Adjusting Quality Measures For Social Risk Factors Can Promote Equity In Health Care

A Scoping Review and Assessment of the Area-Level Composite Measures That Estimate Social Determinants of Health Across the United States

OBJECTIVES

Evidence-informed population health initiatives often leverage data from various sources, such as epidemiologic surveillance data and administrative datasets. Recent interest has arisen in using area-level composite measures describing a community's social risks to inform the development and implementation of health policies, including payment reform initiatives. Our objective was to capture the breadth of available area-level composite measures that describe social determinants of health (SDH) and have potential for application in population health and policy work.

METHODS

We conducted a scoping review of the scientific literature from 2010 to 2022 to identify multifactorial indices and rankings reflected in peer-reviewed literature that estimate SDH and that have publicly accessible data sources. We discovered several additional composite measures incidental to the scoping review process. Literature searches for each composite measure aimed to contextualize common applications in public health investigations.

RESULTS

From 491 studies, we identified 31 composite measures and categorized them into 8 domains: environmental conditions and pollution, opportunity and infrastructure, deprivation and well-being, COVID-19, rurality, food insecurity, emergency response and community resilience, and health. Composite measures are applied most often as an independent variable associated with disparities, risk factors, and/or outcomes affecting individuals, populations, communities, and health systems.

CONCLUSIONS

Area-level composite measures describing SDH have been applied to wide-ranging population health work. Social risk indicators may enable policy makers, evaluators, and researchers to better assess community risks and needs, thereby facilitating the evidence-informed development, implementation, and study of initiatives that aim to improve population health.

Statistical Methods to Examine Racial and Ethnic Disparities in the Surgical Literature: A Review and Recommendations for Improvement

OBJECTIVE

To characterize the quality of statistical methods for studies of racial and ethnic disparities in the surgical-relevant literature during 2021-2022.

BACKGROUND

Hundreds of scientific papers are published each year describing racial and ethnic disparities in surgical access, quality, and outcomes. The content and design quality of this literature have never been systematically reviewed.

METHODS

We searched for 2021 to 2022 studies focused on describing racial and/or ethnic disparities in surgical or perioperative access, process quality, or outcomes. Identified studies were characterized in terms of 3 methodological criteria: (1) adjustment for variables related to both race/ethnicity and outcomes, including social determinants of health (SDOH), (2) accounting for clustering of patients within hospitals or other subunits ("providers"), and (3) distinguishing within-provider and between-provider effects.

RESULTS

We identified 224 papers describing racial and/or ethnic differences. Of the 38 single-institution studies, 24 (63.2%) adjusted for at least one SDOH variable. Of the 186 multisite studies, 113 (60.8%) adjusted for at least one SDOH variable, and 43 (23.1%) accounted for the clustering of patients within providers using appropriate statistical methods. Only 10 (5.4%) of multi-institution studies made efforts to examine how much of the overall disparities were driven by within versus between-provider effects.

CONCLUSIONS

Most recently published papers on racial and ethnic disparities in the surgical literature do not meet these important statistical design criteria and, therefore, may risk inaccuracy in the estimation of group differences in surgical access, quality, and outcomes. The most potent leverage points for these improvements are changes to journal publication guidelines and policies.

Applying Implementation Science to Advance Electronic Health Record-Driven Learning Health Systems: Case Studies, Challenges, and Recommendations

With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.

Impact of Social Determinants of Health on Predictive Models for Outcomes After Congenital Heart Surgery

BACKGROUND

Despite documented associations between social determinants of health and outcomes post-congenital heart surgery, clinical risk models typically exclude these factors.

OBJECTIVES

The study sought to characterize associations between social determinants and operative and longitudinal mortality as well as assess impacts on risk model performance.

METHODS

Demographic and clinical data were obtained for all congenital heart surgeries (2006-2021) from locally held Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources Society of Thoracic Surgeons Congenital Heart Surgery Database data. Neighborhood-level American Community Survey and composite sociodemographic measures were linked by zip code. Model prediction, discrimination, and impact on quality assessment were assessed before and after inclusion of social determinants in models based on the 2020 Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model.

RESULTS

Of 14,173 total index operations across New York State, 12,321 cases, representing 10,271 patients at 8 centers, had zip codes for linkage. A total of 327 (2.7%) patients died in the hospital or before 30 days, and 314 children died by December 31, 2021 (total n = 641; 6.2%). Multiple measures of social determinants of health explained as much or more variability in operative and longitudinal mortality than clinical comorbidities or prior cardiac surgery. Inclusion of social determinants minimally improved models' predictive performance (operative: 0.834-0.844; longitudinal 0.808-0.811), but significantly improved model discrimination; 10.0% more survivors and 4.8% more mortalities were appropriately risk classified with inclusion. Wide variation in reclassification was observed by site, resulting in changes in the center performance classification category for 2 of 8 centers.

