Association Between Travel Burden and Surgical Timeliness for Women with Breast Cancer in Georgia

Abstract

Introduction:

This study evaluates associations between patient distance traveled to surgical care and receipt of timely breast cancer surgery in Georgia.

Methods:

This retrospective cohort study included electronic health record data from women diagnosed with stage I-III breast cancer from 2004–2020 who received upfront surgery. Race was self-reported. Distance from patient home address to surgical treatment facility was measured as crow-fly distance. Multivariable logistic regression models estimated associations between time to surgery, race, and distance.

Results:

Of 18,240 patients (White=61% and Black=39%) with a median distance to surgery of 11.4 miles (IQR: 5.9, 19.6), Black patients had approximately half the odds of receiving surgery within the recommended 60 days of diagnosis (OR: 0.52, 95% CI: 0.47–0.56). Distance traveled was not associated with time to surgery.

Conclusion:

Understanding mechanisms driving surgical care delays and their effects on breast cancer outcomes is critical to reducing inequities in breast cancer care.

INTRODUCTION

Breast cancer is the most common cancer among women in the United States outside of non-melanoma skin cancer, and recent advances in breast cancer screening, diagnosis, and treatment have contributed to significant increases in survival gains for breast cancer patients.¹ However, racial disparities in breast cancer survival are still prevalent, as evidenced by Black women having higher breast cancer mortality rates than White women despite lower disease incidence.² The multifactorial reasons contributing to this disparity are challenging to independently evaluate³; however, a previously identified component is the time from diagnosis of breast cancer to surgery. The standard recommendation from initial breast cancer diagnosis to upfront surgery (excluding neoadjuvant therapy patients) is 60 days, as time beyond this has been associated with worse overall survival.⁴ Numerous studies have demonstrated that Black women experience greater delays in surgical care, which may contribute to racial disparities seen in breast cancer outcomes.^5–10

Another potential factor contributing to racial disparities in breast cancer is travel burden, typically measured as distance traveled from patient residence to treating facility. However, the association of race and travel distance to surgery has not been well-studied. Previous work has demonstrated that patients living in neighborhoods comprised of majority Black residents travel greater distances to receive cancer screenings.¹¹ Some studies have demonstrated greater travel distance to cancer care is associated with differences in cancer care, where those traveling farther have greater likelihood of mastectomy and decreased likelihood of radiation treatment.^12–14 A study of South Carolina breast cancer patients found increased odds of delayed surgery with increasing travel distance (1% increased odds of delayed surgery per additional mile traveled), but there did not appear to be a difference by race.¹⁵

The joint effects of travel distance and race on the time to surgery and receipt of timely surgical care in women with breast cancer could be exacerbated in the Deep South, where there are disparities in breast cancer survival, travel distances to care can be long due to rurality of the region and lack of health care access, and racial disparities are prevalent.^16,17 Therefore, this study evaluates the association between travel distance to treatment facility and time from breast cancer diagnosis to upfront breast cancer surgery in Georgia and its interplay with race. Understanding the mechanisms driving delays in surgical care for breast cancer patients and its effects on breast cancer outcomes is critical to reducing racial disparities in breast cancer.

METHODS

Study Design, Setting, and Participants

This retrospective cohort study included women with breast cancer receiving surgical care between 2004 and 2020 at one of three hospital systems (hereafter Hospital A, Hospital B, and Hospital C) within in the greater Atlanta metropolitan area of Georgia. Data for this study were obtained from hospital system electronic health records which were queried for breast cancer patients who underwent upfront surgery between 2004 and 2020 and self-identified as White or Black/African American. Women with stage 0 and stage IV disease, who received neoadjuvant therapy, with time to treatment of zero days, or self-identified as a race other than White or Black were excluded. Women with stage 0 and IV disease were excluded, as the recommendation for upfront surgery within 60 days is based on studies of patients with stage I-III disease. Additionally, surgery currently does not have a role in the treatment of stage IV disease. The decision to exclude races other than White or Black was made a priori given their small sample sizes and lack of ability to provide meaningful inference. Additionally, women with missing data on date of diagnosis, time to treatment, distance to hospital, or race were excluded.

