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59 (); 137-143
doi:
10.1016/j.jor.2024.10.040

Medicare and Medicaid patients undergoing total joint arthroplasty have more complications and healthcare utilization than privately insured patients

School of Medicine, The Johns Hopkins University, 733 N. Broadway, Baltimore, MD, 21205, USA
Department of Orthopaedic Surgery, 601 N Caroline Street, Baltimore, MD, 21287, USA

⁎Corresponding author: Matthew J. Best. mbest8@jhmi.edu

Disclaimer:
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.

Abstract

Abstract

This study characterized the independent association between insurance type and healthcare outcomes in patients undergoing total joint arthroplasty (TJA).

National data identified patients who underwent total hip, knee, shoulder, elbow, ankle or wrist joint arthroplasty surgery from 2012 to 2020 for osteoarthritis. Medicaid, Medicare≥65 years old, Medicare<65 years old, and uninsured patients were matched to privately insured patients based on age, sex, and comorbidities. Multivariable analysis, controlled for various characteristics, was conducted to quantify various outcome measures by payer status.

Medicaid patients had greater odds of cardiac, genitourinary, hematoma/hemorrhage/seroma, respiratory, and wound dehiscence complications than privately insured (odds ratio [OR]: 1.5, 1.2, 1.6, 1.3, 1.5, respectively; p < 0.01). Medicare patients ≥65 years old had greater odds of cardiac and wound dehiscence complications but fewer central nervous system and genitourinary complications and post-operative infections than privately insured (OR:1.2, 1.2, 0.3, 0.8, 0.7, respectively; p < 0.01). Medicare<65 years old patients had greater odds of cardiac, gastrointestinal, genitourinary, hematoma/hemorrhage/seroma, post-operative anemia, respiratory, and wound dehiscence complications than privately insured (OR: 1.2, 1.4, 1.2, 1.4, 1.2, 1.7, 1.6, respectively; p < 0.01). Medicare≥65, Medicare<65, and Medicaid patients had $2,243, $3,849, and $1170 more total charges, respectively (p < 0.01).

Despite Medicaid expansion through the 2014 Affordable Care Act, marked disparities in complications after TJA between individuals with and without private insurance still exist. Medicare<65 and Medicaid cohorts demonstrated higher complication rates than private payers, possibly attributable to barriers in healthcare such as patient education, access to healthcare, and social determinants of health.

Keywords

Complications
Medicare
Medicaid
Total joint arthroplasty
Health care utilization
Length of stay
Hospital costs
Hospital charges
1

1 Introduction

The number of primary total joint arthroplasty (TJA) procedures, including hip, knee, shoulder, elbow, ankle or wrist, have increased from 2009 to 2019 and continue to rise.1–3 As the number of these procedures continues to grow, it is imperative to identify, and thus ameliorate, factors contributing to suboptimal patient outcomes.

Various studies conducted utilizing national databases have demonstrated Medicaid status to be an independent risk factor for increased morbidity, reoperations, and resource utilization.4–7 For instance, a National Inpatient Sample (NIS) database study by Browne et al. reported patients from 2002 to 2011 with Medicaid insurance undergoing TJA were more likely to experience postoperative complications than TJA patients with non-Medicaid primary insurance. However, this study was limited to hip or knee arthroplasties.4,8 In addition, this study did not examine Medicare patients or patients who self-paid. To the authors’ knowledge, no study has investigated the impact of primary payer status on postoperative outcomes for all major joint arthroplasties.

Our study expands on the existing literature5,9–11 by investigating postoperative complications and resource utilization among patients undergoing inpatient, major primary joint arthroplasties (hip, knee, shoulder, shoulder, ankle, or wrist) from 2012 to 2020 stratified by primary payer status. We hypothesized that Medicare or Medicaid insurance would be independently associated with higher rates of postoperative complications and report higher healthcare resource utilization when compared to patients with private insurance, even when controlling for confounding variables.

2

2 Methods

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) database was used to identify patients who underwent total hip, knee, shoulder, elbow, ankle, or wrist joint arthroplasty surgery from 2012 to 2020. The International Classification of Disease-9 (ICD-9) and ICD-10 codes used are detailed in Table 1.