CONCLUSIONS

Although indiscriminate inclusion of social determinants in clinical risk modeling can conceal inequities, thoughtful consideration can help centers understand their performance across populations and guide efforts to improve health equity.

Health Equity Adjustment and Hospital Performance in the Medicare Value-Based Purchasing Program

Importance

Medicare's Hospital Value-Based Purchasing (HVBP) program will provide a health equity adjustment (HEA) to hospitals that have greater proportions of patients dually eligible for Medicare and Medicaid and that offer high-quality care beginning in fiscal year 2026. However, which hospitals will benefit most from this policy change and to what extent are unknown.

Objective

To estimate potential changes in hospital performance after HEA and examine hospital patient mix, structural, and geographic characteristics associated with receipt of increased payments.

Design, Setting, and Participants

This cross-sectional study analyzed all 2676 hospitals participating in the HVBP program in fiscal year 2021. Publicly available data on program performance and hospital characteristics were linked to Medicare claims data on all inpatient stays for dual-eligible beneficiaries at each hospital to calculate HEA points and HVBP payment adjustments.

Exposures

Hospital Value-Based Purchasing program HEA.

Main Outcomes and Measures

Reclassification of HVBP bonus or penalty status and changes in payment adjustments across hospital characteristics.

Results

Of 2676 hospitals participating in the HVBP program in fiscal year 2021, 1470 (54.9%) received bonuses and 1206 (45.1%) received penalties. After HEA, 102 hospitals (6.9%) were reclassified from bonus to penalty status, whereas 119 (9.9%) were reclassified from penalty to bonus status. At the hospital level, mean (SD) HVBP payment adjustments decreased by $4534 ($90 033) after HEA, ranging from a maximum reduction of $1 014 276 to a maximum increase of $1 523 765. At the aggregate level, net-positive changes in payment adjustments were largest among safety net hospitals ($28 971 708) and those caring for a higher proportion of Black patients ($15 468 445). The likelihood of experiencing increases in payment adjustments was significantly higher among safety net compared with non-safety net hospitals (574 of 683 [84.0%] vs 709 of 1993 [35.6%]; adjusted rate ratio [ARR], 2.04 [95% CI, 1.89-2.20]) and high-proportion Black hospitals compared with non-high-proportion Black hospitals (396 of 523 [75.7%] vs 887 of 2153 [41.2%]; ARR, 1.40 [95% CI, 1.29-1.51]). Rural hospitals (374 of 612 [61.1%] vs 909 of 2064 [44.0%]; ARR, 1.44 [95% CI, 1.30-1.58]), as well as those located in the South (598 of 1040 [57.5%] vs 192 of 439 [43.7%]; ARR, 1.25 [95% CI, 1.10-1.42]) and in Medicaid expansion states (801 of 1651 [48.5%] vs 482 of 1025 [47.0%]; ARR, 1.16 [95% CI, 1.06-1.28]), were also more likely to experience increased payment adjustments after HEA compared with their urban, Northeastern, and Medicaid nonexpansion state counterparts, respectively.

Conclusions and Relevance

Medicare's implementation of HEA in the HVBP program will significantly reclassify hospital performance and redistribute program payments, with safety net and high-proportion Black hospitals benefiting most from this policy change. These findings suggest that HEA is an important strategy to ensure that value-based payment programs are more equitable.

The reliability and validity of lung cancer and melanoma clinical quality survival measures

OBJECTIVE

To develop a risk adjustment approach and test reliability and validity for oncology survival measures.

DATA SOURCES AND STUDY SETTING

We used the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER)-Medicare data from 2010 to 2013, with mortality data through 2015.

STUDY DESIGN

We developed 2-year risk-standardized survival rates (RSSR) for melanoma, non-small cell lung cancer (NSCLC), and small cell lung cancer (SCLC). Patients were attributed to group practices based on the plurality of visits. We identified the risk-adjustment variables via bootstrap and calculated the RSSRs. Reliability was tested via three approaches: (1) signal-to-noise ratio (SNR) reliability, (2) split-half, and (3) test-retest using bootstrap. We tested known group validity by stage at diagnosis using Cohen's d.

DATA COLLECTION/EXTRACTION METHODS

We selected all patients enrolled in Medicare and linked to SEER during the measurement period with an incident first primary diagnosis of stage I-IV melanoma, NSCLC, or SCLC. We excluded patients with missing data on month and/or stage of diagnosis.

