J Reconstr Microsurg 2025; 41(01): 009-018
DOI: 10.1055/a-2277-0236
Original Article

Social Determinants of Health and Patient-reported Outcomes Following Autologous Breast Reconstruction, Using Insurance as a Proxy

Ethan L. Plotsker*
1   Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
,
Francis D. Graziano*
1   Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
,
Minji Kim
1   Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
,
Lillian A. Boe
2   Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
,
Audree B. Tadros
3   Breast Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
,
Evan Matros
1   Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
,
Said C. Azoury
4   Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
,
Jonas A. Nelson
1   Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
› Author Affiliations

Funding This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748, which supports Memorial Sloan Kettering Cancer Center's research infrastructure.
 

Abstract

Background Insurance type can serve as a surrogate marker for social determinants of health and can influence many aspects of the breast reconstruction experience. We aimed to examine the impact of insurance coverage on patients reported outcomes with the BREAST-Q (patient reported outcome measure for breast reconstruction patients, in patients receiving) in patients receiving deep inferior epigastric artery perforator (DIEP) flap breast reconstruction.

Methods We retrospectively examined patients who received DIEP flaps at our institution from 2010 to 2019. Patients were divided into categories by insurance: commercial, Medicaid, or Medicare. Demographic factors, surgical factors, and complication data were recorded. Descriptive statistics, Fisher's exact, Kruskal–Wallis rank sum tests, and generalized estimating equations were performed to identify associations between insurance status and five domains of the BREAST-Q Reconstructive module.

Results A total of 1,285 patients were included, of which 1,011 (78.7%) had commercial, 89 (6.9%) had Medicaid, and 185 (14.4%) had Medicare insurances. Total flap loss rates were significantly higher in the Medicare and Medicaid patients as compared to commercial patients; however, commercial patients had a higher rate of wound dehiscence as compared to Medicare patients. With all other factors controlled for, patients with Medicare had lower Physical Well-being of the Chest (PWBC) than patients with commercial insurance (β = − 3.1, 95% confidence interval (CI): −5.0, −1.2, p = 0.002). There were no significant associations between insurance classification and other domains of the BREAST-Q.

Conclusion Patients with government-issued insurance had lower success rates of autologous breast reconstruction. Further, patients with Medicare had lower PWBC than patients with commercial insurance regardless of other factors, while other BREAST-Q metrics did not differ. Further investigation as to the causes of such variation is warranted in larger, more diverse cohorts.


#

It has become increasingly evident that patient-reported outcomes (PROs) are not solely shaped by clinical procedures and medical interventions. Rather, a complex interplay of social determinants of health (SDOH) play a pivotal role in influencing patients' overall experience and effectiveness of treatment.[1] [2] [3] SDOH encompass the environmental factors in peoples' lives that affect health, functioning, and quality of life.[4] SDOH can be broken down into the following domains: economic stability, access to education, access to health care, neighborhood environment, and social community.[4] Since SDOH can be difficult to study and quantify given multifactorial inputs, insurance status can serve as a valuable surrogate marker for multiple SDOH, including socioeconomic status (SES), race, education level, and comorbidities.[5] As examples, a majority of Medicaid beneficiaries are non-White, whereas individuals with more education are more likely to have better insurance and health benefits.[6] [7] [8] [9]

The Women's Health and Cancer Rights Act in 1998 mandated that all insurances cover breast reconstruction in patients undergoing mastectomy.[10] Research has since attempted to elucidate whether insurance status continues to impact the reconstructive course.[11] [12] [13] [14] [15] [16] [17] [18] [19] [20] Many of these studies have assessed the influence of insurance status on the modality of breast reconstruction as well as clinical outcomes, but little is known about how insurance status—and by extension, SDOH—can affect PROs. Assessing the impact of SDOH on PROs after breast reconstruction is critical to ensure that all patients are satisfied and have an optimal quality of life.[21] BREAST-Q has emerged as one of the most validated tools for examining PROs after breast surgery.[22] [23] [24] [25] [26] One study examined the association between SES and BREAST-Q scores after breast reconstruction, but this study used Zillow to estimate patients' incomes rather than having patients report their incomes, which may contribute to inaccuracies.[27] Therefore, a more comprehensive examination of SDOH and PROs after breast reconstruction is necessary by using insurance status as a proxy.

This study aims to delve into the critical relationship between PROs in autologous breast reconstruction and SDOH, using insurance type as a pragmatic lens to gain a deeper understanding of these intricate dynamics. Autologous breast reconstruction has been shown to confer the strongest increases in BREAST-Q scores following surgery,[25] [28] so our study focused on patients that underwent deep inferior epigastric perforator (DIEP) flaps for breast reconstruction. We hypothesized that, relative to patients with commercial insurance, patients with publicly funded insurance would report worse long-term BREAST-Q scores.

Methods

This was a retrospective study examining all patients receiving unilateral or bilateral DIEP flaps at Memorial Sloan Kettering Cancer Center between January 2010 and December 2019. Institutional Review Board approval was obtained for this study. Patients were included if they were 18 years of age or older. Exclusion criteria included other types of autologous breast reconstruction and implant-based reconstruction.

Demographic characteristics of interest included age, race, ethnicity, marital status, body mass index, smoking status, median household income, history of psychiatric diagnosis, and receipt of either chemotherapy or radiation. Mean household income was determined based on patients' zip code. Insurance payers were categorized into three categories: commercial, Medicare, and Medicaid. Medicaid is generally for patients with limited income and resources while Medicare is for patients aged 65 and older and/or with certain disabilities and conditions.[29] Surgical characteristics included donor site laterality, contralateral or bilateral prophylactic mastectomy, concurrent axillary lymph node dissection, and postoperative length of stay.

We examined postoperative complications to see if these could influence PROs. Complications of interest included abdominal wall hernia, infection/cellulitis, delayed healing, mastectomy skin flap necrosis, total flap loss, seroma, wound dehiscence, hematoma, and systemic complications (deep venous thrombosis, pulmonary embolism, and pneumonia). We also examined presence of any complications as well as readmissions or reoperations related to the index procedure within 90 days of reconstruction.

BREAST-Q scores were examined preoperatively and at 2 weeks, 3 months, 6 months, 1 year, and 2 years postoperatively for Physical Well-being of the Chest (PWBC) and Physical Well-being of the Abdomen (PWBA) domains. The 2-week postoperative survey was not administered for the other domains. Thus, for Satisfaction with Breasts, Psychosocial Well-being, and Sexual Well-being, scores were examined preoperatively and at 3 months, 6 months, 1 year, and 2 years postoperatively. A minimal clinically important difference (MCID) of 4 points was considered clinically important.[30]

Patient demographics and outcomes were compared between each of the insurance categories overall using Fisher's exact and Kruskal–Wallis rank sum tests. Pairwise comparisons of BREAST-Q scores between individual insurance categories were conducted using Wilcoxon rank-sum tests. The generalized estimating equation method was used to identify confounders of differences in PROs across insurance groups. Multiple imputation was used to account for missing BREAST-Q scores, median household income, and postoperative length of stay. All analyses were conducted in R (version). Statistical significance was set at an alpha of 0.05.