Table 1 ICD-9 and ICD-10 codes used to identify total joint arthroplasty patients.
ICD-9
Hip Description
8151 Total Hip Replacement
Knee
8154 Total Knee Replacement
Ankle
8156 Total Ankle Replacement
Shoulder
8180 Total Shoulder Replacement
8188 Reverse Total Shoulder Replacement
Elbow
8184 Total Elbow Replacement
Wrist
8173 Total Wrist Replacement
ICD-10
Hip Description
0SR9 Right Total Hip Replacement
0SRA and 0SRR Right Hip Acetabular Surface Replacement
Right Hip Femoral Surface Replacement
0SRB Left Total Hip Replacement
0SRE and 0SRS Left Hip Acetabular Surface Replacement
Left Hip Femoral Surface Replacement
Knee
0SRC Right Total Knee Replacement
0SRV and 0SRT Right Knee Tibial Surface Replacement
Right Knee Femoral Surface Replacement
0SRD Left Total Knee Replacement
0SRW and 0SRU Left Knee Tibial Surface Replacement
Left Knee Femoral Surface Replacement
Shoulder
0RRJ Right Total Shoulder Replacement
0RRK Left Total Shoulder Replacement
Ankle
0SRF Right Total Ankle Replacement
0SRG Left Total Ankle Replacement
Elbow
0RRL Right Total Elbow Replacement
0RRM Left Total Elbow Replacement
Hand
0RRN Right Total Wrist Replacement
0RRP Left Total Wrist Replacement
2.1

2.1 Inclusion/exclusion criteria

Patients were included in the analysis only if their primary diagnosis code was osteoarthritis of the corresponding joint that was replaced during their inpatient stay. Patients with primary diagnoses such as fractures and osteonecrosis were thus excluded. Entries were excluded if they did not have data in one or more of the following categories: age, sex, race, year of procedure, hospital location, joint replaced, payer status, length of stay (LOS), total charges, total costs, or cost-to-charge ratios. Upon exclusion of incomplete entries, the patients were split into five cohorts: patients whose primary payer was Medicaid, Medicare and ≥65 years old, Medicare and <65 years old, no-insurance, or private insurance.

2.2

2.2 Data analysis

Once cohorts were split, each patient whose primary payer was Medicaid, Medicare and age ≥65 years, Medicare and age <65 years, or no insurance were exactly matched to one patient whose primary payer was private insurance who had an identical combination of age, sex, joint replaced, and the 31 comorbidities described in Table 2. Instead of matching by the exact age, patients were grouped into categories of <30 years old, 30–39 years old, 40–49 years old, then in 5-year ranges from 50 to 90+ (e.g. 50–54 years old, 55–59 years old, etc.), and then matched according to these groupings. This was done to increase the number of patients we could include, as matching by exact age was too stringent.

