records were then aggregated by year 

and independent age group. The accident rate for each 

year-age group combination was computed as the ratio 

of the count of accidents for the year and age group to 

the sum of annualized flight hours (in units of 100,000) 

for the year and age group. The resulting file contained 

80 records (8 age groups x 10 years)3.

Analysis

Descriptive statistics for each Study 5 age group, 

including the sum of annualized flight hours, total cumulative flight hours, and total accidents across the 10 

year time span were calculated. A one-way analysis of 

variance (ANOVA) was conducted to determine if there 

was a trend in the accident rates across the independent 

age groups. A t-test was also used to compare the mean 

accident rates of the 55-59 and 60-63 age groups in a 

planned comparison to contrast age groups that were 

immediately adjacent to the current age limit of 60. 

The trend analysis was conducted in view of previous 

studies suggesting a “U”-shaped function or quadratic 

trend across pilot age groups (Broach et al., 2000a,b; 

Golaszewski 1993; Kay et al., 1994, p. 5-2). All analyses 

were conducted using the SPSS® version 11.5 statistical 

package (SPSS Inc., 1999).

Results

Descriptive statistics for each Study 5 age group, including the sum of annualized flight hours, total cumulative 

flight hours, and total accidents across the 10 year time 

span are presented in Table 4. As in the previous analyses, the sum of annualized and cumulative total flight 

hours across the span of the study both took an inverted 

“U”-shaped distribution across age groups (Figure 3). 

Annual flight hours peaked between ages 35 to 49 and 

then declined with age. Cumulative flight hours peaked 

between ages 50 and 54 and then declined with age. In 

contrast, the raw number of accidents across the study 

period appeared to trend upwards through the 55-59 age 

group (n = 51) but then declined for 60-63 age group 

(n=7), as shown in Table 4.

The mean accident rate for each Study 5 age group, 

along with the 5/95% confidence interval, is illustrated 

in Figure 4. In contrast to the results of Studies 3 and 

4, inspection of the graphed data suggested that a linear 

function might fit the data rather than a “U”-shaped 

function. The ANOVA indicated that a linear function 

best described the trend in mean accident rates across age 

group (F(1) = 9.79, p < .01; see Table 5 and Figure 4). The 

planned comparison revealed that the mean accident rate 

for pilots age 55-59 (M55-59 = .1527 per 100,000 annual 

flight hours) was not significantly different from that of 

the 60-63 age group (M60-63 = ..2276 per 100,000 annual 

flight hours, unequal variances, t(9.411) = -0.56, ns). In 

other words, the accident rate for the 60-63 age group 

was not statistically different from the accident rate for 

the 55-59 age group.

Inspection of the confidence intervals indicated that 

the mean accident rates for the youngest and oldest age 

groups were far more variable than the accident rates for 

the other age groups (30s through 40s). This observation 

was supported by rejection of the assumption of equal 

variances across the age groups (Levene Statistic(7, 72) = 

12.92, p < .001). The number of pilot records aggregated 

in the denominator of the accident rate varied across age 

groups by as much as ten-fold, as in the previous analyses, 

with much the same effect. Estimates of accident rate based 

on fewer records were far more variable than estimates 

based on much larger numbers of records.
13

Table 3

Number of exposure-accidents records by employer and accident 

status for Study 5

Record for accident pilot?

CAIS employer/NTSB operator No Yes Total

Air Nevada 17 1 18

Air Wisconsin 1,450 2 1,452

Alaska Airlines 6,060 2 6,062

Aloha Airlines 1,410 1 1,411

American Airlines 60,640 34 60,674

America West 6,999 6 7,005

Aspen Airways 177 2 179

Atlantic Southeast 3,124 3 3,127

Big Sky Airlines 57 2 59

Britt Airways 339 2 341

Business Express 2,315 1 2,316

Comair 4,636 3 4,639

Contintental Airlines 33,032 15 33,047

Delta Airlines 59,258 27 59,285

Eastern Airlines 6,536 3 6,539

Great Lakes Aviation 11 2 13

Hawaiian Airlines 2,110 1 2,111

Horizon Airlines 3,639 5 3,644

Mesa Airlines 2,168 8 2,176

Mesaba Airlines 2,313 2 2,315

Metroflight Airlines 9 2 11

Northwest Airlines 39,849 8 39,857

Pan American World Airways 6,129 3 6,132

Pennsylvania Airlines 400 2 402

Piedmont Aviation 4,179 4 4,183

Simmons 1,600 6 1,606

Southcentral Air 6 1 7

Southwest Airlines 12,357 8 12,365

Transworld Airlines 21,993 9 22,002

United Airlines 57,998 30 58,028

U.S. Air 37,947 17 37,964

West Air 2,442 1 2,443

Total 381,200 213 381,413
1

4

Table 4 Study 5 total pilot records, annual flight hours, total cumulative flight hours, total accidents, and accident rate descriptive statistics by age group 

 Accident Rate (per 100,000 annualized hours) 