PRINCIPAL FINDINGS

Results are based on patients with melanoma (n = 4344); NSCLC (n = 16,080); and SCLC (n = 2807) diagnosed between 2012 and 2013. The median (interquartile range) for the RSSRs at the group practice-level were 0.89 (0.83-0.87) for melanoma, 0.37 (0.30-0.43) for NSCLC, and 0.19 (0.11-0.25) for SCLC. C-statistics for the models ranged from 0.725 to 0.825. The reliability varied by approach with median SNR 0.20, 0.25, and 0.13; median test-retest 0.59, 0.57, and 0.56; median split-half reliability 0.21, 0.29, and 0.29 for melanoma, NSCLC, and SCLC, respectively. Cohen's d for stage I-IIIa and IIIb+ was 1.27, 0.86, 0.60 for melanoma, NSCLC, and SCLC, respectively.

CONCLUSIONS

Our results suggest that these cancer survival measures demonstrated adequate test-retest reliability and expected findings for the known-group validity analysis. If data limitations and feasibility challenges can be addressed, implementation of these quality measures may provide a survival metric used for oncology quality improvement efforts.

Guiding Risk Adjustment Models Toward Machine Learning Methods

This Viewpoint reviews the history of administrative risk adjustment models used in health care and provides recommendations for modernizing these models to promote their safe, transparent, equitable, and efficient use.

Using the Kitagawa Decomposition to Measure Overall-and Individual Facility Contributions to-Within-facility and Between-facility Differences: Analyzing Racial and Ethnic Wait Time Disparities in the Veterans Health Administration

BACKGROUND

Identifying whether differences in health care disparities are due to within-facility or between-facility differences is key to disparity reductions. The Kitagawa decomposition divides the difference between 2 means into within-facility differences and between-facility differences that are measured on the same scale as the original disparity. It also enables the identification of facilities that contribute most to within-facility differences (based on facility-level disparities and the proportion of patient population served) and between-facility differences.

OBJECTIVES

Illustrate the value of a 2-stage Kitagawa decomposition to partition a disparity into within-facility and between-facility differences and to measure the contribution of individual facilities to each type of difference.

SUBJECTS

Veterans receiving a new outpatient consult for cardiology or orthopedic services during fiscal years 2019-2021.

MEASURES

Wait time for a new-patient consult.

METHODS

In stage 1, we predicted wait time for each Veteran from a multivariable model; in stage 2, we aggregated individual predictions to determine mean adjusted wait times for Hispanic, Black, and White Veterans and then decomposed differences in wait times between White Veterans and each of the other groups.

RESULTS

Noticeably longer wait times were experienced by Hispanic Veterans for cardiology (2.32 d, 6.8% longer) and Black Veterans for orthopedics (3.49 d, 10.3% longer) in both cases due entirely to within-facility differences. The results for Hispanic Veterans using orthopedics illustrate how positive within-facility differences (0.57 d) can be offset by negative between-facility differences (-0.34 d), resulting in a smaller overall disparity (0.23 d). Selecting 10 facilities for interventions in orthopedics based on the largest contributions to within-in facility differences instead of the largest disparities resulted in a higher percentage of Veterans impacted (31% and 12% of Black and White Veterans, respectively, versus 9% and 10% of Black and White Veterans, respectively) and explained 21% of the overall within-facility difference versus 11%.

CONCLUSIONS

The Kitagawa approach allows the identification of disparities that might otherwise be undetected. It also allows the targeting of interventions at those facilities where improvements will have the largest impact on the overall disparity.

Adjustment for Social Risk Factors in a Measure of Clinician Quality Assessing Acute Admissions for Patients With Multiple Chronic Conditions

Importance

Adjusting quality measures used in pay-for-performance programs for social risk factors remains controversial.

Objective

To illustrate a structured, transparent approach to decision-making about adjustment for social risk factors for a measure of clinician quality that assesses acute admissions for patients with multiple chronic conditions (MCCs).

Design, Setting, and Participants

This retrospective cohort study used 2017 and 2018 Medicare administrative claims and enrollment data, 2013 to 2017 American Community Survey data, and 2018 and 2019 Area Health Resource Files. Patients were Medicare fee-for-service beneficiaries 65 years or older with at least 2 of 9 chronic conditions (acute myocardial infarction, Alzheimer disease/dementia, atrial fibrillation, chronic kidney disease, chronic obstructive pulmonary disease or asthma, depression, diabetes, heart failure, and stroke/transient ischemic attack). Patients were attributed to clinicians in the Merit-Based Incentive Payment System (MIPS; primary health care professionals or specialists) using a visit-based attribution algorithm. Analyses were conducted between September 30, 2017, and August 30, 2020.

Exposures

Social risk factors included low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility.

Main Outcomes and Measures

Number of acute unplanned hospital admissions per 100 person-years at risk for admission. Measure scores were calculated for MIPS clinicians with at least 18 patients with MCCs assigned to them.