Table 2 Matching characteristics of each payer type before matching.
Variable
Private (n = 3,396,840) Medicare ≥65 (n = 4,822,470) Medicare <65 (n = 525,795) Medicaid (n = 391,450) None (n = 51,935)
Age category a , y
<30 11035 (0.3; 58.8) 1815 (0.3; 9.7) 5450 (1.4; 29.1) 460 (0.9; 2.5)
30–39 41895 (1.2; 61.3) 9070 (1.7; 13.3) 15980 (4.1; 23.4) 1450 (2.8; 2.1)
40–49 269465 (7.9; 69.9) 45955 (8.7; 11.9) 64280 (16.4; 16.7) 5545 (10.7; 1.4)
50–54 467200 (13.8; 73.8) 79810 (15.2; 12.6) 79025 (20.2; 12.5) 6830 (13.2; 1.1)
55–59 850740 (25.1; 76.4) 152220 (29.0; 13.7) 100645 (25.7; 9.0) 10220 (19.7; 0.9)
60–64 1136205 (33.5; 76.7) 236925 (45.1; 16.0) 96725 (24.7; 6.5) 12255 (23.6; 0.8)
65–69 357015 (10.5; 19.2) 1482205 (30.7; 79.9) 11200 (2.9; 0.6) 5550 (10.7; 0.3)
70–74 149290 (4.4; 9.4) 1428185 (29.6; 89.8) 8975 (2.3; 0.6) 4660 (9.0; 0.3)
75–79 71735 (2.1; 6.3) 1060175 (22.0; 92.9) 6030 (1.5; 0.5) 2980 (5.7; 0.3)
80–84 30165 (0.9; 4.8) 591605 (12.3; 94.6) 2405 (0.6; 0.4) 1460 (2.8; 0.2)
≥85 11095 (0.3; 4.1) 260300 (5.4; 95.5) 735 (0.2; 0.3) 525 (1.0; 0.2)
Female sex 1843845 (54) 3023970 (63) 330190 (63) 243040 (62) 29035 (56)
Joint replaced b
Ankle 17020 (0.5; 38.5) 22310 (0.5; 50.5) 3145 (0.6; 7.1) 1560 (0.4; 3.5) 140 (0.3; 0.3)
Elbow 2235 (0.1; 27.2) 4375 (0.1; 53.2) 940 (0.2; 11.4) 500 (0.1; 6.1) 170 (0.3; 2.1)
Hand 80 (<0.1; 23.5) 170 (<0.1; 50.0) 65 (<0.1; 19.1) 20 (<0.1; 5.9) 5 (<0.1; 1.5)
Hip 1222530 (36; 39.7) 1512130 (31.4; 49.1) 172645 (32.8; 5.6) 149635 (38.2; 4.9) 22300 (42.9; 0.7)
Knee 1972920 (58.1; 37.5) 2753260 (57.1; 52.3) 298820 (56.8; 5.7) 215105 (55; 4.1) 25730 (49.5; 0.5)
Shoulder 181055 (5.3; 22.9) 530225 (11; 67.1) 50180 (9.5; 6.4) 24630 (6.3; 3.1) 3590 (6.9; 0.5)
Comorbidities
Anemia, blood loss 21665 (0.6) 36730 (0.8) 4105 (0.8) 3005 (0.8) 325 (0.6)
Anemia, deficiency 246400 (7.3) 452345 (9.4) 55595 (10.6) 33720 (8.6) 3615 (7.0)
CHF 43565 (1.3) 188240 (3.9) 21640 (4.1) 10475 (2.7) 1075 (2.1)
Coagulopathy 43555 (1.3) 96730 (2) 10895 (2.1) 6455 (1.6) 690 (1.3)
Depression 447845 (13.2) 607760 (12.6) 138610 (26.4) 78415 (20.0) 6635 (12.8)
DM 447980 (13.2) 785205 (16.3) 106455 (20.2) 64075 (16.4) 6655 (12.8)
DM with complications 90305 (2.7) 240470 (5) 33725 (6.4) 16320 (4.2) 1600 (3.1)
Fluid/electrolyte disorder 175800 (5.2) 378580 (7.9) 39875 (7.6) 25360 (6.5) 2990 (5.8)
HIV/AIDS 1920 (0.1) 855 (0) 2090 (0.4) 1255 (0.3) 85 (0.2)
HTN 1791515 (52.8) 3001005 (62.2) 302135 (57.5) 211255 (54) 26310 (50.7)
