Are Neighborhood Characteristics Associated With Outcomes After THA and TKA? Findings From a Large Healthcare System Database.

BACKGROUND

Non-White patients have higher rates of discharge to an extended care facility, hospital readmission, and emergency department use after primary THA and TKA. The reasons for this are unknown. Place of residence, which can vary by race, has been linked to poorer healthcare outcomes for people with many health conditions. However, the potential relationship between place of residence and disparities in these joint arthroplasty outcomes is unclear.

QUESTIONS/PURPOSES: (1) Are neighborhood-level characteristics, including racial composition, marital proportions, residential vacancy, educational attainment, employment proportions, overall deprivation, access to medical care, and rurality associated with an increased risk of discharge to a facility, readmission, and emergency department use after elective THA and TKA? (2) Are the associations between neighborhood-level characteristics and discharge to a facility, readmission, and emergency department use the same among White and Black patients undergoing elective THA and TKA?

METHODS

Between 2007 and 2018, 34,008 records of elective primary THA or TKA for osteoarthritis, rheumatoid arthritis, or avascular necrosis in a regional healthcare system were identified. After exclusions for unicompartmental arthroplasty, bilateral surgery, concomitant procedures, inability to geocode a residential address, duplicate records, and deaths, 21,689 patients remained. Ninety-seven percent of patients in this cohort self-identified as either White or Black, so the remaining 659 patients were excluded due to small sample size. This left 21,030 total patients for analysis. Discharge destination, readmissions within 90 days of surgery, and emergency department visits within 90 days were identified. Each patient's street address was linked to neighborhood characteristics from the American Community Survey and Area Deprivation Index. A multilevel, multivariable logistic regression analysis was used to model each outcome of interest, controlling for clinical and individual sociodemographic factors and allowing for clustering at the neighborhood level. The models were then duplicated with the addition of neighborhood characteristics to determine the association between neighborhood-level factors and each outcome. The linear predictors from each of these models were used to determine the predicted risk of each outcome, with and without neighborhood characteristics, and divided into tenths. The change in predicted risk tenths based on the model containing neighborhood characteristics was compared to that without neighborhood characteristics.The change in predicted risk tenth for each outcome was stratified by race.

RESULTS

After controlling for age, sex, insurance type, surgery type, and comorbidities, we found that an increase of one SD of neighborhood unemployment (odds ratio 1.26 [95% confidence interval 1.17 to 1.36]; p < 0.001) was associated with an increased likelihood of discharge to a facility, whereas an increase of one SD in proportions of residents receiving public assistance (OR 0.92 [95% CI 0.86 to 0.98]; p = 0.008), living below the poverty level (OR 0.82 [95% CI 0.74 to 0.91]; p < 0.001), and being married (OR 0.80 [95% CI 0.71 to 0.89]; p < 0.001) was associated with a decreased likelihood of discharge to a facility. Residence in areas one SD above mean neighborhood unemployment (OR 1.12 [95% CI [1.04 to 1.21]; p = 0.002) was associated with increased rates of readmission. An increase of one SD in residents receiving food stamps (OR 0.83 [95% CI 0.75 to 093]; p = 0.001), being married (OR 0.89 [95% CI 0.80 to 0.99]; p = 0.03), and being older than 65 years (OR 0.93 [95% CI 0.88 to 0.98]; p = 0.01) was associated with a decreased likelihood of readmission. A one SD increase in the percentage of Black residents (OR 1.11 [95% CI 1.00 to 1.22]; p = 0.04) and unemployed residents (OR 1.15 [95% CI 1.05 to 1.26]; p = 0.003) was associated with a higher likelihood of emergency department use. Living in a medically underserved area (OR 0.82 [95% CI 0.68 to 0.97]; p = 0.02), a neighborhood one SD above the mean of individuals using food stamps (OR 0.81 [95% CI 0.70 to 0.93]; p = 0.004), and a neighborhood with an increasing percentage of individuals older than 65 years (OR 0.90 [95% CI 0.83 to 0.96]; p = 0.002) were associated with a lower likelihood of emergency department use. With the addition of neighborhood characteristics, the risk prediction tenths of the overall cohort remained the same in more than 50% of patients for all three outcomes of interest. When stratified by race, neighborhood characteristics increased the predicted risk for 55% of Black patients for readmission compared with 17% of White patients (p < 0.001). The predicted risk tenth increased for 60% of Black patients for emergency department use compared with 21% for White patients (p < 0.001).

CONCLUSION

These results can be used to identify high-risk patients who might benefit from preemptive interventions to avoid these particular outcomes and to create more realistic, comprehensive risk adjustment models for value-based care programs. Additionally, this study demonstrates that neighborhood characteristics are associated with greater risk for these outcomes among Black patients compared with White patients. Further studies should consider that race/ethnicity and neighborhood characteristics may not function independently from each other. Understanding this link between race and place of residence is essential for future racial disparities research.

LEVEL OF EVIDENCE

Level III, therapeutic study.