This study was approved by the Georgia State University Institutional Review Board (IRB) and by the hospitals providing data. The IRB waived the need for consent as medical record data were collected retrospectively.

Exposures and Outcomes

The primary outcome was timely receipt of surgery, operationally defined as receipt of breast cancer surgery within 60 days of breast cancer diagnosis. A secondary outcome was time to surgery from diagnosis, measured in days.

The primary exposures were travel distance from patient residence to surgical treatment location and race. Distance from patient home address, as documented in their electronic health records, to surgical treatment facility was estimated using crow-fly (i.e. straight line) distance in miles. Travel distance was reported as a continuous variable and as three categories approximating urban (<10 miles), suburban (10–30 miles), and rural (>30 miles). Race was self-reported by participants and obtained through patient electronic health records. Race was categorized as White or Black.

Covariates

Patient sociodemographic and clinical characteristics included treating hospital, age, area deprivation index (ADI) category, pathologic stage according to AJCC (6^th, 7^th, and 8^th editions)^18–20, tumor grade, hormone receptor status (estrogen receptor (ER), progesterone receptor (PR)), and human epidermal growth factor-2 (HER2) receptor status. ADI, an area-level measure of resource richness, was calculated utilizing the University of Wisconsin Neighborhood Atlas mapping tool^21,22 at the census tract level. Patient neighborhood ADI ranking was then determined, and ADI scores were divided into two categories (high (<85) neighborhood deprivation and low (85–100) neighborhood deprivation) for ease of interpretation.

Statistical Analysis

Descriptive statistics were used to characterize the patient cohort. Counts and percentages were used for categorical variables and means and standard deviations (SD) were used for normally distributed continuous variables. Medians and interquartile ranges (IQR) were used to describe time to surgery and travel distance given their skewed distributions.

Bivariate analyses used absolute standardized mean differences (SMD) to examine the relationship between race and patient characteristics including age, travel distance, time to surgery, ADI, tumor hormone receptor and HER2 receptor status, tumor grade, and pathologic stage. Kruskal-Wallis ranked sum tests and Pearson’s Chi-squared tests were used to evaluate time to surgery by distance traveled. Kaplan-Meier analyses were used to descriptively examine variation in time to surgery by race and distance.

A multivariable logistic regression model was used to investigate receipt of timely breast cancer surgery adjusting for treating hospital, age, race, categorical distance to treatment facility, ADI, tumor grade, and pathologic stage. Receptor status was not included as confounding control in the multivariable model due to missing values in 38% of the cohort. In addition to a main effects estimation, the model was evaluated with an interaction between distance and race. Sensitivity analyses included an additional multivariable logistic regression model fit with a continuous distance measure, and a Cox proportional hazards model estimating time to surgery by race and distance. All analyses was performed using R (version 4.3.2, 2023) with significance assessed at the 95% confidence level.²³

RESULTS

Cohort Characteristics

Among women with stage I-III breast cancer who received upfront surgery (no neoadjuvant chemotherapy), any patients missing a date of diagnosis (n=7), missing time to treatment or had a time to treatment of 0 days (n=6,919), missing distance to hospital (n=217), and race other than White or Black (n=202) were excluded for a final analytic cohort of 18,240 patients (Figure 1). Of 18,240 patients included in the analytic sample (Table 1), the average age was 60.2 years (SD 12.9), 61% were White (n=11,306), median distance traveled to surgery was 11.4 miles (IQR: 5.9, 19.6), and median time to surgery was 39 days (IQR: 24–58) with 76% of the cohort receiving surgery within 60 days. Over half of patients had stage 1 disease at diagnosis (60%) and were hormone receptor positive (ER+ 83%; PR+ 71%) and HER2 negative (90%).

Figure 1.

Study cohort exclusion cascade

Table 1.

Patient cohort characteristics by race.