HTN with complications 119885 (3.5) 483260 (10) 40430 (7.7) 18960 (4.8) 2675 (5.2)
Hypothyroidism 449240 (13.2) 930535 (19.3) 82505 (15.7) 41350 (10.6) 5785 (11.1)
Liver 43610 (1.3) 50285 (1) 16685 (3.2) 12110 (3.1) 965 (1.9)
Lymphoma 6010 (0.2) 14815 (0.3) 1380 (0.3) 565 (0.1) 80 (0.2)
Metastasis 3915 (0.1) 7340 (0.2) 1015 (0.2) 600 (0.2) 110 (0.2)
Obesity 997985 (29.4) 1023225 (21.2) 181300 (34.5) 123660 (31.6) 13160 (25.3)
Other neurologic 106545 (3.1) 250330 (5.2) 46910 (8.9) 19320 (4.9) 1775 (3.4)
Paralysis 6505 (0.2) 16815 (0.3) 6610 (1.3) 2255 (0.6) 145 (0.3)
Peptic ulcer disease 4580 (0.1) 10055 (0.2) 1295 (0.2) 965 (0.2) 110 (0.2)
Psychoses 51870 (1.5) 68705 (1.4) 45765 (8.7) 23530 (6.0) 910 (1.8)
Pulmonary 429680 (12.7) 745325 (15.5) 149740 (28.5) 94525 (24.1) 6560 (12.6)
Pulmonary hypertension 10635 (0.3) 29260 (0.6) 2960 (0.6) 1535 (0.4) 165 (0.3)
PVD 38765 (1.1) 159775 (3.3) 10900 (2.1) 4960 (1.3) 665 (1.3)
Renal 93360 (2.7) 390410 (8.1) 32950 (6.3) 13435 (3.4) 2000 (3.9)
Rheumatoid arthritis 132175 (3.9) 227660 (4.7) 49245 (9.4) 21200 (5.4) 2090 (4)
Smoking 921615 (27.1) 1329635 (27.6) 213670 (40.6) 168375 (43) 15895 (30.6)
Substance abuse, alcohol 40575 (1.2) 37210 (0.8) 10960 (2.1) 12640 (3.2) 950 (1.8)
Substance abuse, drug 25850 (0.8) 22895 (0.5) 17500 (3.3) 16825 (4.3) 695 (1.3)
Tumor 13145 (0.4) 33240 (0.7) 1975 (0.4) 1420 (0.4) 230 (0.4)
Valvular 70750 (2.1) 229775 (4.8) 11035 (2.1) 5560 (1.4) 1055 (2)
Weight loss 5675 (0.2) 17490 (0.4) 2390 (0.5) 1575 (0.4) 185 (0.4)
Complications
Cardiac 8605 (0.3) 20550 (0.4) 1685 (0.3) 1275 (0.3) 180 (0.3)
Central nervous system 685 (<0.1) 1540 (<0.1) 170 (<0.1) 125 (<0.1) 5 (<0.1)
Deep vein thrombosis 5580 (0.2) 9725 (0.2) 1080 (0.2) 775 (0.2) 115 (0.2)
Gastrointestinal 4460 (0.1) 9180 (0.2) 990 (0.2) 570 (0.1) 75 (0.1)
Genitourinary 5715 (0.2) 14450 (0.3) 915 (0.2) 660 (0.2) 115 (0.2)
HHS 6555 (0.2) 11690 (0.2) 1785 (0.3) 1250 (0.3) 135 (0.3)
Postoperative anemia 551025 (16.4) 935190 (19.7) 102845 (19.8) 65475 (16.9) 8390 (16.2)
Postoperative infection 1230 (<0.1) 1605 (<0.1) 330 (0.1) 255 (0.1) 45 (0.1)
Pulmonary embolism 3520 (0.1) 7245 (0.2) 810 (0.2) 560 (0.1) 70 (0.1)
Respiratory 4935 (0.1) 11760 (0.2) 1665 (0.3) 890 (0.2) 105 (0.2)
Vascular 1505 (<0.1) 2500 (0.1) 260 (<0.1) 215 (0.1) 10 (<0.1)
Wound dehiscence 1530 (<0.1) 2305 (<0.1) 470 (0.1) 355 (0.1) 45 (0.1)
and joint replaced.
which are reported as (% within payor type; % within age or procedure).

