Abstract
PURPOSE:
Geographical barriers can impact access to healthcare, but their influence on complications and long-term patient-reported outcomes (PROs) after breast reconstruction is unclear. This study evaluates the impact of travel distance on postoperative complications and PROs using the BREAST-Q.
METHODS:
Patients who underwent postmastectomy breast reconstruction between 2017 and 2023 were categorized by travel distance into five groups (0–10, 11–25, 26–50, 51–100, 101+ miles). Complications assessed included wound infection, delayed wound healing, hematoma, mastectomy skin flap necrosis (MSFN), and seroma. BREAST-Q domains—physical well-being of the chest (PWBC), psychosocial well-being (PSWB), satisfaction with breasts (SATSB), and sexual well-being (SWB)—were evaluated, when available, preoperatively and at 6 months, 1, 2, 3, 4, and 5 years. Linear mixed-effects (LME) modeling assessed travel distance as an independent predictor of PROs.
RESULTS:
Among 5,600 patients (4,202 implant, 1,398 autologous), wound infection rates differed significantly by travel distance in the implant cohort (p=0.005), but other complications were consistent across groups. PROs were similar across travel distance groups for PWBC, PSWB, and SWB domains at all time points. SATSB scores varied significantly by travel distance at 1-year (p=0.031), and 2-year (p=0.008) postoperatively. LME modeling revealed minimal association between travel distance and PROs. Patients traveling 11–25 miles reported slightly higher SWB scores (p=0.045) than those traveling 0–10 miles, but differences did not meet the minimally clinically important difference (MCID) of 4 points.
CONCLUSIONS:
Travel distance didn’t meaningfully influence clinical outcomes or PROs, confirming patients can safely travel to specialized centers for breast reconstruction without compromising care or well-being.
Keywords: Breast reconstruction, Travel Distance, Complications, Patient-Reported Outcomes
INTRODUCTION
Post-mastectomy breast reconstruction is associated with significant psychosocial and physical benefits that improve a patient’s quality of life after mastectomy.1–4 Despite this, approximately 40–50% of patients forgo reconstruction due to socioeconomic and geographic barriers that limit awareness and reduce access to comprehensive cancer centers performing post-mastectomy reconstruction.5–8 As specialized services, like microvascular reconstruction, are often concentrated in academic and high-volume centers, patients frequently need to travel significant distances to access services for comprehensive breast cancer care.
Proximity to a reconstructive surgeon and distance traveled have been shown to play a significant role in influencing a patient’s likelihood of pursuing reconstruction, with patients traveling further distances to receive reconstruction versus mastectomy alone.9–13 While the need for broader access to reconstructive services is well recognized, the association between travel distance, clinical outcomes, and long-term patient-reported outcomes (PROs) is less understood. Given the demonstrated influence of geographic proximity on the decision to undergo breast reconstruction, it is essential to examine whether barriers such as distance traveled for reconstructive care impact the quality of care and PROs after surgery.
This study aimed to evaluate the impact of travel distance for reconstructive care on clinical outcomes and long-term PROs following breast reconstruction at a single high-volume cancer center. The primary objective was to determine whether travel distance influenced clinical outcomes, specifically rates of postoperative complications. Secondarily, we assessed whether travel distance affected PROs as measured by the BREAST-Q domains. We hypothesized that neither clinical outcomes nor patient satisfaction would be significantly associated with travel distance, with geographic barriers having minimal effect on overall quality of care or well-being.
METHODS
Study Population
This analysis included patients who underwent immediate or delayed implant or autologous reconstruction at Memorial Sloan Kettering Cancer Center (MSKCC), a single National Cancer Institute (NCI)-designated cancer center, from 2017 to 2023. Travel distance, measured in miles, was calculated from each patient’s residential ZIP code to the MSKCC main hospital using the ZIP codes of the patient’s residence and the hospital (ZIP Code 10065). Household income for each patient was estimated based on the median household income for their ZIP code, using data from the 2022 U.S. Census Bureau. Travel distances were categorized into five cohorts: 0–10, 11–25, 26–50, 51–100, and over 100 miles. We determined these distance cohorts based on a combination of prior literature and our understanding of travel patterns within NYC and its surrounding areas.9
Study Variables
Demographic and clinical variables collected included age, race, ethnicity, marital status, employment status, body mass index (BMI), insurance type, smoking history, presence of psychiatric disorders, autoimmune conditions, diabetes, and hypertension. Surgical and oncologic variables of interest included radiation therapy (neoadjuvant, adjuvant, or none), chemotherapy (neoadjuvant, adjuvant, or none), reconstruction timing (immediate versus delayed), laterality (unilateral versus bilateral), type of reconstruction, mastectomy type, and axillary surgery type (axillary lymph node dissection, sentinel lymph node biopsy, or none).
Postoperative complications were recorded after tissue expander placement and definitive reconstruction for both implant and autologous-based patients. Assessed complications included wound infection, delayed wound healing or dehiscence, hematoma, mastectomy skin flap necrosis (MSFN), and seroma formation.
BREAST-Q Patient Reported Outcomes
This study focused on evaluating the following BREAST-Q domains across travel distances and perioperative time points: Physical Well-Being of the Chest (PWBC), Psychosocial Well-Being (PWB), Satisfaction with Breasts (SATSB), and Sexual Well-Being (SWB). Patients completed the BREAST-Q questionnaire as part of standard clinical care at pre-specified intervals.14 The time points analyzed included preoperative (before mastectomy) and postoperative assessments at 6 months, 1, 2, 3, 4, and 5 years. A minimal clinically important difference (MCID) was defined as a 4-point change.15
Statistical Analysis
Patient demographics, surgical characteristics, complications, and BREAST-Q scores were summarized overall and by distance traveled to MSKCC. Medians and interquartile ranges were used to describe continuous variables. Categorical variables were summarized with frequencies and proportions. Comparison of continuous variables across groups was performed using the Kruskal-Wallis rank sum test. Categorical variables were compared with chi-squared and Fisher’s exact test. Any outcomes showing a statistically significant difference across all groups were further examined using pairwise comparisons with the Wilcoxon rank sum, chi-squared, or Fisher’s exact test, where appropriate. Multivariable linear mixed effects regression models were used to evaluate the association between travel distance and BREAST-Q scores over time while adjusting for potential confounders. We constructed one model for each BREAST-Q domain, and all were adjusted for the aforementioned patient-level variables, which we hypothesized a priori to be associated with the outcome. All analyses were run in R version 4.4.1 with the ‘zipcodeR’, ‘lme4’, ‘afex’ and ‘gtsummary’ packages. A p-value <0.05 was considered statistically significant.