Results

There were 4 659 922 patients with MCCs (mean [SD] age, 79.0 [8.0] years; 42.5% male) assigned to 58 435 MIPS clinicians. The median (IQR) risk-standardized measure score was 38.9 (34.9-43.6) per 100 person-years. Social risk factors of low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility were significantly associated with the risk of hospitalization in the univariate models (relative risk [RR], 1.14 [95% CI, 1.13-1.14], RR, 1.05 [95% CI, 1.04-1.06], and RR, 1.44 [95% CI, 1.43-1.45], respectively), but the association was attenuated in adjusted models (RR, 1.11 [95% CI 1.11-1.12] for dual eligibility). Across MIPS clinicians caring for variable proportions of dual-eligible patients with MCCs (quartile 1, 0%-3.1%; quartile 2, >3.1%-9.5%; quartile 3, >9.5%-24.5%, and quartile 4, >24.5%-100%), median measure scores per quartile were 37.4, 38.6, 40.0, and 39.8 per 100 person-years, respectively. Balancing conceptual considerations, empirical findings, programmatic structure, and stakeholder input, the Centers for Medicare & Medicaid Services decided to adjust the final model for the 2 area-level social risk factors but not dual Medicare-Medicaid eligibility.

Conclusions and Relevance

This cohort study demonstrated that adjustment for social risk factors in outcome measures requires weighing high-stake, competing concerns. A structured approach that includes evaluation of conceptual and contextual factors, as well as empirical findings, with active engagement of stakeholders can be used to make decisions about social risk factor adjustment.

Social risk adjustment in the hospital readmission reduction program: Pitfalls of peer grouping, measurement challenges, and potential solutions

OBJECTIVE

To examine the limitations of peer grouping and associated challenges measuring social risk in Medicare's Hospital Readmission Reduction Program (HRRP). Under peer grouping, hospitals are divided into quintiles based on the proportion of a hospital's Medicare inpatients with Medicaid ("dual share"). This approach was implemented to address concerns that the HRRP unfairly penalized hospitals that disproportionately serve disadvantaged patients.

DATA

Public data on hospitals in the HRRP.

DESIGN

We examined the relationship between hospital dual share and readmission rates within peer groups; changes in hospitals' peer group assignments, readmission rates, and penalties; and the relationship between state Medicaid eligibility rules and peer groups.

DATA COLLECTION

Public data on hospital characteristics and readmission rates for 3119 hospitals from 2019 to 2020.

PRINCIPAL FINDINGS

The proportion of dual inpatients among hospitals of the same peer group varied by as much as 69 percentage points (ppt). Within peer groups, a one ppt increase in dual share was associated with a 0.01 ppt increase in the difference from the median readmission rate (p < 0.001). From 2019 to 2020, 8.8% of hospitals switched peer groups. Compared to hospitals that did not switch, those moving to a lower peer group had a higher mean penalty in 2020 (0.096 ppt; p = 0.006); those moving to a higher group had a lower mean penalty (-0.06 ppt; p = 0.079). However, changes in penalties did not correspond to changes in readmission rates. Hospitals in states with higher Medicaid income eligibility limits were more likely to be in higher peer groups.

CONCLUSIONS

Peer grouping is limited in the extent to which it accounts for differences in hospitals' patient populations, and it may not fully insulate hospitals from penalties linked to changes in patient mix. These problems arise from the construction of peer groups and the measure of social risk used to define them.

Association of Insurance Type With Inpatient Surgery 30-Day Complications and Costs

INTRODUCTION

Safety-net hospitals (SNHs) have higher postoperative complications and costs versus low-burden hospitals. Do low socioeconomic status/vulnerable patients receive care at lower-quality hospitals or are there factors beyond providers' control? We studied the association of private, Medicare, and vulnerable insurance type with complications/costs in a high-burden SNH.

METHODS

Retrospective inpatient cohort study using National Surgical Quality Improvement Program (NSQIP) data (2013-2019) with cost data risk-adjusted by frailty, preoperative serious acute conditions (PASC), case status, and expanded operative stress score (OSS) to evaluate 30-day unplanned reoperations, any complication, Clavien-Dindo IV (CDIV) complications, and hospitalization variable costs.

