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. 2022 May 9;480(9):1636–1645. doi: 10.1097/CORR.0000000000002207

Are Income-based Differences in TKA Use and Outcomes Reduced in a Single-payer System? A Large-database Comparison of the United States and Canada

Bella Mehta 1, Kaylee Ho 2, Vicki Ling 3, Susan Goodman 1, Michael Parks 1, Bheeshma Ravi 3,4, Samprit Banerjee 5, Fei Wang 6, Said Ibrahim 6, Peter Cram 3,7
PMCID: PMC9384923  PMID: 35543485

Abstract

Background

Income-based differences in the use of and outcomes in TKA have been studied; however, it is not known if different healthcare systems affect this relationship. Although Canada’s single-payer healthcare system is assumed to attenuate the wealth-based differences in TKA use observed in the United States, empirical cross-border comparisons are lacking.

Questions/purposes

(1) Does TKA use differ between Pennsylvania, USA, and Ontario, Canada? (2) Are income-based disparities in TKA use larger in Pennsylvania or Ontario? (3) Are TKA outcomes (90-day mortality, 90-day readmission, and 1-year revision rates) different between Pennsylvania and Ontario? (4) Are income-based disparities in TKA outcomes larger in Pennsylvania or Ontario?

Methods

We identified all patients hospitalized for primary TKA in this cross-border retrospective analysis, using administrative data for 2012 to 2018, and we found a total of 161,244 primary TKAs in Ontario and 208,016 TKAs in Pennsylvania. We used data from the Pennsylvania Health Care Cost Containment Council, Harrisburg, PA, USA, and the ICES (formally the Institute for Clinical Evaluative Sciences), Toronto, Ontario, Canada. We linked patient-level data to the respective census data to determine community-level income using ZIP Code or postal code of residence and stratified patients into neighborhood income quintiles. We compared TKA use (age and gender, standardized per 10,000 population per year) for patients residing in the highest-income versus the lowest-income quintile neighborhoods. Similarly secondary outcomes 90-day mortality, 90-day readmission, and 1-year revision rates were compared between the two regions and analyzed by income groups.

Results

TKA use was higher in Pennsylvania than in Ontario overall and for all income quintiles (lowest income quartile: 31 versus 18 procedures per 10,000 population per year; p < 0.001; highest income quartile: 38 versus 23 procedures per 10,000 population per year; p < 0.001). The relative difference in use between the highest-income and lowest-income quintile was larger in Ontario (28% higher) than in Pennsylvania (23% higher); p < 0.001. Patients receiving TKA in Pennsylvania were more likely to be readmitted within 90 days and were more likely to undergo revision within the first year than patients in Ontario, but there was no difference in mortality at 1 year. When comparing income groups, there were no differences between the countries in 90-day mortality, readmission, or 1-year revision rates (p > 0.05).

Conclusion

These results suggest that universal health insurance through a single-payer may not reduce the income-based differences in TKA access that are known to exist in the United States. Future studies are needed determine if our results are consistent across other geographic regions and other surgical procedures.

Level of Evidence

Level III, therapeutic study.

Introduction

There is a strong, positive relationship between income and access to healthcare [15]. Globally, there is documentation of decreased healthcare access and worse healthcare outcomes in patients with lower income and in communities that face inequality [21, 22, 50, 51]. In a highly prevalent and disabling disease like knee osteoarthritis, an elective surgery—TKA—is often the only definitive management option. Income-based differences in the use of and outcomes after TKA in the United States are well documented [4, 27, 28, 33, 35, 44, 54], and it is unclear whether some of these differences are unique to the fragmented US health insurance system. Although there are problems with access in single-payer health systems like Canada—there can be long wait times for TKA and disparities exist for patients with lower socioeconomic standing and lower education levels—it is not known how income-based differences in TKA in a universal healthcare system like Canada are different than the United States [26]. Canada’s publicly funded health system provides health insurance to all legal residents irrespective of income or employment and has no copayments or patient cost-sharing [20]. In contrast, about 9% of Americans lack health insurance, and US patients undergoing TKA may incur out-of-pocket costs ranging from USD 11,000 to USD 60,000 [5, 46]. There is some evidence that patients among lower income groups in the US may do worse than those in Canada; however, we do not know if that is the case in an elective procedure like TKA [31].