RESULTS
Patient Demographics and Surgical Characteristics
A total of 5,600 patients were included in the analysis, with 4,202 (75%) undergoing implant-based reconstruction and 1,398 (25%) receiving autologous reconstruction. When analyzed by travel distance, 1,444 (25.89%) patients traveled 0–10 miles, 2,048 (36.6%) traveled 11–25 miles, 1,081 (19.3%) traveled 26–50 miles, 546 (9.75%) traveled 51–100 miles, and 481 (8.6%) traveled over 101 miles. Detailed breakdowns of travel distances by reconstruction type are provided in Table 1. The median age of the cohort was 48 years [Interquartile range (IQR): 41, 56], and the median BMI was 24 (IQR: 21.3, 27.6). Most patients were white (73%), non-smokers (75%), and married or in a domestic partner (72%). Regarding oncologic treatments, 26% received radiation therapy, and 47% received chemotherapy. Most patients underwent a skin-sparing mastectomy (83%), and 60% of patients underwent bilateral breast reconstruction. Immediate reconstruction was performed in 85% of cases, while 15% had delayed reconstruction. Axillary surgery was common, with 87% of patients undergoing sentinel lymph node biopsy and 18% receiving axillary lymph node dissection. Patient demographics and surgical characteristics are summarized in Table 1.
Table 1.
Demographics by travel distance
| Variable | N | Overall | 0–10 | 11–25 | 26–50 | 51–100 | 101+ | p-value2 |
|---|---|---|---|---|---|---|---|---|
| N = 5,6001 | N = 1,4441 | N = 2,0481 | N = 1,0811 | N = 5461 | N = 4811 | |||
|
| ||||||||
| Age at surgery | 5,600 | 48 (41, 56) | 47 (40, 55) | 48 (41, 55) | 49 (42, 56) | 50 (43, 58) | 49 (41, 57) | <0.001 |
| Race | 5,600 | <0.001 | ||||||
| Asian | 541 (9.7%) | 230 (16%) | 203 (9.9%) | 68 (6.3%) | 19 (3.5%) | 21 (4.4%) | ||
| Black | 497 (8.9%) | 229 (16%) | 178 (8.7%) | 32 (3.0%) | 32 (5.9%) | 26 (5.4%) | ||
| Other/Unknown | 466 (8.3%) | 178 (12%) | 166 (8.1%) | 70 (6.5%) | 29 (5.3%) | 23 (4.8%) | ||
| White | 4,096 (73%) | 807 (56%) | 1,501 (73%) | 911 (84%) | 466 (85%) | 411 (85%) | ||
| Ethnicity | 5,600 | <0.001 | ||||||
| Hispanic or Latino | 515 (9.2%) | 209 (14%) | 163 (8.0%) | 77 (7.1%) | 33 (6.0%) | 33 (6.9%) | ||
| Not Hispanic | 4,696 (84%) | 1,125 (78%) | 1,752 (86%) | 924 (85%) | 483 (88%) | 412 (86%) | ||
| Unknown | 389 (6.9%) | 110 (7.6%) | 133 (6.5%) | 80 (7.4%) | 30 (5.5%) | 36 (7.5%) | ||
| Marital Status | 5,600 | <0.001 | ||||||
| Divorced/Separated | 435 (7.8%) | 121 (8.4%) | 154 (7.5%) | 75 (6.9%) | 45 (8.2%) | 40 (8.3%) | ||
| Married/Domestic Partner | 4,004 (72%) | 824 (57%) | 1,539 (75%) | 880 (81%) | 414 (76%) | 347 (72%) | ||
| Single/Widowed | 1,161 (21%) | 499 (35%) | 355 (17%) | 126 (12%) | 87 (16%) | 94 (20%) | ||
| Actively Working | 5,600 | 0.008 | ||||||
| No | 1,004 (18%) | 229 (16%) | 366 (18%) | 212 (20%) | 89 (16%) | 108 (22%) | ||
| Unknown | 1,082 (19%) | 273 (19%) | 422 (21%) | 208 (19%) | 107 (20%) | 72 (15%) | ||
| Yes | 3,514 (63%) | 942 (65%) | 1,260 (62%) | 661 (61%) | 350 (64%) | 301 (63%) | ||
| BMI | 5,600 | 24.0 (21.3, 27.6) | 23.7 (20.9, 27.4) | 23.8 (21.2, 27.3) | 24.4 (21.5, 28.1) | 25.1 (21.9, 28.9) | 23.5 (21.3, 27.2) | <0.001 |
| Smoking Status | 5,600 | 0.005 | ||||||
| Current smoker | 154 (2.8%) | 43 (3.0%) | 62 (3.0%) | 24 (2.2%) | 18 (3.3%) | 7 (1.5%) | ||
| Former smoker | 1,219 (22%) | 298 (21%) | 403 (20%) | 267 (25%) | 139 (25%) | 112 (23%) | ||
| Never Smoker | 4,227 (75%) | 1,103 (76%) | 1,583 (77%) | 790 (73%) | 389 (71%) | 362 (75%) | ||
| Psych Diagnosis | 5,600 | 3,458 (62%) | 906 (63%) | 1,258 (61%) | 652 (60%) | 350 (64%) | 292 (61%) | 0.5 |
| Autoimmune Disease | 5,600 | 203 (3.6%) | 49 (3.4%) | 84 (4.1%) | 36 (3.3%) | 25 (4.6%) | 9 (1.9%) | 0.11 |
| Diabetes | 5,600 | 309 (5.5%) | 93 (6.4%) | 120 (5.9%) | 50 (4.6%) | 24 (4.4%) | 22 (4.6%) | 0.2 |
| Hypertension | 5,600 | 1,231 (22%) | 317 (22%) | 418 (20%) | 229 (21%) | 162 (30%) | 105 (22%) | <0.001 |
| Laterality | 5,600 | <0.001 | ||||||
| Bilateral | 3,373 (60%) | 730 (51%) | 1,306 (64%) | 726 (67%) | 316 (58%) | 295 (61%) | ||
| Unilateral | 2,227 (40%) | 714 (49%) | 742 (36%) | 355 (33%) | 230 (42%) | 186 (39%) | ||
| Chemotherapy | 5,600 | <0.001 | ||||||
| Adjuvant | 1,155 (21%) | 298 (21%) | 430 (21%) | 228 (21%) | 117 (21%) | 82 (17%) | ||
| Neoadjuvant | 1,474 (26%) | 420 (29%) | 555 (27%) | 285 (26%) | 122 (22%) | 92 (19%) | ||
| None | 2,971 (53%) | 726 (50%) | 1,063 (52%) | 568 (53%) | 307 (56%) | 307 (64%) | ||
| Radiation | 5,600 | <0.001 | ||||||
| Adjuvant | 1,174 (21%) | 321 (22%) | 450 (22%) | 246 (23%) | 98 (18%) | 59 (12%) | ||
| Neoadjuvant | 265 (4.7%) | 74 (5.1%) | 94 (4.6%) | 52 (4.8%) | 23 (4.2%) | 22 (4.6%) | ||