Age

Group 

Total 

records1

Total annual 

hours 

Total cumulative 

hours 

Total 

accidents Mean SD Min Max 

LE 29 14,156 11,188,639 58,935338 4 0.0329 0.0464 0.0000 0.1272 30-34 41,748 31,374,921 243,040,985 18 0.0624 0.0485 0.0000 0.1697 

35-39 68,022 49,885,004 488,392,447 22 0.0449 0.0331 0.0000 0.1029 

40-44 70,013 50,722,284 632,728,642 31 0.0607 0.0366 0.0000 0.1469 

45-49 69,700 50,466,881 780,857,281 40 0.0788 0.0269 0.0450 0.1223 

50-54 66,334 46,828,754 917,756,623 40 0.0852 0.0357 0.0232 0.1553 

55-59 47,283 33,002,299 796,601,126 51 0.1527 0.0629 0.0714 0.2580 

60-63 4,157 2,939,132 79,714,801 7 0.2276 0.4157 0.0000 1.1194 

Notes: 1Number of annualized records for age group from 1988 through 1997 
15

0

60,000,00050,000,00040,000,00030,000,00020,000,00010,000,000

LE 29 30-34 35-39 40-44 45-49 50-54 55-59 60-63

Age Group

Total Annualized Hours

1,200,000,0001,000,000,000800,000,000600,000,000400,000,000200,000,0000

Total Flight Hours

Total Annualized HoursTotal Flight Hours

Figure 3 Total annualized flight hours and total flight hours by age group
16 

���

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

LE 29 30-34 35-39 40-44 45-49 50-54 55-59 60-63

Age Group

Accidents per 100,000 Annualized Flight Hours

95% Upper Bound

5% Lower Bound

Mean

Figure 4 

Study 5 mean accident rate and 5%/95% confidence intervals by pilot age group 

Table 5 

Study 5 results for ANOVA of accident rate by age group 

Sum of Squares Df Mean Square F p 

BETWEEN 

GROUPS 

(Combined) .299 7 .043 1/838 .093 

Linear Term Contrast .227 1 .227 9.791 .003 

Deviation .071 6 .012 .512 .797 

Quadratic Term Contrast .054 1 .054 2.318 .132 

Deviation .018 5 .004 .151 .979 

Within Groups 1.671 72 .023 

Total 1.969 79 

Table 6 

Comparison of Age 60 studies conducted on behalf of or by the FAA 

Study Trend with Age 55-59 vs. 60-63 

Kay et al. (1994) Linear trend – Downward with age no comparison made 

Broach et al. (2000a) Quadratic trend – U-shaped Rate60-63 = Rate55-59

Broach et al. (2000b) Quadratic trend – U-shaped 

Linear trend 

Cubic trend 

Rate60-63 > Rate55-59

Broach (2003) Linear trend –upward with age Rate60-63 = Rate55-59
17

Discussion

Comparison of Study 5 Results with Other Studies

On one hand, the results of Study 5 supported the 

first alternative hypothesis (H1alt) of a positive trend in 

fixed-wing, multi-engine airplane accident rates across 

age groups: the non-zero slope indicated that accident 

rate increased with age in this sample of pilots. The trend 

was best described by a linear (straight-line) rather than 

a quadratic (“U”-shaped) function reported in previous 

studies on pilot age and accident rates, including Kay 

et al. (1994) and Broach et al. (2000a, b). On the other 

hand, the null hypothesis (H2null) of no difference in accident rates for the 55-59 and 60-63 age groups could 

not be rejected. That is, the accident rate for these two 

age groups appeared to be statistically the same. However, 

post-hoc comparisons indicated that the accident rate for 

pilots in the 55-59 age group (M55-59 = .1527 per 100,000 

annual flight hours) was statistically greater than the accident rates for pilots age 29 or younger (MLE 29 = .0329), 

30-34 (M30-34 = .0624), age 35-39 (M35-39 = .0449), and 

age 40-44 (M40-44 = .0607).

The observation of a linear trend with age is not 

consistent with previous research conducted by or on 

behalf of the FAA (Table 6). For example, Kay et al. 

(1994) found in their study of pilots holding Class 1 

medical certificates that “... accident rate decreased with 

increased age for the younger pilots leveling off for the 

older pilots ... “ (p. 5-2). While Kay et al. noted that 

exposure for pilots employed by Part 121-certificated 

carriers could not be estimated directly, they used total 

and recent flight time to define a subset of pilots that 

would be “reasonably comparable” to air carrier pilots. 

Based on their analysis of pilots holding Class 1 medical 

certificates, 2,000 or more hours total flight time, and 

at least 700 hours of recent flight time, they went on to 

conclude that their analyses “... provided no support for 

the hypothesis that the pilots of scheduled air carriers had 

increased accident rates as they neared the age of 60” (p. 