#

Results

A total of 1,285 patients were included in the database, of which 1,011 (78.7%) had commercial insurance, 89 (6.9%) had Medicaid, and 185 (14.4%) had Medicare ([Table 1]). Overall, the median age of the patients was 50 (interquartile range: 44, 57) and majority of the patients were White (69%) and non-Hispanic (86%). There were significant differences in age at surgery, race, ethnicity, smoking status, smoking status, marital status, and median household income between the three cohorts. Proportion of donor site laterality, prophylactic mastectomy, and axillary lymph node dissection were significantly different among the insurance types ([Table 1]).

Table 1

Patient demographics

Patient demographics

Overall (n = 1,285)

Commercial (n = 1,011)

Medicaid

(n = 89)

Medicare (n = 185)

p-Value[b]

Age at surgery[a]

50 (44, 57)

49 (44, 55)

46 (41, 54)

63 (57, 66)

<0.001

Race

<0.001

 White

886 (69%)

708 (70%)

44 (49%)

134 (72%)

 Asian

111 (8.6%)

95 (9.4%)

7 (7.9%)

9 (4.9%)

 Black

164 (13%)

121 (12%)

17 (19%)

26 (14%)

 Other

124 (9.6%)

87 (8.6%)

21 (24%)

16 (8.6%)

Ethnicity

0.001

 Non-Hispanic

1,106 (86%)

884 (87%)

63 (71%)

159 (86%)

 Hispanic

132 (10%)

92 (9.1%)

21 (24%)

19 (10%)

 Unknown

47 (3.7%)

35 (3.5%)

5 (5.6%)

7 (3.8%)

Marital status

<0.001

 Married

941 (73%)

773 (76%)

43 (48%)

125 (68%)

 Separated

121 (9.4%)

78 (7.7%)

16 (18%)

27 (15%)

 Single

223 (17%)

160 (16%)

30 (34%)

33 (18%)

Body mass index[a]

26.2 (23.4, 29.7)

26.2 (23.4, 29.7)

26.5 (23.9, 29.8)

26.9 (23.3, 29.8)

0.8

Smoking status

0.024

 Never

948 (74%)

757 (75%)

68 (76%)

123 (66%)

 Current

32 (2.5%)

23 (2.3%)

5 (5.6%)

4 (2.2%)

 Former

305 (24%)

231 (23%)

16 (18%)

58 (31%)

Median household income

96,606 (73,514, 127,680)

102,763 (77,044, 130,121)

66,332 (56,870, 99,572)

87,397 (69,353, 1223,492)

<0.001

History of psychiatric comorbidities

718 (56%)

547 (54%)

58 (65%)

113 (61%)

0.04

Chemotherapy

0.064

 Adjuvant/Neoadjuvant

998 (78%)

777 (77%)

78 (88%)

143 (77%)

Radiation

 Adjuvant/Neoadjuvant

87 (6.8%)

67 (6.6%)

8 (9.0%)

12 (6.5%)

0.7

Donor site laterality

0.007

 Bilateral

628 (49%)

517 (51%)

35 (39%)

76 (41%)

 Unilateral

657 (51%)

494 (49%)

54 (61%)

109 (59%)

Prophylactic

0.012

 None

855 (67%)

650 (64%)

69 (78%)

136 (74%)

 Contralateral prophylactic

391 (30%)

326 (32%)

20 (22%)

45 (24%)

 Bilateral prophylactic

39 (3.0%)

35 (3.5%)

0 (0%)

4 (2.2%)

Axillary lymph node biopsy

763 (59%)

611 (60%)

63 (71%)

89 (48%)

<0.001

Postoperative length of stay in days[a]

3 (3,5)

3 (3,5)

4 (3,6)

4 (3,6)

0.2

a Median (IQR); n (%).


b Kruskal–Wallis rank sum test; Fisher's exact test for count data with simulated p-value.


Complications

There were significant differences in the proportion of patients who developed total flap loss and wound dehiscence. Total flap loss occurred most frequently in patients with Medicaid (4.5%) compared to 0.8% in commercially insured patients and 1.6% in Medicare patients (p = 0.014); wound dehiscence occurred in 5.2% of commercial, 4.5% of Medicaid, and 1.1% of Medicare patients (p = 0.026). No significant differences in insurance type were observed for other complications or the presence of any complication ([Table 2]). Given the low complication events for these three complications, especially in the Medicare cohort, it was determined that it would be difficult to reliably estimate a β coefficient through logistic regression models.

Table 2

Complications

All patients

Overall (n = 1,285)[a]

Commercial (n = 1,011)[a]

Medicaid (n = 89)[a]

Medicare (n = 185)[a]

p-Value[b]

Abdominal wall—hernia

8 (0.6%)

6 (0.6%)

0 (0%)

2 (1.1%)

0.6

Infection/cellulitis

174 (14%)

128 (13%)

16 (18%)

30 (16%)

0.2

Delayed healing

63 (4.9%)

49 (4.8%)

2 (2.2%)

12 (6.5%)

0.3

Mastectomy skin flap/Nipple necrosis

175 (14%)

134 (13%)

9 (10%)

32 (17%)

0.2

Total flap loss

15 (1.2%)

8 (0.8%)

4 (4.5%)

3 (1.6%)

0.014

Seroma

58 (4.5%)

52 (5.1%)

3 (3.4%)

3 (1.6%)

0.085

Wound dehiscence

59 (4.6%)

53 (5.2%)

4 (4.5%)

2 (1.1%)

0.026

Hematoma

94 (7.3%)

70 (6.9%)

5 (5.6%)

19 (10%)

0.2

Systemic comorbidities (DVT, PE, pneumonia)

12 (0.9%)

8 (0.8%)

1 (1.1%)

3 (1.6%)

0.4

Any complication

655 (51%)

509 (50%)

47 (53%)

99 (54%)

0.7

Readmission within 90 days

138 (11%)

100 (9.9%)

14 (16%)

24 (13%)

0.13

Reoperation within 90 days

558 (43%)

440 (44%)

45 (51%)

73 (39%)

0.2

a n (%).


b Kruskal–Wallis rank sum test; Fisher's exact test; Pearson's chi-square test.



#

BREAST-Q

Physical Well-being of the Chest Analysis

BREAST-Q scores across the insurance cohorts at different timepoints are listed in [Table 3] along with the number of patient scores available. Differences in PWBC scores were observed across the three insurance cohorts at 6 months, 1 year, and 2 year postoperatively ([Table 3]). Pairwise comparison showed that commercially insured patients had statistically higher median preoperative PWBC scores than Medicare patients at 6 months and 1 year postoperatively ([Table 4]). At 6 months, commercially insured patients had a median score of 72 (Interquartile Range (IQR): 60, 85) while Medicare patients had a score of 69 (56, 81; p = 0.016; [Fig. 1]). The difference was clinically meaningful at 1 year: commercial: 76 (64, 92) versus Medicare: 68 (56, 80; p = 0.005; [Fig. 2]). At 2 years, commercially insured patients scored 12 points higher than Medicaid patients (76 [64, 92] vs. 64 [55, 83], p = 0.031; [Fig. 3]).