| None | 4,161 (74%) | 1,049 (73%) | 1,504 (73%) | 783 (72%) | 425 (78%) | 400 (83%) | ||
| Timing of Reconstruction | 5,600 | 0.4 | ||||||
| Delayed | 862 (15%) | 220 (15%) | 332 (16%) | 155 (14%) | 91 (17%) | 64 (13%) | ||
| Immediate | 4,738 (85%) | 1,224 (85%) | 1,716 (84%) | 926 (86%) | 455 (83%) | 417 (87%) | ||
| Overall Mastectomy Type | 5,600 | <0.001 | ||||||
| Nipple sparing | 742 (13%) | 217 (15%) | 282 (14%) | 128 (12%) | 42 (7.7%) | 73 (15%) | ||
| Skin sparing | 4,657 (83%) | 1,181 (82%) | 1,682 (82%) | 921 (85%) | 487 (89%) | 386 (80%) | ||
| Unknown | 201 (4%) | 46 (3.2%) | 84 (4.1%) | 32 (3.0%) | 17 (3.1%) | 22 (4.6%) | ||
| Reconstruction Type | 5,600 | 0.5 | ||||||
| Autologous | 1,398 (25%) | 363 (25%) | 519 (25%) | 260 (24%) | 147 (27%) | 109 (23%) | ||
| Implant | 4,202 (75%) | 1,081 (75%) | 1,529 (75%) | 821 (76%) | 399 (73%) | 372 (77%) | ||
| ALND | 5,600 | 1,006 (18%) | 255 (18%) | 362 (18%) | 210 (19%) | 93 (17%) | 86 (18%) | 0.7 |
| SLNB | 5,600 | 4,881 (87%) | 1,264 (88%) | 1,790 (87%) | 948 (88%) | 478 (88%) | 401 (83%) | 0.15 |
| Insurance | 5,600 | <0.001 | ||||||
| Commercial | 4,467 (80%) | 1,069 (74%) | 1,681 (82%) | 903 (84%) | 414 (76%) | 400 (83%) | ||
| Medicaid | 337 (6.0%) | 156 (11%) | 120 (5.9%) | 28 (2.6%) | 23 (4.2%) | 10 (2.1%) | ||
| Medicare | 786 (14%) | 213 (15%) | 245 (12%) | 148 (14%) | 109 (20%) | 71 (15%) | ||
| Self-Pay | 10 (0.2%) | 6 (0.4%) | 2 (<0.1%) | 2 (0.2%) | 0 (0%) | 0 (0%) | ||
| Median Income | 5,600 | 119,057 (88,116, 152,020) | 95,987 (74,839, 145,934) | 137,326 (104,777, 167,039) | 136,375 (115,000, 161,533) | 103,207 (85,539, 118,443) | 85,050 (67,015, 104,868) | <0.001 |
| Travel Distance | 5,600 | 22 (10, 43) | 5 (3, 7) | 18 (14, 23) | 39 (34, 44) | 61 (54, 76) | 213 (141, 977) | <0.001 |
Median (Q1, Q3); n (%)
Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates)
Incidence of Postoperative Complications
In the autologous cohort, wound infection was the most common complication (n=237, 17%), followed by MSFN (n=157, 11%), seroma (n=137, 9.8%), hematoma (n=90, 6.4%), and delayed wound healing or dehiscence (n=68, 4.9%). In the implant cohort, wound infection was also the most frequent complication (n=495, 12%), followed by seroma (n=379, 9%), hematoma (n=201, 4.8%), MSFN (n=92, 2.2%), and delayed wound healing or dehiscence (n=54, 1.3%). Significant differences by travel distance were noted only for wound infection in the implant cohort (p=0.005) (Table 2). Pairwise analysis revealed significant differences in the rates of surgical site infection between individuals traveling 0–10 vs. 101+ miles (11% vs. 7%, p=0.014), 11–25 vs. 26–50 miles (13% vs. 10%, p=0.036), 11–25 vs. 101+ miles (13% vs. 7%, p<0.001), and 51–100 vs. 101+ miles (13% vs. 7%, p=0.004) (Supplemental Digital Content, Table 1).
Table 2.
Complications by travel distance
| Autologous Patients | ||||||||
|---|---|---|---|---|---|---|---|---|
| Variable | N | Overall | 0–10 | 11–25 | 26–50 | 51–100 | 101+ | p-value2 |
| N = 1,3981 | N = 3631 | N = 5191 | N = 2601 | N = 1471 | N = 1091 | |||
|
| ||||||||
| Infection | 1,398 | 237 (17%) | 69 (19%) | 84 (16%) | 39 (15%) | 31 (21%) | 14 (13%) | 0.3 |
| Delayed Wound Healing / Dehiscence | 1,398 | 68 (4.9%) | 20 (5.5%) | 25 (4.8%) | 14 (5.4%) | 6 (4.1%) | 3 (2.8%) | 0.8 |
| Hematoma | 1,398 | 90 (6.4%) | 21 (5.8%) | 35 (6.7%) | 18 (6.9%) | 8 (5.4%) | 8 (7.3%) | >0.9 |
| Mastectomy Skin Flap Necrosis | 1,398 | 157 (11%) | 41 (11%) | 43 (8.3%) | 35 (13%) | 21 (14%) | 17 (16%) | 0.055 |
| Seroma | 1,398 | 137 (9.8%) | 40 (11%) | 50 (9.6%) | 18 (6.9%) | 16 (11%) | 13 (12%) | 0.4 |
|
| ||||||||
| Implant Patients | ||||||||
| Variable | N | Overall | 0–10 | 11–25 | 26–50 | 51–100 | 101+ | p-value2 |
| N = 4,2021 | N = 1,0811 | N = 1,5291 | N = 8211 | N = 3991 | N = 3721 | |||
|
| ||||||||
| Infection | 4,202 | 495 (12%) | 124 (11%) | 206 (13%) | 86 (10%) | 53 (13%) | 26 (7.0%) | 0.005 |
| Delayed Wound Healing / Dehiscence | 4,202 | 54 (1.3%) | 15 (1.4%) | 22 (1.4%) | 9 (1.1%) | 4 (1.0%) | 4 (1.1%) | >0.9 |
| Hematoma | 4,202 | 201 (4.8%) | 62 (5.7%) | 75 (4.9%) | 27 (3.3%) | 21 (5.3%) | 16 (4.3%) | 0.2 |
| Mastectomy Skin Flap Necrosis | 4,202 | 92 (2.2%) | 27 (2.5%) | 29 (1.9%) | 16 (1.9%) | 12 (3.0%) | 8 (2.2%) | 0.6 |
| Seroma | 4,202 | 379 (9.0%) | 103 (9.5%) | 135 (8.8%) | 80 (9.7%) | 37 (9.3%) | 24 (6.5%) | 0.4 |
n (%)
Pearson’s Chi-squared test; Fisher’s exact test
Patient-Reported Outcomes
Analysis of PROs at each time point showed similar scores across all travel distance groups for PWBC, PWB, and SWB. However, SATSB scores differed significantly by travel distance at the 1-year (p=0.031) and 2-year (p=0.008) postoperative intervals (Table 3).
