PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Plast Reconstr Surg. Author manuscript; available in PMC Dec 1, 2011.
Published in final edited form as:
PMCID: PMC3058849
NIHMSID: NIHMS237354
Sacramento Area Breast Cancer Epidemiology Study (SABES): Use of Post-Mastectomy Breast Reconstruction Along the Rural to Urban Continuum
Warren H. Tseng, MD,1 Thomas R. Stevenson, MD,2 Robert J. Canter, MD,1 Steven L. Chen, MD, MBA,1 Vijay P. Khatri, MD,1 Richard J. Bold, MD,1 and Steve R. Martinez, MD, MAS1
1University of California at Davis, Department of Surgery, Division of Surgical Oncology, Sacramento, California
2University of California at Davis, Department of Surgery, Division of Plastic and Reconstructive Surgery, Sacramento, California
Address reprint requests to: Steve R. Martinez, UC Davis Cancer Center, 4501 X Street, Suite 3010, Sacramento, CA 95817. Fax: (916) 703-5267. steve.martinez/at/ucdmc.ucdavis.edu
Background
Health care disparities have been documented in rural populations. We hypothesized that breast cancer (BCa) patients in urban counties would have higher rates of post-mastectomy breast reconstruction (BR) relative to patients in surrounding near-metro and rural counties.
Methods
We used the Surveillance, Epidemiology, and End Results (SEER) database to identify patients diagnosed with BCa and treated with mastectomy in the greater Sacramento area between 2000 and 2006. Counties were categorized as urban, near-metro or rural. Univariate models evaluated the relationship of rural, near-metro or urban location with use of BR via the chi-square test. Multivariate logistic regression models controlling for patient, tumor, and treatment-related factors predicted use of BR. The likelihood of undergoing BR was reported as odds ratios (OR) with 95% confidence intervals (CI); significance was set at p ≤ 0.05.
Results
Complete information was available for 3,552 BCa patients treated with mastectomy. Of these, 718 (20.2%) underwent BR. On univariate analysis, differences in the rates of BR were noted among urban, near-metro and rural areas (p<0.001). On multivariate analysis patients from rural (OR 0.51, CI 0.28-0.93; p<0.03) and near-metro (OR 0.73, CI 0.59-0.89; p=0.002) areas had a decreased likelihood of undergoing BR relative to patients from urban areas.
Conclusions
Patients from near-metro and rural areas as less likely to receive BR following mastectomy for BCa than their urban counterparts. Differences in use of BR detected at a population level should guide future interventions to increase rates of BR at the local level.
Because breast reconstruction (BR) has a significant positive psychosocial impact on patients1-4, it is increasingly seen as a necessary and integral component of post-mastectomy breast cancer (BCa) therapy5. Although patients with BCa who reside in rural areas are 58% more likely than their urban counterparts to receive mastectomy, 6 little is known about their utilization of BR. From the management of chronic disease to the diagnosis and treatment of malignancies, patients living in rural areas are less likely to receive standard care and more likely to have poorer survival than those living in urban areas7-11 We therefore hypothesized that BCa patients in urban counties of Northern California would have higher rates of post-mastectomy BR relative to patients in surrounding near-metro and rural counties.
We used the Surveillance, Epidemiology, and End Results (SEER) database to identify patients diagnosed with infiltrating ductal carcinoma (IDC), infiltrating lobular carcinoma (ILC), or mixed infiltrating ductal and lobular carcinoma (MDLC) of the breast treated with mastectomy in the greater Sacramento area between 2000 and 2006. The Surveillance Epidemiology and End Results (SEER) database of the National Cancer Institute was used to identify patients undergoing mastectomy for IDC, ILC, or MDLC from 1988 to 2006. The registries, attributes, and limitations of the SEER database have been reported previously12-16.
