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J Am Acad Orthop Surg Glob Res Rev
J Am Acad Orthop Surg Glob Res Rev
JAAOS Glob Res Rev
JAAOS Glob Res Rev
JAAOS Global Research & Reviews
2474-7661
Wolters Kluwer Philadelphia, PA

JAAOSGlobal-D-24-00205
10.5435/JAAOSGlobal-D-24-00205
00002
3
011
Research Article
Socioeconomic Status and Time to Treatment in Patients With Traumatic Rotator Cuff Tears
Gutowski Caroline T. BS gutows24@rowan.edu

Wright Branden MD wright-branden@cooperhealth.edu

Romiyo Vineeth BS romiyo28@rowan.edu

Gentile Pietro BS gentile-pietro@cooperhealth.edu

Hunter Krystal PhD, MBA hunter-krystal@cooperhealth.edu

https://orcid.org/0000-0002-8566-912X
Fedorka Catherine J. MD
From the Cooper Medical School of Rowan University Camden, Camden, NJ (Ms. Gutowski, Mr. Romiyo, and Dr. Fedorka), and the Cooper University Healthcare Camden, Camden, NJ (Dr. Wright, Mr. Gentile, Dr. Hunter, and Dr. Fedorka).
Correspondence to Dr. Fedorka: Fedorka-Catherine@CooperHealth.edu
9 2024
05 9 2024
8 9 e24.0020506 6 2024
06 6 2024
Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Orthopaedic Surgeons.
2024
American Academy of Orthopaedic Surgeons
https://creativecommons.org/licenses/by-nd/4.0/ This is an open access article distributed under the Creative Commons Attribution-NoDerivatives License 4.0 (CC BY-ND) which allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.

Introduction:

Socioeconomic status (SES) affects access to care for traumatic rotator cuff (RTC) tears. Delayed time to treatment (TTT) of traumatic RTC tears results in worse functional outcomes. We investigated disparities in TTT and hypothesized that individuals from areas of low SES would have longer time to surgical repair.

Methods:

Patients who underwent repair of a traumatic RTC tear were retrospectively reviewed. Median household income and Social Deprivation Index were used as a proxy for SES. The primary outcome was TTT. Patients were further stratified by preoperative forward flexion and number of tendons torn.

Results:

A total of 221 patients met inclusion criteria. No significant difference in TTT was observed between income classes (P = 0.222) or Social Deprivation Index quartiles (P = 0.785). Further stratification by preoperative forward flexion and number of tendons torn also yielded no significant difference in TTT.

Discussion:

Contrary to delays in orthopaedic care documented in literature, our study yielded no difference in TTT between varying levels of SES, even when stratified by the severity of injury. Thus, we reject our original hypothesis. Based on our findings, mechanisms in place at our institution may have mitigated some of these health disparities within our community.

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pmcRotator cuff (RTC) injuries are the most prevalent tendon injury seen and treated in adults, with tears affecting approximately 30% of those older than 60 years and 62% of those older than 80 years.1 The incidence of these injuries has grown markedly in recent years, rising by 141% between 1996 and 2006.2-4 Given the aging population and the corresponding rising incidence of injuries, the number of RTC repairs is expected to rise as well.5

Socioeconomic factors, such as income, education, and occupation, have been consistently shown to affect health outcomes and access to health care. In the context of RTC repairs, understanding the role of these factors is crucial because it may provide insight into disparities in treatment and outcomes. Current literature suggests that socioeconomic status (SES) affects access to care for traumatic RTC tears.6-8 Studies have found that low-income populations, as represented by Medicaid insurance and lower home zip code income quartile, have lower rates of RTC repair.8 In addition, low-income populations have greater difficulty accessing high-volume surgeons for RTC repair.7 As the orthopaedic community expands its RTC surgical footprint, research into factors that influence access to RTC repair is warranted to ensure equitable care is being provided.