Table 3:
BREAST-Q scores by Travel Distance
| Physical Well-Being Chest | ||||||
|---|---|---|---|---|---|---|
| Time Period | 0–10 miles | 11–25 miles | 26–50 miles | 51–100 miles | 101+ miles | p-value2 |
| N = 1,4441 | N = 2,0481 | N = 1,0811 | N = 5461 | N = 4811 | ||
|
| ||||||
| PreOp | 85 (72, 100) | 85 (74, 100) | 85 (74, 100) | 85 (72, 100) | 85 (74, 100) | 0.2 |
| 6 Months Postoperatively | 72 (60, 85) | 72 (60, 85) | 72 (60, 85) | 72 (60, 85) | 72 (60, 85) | 0.2 |
| 1 Year Postoperatively | 76 (60, 85) | 80 (64, 92) | 76 (60, 92) | 76 (64, 92) | 76 (64, 92) | 0.084 |
| 2 Year Postoperatively | 76 (64, 92) | 80 (64, 92) | 80 (64, 92) | 80 (68, 92) | 76 (64, 92) | 0.3 |
| 3 Year Postoperatively | 76 (64, 92) | 80 (68, 92) | 76 (64, 92) | 80 (64, 92) | 78 (66, 92) | 0.8 |
| 4 Year Postoperatively | 80 (64, 92) | 80 (64, 100) | 80 (64, 92) | 76 (64, 92) | 80 (72, 100) | 0.9 |
|
| ||||||
| Psychosocial Well-Being | ||||||
| Time Period | 0–10 miles | 11–25 miles | 26–50 miles | 51–100 miles | 101+ miles | p-value2 |
| N = 1,4441 | N = 2,0481 | N = 1,0811 | N = 5461 | N = 4811 | ||
|
| ||||||
| PreOp | 66 (57, 82) | 69 (58, 83) | 66 (56, 83) | 66 (58, 80) | 69 (58, 83) | 0.11 |
| 6 Months Postoperatively | 64 (52, 80) | 64 (53, 80) | 66 (53, 80) | 66 (53, 80) | 64 (53, 80) | 0.8 |
| 1 Year Postoperatively | 64 (53, 83) | 69 (56, 87) | 69 (55, 87) | 70 (59, 83) | 69 (58, 83) | 0.1 |
| 2 Year Postoperatively | 65 (53, 83) | 69 (58, 87) | 69 (56, 83) | 69 (56, 83) | 69 (61, 83) | 0.071 |
| 3 Year Postoperatively | 66 (56, 83) | 71 (58, 87) | 69 (56, 83) | 71 (58, 87) | 74 (58, 87) | 0.3 |
| 4 Year Postoperatively | 64 (53, 83) | 70 (60, 93) | 71 (58, 93) | 69 (56, 80) | 69 (60, 83) | 0.11 |
|
| ||||||
| Satisfaction with Breasts | ||||||
| Time Period | 0–10 miles | 11–25 miles | 26–50 miles | 51–100 miles | 101+ miles | p-value2 |
| N = 1,4441 | N = 2,0481 | N = 1,0811 | N = 5461 | N = 4811 | ||
|
| ||||||
| PreOp | 58 (48, 79) | 58 (48, 71) | 58 (48, 71) | 58 (48, 71) | 58 (48, 79) | 0.012 |
| 6 Months Postoperatively | 58 (48, 71) | 58 (48, 71) | 59 (49, 73) | 58 (47, 75) | 59 (48, 71) | 0.5 |
| 1 Year Postoperatively | 58 (49, 73) | 62 (53, 75) | 62 (51, 75) | 61 (49, 75) | 64 (53, 73) | 0.031 |
| 2 Year Postoperatively | 59 (48, 71) | 62 (53, 75) | 62 (52, 75) | 62 (51, 75) | 64 (54, 75) | 0.008 |
| 3 Year Postoperatively | 58 (48, 73) | 64 (53, 78) | 61 (49, 73) | 61 (49, 73) | 64 (52, 73) | 0.2 |
| 4 Year Postoperatively | 59 (48, 71) | 64 (49, 82) | 61 (49, 78) | 64 (49, 73) | 59 (54, 71) | 0.3 |
|
| ||||||
| Sexual Well-Being | ||||||
| Time Period | 0–10 miles | 11–25 miles | 26–50 miles | 51–100 miles | 101+ miles | p-value2 |
| N = 1,4441 | N = 2,0481 | N = 1,0811 | N = 5461 | N = 4811 | ||
|
| ||||||
| PreOp | 59 (46, 70) | 59 (47, 70) | 59 (46, 70) | 59 (46, 67) | 59 (48, 71) | 0.7 |
| 6 Months Postoperatively | 47 (34, 62) | 48 (36, 62) | 48 (36, 62) | 48 (36, 66) | 46 (34, 62) | 0.5 |
| 1 Year Postoperatively | 48 (34, 66) | 50 (36, 66) | 50 (36, 66) | 50 (39, 66) | 53 (38, 66) | 0.064 |
| 2 Year Postoperatively | 48 (34, 66) | 50 (39, 66) | 50 (39, 66) | 53 (36, 66) | 53 (39, 66) | 0.3 |
| 3 Year Postoperatively | 50 (36, 66) | 53 (39, 66) | 50 (39, 66) | 53 (36, 66) | 56 (41, 66) | 0.6 |
| 4 Year Postoperatively | 48 (36, 66) | 56 (39, 70) | 56 (41, 70) | 50 (39, 62) | 53 (42, 66) | 0.022 |
Median (Q1, Q3)
Kruskal-Wallis rank sum test
At 1-year follow-up, significant differences in SATSB scores were noted between the 0–10 and 11–25 mile groups (58 [49, 73] vs. 62 [53, 75], p=0.002) and the 0–10 and over 100-mile groups (58 [49, 73] vs. 64 [53, 73], p=0.021), both meeting the MCID of 4 points. At 2-year follow-up, significant differences in SATB scores were also identified between the 0–10 and 11–25 mile groups (59 [48, 71] vs. 62 [53, 75], p=0.001), 0–10 and 26–50 mile groups (59 [48, 71[ vs. 62 [52, 75], p=0.016), and 0–10 mile group and the over 100-mile group (59 [48, 71] vs. 64 [54, 75], p=0.005) for which the MCID of 4 was met (Supplemental Digital Content, Table 2).
