Comparison of 30-day Outcomes in Matched Population: Private versus Medicaid
The comparison of postoperative outcomes between the matched Medicaid and private insurance cohort is displayed in Table 3. No significant difference in overall morbidity was observed between privately insured patients and those insured by Medicaid (P = 0.167). The rate of medical complications was low (0.6%), regardless of payer type, and the majority of morbidity was attributable to wound complications.
Wound complications occurred around 20% of the time (18.6% vs 23.6%, P = 0.189) in both private and Medicaid cohorts. No significant difference was observed between the 2 cohorts in the rates of SSI (4.0% vs 5.5%, P = 0.449), seroma (3.7% vs 6.1%, P = 0.223), or hematoma (2.1% vs 1.2%, P = 0.471). The most common complication observed was wound dehiscence, although rates did not differ significantly between private and Medicaid patients (7.6% vs 7.9%, P = 0.920). The only significant difference observed in outcomes was for flap loss. The rate of flap failure was significantly lower in the privately insured cohort (2.1% vs 6.1%, P = 0.024), although the overall reoperation rate did not differ between the 2 groups (9.1% for both, P = 0.981). Multivariable logistic regression analyses demonstrated that Medicaid insurance status did not independently increase the risk of surgical complication (Table 4; P = 0.299). Subanalyses of specific complications revealed that Medicaid insurance had no significant relationship with SSI, wound dehiscence, prosthesis loss, seroma, or hematoma (all P > 0.05) but conferred a significantly increased risk of flap failure [odds ratio (OR) = 3.315; P = 0.027].
Comparison of 30-day Outcomes in Matched Population: Private versus Medicare
Table 5 shows the comparison of postoperative outcomes between the matched Medicare and private insurance cohort. Results of the outcome analysis mirrored the findings of the comparison between Medicaid and privately insured patients; no difference in overall morbidity was observed between privately insured patients and those with Medicare (15.8% vs 16.3%, P = 0.861). Again, the rate of medical complications was low (0.9% vs 0.4%, P = 0.486), and the majority of morbidity was attributable to wound complications.
In both the private and Medicare cohorts, wound complications occurred less than 20% of the time (15.1% vs 16.3%, P = 0.681). No significant difference was observed between rates of SSI (3.0% vs 5.2%, P = 0.157), seroma (5.0% vs 4.3%, P = 0.668), hematoma (0.9% vs 1.7%, P = 0.363), wound dehiscence (6.4% vs 4.3%, P = 0.260), or reoperation (5.5% vs 6.9%, P = 0.474). Multivariable logistic regression analyses demonstrated that Medicare insurance status did not independently increase the risk of overall surgical complication or any individual complication (Table 6, all P > 0.05).
Our study is the first to investigate the effect of payer status on surgical outcomes of breast reconstruction, and, to our knowledge, the first to examine this relationship in the plastic surgery literature. In contrast to studies in other surgical specialties that demonstrate higher complication rates in patients with government insurance, our results indicate that this is not the case for breast reconstruction. Our analysis suggests that Medicaid and Medicare insurance status does not independently predict inferior 30-day outcomes in breast reconstruction when compared with private insurance status. Many studies have investigated the effect of inherent patient characteristics and surgical factors on breast reconstruction outcomes. Few authors, however, have examined the role that systemic, health policy–related factors may play in outcomes of breast reconstruction. It is important for plastic surgeons to join this conversation.
In 2010, LaPar et al1 performed the largest study to date comparing outcomes between Medicare, Medicaid, uninsured, and privately insured patients for 8 major, general surgical procedures. They found the odds of in-hospital mortality, wound complications, infection, and a number of medical complications to be independently higher for both the Medicare (OR, 1.54) and Medicaid (OR, 1.74) cohorts when compared to privately insured patients. Stone et al2 recently corroborated these findings in pediatric surgery, and similar results have been reported in a number of other surgical specialties.5–11
Multiple factors likely explain the divergence of our findings from those in other surgical specialties. Patients with Medicaid have been shown to have a higher acuity on presentation and require nonelective, emergent operation more often than privately insured patients.10 Although prior authors have attempted to control for elective versus emergent operative status, a number of additional factors, such as adequate resuscitation, are correlated with emergent operation and are difficult to quantify and control for retrospectively. In contrast to treatment of a ruptured aortic aneurysm, for example, breast reconstruction is always an elective procedure performed in hemodynamically stable patients. This is not to say that the issue of delayed and advanced presentation for Medicaid and Medicare is insignificant in patients with breast cancer. On the contrary, a 2010 analysis of the National Cancer Database showed that uninsured, Medicaid, and Medicare patients more frequently presented with advanced disease (stage III or IV) compared with stage I disease.20 Nonetheless, higher acuity of breast cancer presentation rarely necessitates emergent operation or translates into higher surgical risk. Thus, the phenomenon of poor outcomes as a result of delayed presentation in Medicaid patients is relatively less important in this context.