Time to treatment (TTT) and delay to repair may also be influenced by an individual's SES, which has important clinical implications on postoperative outcomes for traumatic RTC injuries. Research has shown that earlier RTC repair leads to better outcomes, although the ideal timing is still debated.9-12 Duncan et al12 demonstrated that repair within 6 months of injury results in better outcomes than delayed repair, with postoperative Oxford scores of the early repair group nearly double than that of the delayed repair group. Gutman et al10 found that repair within 3 weeks demonstrated markedly better visual analogue scale, American Shoulder and Elbow Surgeons, and single alpha-numeric evaluation scores, but there was also a drop-off in functional outcomes in patients who undergo surgery >4 months after injury. In addition, surgical intervention within 3 weeks resulted in markedly better postoperative University of California-Los Angeles and Constant scores, as well as range of motion, compared with delayed repair after 3 weeks.9 Furthermore, when controlling for age and comorbidity burden, Hantes et al9 found that RTC repair more than 12 months after initial diagnosis correlated with nearly double the odds of undergoing subsequent revision surgery to routine repair between 6 weeks and 12 months after diagnosis.11

Given the effect of TTT on RTC repair outcomes, the authors of this study sought to investigate potential disparities in TTT based on SES at our institution. We hypothesized that individuals from areas of low SES would have longer time from the date of injury to date of surgical repair.

Methods

This retrospective, single-center review was conducted at a level 1 trauma center and tertiary referral center. Institutional review board approval for this study protocol was obtained at our local institution. We queried our institution's orthopaedic database from January 2014 to December 2020 to identify patients, 18 years or older, who underwent arthroscopic RTC repair for a traumatic tear. ICD 10 codes M75.XX and S46.XX and CPT code 29827 were used to identify this sample population. All patients included in the study were treated by 1 of 4 fellowship-trained orthopaedic surgeons at our institution (3 sports medicine, 1 shoulder and elbow). Retrospective review of individual patient charts was conducted to verify documentation of a traumatic full-thickness RTC tear and a current New Jersey, Pennsylvania, or Delaware address. All patients documented an acute injury without previous pain or weakness in that shoulder. MRI review was conducted by a board-certified orthopaedic surgeon (C.J.F.) to assess for Goutallier classification stages. Only patients with stage 0 (normal muscle without fatty streaking) or 1 (few fatty streaks within the muscle) were included in the final cohort.13-16 Patients with home addresses in other states, patients with polytrauma, Goutallier grades 2, 3, or 4 on MRI, and patients with atraumatic, chronic, or partial-thickness tears were excluded from this study.

Median household income and Social Deprivation Index (SDI) were used as a proxy for SES.17 Median household income data were obtained from the US Census Bureau QuickFacts search tool.18 Income classifications were defined by the Federal Reserve and Federal Department of Housing and Urban Development definitions for the Philadelphia metropolitan area17,19,20 (see Supplemental Appendix Table 1, http://links.lww.com/JG9/A350, for income definitions). Income classes were separated into 4 groups: extremely and very low, low, middle, and high. Extremely low and very low (EVL) income classes are considered below the poverty line and thus were combined into one low-income group.17-20

Given SES may be influenced by other factors other than household income, we included SDI as an additional estimator of SES. Social disadvantage was quantified by SDI, a composite measure developed by Butler et al21 to quantify socioeconomic variation in health outcomes. The SDI score is based on 7 household characteristics: (1) below the poverty line; (2) less than 12 years of education; (3) single-parent families with dependents; (4) renter-occupied housing units; (5) overcrowded housing areas; (6) no vehicle; and (7) nonemployed adults younger than 65 years.21 The composite measure is scored on a scale from 0 to 100 to permit quantification and comparison of social determinants of health across geographic areas. SDI values are scored based on the level of deprivation; thus, higher values represent greater deprivation and lower SES.21 Patients were stratified into 4 distinct quartiles based on SDI scores (Supplemental Appendix Table 2, http://links.lww.com/JG9/A351) assigned according to Zip Code Tabulation Areas that represent the 4 SDI subgroups used for analysis.

The primary outcome of our study was TTT, which represents access to care. TTT was defined as the number of days from the date of injury to date of surgery. Additional secondary variables included patient demographics, such as age, race, and sex; BMI; employment status; Charlson Comorbidity Index (CCI); smoking status; alcohol use; and history of intravenous (IV) drug use. Clinical characteristics included surgical laterality and Goutallier classification stage.

