| Literature DB >> 35875145 |
Juanita Elizabeth Quino1,2, Fabian Perez1,2,3, Angelica Perez1, April Pangia Vang1,2, Leonie Avendano4, Julie Dang1, Moon S Chen1, Alexa Morales Arana1,2, Sienna Rocha1,2, Miriam Nuno3,5, Primo N Lara1, Laura Fejerman1,3, Luis G Carvajal-Carmona1,2,6,7.
Abstract
Background: Cancer is the leading cause of death among Latinos, the largest minority population in the United States (US). To address cancer challenges experienced by Latinos, we conducted a catchment area population assessment (CAPA) using validated questions from the National Cancer Institute (NCI) population health assessment supplement at our NCI-designated cancer center in California.Entities:
Keywords: Latino health; health disparities; nativity; needs assessment; preventative screenings
Year: 2022 PMID: 35875145 PMCID: PMC9300947 DOI: 10.3389/fonc.2022.883200
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 5.738
Figure 1UC Davis Cancer Center 19-County Catchment Area.
Demographics of participants stratified by nativity. (N=246).
| Variable | Overall | U.S. Born | Foreign-Born | p-value |
|---|---|---|---|---|
|
| ||||
| 18-30y | 67 (27.5%) | 36 (47.4%) | 27 (17.0%) |
|
| 31-40y | 60 (24.6%) | 17 (22.4%) | 42 (26.4%) | |
| 41-50y | 53 (21.7%) | 12 (15.8%) | 40 (25.2%) | |
| 51-65y | 52 (21.3%) | 7 (9.2%) | 42 (26.4%) | |
| 66y+ | 12 (4.9%) | 4 (5.3%) | 8 (5.0%) | |
|
| 41.9 (14.1) | 36.1 (15.2) | 44.7 (12.7) |
|
|
| ||||
| Male | 55 (21.8%) | 18 (21.7%) | 37 (23.1%) | 0.7994 |
| Female | 197 (78.2%) | 65 (78.3%) | 123 (76.9%) | |
|
| ||||
| English | 41 (16.4%) | 29 (35.4%) | 10 (6.3%) |
|
| Spanish | 158 (63.2%) | 20 (24.4%) | 134 (84.3%) | |
| Both | 51 (20.4%) | 33 (40.2%) | 15 (9.4%) | |
|
| ||||
| Very Well | 72 (38.7%) | 44 (84.6%) | 28 (21.9%) |
|
| Well | 39 (21%) | 7 (13.5%) | 30 (23.4%) | |
| Not Well | 64 (34.4%) | 1 (1.9%) | 61 (47.7%) | |
| Not At All | 11 (5.9%) | 0 (0.0%) | 9 (7.0%) | |
|
| ||||
| <High school | 61 (25.0%) | 3 (3.7%) | 52 (34.0%) |
|
| High school graduate | 57 (23.4%) | 16 (19.5%) | 39 (25.5%) | |
| Some college/Vocational | 65 (26.6%) | 30 (36.6%) | 34 (22.2%) | |
| College grad or higher | 61 (25.0%) | 33 (40.2%) | 28 (18.3%) | |
|
| ||||
| Employed | 140 (57.1%) | 51 (62.2%) | 87 (55.1%) |
|
| Student | 25 (10.20%) | 15 (18.3%) | 10 (6.3%) | |
| Homemaker | 40 (16.3%) | 1 (1.2%) | 39 (24.7%) | |
| Unemployed/Disabled/Retired | 40 (16.3%) | 15 (18.3) | 22 (13.9%) | |
|
| ||||
| <$35k | 107 (47.1%) | 28 (35.9%) | 74 (52.1%) |
|
| $35k-$74.9k | 78 (34.4%) | 28 (35.9%) | 49 (34.5%) | |
| $75k+ | 42 (18.5%) | 22 (28.2%) | 19 (13.4%) | |
Two-sided p-values form Chi-square test are used, unless a cell had less than 10, Fisher’s exact test was used. *denotes Fischer’s exact test. **denotes Independent T-Test for continuous variables. Bolded p-value indicates significance level of p<0.05.
