Burden of motorcyclists without helmets in a state without a universal helmet law: a propensity score analysis

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Figure 1
Patient flow diagram. ATV, all-terrain vehicle.

Table 1

Summary of patient demographics and toxicology screen prior to matching

Table 2

Summary of matched variables in the matched cohort

Table 3

Summary of injury severity, length of stay and patient outcomes between groups

	No helmet (n=2541)	Helmet (n=2541)	P value	OR (95% CI)
ISS	6.0 (3.0–14.0)	5.0 (2.0–13.0)	0.013	–
GCS <15	427 (17.0%)	252 (10.0%)	<0.001	0.54 (0.44 to 0.62)
LOS (days)	1.1 (0.2–3.9)	0.9 (0.2–3.9)	0.028	–
ICU LOS (days)	3.0 (2.0–7.0)	3.0 (2.0–6.0)	0.019	–
Total hospital charges (US$), thousands	37 (18–92)	33 (16–80)	0.001	–
Ventilated	304 (12.0%)	198 (7.8%)	<0.001	0.62 (0.52 to 0.75)
ICU admission	603 (23.7%)	515 (20.3%)	0.002	0.81 (0.70 to 0.93)
Complications (1+)	222 (8.7%)	186 (7.3%)	0.067	0.83 (0.67 to 1.01)
Died	136 (5.4%)	60 (2.4%)	<0.001	0.42 (0.31 to 0.58)

GCS, Glasgow Coma Scale; ICU, intensive care unit; ISS, Injury Severity Score; LOS, length of stay.

Table 4

Summary of linear regression predicting total hospital charges (ln)

Variable, model	Sample	P value for helmet use	Unstandardized B (SE)	Interpretation
Helmet use, adjusted model	Matched cohort	0.029	−0.075 (0.034)	8% average decrease in hospital charges
Helmet use, adjusted model	Entire sample	0.009	−0.080 (0.030)	8% average decrease in hospital charges

Table 5

Summary of logistic regression predicting mortality

Variable, model	Sample	P value for helmet use	OR (95% CI)	Interpretation
Helmet use, adjusted model	Matched cohort	<0.001	0.44 (0.31 to 0.58)	For each death, a 56% decrease in odds of wearing a helmet
Helmet use, adjusted model	Entire sample	<0.001	0.46 (0.35 to 0.61)	For each death, a 54% decrease in odds of wearing a helmet