nassCDS {DAAG}

Airbag and other influences on accident fatalities

Description

US data, for 1997-2002, from police-reported car crashes in which there is a harmful event (people or property), and from which at least one vehicle was towed. Data are restricted to front-seat occupants, include only a subset of the variables recorded, and are restricted in other ways also.

Usage

nassCDS

Format

A data frame with 26217 observations on the following 15 variables.

dvcat

ordered factor with levels (estimated impact speeds) 1-9km/h, 10-24, 25-39, 40-54, 55+

weight

Observation weights, albeit of uncertain accuracy, designed to account for varying sampling probabilities.

dead

factor with levels alive dead

airbag

a factor with levels none airbag

seatbelt

a factor with levels none belted

frontal

a numeric vector; 0 = non-frontal, 1=frontal impact

sex

a factor with levels f m

ageOFocc

age of occupant in years

yearacc

year of accident

yearVeh

Year of model of vehicle; a numeric vector

abcat

Did one or more (driver or passenger) airbag(s) deploy? This factor has levels deploy nodeploy unavail

occRole

a factor with levels driver pass

deploy

a numeric vector: 0 if an airbag was unavailable or did not deploy; 1 if one or more bags deployed.

injSeverity

a numeric vector; 0:none, 1:possible injury, 2:no incapacity, 3:incapacity, 4:killed; 5:unknown, 6:prior death

caseid

character, created by pasting together the populations sampling unit, the case number, and the vehicle number. Within each year, use this to uniquely identify the vehicle.

Details

Data collection used a multi-stage probabilistic sampling scheme. The observation weight, called national inflation factor (national) in the data from NASS, is the inverse of an estimate of the selection probability. These data include a subset of the variables from the NASS dataset. Variables that are coded here as factors are coded as numeric values in that dataset.

Source

https://www.stat.colostate.edu/~meyer/airbags.htm\ https://www.nhtsa.gov/file-downloads

See also\ https://maths-people.anu.edu.au/~johnm/datasets/airbags/

References

Meyer, M.C. and Finney, T. (2005): Who wants airbags?. Chance 18:3-16.

Farmer, C.H. 2006. Another look at Meyer and Finney's ‘Who wants airbags?’. Chance 19:15-22.

Meyer, M.C. 2006. Commentary on "Another look at Meyer and Finney's ‘Who wants airbags?’. Chance 19:23-24.

For analyses based on the alternative FARS (Fatal Accident Recording System) data, and associated commentary, see:

Cummings, P; McKnight, B, 2010. Accounting for vehicle, crash, and occupant characteristics in traffic crash studies. Injury Prevention 16: 363-366. [The relatively definitive analyses in this paper use a matched cohort design,

Olson, CM; Cummings, P, Rivara, FP, 2006. Association of first- and second-generation air bags with front occupant death in car crashes: a matched cohort study. Am J Epidemiol 164:161-169. [The relatively definitive analyses in this paper use a matched cohort design, using data taken from the FARS (Fatal Accident Recording System) database.]

Braver, ER; Shardell, M; Teoh, ER, 2010. How have changes in air bag designs affected frontal crash mortality? Ann Epidemiol 20:499-510.

The web page https://www-fars.nhtsa.dot.gov/Main/index.aspx has a menu-based interface into the FARS (Fatality Analysis Recording System) data. The FARS database aims to include every accident in which there was at least one fatality.

Examples

data(nassCDS)
xtabs(weight ~ dead + airbag, data=nassCDS)
xtabs(weight ~ dead + airbag + seatbelt + dvcat, data=nassCDS)
tab <- xtabs(weight ~ dead + abcat, data=nassCDS,
             subset=dvcat=="25-39"&frontal==0)[, c(3,1,2)]
round(tab[2, ]/apply(tab,2,sum)*100,2)