Table 1 Characteristics of study respondents by the willingness and uptake of COVID-19 testing
Characteristics | Total (%) | Uptake of COVID-19 testing | Willingness of COVID-19 testing | ||
|---|---|---|---|---|---|
Tested/scheduled (%) | Not tested/scheduled (%) | Willing (%) | Unwilling (%) | ||
Total | 2244 (100) | 1177 (52.45) | 1067 (47.55) | 2146 (95.63) | 98 (4.37) |
City |  | P < 0.001 |  | P < 0.001 |  |
 Nanjing | 1091 (48.62) | 626 (57.38) | 465 (42.62) | 1024 (93.86) | 67 (6.14) |
 Chizhou | 1153 (51.38) | 551 (47.79) | 602 (52.21) | 1122 (97.31) | 31 (2.69) |
Gender |  | P = 0.638 |  | P = 0.152 |  |
 Male | 700 (31.19) | 362 (51.71) | 338 (48.29) | 663 (94.71) | 37 (5.29) |
 Female | 1544 (68.81) | 815 (52.78) | 729 (47.22) | 1483 (96.05) | 61 (3.95) |
Age (years) |  | P = 0.099 |  | P = 0.199 |  |
 18–25 | 218 (9.71) | 119 (54.59) | 99 (45.41) | 208 (95.41) | 10 (4.59) |
 26–35 | 1043 (46.48) | 519 (49.76) | 524 (50.24) | 998 (95.69) | 45 (4.31) |
 36–45 | 567 (25.27) | 317 (55.91) | 250 (44.09) | 549 (96.83) | 18 (3.17) |
  >  = 46 | 416 (18.54) | 222 (53.37) | 194 (46.63) | 391 (93.99) | 25 (6.01) |
Residency |  | P = 0.309 |  | P = 0.003 |  |
 Local residents | 1945 (86.68) | 1012 (52.03) | 933 (47.97) | 1870 (96.14) | 75 (3.86) |
 Migrants | 299 (13.32) | 165 (55.18) | 134 (44.82) | 276 (92.31) | 23 (7.69) |
Marital status |  | P = 0.044 |  | P = 0.352 |  |
 Single | 283 (12.61) | 168 (59.36) | 115 (40.64) | 266 (93.99) | 17 (6.01) |
 Married | 1911 (85.16) | 984 (51.49) | 927 (48.51) | 1832 (95.87) | 79 (4.13) |
 Divorced/widow | 50 (2.23) | 25 (50) | 25 (50) | 48 (96) | 2 (4) |
Educational attainment |  | P < 0.001 |  | P = 0.343 |  |
 Middle school or lower | 341 (15.20) | 131 (38.42) | 210 (61.58) | 329 (96.48) | 12 (3.52) |
 High school or technical secondary school | 402 (17.91) | 163 (40.55) | 239 (59.45) | 378 (94.03) | 24 (5.97) |
 Junior college | 558 (24.87) | 289 (51.79) | 269 (48.21) | 536 (96.06) | 22 (3.94) |
 Bachelor's degree or higher | 943 (42.02) | 594 (62.99) | 349 (37.01) | 903 (95.76) | 40 (4.24) |
Occupation |  | P < 0.001 |  | P = 0.001 |  |
 Government agency | 774 (34.49) | 582 (75.19) | 192 (24.81) | 758 (97.93) | 16 (2.07) |
 Service industry | 580 (25.85) | 273 (47.07) | 307 (52.93) | 547 (94.31) | 33 (5.69) |
 Manufacturing industry or agriculture | 302 (13.46) | 102 (33.77) | 200 (66.23) | 290 (96.03) | 12 (3.97) |
 Others | 588 (26.20) | 220 (37.41) | 368 (62.59) | 551 (93.71) | 37 (6.29) |
Annual individual income |  | P = 0.001 |  | P = 0.821 |  |
  < 20 k | 244 (10.87) | 107 (43.85) | 137 (56.15) | 230 (94.26) | 14 (5.74) |
 20-50 k | 373 (16.62) | 181 (48.53) | 192 (51.47) | 359 (96.25) | 14 (3.75) |
 50-100 k | 701 (31.24) | 359 (51.21) | 342 (48.79) | 672 (95.86) | 29 (4.14) |
 100-200 k | 606 (27.01) | 339 (55.94) | 267 (44.06) | 579 (95.54) | 27 (4.46) |
  > 200 k | 320 (14.26) | 191 (59.69) | 129 (40.31) | 306 (95.63) | 14 (4.38) |
Self-reported health status |  | P = 0.382 |  | P = 0.232 |  |
 Good | 2025 (90.24) | 1056 (52.15) | 969 (47.85) | 1940 (95.80) | 85 (4.20) |
 Poor | 219 (9.76) | 121 (55.25) | 98 (44.75) | 206 (94.06) | 13 (5.94) |
Awareness of COVID-19 |  | P = 0.014 |  | P < 0.001 |  |
 High | 2082 (92.78) | 1107 (53.17) | 975 (46.83) | 2007 (96.40) | 75 (3.60) |
 Low | 162 (7.22) | 70 (43.21) | 92 (56.79) | 139 (85.80) | 23 (14.20) |
Perceived susceptibility of COVID-19 |  | P < 0.001 |  | P = 0.157 |  |
 High | 146 (6.51) | 116 (79.45) | 30 (20.55) | 143 (97.95) | 3 (2.05) |
 Low | 2098 (93.49) | 1061 (50.57) | 1037 (49.43) | 2003 (95.47) | 95 (4.53) |
Perceived severity of COVID-19 |  | P = 0.034 |  | P = 0.696 |  |
 Severe or moderate | 424 (18.89) | 242 (57.08) | 182 (42.92) | 404 (95.28) | 20 (4.72) |
 Mild | 1820 (81.11) | 935 (51.37) | 885 (48.63) | 1742 (95.71) | 78 (4.29) |