Association between participation in the subsidy programme for domestic travel and incidence of COVID-19 like symptoms
| Subsidy programme participation | Weighted sample, no. | Weighted incidence, n (%) | Model 1: adjusted for demographics, SES, health and prefecture fixed effects | Model 2: adjusted for the adjustment variables in model 1+preventive measures and fear against COVID-19 | ||||
| Adjusted rate, % (95% CI) | Adjusted OR (95% CI) | Adjusted p value | Adjusted rate, % (95% CI) | Adjusted OR (95% CI) | Adjusted p value | |||
| High fever | ||||||||
| Participants | 3289 | 327 (9.9) | 4.7 (4.2 to 5.2) | 1.83 (1.34 to 2.48) | <0.001 | 4.4 (3.9 to 4.9) | 1.56 (1.09 to 2.23) | 0.04 |
| Non-participants | 22 193 | 633 (2.9) | 3.7 (3.6 to 3.8) | Reference | 3.7 (3.6 to 3.8) | Reference | ||
| Sore throat | ||||||||
| Participants | 3289 | 790 (24.0) | 19.8 (15.0 to 24.6) | 2.09 (1.37 to 3.19) | 0.002 | 18.2 (15.0 to 21.4) | 1.84 (1.35 to 2.52) | <0.001 |
| Non-participants | 22 193 | 2406 (10.8) | 11.3 (10.5 to 12.1) | Reference | 11.6 (11.1 to 12.1) | Reference | ||
| Cough | ||||||||
| Participants | 3289 | 728 (22.1) | 19.0 (14.2 to 23.9) | 1.96 (1.26 to 3.01) | 0.008 | 17.1 (13.9 to 20.2) | 1.66 (1.21 to 2.26) | 0.006 |
| Non-participants | 22 193 | 2417 (10.9) | 11.3 (10.5 to 12.0) | Reference | 11.5 (11.0 to 12.1) | Reference | ||
| Headache | ||||||||
| Participants | 3289 | 1009 (30.7) | 29.2 (27.0 to 31.4) | 1.24 (1.08 to 1.44) | 0.006 | 28.2 (26.3 to 30.2) | 1.17 (1.02 to 1.34) | 0.04 |
| Non-participants | 22 193 | 5612 (25.3) | 25.5 (25.2 to 25.8) | Reference | 25.7 (25.4 to 25.9) | Reference | ||
| Smell and taste disorder | ||||||||
| Participants | 3289 | 167 (5.1) | 2.6 (2.0 to 3.1) | 1.98 (1.15 to 3.40) | 0.01 | 2.3 (1.9 to 2.6) | 1.56 (1.05 to 2.30) | 0.03 |
| Non-participants | 22 193 | 287 (1.3) | 1.8 (1.6 to 1.9) | Reference | 1.8 (1.7 to 1.9) | Reference | ||
We examined the association of participation in the government subsidy programme for domestic travel in the past 1–2 months with the incidence of the five COVID-19 like symptoms within the past month of the survey. For each outcome, we constructed a weighted multivariable logistic regression model with SEs clustered at the prefecture-level. Model 1 adjusted for the respondents’ sociodemographic characteristics, health-related characteristics and prefecture indicator variables. Model 2 adjusted for all the variables included in model 1 plus the preventive measures and fear against the COVID-19 infection. We weighted the regression models using IPW to account for ‘being a respondent in an internet survey’. Adjusted rates were calculated using marginal standardisation. Adjusted p values using the Holm method for multiple testing were shown (the adjusted p value <0.05 was considered to be statistically significant).
IPW, inverse probability weighting.