In addition, it is well documented that patients with Medicaid have poorer overall health maintenance and significantly higher rates of comorbidities as a result of complex socioeconomic factors.41,42 Similarly, as a result of advanced age, the Medicare population often presents with higher rates of cardiopulmonary and renal comorbidities. Our population reflects these findings. However, breast reconstruction patients are generally more likely to be healthier than the patient cohorts for vascular, cardiac, and general surgery that have been previously studied. Patients with significant medical comorbidities often have prohibitively high anesthetic risk and therefore are not candidates for elective breast reconstruction. Additionally, tissue expander reconstructions involve relatively short anesthesia times, are confined to the body wall, and involve minimal blood loss and fluid shifts, which limit the procedural risk of medical complications when compared to many major general surgical procedures. These considerations likely contribute to the divergence of our findings in breast reconstruction from those in other surgical procedures.
Furthermore, surgical outcomes depend not only on the operation itself but also on postoperative care. For tissue expander insertion, which represented nearly 80% of the reconstructions analyzed in our study, patients have far less acuity in the immediate postoperative period when compared to more invasive procedures such as coronary artery bypass grafting or major colon resection. Early postoperative mobilization in our patients translates to lower rates of medical complications such as pulmonary embolism and pneumonia. Notably, the immediate postoperative care plays a more significant role in autologous reconstructions. Particularly, in cases requiring anastomotic revision or difficult dissections, anesthesia duration tends to be longer, increasing the risk of perioperative complications.
Interestingly, flap loss was the only complication in which a disparity was observed, with Medicaid patients demonstrating significantly higher rates than privately insured patients. Similarly, risk-adjusted multivariate regression revealed that Medicaid patients were 3 times as likely to experience flap failure. While the data do not yield an explanation as to why this one rate is higher, it may be that younger surgeons are more likely to perform flap reconstruction in Medicaid patients, as it has been shown that flap survival is related to surgeon experience. The TOPS registry does not provide comprehensive data on the type of hospital, specifically community versus academic, at which procedures were performed. Nor does it provide data on the experience of the surgeon or the case volume of the center where these procedures were performed.
This ties into the broader issue of discrepancies in access to quality care between government and privately insured patients across all specialties. Privately insured patients may have a more flexible network of providers, allowing them to seek out surgeons with higher level of expertise and centers with well-trained ancillary staff to provide perioperative care. Although reimbursement rates are similar between private insurance and Medicare, Medicaid reimbursement rates are consistently lower for both physicians and facilities. A growing number of physicians are declining to accept Medicaid reimbursement because of an inability to cover the basic costs of caring for these patients. An example of this is provided by a survey of otolaryngologists in southern California that found 97% would provide consultation for children with private insurance while only 27% would do so for children with public insurance.43 These realities could have a substantial impact on healthcare access in the coming years as Medicaid coverage is expanded. A smaller network of providers and facilities available to the Medicaid population may contribute to the inferior surgical outcomes reported previously in other specialties.
Low reimbursement from Medicaid affects plastic surgeons in a manner analogous to other specialists. Data reported from a large academic center cite only a 13.0% collection rate for surgeon fees and a 20.4% collection rate for facility fees from Medicaid for breast reconstruction procedures compared to significantly higher numbers for Medicare (37.0% and 33.5%) and private insurance (40.0% and 63.4%).44 Although the WHCRA mandated insurance coverage, it did not establish reimbursement rates or require that a given center provide breast reconstruction services if they find it to be unprofitable. The effect of these economic factors on the ability of Medicaid patients to find a breast reconstructive surgeon has been highlighted recently in the popular press.45
Despite these considerations, our data do not show inferior 30-day outcomes for breast reconstruction in patients with government insurance. As recent changes in health policy have expanded access to breast reconstruction, our data indicate that plastic surgeons have ensured consistent quality of care, irrespective of insurance status.