Zoom Image
Fig. 1 Box plots of Physical Well-being of the Chest at 6 months by insurance type.
Zoom Image
Fig. 2 Box plots of Physical Well-being of the Chest at 1 year by insurance type.
Zoom Image
Fig. 3 Box plots of Physical Well-being of the Chest at 2 years by insurance type.
Table 3

BREAST-Q scores

All patients

N

Commercial (n = 1,011)[a]

N

Medicaid

(n = 89)[a]

N

Medicare (n = 185)[a]

p-Value[b]

Physical Well-being of the Chest

 Preoperative

512

76 (60, 91)

28

73 (60, 91)

89

71 (55, 85)

0.059

 2 weeks

578

60 (50, 71)

46

60 (41, 72)

94

64 (45, 76)

0.5

 3 months

487

68 (59, 80)

27

68 (58, 83)

81

64 (50, 77)

0.3

 6 months

575

72 (60, 85)

48

68 (55, 80)

110

69 (56, 81)

0.016

 1 year

644

76 (64, 92)

44

70 (57, 85)

122

68 (56, 80)

0.005

 2 years

413

76 (64, 92)

27

64 (55, 83)

80

76 (55, 85)

0.031

Physical Well-being of the Abdomen

 Preoperative

482

83 (69, 100)

26

100 (71, 100)

83

76 (63, 100)

0.8

 2 weeks

572

49 (39, 58)

44

47 (39, 58)

92

52 (43, 60)

0.2

 3 months

482

61 (52, 72)

26

61 (47, 71)

78

66 (51, 76)

0.7

 6 months

567

66 (56, 81)

46

64 (51, 75)

104

66 (54, 81)

0.4

 1 year

629

69 (58, 81)

42

66 (56, 80)

120

66 (56, 88)

0.8

 2 years

409

69 (58, 88)

26

76 (58, 88)

77

69 (58, 81)

0.7

Satisfaction with Breasts

 Preoperative

501

48 (33, 58)

26

41 (30, 52)

88

44 (34, 58)

0.7

 3 months

464

59 (52, 69)

25

61 (55, 67)

76

58 (46, 71)

0.8

 6 months

568

62 (53, 75)

48

59 (52, 71)

102

58 (48, 71)

0.08

 1 year

634

64 (53, 78)

45

58 (49, 75)

119

61 (48, 75)

0.09

 2 years

412

65 (53, 78)

26

64 (53, 75)

79

65 (52, 75)

0.5

Psychosocial Well-being

 Preoperative

500

60 (47, 74)

27

62 (43, 75)

88

60 (48, 75)

0.9

 3 months

466

65 (52, 83)

25

69 (52, 79)

78

64 (51, 82)

>0.9

 6 months

564

66 (53, 83)

47

64 (50, 85)

106

61 (52, 79)

0.2

 1 year

635

69 (56, 87)

43

60 (52, 70)

119

65 (53, 83)

0.1

 2 years

410

69 (56, 87)

27

64 (52, 94)

80

68 (58, 86)

0.7

Sexual Well-being

 Preoperative

478

43 (29, 59)

24

41 (24, 55)

80

43 (24, 62)

0.8

 3 months

445

48 (34, 59)

22

53 (30, 64)

68

46 (29, 59)

0.9

 6 months

544

48 (36, 63)

42

48 (25, 59)

96

43 (31, 59)

0.2

 1 year

612

52 (39, 66)

40

42 (24, 57)

106

48 (32, 63)

0.049

 2 years

400

53 (39, 66)

24

43 (32, 61)

68

48 (36, 66)

0.8

a Median (IQR).


b One way ANOVA.


Table 4

Pairwise comparisons, BREAST-Q median scores

All patients

Commercial versus Medicaid (p-value)

Commercial versus Medicare (p-value)

Medicaid versus Medicare (p-value)

Physical Well-being of the Chest

 6 months

72 vs. 68 (0.06)

72 vs. 69 (0.039)

68 vs. 69 (0.7)

 1 year

76 vs. 70 (0.064)

76 vs. 68 (0.003)

70 vs. 68 (>0.9)

 2 years

76 vs. 64 (0.032)

76 vs. 76 (0.10)

64 vs. 76 (0.4)

Satisfaction with Breasts

 1-year

64 vs. 58 (0.059)

64 vs. 61 (0.040)

58 vs. 61 (0.7)

Sexual Well-being

 6 months

48 vs. 48 (0.3)

48 vs. 43 (0.019)

48 vs. 43 (0.7)

 1 year

52 vs. 42 (0.007)

52 vs. 48 (0.045)

42 vs. 48 (0.3)

Generalized estimation equation modeling showed that when controlling for all other factors, postoperative PWBC scores at 2 weeks and 3 months were significantly lower than the preoperative score. With all other factors controlled for, including the time of BREAST-Q administration, patients with Medicare have significantly lower PWBC than patients with commercial insurance (β = − 3.1, 95% CI: −5.0, −1.2, p = 0.002). Patients who identified as Asian, Black, or Other had significantly lower PWBC scores than White patients, as did patients with a history of psychiatric diagnosis and longer postoperative stay ([Table 5]).

Table 5

GEE models for BREAST-Q scores over time

Physical Well-being of the Chest

Physical Well-being of the Abdomen

Satisfaction with Breasts

Psychosocial Well-being

Sexual Well-being

Patient demographics

β (95% CI)

p-Value

β (95% CI)

p-Value

β (95% CI)

p-Value

β (95% CI)

p-Value

β (95% CI)

p-Value

Time

 Preop

 2 weeks

−14 (−16, −12)

<0.001

−26 (−28, −23)

<0.001

X

X

X

 3 months

−4.8 (−6.9, −2.7)

<0.001

−12 (−14, −9.2)

<0.001

15 (13, 18)

<0.001

9.4 (6.7, 12)

<0.001

4.3 (1.4, 7.1)

0.004

 6 months

−1.2 (−3.3, 0.85)

0.2

−7.7 (−10, −5)

<0.001

18 (16, 20)

<0.001

10 (7.8, 12)

<0.001

6.2 (3.5, 8.9)

<0.001

 1 year

1.0 (−1.0, 3.1)

0.3

−5.3 (−7.6, −2.9)

<0.001

19 (17, 22)

<0.001

12 (9.9, 14)

<0.001

9.0 (6.3, 12)

<0.001

 2 years

2.1 (−0.69, 4.9)

0.14

−5.3 (−8.0, −2.6)

<0.001

20 (18, 23)

<0.001

12 (9.7, 15)

<0.001

11 (7.7, 14)

<0.001

Insurance

 Commercial

 Medicaid

−1.8 (−4.8, 1.1)

0.2

−0.31 (−3.4, 2.7)

0.8

−0.34 (−3.7, 3.1)

0.8

1.1 (−2.2, 4.4)

0.5

−1.5 (−5.4, 2.5)

0.5

 Medicare

−3.1 (−5.0, −1.2)