All cases of primary, histologically confirmed, IDC, ILC, or MDLC were eligible. Patients with metastatic disease, and those identified by death certificate or autopsy were excluded. The final sample included 7,207 patients. Fourteen counties, including Sacramento County, were assessed for this study. We used the 2003 rural-urban continuum codes for California from the United States Department of Agriculture (USDA) to make decisions regarding whether a county was to be considered rural, near-metro, or urban ( http://www.usda.gov/wps/portal/usdahome).
The USDA assigns counties a code number from 1 to 9, indicating progressive rurality. Counties coded as “1” (El Dorado, Placer, Sacramento, and Yolo Counties) were considered urban. Counties coded as “2, 3, 4, or 5” (Butte, Nevada, San Joaquin, Stanislaus, Sutter, and Yuba Counties) were considered near-metro. Counties coded as “6, 7, 8, or 9” (Alpine, Amador, Calaveras, and Colusa Counties) were considered rural.
Univariate models evaluated the relationship of rural, near-metro or urban location with use of BR via the chi-square test. Covariates assessed included patient age (median split, ≤ 62 years vs. ≥ 63 years), sex, race/ethnicity (Asian/Pacific Islander, black, Hispanic, native American, White), American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, tumor grade, hormone receptor status (positive, negative, equivocal, unknown), tumor histology (IDC, ILC, MDLC), type of mastectomy (unilateral vs. bilateral) and use of radiation therapy (yes, no, unknown).
We used multivariate logistic regression models to assess the role of rural, near-metro, or urban status on the likelihood of receiving BR while controlling for all factors assessed in the univariate analysis, except sex. Age was assessed as a continuous variable in the multivariate analyses. Patients for whom BR status was unknown were excluded, leaving 3,552 patients for analysis. For categorical and ordinal variables, the most prevalent or clinically relevant variable served as the referent group. Additional multivariate logistic regression models were constructed to assess the likelihood of receiving BR for each county relative to Sacramento County. Likelihood of undergoing BR was reported as odds ratios (OR) with 95% confidence intervals (CI); significance was set at p ≤ 0.05.
Patient, tumor, and treatment-specific characteristics of the study population are presented in Table 1. The total population of patients included 7,207 BCa patients treated with mastectomy. Briefly, the median age of patients was 62 years. Men represented 1% of the study population. The majority of patients were white (80.6%). As expected, most patients were from urban (58.7%) or near-metro (37.8%) counties; only 3.4% were from rural counties. T1 and T2 tumors represented 87.3% of primary tumors. Lymph node metastases were reported in 42.1% of patients. Grade was low (I/II) in 58.8% and high (III/IV) in 32.3% of patients. Tumor histology was IDC, ILC and MDLC in 78.2%, 11.1%, and 10.8% of patients, respectively. The majority of tumors were estrogen receptor (68.2%) and progesterone receptor (56.6%) positive. Unilateral mastectomies (97.9%) were more common than bilateral mastectomies (2.1%). The majority of patients (79%) did not receive adjuvant radiation therapy. Ten percent of all study patients underwent BR, while 39.3% did not. It was unknown whether or not a patient had BR in 50.7% of cases.
Table 1
Table 1
Patient, tumor and treatment-specific characteristics of the study population.
Differences among the study population were further examined according to whether patients were from rural, near-metro, or urban counties (Table 2). Significant differences were noted among rural, near-metro, and urban patients with respect to race/ethnicity (P<0.001), tumor grade (P=0.003), histology (P<0.001), estrogen receptor status (P=0.001), progesterone receptor status (P<0.001), use of breast reconstruction (P<0.001) and use of radiation therapy (P=0.021).
Table 2
Table 2
Patient, tumor, and treatment-related characteristics of the study population according to county status along the rural-urban continuum.
Two multivariate models were constructed. The first model (Model A) assessed the varying categories of counties (rural, near-metro, urban) by the use of BR while the second model (Model B) did not categorize according to county type but rather included each county separately to assess the likelihood of receiving BR.
