TABLE 3.

Multiple logistic regression models using online crowdsourced data to examine the relationship between county residence and kratom use while controlling for demographic and substance use factors.

Lifetime kratom use - mTurk	OR	95% CI		Z	p	VIF
		Lower	Upper
Intercept	0.02	0.00	0.03	-17.77	<0.001
Young Age (<35)	1.64	1.24	2.16	3.52	<0.001	1.03
Sex/gender (Male – Nonmale)	1.79	1.37	2.34	4.24	<0.001	1.02
Race (White - US Minority)	0.81	0.60	1.09	-1.39	0.16	1.01
Education (Highschool - College)	1.39	1.04	1.87	2.20	0.03	1.16
Employed (Unemployed - Employed)	1.10	0.79	1.54	0.55	0.58	1.16
Below Poverty Line Annual Income	1.01	0.72	1.40	0.03	0.98	1.20
Rural - Urban Continuum
Non-metro - Large Metro	1.23	0.95	1.59	1.57	0.12	1.05
Small Metro - Large Metro	1.04	0.82	1.32	0.32	0.75	1.05
Lifetime Non-Medical Opioid Use	5.13	3.80	6.94	10.62	<0.001	1.17
Moderate to Severe SUD	2.00	1.49	2.68	4.61	<0.001	1.17
Lifetime SUD Treatment	1.53	1.09	2.14	2.47	0.01	1.16

Past-Year Kratom Use - mTurk	OR	95% CI		Z	p	VIF
		Lower	Upper
Intercept	0.02	0.00	0.03	-15.97	<0.001
Young Age (<35)	1.37	0.98	1.90	1.84	0.07	1.03
Sex/gender (Male - Nonmale)	1.47	1.06	2.03	2.29	0.02	1.03
Race (White - US Minority)	0.77	0.54	1.10	-1.42	0.15	1.01
Education (Highschool - College)	1.44	1.00	2.08	1.97	0.05	1.17
Employed (Unemployed - Employed)	1.30	0.88	1.93	1.31	0.19	1.17
Below Poverty Line Annual Income	0.92	0.61	1.37	-0.42	0.67	1.20
Rural - Urban Continuum
Non-metro - Large Metro	1.26	0.93	1.72	1.51	0.13	1.05
Small Metro - Large Metro	1.08	0.80	1.44	0.49	0.62	1.05
Lifetime Non-Medical Opioid Use	5.22	3.56	7.66	8.46	<0.001	1.17
Moderate to Severe SUD	1.94	1.34	2.79	3.54	<0.001	1.18
Lifetime SUD Treatment	1.20	0.80	1.79	0.87	0.39	1.16

χ ² (11) = 331.38; Pseudo-R², 0.24; p = <0.01; AIC, 1,510.53. χ ² (11) = 192.74; Pseudo-R², 0.18; p = <0.01; AIC, 1,110.48. Statistically significant explanatory variables are denoted by bolded text.