Table 3.

Adolescent drivers who read or typed a text message or e-mail while driving at least once in the 30 days before the survey and the association with cellphone use while driving legislation

Characteristic	Law	Model 1^a		Model 2^a		Model 3^a		p Value^a
Characteristic	Law	RR	95% CI	RR	95% CI	RR	95% CI	p Value^a
Overall	UTB	.91	.77–1.06	.92	.80–1.07	.92	.80–1.06
	UHB			.88	.70–1.09	.88	.71–1.10
	YDB			1.25	1.05–1.49	1.26	1.07–1.48
Age								.8854
16–17	UTB	.90	.72–1.12	.89	.75–1.07	.88	.73–1.07
	UHB			.86	.66–1.13	.87	.66–1.14
	YDB			1.34	1.06–1.69	1.37	1.09–1.73
18	UTB	.90	.68–1.18	1.01	.77–1.33	.97	.74–1.27
	UHB			.90	.66–1.24	.91	.67–1.26
	YDB			1.09	.87–1.35	1.10	.88–1.37
Sex								.6887
Male	UTB	.89	.73–1.09	.95	.76–1.17	.96	.76–1.21
	UHB			.76	.58–.99	.76	.59–1.00
	YDB			1.21	.94–1.56	1.20	.93–1.55
Female	UTB	.95	.74–1.20	.92	.73–1.16	.89	.71–1.10
	UHB			1.01	.74–1.39	.99	.70–1.40
	YDB			1.32	1.11–1.57	1.36	1.15–1.62
Race/ethnicity								.3421
White, non-Hispanic	UTB	.85	.68–1.06	.85	.69–1.06	.86	.70–1.05
	UHB			.85	.62–1.16	.84	.63–1.14
	YDB			1.23	1.03–1.46	1.23	1.06–1.44
Other	UTB	.96	.76–1.22	.97	.76–1.24	1.01	.79–1.30
	UHB			.95	.71–1.25	.93	.70–1.23
	YDB			1.35	.91–1.99	1.33	.90–1.97
Location								.8967
Urban	UTB	.90	.77–1.04	.95	.80–1.12	.95	.81–1.11
	UHB			.88	.68–1.14	.88	.68–1.13
	YDB			1.18	.95–1.47	1.19	.97–1.46
Rural	UTB	.94	.61–1.45	.82	.55–1.22	.81	.55–1.19
	UHB			1.10	.72–1.70	1.12	.74–1.68
	YDB			1.61	.96–2.70	1.62	.98–2.66

CI = confidence interval; RR = estimated risk ratio; UHB = universal handheld calling ban; UTB = universal texting ban; YDB = young driver all cellphone ban.

The outcome was whether or not the driver self-reported reading or typing a text message or e-mail 30 days before the survey. The exposure was the cellphone use while driving legislation. The RR presented compares drivers exposed to the ban with those who were not exposed; although the models contained several variables, only the RRs pertaining to the cellphone legislation were shown for ease of presentation. Model 1 contained variables for the presence of a texting ban (binary) only. Model 2 contained variables for the presence of a texting ban (binary), the presence of universal handheld calling ban (binary), the presence of YDB (binary), and the year of survey. Model 3 contained all terms from Model 2 and additionally controlled for sex, age, and race/ethnicity. A fourth model containing variables from Model 2 with the driver characteristic and an interaction term between the legislation and the driver characteristic were run to formally test for subgroup differences. The p value presented applies to the interaction term between the presence of a universal texting ban and the driver characteristic. The null hypothesis was the driver subgroups were equal.