Comparing data from the United States and Canada would allow us to examine whether universal health insurance can eliminate or reduce income-based disparities in elective TKA [41]. We selected Pennsylvania, USA, and Ontario, Canada, based on several factors, including similarities in population size and income, as well as geographic proximity.

Thus, our primary objective was to compare income-based differences in TKA use and outcomes between Pennsylvania and Ontario. We therefore asked: (1) Does TKA use differ between Pennsylvania and Ontario? (2) Are income-based disparities in TKA use larger in Pennsylvania or Ontario? (3) Are TKA outcomes (90-day mortality, 90-day readmission, and 1-year revision rates) different between Pennsylvania and Ontario? (4) Are income-based disparities in TKA outcomes larger in Pennsylvania or Ontario?

Patients and Methods

Study Design and Setting

We conducted an international comparative study (between USA and Canada), using retrospective data from large regional datasets in Pennsylvania and Ontario. We linked patient-level data in the cohorts of Pennsylvania and Ontario to respective census data to determine the community-level median household income and examined its association with TKA use and outcomes.

Selection of the Regions in United States and Canada

We selected the cohorts from Pennsylvania and Ontario because of their geographical proximity (they share a border) and similarities in terms of their population size and socioeconomics. Pennsylvania has a population of 12.7 million people; the population of Ontario is 13.6 million people [42, 47]. The median household income in Pennsylvania is USD 59,500, and that in Ontario is USD 57,070 (CAD 74,287) [3, 38]. In Pennsylvania, 13% of the population live below the poverty line compared with 14% in Ontario [16, 48]. In 2019, the unemployment rate in Pennsylvania was 4%, and it was 6% in Ontario [3, 37, 44].

Data Sources

We used population-based administrative data from Pennsylvania and Ontario to identify all adult patients who underwent TKA between 2012 and 2018 (this helped us analyze more than 5 years of the most recently available data).

For Pennsylvania, we used patient-level data from the Pennsylvania Health Care Cost Containment Council, a database that comprises administrative data for all inpatient discharges from acute-care hospitals with unique patient and hospital identifiers, as described elsewhere [45]. It is a detailed statewide dataset that includes patients with all insurances and has data on primary and secondary ICD-9 and ICD-10 codes as well as discharge disposition that would help select the population to conduct our analysis.

For Ontario, we used patient-level administrative data from the ICES (formerly the Institute for Clinical Evaluative Sciences), an independent, government-supported research center that houses provincial and national Canadian datasets for use in approved research projects. Specific files germane to this research include the Canadian Institute for Health Information Discharge Abstract Database and the Ontario Registered Persons Database for information on deaths; these datasets have been used extensively in prior health services research because it includes linkable, coded data for all Ontario patients eligible for universal healthcare coverage. The Discharge Abstract Database contains administrative data for all hospital admissions in Ontario. The data repository allows for analyses of patients with specific procedures, such as TKA, and patient outcomes, like length of hospital stay, mortality, and other complications that are germane to our research question.

We linked each primary TKA recipient to census data from the respective countries to obtain community-level income [49, 53]. The American Community Survey of the US Census Bureau collects data on area-based measures (ABMs) including poverty, housing, and race. Although most ABMs are available at the ZIP Code level, some data are available only at the census tract level (smaller units than ZIP Codes). We used crosswalks between ZIP Codes and census tracts to estimate community income and assess outcomes. Similarly, Statistics Canada conducts a national census of the Canadian population [5, 18]. We used geospatial localization (geocoding) of patients’ ZIP Codes (Pennsylvania) and postal codes (Ontario) to link each patient’s address of residence to neighborhood income.