0.002

0.40 (−1.4, 2.2)

0.7

−1.1 (−3.3, 1.1)

0.3

−0.62 (−2.7, 1.5)

0.6

−1.3 (−4.0, 1.3)

0.3

Race

 White

 Asian

−3.9 (−6.4, −1.5)

0.002

−2.7 (−5.7, 0.37)

0.084

−0.79 (−3.5, 1.9)

0.6

−4.2 (−7.1, −1.2)

0.007

−2.6 (−5.8, 0.062)

0.11

 Black

−3.2 (−5.3, −1.1)

0.004

−3.8 (−5.9, −1.7)

<0.001

−1.3 (−3.8, 1.1)

0.3

−1.6 (−3.9, 0.75)

0.2

0.26 (−2.7, 3.2)

0.9

 Other

−4.5 (−6.8, −2.3)

<0.001

−2.0 (−4.7, 0.71)

0.15

−4.7 (−7.1, −2.2)

<0.001

−6.0 (−9.0, −3.0)

<0.001

−4.9 (−8.1, −1.6)

0.004

History of psychiatric comorbidities

−4.1 (−5.5, −2.6)

<0.001

−6.0 (−7.4, −4.5)

<0.001

−5.8 (−7.2, −4.3)

<0.001

−10 (−12, −8.4)

<0.001

−10 (−12, −8.6)

<0.001

Median household income

0.000004 (−0.000016, 0.000024)

0.7

0.000008 (−0.00001, 0.00003)

0.4

0.00002 (−0.000003, 0.00004)

0.093

0.00003 (0.000008, 0.00005)

0.008

0.000029 (0.000003, 0.00006)

0.031

Chemotherapy

−0.45 (−2.0, 1.1)

0.6

4.3 (2.3, 6.2)

<0.001

1.4 (−0.23, 3.1)

0.091

2.8 (0.77, 4.7)

0.007

1.7 (−0.53, 3.9)

0.13

Radiation

−1.7 (−4.3, 0.90)

0.2

2.0 (−0.92, 4.8)

0.2

1.0 (−1.9, 3.9)

0.5

3.1 (0.28, 5.9)

0.031

3.0 (−0.73, 6.8)

0.11

Donor site laterality

 Bilateral

 Unilateral

0.79 (−0.89, 2.5)

0.4

2.6 (0.63, 4.5)

0.010

−1.1 (−3.0, 0.78)

0.3

1.0 (−0.96, 3.0)

0.3

0.79 (−1.4, 3.0)

0.5

Prophylactic

 Bilateral

 Not prophylactic

−1.4 (−4.8, 2.1)

0.4

−1.9 (−6.1, 2.2)

0.4

−1.3 (−5.2, 2.6)

0.5

−0.14 (−4.3, 4.1)

>0.9

−0.46 (−4.6, 3.7)

0.8

 Contralateral

−0.67 (−3.9, 2.6)

0.7

−1.1 (−4.7, 2.6)

0.6

0.22 (−3.5, 3.9)

>0.9

0.97 (−3.0, 4.9)

0.6

0.82 (−3.1, 4.7)

0.7

Postoperative length of stay

−0.0061 (−0.0096, −0.0025)

0.001

−0.0074 (−0.012, −0.003)

<0.001

−0.0037 (−0.008, 0.0007)

0.10

−0.0041 (−0.008, 0.0002)

0.064

−0.0054 (−0.01, −0.0005)

0.032

Complications

−0.61 (−1.9, 0.69)

0.4

−0.05 (−1.7, 1.6)

>0.9

−1.6 (−3.1, −0.10)

0.037

−0.98 (−2.4, 0.42)

0.2

−2.0 (−4.2, 0.24)

0.079


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Physical Well-being of the Abdomen Analysis

We observed no differences in PWBA at any point preoperatively or postoperatively when comparing all three insurance cohorts at once.

Generalized estimation equation modeling showed that when all other factors were controlled for, all postoperative PWBA scores over time were lower than the preoperative score, although it improved over time (p < 0.001). It also showed that insurance type was not an independent predictor of PWBA. Patients who identified as Black (vs. White), have a history of psychiatric diagnosis, and longer postoperative stay had significantly lower PWBA scores while those with a receipt of chemotherapy and unilateral donor site (vs. bilateral) had higher PWBA scores (Table 5).


#

Satisfaction with Breasts

Although not significant, notable differences were seen in Satisfaction with Breasts at 6 months and 1 year across the three insurance types (p = 0.08, p = 0.09, respectively). Pairwise comparison demonstrated that commercially insured patients had significantly higher Satisfaction with Breasts than Medicare patients at 1 year (64 [53, 78] vs. 61 [46, 75], p = 0.04). This, however, was not clinically significant.

Modeling for Satisfaction with Breasts showed that all postoperative timepoints were associated with higher Satisfaction with Breasts scores relative to preoperative, holding all else constant. No association was observed between Satisfaction with Breasts and insurance type. Patients who identified as “Other” (vs. White), have a history of psychiatric diagnosis, and experienced complications had significantly lower Satisfaction with Breasts scores.


#

Psychosocial Well-being Analysis

No significant differences were seen for Psychosocial Well-being at any timepoint.

Generalized estimating equations (GEE) models for Psychosocial Well-being showed no significant differences by insurance type. Patients who identified as Asian or “Other” (vs. White) and have a history of psychiatric diagnosis had significantly lower Psychosocial Well-being scores. Patients with a receipt of chemotherapy, radiation, or have higher median household income had significantly higher Psychosocial Well-being.


#

Sexual Well-being Analysis

Sexual Well-being was significantly different among the insurance types at 1 year (p = 0.049). Pairwise analysis indicated that there were significant differences between commercial (48 [36, 63]) and Medicare (43 [31, 59]) patients at 6 months (p = 0.019), meeting the MCID of 4. The difference was also observed at 1 year, in which commercially insured patients scored 4 points higher on Sexual Well-being than Medicare patients (52 [39, 66] vs. 48 [32, 63], p = 0.045). Patients with commercial insurance also scored 10 points higher than patients with Medicaid at this timepoint (52 [39, 66] vs. 42 [24, 57], p = 0.007).

Generalized estimation equation modeling for Sexual Well-Being showed that, holding all other factors constant, all postoperative scores were significantly higher than preoperative scores (p < 0.001). Insurance status was not an independent predictor of Sexual Well-Being scores. Patients who identified as “Other” (vs. White), have a history of psychiatric diagnosis, and stayed in the hospital for longer had significantly lower Sexual Well-being. Median household income was positively correlated with Sexual Well-being (Table 5).


#
#
#

Discussion

Insurance status can be used as a proxy for many SDOH, including SES, race, education level, and comorbidities.[5] In this study, we examined how insurance type correlates with complication rates and PROs following DIEP breast reconstruction. Total flap loss and wound dehiscence were significantly different across the insurance types. We also found that insurance type most profoundly influences PWBC, with Medicare patients having significantly lower PWBC scores than commercially insured patients.