Health characteristics of participants stratified by nativity. (N = 246).
| Variable | Overall | U.S. Born ( | Foreign-Born ( | p-value |
|---|---|---|---|---|
|
| ||||
| Private | 124 (54.4%) | 52 (67.5%) | 70 (48.3%) |
|
| Public | 31 (13.6%) | 14 (18.2%) | 16 (11.0%) | |
| Some other Source | 10 (4.4%) | 7 (9.1%) | 3 (2.07%) | |
| None | 63 (27.6%) | 4 (5.2%) | 56 (38.6%) | |
|
| ||||
| Clinic or health center | 122 (51.7%) | 29 (37.2%) | 88 (52.3%) |
|
| Doctor’s office or HMO | 72 (30.5%) | 41 (52.6%) | 31 (20.5%) | |
| Hospital emergency room | 8 (3.4%) | 2 (2.6%) | 6 (4.0%) | |
| Some other place | 4 (1.7%) | 0 (0.0%) | 4 (2.6%) | |
| There is no place | 30 (12.7%) | 6 (7.7%) | 22 (14.6%) | |
|
| ||||
| <1y | 169 (70.7%) | 62 (78.5%) | 103 (66.9%) | 0.1298* |
| 1-2y | 33 (13.8%) | 11 (13.9%) | 21 (13.6%) |
|
| 2-5y | 13 (5.4%) | 3 (3.8%) | 10 (6.5%) | |
| 5y+ | 16 (6.7%) | 3 (3.8%) | 12 (7.8%) | |
| Never | 8 (3.3%) | 0 (0.0%) | 8 (5.2%) | |
|
| ||||
| Yes | 34 (13.7%) | 9 (11.1%) | 25 (15.4%) | 0.4352* |
| No | 214 (86.3%) | 72 (88.9%) | 137 (84.6%) | |
|
| ||||
| Yes | 70 (30.2%) | 21 (26.6%) | 47 (31.8%) | 0.4175 |
| No | 162 (69.8%) | 58 (73.4%) | 101 (68.2%) | |
|
| ||||
| Excellent | 19 (7.9%) | 8 (9.8%) | 10 (6.6%) | 0.7256* |
| Very Good | 43 (17.9%) | 17 (20.7%) | 26 (17.2%) | |
| Good | 108 (22.1%) | 34 (41.5%) | 70 (46.4%) | |
| Fair | 53 (22.1%) | 19 (23.2%) | 33 (21.9%) | |
| Poor | 17 (7.1%) | 4 (4.9%) | 12 (7.9%) | |
|
| ||||
| Completely Confident | 60 (25.0%) | 30 (37.0%) | 30 (19.7%) | 0.0744* |
| Very Confident | 68 (28.3%) | 20 (24.7%) | 46 (30.3%) | |
| Somewhat confident | 71 (29.6%) | 21 (25.9%) | 48 (31.6%) | |
| A little confident | 30 (12.5%) | 7 (8.6%) | 22 (14.5%) | |
| Not Confident at all | 11 (4.6%) | 3 (3.7%) | 6 (3.9%) | |
|
| ||||
| At least 3 doses | 80 (57.6%) | 36 (67.9%) | 43 (53.1%) |
|
| Less than 3 doses | 13 (9.4%) | 7 (13.2%) | 4 (4.9%) | |
| No doses | 46 (33.1%) | 10 (18.9%) | 34 (42.0%) | |
|
| ||||
| Yes | 18 (7.6%) | 6 (7.3%) | 12 (7.8%) | 1.0000* |
| No | 218 (92.4%) | 76 (92.7%) | 141 (92.2%) | |
|
| ||||
| Yes | 65 (28.8%) | 32 (42.1%) | 33 (23.1%) |
|
| No | 161 (71.2%) | 44 (57.9%) | 110 (76.9%) | |
|
| ||||
| Yes | 148 (66.7%) | 41 (53.2%) | 104 (74.3%) |
|
| No | 74 (33.3%) | 36 (46.8%) | 36 (25.7%) | |
|
| ||||
| Yes | 133 (60.7%) | 44 (57.1%) | 87 (63.5%) | 0.3594 |
| No | 86 (39.3%) | 33 (42.9%) | 50 (36.5%) | |
|
| ||||
| 0-3 days | 98 (46.0%) | 31 (43.1%) | 63 (47.0%) | 0.5864 |
| 4-7 days | 115 (54.0%) | 41 (56.9%) | 71 (53.0%) | |
|
| ||||
| Normal | 51 (23.9%) | 24 (30.8%) | 27 (20.8%) | 0.1251 |