However, after multivariable adjustment, we observed an association between distance traveled and SWB. Patients traveling 11–25 miles had estimated SWB scores 1.5 points higher than those traveling 0–10 miles across all time points, though this did not meet the MCID. All other BREAST-Q domains showed consistent scores based on travel distance as the covariant of interest (Table 4).
Table 4.
Linear Mixed Effects Models, BREAST-Q Scores by Travel Distance
| Physical Well-Being | Psychosocial Well-Being | Satisfaction with Breasts | Sexual Well-Being | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||||
| Characteristic | Beta | 95% CI1 | p-value | Beta | 95% CI1 | p-value | Beta | 95% CI1 | p-value | Beta | 95% CI1 | p-value |
|
| ||||||||||||
| Time | ||||||||||||
| PreOp | — | — | — | — | — | — | ||||||
| 6Months | −11 | −12, −11 | <0.001 | −3.6 | −4.5, −2.7 | <0.001 | −2.6 | −3.6, −1.5 | <0.001 | −11 | −12, −9.9 | <0.001 |
| 1Year | −8 | −8.7, −7.3 | <0.001 | −0.96 | −1.7, −0.25 | 0.009 | −0.26 | −1.1, 0.57 | 0.5 | −7.9 | −8.7, −7.1 | <0.001 |
| 2Year | −6.4 | −7.2, −5.6 | <0.001 | −0.45 | −1.3, 0.40 | 0.3 | 0.43 | −0.55, 1.4 | 0.4 | −7.2 | −8.2, −6.3 | <0.001 |
| 3Year | −5.9 | −6.8, −4.9 | <0.001 | 0.02 | −0.95, 0.98 | >0.9 | 0.34 | −0.79, 1.5 | 0.6 | −6.7 | −7.8, −5.6 | <0.001 |
| 4Year | −4.9 | −6.0, −3.8 | <0.001 | 0.48 | −0.71, 1.7 | 0.4 | 0.81 | −0.57, 2.2 | 0.3 | −5.8 | −7.1, −4.4 | <0.001 |
| Distance Range | ||||||||||||
| 0–10 | — | — | — | — | — | — | — | — | ||||
| 11–25 | 0.73 | −0.35, 1.8 | 0.2 | 0.8 | −0.42, 2.0 | 0.2 | 1.1 | −0.10, 2.3 | 0.073 | 1.5 | 0.03, 3.0 | 0.045 |
| 26–50 | 0.27 | −1.0, 1.5 | 0.7 | −0.1 | −1.5, 1.3 | 0.9 | 0.42 | −0.99, 1.8 | 0.6 | 1.2 | −0.57, 2.9 | 0.2 |
| 51–100 | 0.59 | −0.95, 2.1 | 0.5 | 0.57 | −1.2, 2.3 | 0.5 | −0.23 | −1.9, 1.5 | 0.8 | 0.8 | −1.3, 2.9 | 0.5 |
| 101+ | 0.16 | −1.4, 1.8 | 0.8 | 0.67 | −1.1, 2.5 | 0.5 | 1.3 | −0.47, 3.1 | 0.15 | 0.52 | −1.7, 2.7 | 0.6 |
| Income Range | ||||||||||||
| Low (<$61,000) | — | — | — | — | — | — | — | — | ||||
| Medium ($61,000 - $183,000) | 1.4 | −0.21, 3.0 | 0.09 | 0.01 | −1.8, 1.8 | >0.9 | 0.46 | −1.3, 2.3 | 0.6 | −0.54 | −2.8, 1.7 | 0.6 |
| High (>$183,000) | 1.8 | −0.24, 3.9 | 0.083 | 2.6 | 0.22, 5.0 | 0.033 | 1.2 | −1.2, 3.6 | 0.3 | 0.49 | −2.4, 3.4 | 0.7 |
| Age at Surgery | −0.03 | 0.3 | 0.2 | 0.16 | 0.10, 0.22 | <0.001 | −0.07 | −0.12, −0.01 | 0.016 | −0.09 | −0.16, −0.02 | 0.01 |
| Race | ||||||||||||
| Asian | — | — | — | — | — | — | — | — | ||||
| Black | 0.82 | −1.1, 2.8 | 0.4 | 3.7 | 1.5, 5.9 | 0.001 | 3.4 | 1.2, 5.6 | 0.003 | 4.5 | 1.8, 7.1 | 0.001 |
| Other/Unknown | 0.46 | −1.5, 2.4 | 0.6 | 0.08 | −2.1, 2.3 | >0.9 | −0.53 | −2.7, 1.7 | 0.6 | 1.1 | −1.5, 3.8 | 0.4 |
| White | 3.3 | 1.8, 4.7 | <0.001 | 3 | 1.4, 4.6 | <0.001 | 2.2 | 0.53, 3.8 | 0.009 | 2.7 | 0.73, 4.7 | 0.007 |
| Marital Status | ||||||||||||
| Divorced/Separated | — | — | — | — | — | — | — | — | ||||
| Married/Domestic Partner | 1.7 | 0.22, 3.2 | 0.024 | 2.7 | 1.0, 4.4 | 0.002 | 1.2 | −0.43, 2.9 | 0.14 | 4.1 | 2.0, 6.2 | <0.001 |
| Single/Widowed | 1.2 | −0.53, 2.9 | 0.2 | 1.8 | −0.13, 3.7 | 0.068 | 1.9 | 0.04, 3.8 | 0.046 | 1.1 | −1.2, 3.5 | 0.3 |
| BMI | −0.14 | −0.23, −0.06 | 0.001 | −0.34 | −0.44, −0.24 | <0.001 | −0.4 | −0.50, −0.30 | <0.001 | −0.46 | −0.58, −0.34 | <0.001 |
| Smoking Status | ||||||||||||
| Current smoker | — | — | — | — | — | — | — | — | ||||
| Former smoker | 2.6 | 0.05, 5.2 | 0.046 | 2.3 | −0.64, 5.3 | 0.13 | 2 | −1.0, 4.9 | 0.2 | 1.4 | −2.2, 4.9 | 0.4 |
