Diabetes-Focused Health Care Utilization Among Adolescents and Young Adults With Type 1 Diabetes

OBJECTIVE

To describe the current rates of health services use with various types of providers among adolescents and young adults (AYA) with type 1 diabetes (T1D) and evaluate which patient factors are associated with rates of service use from different provider types.

METHODS

Using 2012-16 claims data from a national commercial insurer, we identified 18,927 person-years of AYA with T1D aged 13 to 26 years and evaluated the frequency at which: 1) AYA skipped diabetes care for a year despite being insured; 2) received care from pediatric or non-pediatric generalists or endocrinologists if care was sought; and 3) received annual hemoglobin A1c (HbA1c) testing as recommended for AYA. We used descriptive statistics and multivariable regression to examine patient, insurance, and physician characteristics associated with utilization and quality outcomes.

RESULTS

Between ages 13 and 26, the percentage of AYA with: any diabetes-focused visits declined from 95.3% to 90.3%; the mean annual number of diabetes-focused visits, if any, decreased from 3.5 to 3.0; receipt of ≥2 HbA1c tests annually decreased from 82.3% to 60.6%. Endocrinologists were the majority providers of diabetes care across ages, yet the relative proportion of AYA whose diabetes care was endocrinologist-dominated decreased from 67.3% to 52.7% while diabetes care dominated by primary care providers increased from 19.9% to 38.2%. The strongest predictors of diabetes care utilization were younger age and use of diabetes technology (pumps and continuous glucose monitors).

CONCLUSIONS

Several provider types are involved in the care of AYA with T1D, though predominate provider type and care quality changes substantially across age in a commercially-insured population.

Organization of Cancer Specialists in US Physician Practices and Health Systems

PURPOSE

To describe the supply of cancer specialists, the organization of cancer care within versus outside of health systems, and the distance to multispecialty cancer centers.

METHODS

Using the 2018 Health Systems and Provider Database from the National Bureau of Economic Research and 2018 Medicare data, we identified 46,341 unique physicians providing cancer care. We stratified physicians by discipline (adult/pediatric medical oncologists, radiation oncologists, surgical/gynecologic oncologists, other surgeons performing cancer surgeries, or palliative care physicians), system type (National Cancer Institute [NCI] Cancer Center system, non-NCI academic system, nonacademic system, or nonsystem/independent practice), practice size, and composition (single disciplinary oncology, multidisciplinary oncology, or multispecialty). We computed the density of cancer specialists by county and calculated distances to the nearest NCI Cancer Center.

RESULTS

More than half of all cancer specialists (57.8%) practiced in health systems, but 55.0% of cancer-related visits occurred in independent practices. Most system-based physicians were in large practices with more than 100 physicians, while those in independent practices were in smaller practices. Practices in NCI Cancer Center systems (95.2%), non-NCI academic systems (95.0%), and nonacademic systems (94.3%) were primarily multispecialty, while fewer independent practices (44.8%) were. Cancer specialist density was sparse in many rural areas, where the median travel distance to an NCI Cancer Center was 98.7 miles. Distances to NCI Cancer Centers were shorter for individuals living in high-income areas than in low-income areas, even for individuals in suburban and urban areas.

CONCLUSION

Although many cancer specialists practiced in multispecialty health systems, many also worked in smaller-sized independent practices where most patients were treated. Access to cancer specialists and cancer centers was limited in many areas, particularly in rural and low-income areas.

Opportunities for Savings in Risk Arrangements for Oncologic Care

Importance

As the US accelerates adoption of alternative payment through global payment models such as Accountable Care Organizations (ACOs) or Medicare Advantage (MA), high spending for cancer care is a potential target for savings.

Objective

To quantify the extent to which ACOs and other risk-bearing organizations operating in a specific geographic area (hospital referral region [HRR]) could achieve savings by steering patients to efficient medical oncology practices.

Design, Setting, and Participants

This observational study included serial cross-sections of Medicare beneficiaries with cancer from 2010 to 2018. Data were analyzed from August 2021 to March 2023.

Main Outcomes and Measures

Total spending and spending by category in the 1-year period following an index visit for a patient with newly diagnosed (incident) or poor-prognosis cancer.