We speculated that the number of torn tendons involved and preoperative forward flexion (FF) may be confounding variables for TTT. Patients with RTCs involving one tendon or RTCs with FF greater than 90° may exhibit enough functionality to defer seeking medical attention. For all income classes and SDI quartiles, patients were further stratified into 2 groups based on preoperative FF: FF greater than or equal to 90° (FF ≥ 90°) and FF less than 90° (FF < 90°). Patients were also stratified by the number of tendons involved: patients with greater than or equal to 2 tendons involved (GTTT) and those with only 1 tendon involved (OT).

One-way analysis of variance (ANOVA) was conducted to analyze differences in age, BMI, and CCI between income groups and between SDI quartiles. Chi-squared tests were used to examine differences in sex, race, employment, and smoking status. One-way ANOVA tests were used to examine differences in TTT between FF ≥ 90° and FF < 90° groups and between GTTT and OT classes. Pairwise analysis was conducted on FF ≥ 90°, FF < 90°, GTTT, and OT subgroups for both income classes and SDI quartiles. Post hoc analysis was conducted by the Turkey test to make pairwise comparison to confirm statistical significance in TTT between income classes and SDI subgroups. A post hoc power analysis was run for FF ≥ 90° and OT. Statistical significance was set at an α value of 0.05. All statistical analyses were conducted using the statistical software package SPSS v27.0 (IBM).

Results

A total of 221 patients met inclusion criteria and were included in the final analysis. Demographic analysis of income classes did not differ in age, race, sex, smoking status, IV drug use, employment status, CCI, surgical laterality, or Goutallier stage; however, there were significant differences in BMI and alcohol use (Table 1). SDI quartiles did not differ in any demographic characteristics apart from IV drug use and alcohol use (Table 2).

Table 1 Income Class Demographics

Variable	EVL (n = 49)	Low (n = 96)	Middle (n = 60)	High (n = 16)	P	
Agea	57.88 ± 9.12	56.69 ± 9.93	56.33 ± 8.12	56.81 ± 11.04	0.850	
Raceb						
 Caucasian	31 (63.3%)	66 (68.8%)	31 (51.7%)	13 (81.3%)	0.245	
 African American	10 (20.4%)	16 (16.7%)	11 (18.3%)	1 (6.3%)	
 Asian	0 (0.0%)	0 (0.0%)	2 (3.3%)	0 (0.0%)	
 Hispanic	7 (14.3%)	9 (9.4%)	13 (21.7%)	2 (12.5%)	
 Other	1 (2.0%)	5 (5.2%)	3 (5.0%)	0 (0.0%)	
Sexb						
 Male	29 (59.2%)	56 (58.3%)	31 (51.7%)	12 (75.0%)	0.405	
 Female	20 (40.8%)	40 (41.7%)	29 (48.3%)	4 (25.0%)	
BMI (kg/m2)a	33.15 ± 5.94	30.79 ± 6.16	30.75 ± 6.13	28.55 ± 4.05	0.033 c	
Smokingb						
 None	33 (67.3%)	60 (62.5%)	32 (53.3%)	9 (56.3%)	0.785	
 Current	10 (20.4%)	19 (19.8%)	14 (23.3%)	4 (25.0%)	
 Quit	6 (12.2%)	17 (17.7%)	14 (23.3%)	3 (18.8%)	
Alcoholb						
 No	32 (65.3%)	37 (38.5%)	30 (50.0%)	7 (43.7%)	0.023 c	
 Yes	17 (34.7%)	59 (61.5%)	30 (50.0%)	9 (56.3%)	
IV drug useb						
 Never	47 (95.9%)	94 (97.9%)	68 (100%)	16 (100%)	0.527	
 Current	2 (4.1%)	1 (1.0%)	0 (0.0%)	0 (0.0%)	
 Former	0 (0.0%)	1 (1.0%)	0 (0.0%)	0 (0.0%)	
CCIa	3.55 ± 1.99	3.08 ± 2.41	2.90 ± 1.80	2.44 ± 1.09	0.221	
Surgical lateralityb						
 Right	28 (57.1%)	63 (65.6%)	39 (65.0%)	8 (50.0%)	0.527	
 Left	21 (42.9%)	33 (34.4%)	21 (35.0%)	8 (50.0%)	
Goutallier stage						
 0	34 (69.4%)	77 (80.2%)	44 (73.3%)	10 (62.5%)	0.309	
 1	15 (30.6%)	19 (19.8%)	16 (26.7%)	6 (37.5%)	
BMI = body mass index, CCI = Charlson Comorbidity Index, EVL = extremely/very low, IV = intravenous, SDI = Social Deprivation Index

a Statistics conducted by the one-way ANOVA test.

b Statistics conducted by the chi-square test.

c Statistical significance (P ≤ 0.05).