RESULTS

Cases (Private 1517; Medicare 1224; Vulnerable 3648) with patient mean age 52.3 y [standard deviation = 14.7] and 47.3% male. Adjusting for frailty and OSS, vulnerable patients had higher odds of PASC (aOR = 1.71, CI = 1.39-2.10, P < 0.001) versus private. Adjusting for frailty, PASC and OSS, Medicare (aOR = 1.27, CI = 1.06-1.53, P = 0.009), and vulnerable (aOR = 2.44, CI = 2.13-2.79, P < 0.001) patients were more likely to undergo urgent/emergent surgeries. Vulnerable patients had increased odds of reoperation and any complications versus private. Variable cost percentage change was similar between private and vulnerable after adjusting for case status. Urgent/emergent case status increased percentage change costs by 32.31%. We simulated "switching" numbers of private (3648) versus vulnerable (1517) cases resulting in an estimated variable cost of $49.275 million, a 25.2% decrease from the original $65.859 million.

CONCLUSIONS

Increased presentation acuity (PASC and urgent/emergent surgeries) in vulnerable patients drive increased odds of complications and costs versus private, suggesting factors beyond providers' control. The greatest impact on outcomes may be from decreasing the incidence of urgent/emergent surgeries by improving access to care.

Review of the National Quality Forum's Measure Endorsement Process

ABSTRACT

The National Quality Forum (NQF) evaluates healthcare performance measures for endorsement based on a broad set of criteria. We extracted data from NQF technical reports released between spring 2018 and spring 2019. Measures were commonly stewarded by federal agencies (44.29%), evaluated for maintenance (67.14%), classified as outcome (42.14%) or process (39.29%) measures, and used a statistical model for risk adjustment (48.57%). For 80% of the measures reviewed, a patient advocate was present on the reviewing committee. Validity was evaluated using face validity (65.00%) or score-level empirical validity (67.14%), and reliability was frequently evaluated using score-level testing (71.43%). Although 91.56% of all reviewed measures were endorsed, most standing committee members voted moderate rather than high support on key assessment criteria like measure validity, measure reliability, feasibility of use, and whether the measure addresses a key performance gap. Results show that although the Consensus Development Process includes multidisciplinary stakeholder input and thorough evaluations of measures, continued work to identify and describe appropriate and robust methods for reliability and validity testing is needed. Further work is needed to study the extent to which stakeholder input is truly representative of diverse viewpoints and improve processes for considering social factors when risk adjusting.

Assessing Hospital Quality Scores By Proportion Of Patients Dually Eligible For Medicare And Medicaid

The Centers for Medicare and Medicaid Services has been reporting hospital star ratings since 2016. Some stakeholders have criticized the star ratings methodology for not adjusting for social risk factors. We examined the relationship between 2021 star rating scores and hospitals' proportion of Medicare patients dually eligible for Medicaid. We found that, on average, hospitals caring for a greater proportion of dually eligible patients had lower star ratings, but there was significant overlap in performance among hospitals when we stratified them by quintile of dually eligible patients. Hospitals in the highest quintile (those with the greatest proportion of dually eligible patients) had the best mean mortality scores (0.28) but the worst readmission (-0.44) and patient experience (-0.78) scores. We assigned star ratings after stratifying the readmission measure group by proportion of dually eligible patients and found that a total of 142 hospitals gained a star and 161 hospitals lost a star, of which 126 (89 percent) and 1 (<1 percent) were in the highest quintile, respectively. Adjusting public reporting tools such as star ratings for social risk factors is ultimately a policy decision, and views on the appropriateness of accounting for factors such as proportion of dually eligible patients are mixed, depending on the organization and stakeholder.

Adjusting for Patient Economic/Access Issues in a Hypertension Quality Measure

INTRODUCTION

The American Heart Association and American College of Cardiology have proposed adjusting hypertension-related care quality measures by excluding patients with economic/access issues from the denominator of rate calculations. No research to date has explored the methods to operationalize this recommendation or how to measure economic/access issues. This study applied and compared different approaches to populating these denominator exceptions.

METHODS

Electronic health record data from 2019 were used in 2021 to calculate hypertension control rates in 84 community health centers. A total of 10 different indicators of patient economic/access barriers to care were used as denominator exclusions to calculate and then compare adjusted quality measure performance. Data came from a nonprofit health center‒controlled network that hosts a shared electronic health record for community health centers located in 22 states.

RESULTS

A total of 5 of 10 measures yielded an increase in adjusted hypertension control rates in ≥50% of clinics (average rate increases of 0.7-3.71 percentage points). A total of 3 of 10 measures yielded a decrease in adjusted hypertension control rates in >50% of clinics (average rate decreases of 1.33-13.82 percentage points). A total of 5 measures resulted in excluding >50% of the clinic's patient population from quality measure assessments.

CONCLUSIONS

Changes in clinic-level hypertension control rates after adjustment differed depending on the measure of economic/access issue. Regardless of the exclusion method, changes between baseline and adjusted rates were small. Removing community health center patients experiencing economic/access barriers from a hypertension control quality measure resulted in excluding a large proportion of patients, raising concerns about whether this calculation can be a meaningful measure of clinical performance.