Our study is not without limitations. Although the methodology of propensity score matching allows us to minimize confounding, it is impossible to eliminate all bias inherent to a retrospective design. Moreover, we are unable to determine whether cases excluded due to incomplete data systematically differed from those with complete data, raising the possibility of selection bias. Although our analysis of 30-day postoperative outcomes likely captures the majority of perioperative wound and medical complications, our data likely underestimate complications, as events such as capsular contracture, reoperation, and explanation may not be fully accounted for within the 30-day postoperative period. Finally, information regarding facility type and subjective endpoints such as aesthetic outcome and patient satisfaction were not considered in our analysis, as TOPS does not record these data points.
This study is the first to examine the effect of primary payer status on outcomes following breast reconstruction. Our results suggest that Medicaid and Medicare insurance do not independently predict increased overall complication rates in breast reconstruction. This finding underscores the efforts and commitment of the plastic surgeon to provide consistent care for patients, irrespective of insurance status. Further work should examine whether these cohorts of patients differ with respect to longer term and aesthetic outcomes.
1. LaPar DJ, Bhamidipati CM, Mery CM, et al. Primary payer status affects mortality for major surgical operations. Ann Surg. 2010;252:544–550; discussion 550–551
2. Stone ML, LaPar DJ, Mulloy DP, et al. Primary payer status is significantly associated with postoperative mortality, morbidity, and hospital resource utilization in pediatric surgical patients within the United States. J Pediatr Surg. 2013;48:81–87
3. Abdo A, Trinh QD, Sun M, et al. The effect of insurance status on outcomes after partial nephrectomy. Int Urol Nephrol. 2012;44:343–351
4. Azzopardi J, Walsh D, Chong C, et al. Surgical treatment for women with breast cancer in relation to socioeconomic and insurance status. Breast J. 2014;20:3–8
5. Bradley CJ, Dahman B, Given CW. Treatment and survival differences in older Medicare patients with lung cancer as compared with those who are dually eligible for Medicare and Medicaid. J Clin Oncol. 2008;26:5067–5073
6. Dasenbrock HH, Wolinsky JP, Sciubba DM, et al. The impact of insurance status on outcomes after surgery for spinal metastases. Cancer. 2012;118:4833–4841
7. Kelz RR, Gimotty PA, Polsky D, et al. Morbidity and mortality of colorectal carcinoma surgery differs by insurance status. Cancer. 2004;101:2187–2194
8. LaPar DJ, Stukenborg GJ, Guyer RA, et al. Primary payer status is associated with mortality and resource utilization for coronary artery bypass grafting. Circulation. 2012;126(11 Suppl 1):S132–S139
9. Lapar DJ, Bhamidipati CM, Walters DM, et al. Primary payer status affects outcomes for cardiac valve operations. J Am Coll Surg. 2011;212:759–767
10. Murphy EH, Stanley GA, Arko MZ, et al. Effect of ethnicity and insurance type on the outcome of open thoracic aortic aneurysm repair. Ann Vasc Surg. 2013;27:699–707
11. Robbins AS, Chen AY, Stewart AK, et al. Insurance status and survival disparities among nonelderly rectal cancer patients in the National Cancer Data Base. Cancer. 2010;116:4178–4186
12. Rosen H, Saleh F, Lipsitz SR, et al. Lack of insurance negatively affects trauma mortality in US children. J Pediatr Surg. 2009;44:1952–1957
13. Schoenfeld AJ, Belmont PJ Jr, See AA, et al. Patient demographics, insurance status, race, and ethnicity as predictors of morbidity and mortality after spine trauma: a study using the National Trauma Data Bank. Spine J. 2013;13:1766–1773
14. Short SS, Liou DZ, Singer MB, et al. Insurance type, not race, predicts mortality after pediatric trauma. J Surg Res. 2013;184:383–387
15. Slatore CG, Au DH, Gould MKAmerican Thoracic Society Disparities in Healthcare Group. . An official American Thoracic Society systematic review: insurance status and disparities in lung cancer practices and outcomes. Am J Respir Crit Care Med. 2010;182:1195–1205