6-2). Kay et al. concluded that they “... saw no hint of 

an increase in accident rate for pilots of scheduled air 

carriers as they neared their 60th birthday” (p. 6-2). In 

contrast, the work by Broach et al. (2000a, b) reported 

that a “U”-shaped function fit the trend in accident rate 

across age groups. Moreover, the accident rate for pilots 

in the 60-63 age group was statistically greater than the 

accident rate for pilots in the 55-59 age group in Broach 

et al.’s Study 4 (2000b).

A different result was obtained in the present analysis, 

with an overall upward linear trend, suggesting more than 

a hint of an increase in accident rate for pilots identifiable 

scheduled air carriers as they neared their 60th birthday. 

But the accident rate for the 60-63 age group was not 

significantly different from that of the 55-59 age group. 

As noted in Studies 3 and 4, the greater variability in accident rate for the oldest age group (compared with that 

of the next younger (55-59) age group) was attributed 

to the relatively fewer records for the older pilots. This 

greater variability reduced the likelihood of detecting 

any difference between the age groups that may in fact 

exist. Moreover, the differences to be detected were very 

small in absolute terms. For example, the difference in 

mean accident rates for the 55-59 and 60-63 age groups 

in Study 3 was just .0443 accidents/100,000 flight hours. 

This small difference relative to the wide variability in 

accident rates within the two age groups would be difficult to detect with conventional significance testing. 

Post-hoc power analysis supported this line of reasoning 

(Table 7).

The standardized effect size for age in Study 3 was 

d=.20. With just 10 cases per comparison group, post-hoc

power analysis indicated very low (.09) statistical power to 

detect a difference of this magnitude using conventional 

significance testing. In other words, while the risk of a false 

positive (Type I error – finding a difference in accident 

rates that was not real) was held at 5% (e.g., α=.05), the 

likelihood of a false negative (Type II error – failing to 

detect a difference in accident rates that was real) was 

about 91%. In contrast, the effect size in Study 4 was d= 

1.16, which resulted in greater available statistical power 

of .71. That is, there was a greater likelihood of detecting 

Table 7 

Post-hoc power analysis for planned comparison of accident rates at =.05 and n=10 in each 

age group by study 

Study Age Group M SD 5% 95% d Power 

3 55-59 0.2057 0.0959 0.1370 0.2740 

 60-63 0.2500 0.3465 0.0020 0.4980 0.20 .09 

4 55-59 0.2667 0.1138 0.1853 0.3482 

 60-63 0.5211 0.3246 0.2889 0.7533 1.16 .71 

5 55-59 0.1527 0.0629 0.1077 0.1976 

 60-63 0.2276 0.4157 -0.0697 0.5250 0.47 .12 
18 

the difference in accident rates on either side of the “Age 

60 Rule” in Study 4 than in Study 3.

The difference in accident rates in the present study 

for the pilots on either side of the “Age 60 Rule” was 

equally small at .0749 accidents/100,000 recent flight 

hours (M60-63=.2276, M55-59=.1527), or an effect size of 

d=.47. Holding the likelihood of a false positive (Type I 

error) at 5% through conventional significance testing, 

the likelihood of a false negative (Type II error) in the 

present study was about 88%. The difference in accident rates for the 55-59 and 60-63 age groups was not 

statistically significant in the present study. The post-hoc

power analysis suggested that the design provided little 

statistical power (.12) to detect the small difference in 

accident rates between the two age groups. This line of 

reasoning is buttressed by (a) the overall trend toward 

increasing accident rate with age, and (b) the finding 

that the accident rate for the 55-59 age group was statistically greater than the accident rate for younger pilots 

up through age 44.

Finally, the results of this study differ sharply from 

those recently reported by Li et al. (2003). In their study, 

they followed a cohort of 3,306 commuter air carrier and 

air taxi pilots from 1987 through 1997. The pilots were 

45 to 54 years of age at the start of the study and were 

employed by Part 135 operators. Baseline characteristics 

included age, sex, total flight hours at baseline, recent 

flight time (in previous 6 months), use of corrective lenses, 

and any medical pathology. The effects of these baseline 

characteristics and cumulative exposure on accident rates 

were examined through the proportional hazards model 

(e.g., Cox regression). Overall, the cohort experienced 66 

accidents while accumulating 12.9 million flight hours. 

There were just 3 accidents for pilots age 60-64 during 

the study period. Although the accident rate for the oldest 

pilots of 11.05 per million flight hours was approximately 

twice the rate for younger pilots (5.47 per million flight 

hours), the difference in relative risk was not statistically 

significant. Li et al. concluded that accident risk was not 

significantly associated with advancing age.

Possible Methodological Explanations for Differences in 

Results

The differences between the results of the present 

study, previous FAA research as summarized in Table 6, 

and the work of Li et al. (2003) should be considered in 

light of methodological differences in the studies. These 

methodological differences include (a) accident inclusion 

criteria (b) pilot inclusion/exclusion criteria, (c) method 

for annualizing flight hours, and (d) analytic strategy, 

including statistical power available in an analysis.

