How comparable are road traffic crash cases in hospital admissions data and police records? An examination of data linkage rates.
OBJECTIVE
An assessment of linked data was used to investigate the scope and the extent to which hospitalisations data and police crash records represent road crashes in New South Wales (NSW).


METHODS
Hospital separation records for the period 1 July 2000 to 30 June 2001, inclusive, were linked to police crash casualty records for the same period using probabilistic record linkage techniques. Multivariable logistic regression techniques were used to identify factors independently associated with the probability of record linkage.


RESULTS
Of 17,552 road transport-related hospital records, 45.1% matched to police crash casualty records. When the analysis was restricted to road traffic crashes, 69.2% of the 9,178 records had a matching police crash casualty record. Multivariable analysis found the most significant factors contributing to the likelihood of linkage to be road user type, payment status and principal diagnosis of injury variables. Motor vehicle controllers, cases entitled to financial compensation and cases with a principal diagnosis of injury were significantly more likely to be linked than all other cases.


CONCLUSIONS
The findings indicate that researchers and policy makers should be cautious when examining traffic crashes based on a separate analysis of the hospitalisations data and police crash records. This is particularly true for crashes involving pedestrians, cyclists and motorcyclists, and those resulting in less severe injuries.


CONCLUSIONS
The findings have implications for use of both police crash records and hospital records in informing the development of strategies designed to prevent road trauma in the community.
Lujic
Sanja
S
NSW Injury Risk Management Research Centre, University of New South Wales.
Finch
Caroline
C
Boufous
Soufiane
S
Hayen
Andrew
A
Dunsmuir
William
W
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001336:Automobiles / Q000706:statistics & numerical data*; D002648:Child; D002675:Child, Preschool; D003625:Data Collection; D003627:Data Interpretation, Statistical; D005260:Female; D006291:Health Policy; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D035846:Law Enforcement; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D012449:Safety