When examining all surgical specialties, including general surgery, orthopaedic, gynecology, and plastic surgery, Medicare and Medicaid patients experience higher rates of complications than commercial payers.[31] In implant- and autologous-based breast reconstruction, patients under Medicaid and Medicare are more likely to develop infections, cellulitis, and tissue necrosis than those who are commercially insured.[32] In our focused examination of DIEP patients, we found that the overall presence of complications did not differ by payer type, but we did find that total flap loss and wound dehiscence were significantly different. Notably, Medicaid patients had the highest and commercial patients had the lowest rates of flap loss. This may be related to how Medicaid patients in our study have higher proportion of current smokers, relative to commercial and Medicare patients. Smoking is a well-known independent risk factor for complications in autologous breast reconstruction as it can cause vasoconstriction and reduced blood flow.[33] [34] [35] We also found that commercial patients had a significantly higher rate of wound dehiscence as compared to Medicare patients. It is important to highlight that due to the minimal occurrence of complication events, particularly within the Medicare and Medicaid cohorts, interpreting these findings should be approached with caution. Additional studies with greater sample sizes are necessary to control for such confounding variables and to assess whether insurance type truly impacts complication rates after DIEP flap reconstruction.

The Women's Health and Cancer Rights Act in 1998 mandated that all breast reconstruction after mastectomy is covered, yet health disparities still exist in accessing reconstructive services. Medicare patients are least likely to undergo breast reconstruction compared to Medicaid and commercial patients, and publicly insured patients are less likely to undergo ABR than commercially insured patients.[16] [36] Our research extends the current body of literature by demonstrating that Medicare patients have significantly lower PWBC than commercially insured patients. The pairwise tests showed that Medicaid patients also have lower PWBC than commercially insured patients at 2 years. The differences may be for several reasons. PWBC scale may be reflective of general, overall health as it asks patients about symptoms including breast pain, breast tightness, and difficulty in moving arms.[37] Given that Medicare services' patients aged 65 or older or those with certain comorbidities,[38] it is logical that these patients would have a lower PWBC. We also found that patients with Medicare have higher rates of psychiatric diagnosis, which has been previously associated with lower PWBC scores.[26] Furthermore, although not significant, patients with Medicaid experienced higher rates of radiotherapy compared to patients with commercial insurance. Radiation has been associated with increased chronic postsurgical pain and chest/upper body discomfort in patients undergoing breast reconstruction by causing skin retraction and tightness.[39] [40] [41] Lastly, physical therapy is often advised for patients undergoing breast reconstruction to alleviate postsurgical pain.[42] However, patients with public insurances may be less inclined to attend physical therapy and other rehabilitative programs, and may not realize the health benefits associated with physical therapy.[43] Providers should recognize the potentially lower PWBC in publicly insured patients and provide necessary interventions to optimize their postoperative function and mobility.[44]

While we no longer observed statistical significance in other BREAST-Q domains by insurance type, race and income were significantly associated with these domains. Patients who identified as Asian, Black, or Other had lower BREAST-Q scores than White patients. In the existing literature, Hanson et al found that American Indian/Alaska Native patients have lower BREAST-Q scores while Oskar et al did not observe any postoperative BREAST-Q score differences across racial groups; however, sample sizes in these two studies were smaller.[45] [46] Race is intricately tied with insurance coverage, but the expansion of Medicaid has helped to reduce racial disparities in accessing health care.[8] [47] Our results indicate that BREAST-Q domains, except for PWBC, may be driven by inherent, racial, and cultural differences rather than insurance coverage. Moreover, we found that patients with higher income had improved Psychosocial and Sexual Well-being. Another study also observed that higher SES was tied to better Satisfaction with Breasts, Psychosocial Well-being, and Sexual Well-being, implying that insurance type and income may not always be correlated.[27] [45] Patients with lower SES, for instance, may have commercial insurance because of employer-sponsored plans while patients with higher SES can still qualify for Medicaid due to preexisting disabilities.[48] [49] As such, while SES and race are critical SDOH to take into account, our research underscores the importance of evaluating insurance status independently as distinct SDOH factors may exert varying influences on outcomes. Further work is warranted to address existing SDOH in breast reconstruction and to minimize the impact of these nonmedical factors that influence health outcomes.

The strengths of this study include a long follow-up time, up to 2 years postoperatively, and a rigorous statistical analysis to control for outside influences that may confound the effect of insurance coverage on BREAST-Q outcomes. Nonetheless, there are several limitations. First, this study is retrospective, which inherently limit the data we can collect and variables for analysis. It is feasible that there are other unknown variables which, if included in our modeling, could impact our results, but we are unable to control for all possible confounding factors. For example, Dinis et al demonstrated that publicly insured patients are less likely to undergo perforator flaps than commercially insured patients, indicating that selection bias may play a role in our findings.[13] Another limitation is the limited cohort size in the publicly insured cohorts. For example, we were not able to conduct logistic regression models to highlight the actual impact of insurance status on complications given the low complication events across the insurance types. Given that not all patients completed BREAST-Q at every timepoint, we utilized multiple imputation to account for missing scores; still, the limited number of available scores in the Medicare and Medicaid cohorts is a limitation to our study. There may be additional significant differences that we were not able to observe due to the limited sample size. Lastly, our patient cohort was largely White, non-Hispanic, and had a median income that is higher than the majority of the United States. This may contribute to the lower number of patients in the publicly insured cohorts and our findings may not be generalizable to other patient populations; they must be interpreted with caution. Nonetheless, as a cancer center that performs countless autologous breast reconstruction, we provide robust results on the impact of insurance on PROs. Future studies should continue to explore the relationship between insurance type and PROs in breast reconstruction in different patient populations as well as the factors that modify the effect of insurance status on PROs.


#

Conclusion

In this study, we used insurance status as a proxy for SDOH in breast reconstruction. Total flap loss and wound dehiscence were significantly different across the insurance types. Medicare patients have significantly lower PWBC than commercial payers regardless of other factors, while PWBA, Sexual Well-Being, Psychosocial Well-Being, and Satisfaction with Breasts do not display a correlation with insurance status. Future work should be done to further delineate how to reduce the discrepancies that may draw from differential insurance coverage.


#
#

Conflict of Interest

J.A.N. is a consultant for RTI. Other authors have no conflicts of interest to disclose.

* These authors contributed equally to this manuscript and should be viewed as cofirst authors.