Model A is summarized in Table 3. When compared to patients from urban counties, those from rural (OR 0.73, CI 0.59-0.89; P=0.002) and near-metro counties (OR 0.51, CI 0.28-0.93; P=0.028) demonstrated a decreased likelihood of receiving BR. Additional factors predicting a decreased likelihood of BR included increasing age (OR 0.92, CI 0.91-0.92; p<0.001), Asian race (OR 0.42, CI 0.27-0.67; p<0.001), black race (OR 0.55, CI 0.31-0.98; p=0.043), Hispanic ethnicity (OR 0.67, CI 0.46-0.97; p=0.032), N2 (OR 0.55, CI 0.37-0.82; p=0.003), N3 (OR 0.16, CI 0.07-0.35; p<0.001) and NX (OR 0.57, CI 0.34-0.97; p=0.038) status, unknown tumor grade (OR 0.61, CI 0.40-0.93; p=0.021), and use of radiation therapy (OR 0.39, CI 0.29-0.53; p<0.001, Table 3). The only factors predicting an increased likelihood of BR were receipt of bilateral mastectomy surgery (OR 3.45, CI 2.10-5.66; p<0.001), ILC histology (OR 1.75, CI 1.25-2.45; p=0.001), and T4 tumors (OR 35.23, CI 3.24-383.18; p=0.003).
Table 3
Table 3
Multivariate logistic regression model of the entire study population predicting the likelihood of receiving breast reconstruction.
Model B is summarized in Table 4. When compared to Sacramento County, the following counties were associated with a decreased likelihood of receiving BR: Amador (OR 0.36, CI 0.14-0.90; p=0.03), Butte (OR 0.24, CI 0.12-0.49; p<0.001), San Joaquin (OR 0.62, CI 0.45-0.85; p=0.003), and Stanislaus (OR 0.53, CI 0.37-0.76; p=0.001) counties. Nevada County was the only county associated with an increased likelihood (OR 2.25, CI 1.46-3.48; p<0.001) of receiving BR. The only other factors predicting an increased likelihood of BR were receipt of bilateral mastectomy surgery (OR 3.39, CI 2.05-5.60; p<0.001), ILC histology (OR 1.70, CI 1.21-2.38; p=0.002), and T4 tumors (OR 52.33, CI 4.41-620.49; p=0.002). Additional factors predicting a decreased likelihood of receiving BR were increasing age (OR 0.91, CI 0.91-0.92; p<0.001), Asian race (0.42, CI 0.26-0.67; p<0.001), black race (OR 0.53, CI 0.29-0.94; p=0.029), N2 (OR 0.53, CI 0.35-0.79; p=0.002), N3 (OR 0.15, CI 0.07-0.34; p<0.001) and NX (OR 0.58, CI 0.34-0.99; p=0.046) status, and unknown tumor grade (OR 0.65, CI 0.43-0.99; p=0.046).
Table 4
Table 4
Multivariate logistic regression model of the entire study population to assess the likelihood of receiving breast reconstruction for each county relative to Sacramento County.
Nationally, approximately 5.6-42% of women undergoing mastectomy receive immediate or early- delayed BR17-20. BR has been shown to have a significant positive psychosocial impact on patients1-4 with overall good to excellent patient satisfaction3, 21-22. Appropriately, the Women’s Health and Cancer Rights Act of 1998 requires all medical insurers providing mastectomy coverage to also cover all stages of reconstruction of the affected breast and reconstruction of the contralateral breast to provide a symmetrical appearance23. Because post-mastectomy BR has become an expected component of quality cancer care, because residents of rural areas have demonstrated health care disparities relative to their urban-dwelling counterparts for other health indicators, and because rural residents have been shown to more likely undergo mastectomy for the primary treatment of their BCa, we hypothesized that patients from more rural areas would be less likely to receive post-mastectomy BR than their urban counterparts.