Table 2 SDI Quartile Demographics

Variable	SDI Quartile 4 (n = 49)	SDI Quartile 3 (n = 36)	SDI Quartile 2 (n = 58)	SDI Quartile 1 (n = 78)	P	
Agea	58.39 ± 9.42	56.53 ± 8.48	56.47 ± 9.23	56.36 ± 9.79	0.643	
Raceb	
 Caucasian	30 (61.2%)	29 (80.6%)	34 (58.6%)	48 (61.5%)	0.341	
 African American	12 (24.5%)	3 (8.3%)	10 (17.2%)	13 (16.7%)	
 Asian	0 (0.0%)	0 (0.0%)	0 (0.0%)	2 (2.6%)	
 Hispanic	6 (12.2%)	3 (8.3%)	12 (20.7%)	10 (12.8%)	
 Other	1 (2.0%)	1 (2.8%)	2 (3.4%)	5 (6.4%)	
Sexb	
 Male	29 (59.2%)	20 (55.6%)	34 (58.6%)	45 (57.7%)	0.988	
 Female	20 (40.8%)	16 (44.4%)	24 (41.4%)	33 (42.3%)	
BMI (kg/m2)a	32.62 ± 5.84	31.21 ± 5.94	31.74 ± 6.40	29.75 ± 5.83	0.056	
Smokingb	
 Never	34 (69.4%)	20 (55.6%)	31 (53.4%)	49 (62.8%)	0.304	
 Current	11 (22.4%)	6 (16.7%)	14 (24.1%)	16 (20.5%)	
 Quit	4 (8.2%)	10 (27.8%)	13 (22.4%)	13 (16.7%)	
Alcoholb	
 No	32 (65.3%)	13 (36.1%)	25 (43.1%)	36 (46.2%)	0.035 c	
 Yes	17 (34.7%)	23 (63.9%)	33 (56.9%)	42 (53.8%)	
IV drug useb	
 Never	46 (93.9%)	36 (100.0%)	57 (98.3%)	78 (100.0%)	0.036 c	
 Current	3 (6.3%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	
 Former	0 (0.0%)	0 (0.0%)	1 (1.7%)	0 (0.0%)	
CCIa	3.69 ± 1.97	2.86 ± 1.81	3.21 ± 1.97	2.73 ± 2.32	0.072	
Surgical lateralityb	
 Right	28 (57.1%)	26 (72.2%)	37 (63.8%)	47 (60.3%)	0.520	
 Left	21 (42.9%)	10 (27.8%)	21 (36.2%)	31 (39.7%)	
Goutallier stage	
 0	35 (71.4%)	28 (77.8%)	45 (77.6%)	57 (73.1%)	0.844	
 1	14 (28.6%)	8 (22.2%)	12 (22.4%)	21 (26.9%)	
BMI = body mass index, CCI = Charlson Comorbidity Index, IV = intravenous, SDI = Social Deprivation Index

a Statistics conducted by the one-way ANOVA test.

b Statistics conducted by the chi-square test.

c Statistical significance (P ≤ 0.05).

One-way ANOVA analysis found no difference in TTT when stratifying by income classes alone (P = 0.222, Table 3). When stratified by preoperative FF, no significant difference in TTT between income classes was detected in patients with FF ≥ 90° (P = 0.075) or FF < 90° (P = 0.401, Table 4). Pairwise analysis of income classes with FF ≥ 90°, however, yielded significant differences between the EVL and middle-income classes (P = 0.034) and the EVL and high-income classes (P = 0.031, Table 5). EVL class patients with FF ≥ 90° averaged 191.25 days between injury and intervention, compared with 129.67 days in low-income and 120.47 days in middle-income class patients (Table 4). Pairwise analysis of income classes with FF < 90° yielded no notable findings.