| Never Smoker | 3.6 | 1.1, 6.1 | 0.005 | 3.5 | 0.68, 6.4 | 0.015 | 3.9 | 1.0, 6.7 | 0.008 | 2.8 | −0.60, 6.2 | 0.11 |
| Psych Diagnosis | ||||||||||||
| No | — | — | — | — | — | — | — | — | ||||
| Yes | −2.9 | −3.7, −2.0 | <0.001 | −6.7 | −7.7, −5.7 | <0.001 | −4.4 | −5.3, −3.4 | <0.001 | −6.5 | −7.7, −5.3 | <0.001 |
| Laterality | ||||||||||||
| Bilateral | — | — | — | — | — | — | — | — | ||||
| Unilateral | 0.14 | −0.76, 1.0 | 0.8 | 0.54 | −0.46, 1.5 | 0.3 | −0.05 | −1.0, 0.94 | >0.9 | 1.2 | 0.00, 2.4 | 0.051 |
| Chemotherapy | ||||||||||||
| Adjuvant | — | — | — | — | — | — | — | — | ||||
| Neoadjuvant | 0.89 | −0.31, 2.1 | 0.15 | −0.07 | −1.4, 1.3 | >0.9 | 0.04 | −1.3, 1.4 | >0.9 | −1.5 | −3.2, 0.13 | 0.071 |
| None | −0.68 | −1.8, 0.45 | 0.2 | −0.21 | −1.5, 1.1 | 0.7 | 0.36 | −0.89, 1.6 | 0.6 | 0.83 | −0.71, 2.4 | 0.3 |
| Radiation | ||||||||||||
| Adjuvant | — | — | — | — | — | — | — | — | ||||
| Neoadjuvant | −0.7 | −2.8, 1.4 | 0.5 | −0.49 | −2.9, 1.9 | 0.7 | 0.44 | −1.9, 2.8 | 0.7 | −0.95 | −3.8, 1.9 | 0.5 |
| None | 3.1 | 1.9, 4.3 | <0.001 | 1.4 | 0.06, 2.8 | 0.041 | 2.7 | 1.4, 4.1 | <0.001 | 1.6 | −0.11, 3.2 | 0.066 |
| Timing of Reconstruction | ||||||||||||
| Delayed | — | — | — | — | — | — | — | — | ||||
| Immediate | 2.1 | 0.76, 3.4 | 0.002 | 2.1 | 0.60, 3.6 | 0.006 | 4.3 | 2.8, 5.8 | <0.001 | 2.1 | 0.22, 3.9 | 0.028 |
| Overall Mastectomy Type | ||||||||||||
| Nipple sparing | — | — | — | — | — | — | — | — | ||||
| Skin sparing | −1.7 | −2.9, −0.41 | 0.009 | −2.1 | −3.5, −0.71 | 0.003 | −1.1 | −2.5, 0.26 | 0.11 | −2.3 | −4.0, −0.57 | 0.009 |
| Unknown | −2.1 | −4.7, 0.40 | 0.1 | −2.1 | −5.0, 0.85 | 0.2 | −2.4 | −5.3, 0.46 | 0.1 | −1.6 | −5.1, 2.0 | 0.4 |
| Reconstruction Type | ||||||||||||
| Autologous | — | — | — | — | — | — | — | — | ||||
| Implant | −0.98 | −2.1, 0.13 | 0.084 | −1.7 | −2.9, −0.43 | 0.008 | −4.5 | −5.7, −3.3 | <0.001 | −1.9 | −3.4, −0.37 | 0.014 |
| Insurance | ||||||||||||
| Commercial | — | — | — | — | — | — | — | — | ||||
| Medicaid | −3.8 | −5.6, −2.0 | <0.001 | −1 | −3.0, 0.99 | 0.3 | −0.65 | −2.7, 1.4 | 0.5 | 0.8 | −1.6, 3.2 | 0.5 |
| Medicare | −2.4 | −3.8, −1.0 | <0.001 | −1.4 | −3.0, 0.17 | 0.08 | −0.52 | −2.1, 1.1 | 0.5 | −0.2 | −2.2, 1.8 | 0.8 |
| Self-Pay | −4.4 | −14, 4.8 | 0.4 | −2 | −12, 8.0 | 0.7 | −4.2 | −14, 5.6 | 0.4 | 3.9 | −8.0, 16 | 0.5 |
CI = Confidence Interval
DISCUSSION
In this retrospective review of 5,600 patients who underwent implant and autologous breast reconstruction, we investigated the association between travel distance, complication rates, and long-term PROs at a high-volume cancer center. Complication rates remained relatively consistent across all travel distance cohorts, affirming that distance does not compromise the quality of reconstructive care provided at specialized centers. Additionally, using the BREAST-Q to assess overall patient satisfaction, our findings demonstrate that satisfaction levels remained consistent across all distance groups, with similar outcomes reported regardless of patients’ travel distance for breast reconstruction. This was consistent across all four BREAST-Q domains, suggesting that logistical challenges related to longer travel distances have minimal effect on patient satisfaction and quality of life. These results contribute to our understanding of access issues in breast reconstruction, emphasizing that travel-related obstacles should not be viewed as a barrier to receiving high-quality care.
