Understanding disruptions in cancer care to reduce increased cancer burden
- Department of Surgery, Public Health Sciences, School of Medicine, Washington University in St. Louis, United States
- Brown School, Washington University in St. Louis, United States
Figures
Figure 1
Care disruption by cancer screening/appointment type across Missouri and Southern Illinois (July-August 2020).
N shown is the number who were planning to have a screening test between March 2020 and the end of 2020; For Cancer-related care, we calculate the percentage out of those who self-reported ever being diagnosed as having cancer (n-53).
Tables
Table 1
Characteristics of residents across Missouri and Southern Illinois by care disruption status (July-August 2020).
| Variable | Category | Total sample (N=680)– N (%) | No care disruption (N=304)– N (%) | Care disruption (N=376)– N (%) |
|---|---|---|---|---|
| Race | White | 399 (58.7%) | 186 (61.2%) | 213 (56.7%) |
| Black or African American | 212 (31.2%) | 90 (29.6%) | 122 (32.5%) | |
| Asian/ Native Hawaiian or Other Pacific Islander | 21 (3.1%) | 12 (4.0%) | 9 (2.4%) | |
| Other, including multiple groups | 48 (7.1%) | 16 (5.3%) | 32 (8.5%) | |
| Hispanic, Latino/a, or Spanish origin | Yes | 15 (2.2%) | 5 (1.6%) | 10 (2.7%) |
| No | 664 (97.8%) | 299 (98.4%) | 365 (97.3%) | |
| Gender Identity* | Woman | 464 (68.2%) | 192 (63.2%) | 272 (72.3%) |
| Man | 206 (30.1%) | 110 (36.2%) | 96 (25.5%) | |
| Transgender / Gender Diverse | 5 (0.7%) | 1 (0.3%) | 4 (1.1%) | |
| Prefer not to answer | 5 (0.7%) | 1 (0.3%) | 4 (1.1%) | |
| Sex assigned at birth* | Female | 472 (69.4%) | 193 (63.5%) | 279 (74.2%) |
| Male | 204 (30.0%) | 110 (36.2%) | 94 (25.0%) | |
| Prefer not to answer | 4 (0.6%) | 1 (0.3%) | 3 (0.8%) | |
| Sexual Orientation | LGBTQIA+ | 76 (11.2%) | 25 (8.2%) | 51 (13.6%) |
| Straight or Heterosexual | 590 (86.8%) | 272 (89.5%) | 318 (84.6%) | |
| Prefer not to answer | 14 (2.1%) | 7 (2.3%) | 7 (1.9%) | |
| Education* | Less than High School or GED | 31 (4.6%) | 17 (5.6%) | 14 (3.7%) |
| Grade 12 or GED (High school graduate) | 120 (17.7%) | 64 (21.1%) | 56 (14.9%) | |
| Some college, but did not graduate | 159 (23.4%) | 78 (25.7%) | 81 (21.5%) | |
| Associates Degree or Technical School Certification | 111 (16.4%) | 42 (13.9%) | 69 (18.4%) | |
| College 4 years or more (College graduate) | 143 (21.1%) | 63 (20.8%) | 80 (21.3%) | |
| Graduate or professional school | 115 (16.9%) | 39 (12.9%) | 76 (20.2%) | |
| Annual Household Income | $0 to $9,999 | 57 (8.4%) | 32 (10.6%) | 25 (6.7%) |
| $10,000 to $14,999 | 53 (7.8%) | 19 (6.3%) | 34 (9.1%) | |
| $15,000 to $19,999 | 36 (5.3%) | 14 (4.6%) | 22 (5.9%) | |
| $20,000 to $34,999 | 105 (15.5%) | 43 (14.2%) | 62 (16.5%) | |