Social Risk Adjustment In The Hospital Readmissions Reduction Program: A Systematic Review And Implications For Policy

Value-based payment programs adjust payments to providers based on spending, quality, or health outcomes. Concern that these programs penalize providers disproportionately serving vulnerable patients prompted calls to adjust performance measures for social risk factors. We reviewed fourteen studies of social risk adjustment in Medicare's Hospital Readmissions Reduction Program (HRRP), a value-based payment model that initially did not adjust for social risk factors but subsequently began to do so. Seven studies found that adding social risk factors to the program's base risk-adjustment model (which adjusts only for age, sex, and comorbidities) reduced differences in risk-adjusted readmissions and penalties between safety-net hospitals and other hospitals. Three studies found that peer grouping, the HRRP's current approach to social risk adjustment, reduced penalties among safety-net hospitals. Two studies found that differences in risk-adjusted readmissions and penalties were further narrowed when augmentation of the base model was combined with peer grouping. Two studies showed that it is possible to adjust for social risk factors without obscuring quality differences between hospitals. These findings support the use of social risk adjustment to improve provider payment equity and highlight opportunities to enhance social risk adjustment in value-based payment programs.

Association Between Self-reported Health-Related Social Needs and Acute Care Utilization Among Older Adults Enrolled in Medicare Advantage

Importance

There is increased focus on identifying and addressing health-related social needs (HRSNs). Understanding how different HRSNs relate to different health outcomes can inform targeted, evidence-based policies, investments, and innovations to address HRSNs.

Objective

To examine the association between self-reported HRSNs and acute care utilization among older adults enrolled in Medicare Advantage.

Design Setting and Participants

This cross-sectional study used data from a large, national survey of Medicare Advantage beneficiaries to identify the presence of HRSNs. Survey data were linked to medical claims, and regression models were used to estimate the association between HRSNs and rates of acute care utilization from January 1, 2019, through December 31, 2019.

Exposures

Self-reported HRSNs, including food insecurity, financial strain, loneliness, unreliable transportation, utility insecurity, housing insecurity, and poor housing quality.

Main Outcomes and Measures

All-cause hospital stays (inpatient admissions and observation stays), avoidable hospital stays, all-cause emergency department (ED) visits, avoidable ED visits, and 30-day readmissions.

Results

Among a final study population of 56 155 Medicare Advantage beneficiaries (mean [SD] age, 74.0 [5.8] years; 32 779 [58.4%] women; 44 278 [78.8%] White; and 7634 [13.6%] dual eligible for Medicaid), 27 676 (49.3%) reported 1 or more HRSNs. Health-related social needs were associated with statistically significantly higher rates of all utilization measures, with the largest association observed for avoidable hospital stays (incident rate ratio for any HRSN, 1.53; 95% CI, 1.35-1.74; P < .001). Compared with beneficiaries without HRSNs, beneficiaries with an HRSN had a 53.3% higher rate of avoidable hospitalization (incident rate ratio, 1.53; 95% CI, 1.35-1.74; P < .001). Financial strain and unreliable transportation were each independently associated with increased rates of hospital stays (marginal effects of 26.5 [95% CI, 14.2-38.9] and 51.2 [95% CI, 30.7-71.8] hospital stays per 1000 beneficiaries, respectively). All HRSNs, except for utility insecurity, were independently associated with increased rates of ED visits. Unreliable transportation had the largest association with increased hospital stays and ED visits, with marginal effects of 51.2 (95% CI, 30.7-71.8) and 95.5 (95% CI, 65.3-125.8) ED visits per 1000 beneficiaries, respectively. Only unreliable transportation and financial strain were associated with increased rates of 30-day readmissions, with marginal effects of 3.3% (95% CI, 2.0%-4.0%) and 0.4% (95% CI, 0.2%-0.6%), respectively.

Conclusions and Relevance

In this cross-sectional study of older adults enrolled in Medicare Advantage, self-reported HRSNs were common and associated with statistically significantly increased rates of acute care utilization, with variation in which HRSNs were associated with different utilization measures. These findings provide evidence of the unique association between certain HRSNs and different types of acute care utilization, which could help refine the development and targeting of efforts to address HRSNs.

Neighborhood deprivation and Medicare expenditures for common surgical procedures

INTRODUCTION

The Center of Medicare and Medicaid Services valued based payments for inpatient surgical hospitalizations are adjusted for clinical but not social risk factors. While research has shown that social risk is associated with worse surgical patient outcomes, it is unknown if inpatient surgical episode Medicare payments are affected by social risk factors.