In agreement with our stated hypothesis, patients from rural and near-metro areas were less likely to receive post-mastectomy BR relative to their urban-dwelling counterparts, even after controlling for known patient, tumor, and treatment-specific factors. The reason for the observed rural-urban disparity in usage of BR is unclear, but is likely multifactorial. In addition to demonstrating the BR disparities among rural, near-metro, and urban counties, the present study validates previously reported predictors of lower likelihood of BR including age24, ethnicity18, 25, removal of the contralateral breast2, tumor factors predictive of local recurrence including T stage2, 26-27, and tumor grade28. The only factors associated with an increased likelihood of BR in our study included performance of a bilateral mastectomy procedure, ILC histology, and T4 tumors. Patients undergoing bilateral mastectomy for prophylactic reasons may be more motivated to undergo BR to obtain chest wall symmetry and simultaneously reduce their risk of contralateral breast cancer. Similarly, ILC more often is bilateral than IDC and these patients may therefore more likely choose or require bilateral mastectomy for treatment of their breast cancer. It seems counterintuitive that T4 tumors would be more associated with BR than smaller tumors. However, T4 tumors may necessitate more radical resections of the chest wall, which may require subsequent reconstruction for wound closure. These procedures may therefore be coded as BR procedures.
Why should rural patients receive lower rates of BR than urban patients? One possibility is that rural and near-metro areas may have fewer plastic surgeons. To investigate this possibility, we researched the number of plastic surgeons within each county using the American Society of Plastic Surgery database. Those counties identified as “rural” had no plastic surgeons serving their areas. The counties identified as near-metro had a total of 20 plastic surgeons serving their areas. Urban counties, however, had 25 plastic surgeons servicing their areas. This would indicate that the supply of plastic surgeons in rural and near-metro areas may contribute to the lower rate of BR seen in these populations.
Patients from rural or near-metro areas may attempt to alleviate this plastic surgeons supply problem by traveling to an area where a plastic surgeon is available. Travel itself, however, may be an issue. Research has investigated the role of distance to travel for care as a predictor of compliance and receipt of obstetric, medical, and cancer care29-35. Athas et al., in their analysis of New Mexican women undergoing care for BCa, found an inverse relationship between travel distance and receipt of post lumpectomy radiation therapy36. Nair et al. evaluated the effect of travel distance on bilateral breast reduction utilization among symptomatic women living near Edinburgh, Scotland. The Scottish health care system is socialized and provides free health care to all permanent residents, and breast reduction is fully covered for eligible women. They found that the likelihood of uptake of breast reduction surgery decreased with both travel time and distance traveled to the operative hospital. In this study, satellite plastic surgery clinics that assessed women preoperatively and that were strategically located within rural communities had a strong positive effect on qualified, symptomatic women receiving breast reduction37. Difficulty in attaining plastic surgeon consultation and travel barriers may negatively influence surgeon-patient discussions of and patient decisions about BR.
Finding a plastic surgeon, either locally or via distant travel, does not guarantee access to BR. Surgeon preference is cited as an important predictor of BCa treatment38-40. Higher BR rates are seen in patients who have pre-mastectomy discussions of BR with their cancer surgeon41-42 and plastic surgeon43. Surgeons most likely to refer patients for BR are more likely women (OR 2.3, p=0.03), with high volume breast practices (OR 4.1, p=0.01), in cancer centers (OR 2.4, p=0.01)44. Surgeons least likely to refer patients for BR may believe that their patients have more barriers (cost, plastic surgeon availability) and lower desire for BR44. Although these studies were accomplished in exclusively urban areas, these findings raise the question of whether or not similar referral patterns and biases exist among surgeons practicing in rural areas. It is possible that rural surgeons may be less likely to have discussions regarding BR with their patients and be less likely to recommend BR. These biases could contribute to the BR rate disparities seen in the current study and should be a point of future research.
