EDITORIAL

Holiday time! evidence-based flying revisited

As we approach the winter holiday season and finalise

our travel plans, we steel ourselves in preparation for

the inevitable delays in getting to our destinations.

However, if we fly, not all airlines all equal, and not

all travel hubs share the same misery. In a prior per-

spective, published on-line on April 1, the traditional

day for off-beat editorials, the idea of evidence-based

flying (EBF) was introduced (1). The calculation of

number needed to fly (NNF), defined as the number

of flights one has to take with one airline vs. another

before expecting to encounter (or avoid) one addi-

tional departure delay, was demonstrated. Number

needed to upgrade (NNU) was also discussed, and

was found to be in the single digits for members of

loyalty clubs who have achieved high status. The like-

lihood to be upgraded or delayed (LUD) can thus be

calculated, with the result being of some utility in fly-

ing decision-making. As noted, in any of these calcu-

lations, using any frequent flyer outcome measures,

the personal preferences and values of the flyer are

key to making flyer-relevant decisions. A follow-up

letter to the editor, by a dear friend and colleague,

expanded on these concepts and proposed additional

data to be routinely provided on publically-accessible

Airline Registries (2). Also suggested were measures

such as number-needed-to-fly-for-free (NNTripleF)

coach or business, for domestic and international

flights, and the calculation of likelihood to fly-for-

free or be delayed (LTripleFD).

In a recent article in The Wall Street Journal (3),

read of course when flying, data for on-time arrivals

by airline and travel hub were provided. Although

US-centric (thus not necessarily applicable to many

of our readers), and lacking numerators and denomi-

nators (and thus making the calculation of confi-

dence intervals impossible), nonetheless, these data

can be reinterpreted using a new metric, number

needed fly and arrive on time (NNFAOT), for both

airlines and destinations.

Selecting United Airlines (my usual airline) and

Liberty International Airport in Newark, New Jersey

(EWR) (my usual airport), as the reference interven-

tion and the other airlines and airports as the com-

parator, the calculation of NNFAOT involves taking

the reciprocal of the difference in percentages for on-

time arrival, and then rounding up to the next high-

est whole number. For the sake of convenience, we

will use the convention that a positive NNFAOT

would indicate an advantage for the reference airline

or airport. A negative NNFAOT would indicate an

advantage for the comparator airline or airport.

Tables 1 and 2 illustrate these outcomes. My

favoured airline (United) did rather well, except for

the comparison with Alaska Airlines, where I can

expect for every 12 flights taken on Alaska instead of

United, I would avoid one additional arrival delay.

But then again, how often do I fly to Alaska? Unfor-

tunately, my favoured hub airport, Newark, did not

Table 1 Percentage of on-time arrivals by airline for

June–August 2013 and number needed to fly and arrive

on time (NNFAOT); positive numbers indicate an

advantage for United Airlines

Airline

Percentage of

on-time arrivals NNFAOT vs. United

United (reference

airline)

76 NA

Spirit 53 5

Jet Blue 68 13

Allegiant 70 17

Air Tran 72 25

Virgin America 73 34

Southwest 74 50

Frontier 75 100

American 75 100

US Airways 76 No difference

Delta 78 �50

Alaska 85 �12

Data from (3).

Table 2 Percentage of on-time arrivals by airport for

June–August 2013 and number needed to fly and

arrive on time (NNFAOT); positive numbers indicate

an advantage for Liberty International Airport, Newark,

New Jersey

Airport

Percentage

of

on-time

arrivals

NNFAOT vs.

Newark

Newark (reference

airport)

67 NA

San Francisco 62 20

JFK 65 50

Ft. Lauderdale 69 �50

LaGuardia 69 �50

Philadelphia 69 �50

Boston 71 �25

Chicago 73 �17

Atlanta 73 �17

Data from (3).

Come fly with

me, let’s fly,

let’s fly

away…

ª 2013 John Wiley & Sons LtdInt J Clin Pract, December 2013, 67, 12, 1213–1219 1213

fare well. Beating JFK with a NNFAOT of 50 is faint

praise. Perhaps I should move to Chicago, where for

every 17 flights arriving there, instead of Newark, I

would arrive on-time.

However, it may be more useful to display a grid

of possible NNFAOT estimates (Table 3), much in

the same way grids of outcomes have been displayed

for studies of medications, including antipsychotics

(4). Akin to a mileage chart, the practice of EBF is

facilitated by access to easy-to-read research data for

on-time arrivals, ready to be incorporated with one’s

own personal flying experiences and individual pref-

erences and values.

Happy trails, and may your holiday travel plans be

smooth and uninterrupted!

Disclosures

Leslie Citrome belongs to the loyalty programmes for

all of the airlines he flies on and has Global Services

status with United Airlines. The initial draft of this

manuscript was written in the air on a domestic

flight between Newark, NJ, USA, and Jacksonville,

FL, USA.

L. CitromeNew York Medical College, Valhalla, NY, USA

Email: citrome@cnsconsultant.com

References1 Citrome L. Evidence-based flying: a new paradigm

for frequent flyers. Int J Clin Pract 2010; 64: 667–8.

2 Correll CU. Evidence-based flying taking off: maxi-

mising the effectiveness of a novel airline user deci-

sion-making tool. Int J Clin Pract 2010; 64: 1836–7.

3 McCartney S. The story behind all those delays.

Wall Street J 12 September 2013, D1, D4.

4 Citrome L. Interpreting and applying the CATIE

results: with CATIE, context is key, when sorting

out phases 1, 1A, 1B, 2E, and 2T. Psychiatry (Edg-

mont) 2007; 4(10): 23–9.

doi: 10.1111/ijcp.12348

ED ITORIAL

Targeted lipid treatment: decades of failure suggest newtargets are in order

In this issue, Jameson et al. (1), demonstrate that yet

again patients thought to be at high risk from their

elevated lipids fail to be treated to achieve targets. It

is ‘yet again’ because there have been so many previ-

ous studies that have shown that we cannot achieve

targets (2,3).

Table 3 Number needed to fly and arrive on time (NNFAOT), airline vs. airline; positive numbers indicate an advantage for the reference airline

Reference

below United Spirit

Jet

Blue Allegiant

Air

Tran

Virgin

America Southwest Frontier American US Airways Delta Alaska

United X 5 13 17 25 34 50 100 100 No difference �50 �12

Spirit �5 X �7 �6 �6 �5 �5 �5 �5 �5 �4 �4

Jet Blue �13 7 X �50 �25 �20 �17 �15 �15 �13 �10 �6

Allegiant �17 6 50 X �50 �34 �25 �20 �20 �17 �13 �7

Air Tran �25 6 25 50 X �100 �50 �34 �34 �25 �17 �8

Virgin Am. �34 5 20 34 100 X �100 �50 �50 �34 �20 �9

Southwest �50 5 17 25 50 100 X �100 �100 �50 �25 �10

Frontier �100 5 15 20 34 50 100 X No difference �100 �34 �10

American �100 5 15 20 34 50 100 No difference X �100 �34 �10

US Airways No difference 5 13 17 25 34 50 100 100 X �50 �12

Delta 50 4 10 13 17 20 25 34 34 50 X �15

Alaska 12 4 6 7 8 9 10 10 10 12 15 X

Data from (3).

Linked Comment: Jameson et al. Int J Clin Pract 2013; 67: 1228–37.

ª 2013 John Wiley & Sons LtdInt J Clin Pract, December 2013, 67, 12, 1213–1219

1214 Editorials