METHODS

Retrospective review of Medicare beneficiaries, age 65-99, undergoing appendectomy, colectomy, hernia repair, or cholecystectomy between 2014 and 2018. Neighborhood deprivation measured by Area Deprivation Index for beneficiary census tract. We evaluated Medicare payments for a total episode of surgical care comprised of index hospitalization, physician fees, post-acute care, and readmission by beneficiary neighborhood deprivation.

RESULTS

A total of 809,059 patients (Women, 56.0%) and mean (SD) age of 75.7 (7.4 years were included. A total of 145,351 beneficiaries lived in the least deprived neighborhoods and 134,188 who lived in the most deprived neighborhoods. Total surgical episode spending was $2654 higher among beneficiaries from the most deprived neighborhoods compared to those from the least after risk adjustment for clinical and hospital factors. These differences were driven in part by higher rates of readmissions (12.9% vs 10.8%, P < 0.001) and post-acute care (67.8% vs. 61.2%, P < 0.001) among beneficiaries living in the most deprived neighborhoods.

CONCLUSION

These findings suggest that value-based payment models with inclusion of social risk adjustment may be needed for surgical cohorts. Moreover, efforts focused on investing in deprived communities may be aligned with surgical quality improvement.

Financing Care for CYSHCN in the Next Decade: Reducing Burden, Advancing Equity, and Transforming Systems

Blueprint for Change: Guiding Principles for a System of Services for CYSHCN and Their Families (Blueprint for Change), presented by the Maternal and Child Health Bureau at the Health Resources and Services Administration, outlines principles and strategies that can be implemented at the federal and state levels and by health systems, health care providers, payors, and advocacy organizations to achieve a strong system of care for children and youth with special health care needs (CYSHCN). The vision for the financing of services outlined in the Blueprint for Change: Guiding Principles for a System of Services for CYSHCN and their Families is one in which health care and other related services are accessible, affordable, comprehensive, continuous, and prioritize the wellbeing of CYSHCN and their families. There are several barriers caused or exacerbated by health care financing policies and structures that pose significant challenges for families of CYSHCN, including finding appropriate and knowledgeable provider care teams, ensuring adequate and continuous coverage for services, and ensuring benefit adequacy. Racial disparities and societal risks all exacerbate these challenges. This article outlines recommendations for improving financing for CYSHCN, including potential innovations to address barriers, such as state Medicaid expansion for CYSHCN, greater transparency in medical necessity processes and determinations, and adequate reimbursement and funding. Financing innovations must use both current and new measures to assess value and provide evidence for iterative improvements. These recommendations will require a coordinated approach among federal and state agencies, the public sector, the provider community, and the families of CYSHCN.

Measuring Racial Inequities In The Quality Of Care Across Oncology Practices In The US

Racial inequities in clinical performance diminish overall health care system performance; however, quality assessments have rarely incorporated reliable measures of racial inequities. We studied care for more than one million Medicare fee-for-service beneficiaries with cancer to assess the feasibility of calculating reliable practice-level measures of racial inequities in chemotherapy-associated emergency department (ED) visits and hospitalizations. Specifically, we used hierarchical models to estimate adjusted practice-level Black-White differences in these events and described differences across practices. We calculated reliable inequity measures for 426 and 322 practices, depending on the measure. These practices reflected fewer than 10 percent of practices treating Medicare beneficiaries with chemotherapy, but they treated approximately half of all White and Black Medicare beneficiaries receiving chemotherapy and two-thirds of Black Medicare beneficiaries receiving chemotherapy. Black patients experienced chemotherapy-associated ED visits and hospitalizations at higher rates (54.2 percent and 35.8 percent, respectively) than White patients (45.7 percent and 31.9 percent, respectively). The median within-practice Black-White difference was 8.1 percentage points for chemotherapy-associated ED visits and 2.7 percentage points for chemotherapy-associated hospitalizations. Additional research is needed to identify other reliable measures of racial inequities in health care quality, measure care inequities in smaller practices, and assess whether providing practice-level feedback could improve equity.

Health Equity: The Only Path Forward for Primary Care

The 2021 National Academies of Sciences, Engineering, and Medicine (NASEM) report on Implementing High-Quality Primary Care identifies 5 high-level objectives regarding payment, access, workforce development, information technology, and implementation. Nine junior primary care leaders (3 internal medicine, 3 family medicine, 3 pediatrics) invited from broad geographies, practice settings, and academic backgrounds used appreciative inquiry to identify priorities for the future of primary care. Highlighting the voices of these early career clinicians, we propose a response to the report from the perspective of early career primary care physicians. Health equity must be the foundation of the future of primary care. Because Barbara Starfield's original 4 Cs (first contact, coordination, comprehensiveness, and continuity) may not be inclusive of the needs of under-resourced communities, we promote an extension to include 5 additional Cs: convenience, cultural humility, structural competency, community engagement, and collaboration. We support the NASEM report's priorities and its focus on achieving health equity. We recommend investing in local communities and preparatory programs to stimulate diverse individuals to serve in health care. Finally, we support a blended value-based care model with risk adjustment for the social complexity of our patients.Appeared as Annals "Online First" article.