Race/ethnicity can influence rates of BR. Using the SEER registries from Detroit and Los Angeles, Alderman et al. showed that 40.9% of whites received BR while 33.5% of blacks received post mastectomy BR25. Interestingly, assimilated Hispanics showed rates of BR of 41.2%, while un-assimilated Hispanics had BR rates of only 13.5%. The authors further showed that non-white women were less likely than white women to see a plastic surgeon before initial surgery but were more likely to desire information regarding BR. Our data confirm the role of race/ethnicity on use of BR; black and Hispanic women had a 45% and 33% decreased likelihood, respectively, of receiving BR relative to white women. Even with the incorporation of race/ethnicity into several multivariate analyses, rural areas continued to show lower rates of BR relative to urban areas.
Patient income17 and insurance status20 may also influence receipt of BR. Christian et al. demonstrated a 42% rate of BR within 8 National Comprehensive Cancer Network Centers—a rate significantly higher than previously reported in population-based studies45--and found that patients with Medicare/ Medicaid were significantly less likely to receive BR than those with managed care insurance. Even among a fully insured patient population, insurance status represents an important barrier to health care, and there are others17. A major reason patients express for not undergoing BR is a desire for no further surgery27, 38. Unfortunately, our SEER data do not allow us to comment on individual patient income, insurance status, or preferences.
Nevada county was the only geographic area to demonstrate an association with a higher likelihood of BR as compared to Sacramento county (OR 2.25, 95% CI 1.46-3.48, P<0.001). Sixty miles from the city of Sacramento, California and 88 miles from the city of Reno, Nevada, median household income in Nevada County was $52,700 (2000, 3rd highest among the counties examined-data not shown46) as compared to a median household income of $50,700 for Sacramento County. The percentage of persons living under the poverty line in the county was 8.1% (3rd lowest among the counties examined). In contrast, residents in Butte County demonstrated the lowest likelihood of receiving BR (OR 0.24, 95% CI 0.12-0.49, p<0.001). Median household income was $41,000, and nearly 20% of the county lived below the poverty line. Residents of Amador County had the 2nd lowest likelihood of receiving BR (OR 0.36, 95% CI 0.14-0.90, p=0.03). Median household income of the county was $51,200 (4th highest) with 9.2% of the county populace living below the poverty line. These findings suggest that socioeconomic status is not the only population variable affecting delivery of post-mastectomy BR.
Limitations of this study include the fact that SEER codes only treatment received during the “first course” of therapy. We may have underestimated the number of patients receiving BR. It was unknown if 50.7% of patients in our series received BR. Furthermore, we determined that 18% of patients received post-mastectomy radiation therapy, presumably due to locally advanced disease. It is plausible, then, that those patients receiving late or delayed reconstruction due to intervening chest wall radiation therapy are not included in our analysis47. SEER also does not abstract medical comorbidities. It is possible that women with BCa from rural areas had a higher prevalence of significant relative contraindications to breast reconstruction such as smoking and diabetes. We also recognize that there is likely geographic clustering at the county and local level. Population based analyses such as these are not meant to be generalizeable at the individual level.
The decision to undergo BR is a personal decision. Those who decide to undergo BR report excellent rates of satisfaction and may receive psychological and social benefits from their decision. So, too, may women who make the informed choice not to perform BR. It is important to note, that patients may choose not to have BR because they do not feel it is necessary for their physical or emotional well-being48.
Our findings generate a number of questions. In addition to the complex interaction between ethnicity and socioeconomic characteristics such as income, educational level, and employment status there likely exists an interaction between the rural- urban continuum and these factors. Even after controlling for previously investigated prognostic factors for BR, differences in BR rates among the counties studied existed.
Differences in use of BR detected at a population level should guide future studies and interventions to increase rates of BR at the local level. Findings from the current study suggest differences in the utilization of BR in rural and urban settings in Northern California. Further studies are needed to evaluate the causes of these disparities and identify potential areas of improvement with a goal of providing patient- centered BCa care.