Accident inclusion criteria. The study by Kay et al. in 

1994 included 762 NTSB accidents from 1976 through 

1988 that involved pilots holding Class 1 medical certificates without regard to the regulation under which the 

flight was operated or aircraft characteristics (see Figure 

5-5 and p. B-6 in Kay et al.). In other words, their data 

included accidents occurring under Parts 121 (domestic, 

flag, and supplemental), 135 (on-demand and air taxi), 

91 (general aviation), and other regulations. In contrast, 

the previous studies conducted by Broach and colleagues 

included 1,334 accidents that occurred between 1988 

and 1997 for flights operating under 14 C.F.R. §121 or 

§135 only, or approximately 98% of the 1,359 accidents 

reported by NTSB under these flight regulations. However, the data set for Studies 3 and 4 included rotorcraft, 

single-engine airplanes and cargo-only operations. The 

only criterion for inclusion in the Li et al. survival analysis 

was that the accident involved a pilot from the cohort 

being followed. The 66 accidents reported for the cohort 

represented just 0.3% of the accidents reported by NTSB 

for flights conducted under Part 135 (n=1,1,27) and Part 

91 (n=21,797) during the study period.

The present study refined the accident inclusion 

criteria previously used by Broach et al. in light of the 

“One Level of Safety Commuter Rule” and included only 

fixed-wing, multi-engine land airplanes with 10 or more 

seats engaged in scheduled air carrier or commuter passenger operations. The 213 accidents in the present study 

accounted for 15.6% of accidents reported by NTSB for 

flights conducted under Part 121 or 135 for the period 

1988-1997. Given the sample sizes used in the studies, it 

is possible that the differences in results may be attributable, at least in part, to sampling error4

.

Pilot inclusion criteria. Another possible explanation 

of differences might be in the criteria for including pilots 

from whom estimates of exposure were derived. Kay et 

al. (1994) initially defined their sample as pilots between 

the ages of 20 and 74 who held a first-, second-, or thirdclass medical certificate and had recent and total flight 

time greater than zero (p. 3-7). Kay et al. argued that 

data for pilots with Class 1 medical certificates would be 

pertinent to accident rates for pilots employed by Part 

121-certificated air carriers (p. 4-1). In their view, only 

pilots younger than age 60 should have been included 

in the analyses “because no Part 121 pilots flew after 

60” (p. 4-1). In particular, they focused their attention 

on “... those Class I pilots most like pilots of scheduled 

air carriers by considering only those pilots who had the 

necessary minimum total flight hours [2,000] and who 

had the number of recent flight hours [700] characteristic 

of pilots of scheduled air carriers” (p. 4-2). As a result, they 

did not include pilots with first-class medical certificates 

over age 60 in their analyses.

However, not all pilots holding Class 1 medical certificates are employees of air carriers certified for operations 
19

under Part 121. Even with the requirement for 2,000 total 

and 700 recent flight hours, it may be the case that there 

was extraneous variance in exposure hours attributable to 

pilots not working for air carriers. As noted previously, 

inclusion of exposure hours from pilots not in the target 

group would tend to depress the observed accident rate. 

In other words, the decrease in accident rates for Class 1 

pilots with at least 2,000 total and 700 recent flight hours 

illustrated in Figure 5-5 of Kay et al.’s report might be an 

artifact of the pilot inclusion criteria rather than age.

In comparison, three pilot inclusion criteria were used 

in the previous study on the “Age 60 Rule” (Broach et 

al., 2000b) to identify the pilot population required by 

Senate Report 106-55: ATP or CP certificates; Class 1 or 

2 medical certificates; and “professional pilot” reported 

as the occupation. As noted in the reports for Studies 3 

and 4, these criteria resulted in samples that represented 

approximately about 50% to 85% of pilots issued ATP 

or Class 1 medical certificates. However, these inclusion 

criteria did not fully eliminate the possibility of inclusion 

of extraneous pilots in the analyses.

Pilots were eligible for inclusion in the Li et al. prospective cohort study if, in 1987, they (a) held a Class 

1 medical certificate, (b) were employed by an identifiable Part 135 operator, (c) were 45 to 54 years of age, 

(d) flew for business or both business and pleasure, (e) 

listed “pilot” or “commercial pilot” as their occupation, 

(f) had 20 or more hours recent (in the last 6 months) 

flight time, and (g) had 500 or more hours of total flight 

time. Their sample of 3,306 represented about 5% of 

the approximately 65,000 pilots they reported as flying 

in Part 135 operations, and about 5% of the ATP and 

Class 1 certificates issued in 1987.

