Table 2.

The influencing factors of the two-week prevalence rate according to the univariate and multivariate analysis of individuals from Tibet in 2018

Influence factor		Crude OR	95% CI	Adjusted OR	95% CI	&p value
Gender	Female	1.000		1.000
	Male	0.627	(0.569,0.692)	0.691	(0.622,0.767)	< 0.001
Age	15–29	1.000		1.000
	30–44	1.912	(1.595,2.290)	1.464	(1.201,1.784)	< 0.001
	45–59	3.944	(3.318,4.689)	2.804	(2.304,3.413)	< 0.001
	60-	5.000	(4.158,6.013)	2.968	(2.367,3.722)	< 0.001
Residence	Urban	1.000		1.000
	Rural	0.574	(0.514,0.640)	0.610	(0.541,0.689)	< 0.001
Education	Illiterate	1.000		1.000
	Primary school	0.772	(0.694,0.859)	0.921	(0.822,1.032)	0.155
	Junior middle school	0.481	(0.401,0.576)	0.876	(0.717,1.072)	0.199
	High school	0.573	(0.454,0.724)	0.974	(0.741,1.279)	0.849
	University and above	0.251	(0.132,0.480)	0.568	(0.288,1.120)	0.102
Economic level	Low	1.000		1.000
	Medium	1.148	(1.017,1.296)	1.134	(1.000,1.287)	0.051
	High	1.240	(1.081,1.423)	1.106	(0.953,1.283)	0.185
Marital status	Married	1.000		1.000
	Unmarried	0.407	(0.340,0.486)	0.684	(0.562,0.832)	< 0.001
	Widow	2.182	(1.856,2.565)	1.279	(1.070,1.529)	0.007
	Divorce	1.781	(1.324,2.478)	1.644	(1.187,2.277)	0.003
	Others	1.134	(0.648,1.984)	1.023	(0.575,1.819)	0.940
Employment status	Employed	1.000		1.000
	Retired	2.696	(2.022,3.593)	1.295	(0.945,1.776)	0.108
	Laid-off	3.251	(2.380,4.440)	2.360	(1.695,3.287)	< 0.001
	Unemployed	1.868	(1.655,2.110)	1.238	(1.075,1.424)	0.003
	Student	0.131	(0.065,0.266)	0.334	(0.159,0.701)	0.004

&: p value adjusted by multivariate logistic regression analysis

/: In multivariate regression analysis, the Enter method was used to adjust for confounding factors