Association Between Race, Neighborhood, and Medicaid Enrollment and Outcomes in Medicare Home Health Care

Maddox, Karen E. Joynt; Chen, Lena M.; Zuckerman, Rachael; Epstein, Arnold M.

doi:10.1111/jgs.15082

Cited by 47 publications

(63 citation statements)

References 20 publications

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“…Our findings are consistent with prior studies demonstrating a relationship between claims‐based measures of social risk and readmission rates, though to our knowledge this is the first to combine individual, community, and housing variables in such a manner. One prior study examined the relationship between adjusting for social risk and penalties under the HRRP, and despite similar quantitative findings, came to different conclusions, but that study only evaluated the dichotomous outcome of going from penalty to no penalty, and likely had more imprecise geographic social measures than the ones used here .…”

Section: Discussionsupporting

confidence: 91%

“…Another possibility is that social risk is associated with higher readmission rates due to post‐discharge issues such as access to primary care, transportation to follow‐up appointments, health literacy, ability to adhere to self‐care regimens, and ability to afford prescriptions, all of which are more difficult in the presence of social risk. Finally, prior evidence suggests that social risk is associated with the receipt of poorer‐quality post‐acute care, which may impact readmission rates independent of hospital quality …”

Section: Discussionmentioning

confidence: 99%

“…While a large body of evidence has demonstrated that social risk factors such as individual poverty and residence in a highly disadvantaged community are powerfully linked to readmission, less is known about how these relationships might impact performance under the HRRP or the magnitude of penalties levied against safety‐net hospitals. In the first year of the HRRP, safety‐net hospitals were more likely to receive maximum penalties than non–safety‐net hospitals, but as the program expanded in its first few years, these differences became smaller .…”

Section: Introductionmentioning

confidence: 99%

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Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program

Maddox

Reidhead

et al. 2019

Health Services Research

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161

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Objective Medicare's Hospital Readmissions Reduction Program (HRRP) does not account for social risk factors in risk adjustment, and this may lead the program to unfairly penalize safety‐net hospitals. Our objective was to determine the impact of adjusting for social risk factors on HRRP penalties. Study Design Retrospective cohort study. Data Sources/Study Setting Claims data for 2 952 605 fee‐for‐service Medicare beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF) or pneumonia from December 2012 to November 2015. Principal Findings Poverty, disability, housing instability, residence in a disadvantaged neighborhood, and hospital population from a disadvantaged neighborhood were associated with higher readmission rates. Under current program specifications, safety‐net hospitals had higher readmission ratios (AMI, 1.020 vs 0.986 for the most affluent hospitals; pneumonia, 1.031 vs 0.984; and CHF, 1.037 vs 0.977). Adding social factors to risk adjustment cut these differences in half. Over half the safety‐net hospitals saw their penalty decline; 4‐7.5 percent went from having a penalty to having no penalty. These changes translated into a $17 million reduction in penalties to safety‐net hospitals. Conclusions Accounting for social risk can have a major financial impact on safety‐net hospitals. Adjustment for these factors could reduce negative unintended consequences of the HRRP.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program

Maddox

Reidhead

et al. 2019

Health Services Research

Self Cite

161

118

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“…Moreover, in a recent study to evaluate clinical outcomes in Medicare beneficiaries using HHC, those dually enrolled in both Medicare and Medicaid were more likely to have 30‐day readmissions and 30‐day emergency department visits following hospital discharge (OR = 1.08 [ P < .001] and OR = 1.20 [ P < .001], respectively) . This latter study also noted a similar association for beneficiaries living in a low‐income zip code and for black race . Overall, these findings suggest a need to consider and incorporate functional outcomes and social determinants of health into models that assess and address readmission risk.…”

Section: Discussionmentioning

confidence: 60%

“…In another study, including social variables, such as housing instability, improved model performance . Moreover, in a recent study to evaluate clinical outcomes in Medicare beneficiaries using HHC, those dually enrolled in both Medicare and Medicaid were more likely to have 30‐day readmissions and 30‐day emergency department visits following hospital discharge (OR = 1.08 [ P < .001] and OR = 1.20 [ P < .001], respectively) . This latter study also noted a similar association for beneficiaries living in a low‐income zip code and for black race .…”