Acknowledgments
Supported by Grant Number UL1 RR024146 from the National Center for Research Resources (NCRR) a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NNCRR or NIH. Information on NCRR is available athttp://www.ncrr.nih.gov/. Information on Re-engineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp
Footnotes
Presented, in part, at the Society of Surgical Oncology 63rd Annual Cancer Symposium, March 3-7, 2010, Saint Louis, MO.
1. Atisha D, Alderman AK, Lowery JC, Kuhn LE, Davis J, Wilkins EG. Prospective analysis of long-term psychosocial outcomes in breast reconstruction: two-year postoperative results from the Michigan Breast Reconstruction Outcomes Study. Ann Surg. 2008 Jun;247(6):1019–1028. [PubMed]
2. Joslyn SA. Patterns of care for immediate and early delayed breast reconstruction following mastectomy. Plast Reconstr Surg. 2005 Apr 15;115(5):1289–1296. [PubMed]
3. Al-Ghazal SK, Sully L, Fallowfield L, Blamey RW. The psychological impact of immediate rather than delayed breast reconstruction. Eur J Surg Oncol. 2000 Feb;26(1):17–19. [PubMed]
4. Knottenbelt A, Spauwen PH, Wobbes T. The oncological implications of immediate breast reconstruction. Eur J Surg Oncol. 2004 Oct;30(8):829–833. [PubMed]
5. Birido N, Geraghty JG. Quality control in breast cancer surgery. Eur J Surg Oncol. 2005 Aug;31(6):577–586. [PubMed]
6. Jacobs LK, Kelley KA, Rosson GD, Detrani ME, Chang DC. Disparities in urban and rural mastectomy populations : the effects of patient- and county-level factors on likelihood of receipt of mastectomy. Ann Surg Oncol. 2008 Oct;15(10):2644–2652. [PubMed]
7. Casper M, Nwaise I, Croft JB, Hong Y, Fang J, Greer S. Geographic disparities in heart failure hospitalization rates among Medicare beneficiaries. J Am Coll Cardiol. 2010 Jan 26;55(4):294–299. [PubMed]
8. Sonnenday CJ, Dimick JB, Schulick RD, Choti MA. Racial and geographic disparities in the utilization of surgical therapy for hepatocellular carcinoma. J Gastrointest Surg. 2007 Dec;11(12):1636–1646. discussion 1646. [PubMed]
9. Baker P, Hoel D, Mohr L, Lipsitz S, Lackland D. Racial, age, and rural/urban disparity in cervical cancer incidence. Ann Epidemiol. 2000 Oct 1;10(7):466–467. [PubMed]
10. Leira EC, Hess DC, Torner JC, Adams HP., Jr Rural-urban differences in acute stroke management practices: a modifiable disparity. Arch Neurol. 2008 Jul;65(7):887–891. [PubMed]
11. Hale NL, Bennett KJ, Probst JC. Diabetes Care and Outcomes: Disparities Across Rural America. J Community Health. 2010 Apr 14; [PubMed]
12. Martinez SR, Beal SH, Canter RJ, Chen SL, Khatri VP, Bold RJ. Medullary carcinoma of the breast: a population-based perspective. Med Oncol. 2010 Apr 14; [PubMed]
13. Canter RJ, Beal S, Borys D, Martinez SR, Bold RJ, Robbins AS. Interaction of histologic subtype and histologic grade in predicting survival for soft-tissue sarcomas. J Am Coll Surg. 2010 Feb;210(2):191–198. e192. [PubMed]
14. Martinez SR, Robbins AS, Meyers FJ, Bold RJ, Khatri VP, Goodnight JE., Jr Racial and ethnic differences in treatment and survival among adults with primary extremity soft-tissue sarcoma. Cancer. 2008 Mar 1;112(5):1162–1168. [PubMed]
15. Martinez SR, Chen SL, Bilchik AJ. Treatment disparities in Hispanic rectal cancer patients: a SEER database study. Am Surg. 2006 Oct;72(10):906–908. [PubMed]