Comorbidities were characterized by analyzing the ICD-9 and ICD-10 codes available in the non-primary diagnosis codes in the database with the Clinical Classification Software from Agency for Healthcare Research and Quality. This was done efficiently and accurately using the ‘icd’ package12 in R software.13 Complications were categorized as cardiac, central nervous system, deep vein thrombosis, genitourinary, hemorrhage/hematoma/seroma, post-operative anemia, post-operative infection, pulmonary embolus, respiratory, vascular, and wound dehiscence complications. ICD-9 codes cannot differentiate between intra-operative and post-operative complications. ICD-9 codes began transition to ICD-10 codes in 2015 and were fully in use in 2016. Thus, the authors decided to include both intra-operative and post-operative complications when determining which ICD-10 codes to include in order to keep complications consistent across years (Supplementary Table 1).

Univariate analysis was conducted comparing all the characteristics available in the HCUP NIS database. Medicare ≥65, Medicare <65, Medicaid, and no insurance cohorts in the database were compared to private insurance. Continuous variables were compared using Student's t-test, and categorical variables were analyzed using chi-square tests. For each complication, multivariable regressions were used to assess the odds ratio of each payer status compared to the reference of private insurance. In addition, a generalized linear model using Gamma log link was used to analyze total charges and total cost. These values were log transformed to account for non-normal distribution of the data.14 The values for length of stay were transformed into a binary measure of either “abnormally long LOS” or “normal LOS”. This was determined by taking the 90th percentile value for each procedure and considering LOS less than the 90th percentile as “normal LOS” and above as “abnormally long LOS”. Total charge was inflation adjusted using 2020 consumer price index as the reference year. Total cost was calculated by multiplying the adjusted total charge by the respective hospital's cost-to-charge ratio (CCR). Multivariable regressions were adjusted for age, sex, race, year of procedure, hospital location, joint replaced, and the number of comorbidities of the 31 previously described, and the independent variable of payer status was examined by calculating the associated odds ratio. The odds ratios of suffering a complication for each payer status relative to private insurance payers were reported. The respective multivariable coefficient estimates for total charge and total cost relative to private insurance payers were reported and multiplied by the cohort average of these measures to put the results in context. LOS was analyzed via multivariate logistic regression to examine association between payer type and “abnormally long LOS.” Odds ratios between the Medicare≥65 cohort and Medicare<65 cohort were compared at p < 0.05.15,16

All analysis was conducted using R statistical software.13 Significance for multivariable and univariate analysis was set at p < 0.01 because of large sample size and multiple comparisons of differing payer status. This study is IRB exempt because of the use of a national deidentified sample database. No patient consent was needed or obtained.

3

3 Results

3.1

3.1 General characteristics

The number of entries identified from the HCUP NIS database from 2012 to 2020 was 1,940,147, the methodology which is described in Fig. 1. After elimination of entries with incomplete information, 1,875,189 entries remained. After only including entries with payer status as Medicaid, Medicare, private, or no insurance status, 1,813,611 entries remained. These patients were then split into the following cohorts: Medicare ≥65 years old (n = 964,494), Medicare <65 years old (n = 105,159), Medicaid (n = 78,290), no insurance (n = 10,387), and private insurance (n = 679,168). By examining Table 2, matching is clearly necessary; patients in the Medicare ≥65 years cohort tended to be older within their stratification, as 60.3 % of entries were in the 65–74-year age group, while only 14.9 % of private entries were in the 65–74-year age group. The statistics in Table 2 were multiplied by NIS-provided weights to provide national estimates.

Flowchart showing the matching and analysis of the population. Filtering of the data after acquisition from the NIS and subsequent matching process. Cohorts used for multivariable analysis.
Fig. 1 Flowchart showing the matching and analysis of the population. Filtering of the data after acquisition from the NIS and subsequent matching process. Cohorts used for multivariable analysis.
3.2

3.2 Matching results

The matched cohorts are as follows: 62,799 Medicaid entries, 109,589 Medicare≥65 years old entries, 77,610 Medicare<65 years old entries, 8906 no-insurance entries matched to an equal number of private-insurance patients. Patients with no insurance could not be analyzed for complications because smaller sample size risked overfitting the data.

3.2.1

3.2.1 Medicare ≥65 years old

Multivariable logistic regression revealed that patients with Medicare≥65 years were 1.21, 0.33, 0.79, 0.68, and 1.21 times as likely to experience a cardiac, central nervous system complication, genitourinary, post-operative infection, and wound dehiscence complication, respectively, than patients with private insurance (p < 0.01) (Table 3). Odds ratios <1 indicate a complication is less likely to occur, while >1 is more likely.