Section: Discussionmentioning

confidence: 68%

Predicting Hospital Readmissions from Home Healthcare in Medicare Beneficiaries

Jones

Falvey

Hess³

et al. 2019

J American Geriatrics Society

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OBJECTIVE To use patient‐level clinical variables to develop and validate a parsimonious model to predict hospital readmissions from home healthcare (HHC) in Medicare fee‐for‐service beneficiaries. DESIGN Retrospective analysis using multivariable logistic regression and gradient boosting machine (GBM) learning to develop and validate a predictive model. SETTING/PARTICIPANTS/MEAUREMENTS A 5% national sample of patients, aged 65 years or older, with Medicare fee‐for‐service who received skilled HHC services within 5 days of hospital discharge in 2012 (n = 43 407). Multiple data sets were merged, including Medicare Outcome and Assessment Information Set, Home Health Claims, Medicare Provider Analysis and Review, and Master Beneficiary Summary Files, to extract patient‐level variables from the first HHC visit after discharge and measure 30‐day readmission outcomes. RESULTS Among 43 407 patients with inpatient hospitalizations followed by HHC, 14.7% were readmitted within 30 days. Of the 53 candidate variables, seven remained in the final model as individually predictive of outcome: Elixhauser comorbidity index, index hospital length of stay, urinary catheter presence, patient status (ie, fragile health with high risk of complications or serious progressive condition), two or more hospitalizations in prior year, pressure injury risk or presence, and surgical wound presence. Of interest, surgical wounds, either from a total hip or total knee arthroplasty procedure or another surgical procedure, were associated with fewer readmissions. The optimism‐corrected c‐statistics for the full model and parsimonious model were 0.67 and 0.66, respectively, indicating fair discrimination. The Brier score for both models was 0.120, indicating good calibration. The GBM model identified similar predictive variables. CONCLUSION Variables available to HHC clinicians at the first postdischarge HHC visit can predict readmission risk and inform care plans in HHC. Future analyses incorporating measures of social determinants of health, such as housing instability or social support, have the potential to enhance prediction of this outcome. J Am Geriatr Soc 67:2505–2510, 2019

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Assessment of Racial Disparities in Primary Care Physician Specialty Referrals

et al. 2021

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IMPORTANCE Disparities in quality of care according to patient race and socioeconomic status persist in the US. Differential referral patterns to specialist physicians might be associated with observed disparities. OBJECTIVE To examine whether differences exist between Black and White Medicare beneficiaries in the observed patterns of patient sharing between primary care physicians (PCPs) and physicians in the 6 specialties to which patients were most frequently referred. DESIGN, SETTING, AND PARTICIPANTSThis cross-sectional observational study of Black and White Medicare beneficiaries used claims data from 2009 to 2010 on 100% of traditional Medicare beneficiaries who were seen by PCPs and selected high-volume specialists in 12 health care markets with at least 10% of the population being Black. Statistical analyses were conducted from December 20, 2017, to September 30, 2020. EXPOSURES Differences in patterns of patient sharing among Black and White patients. MAIN OUTCOMES AND MEASURES Primary care physician and specialist degree (the number of other PCPs or specialists to whom each physician is connected) and strength (the number of shared patients per connection, overall, for Black patients and White patients and after equalizing the numbers of Black and White patients per PCP), as well as distance between PCP and patient and specialist zip code centroids. RESULTSThe 12 selected markets ranged in size from Manhattan, New York (187 054 Black or White beneficiaries seen by at least 2 physicians within an episode of care; 9794 total physicians), to Tallahassee, Florida (44 644 Black or White beneficiaries seen by at least 2 physicians within an episode of care; 847 total physicians). The percentage of Black beneficiaries ranged from 11.5% (Huntsville, Alabama) to 46.8% (Chicago, Illinois). The mean PCP-specialist degree (number of specialists with whom a PCP shares patients) was lower for Black patients than for White patients.For instance, the mean PCP-cardiologist degree across all markets for White patients was 17.5 compared with 8.8 for Black patients. After sampling White patients to equalize the numbers of patients seen, the degree differences narrowed but were still not equivalent in many markets (eg, for all specialties in Baton Rouge, Louisiana: 4.5 for Black patients vs 5.7 for White patients). Specialist networks among White patients were much larger than those constructed based just on Black patients (eg, for cardiology across all markets: 135 for Black patients vs 330 for White patients), even after equalizing the numbers of patients seen per PCP (123 for Black patients vs 211 for White patients). The overall test for differences in referral patterns was statistically significant for all 6 specialties examined in 7 of the 12 markets and in 5 specialties for another 3. (continued) Key Points Question Does patient sharing among primary care physicians and specialists differ based on the patient's race? Findings In this cross-sectional study of Medicare beneficiaries, primary care physicians shared Bla...

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Association Between Race, Neighborhood, and Medicaid Enrollment and Outcomes in Medicare Home Health Care

Abstract: Medicare beneficiaries receiving home health services who are dually enrolled, live in a low-income neighborhood, or are black have higher rates of adverse clinical outcomes. These populations may be an important target for quality improvement under Home Health Value-Based Purchasing.

Cited by 47 publications

References 20 publications

Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program

Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program

Predicting Hospital Readmissions from Home Healthcare in Medicare Beneficiaries

Assessment of Racial Disparities in Primary Care Physician Specialty Referrals

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