16. Beal SH, Martinez SR, Canter RJ, Chen SL, Khatri VP, Bold RJ. Survival in 12,653 breast cancer patients with extensive axillary lymph node metastasis in the anthracycline era. Med Oncol. 2010 Jan 5; [PMC free article] [PubMed]
17. Morrow M, Scott SK, Menck HR, Mustoe TA, Winchester DP. Factors influencing the use of breast reconstruction postmastectomy: a National Cancer Database study. J Am Coll Surg. 2001 Jan;192(1):1–8. [PubMed]
18. Tseng JF, Kronowitz SJ, Sun CC, et al. The effect of ethnicity on immediate reconstruction rates after mastectomy for breast cancer. Cancer. 2004 Oct 1;101(7):1514–1523. [PubMed]
19. Staradub VL, Hsieh YC, Clauson J, Langerman A, Rademaker AW, Morrow M. Factors that influence surgical choices in women with breast carcinoma. Cancer. 2002 Sep 15;95(6):1185–1190. [PubMed]
20. Reuben BC, Manwaring J, Neumayer LA. Recent trends and predictors in immediate breast reconstruction after mastectomy in the United States. Am J Surg. 2009 Aug;198(2):237–243. [PubMed]
21. Guyomard V, Leinster S, Wilkinson M. Systematic review of studies of patients’ satisfaction with breast reconstruction after mastectomy. Breast. 2007 Dec;16(6):547–567. [PubMed]
22. Gui GP, Tan SM, Faliakou EC, Choy C, A’Hern R, Ward A. Immediate breast reconstruction using biodimensional anatomical permanent expander implants: a prospective analysis of outcome and patient satisfaction. Plast Reconstr Surg. 2003 Jan;111(1):125–138. discussion 139-140. [PubMed]
23. 1998 WsHaCRAo. Women’s Health and Cancer Rights Act of 1998. US Department of Labor Web Site. 1998
24. Alderman AK, McMahon L, Jr, Wilkins EG. The national utilization of immediate and early delayed breast reconstruction and the effect of sociodemographic factors. Plast Reconstr Surg. 2003 Feb;111(2):695–703. discussion 704-695. [PubMed]
25. Alderman AK, Hawley ST, Janz NK, et al. Racial and ethnic disparities in the use of postmastectomy breast reconstruction: results from a population- based study. J Clin Oncol. 2009 Nov 10;27(32):5325–5330. [PMC free article] [PubMed]
26. Morrow M, Jagsi R, Alderman AK, et al. Surgeon recommendations and receipt of mastectomy for treatment of breast cancer. JAMA. 2009 Oct 14;302(14):1551–1556. [PubMed]
27. Morrow M, Mujahid M, Lantz PM, et al. Correlates of breast reconstruction: results from a population-based study. Cancer. 2005 Dec 1;104(11):2340–2346. [PubMed]
28. Fernandez-Frias AM, Aguilar J, Sanchez JA, Merck B, Pinero A, Calpena R. Immediate reconstruction after mastectomy for breast cancer: which factors affect its course and final outcome? J Am Coll Surg. 2009 Jan;208(1):126–133. [PubMed]
29. Kornelsen J, Moola S, Grzybowski S. Does distance matter? Increased induction rates for rural women who have to travel for intrapartum care. J Obstet Gynaecol Can. 2009 Jan;31(1):21–27. [PubMed]
30. Lazovich DA, White E, Thomas DB, Moe RE. Underutilization of breast-conserving surgery and radiation therapy among women with stage I or II breast cancer. JAMA. 1991 Dec 25;266(24):3433–3438. [PubMed]
31. Celaya MO, Rees JR, Gibson JJ, Riddle BL, Greenberg ER. Travel distance and season of diagnosis affect treatment choices for women with early-stage breast cancer in a predominantly rural population (United States) Cancer Causes Control Aug. 2006;17(6):851–856. [PubMed]
