Table 3.

Difference in the adjusted rates of telemedicine use between April 2020 and August-September 2020 by socioeconomic status measures.

Measure		Number of participants	Adjusted rate, %		Differencea, % (95% CI)	P valueb	Difference in differencesa (95% CI)	P valueb
			April 2020	August-September 2020
Educational attainment
	University or higher	7915	2.3	6.6	4.3 (2.7 to 5.9)	<.001	Reference	N/Ac
	College	4581	1.9	4.0	2.1 (1.3 to 2.9)	<.001	−2.2 (−4.0 to −0.4)	.01
	High school or lower	12,030	1.9	3.5	1.6 (0.9 to 2.4)	<.001	−2.7 (−4.4 to −0.9)	.003
Urbanicity of residence
	Urban	14,666	2.1	5.2	3.2 (2.4 to 3.9)	<.001	Reference	N/A
	Rural	9860	2.0	3.8	1.8 (1.2 to 2.4)	<.001	−1.4 (−2.3 to −0.4)	.004
Income level
	High	5458	1.7	4.6	2.8 (1.8 to 3.8)	<.001	Reference	N/A
	Medium	6814	1.9	4.7	2.8 (1.5 to 4.2)	<.001	0 (−1.6 to 1.7)	.99
	Low	7151	2.3	4.4	2.1 (1.3 to 2.9)	<.001	−0.7 (−2.0 to 0.6)	.34
	Not answered	5103	2.1	5.4	3.2 (1.1 to 5.4)	.003	0.4 (−2.0 to 2.8)	.79

^aWe calculated the differences in the adjusted rates of telemedicine use between April 2020 and August-September 2020 for each socioeconomic status measure (educational attainment, urbanicity of residence, or income level). Then, we examined how the difference in the rates of telemedicine use between the two time points varied by educational attainment, urbanicity of residence, or income level (difference in differences). The analyses were weighted to account for selection in an internet survey. For each analysis, standard errors were clustered at the prefecture level.

^bThe P values were adjusted post hoc to account for multiple comparisons with the use of the Benjamini-Hochberg method.

^cN/A: not applicable.