Cohort Generation

We identified patients aged 18 years and older who underwent primary TKA between January 1, 2012, and September 30, 2018, in Pennsylvania and Ontario. Data from September 30 to December 31, 2018, were used to ascertain 90-day readmission rates only. In Pennsylvania, patients were identified using ICD-9 procedure code 81.54 from 2012 through September 2015 and ICD-10 procedure codes 0SR90xx or 0SRB0xx for TKA thereafter. In Ontario, we used previously validated Canadian Classification of Health Interventions codes for primary elective TKA [11, 12, 18, 19]. We excluded patients with diagnostic codes suggesting inflammatory arthritis (rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, and spondyloarthropathy), pathologic fracture, avascular necrosis, as well as metastatic and bone cancer. We excluded patients with nonelective admissions or interhospital transfers before TKA, and those who underwent at least one or bilateral TKA during the index hospitalization. We excluded patients who resided outside Pennsylvania or Ontario and patients missing key variables, including age, gender, residential ZIP Code or postal code, ZIP Code or postal code income level, or unique patient identifiers. In all, 17% (41,837 of 249,853) of patients were excluded in Pennsylvania and 5% (8820 of 170,046) of patients in Ontario using these criteria. The patients with missing income were less than 0.1% (255 of 170,064) of the cohort, thus there was no differential missingness due to income. Five percent (11,530 of 249,853) of the cohort was excluded in Pennsylvania because they had codes meeting exclusions for clinical diagnosis, which was not the case in Ontario. This reflects coding differences and coding incentives which are much more common in the US than in Canada (Supplementary Fig. 1; http://links.lww.com/CORR/A786). Comorbid conditions at the index hospitalization were identified using the Elixhauser comorbidity index [21, 43]. Hospital-level variables included TKA volume (mean number of TKA procedures per hospital per year, annualized for 12 months). Patients were classified as residing in urban or rural areas using identifiers available in the dataset.

We identified 161,244 primary TKAs in Ontario and 208,016 TKAs in Pennsylvania (Table 1). Among TKA recipients, 16% (26,393 of 161,244) in Ontario and 17% (35,096 of 208,016) in Pennsylvania resided in the lowest-income quintile neighborhoods, whereas 20% (32,063 of 161,244) from Ontario and 29% (59,343 of 208,016) from Pennsylvania lived in the highest-quintile income neighborhoods (Appendix 1; http://links.lww.com/CORR/A787).

Table 1.

Characteristics of patients and hospitals who underwent TKA in Ontario and Pennsylvania

Lowest quintile community income Second to fourth quintile community income Highest quintile community income
Variable Ontario (n = 26,393) Pennsylvania (n = 35,096) p value Ontario (n = 102,788) Pennsylvania (n = 113,577) p value Ontario (n = 32,063) Pennsylvania (n = 59,343) p value
Age in years 68 ± 10 65 ± 10 < 0.001 68 ± 9 66 ± 10 < 0.001 68 ± 9 67 ± 10 < 0.001
 < 50 3 (702) 6 (2001) < 0.001 2 (2046) 4 (4785) < 0.001 2 (575) 3 (1749) < 0.001
 50-64 34 (8988) 42 (14,785) < 0.001 35 (35,722) 38 (43,710) < 0.001 34 (11,004) 35 (20,917) 0.005
 ≥ 65 63 (16,703) 52 (18,310) < 0.001 63 (65,020) 57 (65,082) < 0.001 64 (20,484) 62 (36,677) < 0.001
Gender - women 67 (17,805) 66 (23,109) < 0.001 61 (63,028) 62 (70,425) 0.001 59 (19,051) 60 (35,830) 0.005
Elixhauser comorbidity index
 0 comorbidities 54 (14,120) 9 (3326) < 0.001 59 (60,159) 11 (12,766) < 0.001 60 (19,116) 12 (7132) < 0.001
 1-4 comorbidities 46 (12,206) 82 (28,715) < 0.001 41 (42,451) 82 (92,807) < 0.001 40 (12,907) 81 (48,136) < 0.001
 ≥ 5 comorbidities 0.3 (67) 9 (3055) < 0.001 0.2 (178) 7 (8004) < 0.001 0.1 (40) 7 (4075) < 0.001
Annual volume of procedures (by facility)
 < 25 procedures a 2 (593) < 0.001 0 (28) 1 (1121) < 0.001 a 1 (479) < 0.001
 25-99 procedures 3 (720) 11 (3847) < 0.001 3 (3051) 7 (7924) < 0.001 1 (351) 4 (2348) < 0.001
 100-199 procedures 8 (2142) 24 (8356) < 0.001 8 (8049) 18 (20,237) < 0.001 5 (1645) 9 (5422) < 0.001
 200-299 procedures 8 (1983) 12 (4198) < 0.001 7 (6667) 12 (14,110) < 0.001 4 (1151) 16 (9246) < 0.001
 ≥ 300 procedures 82 (21,546) 52 (18,102) < 0.001 83 (84,993) 62 (70,185) < 0.001 90 (28,911) 71 (41,848) < 0.001
Urban or rural (based on patient ZIP Code or postal code), rural 14 (3775) 26 (9179) < 0.001 20 (20,865) 18 (20,915) < 0.001 4 (1170) 0 (189) < 0.001
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Data presented as mean ± SD or (n); comparisons of continuous variables were performed with one-way ANOVA; comparisons of categorical variables were performed with logistic regression.