| Overweight | 63 (29.6%) | 17 (21.8%) | 43 (33.1%) | |
| Obese | 99 (46.5%) | 37 (47.4%) | 60 (46.2%) | |
|
| ||||
| Yes | 26 (10.6%) | 7 (8.5%) | 18 (11.4%) | 0.6566* |
| No | 220 (89.4%) | 75 (91.5%) | 140 (88.6%) | |
|
| ||||
| Yes | 118 (51.1%) | 35 (45.5%) | 80 (53.7%) | 0.2404 |
| No | 113 (48.9%) | 42 (54.5%) | 69 (46.3%) | |
Two-sided p-values from Chi-square test are reported, unless a cell had less than 10, then Fisher’s exact test was used. *denotes Fischer’s exact test. Bolded p-value indicates significance level of p<0.05.
Figure 2Cancer screening rates in survey participants. Two-sided p-values from Chi-square test or Fisher’s exact test are reported. Age inclusion: 21-65yo females for papsmear (N = 158); 40-75yo females for mammogram (N = 85); 50-75yo for colonoscopy/FIT (N = 43).
Unadjusted and adjusted odds ratios (OR) from logistic regression for factors associated with cervical, breast and colorectal cancer screening.
| Cervical Cancer | Breast Cancer | Colorectal Cancer | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | Unadjusted | Adjusted | p-value | Unadjusted | Adjusted | p-value | Unadjusted | Adjusted | p-value |
|
| 1.04 (1.00-1.08) | 1.09 (1.03-1.15) |
| 1.12 (1.02-1.22) | 1.10 (0.97-1.25) | 0.1199 | 1.27 (1.1-1.45) | 1.24 (1.05-1.46) |
|
|
| |||||||||
| Some college or more | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | |||
| High school or less | 0.37 (0.17-0.83) | 0.23 (0.08-0.7) |
| 0.55 (0.18-1.67) | 1.05 (0.22-4.94) | 0.9501 | 0.32 (0.1-0.97) | 0.37 (0.08-1.70) | 0.1997 |
|
| |||||||||
| ≤ 1 year | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | |||
| 1-2 years | 0.30 (0.11-0.83) | 0.42 (0.12-1.41) | 0.1615 | 0.24 (0.06-1.04) | 0.64 (0.09-4.64) | 0.6590 | 0.66 (0.04-11.12) | NE | |
| 2 or more years | 0.15 (0.05-0.41) | 0.16 (0.04-0.59) |
| 0.14 (0.04-0.51) | 0.34 (0.05-2.38) | 0.2753 | 0.09 (0.01-0.82) | NE | |
|
| |||||||||
| U.S. born | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | |||
| Foreign born | 0.86 (0.36-2.04) | 0.84 (0.24-2.92) | 0.7843 | 0.43 (0.09-2.08) | 3.73 (0.4-34.86) | 0.2487 | 0.41 (0.09-1.78) | 1.30 (0.21-8.07) | 0.7794 |
|
| |||||||||
| Yes | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | |||
| No | 0.59 (0.26-1.38) | 0.82 (0.26-2.62) | 0.7425 | 0.11 (0.04-0.37) | 0.06 (0.01-0.34) |
| 0.03 (0.00-0.28) | 0.06 (0.01-0.55) |
|
NE: Not estimated to avoid model overfitting due to small sample size. P-values for the adjusted model are reported, unadjusted p-values not shown. Bolded p-value indicates significance level of p<0.05.
Figure 3Area Under the curve for multiple logistic regression screening models.