A review of socioeconomic factors associated with acute myocardial infarction-related mortality and hospital readmissions

BACKGROUND

Risk-adjustment models are widely used methodological approaches within the healthcare industry to measure hospital performance and quality of care. However, the Centers for Medicare and Medicaid Services (CMS) do not fully adjust for socioeconomic status (SES) in acute myocardial infarction (AMI) models. A review and evidence synthesis was conducted to identify associations of SES factors with hospital readmission and mortality in AMI patients.

METHODS

Multiple electronic databases were queried to identify studies assessing risk for AMI-related mortality or hospital readmissions and SES factors. Identified studies were screened by title and abstract. Full-text reviews followed for articles meeting the inclusion criteria, including quality assessments. Data were extracted from all included studies, and evidence synthesis was performed to identify associations between SES factors and outcome variables.

RESULTS

Ten studies were included in the review. One study showed that Black patients had higher AMI-related readmission rates compared to White patients (mean difference 4.3% [SD 1.4%], p < 0.001). Another study showed that income inequality was associated with increased risk of AMI-related readmissions (RR 1.18 [95% CI], 1.13-1.23). One study found that unemployed individuals experienced significantly greater rates of AMI-related mortality than those working full-time (HR 2.08, 1.51-2.87). According to another study, lack of health insurance was associated with worse rates for in-hospital AMI-related mortality (OR 1.77, 1.72-1.82). Based on one study, AMI-related mortality was higher in those with <8 years of education compared to those with >16 years (17.5% vs. 3.5%, p < 0.0001). Five of six studies found a significant association between ZIP code/neighborhood/location and AMI-related readmission or mortality.

CONCLUSION

Race, ZIP code/neighborhood/location, insurance status, income/poverty, and education comprise SES factors found to be associated with AMI-related mortality and/or readmission outcomes. Including these SES factors in future updates of CMS's risk-adjusted models has the potential to provide more appropriate compensation mechanisms to hospitals.

Association of low-income subsidy, medicaid dual eligibility, and disability status with high-risk medication use among Medicare Part D beneficiaries

BACKGROUND

Low-income subsidy/dual eligibility (LIS/DE) status and disability status may be associated with high-risk medication (HRM) use but are not usually accounted for in medication-use quality measures.

OBJECTIVE

To examine the association of: 1) LIS/DE status and HRM use; and 2) disability status and HRM use, while controlling for both health plan level effects and patient characteristics for Medicare beneficiaries enrolled in Medicare Advantage Prescription Drug Plans (MA-PD) and stand-alone Prescription Drug Plans (PDP).

METHODS

This retrospective cross-sectional study used 2013 Medicare data to determine if LIS/DE status and disability status were independently associated with HRM use (using the Pharmacy Quality Alliance HRM measure) in MA-PDs and PDPs. Multivariable generalized linear mixed models assessed the association of LIS/DE and HRM use, and disability and HRM use, after adjusting for health plan effect and patient-level confounders for MA-PD and PDP beneficiaries.

RESULTS

Of 520,019 MA-PD beneficiaries, 88,693 (17.1%) were LIS/DE and 48,997 (9.4%) were disabled. Of 881,264 PDP beneficiaries, 213,096 (24.2%) were LIS/DE, and 83,593 (9.5%) were disabled. LIS/DE beneficiaries had a higher percent of HRM users compared to non-LIS/DE MA-PD (13.3% vs. 9.7%, p < 0.001) and PDP (17.1% vs. 13.2%, p < 0.001) beneficiaries. Disabled beneficiaries had a higher percent of HRM users compared to non-disabled MA-PD (17.0% vs. 9.6%, p < 0.001) and PDP (22.9% vs. 13.2%, p < 0.001) beneficiaries. Multivariable analyses showed LIS/DE (adjusted odds ratio [AOR] = 1.07; 95% CI = 1.04, 1.10) and disability (AOR = 1.38; 95% CI = 1.34, 1.42) were associated with HRM use among MA-PD and PDP beneficiaries (LIS/DE AOR = 1.14; 95% CI = 1.12, 1.16; disability AOR = 1.37; 95% CI = 1.34, 1.40).

CONCLUSIONS

The association of LIS/DE and disability with higher HRM use in both MA-PD and PDP beneficiaries, when controlling for health plan effects and patient characteristics, suggests these factors should be considered when comparing health plan performance on HRM measures.