a

Cell sizes ≤ 10 have been omitted to protect patient confidentiality.

Exposure Variables

Our primary exposure variable was the median household income quintile of residence (the census reports pretax income), calculated for each patient who underwent TKA, developed independently for the regions of Pennsylvania and Ontario. Patient income quintiles were stratified into low (lowest quintile), middle (quintile 2 to quintile 4), and high (highest quintile) neighborhood income groups for each region and then analyzed.

Sensitivity Analysis

We performed a sensitivity analysis using the community-level Gini index in place of income [13, 39, 52]. The Gini index is a widely used measure of statistical dispersion intended to represent income inequality or wealth distribution [6, 7]. The coefficient ranges from 0 (or 0%) to 1 (or 100%), with 0 representing perfect equality and 1 representing perfect inequality.

Primary and Secondary Outcome Measures

The primary outcome of interest was TKA use (age and gender standardized per 10,000 population per year) between the United States and Canada and further compared the highest-income and lowest-income quintile neighborhoods both regions (Fig. 1).

Fig. 1.

Fig. 1

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This graph shows TKA use across income groups in Ontario and Pennsylvania.

Secondary outcomes were 90-day all-cause mortality, all-cause hospital readmission within 90 days of discharge, and 1-year revision rates in the United States and Canada and comparing income quintiles as above (Fig. 2).

Fig. 2.

Fig. 2

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A-B These graphs show the adjusted OR of the three TKA outcomes (1-year revision, 90-day mortality, and 90-day readmission) for patients who lived in the lowest income quintile compared with patients living in the highest income quintile in (A) Pennsylvania and (B) Ontario. Adjusted model 1 (solid line) adjusted for age, sex, hospital volume of cases, and rural/urban. Adjusted model 2 (dotted line) adjusted for age, sex, hospital volume of cases, rural/urban, and Elixhauser Index. Comparison tests for country differences based on model estimates.

We also assessed hospital length of stay and discharge disposition, anticipating that Ontario’s single-payer system could attenuate income-based differences in these outcomes previously identified in the United States.

Statistical Analysis

First, we compared patient demographics, the Elixhauser comorbidity index (likely to be differentially captured between the United States and Canada because of the different payment incentives for hospitals), and patient-level and hospital-level variables for patients who underwent TKA in Pennsylvania and Ontario (Table 1). We compared all continuous variables using the t-test for two independent samples and categorical variables using the chi-square or the Fisher exact test, as appropriate.

Second, we calculated annualized primary TKA use rates (TKAs per 10,000 population per year) for Pennsylvania and Ontario. The numerator for these calculations was the annualized number of TKA procedures performed each year, and the denominator was the number of adults residing in each ZIP Code or postal code. We calculated use for the entire study population, by age-specific population strata (< 50 years, 50 years to 65 years, and ≥ 65 years) and by patient gender (men and women). We then calculated TKA use rates for both regions using direct standardization, with Pennsylvania serving as the reference population. We compared use for residents of the highest income quintile, the middle three quintiles, and the lowest quintile neighborhoods [18] (Fig. 1).

Third, we examined each study secondary outcome using logistic regression models, with income group as the primary predictor. We conducted statistical models for each outcome: model 1 adjusted for select patient-level and hospital-level variables (income, age, gender, hospital volume of procedures, and rural and urban locations). Model 2 adjusted for all model 1 variables in addition to comorbidities. To compare outcomes between the two countries, we used the parameter estimates and standard errors of each country’s regression model to compute a Z statistic and its corresponding p value.

Lastly, we conducted a sensitivity analysis in which we replaced the independent variable of community income with the Gini coefficient. The Gini index was categorized as low (less inequality), middle (quintile 2 to quintile 4), and high (more inequality) for analyses.