Previous studies have focused on travel distance as a factor influencing the likelihood of undergoing post-mastectomy breast reconstruction versus mastectomy alone.9,11,16–18 Many show that patients are willing to travel longer distances to undergo reconstruction, particularly at academic institutions and for autologous procedures. For example, using a nationwide database, Albornoz et al. observed that patients opting for breast reconstruction traveled farther than those choosing mastectomy alone, with distances varying by facility type.9 Similarly, on a state level, Kirkpatrick et al. found that as travel distance increased, the rate of post-mastectomy breast reconstruction also increased.19 Academic centers have been associated with the highest rates of reconstruction. However, they are often located in metropolitan areas, which may be out of reach for some patients in rural areas.20,21 These studies highlight disparities in access but often neglect the impact of travel distance on care quality and satisfaction. Examining clinical outcomes and PROs across travel distances offers valuable insights to optimize reconstructive care and patient satisfaction.22
In our study, clinical outcomes remained relatively consistent across travel distances. This is reassuring, as concerns over the potential for major complications may influence patients’ decisions regarding breast reconstruction.23 While minor differences in infection rates were noted among implant patients across travel distances, these differences were not clinically significant. This consistency may reflect the standardized protocols, high-quality surgical care provided at specialized centers, and robust on-call services that ensure patients have timely access to medical advice and care for diagnosing and addressing any concerns. Additionally, patients traveling longer distances may exhibit lower complication rates in our dataset due to follow-up care at local institutions, potentially resulting in underreported events. Despite this limitation, our findings are consistent with prior studies, demonstrating that travel distance does not significantly affect post-operative outcomes after breast reconstruction.11
Our study also investigated the impact of travel distance on patient satisfaction and well-being after surgery. We observed that patients who traveled longer distances for breast reconstruction reported quality of life outcomes comparable to those of patients traveling shorter distances, with only SATSB scores showing minor, nonsignificant variations after adjustment, reflecting the overall similarity seen across all BREAST-Q domains. These findings suggest that, despite the challenges of traveling to an academic center for reconstructive care, patients continue to demonstrate improved PROs after surgery. Our findings may guide patients in making informed decisions about reconstruction and reinforce that access to high-quality care provides meaningful, individualized, long-term benefits, irrespective of the distance traveled.
The consistency of PROs across varying travel distances can be attributed to several factors. Telemedicine, as noted by Contreras et al., has significantly reduced travel-related burdens such as lost wages, childcare challenges, and travel expenses, while streamlining care coordination for cancer patients requiring multiple visits.24 Virtual platforms also enhance accessibility to specialists, allowing patients to receive expert care without the need for frequent travel. By improving preoperative and follow-up care, telemedicine alleviates the stress and costs of long-distance travel.25–28 Additionally, cancer care centers can still offer multidisciplinary follow-up care through virtual platforms, ensuring comprehensive physical and psychosocial support that promotes high patient satisfaction across all distance groups.
These findings carry important clinical implications for patient outreach and practice. While the limited number of academic institutions often necessitates extended travel for reconstructive services, our results reassure patients and healthcare providers that surgical outcomes and patient satisfaction in breast reconstruction remain consistent, irrespective of travel distance. By adjusting for key confounders, our analysis emphasizes that despite traveling long distances, patients can pursue reconstructive care without compromising their outcomes or postoperative satisfaction.
Study Limitations
This study has several limitations. First, its retrospective design limits our ability to establish causal relationships between travel distance and outcomes, as we are dependent on existing records and may be subject to selection bias. Additionally, travel distance was calculated as straight-line miles between ZIP codes, which does not account for variables such as actual travel time, traffic, and public transportation availability. These factors may influence patients’ willingness to travel and affect reported outcomes. Furthermore, the study relies on follow-up data captured within our system, which may miss complications managed locally for patients who live farther from the center. Future prospective studies incorporating detailed travel factors and tracking of local follow-up care would provide a more comprehensive understanding of the relationship between travel distance and outcomes in breast reconstruction.
CONCLUSION
Our study demonstrates that travel distance does not compromise clinical outcomes and PROs in post-mastectomy breast reconstruction. Patients traveling longer distances to a high-volume cancer center experience similar complication rates while reporting comparable satisfaction and quality of life to those traveling shorter distances. These findings highlight that geographic distance should not deter patients from seeking reconstructive care, as outcomes remain consistently favorable regardless of the distance traveled.
Supplementary Material
Supplemental Digital Content, Table 1: Pairwise Comparisons, Complications of Implant Patients by Travel Distance
Supplemental Digital Content, Table 2: Pairwise Comparisons, BREAST-Q Scores (Satisfaction with Breasts) by Travel Distance
Financial Disclosure:
This research was partially funded by the NIH/NCI Cancer Center Support Grant P30 CA008748, which funds the research infrastructure at Memorial Sloan Kettering Cancer Center.
REFERENCES
- 1.Nelson JA, Allen RJ Jr., Polanco T, et al. Long-term Patient-reported Outcomes Following Postmastectomy Breast Reconstruction: An 8-year Examination of 3268 Patients. Ann Surg. Sep 2019;270(3):473–483. doi: 10.1097/SLA.0000000000003467 [DOI] [PubMed] [Google Scholar]
- 2.Santosa KB, Qi J, Kim HM, Hamill JB, Wilkins EG, Pusic AL. Long-term Patient-Reported Outcomes in Postmastectomy Breast Reconstruction. JAMA Surg. Oct 1 2018;153(10):891–899. doi: 10.1001/jamasurg.2018.1677 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Eltahir Y, Werners L, Dreise MM, van Emmichoven IAZ, Jansen L, Werker PMN, de Bock GH. Quality-of-life outcomes between mastectomy alone and breast reconstruction: comparison of patient-reported BREAST-Q and other health-related quality-of-life measures. Plast Reconstr Surg. Aug 2013;132(2):201e–209e. doi: 10.1097/PRS.0b013e31829586a7 [DOI] [PubMed] [Google Scholar]