The present study refined the inclusion criteria used in 

Studies 3 and 4 (Broach et al., 2000a,b) by requiring (a) at 

least 200 recent flight hours in the last six months, (b) at 

least 1,500 total flight hours as of the medical examination, 

(c) the same employer for medical examinations conducted 

in a given calendar year, and (d) the same employer as 

the accident pilots. The flight hours criteria were based 

on examination of the distribution of total and recent 

flight time by type of employer (identifiable Part 121 or 

135 operator). The same employer criterion focused on 

pilots with stable employment. The “same employer as 

the accident pilots” criterion ensured an apples-to-apples 

comparison, that is, of accident to non-accident pilots 

working for the same employers. As a result, the majority 

(88%) of the medical examination records that served as 

the basis for estimating exposure came from pilots working for identifiable large flag, domestic, or supplemental 

carriers; the other 12% came from pilots employed by 

identifiable, smaller operators. Overall, between 63% and 

75% of pilots for identifiable Part 121 and 135 carriers 

each year, as listed in the FAA CAIS, were included in 

Study 5 (Table 8). This represents a larger sample than 

the cohort studied by Li et al., and would be much less 

susceptible to sampling error. It is also a much more 

rigorously defined sample than the one examined by Kay 

Table 8 

Comparison of number of ATP Certificates, Class 1 medical certificates, airline pilots, 

and number of CAIS records available in Study 5 for analysis for each year, 1988-1997 

Year ATP 

Certificates1

Class 1 

Certificates2 Airline Pilots3

 Study 5 Pilots 

1988 96,968 70,388 46,701 34,980 

1989 102,087 83,254 49,380 34,041 

1990 107,732 81,055 59,259 38,795 

1991 112,167 90,859 58,962 40,652 

1992 115,855 89,879 57,185 41,414 

1993 117,070 87,654 56,715 37,383 

1994 117,434 75,662 55,413 35,220 

1995 123,877 78,662 57,443 40,680 

1996 127,486 82,200 59,164 40,260 

1997 130,858 84,732 60,741 37,988 

Notes: 1As reported each year in the annual report, U.S. Civil Airmen Statistics

2

As reported each year in the annual report, Aeromedical Certification Statistical 

Handbook, Table IV.A, for Class 1 certificates that have not lapsed (e.g., are 

"effective") 

3

As reported each year in the annual report, Aeromedical Certification Statistical 

Handbook, Table IV.N or IV.M 
20 

et al. (1994), and represents a sizable proportion of the 

population of pilots covered by the “Age 60 Rule.”

Moreover, it is reasonable to assume that pilots working 

for the same employers flew similar aircraft, routes, and 

schedules. This would result in more uniform exposure 

and less extraneous variance in the denominator. As the 

employer in the present study is known rather than 

assumed to be a certificated air carrier, it was unlikely 

that pilots not working for air carriers contributed any 

exposure hours. Therefore, the accident rates analyzed 

in Study 5 are unlikely to be artificially depressed by 

inclusion of extraneous exposure hours for pilots not in 

the target population. Finally, the sample of pilots in the 

present study was drawn from the population of working 

airline pilots most likely to be covered by the “Age 60 

Rule” as of 1999.

Another factor that may explain finding a linear trend 

in accident rates with increasing age in Study 5 rather 

than the quadratic (“U”-shaped) trend reported in Studies 3 and 4 might be the relative sparseness of data for 

younger pilots. The pilot inclusion criteria for the present 

study resulted in substantially fewer younger (age 29 or 

less) pilots contributing exposure hours to the accident 

rate denominator than in the previous Congressionallymandated studies. There were 44,625 records for medical 

examinations of pilots age 29 or younger in the analysis 

of accident rates for professional pilots holding ATP 

and Class 1 medical certificates (Broach et al., 2000a). 

Exposure estimates in the analysis of accident rates for 

professional pilots holding ATP or CP and Class 1 or 

2 medical certificates (Broach et al., 2000b) included 

52,706 records for medical examinations of pilots age 29 

or younger (see Table 1). In contrast, the present study 

was based on just 14,213 records of medical examinations 

for pilots age 29 or younger reporting employment by the 

same certificated air carrier or commuter operators as the 

accident pilots. The relative sparseness of the data for the 

younger pilots, compared with older pilots, may explain 

why a significant linear, rather than the classic “U”-shaped 

relationship between age and performance, was observed 

in the present study. However, the more important point 

to be made is that, whether the relationship is linear or 

quadratic, accident rates increased with pilot age starting 

in the late 40s in the present study and in the previous 

studies (Studies 3 and 4) conducted for Congress.

As in all other previous studies, the exposure data were 

based on self-reported recent and total hours without 

regard to type of operation. Therefore, as noted in Studies 

3 and 4, the exposure estimates were likely inflated in 

some degree relative to actual hours flown under 14 C.F.R. 

§121 and §135. Accident rates computed from these 

inflated exposure estimates were likely to underestimate 

the actual accident rates. Note that every estimate of exposure to the risks of flight based on self-reported recent 

and total flight hours at the time of medical examination for airline pilots has the same problem of possible 

heterogeneity. However, it is reasonable to assume that 

most flight hours for professional airline pilots were accumulated through their employment rather than other 

types of flying4. Given that assumption, Study 5 differed from the previous studies by selecting records for 

non-accident pilots reporting employment by the same 

certificated carriers as the accident pilots and who held 

both an ATP certificate and Class 1 medical certificate. 