Table 3 Multivariable adjusted odds ratios of each comorbidity by payer type relative to private payers.
Complications Odds Ratio (95 % Confidence Interval)
Medicare ≥65 Medicare <65 Medicaid
Cardiac 1.2 (1.1, 1.3) 1.2 (1.1, 1.3) 1.5 (1.3, 1.65)
Central nervous system 0.33 (0.24, 0.47) b , a 1.5 (1.1, 2.1) a 1.5 (1.04, 2.1) a
Deep vein thrombosis 1.0 (0.91, 1.1) 1.1 (1.01, 1.3) 0.94 (0.82, 1.1)
Gastrointestinal 0.98 (0.89, 1.1)b 1.4 (1.2, 1.6) 1.01 (0.87, 1.2)a
Genitourinary 0.79 (0.73, 0.86) b 1.2 (1.1, 1.4) 1.2 (1.1, 1.4)
Hemorrhage, hematoma, seroma 0.95 (0.87, 1.04) 1.4 (1.3, 1.6) 1.6 (1.45, 1.8)
Postoperative anemia 0.98 (0.93, 1.0)b 1.2 (1.1, 1.2) 0.99 (0.98, 1.01)
Postoperative infection 0.68 (0.53, 0.88) b , a 1.04 (0.85, 1.3)a 1.2 (0.90, 1.5)a
Pulmonary embolus 1.1 (0.96, 1.2) 1.02 (0.82, 1.3)a 1.2 (0.95, 1.5) †
Respiratory 1.1 (0.98, 1.2)b 1.65 (1.5, 1.8) 1.3 (1.2, 1.5)
Vascular 0.81 (0.65, 0.97) b , a 1.14 0.89, 1.4)a 1.1 (0.85, 1.5)a
Wound dehiscence 1.2 (1.1, 1.3) b , a 1.6 (1.4, 2.0) a 1.52 (1.2, 1.9) a
This model has less than 150 occurrences and thus is at risk of being underpowered.
Odds ratios for this complication differ significantly between the Medicare≥65 and Medicare<65 cohorts.

Multivariable linear regression on LOS revealed that patients with Medicare ≥65 years old tended to have 15 % decreased odds of remaining in the hospital for an abnormally long time compared to privately insured patients (p < 0.01). In addition, these patients tended to have $421 less in total hospital costs and the hospital charges were $2243 more than for patients with private insurance (p < 0.01) (Table 4).

Table 4 Multivariate adjusted beta of cost and length of stay by payer type relative to private payers.
Variable Insurance Type
Medicare ≥65 Medicare <65 Medicaid None
Length of stay (d) 0.85 (0.84, 0.86)a 1.76 (1.74, 1.78) 1.85 (1.82, 1.88) 1.35 (1.30, 1.39)
Charge ($) 2243 (1959, 2528)a 3849 (3492, 4208) 1170 (783, 1559) −4342 (−5202, −3467)
Cost ($) −421 (−485, −357)a 441 (362, 519) 609 (516, 703) 160 (−65, 389)
Odds ratios for this measure differ significantly between the Medicare≥65 and Medicare<65 cohorts.
3.2.2

3.2.2 Medicare <65 years old

Multivariable logistic regression revealed patients with Medicare<65 years old to be 1.23, 1.39, 1.24, 1.44, 1.16, 1.65, and 1.64 more likely to experience a cardiac, gastrointestinal, genitourinary, hemorrhage/hematoma/seroma, post-operative anemia, respiratory, and wound dehiscence complication (p < 0.01) (Table 3).

Multivariable linear regression on LOS revealed that patients <65 years old tended to have 76 % increased odds of remaining in the hospital for an abnormally long time compared to privately insured patients (p < 0.01). In addition, these patients had $441 more in hospital costs and the hospital charges were $3849 more than for patients with private insurance (p < 0.01) (Table 4).

3.2.3

3.2.3 Medicaid

Multivariable logistic regression revealed patients with Medicaid to be 1.48, 1.23, 1.62, 1.34, and 1.52 times more likely to experience a cardiac, genitourinary, hemorrhage/hematoma/seroma, respiratory, and wound dehiscence complication than patients with private insurance (p < 0.01) (Table 3).