  • References

  • 1 Braveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep 2014; 129 (Suppl. 02) 19-31
  • 2 Gerald MJ, Strand N, Dugue D, Asanbe OA, Jones CM. Beginning to find the missing piece: social determinants of health as a contributor to disparities in plastic surgery. Plast Reconstr Surg 2021; 147 (04) 724e-725e
  • 3 Andermann A. CLEAR Collaboration. Taking action on the social determinants of health in clinical practice: a framework for health professionals. CMAJ 2016; 188 (17–18): E474-E483
  • 4 U.S. Department of Health and Human Services Office of Disease Prevention and Health Promotion; . Healthy People 2030. Available at: https://health.gov/healthypeople Accessed July 7, 2023
  • 5 Snyder RA, Chang GJ. Insurance status as a surrogate for social determinants of health in cancer clinical trials. JAMA Netw Open 2020; 3 (04) e203890-e203890
  • 6 Marcin JP, Schembri MS, He J, Romano PS. A population-based analysis of socioeconomic status and insurance status and their relationship with pediatric trauma hospitalization and mortality rates. Am J Public Health 2003; 93 (03) 461-466
  • 7 Foraker RE, Rose KM, Whitsel EA, Suchindran CM, Wood JL, Rosamond WD. Neighborhood socioeconomic status, Medicaid coverage and medical management of myocardial infarction: atherosclerosis risk in communities (ARIC) community surveillance. BMC Public Health 2010; 10: 632
  • 8 Medicaid and CHIP Payment and Access Commission (MACPAC); Racial and Ethnic Disparities in Medicaid: An Annotated Bibliography. Washington DC: Medicaid and CHIP Payment and Access Commission; 2021
  • 9 Dewar DM. Do those with more formal education have better health insurance opportunities?. Econ Educ Rev 1998; 17 (03) 267-277
  • 10 Centers for Medicare & Medicaid Services. Women's Health and Cancer Rights Act (WHCRA). 2023. Available at: https://www.cms.gov/cciio/programs-and-initiatives/other-insurance-protections/whcra_factsheet Accessed September 18, 2023
  • 11 Matros E, Shamsunder M, Disa JJ. Discussion: The effect of the breast cancer provider discussion law on breast reconstruction rates in New York State. Plast Reconstr Surg 2019; 144 (03) 569-570
  • 12 Panchal H, Shamsunder MG, Sheinin A. et al. Impact of physician payments on microvascular breast reconstruction: an all-payer claim database analysis. Plast Reconstr Surg 2020; 145 (02) 333-339
  • 13 Dinis J, Junn A, Chouairi F. et al. Impact of insurance payer and socioeconomic status on type of autologous breast reconstruction. Surg Oncol 2021; 39: 101661
  • 14 Huynh KA, Jayaram M, Wang C. et al. Factors associated with state-specific medicaid expansion and receipt of autologous breast reconstruction among patients undergoing mastectomy. JAMA Netw Open 2021; 4 (08) e2119141
  • 15 Meade AE, Cummins SM, Farewell JT. et al. Breaking barriers to breast reconstruction among socioeconomically disadvantaged patients at a large safety-net hospital. Plast Reconstr Surg Glob Open 2022; 10 (07) e4410
  • 16 Siotos C, Azizi A, Assam L. et al. Breast reconstruction for medicaid beneficiaries: a systematic review of the current evidence. J Plast Surg Hand Surg 2020; 54 (02) 77-82
  • 17 Vieira BL, Lanier ST, Mlodinow AS. et al. A multi-institutional analysis of insurance status as a predictor of morbidity following breast reconstruction. Plast Reconstr Surg Glob Open 2014; 2 (11) e255
  • 18 Friedman-Eldar O, Burke J, de Castro Silva I. et al. Stalled at the intersection: insurance status and disparities in post-mastectomy breast reconstruction. Breast Cancer Res Treat 2022; 194 (02) 327-335
  • 19 Shippee TP, Kozhimannil KB, Rowan K, Virnig BA. Health insurance coverage and racial disparities in breast reconstruction after mastectomy. Womens Health Issues 2014; 24 (03) e261-e269
  • 20 Yang RL, Newman AS, Lin IC. et al. Trends in immediate breast reconstruction across insurance groups after enactment of breast cancer legislation. Cancer 2013; 119 (13) 2462-2468
  • 21 Cohen-Levy WB, Lans J, Salimy MS, Melnic CM, Bedair HS. The significance of race/ethnicity and income in predicting preoperative patient-reported outcome measures in primary total joint arthroplasty. J Arthroplasty 2022; 37 (7S): S428-S433
  • 22 Cohen WA, Mundy LR, Ballard TN. et al. The BREAST-Q in surgical research: a review of the literature 2009-2015. J Plast Reconstr Aesthet Surg 2016; 69 (02) 149-162
  • 23 Liu LQ, Branford OA, Mehigan S. BREAST-Q measurement of the patient perspective in oncoplastic breast surgery: a systematic review. Plast Reconstr Surg Glob Open 2018; 6 (08) e1904
  • 24 Nelson JA, Chu JJ, McCarthy CM. et al. BREAST-Q REACT: clinical reference values for the BREAST-Q in post-mastectomy breast reconstruction patients. Ann Surg Oncol 2022; 29 (08) 5280-5293
  • 25 Nelson JA, Allen Jr RJ, Polanco T. et al. Long-term patient-reported outcomes following postmastectomy breast reconstruction: an 8-year examination of 3268 patients. Ann Surg 2019; 270 (03) 473-483
  • 26 Shamsunder MG, Chu JJ, Polanco TO. et al. the impact of psychiatric diagnoses on patient-reported satisfaction and quality of life in post-mastectomy breast reconstruction. Ann Surg 2023; 277 (06) e1313-e1323
  • 27 Le NK, Gabrick KS, Chouairi F, Mets EJ, Avraham T, Alperovich M. Impact of socioeconomic status on psychological functioning in survivorship following breast cancer and reconstruction. Breast J 2020; 26 (09) 1695-1701
  • 28 Pirro O, Mestak O, Vindigni V. et al. Comparison of patient-reported outcomes after implant versus autologous tissue breast reconstruction using the BREAST-Q. Plast Reconstr Surg Glob Open 2017; 5 (01) e1217
  • 29 U.S. Department of Health and Human Services. What's the difference between Medicare and Medicaid?. Washington, D.C.: : HHS Headquarters. 2022
  • 30 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 2020; 145 (01) 11e-20e
  • 31 Armstrong JG, Weigel PA, Cromwell JW, Byrn JC. Postoperative complications and patient satisfaction: does payer status have an impact?. Am J Surg 2016; 211 (06) 1099-1105.e1
  • 32 Marquez J, Makadia A, Zlatopolsky A, Hou W, Hajagos J, Khan S. Abstract 184: The effect of insurance payer type on outcomes and readmission rates in patients undergoing breast reconstruction. Plast Reconstr Surg Glob Open 2020; 8 (4S): 124-125
  • 33 Hwang K, Son JS, Ryu WK. Smoking and flap survival. Plast Surg (Oakv) 2018; 26 (04) 280-285
  • 34 Prantl L, Moellhoff N, Fritschen UV. et al. Impact of smoking status in free deep inferior epigastric artery perforator flap breast reconstruction: a multicenter study. J Reconstr Microsurg 2020; 36 (09) 694-702
  • 35 Ooms M, Puladi B, Houschyar KS. et al. Smoking and microvascular free flap perfusion in head and neck reconstruction: radial free forearm flaps and anterolateral thigh flaps. Sci Rep 2022; 12 (01) 13902
  • 36 Chouairi F, Mets EJ, Gabrick KS, Dinis J, Avraham T, Alperovich M. Impact of insurance payer on type of breast reconstruction performed. Plast Reconstr Surg 2020; 145 (01) 1e-8e
  • 37 Leatherman J, Nicholas C, Cusick T. et al. Intra-operative radiation therapy versus whole breast external beam radiotherapy: a comparison of patient-reported outcomes. Kans J Med 2021; 14: 170-175
  • 38 Card D, Dobkin C, Maestas N. Does Medicare save lives?. Q J Econ 2009; 124 (02) 597-636
  • 39 Nepon H, Safran T, Reece EM, Murphy AM, Vorstenbosch J, Davison PG. Radiation-induced tissue damage: clinical consequences and current treatment options. Semin Plast Surg 2021; 35 (03) 181-188
  • 40 Nelson JA, Cordeiro PG, Polanco T. et al. Association of radiation timing with long-term satisfaction and health-related quality of life in prosthetic breast reconstruction. Plast Reconstr Surg 2022; 150 (01) 32e-41e
  • 41 Roth RS, Qi J, Hamill JB. et al. Is chronic postsurgical pain surgery-induced? A study of persistent postoperative pain following breast reconstruction. Breast 2018; 37: 119-125
  • 42 Mazuquin B, Sunemi MMO, E Silva MPP, Sarian LOZ, Williamson E, Bruce J. Current physical therapy care of patients undergoing breast reconstruction for breast cancer: a survey of practice in the United Kingdom and Brazil. Braz J Phys Ther 2021; 25 (02) 175-185
  • 43 Sharpe JA, Martin BI, Fritz JM. et al. Identifying patients who access musculoskeletal physical therapy: a retrospective cohort analysis. Fam Pract 2021; 38 (03) 203-209
  • 44 Higgins MJ, Kale N, Homsy C. et al. Patient perspective on post-breast reconstruction exercise and physical therapy. JPRAS Open 2021; 30: 160-169
  • 45 Hanson SE, Lei X, Roubaud MS. et al. Long-term quality of life in patients with breast cancer after breast conservation vs mastectomy and reconstruction. JAMA Surg 2022; 157 (06) e220631
  • 46 Oskar S, Nelson JA, Hicks MEV. et al. The impact of race on perioperative and patient-reported outcomes following autologous breast reconstruction. Plast Reconstr Surg 2022; 149 (01) 15-27
  • 47 Cross-Call J. Medicaid Expansion Has Helped Narrow Racial Disparities in Health Coverage and Access to Care. Center on Budget and Policy Priorities. 2020
  • 48 Long SK, Shen YC. Low-income workers with employer-sponsored insurance: who's at risk when employer coverage is no longer an option?. Med Care Res Rev 2004; 61 (04) 474-494
  • 49 New York State Department of Health; Medicaid Buy-In Program for Working People with Disabilities. 2023. . Available at: https://www.health.ny.gov/health_care/medicaid/program/buy_in/ Accessed August 2, 2023