Cohort Characteristic	Total* (N=18,240)	White (N=11,306, 62%)	Black (N=6,934, 38%)	Absolute Standardized Mean Difference (95% CI)
Data Source (N, %)				0.62 (0.59 – 0.65)
Hospital A	8,855 (49%)	4,974 (44%)	3,881 (56%)
Hospital B	8,406 (46%)	6,216 (55%)	2,190 (32%)
Hospital C	979 (5%)	116 (1%)	863 (12%)
Age at Diagnosis (mean, SD)	60.2 (12.9)	61.4 (13.0)	58.4 (12.7)	0.24 (0.21 – 0.27)
Distance to Hospital in Miles (median, IQR)	11.4 (5.9, 19.6)	11.6 (5.5, 23.4)	11.1 (6.4, 16.6)	0.08 (0.05 – 0.11)
Grouped Distances to Hospital in Miles (N, %)				0.35 (0.32 – 0.38)
<10 miles	7,789 (43%)	4,856 (43%)	2,933 (42%)
10 to 30 miles	7,984 (44%)	4,462 (39%)	3,522 (51%)
>30 miles	2,467 (14%)	1,988 (18%)	479 (7%)
Time to Surgery in Days (median, IQR)	39 (24, 58)	35 (22, 52)	46 (29, 68)	0.10 (0.07 – 0.13)
Surgery within 60 Days (N, %)				0.33 (0.31 – 0.36)
No	4,367 (24%)	2,096 (19%)	2,271 (33%)
Yes	13,873 (76%)	9,210 (81%)	4,663 (67%)
ADI Category* (N, %)				0.55 (0.51 – 0.58)
ADI <85	15,642 (89%)	10,439 (96%)	5,203 (78%)
ADI 85 – 100	1,971 (11%)	479 (4%)	1,492 (22%)
Estrogen Receptor Status* (N, %)				0.24 (0.21 – 0.27)
Negative	2,659 (17%)	1,329 (14%)	1,330 (23%)
Positive	12,697 (83%)	8,270 (86%)	4,427 (77%)
Progesterone Receptor Status* (N, %)				0.24 (0.21 – 0.27)
Negative	4,405 (29%)	2,353 (25%)	2,052 (36%)
Positive	10,957 (71%)	7,233 (75%)	3,724 (64%)
HER2 Receptor Status* (N, %)				0.08 (0.05 – 0.12)
Negative	10,726 (90%)	6,978 (91%)	3,748 (88%)
Positive	1,218 (10%)	714 (9.3%)	504 (12%)
Tumor Grade* (N, %)				0.27 (0.24 −0.30)
1	4,262 (24%)	3,044 (28%)	1,218 (19%)
2	7,538 (43%)	4,772 (43%)	2,766 (42%)
3	5,739 (33%)	3,157 (29%)	2,582 (39%)
Pathologic Stage* (N, %)				0.19 (0.16 – 0.23)
1	9,253 (60%)	6,244 (63%)	3,009 (54%)
2	5,001 (32%)	2,962 (30%)	2,039 (36%)
3	1,214 (8%)	674 (7%)	540 (10%)

^*Total for ADI category, stage, and receptor statuses do not equal cohort total due to missing values (n missing for ADI =627, n missing for ER status =2,884, n missing for PR status =2,878, n missing for HER2 status=6,296, n missing for grade=701, n missing for stage =2,772)

Bivariate Analyses

Black breast cancer patients (n=6,934, 38%) were diagnosed at a younger mean age compared to White patients (58.4 [SD: 12.7] years vs. 61.4 [SD: 13.0] years, respectively; SMD=0.24, 95% CI: 0.21–0.27) and more likely to reside in an area with a high ADI (22% and 4% respectively, SMD=0.55, 95% CI: 0.51–0.58). Black patients presented with later stages of disease (10% and 7% with stage 3 disease for Black and White patients, respectively, SMD=0.19, 95% CI: 0.16–0.23) and higher tumor grade than their White counterparts (39% and 29% with grade three tumor for Black and White patients, respectively; SMD=0.27, 95% CI: 0.24–0.30). Black patients experienced increased median time to surgery as compared to White patients (46 [IQR: 29–68] vs. 35 days [IQR: 22–52], respectively; SMD=0.10, 95% CI: 0.07– 0.13), and a smaller percentage of Black patients received surgery within 60 days of diagnosis compared to White patients (SMD=0.33, 95% CI: 0.31–0.36). Kaplan Meier analysis similarly demonstrated that Black patients had longer times to surgery compared to White patients (p<0.001) (Figure 2).

