Table 3.

Multiple logistic regression modeling the association between telehealth availability and year*rurality interaction

Variables1	Model 1: 2015–2019			Model 2: 2015–2020
	aOR	95% CI	p-value	aOR	95% CI	p-value
Year*rurality interaction	Joint test p < 0.0001			Joint test p < 0.0001
Within-rural estimates of year2
Rurality3
< 10% rural	1.33	1.26, 1.40	< 0.0001	1.79	1.72, 1.86	< 0.0001
10 to < 20%	1.31	1.26, 1.37	< 0.0001	1.71	1.66, 1.77	< 0.0001
20 to < 30%	1.26	1.21, 1.31	< 0.0001	1.56	1.51, 1.62	< 0.0001
$\ge$ 30%	1.14	1.09, 1.19	< 0.0001	1.43	1.39, 1.48	< 0.0001
Within-year estimates of rurality	Within-2015			Within-2015
Rurality
< 10% rural	Ref			Ref
10 to < 20%	1.12	0.93, 1.34	0.22	1.19	0.99, 1.42	0.06
20 to < 30%	1.56	1.30, 1.86	< 0.0001	1.87	1.56, 2.24	< 0.0001
$\ge$ 30%	2.69	2.26, 3.20	< 0.0001	3.22	2.71, 3.82	< 0.0001
Rurality	Within-2019			Within-2020
< 10% rural	Ref			Ref
10 to < 20%	1.07	0.93, 1.24	0.35	0.94	0.83, 1.07	0.36
20 to < 30%	1.25	1.08, 1.45	< 0.01	0.95	0.83, 1.08	0.43
$\ge$ 30%	1.47	1.27, 1.71	< 0.0001	1.05	0.92, 1.20	0.46

¹Models adjusted for facility owner type, health department licensure, CARF accreditation, and integrated primary care

²Year is a continuous variable

³Percentage of state residents living in rural areas was derived from the US Census Bureau’s 2020 American Community Survey, defined as anyone not residing in an urbanized area of 50,000 people or more, or an urban cluster of at least 2500 and less than 50,000