16. Trinh QD, Schmitges J, Sun M, et al. Morbidity and mortality of radical prostatectomy differs by insurance status. Cancer. 2012;118:1803–1810
17. Boomer L, Freeman J, Landrito E, et al. Perforation in adults with acute appendicitis linked to insurance status, not ethnicity. J Surg Res. 2010;163:221–224
18. Greenstein AJ, Moskowitz A, Gelijns AC, et al. Payer status and treatment paradigm for acute cholecystitis. Arch Surg. 2012;147:453–458
19. Giacovelli JK, Egorova N, Nowygrod R, et al. Insurance status predicts access to care and outcomes of vascular disease. J Vasc Surg. 2008;48:905–911
20. Ward EM, Fedewa SA, Cokkinides V, et al. The association of insurance and stage at diagnosis among patients aged 55 to 74 years in the national cancer database. Cancer J. 2010;16:614–621
21. Rosenthal BD, Hulst JB, Moric M, et al. The effect of payer type on clinical outcomes in total knee arthroplasty. J Arthroplasty. 2014;29:295–298
22. Garfein ES. The privilege of advocacy: legislating awareness of breast reconstruction. Plast Reconstr Surg. 2011;128:803–804
23. Hershman DL, Richards CA, Kalinsky K, et al. Influence of health insurance, hospital factors and physician volume on receipt of immediate post-mastectomy reconstruction in women with invasive and non-invasive breast cancer. Breast Cancer Res Treat. 2012;136:535–545
25. Horner-Taylor C. The Breast Reconstruction Advocacy Project: one woman can make a difference. Am J Surg. 1998;175:85–86
26. Wilkins EG, Alderman AK. Breast reconstruction practices in North America: current trends and future priorities. Semin Plast Surg. 2004;18:149–155
27. Yang RL, Newman AS, Lin IC, et al. Trends in immediate breast reconstruction across insurance groups after enactment of breast cancer legislation. Cancer. 2013;119:2462–2468
29. Hume KM, Crotty CA, Simmons CJ, et al. Medical specialty society-sponsored data registries: opportunities in plastic surgery. Plast Reconstr Surg. 2013;132:159e–167e
31. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150–161
32. Austin PC. Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations. Biom J. 2009;51:171–184
33. Austin PC, Schuster T. The performance of different propensity score methods for estimating absolute effects of treatment on survival outcomes: a simulation study. Stat Methods Med Res. 2014 Feb 3 [Epub ahead of print]
34. Thoemmes F, Kim ES. A systematic review of propensity score methods in the social sciences. Multivariate Behav Res. 2011;46:90–118
35. Stukel TA, Fisher ES, Wennberg DE, et al. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA. 2007;297:278–285
36. Dehejia RH, Wahba S. Propensity score-matching methods for non-experimental causal effects. Biometrika. 1983;70:41–55
37. Little RJ, Rubin DB. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu Rev Public Health. 2000;21:121–145
38. D’Agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281
39. Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39:33–38
40. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med. 2007;26:734–753
41. Lantz PM, House JS, Lepkowski JM, et al. Socioeconomic factors, health behaviors, and mortality: results from a nationally representative prospective study of US adults. JAMA. 1998;279:1703–1708
42. Cohen JW. Medicaid policy and the substitution of hospital outpatient care for physician care. Health Serv Res. 1989;24:33–66
43. Wang EC, Choe MC, Meara JG, et al. Inequality of access to surgical specialty health care: why children with government-funded insurance have less access than those with private insurance in Southern California. Pediatrics. 2004;114:e584–e590
44. Alderman AK, Storey AF, Nair NS, et al. Financial impact of breast reconstruction on an academic surgical practice. Plast Reconstr Surg. 2009;123:1408–1413
© 2014 American Society of Plastic Surgeons
45. Rabin RC No Easy Choices on Breast Reconstruction. May 20, 2013 New York Times Wellness Blog