Figure 2.

Kaplan Meier curve of time to surgery by race.

The median distance traveled from patient residence to treatment facility was 11.4 miles (IQR: 5.9–19.6) with median distance being slightly larger for White patients (White: 11.6 miles [IQR: 5.5 – 23.4] and Black: 11.1 miles [IQR: 6.4 – 16.6], SMD=0.08, 95% CI: 0.05–0.11) (Table 1). Kaplan Meier analysis evaluated the impact of miles traveled, stratified into three groups (<10 miles, 10–30 miles, >30 miles), on time to surgery (Figure 3) and demonstrated that those traveling 10–30 miles had greater time to surgery (p<0.001).

Figure 3.

Kaplan Meier curve of time to surgery by distance to surgery.

Patients living <10 miles from their treatment facility had a median time to surgery of 38 days (IQR: 24–57), those 10–30 miles from their treatment facility experienced a median wait of 40 days (IQR: 26–60), and those who traveled >30 miles had a median wait of 36 days (IQR:22–57) (P<0.001) (Table 2). There was only a 2-percentage point difference in patients receiving surgery at 60 days who traveled 10–30 miles compared to those traveling <10 miles or >30 miles (75% for 10–30 miles vs. 77% for >30 miles and <10 miles, p=0.003).

Table 2.

Time to surgery by distance traveled.

Cohort Characteristic	Total (N=18,240)	<10 miles (N=7,789)	10–30 miles (N=7,984)	>30 miles (N=2,467)	P-value
Time to Surgery in Days (median, IQR)	39 (24 – 58)	38 (24 – 57)	40 (26 – 60)	36 (22 – 57)	<0.0011
Surgery within 60 Days (N, %)					0.0032
No	4,367 (24%)	1,783 (23%)	2,009 (25%)	575 (23%)
Yes	13,873 (76%)	6,006 (77%)	5,975 (75%)	1,892 (77%)

¹Kruskal-Wallis rank sum test;

²Pearson’s Chi-squared test

Multivariable Model

A logistic regression model showed no significant difference in the odds of having surgery within 60 days for those traveling >30 miles compared to those traveling <10 miles after adjusting for patient and tumor characteristics (OR: 0.92, 95% CI 0.81–1.05) (Table 3); however, those traveling 10–30 miles had lower odds of surgery within 60 days compared to those traveling <10 miles (OR: 0.91, 95% CI: 0.83–0.99). Black patients had 0.5x the odds of surgery within 60 days of diagnosis compared to White patients (OR: 0.52, 95% CI: 0.47 – 0.56). A distance by race interaction term suggested no modification of the effect of distance by race (p=0.166). Sensitivity analyses including a logistic regression model with distance as a continuous variable (Supplemental Table 1) and a Cox proportional hazards model (Supplemental Table 2) similarly showed that Black patients had lower odds than White patients to receive upfront surgery within 60 days of diagnosis (OR: 0.51 [95% CI: 0.47–0.56] and HR: 0.74 [95% CI: 0.71–0.76] respectively).

Table 3.

Logistic regression model for time to surgery occurring within 60 days of diagnosis.

Model Covariates	Odds Ratio (95% CI)	P-value
Data Source
Hospital A	Reference
Hospital B	1.14 (0.96 – 1.36)	0.15
Hospital C	1.57 (1.45 – 1.71)	<0.001
Age at Diagnosis	1.00 (1.00 – 1.01)	0.061
Race
White	Reference
Black	0.52 (0.47 – 0.56)	<0.001
Distance to Hospital
<10 miles	Reference
10 to 30 miles	0.91 (0.83 – 0.99)	0.029
>30 miles	0.92 (0.81 – 1.06)	0.26
ADI Category
ADI <85	Reference
ADI 85 – 100	0.93 (0.82 – 1.05)	0.23
Tumor Grade
1	Reference
2	1.17 (1.06 – 1.30)	0.002
3	1.54 (1.38 – 1.73)	<0.001
Pathologic Stage
1	Reference
2	0.83 (0.76 – 0.91)	<0.001
3	0.68 (0.59 – 0.78)	<0.001

DISCUSSION

This study population uniquely encompasses a larger Black patient population that is routinely underrepresented in cancer outcome analysis and in national cancer databases. This large Black patient population with extended follow-up and consideration for resource availability allows researchers to better understand socioeconomic factors that specifically impact Black breast cancer patients.

