Table 1.
Patient characteristics in the newly diagnosed breast cancer cohort
| Patient characteristic | Full cohort (1 January2019 to 30 September 2021) N = 2681 |
Subgroup | |||||
|---|---|---|---|---|---|---|---|
| Four subgroups overall N = 651 |
Pre-lockdown (25 March 2019 to 16 June 2019) N = 240 |
Lockdown (23 March 2020 to 3 May 2020) N = 79 |
Reopening (4 May 2020 to 14 June 2020) N = 68 |
Post-lockdown (22 March 2021 to 13 June 2021) N = 264 |
p-value | ||
| Age at diagnosis by median (25th, 75th percentiles) | 62.0 (51.0, 70.0) | 62.0 (51.0, 70.0) | 63.0 (52.0, 70.0) | 60.0 (49.0, 67.0) | 59.0 (49.0, 67.0) | 63.0 (52.0, 71.0) | 0.0371* |
| Sex | 0.5108 | ||||||
| Female | 2636 (99.21%) | 637 (98.61%) | 236 (98.74%) | 79 (100.0%) | 65 (97.01%) | 257 (98.47%) | |
| Male |
21 (0.79%) |
9 (1.39%) |
3 (1.26%) |
2 (2.99%) |
4 (1.53%) |
||
| Race | 0.4278 | ||||||
| White | 2215 (83.33%) | 532 (82.23%) | 198 (82.85%) | 62 (78.48%) | 52 (77.61%) | 220 (83.97%) | |
| Black | 296 (11.14%) | 88 (13.60%) | 33 (13.81%) | 12 (15.19%) | 13 (19.40%) | 30 (11.45%) | |
| Others (Asian, American Indian/Alaska Native, Native Hawaiian/Pacific Islander) |
91 (3.42%) |
17 (2.63%) |
7 (2.93%) |
2 (2.53%) |
1 (1.49%) |
7 (2.67%) |
|
| Unknown or refused |
91 (3.42%) |
17 (2.63%) |
7 (2.93%) |
2 (2.53%) |
1 (1.49%) |
7 (2.67%) |
|
| Ethnicity | 0.8760 | ||||||
| Hispanic or Latino |
52 (1.96%) |
20 (3.09%) |
7 (2.93%) |
3 (3.80%) |
3 (4.48%) |
7 (2.67%) |
|
| - Not Hispanic or Latino | 2545 (95.82%) | 617 (95.36%) | 227 (94.98%) | 76 (96.20%) | 63 (94.03%) | 251 (95.80%) | |
| Declined or unknown |
59 (2.22%) |
10 (1.55%) |
5 (2.09%) |
1 (1.49%) |
4 (1.53%) |
||
| Estrogen receptor status | 0.3224 | ||||||
| Negative | 383 (14.29%) | 117 (17.97%) | 39 (16.25%) | 19 (24.05%) | 14 (20.59%) | 45 (17.05%) | |
| Positive | 1901 (70.91%) | 448 (68.82%) | 164 (68.33%) | 49 (62.03%) | 43 (63.24%) | 192 (72.73%) | |
| Unknown | 397 (14.81%) | 86 (13.21%) | 37 (15.42%) | 11 (13.92%) | 11 (16.18%) | 27 (10.23%) | |
| Human epidermal growth factor receptor-2 status | 0.1530 | ||||||
| Negative | 1572 (58.64%) | 386 (59.29%) | 148 (61.67%) | 39 (49.37%) | 44 (64.71%) | 155 (58.71%) | |
| - Positive | 323 (12.05%) | 79 (12.14%) | 27 (11.25%) | 17 (21.52%) | 7 (10.29%) | 28 (10.61%) | |
| Unknown | 786 (29.32%) | 186 (28.57%) | 65 (27.08%) | 23 (29.11%) | 17 (25.00%) | 81 (30.68%) | |
| Triple-negative breast cancer | 0.0017* | ||||||
| No | 2262 (84.37%) | 530 (81.41%) | 191 (79.58%) | 54 (68.35%) | 55 (80.88%) | 230 (87.12%) | |
| Yes | 419 (15.63%) | 121 (18.59%) | 49 (20.42%) | 25 (31.65%) | 13 (19.12%) | 34 (12.88%) | |
| Charlson Comorbidity Index | 2.0 (0.0, 2.0) | 1.0 (0.0, 2.0) | 1.0 (0.0, 2.0) | 1.0 (0.0, 2.0) | 2.0 (0.0, 2.0) | 1.0 (0.0, 2.0) | 0.6168 |
| Insurance | 0.0018* | ||||||
| Managed care | 1104 (41.66%) | 270 (41.80%) | 79 (33.05%) | 38 (48.10%) | 34 (50.75%) | 119 (45.59%) | |
| Medicaid | 129 (4.87%) | 27 (4.18%) | 17 (7.11%) | 3 (3.80%) | 5 (7.46%) | 2 (0.77%) | |
| Medicare | 1199 (45.25) | 291 (45.05%) | 116 (48.54%) | 32 (40.51%) | 25 (37.31%) | 118 (45.21%) | |
| Others | 218 (8.23%) | 58 (8.98%) | 27 (11.30%) | 6 (7.59%) | 3 (4.48%) | 22 (8.43%) | |
| Stage at diagnosis** | 199 | 190 | 0.0505 | ||||
| 0 | 222 (8.28%) | 53 (8.14%) | 15 (6.25%) | 6 (7.59%) | 4 (5.88%) | 28 (10.61%) | |