| $35,000 to $49,999 | 110 (16.2%) | 50 (16.5%) | 60 (16.0%) | |
| $50,000 to $74,999 | 121 (17.9%) | 60 (19.8%) | 61 (16.3%) | |
| $75,000 to $99,999 | 91 (13.4%) | 45 (14.9%) | 46 (12.3%) | |
| $100,000 or more | 105 (15.5%) | 40 (13.2%) | 65 (17.3%) | |
| Metro or Non-Metro Area (RUCC codes by ZIP Code) | Metro | 493 (72.5%) | 222 (73.0%) | 271 (72.1%) |
| Non-Metro | 187 (27.5%) | 82 (27.0%) | 105 (27.9%) | |
| Employment (pre-COVID) | Employed Full-time | 321 (47.4%) | 148 (49.2%) | 173 (46.0%) |
| Employed Part-time | 72 (10.6%) | 30 (10.0%) | 42 (11.2%) | |
| Unemployed | 61 (9.0%) | 29 (9.6%) | 32 (8.5%) | |
| Homemaker | 65 (9.6%) | 22 (7.3%) | 43 (11.4%) | |
| Student | 4 (0.6%) | 2 (0.7%) | 2 (0.5%) | |
| Retired | 84 (12.4%) | 42 (14.0%) | 42 (11.2%) | |
| Disabled | 62 (9.2%) | 23 (7.6%) | 39 (10.4%) | |
| Self-Employed/Other | 8 (1.2%) | 5 (1.7%) | 3 (0.8%) | |
| Insurance | Private | 314 (46.2%) | 135 (44.4%) | 179 (47.6%) |
| Medicare/Medicare + | 126 (18.5%) | 59 (19.4%) | 67 (17.8%) | |
| Medicaid | 120 (17.7%) | 47 (15.5%) | 73 (19.4%) | |
| Other/Unknown | 22 (3.2%) | 12 (4.0%) | 10 (2.7%) | |
| Currently do not have insurance | 98 (14.4%) | 51 (16.8%) | 47 (12.6%) | |
| Telehealth appointment* | Yes | 233 (34.3%) | 85 (28.0%) | 148 (39.4%) |
| No | 447 (65.7%) | 219 (72.0%) | 228 (60.6%) | |
| Telehealth appointment type | Cancer Care | 6 (2.6%) | 0 (0%) | 6 (4.1%) |
| General Health Care | 218 (94.0%) | 81 (96.4%) | 137 (92.6%) | |
| Both | 8 (3.5%) | 3 (3.6%) | 5 (3.4%) | |
| Access to Private Vehicle (own or others) | Yes | 611 (89.9%) | 275 (90.5%) | 336 (89.4%) |
| No | 69 (10.2%) | 29 (9.5%) | 40 (10.6%) | |
| Laid off Job or had to close own business* | Yes | 135 (19.9%) | 50 (16.5%) | 85 (22.6%) |
| No | 423 (62.2%) | 204 (67.1%) | 219 (58.2%) | |
| Don’t Know/Not Sure/Prefer Not to Answer | 15 (2.2%) | 2 (0.7%) | 13 (3.5%) | |
| Not Applicable | 107 (15.7%) | 48 (15.8%) | 59 (15.7%) | |
| Variable | Mean (SD) | |||
| Age | 46.2 (12.6) | 46.0 (13.3) | 46.5 (12.0) | |
| Discrimination † | 1.8 (0.8) | 1.7 (0.8) | 1.9 (0.9) | |
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Missing values: 1 Hispanic/Latina(a)/Spanish origin; 1 Education; 2 Income; 3 Employment.
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*
Statistically significant difference (P<0.05; Chi-square or Fischer’s test for categorical, t-test or Wilcoxon rank sum for continuous).
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†
Average score of 7 items on a scale of (1) never, (2) once, (3) 2 or 3 times, and (4) 4 times or more; higher scores indicate more discrimination.
Table 2
Odds of any care disruption compared to no care disruption by social factors across Missouri and Southern Illinois (July-August 2020).
