Optimizing sobriety checkpoints to maximize public health benefits and minimize operational costs

Table 4 Logistic regression models (n = 112)

	Support for Sobriety Checkpoints (Several Times Every Month)				Momentary Perceived Stress
	OR	(95%	CI)	p-value	OR	(95%	CI)	p-value
Prior stress					1.02	0.31	3.28	0.980
Drove after drinking in last 90 days	0.55	0.16	1.84	0.332
Alcohol consumption
None [ref]
Monthly	0.40	0.11	1.40	0.150	3.88	0.85	17.77	0.081
Weekly	0.42	0.11	1.67	0.218	14.35	2.96	69.57	0.001
Daily	0.31	0.06	1.67	0.174	5.67	0.89	35.91	0.066
Male	1.23	0.51	2.97	0.647	0.62	0.24	1.64	0.338
Age
21–28 years [ref]
29–39 years	1.71	0.45	6.46	0.430	0.51	0.12	2.19	0.363
40–49 years	1.42	0.40	5.01	0.583	0.78	0.20	3.11	0.724
50–75 years	1.66	0.44	6.33	0.458	0.50	0.12	2.18	0.359
Race/ethnicity
Non-Hispanic White [ref]
Non-Hispanic Black	0.70	0.20	2.49	0.585	0.71	0.18	2.81	0.625
Hispanic	0.85	0.17	4.35	0.850	0.53	0.07	3.86	0.529
Other	0.45	0.12	1.78	0.257	1.55	0.34	7.09	0.569
Income
< $40 k [ref]
$40–80 k	0.81	0.21	3.16	0.759	1.53	0.36	6.56	0.567
$80–120 k	1.27	0.26	6.21	0.770	0.19	0.03	1.39	0.103
> $120 k	0.76	0.18	3.17	0.704	1.05	0.22	4.90	0.954
Chose not to answer	2.20	0.16	30.09	0.554	1.35	0.13	14.02	0.800

ISSN: 2197-1714