This study found a median time of 39 days to upfront surgery for a diverse group of patients in Georgia. This is within the standard of care, which is generally accepted to be surgical treatment within 60 days of diagnosis (not including those undergoing neoadjuvant chemotherapy, who were excluded in this study). Beyond 60 days, negative impacts on survival start to be seen.⁹ Other works have found impacts on overall survival for patients treated with surgery beyond four weeks.²⁴ In this study, Black patients had significantly lower odds of receiving surgery within the recommended 60-day time frame when accounting for other factors including age, stage at diagnosis, and tumor grade. When comparing median time to treatment for Black and White patients, Black patients experienced surgical treatment approximately 11 days later. However, the available literature does not offer the granular analysis needed to evaluate the clinical relevance of an 11-day difference within the recommended first 60 days after diagnosis, as was seen between races in this study. It is possible that the delay contributes to worse clinical outcomes as well as quality of life, as the pre-operative period can be a time of great anxiety and depression for some patients.²⁵ Other studies have shown similar delays for breast cancer patients of other races, as well.²⁶

While greater travel to surgery has been associated with lower overall and disease specific survival for breast cancer patients,^27–29 in this study, though statistically significant, a clinically meaningful relationship between travel distance and time to surgery was not observed given the percentage of patients receiving surgery within 60 days ranged by only 2 percentage points across travel distances. One possible explanation is that patients with shorter travel time, a proxy for urbanicity, were more likely to be treated at an academic center rather than a community practice, as prior findings suggest that care at an academic center is associated with greater time to upfront surgery.³⁰ This finding may be explained by multidisciplinary discussions, second opinions, and comprehensive offerings including reconstruction and fertility preservation, all of which take additional time.

A potential explanation for the greater delays to surgery for Black patients was area deprivation. Previous data has shown that Black breast cancer patients in Georgia are more likely to reside in higher ADI census tract groups, but that lower ADI amongst Black women in Georgia was not associated with lower mortality.³¹ In this study, greater area deprivation was not associated with longer time to surgery; however, utilizing ADI as a metric for resource availability does not fully encompass the granular socioeconomic portrait displayed by our patients. For example, it does not include a direct measure of health insurance status. Disparities associated with delays in time to oncologic treatment are vast and in a recent analysis have been found to include legal help, transportation, housing stability, and responsibility for other persons.³² ADI was used in this study as a surrogate; however, more detailed understanding of patient resources is warranted.

Study findings should be interpreted in the context of some limitations. Our dataset did not delineate between the types of surgery patients received. Receptor status was not able to be included in the multivariable models due to missing data. Trastuzumab was not approved until 2006 for early-stage HER2+ breast cancer, so some of the missing receptor status may be related to HER2 testing not being performed in the early period of this study. It is plausible that patients who were HER2+, time to surgery may have been delayed due to discussions with medical oncology. An additional limitation is that distance from patient residence to location of surgery is estimated by crow-fly analysis; however, specific travel arrangements and accessibility could not be measured. Different modes of travel each offer variable flexibility and burden to users and transportation barriers are known to impact access to healthcare.³³ Lastly, the findings of the study are based on data from an area within the Deep South and may not necessarily extrapolate to other areas within the United States.

CONCLUSIONS

Black patients had approximately half the odds of receiving upfront breast cancer surgery within the recommended first 60 days of diagnosis compared to White patients. While distance traveled by patients to receive surgery impacts access to care, it did not explain the relationship between race and surgical timeliness. Further work is needed to elucidate the moderators of delays in surgical care by race. Understanding the mechanisms driving delays in surgical care for breast cancer patients and its effects on breast cancer outcomes is critical to reducing racial disparities in breast cancer.