| I | 1080 (40.28%) | 248 (38.10%) | 97 (40.42%) | 29 (36.71%) | 22 (32.35%) | 100 (37.88%) | |
| II | 372 (13.88%) | 91 (13.98%) | 36 (15.00%) | 15 (18.99%) | 13 (19.12%) | 27 (10.23%) | |
| III | 290 (10.82%) | 78 (11.98%) | 32 (13.33%) | 9 (11.39%) | 12 (17.65%) | 25 (9.47%) | |
| IV | 136 (5.07%) | 37 (5.68%) | 19 (7.92%) | 5 (6.33%) | 3 (4.41%) | 10 (3.79%) | |
| Unknown | 581 (21.67%) | 144 (22.12%) | 41 (17.08%) | 15 (18.99%) | 14 (20.59%) | 74 (28.03%) | |
| Surgery | 0.9220 | ||||||
| Conserving surgery (BCS) | 1223 (45.62%) | 296 (45.47%) | 117 (48.75%) | 35 (44.30%) | 28 (41.18%) | 116 (43.94%) | |
| Mastectomy | 490 (18.28%) | 126 (19.35%) | 43 (17.92%) | 15 (18.99%) | 15 (22.06%) | 53 (20.08%) | |
| No surgery or no further encounter | 968 (36.11%) | 229 (35.18%) | 80 (33.33%) | 29 (36.71%) | 25 (36.76%) | 95 (35.98%) | |
| Nonsurgery treatment | |||||||
| Endocrine therapy | 886 | 205 | 109 | 40 | 29 | 27 | |
| Neoadjuvant | 15 (1.69%) | 5 (2.44%) | 2 (1.83%) | 1 (2.50%) | 1 (3.45%) | 1 (3.70%)) | |
| Adjuvant endocrine therapy | 650 (73.36%) | 144 (70.24%) | 83 (76.15%) | 21 (52.50%) | 24 (82.76%)) | 16 (59.26%) | |
| Neoadjuvant + adjuvant endocrine therapy |
10 (1.13%) |
4 (1.95%)) |
0 (0.00%) |
4 (10.00%) |
0 (0.00%) |
0 (0.00%) |
|
| Status unknown | 211 (23.81%) | 52 (25.37%) | 24 (22.02%) | 14 (35.00%) | 4 (13.79%) | 10 (37.04%) | |
| Chemotherapy | 1219 | 303 | 129 | 55 | 42 | 77 | |
| Neoadjuvant chemotherapy | 106 (8.70%) | 27 (8.91%) | 11 (8.53%) | 2 (3.64%) | 6 (14.29%) | 8 (10.39%) | |
| Adjuvant chemotherapy | 636 (52.17%) | 143 (47.19%) | 71 (55.04%) | 20 (36.36%) | 20 (47.62%) | 32 (41.56%) | |
| Neoadjuvant + adjuvant endocrine therapy | 111 (9.11%) | 33 (10.89%) | 15 (11.63%) | 13 (23.64%) | 5 (11.90%) | 0 (0.00%) | |
| Status unknown | 366 (30.02%) | 100 (33.00%) | 32 (24.81%) | 20 (36.36%) | 11 (26.19%) | 37 (48.05%) | |
| Radiation | 227 | 47 | 22 | 4 | 5 | 16 | |
| Neoadjuvant chemotherapy |
6 (2.64%) |
2 (4.26%) |
2 (9.09%) |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
|
| Adjuvant chemotherapy | 153 (67.40%) | 32 (68.09%) | 12 (54.55%) | 2 (50.00%) | 3 (60.00%) | 15 (93.75%) | |
| Neoadjuvant + adjuvant endocrine therapy |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
|
| Status unknown | 68 (29.96%) | 13 (27.66%) | 8 (36.36%) | 2 (50.00%) | 2 (40.00%) | 1 (6.25%) | |
Values are expressed as n (%) or median (25th, 75th percentiles). The p-value comparisons across subgroup categories are based on chi-square test or Fisher’s exact test or Monte Carlo estimate for the exact test for categorical variables; p-values for continuous variables are based on Kruskal–Wallis test for median
*Significant at p < 0.05
**An in-house natural language processing tool (staging state machine) dedicated to cancer stage identification in the unstructured electronic health record data was used to search among a variety of breast cancer-related notes (clinical, radiology, pathology, operational, radiation, etc.). The algorithm extracted stage groups I–IV and TNM staging information separately. The TNM stages were later consolidated into stage groups I–IV for reporting. The staging state machine did not differentiate whether the stage was clinical or pathologic nor which staging system