All p values were two-sided with statistical significance evaluated at the 0.05 alpha level. All analyses were performed using SAS 9.4 (SAS Institute) or R/R Studio version 4.0.3 statistical software (JJ Allaire).

Analyses were conducted separately for the United States and Canada using the identical study protocols we have used in prior international comparisons after the institutional review board’s ethical approval [17, 18]. The use of the data in this project is authorized under section 45 of Ontario’s Personal Health Information Protection Act and does not require review by a research ethics board.

Results

Does TKA Use Differ Between Pennsylvania and Ontario?

TKA was performed more commonly in Pennsylvania than in Ontario. TKA use in Pennsylvania was higher than in Ontario overall (30 versus 23 per 10,000 population per year; p < 0.001), within subgroups defined by age and gender (Table 2) and in all income quintiles (Fig. 1).

Table 2.

Per-capita numbers and use of TKA in Ontario and Pennsylvania

TKA use rates a
Ontario Pennsylvania p value
Total 23 30 < 0.001
Income quintile
 Lowest 18 31 < 0.001
 2nd to 4th 24 26 < 0.001
 Highest 23 38 < 0.001
Age
 < 50 years 0.9 2 < 0.001
 50-65 years 29 42 < 0.001
 ≥ 65 years 67 79 < 0.001
Gender
 Men 18 23 < 0.001
 Women 27 36 < 0.001
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a

Values are the total use rate, or TKA per 10,000 population per year; the Ontario and Pennsylvania populations were older than 18 years of age and were directly standardized to match the age and gender.

Are Income-based Disparities in TKA Use Larger in Pennsylvania or Ontario?

We found that patients in the highest-income quintiles were more likely to undergo TKA than those in lowest-income quintiles, regardless of country (Fig. 1). We found a larger income-based disparity in Ontario than in Pennsylvania. The difference in use between the highest-income (Pennsylvania versus Ontario: 38 versus 23 procedures per 10,000 population per year; p < 0.001) and lowest-income quintile (Pennsylvania versus Ontario: 31 versus 18 procedures per 10,000 population per year; p < 0.001) was larger in Ontario than in Pennsylvania (28% versus 23% difference; p < 0.001).

TKA use in Pennsylvania was higher than in Ontario in the highest Gini quintile (most inequality) (18 versus 17 per 10,000; p < 0.001) but not in the lowest Gini quintile (least inequality) (25 and 25 per 10,000; p < 0.001). The difference in use between the lowest and highest Gini quintile was larger in Ontario (47%) than in Pennsylvania (39%).

Are TKA Outcomes Different Between Pennsylvania and Ontario?

From 2012 to 2018, patients receiving TKA in Pennsylvania were more likely to be readmitted within 90 days and were more likely to undergo revision within the first year than patients in Ontario, but there was no difference in mortality at 1 year (Supplementary Table 1; http://links.lww.com/CORR/A788).

Are Income-based Disparities in TKA Outcomes Larger in Pennsylvania or Ontario?

Patients in the lowest-income quintile in Pennsylvania had higher odds of 1-year revision than patients in the highest-income quintile (adjusted odds ratio 1.44 [95% confidence interval 1.24 to 1.67]), 90-day mortality (adjusted OR 1.58 [95% CI 1.19 to 2.08]), and 90-day readmission (adjusted OR 1.24 [95% CI 1.18 to 1.30]), accounting for all covariates (Fig. 2). This was significant in our fully adjusted model including all covariates (model 2) and our model without comorbidities (model 1). In Ontario, we found similar results for 90-day mortality (adjusted OR 1.47 [95% CI 1.00 to 2.16]) and 90-day readmission rates (adjusted OR 1.3 [95% CI 1.21 to 1.40]), but not for 1-year revision rates. There were no significant differences between the two countries in all adjusted outcomes (p > 0.05) (Fig. 2).