This resulted in the aggregation of exposure data from a 

substantially more homogenous group of pilots than in 

previous analyses, resulting in less unmeasured heterogeneity in the flight hours comprising the denominator of the 

accident rate. Moreover, the accidents in the numerator 

of the accident rate were also more homogenous than in 

previous analyses. For example, rotary wing and singleengine aircraft were included in the previous analyses 

by Broach et al. (2000a, b). The accidents included in 

the rate numerator in Study 5 were for multi-engine, 

fixed-wing land aircraft with 10 or more seats. It may be 

the case that heterogeneity – in both the denominator 

and between numerator and denominator – masked the 

relationship of accident rate to age in previous studies. 

In other words, by enforcing some degree of homogeneity in the data from which accident rate was derived, an 

overall positive trend in accident rate with age was more 

easily detected in the present analysis.

Annualizing flight hours. Differences in how flight 

hours were annualized might also contribute to differences in results between studies. Previous studies 

annualized recent flight hours for pilots by multiplying by 2 the self-reported hours in the last six months. 

This approach was based on the assumption that, on 

average, pilots holding a Class 1 medical certificate 

took 2 medical examinations per year. Doubling the 

self-reported recent flight hours for pilots with just 1 

examination in a given year, as in previous studies, was 

a reasonable rule for estimating annualized flight hours 

for those pilots. However, doubling those hours for pilots 

with 2 examinations in a year would likely result in an 

overestimate. Therefore, as described in Broach et al. 

(2000a,b), the present study (Study 5) adopted a more 

complex set of rules for annualizing flight hours. The 

different approach to annualizing flight hours in this 

study may have resulted in different exposure estimates 

for the accident-rates denominator. Clearly, development 

and adoption of a standard set of rules for annualizing 

recent flight hours is warranted to ensure comparability 

between studies of aviation safety.
21

Analytic strategy. Differences in analytic strategies are 

another possible explanation for differences in study 

outcomes as well as the results of an overall trend across 

age groups. The analytic strategy for this study (Study 5) 

differed from the work of Kay et al. (1994) in technical 

details of conducting the ANOVA of accident rates by age. 

Specifically, the ANOVA design they used treated the data 

as proportions. In their analysis, the degrees of freedom 

for the denominator were assumed to be infinite, as the 

estimate of the variance was exact. The ANOVA strategy 

used in the present analysis was more conservative by treating accident rate as a rate rather than a proportion. The 

degrees of freedom associated with the denominator were 

defined in terms of the number of aggregated observations 

available. For example, Study 5 analyzed 80 observations 

(8 independent age groups x 10 years), resulting in 79 

degrees of freedom in the denominator of the overall test 

for the main effect of age. This conservative approach 

resulted in a design with limited statistical power to detect 

significant differences between age groups. The limited 

statistical power resulted in a greater likelihood of a Type 

II (false negative) error for the planned comparison, as 

shown in Table 7, while controlling the likelihood of a 

Type I error (false positive) at the conventional 5% level. 

Extension of the data set over more years may result in 

a design with more statistical power to detect any small 

differences in accident rate across age and thereby ensure 

the stability and generalizability of the results.

GENERAL DISCUSSION

Previous studies of pilot age and accident rates in the 

context of the “Age 60 Rule” have yielded mixed results. 

On one hand, Kay et al. (1994) found no evidence of an 

increase in accident rate as pilots holding Class 1 medical 

certificates neared age 60. On the other hand, Broach et 

al. (2000a,b) reported that a “U”-shaped function best 

described the Part 121 and 135 accident rate for professional pilots holding ATP or COM pilot and Class 1 or 

2 medical certificates as a function of pilot age. Moreover, 

Broach and his colleagues reported that the accident rate 

for the 60-63 age group was significantly higher than 

the rate for the 55-59 age group. However, Wilkening 

(2002) raised methodological concerns about that study. 

In particular, she suggested that the observed difference 

was more likely attributable to differential exposure to the 

historically higher risks associated with Part 135 operations than pilot age in and of itself (p. 200). Moreover, 

the inherent heterogeneity of exposure hours, due to how 

flight hours are reported, may result in an apparent mismatch between the numerator and denominator of the 

accident rate particularly for pilots age 60-63 compared 

with younger pilots. Comparison of employer type (as a 

proxy for type of flying) for pilots age 55-59 and 60-63 

in Studies 3 and 4 suggested that the groups differed 

significantly on at least one dimension other than age 

(employer type). This difference in employer type suggested heterogeneity of exposure between the age groups 

of interest. The observed statistical difference in accident 

rate for pilots age 55-59 and 60-63 reported by Broach et 

al. (2000b) might therefore be attributable to employer 

type (as a proxy for type of flying), a mismatch between 

numerator and denominator between groups, as well as 

age. It has been argued that pilot age is confounded with 

type of flying as an inevitable consequence of the “Age 

60 Rule” itself (Woolsey, 2003a).

The present study addressed some of this criticism 

by refining the accident and pilot inclusion criteria 

to ensure greater homogeneity of exposure and closer 

match between the numerator and denominator of the 

accident rate between age groups. This study also serves 

to illustrate the impact of methodological choices on 

study outcomes. The numerator for the accident rates 

included only accidents involving fixed-wing, multiengine land airplanes with 10 or more seats engaged in 