Multivariable linear regression on LOS revealed that patients with Medicaid tended to have 85 % increased odds of remaining in the hospital for an abnormally long time compared to privately insured patients (p < 0.01). In addition, patients with Medicaid tended to have $609 more in hospital costs and the hospital charges were $1170 more than for patients with private insurance (p < 0.01) (Table 4).

3.2.4

3.2.4 No insurance

Multivariable linear regression on LOS revealed that patients with no insurance tended to have 35 % increased odds of remaining in the hospital for an abnormally long time compared to privately insured patients (p < 0.01). In addition, patients with no insurance tended to have $5156 less in hospital charges than patients with private insurance (p < 0.01).

4

4 Discussion

The increased incidence of complications and healthcare utilization occurring in Medicaid patients undergoing orthopaedic procedures has been well documented in the literature.4,7,17,18 With the ever-expanding prevalence of Medicaid being adopted by uninsured patients in the United States, especially since the COVID-19 pandemic,19–21 it is important to continue to understand why such patterns exist. Our study adds to the existing literature by exploring the effect, not only of Medicaid, but also of Medicare and uninsured patients, on postoperative complications and healthcare utilization after TJA.

Medicaid payer status has been demonstrated to act as an independent risk factor for worse outcome measures with TJA, as enrollees have higher rates of postoperative mortality, complications, resource utilization, longer lengths of stay, and 90-day ED visits and readmissions than patients with Medicare or private insurance.9,10,22,23 In total hip arthroplasty, both Medicaid and Medicare patients have shown higher hazard ratios for hospital charges and increased complications, such as periprosthetic infections, when compared to privately insured patients.11,24 In our study, hospital charges were $1170 more for Medicaid patients than they were for patients with private insurance. Given the demographics that constitute the Medicaid patient population—differences in social determinants of health, such as a lack of regular doctor appointments; the presence of more complicated, chronic conditions along with a myriad of social factors—the costly nature of these patients’ admissions should not be entirely surprising, though some studies suggest that Medicaid patients are less costly to beneficiaries, so more research is needed to elucidate these differences.25–27

Our data further revealed that Medicaid patients tended to stay in the hospital for 0.21 days longer than patients with private insurance. These results were in line with studies suggesting longer lengths of stay for non-privately insured patients, particularly Medicaid patients.28–30 Previously, increased LOS has also been associated with more short-term complications, which further explains the increased risk of complications for both Medicaid and Medicare <65 patients in this study.27,31 These increases in LOS are likely secondary to modifiable factors acting as barriers to discharge. For example, the increased incidence of smoking and illicit drug use, social concerns resulting in inadequate pain control, socioeconomic factors, and delay in post-operative mobilization have all been cited as barriers to discharge in this population.32–34

Medicare patients <65 years old were more likely than Medicare patients ≥65 years old to experience a central nervous system complication and genitourinary complications relative to privately insured patients. Despite our matching and controlling for comorbidities, these differences in estimated relative risk could potentially be explained by the severity of comorbidities impacting Medicare <65 patients who typically qualify for Medicare.35 This includes patients who suffered severe, traumatic accidents, patients with amyotrophic lateral sclerosis, or patients with permanent kidney failure.36–38 Studies have demonstrated that Medicare patients <65 undergoing lumbar fusion have worse pre- and post-operative patient-reported outcome measures.11,39 Moreover, it has been shown that Medicare patients with disabilities less than 65 years of age are more likely than elderly beneficiaries to be impoverished minorities, factors which both can contribute to worse outcomes.40 Our results showed that hospital charges were $3849 more for Medicare patients <65 and $2243 more for patients ≥65 when compared to patients with private insurance. In recent studies, individuals with private insurance have reported higher costs of care, suggesting that those with public insurance such as Medicare have more cost-effective care; however, this was not focused on surgical procedures.41 We hypothesize the severity of comorbidities and quality of private insurance coverage in patients who can afford TJA in the United States to be an explanation for our findings.11