Discussion

In this retrospective analysis of patients receiving primary TKA, we compared cohorts from Pennsylvania and Ontario to examine whether universal health insurance in Canada could eliminate or reduce income-based disparities. We found a higher use of TKA in Pennsylvania than in Ontario overall and across all neighborhood income subgroups. Surprisingly, in contrast to our assumption, we observed smaller highest versus lowest income differences in TKA use in Pennsylvania than in Ontario. In our sensitivity analysis using the Gini coefficient, we observed the same. There were no differences between the lowest-income and highest-income quintiles for 90-day mortality, 90-day readmission rates, or 1-year revision rates between the two countries. In the context of the ongoing debate over healthcare reform in the United States, our findings suggest that a Canadian-style single-payer system is unlikely to eliminate income-based differences in the use of TKA.

Limitations

Our study has several limitations. First, ZIP Code-level income reflects only community-level metrics. It may not represent homogenous populations in some zip/postal codes. Second, our analysis was restricted to a single procedure (TKA) in one US state and one Canadian province and may not be generalizable due to differences within regions of the two countries. Although we accounted for many of these confounders, our findings should be interpreted in the context that along with the healthcare system there may be other factors, such as a lower number of orthopaedic surgeons per capita or higher wait times for surgery in Canada, which also play a role in TKA use and outcomes. Future studies should be conducted using different surgical procedures and medical diagnoses, a broader sample of US states and Canadian provinces, and other countries. Third, we were not able to evaluate the role of race and ethnicity because these variables were not available in our Ontario data [29, 30]. Our study highlights how epigenetics such as community and its socioeconomics play a role in TKA use and outcomes. And thus, we believe our paper strongly highlights the concept that “place”, or the community that is directly linked to the patient, rather than the social construct of race is strongly associated with TKA use. Fourth, our study relied on hospital administrative data and lacks important clinical variables as well as patient-reported outcomes and patient-reported experience measures. However, collecting patient-reported outcomes and experience measures require massive investment in data collection and is not easily scalable to make cross-border comparisons. Fifth, because of differences in coding and billing practices, certain variables (for example, comorbidities) may be disparate in the two countries. Thus, we presented two models, one that adjusts for the comorbidities and one that does not. We found that the final results did not change substantially.

Does TKA Use and Income-based Disparities in TKA Use Differ Between Pennsylvania and Ontario?

We demonstrated that across all community-level income groups, Pennsylvania had higher TKA use than Ontario. Further, our analysis suggests that the highest versus lowest income differences in the use of TKA were increased in Canada compared with the United States. There has been an assumption that a universal health insurance system, such as the one in Canada, would eliminate some of the inequality that has been extensively documented in the United States that is attributed to a lack of insurance or underinsurance. Our findings contradict this notion with respect to TKA. Few studies that we know of have empirically compared income (or wealth)-based differences in the United States with that in other high-income countries using patient-level data. Pang et al. [40] demonstrated larger income-based differences in rates of pancreatectomy, radical prostatectomy, and nephrectomy in the United States than in Canada. Gorey et al. [24, 25] have similarly demonstrated larger income-based differences in breast and colon cancer in the United States than in Canada. These studies suggest that fragmented insurance coverage in the United States (Medicare, Medicaid, private insurance, or lack of insurance) may exacerbate income-based disparities in cancer incidence and treatment relative to Canada. A recent study comparing the prevalence of 16 common health outcomes in the United States and England (which, similar to Canada, provides universal health insurance) suggested that among individuals in the lowest-income group, patients in the United States had worse outcomes than patients in England [15]. Contrary to these studies, we found higher use of TKA in the United States than in Canada, even in low-income groups. Thus, our finding provides evidence that income-based differences in the use of TKA exist in both countries, but that the magnitude of the income-based difference is different between countries. In a prior US single-site study, we found that community-level socioeconomic factors worsen TKA outcomes [23]. Several prior studies from outside the United States have also described lower use and worse TKA outcomes in low-income communities [1, 9, 10, 36]. Both are attributed to less access to care for patients in lower-income communities [2, 8, 14, 32, 34]. There was reason to believe that Canada’s single-payer health system might reduce income-based differences in TKA use relative to the United States. Although previous work comparing TKA use and outcomes in Ontario and New York found higher overall use in Ontario than in New York [18], we are unaware of any prior studies that have evaluated whether income-based differences observed in the United States are reduced in Canada.