Address for correspondence

Jonas A. Nelson, MD, MPH
Plastic and Reconstructive Surgery Service, Memorial Sloan Kettering Cancer Center
321 East 61st Street, New York, NY 10065

Publication History

Received: 25 January 2024

Accepted: 26 February 2024

Accepted Manuscript online:
27 February 2024

Article published online:
12 April 2024

© 2024. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

  • References

  • 1 Braveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the causes. Public Health Rep 2014; 129 (Suppl. 02) 19-31
  • 2 Gerald MJ, Strand N, Dugue D, Asanbe OA, Jones CM. Beginning to find the missing piece: social determinants of health as a contributor to disparities in plastic surgery. Plast Reconstr Surg 2021; 147 (04) 724e-725e
  • 3 Andermann A. CLEAR Collaboration. Taking action on the social determinants of health in clinical practice: a framework for health professionals. CMAJ 2016; 188 (17–18): E474-E483
  • 4 U.S. Department of Health and Human Services Office of Disease Prevention and Health Promotion; . Healthy People 2030. Available at: https://health.gov/healthypeople Accessed July 7, 2023
  • 5 Snyder RA, Chang GJ. Insurance status as a surrogate for social determinants of health in cancer clinical trials. JAMA Netw Open 2020; 3 (04) e203890-e203890
  • 6 Marcin JP, Schembri MS, He J, Romano PS. A population-based analysis of socioeconomic status and insurance status and their relationship with pediatric trauma hospitalization and mortality rates. Am J Public Health 2003; 93 (03) 461-466
  • 7 Foraker RE, Rose KM, Whitsel EA, Suchindran CM, Wood JL, Rosamond WD. Neighborhood socioeconomic status, Medicaid coverage and medical management of myocardial infarction: atherosclerosis risk in communities (ARIC) community surveillance. BMC Public Health 2010; 10: 632
  • 8 Medicaid and CHIP Payment and Access Commission (MACPAC); Racial and Ethnic Disparities in Medicaid: An Annotated Bibliography. Washington DC: Medicaid and CHIP Payment and Access Commission; 2021
  • 9 Dewar DM. Do those with more formal education have better health insurance opportunities?. Econ Educ Rev 1998; 17 (03) 267-277
  • 10 Centers for Medicare & Medicaid Services. Women's Health and Cancer Rights Act (WHCRA). 2023. Available at: https://www.cms.gov/cciio/programs-and-initiatives/other-insurance-protections/whcra_factsheet Accessed September 18, 2023
  • 11 Matros E, Shamsunder M, Disa JJ. Discussion: The effect of the breast cancer provider discussion law on breast reconstruction rates in New York State. Plast Reconstr Surg 2019; 144 (03) 569-570
  • 12 Panchal H, Shamsunder MG, Sheinin A. et al. Impact of physician payments on microvascular breast reconstruction: an all-payer claim database analysis. Plast Reconstr Surg 2020; 145 (02) 333-339
  • 13 Dinis J, Junn A, Chouairi F. et al. Impact of insurance payer and socioeconomic status on type of autologous breast reconstruction. Surg Oncol 2021; 39: 101661
  • 14 Huynh KA, Jayaram M, Wang C. et al. Factors associated with state-specific medicaid expansion and receipt of autologous breast reconstruction among patients undergoing mastectomy. JAMA Netw Open 2021; 4 (08) e2119141
  • 15 Meade AE, Cummins SM, Farewell JT. et al. Breaking barriers to breast reconstruction among socioeconomically disadvantaged patients at a large safety-net hospital. Plast Reconstr Surg Glob Open 2022; 10 (07) e4410
  • 16 Siotos C, Azizi A, Assam L. et al. Breast reconstruction for medicaid beneficiaries: a systematic review of the current evidence. J Plast Surg Hand Surg 2020; 54 (02) 77-82
  • 17 Vieira BL, Lanier ST, Mlodinow AS. et al. A multi-institutional analysis of insurance status as a predictor of morbidity following breast reconstruction. Plast Reconstr Surg Glob Open 2014; 2 (11) e255
  • 18 Friedman-Eldar O, Burke J, de Castro Silva I. et al. Stalled at the intersection: insurance status and disparities in post-mastectomy breast reconstruction. Breast Cancer Res Treat 2022; 194 (02) 327-335
  • 19 Shippee TP, Kozhimannil KB, Rowan K, Virnig BA. Health insurance coverage and racial disparities in breast reconstruction after mastectomy. Womens Health Issues 2014; 24 (03) e261-e269
  • 20 Yang RL, Newman AS, Lin IC. et al. Trends in immediate breast reconstruction across insurance groups after enactment of breast cancer legislation. Cancer 2013; 119 (13) 2462-2468
  • 21 Cohen-Levy WB, Lans J, Salimy MS, Melnic CM, Bedair HS. The significance of race/ethnicity and income in predicting preoperative patient-reported outcome measures in primary total joint arthroplasty. J Arthroplasty 2022; 37 (7S): S428-S433
  • 22 Cohen WA, Mundy LR, Ballard TN. et al. The BREAST-Q in surgical research: a review of the literature 2009-2015. J Plast Reconstr Aesthet Surg 2016; 69 (02) 149-162
  • 23 Liu LQ, Branford OA, Mehigan S. BREAST-Q measurement of the patient perspective in oncoplastic breast surgery: a systematic review. Plast Reconstr Surg Glob Open 2018; 6 (08) e1904