Results

The incident cohort included 1 309 825 patients with a mean age of 74.0 years; the most common cancer types were breast (21.4%), lung (16.7%), and colorectal cancer (10.0%). The poor prognosis cohort included 1 429 973 (mean age, 72.7 years); the most common cancer types were lung (26.6%), lymphoma (11.7%), and leukemia (7.3%). Options for steering varied across markets; the top quartile market had 10 or more oncology practices, but the bottom quartile had 3 or fewer oncology practices. Total spending (including Medicare Part D) in the incident cohort increased from a mean of $57 314 in 2009 to 2010 to $66 028 in 2016 to 2017. Within markets, total spending for practices in the highest spending quartile was 19% higher than in the lowest quartile. Hospital spending was the single largest component of spending in both time periods ($20 390 and $19 718, respectively) followed by Part B (infused) chemotherapy ($8022 and $11 699). Correlations in practice-level spending between the first-year (2009) and second-year (2010) spending were high (>0.90 in all categories with most >0.98), but these attenuated over time.

Conclusions and Relevance

These results suggest there may be opportunities for ACOs and other risk-bearing organizations to select or drive referrals to lower-spending oncology practices in many local markets.

Specialist use among privately insured children with disabilities

OBJECTIVE

To investigate primary care practice ownership and specialist-use patterns for commercially insured children with disabilities.

DATA SOURCES AND STUDY SETTING

A national commercial claims database and the Health Systems and Provider Database from 2012 to 2016 are the data sources for this study.

STUDY DESIGN

This cross-sectional, descriptive study examines: (1) the most visited type of pediatric primary care physician and practice (independent or system-owned); (2) pediatric and non-pediatric specialist-use patterns; and (3) how practice ownership relates to specialist-use patterns.

DATA COLLECTION/EXTRACTION METHODS

This study identifies 133,749 person-years of commercially insured children with disabilities aged 0-18 years with at least 24 months of continuous insurance coverage by linking a national commercial claims data set with the Health Systems and Provider Database and applying the validated Children with Disabilities Algorithm.

PRINCIPAL FINDINGS

Three-quarters (75.9%) of children with disabilities received their pediatric primary care in independent practices. Nearly two thirds (59.6%) used at least one specialist with 45.1% using nonpediatric specialists, 28.8% using pediatric ones, and 17.0% using both. Specialist-use patterns varied by both child age and specialist type. Children with disabilities in independent practices were as likely to see a specialist as those in system-owned ones: 57.1% (95% confidence interval [95% CI] 56.7%-57.4%) versus 57.3% (95% CI 56.6%-58.0%), respectively (p = 0.635). The percent using two or more types of specialists was 46.1% (95% CI 45.4%-46.7%) in independent practices, comparable to that in systems 47.1% (95% CI 46.2%-48.0%) (p = 0.054). However, the mean number of specialist visits was significantly lower in independent practices than in systems-4.0 (95% CI 3.9%-4.0%) versus 4.4 (95% CI 4.3%-4.6%) respectively-reaching statistical significance with p < 0.0001.

CONCLUSIONS

Recognizing how privately insured children with disabilities use pediatric primary care from pediatric and nonpediatric primary care specialists through both independent and system-owned practices is important for improving care quality and value.

Racial and Ethnic Disparities in Opioid Access and Urine Drug Screening Among Older Patients With Poor-Prognosis Cancer Near the End of Life

PURPOSE

To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms.

METHODS

Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS.

RESULTS

Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, -4.3 percentage points, 95% CI, -4.8 to -3.6; Hispanic, -3.6 percentage points, 95% CI, -4.4 to -2.9) and long-acting opioids (Black, -3.1 percentage points, 95% CI, -3.6 to -2.8; Hispanic, -2.2 percentage points, 95% CI, -2.7 to -1.7). They also received lower daily doses (Black, -10.5 MMED, 95% CI, -12.8 to -8.2; Hispanic, -9.1 MMED, 95% CI, -12.1 to -6.1) and lower total doses (Black, -210 MMEs, 95% CI, -293 to -207; Hispanic, -179 MMEs, 95% CI, -217 to -142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities.

CONCLUSION

There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.

Practice-Level Variation in Molecular Testing and Use of Targeted Therapy for Patients With Non-Small Cell Lung Cancer and Colorectal Cancer

Importance

All patients with newly diagnosed non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) should receive molecular testing to identify those who can benefit from targeted therapies. However, many patients do not receive recommended testing and targeted therapies.

Objective

To compare rates of molecular testing and targeted therapy use by practice type and across practices.

Design, Setting, and Participants

This cross-sectional study used 100% Medicare fee-for-service data from 2015 through 2019 to identify beneficiaries with new metastatic NSCLC or CRC diagnoses receiving systemic therapy and to assign patients to oncology practices. Hierarchical linear models were used to characterize variation by practice type and across practices. Data analysis was conducted from June 2019 to October 2022.

Exposures

Oncology practice providing care.

Outcomes

Primary outcomes were rates of molecular testing and targeted therapy use for patients with NSCLC and CRC. Secondary outcomes were rates of multigene testing for NSCLC and CRC.