The lower use of TKA in low-income neighborhoods in both countries could be caused by many different factors, such as reduced willingness to undergo TKA, reduced access to primary care and orthopaedic surgeons, or less social support, making residents of lower-income neighborhoods less-desirable candidates for TKA. There are also unanswered questions about the underlying need for and severity of osteoarthritis among residents of lower-income and higher-income neighborhoods and whether the higher use among residents in high-income neighborhoods could represent overuse. Further, when we used the Gini index as an added measure of socioeconomics, we found similar results. Although the Gini index measures a different aspect of socioeconomics—inequality compared with the median household income—our results still demonstrate the same pattern.

Are TKA Outcomes and Their Income-based Disparities Different Between Pennsylvania and Ontario?

Overall, 1-year revision rates and 90-day all-cause mortality rates were low (< 1%) across income levels in both Pennsylvania and Ontario. The odds of 90-day readmission in Pennsylvania and Ontario were greater in the lowest-income quintile, as expected. The difference in 1-year revision rates for patients in the lowest-income versus highest-income communities was larger in Pennsylvania than in Ontario (0.3% versus 0.1%). However, in our adjusted outcomes, although the odds of 1-year revision in low-income versus high-income communities were greater in Pennsylvania than in Ontario, there was no statistically significant difference between the two. This may be because 1-year revision is a rare outcome.

Conclusion

We found a higher use of TKA in Pennsylvania than in Ontario overall and across all neighborhood income subgroups; also, the highest versus lowest income differences in TKA use were smaller in the United States than in Canada. Also, we found that there were no income-based differences in TKA outcomes we studied: 90-day mortality, 90-day readmission, and 1-year revision. This study suggests that the simplistic assumption that a single-payer system will help vulnerable low-income communities get access to TKA is not necessarily true and that deeper understanding of social and community factors from a health policy perspective is needed to improve access. Thus, surgeons should consider the impact of social and healthcare system factors on TKA use and outcomes when planning surgery. Future studies should examine whether our findings in TKA are more broadly generalizable across different regions and countries and also to other elective surgical procedures.

Footnotes

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health. Parts of this material are based on data and information compiled and provided by the Ontario Ministry of Health and the Canadian Institute for Health Information. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred.

The institution of one author (BM) has received, during the study period, funding from the Kellen Scholar Award, which is supported by the Anna Marie and Stephen Kellen Foundation Total Knee Improvement Program. The institution of one author (SI) has received, during the study period, funding from grant K24AR055259 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. The institution of one author (SG) has received, during the study period, funding from Novartis. The institution of one author (PC) has received, during the study period, funding from a grant from the NIH (R01AG058878). The institution of one author (SB) has received, during the study period, funding from 4R33MH110542-03, 5P50 MH113838, 2UL 1 TR000457, R01 MH119177, R0 1MH121922, and CER-2017C3-9230. This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health.

One of the authors (SG) certifies receipt of personal payments or benefits, during the study period, in an amount of less than USD 10,000 from consulting fees for Union Chimique Belge. One of the authors (MP) certifies receipt of personal payments or benefits, during the study period, in an amount of USD 10,000 to USD 100,000 for consulting fees from Zimmer Biomet. One of the authors (PC) certifies receipt of personal payments or benefits, during the study period, in an amount of less than USD 10,000 in honoraria from the Centers for Medicare & Medicaid Services. One of the authors (BM) certifies receipt of personal payments or benefits, during the study period, in an amount of less than USD 10,000 from Novartis and Janssen.

All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request.

Ethical approval for this study was waived by Weill Cornell Medicine, New York, NY, USA (number 1808019505).

This work was performed at the Hospital for Special Surgery New York, NY, USA, and ICES, which is an independent, nonprofit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze healthcare and demographic data, without consent, for health system evaluation and improvement.

Contributor Information

Kaylee Ho, Email: kah4001@med.cornell.edu.

Vicki Ling, Email: vicki.ling@ices.on.ca.

Susan Goodman, Email: goodmans@hss.edu.

Michael Parks, Email: parksm@hss.edu.

Bheeshma Ravi, Email: bheeshma.ravi@sunnybrook.ca.

Samprit Banerjee, Email: sab2028@med.cornell.edu.

Fei Wang, Email: few2001@med.cornell.edu.

Said Ibrahim, Email: sai2009@med.cornell.edu.

Peter Cram, Email: pecram@utmb.edu.

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