Table 3 Time to Treatment by SDI and Income Classes

Variable	EVL (n)	Low (n)	Middle (n)	High (n)	P	
TTTa	144.18 ± 132.18 (49)	109.92 ± 92.7 (96)	110.97 ± 96.34 (60)	136.13 ± 90.11 (16)	0.222	
	SDI Quartile 4 (n)	SDI Quartile 3 (n)	SDI Quartile 2 (n)	SDI Quartile 1 (n)	P	
TTTa	126.24 ± 121.45 (49)	129.67 ± 103.38 (36)	109.67 ± 102.35 (58)	118.44 ± 94.04 (78)	0.785	
EVL = extremely/very low, SDI = Social Deprivation Index, TTT = time to treatment

a Statistics conducted by the one-way ANOVA test.

Table 4 Time to Treatment Stratified by FF

Variable	EVL (n)	Low (n)	Middle (n)	High (n)	P	
TTT (FF < 90°)a	73.39 ± 64.1 (18)	88.5 ± 67.16 (34)	102.7 ± 97.95 (27)	132.43 ± 124.52 (7)	0.401	
TTT (FF ≥ 90°)a	191.25 ± 149.22 (28)	129.67 ± 106.52 (54)	120.47 ± 96.2 (32)	137.63 ± 63.77 (8)	0.075	
	SDI Quartile 4 (n)	SDI Quartile 3 (n)	SDI Quartile 2 (n)	SDI Quartile 1 (n)	P	
TTT (FF < 90°)a	82.57 ± 71.74 (21)	92.30 ± 63.63 (20)	76.93 ± 59.98 (27)	112.35 ± 105.54 (31)	0.359	
TTT (FF ≥ 90°)a	159.0 ± 140.76 (28)	176.38 ± 125 (16)	138.19 ± 122.4 (31)	122.45 ± 86.59 (47)	0.335	
EVL = extremely/very low, FF = forward flexion, SDI = Social Deprivation Index, TTT = time to treatment

a Statistics conducted by the one-way ANOVA test.

Table 5 Pairwise Analysis of Income Classes With Preoperative Forward Flexion ≥90°

Variable	EVL	Low	Middle	High	
EVL		0.034a	0.031a	0.333	
Low	0.034a		0.740	0.878	
Middle	0.031a	0.740		0.655	
High	0.333	0.878	0.655		
EVL = extremely/very low

a Statistical significance (P ≤ 0.05).

When stratified by the number of tendons torn, subgroup analysis yielded no significant differences between EVL, low-income, middle-income, and high-income classes in OT (P = 0.118) or the GTTT (P = 0.776, Table 6) groups. Pairwise analysis of the OT group, however, detected significant differences between the EVL and low-income classes (P = 0.045, Table 7). EVL class patients with OT involved averaged 196.9 days to treatment while low-income class patients averaged 127.0 days (Table 6). Pairwise analysis of the GTTT group demonstrated no notable findings.

Table 6 Time to Treatment Stratified by Number of Tendons Torn

Variable	EVL (n)	Low (n)	Middle (n)	High (n)	P	
TTT (GTTT)a	107.83 ± 95.21 (29)	97.18 + 81.56 (55)	103.15 ± 93.24 (34)	129.78 ± 110.52 (9)	0.776	
TTT (OT)a	196.9 ± 160.75 (20)	127.0 ± 104.43 (41)	121.19 ± 101.18 (26)	144.29 ± 62.25 (7)	0.118	
	SDI Quartile 4 (n)	SDI Quartile 3 (n)	SDI Quartile 2 (n)	SDI Quartile 1 (n)	P	
TTT (GTTT)a	93.52 ± 69.58 (29)	127.47 ± 114.57 (19)	100.27 ± 98.82 (37)	102.45 ± 81.0 (42)	0.624	
TTT (OT)a	173.7 ± 161.62 (20)	132.12 ± 92.75 (17)	126.24 ± 108.76 (21)	137.08 ± 105.39 (36)	0.577	
EVL = extremely/very low, FF = forward flexion, GTTT = greater than or equal to 2 tendons, OT = 1 tendon, SDI = Social Deprivation Index, TTT = time to treatment

a Statistics conducted by the one-way ANOVA test.