scheduled air carrier or commuter passenger operations 

under Part 121 or 135 at the time of the accident. The 

denominator, representing exposure, was derived from 

self-reported hours flown by professional pilots employed 

by the same identifiable operators as the accident pilots. In 

addition, the pilots contributing hours to the denominator held (a) an ATP certificate, (b) a Class 1 certificate, 

and (c) reported at least 200 recent and 1,500 total flight 

hours. Selecting exposure records for pilots employed 

by the same operator reduced heterogeneity in both the 

numerator and denominator of the accident rate as well 

as between pilots on the premises that (a) pilots working for the same airline were likely to fly similar aircraft 

on similar routes on similar schedules, and (b) most, if 

not all, flight hours would have been accumulated on 

the job for most of the pilots. Accident and exposure 

records for the period 1988 through 1997 were matched 

by year and pilot identifier, and then aggregated by age 

group. Analysis of variance (ANOVA) found an overall 

linear trend in accident rate across age groups. On one 

hand, the a priori test found that the accident rate for 

the 60-63 age group was not significantly different from 

the accident rate for the 55-59 age group. On the other 

hand, the post-hoc comparisons found that the accident 

rate for pilots age 55-59 was statistically greater than the 

rate for pilots age 44 and younger.

The findings of the present study were compared 

with previous studies by Kay et al. (1994), Broach et al. 

(2000a, b), and Li et al. (2003). The most striking difference is that the present study found a linear trend of 

increasing accident rate with age while Kay et al. reported 
22 

a declining accident rate with age, Broach et al. reported 

a “U”-shaped relationship, and Li et al. reported no relationship. Possible explanations for differences in results 

included (a) accident inclusion criteria, (b) pilot inclusion criteria, (c) rules for annualizing recent flight hours, 

and (d) analytic strategies. Comparison of results across 

studies suggest that outcomes of studies on the topic of 

accidents and pilot age are sensitive to sample definitions 

as represented by the accident and pilot inclusion criteria 

and analytic strategies. However, while study outcomes 

vary, the overall pattern of results suggest that there may 

be some risk associated with allowing pilots age 60 and 

older to operate complex, multi-engine with 10 seats or 

more in passenger operations. Therefore, changes to the 

“Age 60 Rule” should be approached cautiously.

REFERENCES

Baker et al. v. Federal Aviation Administration, 917 F.2d. 

318, 7th Circuit, 1990.

Broach, D., Joseph, K., & Schroeder, D. (2000a). Pilot age 

and accident rates report 3: An analysis of professional 

air transport pilot accident rates by age. (Unpublished 

Civil Aeromedical Report prepared for the U.S. 

Congress). Oklahoma City, OK: Federal Aviation 

Administration Civil Aerospace Medical Institute 

Human Resources Research Division5.

Broach, D., Joseph, K., & Schroeder, D. (2000b). Pilot 

age and accident rates report 4: An analysis of professional ATP and commercial pilot accident rates by age.

(Unpublished Civil Aeromedical Report prepared 

for U.S. Congress). Oklahoma City, OK: Federal 

Aviation Administration Civil Aerospace Medical 

Institute Human Resources Research Division.

Federal Aviation Administration. (1995a). Commuter 

operations and general certification and operations requirements; Proposed rule. Federal Register, 

60(60), 16,229–296, March 29, 1995.

Federal Aviation Administration (1995b). Commuter 

operations and general certification and operations 

requirements; Final rule. Federal Register, 60(244), 

65,831–940, December 20, 1995.

Federal Aviation Administration. (1999). Aviation safety 

statistical handbook. Washington, DC: FAA Air 

Traffic Resource Management Program Planning, 

Information, and Analysis Division (ATX-400).

Golaszewski, R.S. (1993). Additional analysis of general 

aviation pilot proficiency. Jenkintown, PA: Gelman 

Research Associates.

Kay, E.J., Hillman, D.J., Hyland, D.T., Voros, R.S., Harris, R.M., & Deimler, J.D. (1994). Age 60 rule 

research, Part III: Consolidated data base experiments 

final report. (DOT/FAA/AM-92/22). Washington, 

DC: Federal Aviation Administration Office of 

Aerospace Medicine5.

Li, G. (1994). Pilot-related factors in aircraft crashes: A 

review of epidemiologic studies. Aviation, Space, 

and Environmental Medicine, 65, 944-52.

Li, G., & Baker, S.P. (1994). Prior crash and violation 

records of pilots in commuter and air taxi crashes: 

A case-control study. Aviation, Space, and Environmental Medicine, 65, 979-85.