  • 24 Nelson JA, Chu JJ, McCarthy CM. et al. BREAST-Q REACT: clinical reference values for the BREAST-Q in post-mastectomy breast reconstruction patients. Ann Surg Oncol 2022; 29 (08) 5280-5293
  • 25 Nelson JA, Allen Jr RJ, Polanco T. et al. Long-term patient-reported outcomes following postmastectomy breast reconstruction: an 8-year examination of 3268 patients. Ann Surg 2019; 270 (03) 473-483
  • 26 Shamsunder MG, Chu JJ, Polanco TO. et al. the impact of psychiatric diagnoses on patient-reported satisfaction and quality of life in post-mastectomy breast reconstruction. Ann Surg 2023; 277 (06) e1313-e1323
  • 27 Le NK, Gabrick KS, Chouairi F, Mets EJ, Avraham T, Alperovich M. Impact of socioeconomic status on psychological functioning in survivorship following breast cancer and reconstruction. Breast J 2020; 26 (09) 1695-1701
  • 28 Pirro O, Mestak O, Vindigni V. et al. Comparison of patient-reported outcomes after implant versus autologous tissue breast reconstruction using the BREAST-Q. Plast Reconstr Surg Glob Open 2017; 5 (01) e1217
  • 29 U.S. Department of Health and Human Services. What's the difference between Medicare and Medicaid?. Washington, D.C.: : HHS Headquarters. 2022
  • 30 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 2020; 145 (01) 11e-20e
  • 31 Armstrong JG, Weigel PA, Cromwell JW, Byrn JC. Postoperative complications and patient satisfaction: does payer status have an impact?. Am J Surg 2016; 211 (06) 1099-1105.e1
  • 32 Marquez J, Makadia A, Zlatopolsky A, Hou W, Hajagos J, Khan S. Abstract 184: The effect of insurance payer type on outcomes and readmission rates in patients undergoing breast reconstruction. Plast Reconstr Surg Glob Open 2020; 8 (4S): 124-125
  • 33 Hwang K, Son JS, Ryu WK. Smoking and flap survival. Plast Surg (Oakv) 2018; 26 (04) 280-285
  • 34 Prantl L, Moellhoff N, Fritschen UV. et al. Impact of smoking status in free deep inferior epigastric artery perforator flap breast reconstruction: a multicenter study. J Reconstr Microsurg 2020; 36 (09) 694-702
  • 35 Ooms M, Puladi B, Houschyar KS. et al. Smoking and microvascular free flap perfusion in head and neck reconstruction: radial free forearm flaps and anterolateral thigh flaps. Sci Rep 2022; 12 (01) 13902
  • 36 Chouairi F, Mets EJ, Gabrick KS, Dinis J, Avraham T, Alperovich M. Impact of insurance payer on type of breast reconstruction performed. Plast Reconstr Surg 2020; 145 (01) 1e-8e
  • 37 Leatherman J, Nicholas C, Cusick T. et al. Intra-operative radiation therapy versus whole breast external beam radiotherapy: a comparison of patient-reported outcomes. Kans J Med 2021; 14: 170-175
  • 38 Card D, Dobkin C, Maestas N. Does Medicare save lives?. Q J Econ 2009; 124 (02) 597-636
  • 39 Nepon H, Safran T, Reece EM, Murphy AM, Vorstenbosch J, Davison PG. Radiation-induced tissue damage: clinical consequences and current treatment options. Semin Plast Surg 2021; 35 (03) 181-188
  • 40 Nelson JA, Cordeiro PG, Polanco T. et al. Association of radiation timing with long-term satisfaction and health-related quality of life in prosthetic breast reconstruction. Plast Reconstr Surg 2022; 150 (01) 32e-41e
  • 41 Roth RS, Qi J, Hamill JB. et al. Is chronic postsurgical pain surgery-induced? A study of persistent postoperative pain following breast reconstruction. Breast 2018; 37: 119-125
  • 42 Mazuquin B, Sunemi MMO, E Silva MPP, Sarian LOZ, Williamson E, Bruce J. Current physical therapy care of patients undergoing breast reconstruction for breast cancer: a survey of practice in the United Kingdom and Brazil. Braz J Phys Ther 2021; 25 (02) 175-185
  • 43 Sharpe JA, Martin BI, Fritz JM. et al. Identifying patients who access musculoskeletal physical therapy: a retrospective cohort analysis. Fam Pract 2021; 38 (03) 203-209
  • 44 Higgins MJ, Kale N, Homsy C. et al. Patient perspective on post-breast reconstruction exercise and physical therapy. JPRAS Open 2021; 30: 160-169
  • 45 Hanson SE, Lei X, Roubaud MS. et al. Long-term quality of life in patients with breast cancer after breast conservation vs mastectomy and reconstruction. JAMA Surg 2022; 157 (06) e220631
  • 46 Oskar S, Nelson JA, Hicks MEV. et al. The impact of race on perioperative and patient-reported outcomes following autologous breast reconstruction. Plast Reconstr Surg 2022; 149 (01) 15-27
  • 47 Cross-Call J. Medicaid Expansion Has Helped Narrow Racial Disparities in Health Coverage and Access to Care. Center on Budget and Policy Priorities. 2020
  • 48 Long SK, Shen YC. Low-income workers with employer-sponsored insurance: who's at risk when employer coverage is no longer an option?. Med Care Res Rev 2004; 61 (04) 474-494
  • 49 New York State Department of Health; Medicaid Buy-In Program for Working People with Disabilities. 2023. . Available at: https://www.health.ny.gov/health_care/medicaid/program/buy_in/ Accessed August 2, 2023

Zoom Image
Fig. 1 Box plots of Physical Well-being of the Chest at 6 months by insurance type.
Zoom Image
Fig. 2 Box plots of Physical Well-being of the Chest at 1 year by insurance type.
Zoom Image
Fig. 3 Box plots of Physical Well-being of the Chest at 2 years by insurance type.