A decrease in the number of private-enrolled patients in our sample of patients >65 could be due to several factors. Adults >65 years older are the age group most likely to be covered by more than one plan of insurance secondary to a desire to supplement Medicare coverage with privately purchased plans or coverage through a current or previous employer,42 with 29.5 million people aged 65 or older having multiple coverage in 2021. Private insurance is the primary payer when an individual does not sign up for Medicare, or they work for a large company.35 Medicare is the primary payer, typically among retirees and with 86 % of adults 75 and older being retired; this justified the decrease in private-insurance primary payers in our sample above the age of 65.43 The most common type of multiple coverage among this patient population was a combination of Medicare and private insurance.42 This puts into context the cohorts being compared when looking at primary payers of Medicare ≥65 years old.

At 65, the age of Medicare eligibility, patients are likely to undergo more frequent screening for malignancies, such as prostate cancer, especially if they are no longer employed and have slightly less strenuous work schedules.44,45 As such, this cohort of patients may ultimately find malignancies at a lower stage at diagnosis relative to the rest of the population. Patients aged >65 with primary coverage via private insurance are likely still in the workforce46 and, as such, are less likely to undergo such consistent screening or consistent visits to the doctor's office, potentially contributing to the increased risk of genitourinary complications we found. In addition, we found patients with primary Medicare coverage to be more likely to experience a cardiac complication than privately insured patients; this is consistent with the literature, which suggests that continued work beyond the age of 65 is likely to result in better health outcomes relative to those who do not work beyond that point.47 This may be due to higher activity rates of individuals who remain in the workforce at a later age.

Our multivariable analysis was a strength of this study and allowed us to analyze a large number of patient records while also controlling for several patient variables through matching. Despite this, our matching did not consider race and other, differing hospital characteristics such as hospital location (rural, urban teaching, urban nonteaching). We attempted to account for this by including these characteristics as covariates in our analysis. In addition, dual-eligible patients were unable to be identified because of database limitations. Most dual-eligible patients are classified as Medicaid payers in the NIS. In addition, the analysis of no-insurance-based patient data did not allow us to make any significant conclusions regarding complications in that population. It is important to consider that while our paper demonstrates associations according to payer status and overall postoperative outcomes when it comes to TJAs, the retrospective methodology precludes the establishment of causality. These differences could be accounted for with nonquantifiable social determinants of health, and, as such, a retrospective multivariable analysis cannot aptly capture the full picture.

5

5 Conclusion

Ultimately, after exact matching for comorbidities, our study found that Medicaid and Medicare patients appeared to have higher risks for postoperative complications after joint arthroplasty. In addition, hospital charges were greater for both Medicare and Medicaid patients, regardless of age. Our findings build upon the available literature investigating insurance status on post-operative outcomes and are important to analyze as the utilization of Medicaid and Medicare continues to expand among US patients, especially after the expansion of the Affordable Care Act. However, we also found that patients who are older than 65 and insured with Medicare are not as affected by payer status, and that the more vulnerable populations on Medicare and Medicaid need to be targeted for healthcare access and better care. Further research will allow a better understanding of which precise factors lead to suboptimal outcomes in these patient populations. Such research could examine how hospital location and accessibility to care, which may be associated with payer status, could affect these results.

CRediT authorship contribution statement

William ElNemer: contributed, Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, and, Funding acquisition. Sribava Sharma: contributed, Conceptualization, Methodology, Software, Writing – original draft, and, Writing – review & editing. John P. Avendano: contributed, Conceptualization, Writing – original draft, and, Writing – review & editing. Myung-Jin Cha: contributed contributed, Conceptualization, Writing – original draft, and, Writing – review & editing. Majd Marrache: contributed, Writing – original draft, Writing – review & editing. Andrew B. Harris: contributed, Writing – original draft, Writing – review & editing, Supervision, Project administration, and, Funding acquisition. Umasuthan Srikumaran: contributed, Writing – original draft, Writing – review & editing, Supervision, Project administration, and, Funding acquisition. Matthew J. Best: contributed, Writing – original draft, Writing – review & editing, Supervision, Project administration, and, Funding acquisition.

Ethical statement

This study is IRB exempt because of the use of a national deidentified sample database.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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