| Variable | Overall Sample (N=663) | Non-Hispanic Black or African American (N=205) | Non-Hispanic White (N=387) | |||
|---|---|---|---|---|---|---|
| Odds Ratio | 95% CI | Odds Ratio | 95% CI | Odds Ratio | 95% CI | |
| Race/Ethnicity | ||||||
| Non-Hispanic Black or African American | 1.15 | 0.77, 1.72 | -- | -- | -- | -- |
| Other Race/Ethnicity | 1.40 | 0.79, 2.45 | -- | -- | -- | -- |
| Non-Hispanic White (ref) | -- | -- | -- | -- | -- | -- |
| Sex Assigned at Birth* ‡ | ||||||
| Female | 1.60 | 1.12, 2.30 | 1.11 | 0.56, 2.19 | 1.90 | 1.17, 3.08 |
| Male (ref) | -- | -- | -- | -- | -- | -- |
| Sexual Orientation | ||||||
| LGBTQIA+ | 1.53 | 0.88, 2.65 | 0.68 | 0.27, 1.72 | 1.65 | 0.73, 3.73 |
| Straight or Heterosexual (ref) | -- | -- | -- | -- | -- | -- |
| Area designation (by ZIP code) | ||||||
| Non-Metro | 1.23 | 0.82, 1.84 | -- | -- | -- | -- |
| Metro (ref) | -- | -- | -- | -- | -- | -- |
| Telehealth Appointment* ‡ | ||||||
| Yes | 1.51 | 1.07, 2.15 | 1.06 | 0.57, 1.99 | 1.62 | 1.01, 2.59 |
| No (ref) | -- | -- | -- | -- | -- | -- |
| Access to Private Vehicle (own or others) | ||||||
| Yes | 0.74 | 0.41, 1.33 | 0.79 | 0.33, 1.90 | 0.74 | 0.27, 1.98 |
| No (ref) | -- | -- | -- | -- | -- | -- |
| Health Insurance | ||||||
| Medicare/Medicare + | 0.71 | 0.41, 1.24 | 0.88 | 0.31, 2.47 | 0.75 | 0.37, 1.52 |
| Medicaid | 1.02 | 0.59, 1.75 | 0.76 | 0.32, 1.77 | 1.43 | 0.65, 3.15 |
| Other/Unknown | 0.63 | 0.24, 1.66 | 0.75 | 0.15, 3.92 | 0.38 | 0.09, 1.65 |
| Currently do not have insurance | 0.66 | 0.38, 1.13 | 0.58 | 0.22, 1.52 | 0.87 | 0.42, 1.80 |
| Private (ref) | -- | -- | -- | -- | -- | -- |
| Laid off Job or had to close own business | ||||||
| Yes | 1.55 | 0.994, 2.41 | 1.55 | 0.75, 3.20 | 1.52 | 0.80, 2.89 |
| No (ref) | -- | -- | -- | -- | -- | -- |
| Don’t Know/Not Sure/Prefer Not to Answer/Not Applicable | 1.42 | 0.89, 2.25 | 2.12 | 0.87, 5.18 | 1.06 | 0.59, 1.93 |
| Education ‡†* | 1.26 | 1.11, 1.43 | 1.45 | 1.13, 1.85 | 1.39 | 1.17, 1.65 |
| Income | 0.99 | 0.89, 1.09 | 0.93 | 0.78, 1.13 | 0.99 | 0.87, 1.13 |
| Discrimination* | 1.40 | 1.13, 1.72 | 1.26 | 0.89, 1.78 | 1.29 | 0.96, 1.74 |
| Age† | 1.01 | 0.995, 1.03 | 0.99 | 0.96, 1.02 | 1.02 | 1.001, 1.04 |
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*
Statistically significant (p<0.05) overall variable effect – overall model.
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†
Statistically significant (p<0.05) overall variable effect – Non-Hispanic White model.
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‡
Statistically significant (p<0.05) overall variable effect – Non-Hispanic Black or African American model.
Additional files
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Supplementary file 1
Survey item information.
- https://cdn.elifesciences.org/articles/85024/elife-85024-supp1-v2.docx
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Supplementary file 2
Characteristics of residents across Missouri and Southern Illinois by race (July-August 2020).
- https://cdn.elifesciences.org/articles/85024/elife-85024-supp2-v2.docx
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MDAR checklist
- https://cdn.elifesciences.org/articles/85024/elife-85024-mdarchecklist1-v2.docx
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Source code 1
Care disruption analysis SAS code file.
- https://cdn.elifesciences.org/articles/85024/elife-85024-code1-v2.zip
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Understanding disruptions in cancer care to reduce increased cancer burden
eLife 12:e85024.
https://doi.org/10.7554/eLife.85024