Table 7 Pairwise Analysis of Income Classes with Only One Tendon Torn

Variable	EVL	Low	Middle	High	
EVL		0.045a	0.057	0.411	
Low	0.045a		0.854	0.757	
Middle	0.057	0.854		0.611	
High	0.411	0.757	0.611		
EVL = extremely/very low

a Statistical significance (P ≤ 0.05).

One-way ANOVA analysis of SDI quartiles found no significant differences in TTT (P = 0.785, Table 3). Subgroup analysis of SDI quartiles with FF ≥ 90° (P = 0.335) and FF < 90° (P = 0.359) yielded no differences (Table 4). Additional pairwise analysis of these subgroups did not detect any significance. Similarly, subgroup analysis of SDI quartiles for OT (P = 0.577) and GTTT (P = 0.624) involved also yielded no significant differences (Table 6). Pairwise analysis of SDI quartiles within the OT and GTTT subgroups did not yield any significance.

A post hoc power analysis was also run for FF ≥ 90° and OT subgroups and found that a sample size of 12,580 RTC tears would need to be included to appropriately power this study.

Discussion

The goal of this study was to explore the role of SES on TTT for rotator cuff repairs. On initial analysis, there were no significant differences in TTT between income classes or SDI quartiles. When further stratified by (1) FF ≥ 90°, (2) FF < 90°, (3) 2 or more torn tendons, and (4) 1 torn tendon, there were no significant differences in TTT for either the SES income classes or SDI quartiles.

It has been previously reported in multiple orthopaedic subspecialties that social determinants of health do affect patient care and outcomes. Patients in lower income categories who had an anterior cruciate ligament injury experienced longer delays in initial presentation, diagnosis, and repair.22 These findings were consistent with similar results for low-income hip arthroplasty patients.23 Ziedas et al22 suggested that publicly insured and uninsured patients face difficulties in obtaining office visits with an orthopaedic surgeon, further contributing to the delay in diagnosis and treatment. Furthermore, it has been shown that privately insured patients have 8.8 to 57 times greater odds of scheduling an appointment for sports-related injuries compared with Medicaid patients.24 Research on shoulder and sports medicine specifically demonstrated that patients with Medicaid experienced markedly greater wait time and longer periods from injury to surgery in comparison with those with other insurances.24 Despite the aforementioned barriers suggesting that lower income patients may face a markedly increased TTT, the results of the study demonstrated no differences in TTT for RTC repair. The authors of this study hypothesize that these results may be attributed to the mechanisms in place in our health system to alleviate barriers to care, for example, clinics accepting all state Medicaid plans. For uninsured patients, our institution also offers charity care in certain circumstances.

Structural barriers to care in orthopaedics related to the geographic concentration of specialized surgeons, distance and transportation to facilities, and access to high-volume hospitals represent areas of improvement to provide more equitable care.24 It was demonstrated that publicly insured and uninsured patients were less likely to be seen by local orthopaedic surgeons and, consequently, required greater travel distances to receive outpatient orthopaedic care. Limitations in personal resources including transportation to tertiary referral centers only further exacerbate higher no-show rates to outpatient appointments and barriers to care.25 Rabah et al25 also suggested that secondary to deficits in health literacy, patients may not know what symptoms warrant acute orthopaedic attention. To alleviate potential structural barriers, the orthopaedic department at our institution offers clinic availability 5 days a week in a heavily underserved urban area, which facilitates access to care where many patients may not have individual means of transportation. Furthermore, we have urgent clinic appointments available with advanced practice providers for new patients within 72 hours. In addition, patients who already have MRI positive for a traumatic RTC tear are allotted urgent time slots with surgeons to fast-track them to surgery. Our department has also dedicated time to educating local primary care physicians on the diagnosis and management of traumatic RTC tears, which has further improved early identification and streamlined referrals to orthopaedic surgeons. We hypothesize that these mechanisms may have alleviated some of the potential barriers to accessing orthopaedic services and time to intervention.