- 4.Pusic AL, Matros E, Fine N, et al. Patient-Reported Outcomes 1 Year After Immediate Breast Reconstruction: Results of the Mastectomy Reconstruction Outcomes Consortium Study. J Clin Oncol. Aug 1 2017;35(22):2499–2506. doi: 10.1200/JCO.2016.69.9561 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Filipe MD, Siesling S, Vriens MR, van Diest PJ, Witkamp AJ, Mureau MAM. Socioeconomic status significantly contributes to the likelihood of immediate postmastectomy breast reconstruction in the Netherlands: A nationwide study. European Journal of Surgical Oncology. 2021/02/01/ 2021;47(2):245–250. doi: 10.1016/j.ejso.2020.09.016 [DOI] [PubMed] [Google Scholar]
- 6.Schumacher JR, Taylor LJ, Tucholka JL, et al. Socioeconomic Factors Associated with Post-Mastectomy Immediate Reconstruction in a Contemporary Cohort of Breast Cancer Survivors. Ann Surg Oncol. Oct 2017;24(10):3017–3023. doi: 10.1245/s10434-017-5933-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wexelman B, Schwartz JA, Lee D, Estabrook A, Ma AMT. Socioeconomic and Geographic Differences in Immediate Reconstruction after Mastectomy in the United States. The Breast Journal. 2014;20(4):339–346. doi: 10.1111/tbj.12274 [DOI] [PubMed] [Google Scholar]
- 8.Panchal H, Matros E. Current Trends in Postmastectomy Breast Reconstruction. Plast Reconstr Surg. Nov 2017;140(5S Advances in Breast Reconstruction):7S–13S. doi: 10.1097/PRS.0000000000003941 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Albornoz CR, Cohen WA, Razdan SN, et al. The Impact of Travel Distance on Breast Reconstruction in the United States. Plast Reconstr Surg. Jan 2016;137(1):12–18. doi: 10.1097/PRS.0000000000001847 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Clegg DJ, Salomon BJ, Porter CG, et al. The Impact of Travel Distance and Income on Breast Reconstruction after Mastectomy in a Rural Population. Plastic and Reconstructive Surgery – Global Open. 2023;11(2):e4802. doi: 10.1097/gox.0000000000004802 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Silverstein ML, Nesbit RD, Collins MS, Willson TD. The impact of geographical access challenges on outcomes of postmastectomy breast reconstruction. Annals of Breast Surgery. 2023;7 [Google Scholar]
- 12.Teng TL, Hall R, Graham RA, Reiland J, Chatterjee A. Poor Access to Breast Reconstruction: A Geographical Shortage of Plastic Surgeons Relative to Breast Surgeons in the United States. Annals of Plastic Surgery. 2019;82(4S):S256–S258. doi: 10.1097/sap.0000000000001846 [DOI] [PubMed] [Google Scholar]
- 13.Anderson SR, Sieffert MR, Talarczyk CMR, Johnson RM, Fox MJP. Geographic Variation in Breast Reconstruction Modality Use Among Women Undergoing Mastectomy. Annals of Plastic Surgery. 2019;82(4):382–385. doi: 10.1097/sap.0000000000001746 [DOI] [PubMed] [Google Scholar]
- 14.Pusic AL, Klassen AF, Scott AM, Klok JA, Cordeiro PG, Cano SJ. Development of a new patient-reported outcome measure for breast surgery: the BREAST-Q. Plast Reconstr Surg. Aug 2009;124(2):345–353. doi: 10.1097/PRS.0b013e3181aee807 [DOI] [PubMed] [Google Scholar]
- 15.Voineskos SH, Klassen AF, Cano SJ, Pusic AL, Gibbons CJ. Giving Meaning to Differences in BREAST-Q Scores: Minimal Important Difference for Breast Reconstruction Patients. Plast Reconstr Surg. Jan 2020;145(1):11e–20e. doi: 10.1097/PRS.0000000000006317 [DOI] [PubMed] [Google Scholar]
- 16.Clegg DJ, Salomon BJ, Porter CG, et al. The Impact of Travel Distance and Income on Breast Reconstruction after Mastectomy in a Rural Population. Plast Reconstr Surg Glob Open. Feb 2023;11(2):e4802. doi: 10.1097/GOX.0000000000004802 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kaoutzanis C, McCranie A. Geographical access to breast reconstruction: does location matter? Annals of Breast Surgery. 2023;8 [Google Scholar]
- 18.Roughton MC, DiEgidio P, Zhou L, Stitzenberg K, Meyer AM. Distance to a Plastic Surgeon and Type of Insurance Plan Are Independently Predictive of Postmastectomy Breast Reconstruction. Plastic and Reconstructive Surgery. 2016;138(2):203e–211e. doi: 10.1097/prs.0000000000002343 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kirkpatrick DR, Markov NP, Fox JP, Tuttle RM. Initial Surgical Treatment for Breast Cancer and the Distance Traveled for Care. The American Surgeon™. 2021;87(8):1280–1286. doi: 10.1177/0003134820973733 [DOI] [PubMed] [Google Scholar]
- 20.Albornoz CR, Bach PB, Pusic AL, et al. The influence of sociodemographic factors and hospital characteristics on the method of breast reconstruction, including microsurgery: a U.S. population-based study. Plast Reconstr Surg. May 2012;129(5):1071–1079. doi: 10.1097/PRS.0b013e31824a29c5 [DOI] [PubMed] [Google Scholar]
- 21.Siegel E, Tseng J, Giuliano AE, Amersi F, Alban RF. Breast Cancer Treatment at Academic Centers Increases Likelihood of Reconstruction after Mastectomy. Journal of the American College of Surgeons. 2018;227(4):S31–S32. doi: 10.1016/j.jamcollsurg.2018.07.048 [DOI] [PubMed] [Google Scholar]
- 22.Sala L, Bonomi S, Ciniselli CM, Verderio P, Pastori M, Maurichi A, Cortinovis U. Patient-reported outcome measurements in post-mastectomy implant-based breast reconstruction and radiotherapy: Analysis of BREAST-Q data. Tumori Journal. 2023;109(3):295–300. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Shammas RL, Hung A, Mullikin A, et al. Patient Preferences for Postmastectomy Breast Reconstruction. JAMA Surg. Sep 27 2023;doi: 10.1001/jamasurg.2023.4432 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Contreras CM, Metzger GA, Beane JD, Dedhia PH, Ejaz A, Pawlik TM. Telemedicine: Patient-Provider Clinical Engagement During the COVID-19 Pandemic and Beyond. Journal of Gastrointestinal Surgery. 2020/07/01/ 2020;24(7):1692–1697. doi: 10.1007/s11605-020-04623-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Vyas KS, Hambrick HR, Shakir A, et al. A systematic review of the use of telemedicine in plastic and reconstructive surgery and dermatology. Annals of plastic surgery. 2017;78(6):736–768. [DOI] [PubMed] [Google Scholar]
- 26.Gfrerer L, Eberlin KR, Figura L, et al. Telemedicine and plastic surgery: principles from the american society of plastic surgeons health policy committee. Plastic and reconstructive surgery. 2022;150(1):221e–226e. [DOI] [PubMed] [Google Scholar]
- 27.Saad NH, AlQattan HT, Ochoa O, Chrysopoulo M. Telemedicine and Plastic and Reconstructive Surgery: Lessons from the COVID-19 Pandemic and Directions for the Future. Plastic and Reconstructive Surgery. 2020;146(5):680e–683e. doi: 10.1097/prs.0000000000007344 [DOI] [PubMed] [Google Scholar]
- 28.Stearns SA, Lee D, Bustos VP, et al. Enhancing Post-Mastectomy Care: Telehealth’s Impact on Breast Reconstruction Accessibility for Breast Cancer Patients. Cancers (Basel). Jul 16 2024;16(14)doi: 10.3390/cancers16142555 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Digital Content, Table 1: Pairwise Comparisons, Complications of Implant Patients by Travel Distance
Supplemental Digital Content, Table 2: Pairwise Comparisons, BREAST-Q Scores (Satisfaction with Breasts) by Travel Distance