32. Meden T, St John-Larkin C, Hermes D, Sommerschield S. MSJAMA. Relationship between travel distance and utilization of breast cancer treatment in rural northern Michigan. JAMA. 2002 Jan 2;287(1):111. [PubMed]
33. Monnet E, Collin-Naudet E, Bresson-Hadni S, et al. Place of residence and distance to medical care influence the diagnosis of hepatitis C: a population-based study. J Hepatol. 2006 Mar;44(3):499–506. [PubMed]
34. Piette JD, Moos RH. The influence of distance on ambulatory care use, death, and readmission following a myocardial infarction. Health Serv Res. 1996 Dec;31(5):573–591. [PMC free article] [PubMed]
35. Strauss K, MacLean C, Troy A, Littenberg B. Driving distance as a barrier to glycemic control in diabetes. J Gen Intern Med. 2006 Apr;21(4):378–380. [PMC free article] [PubMed]
36. Athas WF, Adams-Cameron M, Hunt WC, Amir-Fazli A, Key CR. Travel distance to radiation therapy and receipt of radiotherapy following breast-conserving surgery. J Natl Cancer Inst. 2000 Feb 2;92(3):269–271. [PubMed]
37. Nair S, Richardson EA, Thompson WR, Shortt NK, Stewart KJ. The influence of geography on uptake of plastic surgery services - analysis based on bilateral breast reduction data. J Plast Reconstr Aesthet Surg. 2010 Apr;63(4):666–672. [PubMed]
38. Lee CN, Hultman CS, Sepucha K. What Are Patients’ Goals and Concerns About Breast Reconstruction After Mastectomy? Ann Plast Surg. 2010 Mar 29; [PubMed]
39. Mendick N, Young B, Holcombe C, Salmon P. The ethics of responsibility and ownership in decision-making about treatment for breast cancer: Triangulation of consultation with patient and surgeon perspectives. Soc Sci Med. 2010 Mar 19; [PubMed]
40. Schou I, Ekeberg O, Ruland CM, Karesen R. Do women newly diagnosed with breast cancer and consulting surgeon assess decision-making equally? Breast. 2002 Oct;11(5):434–441. [PubMed]
41. Ananian P, Houvenaeghel G, Protiere C, et al. Determinants of patients’ choice of reconstruction with mastectomy for primary breast cancer. Ann Surg Oncol. 2004 Aug;11(8):762–771. [PubMed]
42. Greenberg CC, Schneider EC, Lipsitz SR, et al. Do variations in provider discussions explain socioeconomic disparities in postmastectomy breast reconstruction? J Am Coll Surg. 2008 Apr;206(4):605–615. [PubMed]
43. Rowland JH, Dioso J, Holland JC, Chaglassian T, Kinne D. Breast reconstruction after mastectomy: who seeks it, who refuses? Plast Reconstr Surg. 1995 Apr;95(5):812–822. discussion 823. [PubMed]
44. Alderman AK, Hawley ST, Waljee J, Morrow M, Katz SJ. Correlates of referral practices of general surgeons to plastic surgeons for mastectomy reconstruction. Cancer. 2007 May 1;109(9):1715–1720. [PubMed]
45. Christian CK, Niland J, Edge SB, et al. A multi-institutional analysis of the socioeconomic determinants of breast reconstruction: a study of the National Comprehensive Cancer Network. Ann Surg. 2006 Feb;243(2):241–249. [PubMed]
46. California Department of Finance Statistical Abstract. 2009. [4-19, 2010]. http://www.dof.ca.gov/html/fs_data/stat-abs/Toc_xls.htm#top.
47. Desch CE, Penberthy LT, Hillner BE, et al. A sociodemographic and economic comparison of breast reconstruction, mastectomy, and conservative surgery. Surgery. 1999 Apr;125(4):441–447. [PubMed]
48. Reaby LL. Reasons why women who have mastectomy decide to have or not to have breast reconstruction. Plast Reconstr Surg. 1998 Jun;101(7):1810–1818. [PubMed]