It is important to note, however, that while initial analysis yielded no differences in TTT, pairwise analysis demonstrated significant differences between both EVL/low-income and EVL/middle-income classes in patients with preoperative FF ≥ 90°. Similarly, pairwise analysis of TTT in patients with only 1 tendon torn yielded significance between EVL and low-income classes.

These results may represent a percentage of patients who maintain some functionality despite their injury. It is worth noting though that if we were to apply a Bonferroni correction to account for multiple comparisons and set the P value to <0.001, our results would not be statistically significant. However, the authors do suggest that they may be clinically significant, given that the average TTT differed by almost 30 to 60 days between groups. This study may be underpowered to truly detect this difference.

While there were no differences found based on SES, it is important to note some of the other trends that were seen within our data. Patients who had higher functional limitations before surgery (FF < 90°) did have a faster TTT; however, interestingly, the gap was larger in our patients with lower SES and higher SDI than in the patients in our higher income and lower SDI categories (Table 4). We should also note that while we did not find differences between groups, our average TTT for most of our groups was >3 months, indicating that while our care is equitable, we can do better to get these patients into the operating room sooner. Additional research is warranted to determine the effects of this delay in treatment on clinical and functional outcomes.

There exists a plethora of evidence within the orthopaedic literature that ought to raise orthopaedic surgeons' concern when practicing in low-income regions. The findings of this study highlight possible solutions to minimize treatment disparities between socioeconomic classes in RTC injuries, with particular attention to patients who require access to outpatient evaluation. Thus, to practice in an equitable manner, clinicians should be aware of this disparity and place additional emphasis on identifying this subgroup of patients within the clinic and community. By increasing awareness and implementing system-level practice patterns mentioned earlier in this article, it is hoped that this subgroup of patients with traumatic RTC tears can receive the proper orthopaedic care.

This study has several limitations, including the retrospective nature of the study and thus the common biases of retrospectively collected data. Our patient population was sampled from a single institution, which may limit the external validity and generalizability of the study results. The use of zip code median income and SDI zip code as proxies for SES represents an inherent limitation to the study. Although literature supports the use of geocoding to estimate SES, it should be cautiously interpreted because area-level indicators may not represent individual-level SES with absolute certainty.26 Last, stratification by preoperative FF and number of tendons torn reduced the subcohort sample size, and thus, our analysis may be underpowered. Finally, our study may be underpowered to show true differences because a post hoc power analysis showed that we would need over 12,000 patients to reach statistical significance, which would require a multicentered approach. This is, however, to our knowledge the largest study cohort published to date of patients with acute rotator cuff tear. Future multicenter studies with larger sample sizes would offer more generalizable results and potentially be powered to detect true differences in TTT in these patients.

Conclusions

SES has been shown to affect access to care for traumatic RTC tears in previous studies. Delay to surgical treatment has important clinical implications and ultimately may compromise postoperative functional outcomes and require revision repair.9-12 This study investigated the relationship between SES and TTT for traumatic RTC tears. The results of our study yielded overall no significant differences in TTT between SESs, even when stratified by severity of injury and functional limitations as determined by preoperative FF and number of torn tendons involved. Documented explanations for delay to care in underserved populations include (1) number of providers accepting all insurances, (2) geographic distribution of tertiary referral centers, and (3) community education on orthopaedic injuries.22,24,25 Based on our findings, we hypothesize that mechanisms in place at our institution may have mitigated some health disparities within our community and we hope to offer a model to other healthcare systems to deliver more equitable care.

This study has been previously presented at Shoulder360 on April 13 to 15, 2023 and the International Congress on Shoulder and Elbow Surgery (2023) on September 5, 2023.

Dr. Fedorka or an immediate family member serves as a paid consultant to Stryker Corporation. None of the following authors or any immediate family member has received anything of value from or has stock or stock options held in a commercial company or institution related directly or indirectly to the subject of this article: Ms. Gutowski, Dr. Wright, Mr. Romiyo, Mr. Gentile, and Dr. Hunter.

This study received Institutional Review Board committee approval from Cooper Health System: #21-285.
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