Li, G., Baker, S.P., Grabowski, J.G., Qiang, Y., McCarthy, 

M.L., & Rebok, G.W. (2003). Age, flight experience, and risk of crash involvement in a cohort of 

professional pilots. American Journal of Epidemiology, 157, 874-80.

National Transportation Safety Board. (2002). Annual 

review of aircraft accident data: U.S. air carrier operations calendar year 1998. (NTSB/ARC-02/02). 

Washington, DC: Author.

SPSS, Inc. (1997). SPSS Missing Values AnalysisTM 7.5. 

Chicago, IL: Author.

SPSS, Inc. (1999). SPSS 11.5. Chicago, IL: Author.

U.S. Senate. (1999). Department of Transportation and related agencies appropriations bill, 2000. (Senate report 

106-55). Washington, DC: Government Printing 

Office.

Wilkening, R. (2002). The Age 60 rule: Age discrimination in commercial aviation. Aviation, Space, and 

Environmental Medicine, 73, 194-202.

Woolsey, S. D. (2003a). Data quality complaint and request for correction of Age 60 studies, dated January 

15, 2003. (DOT Docket FAA-2003-14951-1). 

Washington, DC: Department of Transportation. (Available from http://dms.dot.gov/search/

searchFormSimple.cfm).

Woolsey, S. D. (2003b). Second amendment to request 

for reconsideration, dated November 7, 2003.

Danville, CA: Author. (Available from http://

www.age60rule.com).
23

ENDNOTES

1

The following accidents were eliminated from the data 

set for this analysis: ANC97LA145B; and LAX94T#A02. 

ANC97LA145B: Bering Air Inc. (ANC97LA145B) aircraft 

was standing when Hageland Aviation Services (ANC97145A) 

aircraft turned on taxi and its rudder impacted the Bering 

Air aircraft. The record for “offending” pilot (e.g., pilot who 

made error in turn resulting in accident – ANC97145A) was 

retained, and the record for “victim” pilot (e.g., standing aircraft 

– ANC97145B) was removed. The LAX94T#A02 accident 

record listed “PUBLIC USE” for “regulation under which 

flight was conducted” and was missing data for the type of 

operation. Moreover, the NTSB record indicated that while 

a computer report was filed, the NTSB did not conduct an 

investigation.

2

Class 1 medical examinations are required every 6 months, 

so the number of exams expected each year should be two. 

However, in some cases the interval between examinations 

was slightly less than 6 months, resulting in 3 examinations 

in a year. In other cases, only one examination record was 

available for some pilots in a given year. As noted in Table 8, 

the medical examinations data represented a large proportion 

of examinations given, but did not include every record for 

every pilot.

3

Matched accident-exposure records (de-identified) and 

aggregated data are available from the author on request.

4

The premise that most exposure hours are likely to have 

been accumulated in occupational rather than recreational or 

personal flying can be partially tested by comparing the actual 

distribution of flight hours to (a) average flight hours for pilots 

in commercial aviation as reported by the federal Bureau of 

Labor Statistics and (b) maximum occupational hours allowed 

under Parts 121 and 135. The BLS indicates that commercial 

pilots, on average, fly about 75 hours per month for airlines, 

or about 900 hours annually 

http://www.bls.gov/oco/ocos107.htm

The maximum flight hours allowed in operations under 

Part 121 is 1,000 annual hours, and 1,200 under Part 135. 

Overall, pilots included in this study reported an average 

of 725 annualized flight hours. The mode for annual flight 

hours was 712 hours. As shown in the table below, 75% of the 

exposure records reported between 500 and 899 annual hours. 

In view of contractual provisions and seniority, it is more likely 

than not that most of these annual hours are occupational. 

Moreover, just 8.7% of the records were for pilots reporting 

more than the regulatory maximum limits under Parts 121 and 

135; flight hours in excess of the regulatory maxima are likely 

to be recreational. If one accepts the premise that employers 

will schedule the pilots near or at the maximums allowed 

by contract and regulation, then it appears that most of the 

hours are more likely to have been occupational rather than 

recreational for most (75% or more) of the pilots included in 

this study. In other words, the assumption that most flight 

hours are occupational for most pilots is reasonable for this 

dataset. Woolsey (2003b) also argues, based on his review of 

Kay, et al., that “... the inflation of denominator values by 

recreational flight hours by these professional pilots can only 

be miniscule” (p. 7, footnote 13).

5

This publication and all Office of Aerospace Medicine technical 

reports are available in full-text from the Civil Aerospace Medical 

Institute’s publications Web site at http://www.cami.jccbi.gov/

aam-400A/index.html

Flight Hours Range N %

LE 499 AHrs 27,233 7.1

500-699 AHrs 128,439 33.7

700-799 AHrs 66,488 17.4

800-899 Ahrs 91,171 23.9

900-999 AHrs 35,007 9.2

1000-1199 AHrs 28,066 7.4

GE 1200 AHrs 5,009 1.3

Total 381,413 100.0