Results

There were 106 228 Medicare beneficiaries with incident NSCLC (31 521 [29.7%] aged 65-69 years; 50 348 [47.4%] female patients; 2269 [2.1%] Asian, 8282 [7.8%] Black, and 91 215 [85.9%] White patients) and 39 512 beneficiaries with incident CRC (14 045 [35.5%] aged 65-69 years; 17 518 [44.3%] female patients; 896 [2.3%] Asian, 3521 [8.9%] Black, and 32 753 [82.9%] White patients) between 2015 and 2019. Among these beneficiaries, 18 435 (12.9%) were treated at National Cancer Institute (NCI)-designated centers, 8187 (5.6%) were treated at other academic centers, and 94 329 (64.7%) were treated at independent oncology practices. Molecular testing rates increased from 74% to 85% for NSCLC and 45% to 65% for CRC. First-line targeted therapy use decreased from 12% to 8% among patients with NSCLC and was constant at 5% for patients with CRC. For NSCLC, molecular testing rates were similar across practice types while rates of multigene panel use (13.2%) and targeted therapy use (16.6%) were highest at NCI-designated cancer centers. For CRC, molecular testing rates were 3.8 (95% CI: 1.2-6.5), 3.3 (95% CI, 0.4-6.1), and 12.2 (95% CI, 9.1-15.3) percentage points lower at hospital-owned practices, large independent practices, and small independent practices, respectively. Rates of targeted therapy use for CRC were similar across practice types. After adjusting for patient characteristics, there was moderate variation in molecular testing and targeted therapy use across oncology practices.

Conclusions and Relevance

In this cross-sectional study of Medicare beneficiaries, molecular testing rates for NSCLC and CRC increased in recent years but remained lower than recommended levels. Rates of targeted therapy use decreased for NSCLC and remained stable for CRC. Variation across practices suggests that where a patient was treated may have affected access to recommended testing and efficacious treatments.

Adoption of Innovative Therapies Across Oncology Practices-Evidence From Immunotherapy

Importance

Immunotherapies reflect an important breakthrough in cancer treatment, substantially improving outcomes for patients with a variety of cancer types, yet little is known about which practices have adopted this novel therapy or the pace of adoption.

Objective

To assess adoption of immunotherapies across US oncology practices and examine variation in adoption by practice type.

Design, Setting, and Participants

This cohort study used data from Medicare fee-for-service beneficiaries undergoing 6-month chemotherapy episodes between 2010 and 2017. Data were analyzed January 19, 2021, to September 28, 2022, for patients with cancer types for which immunotherapy was approved by the US Food and Drug Administration (FDA) during the study period: melanoma, kidney cancer, lung cancer, and head and neck cancer.

Exposures

Oncology practice location (rural vs urban), affiliation type (academic system, nonacademic system, independent), and size (1 to 5 physicians vs 6 or more physicians).

Main Outcomes and Measures

The primary outcome was whether a practice adopted immunotherapy. Adoption rates for each practice type were estimated using multivariate linear models that adjusted for patient characteristics (age, sex, race and ethnicity, cancer type, Charlson Comorbidity Index, and median household income).

Results

Data included 71 659 episodes at 1732 oncology practices. Of these, 264 practices (15%) were rural, 900 (52%) were independent, and 492 (28%) had 1 to 5 physicians. Most practices adopted immunotherapy within 2 years of FDA approval, but there was substantial variation in adoption rates across practice types. After FDA approval, adoption of immunotherapy was 11 (95% CI, -16 to -6) percentage points lower at rural practices than urban practices and 27 (95% CI, -32 to -22) percentage points lower at practices with 1 to 5 physicians than practices with 6 or more physicians. Adoption rates were similar at independent practices and nonacademic systems; however, both practice types had lower adoption than academic systems (independent practice difference, -6 [95% CI, -9 to -3] percentage points; nonacademic systems difference, -9 [95% CI, -11 to -6] percentage points).

Conclusions and Relevance

In this cohort study of Medicare claims, practice characteristics, especially practice size and rural location, were associated with adoption of immunotherapy. These findings suggest that there may be geographic disparities in access to important innovations for treating patients with cancer.

Organization and Performance of US Health Systems

IMPORTANCE

Health systems play a central role in the delivery of health care, but relatively little is known about these organizations and their performance.

OBJECTIVE

To (1) identify and describe health systems in the United States; (2) assess differences between physicians and hospitals in and outside of health systems; and (3) compare quality and cost of care delivered by physicians and hospitals in and outside of health systems.

EVIDENCE REVIEW

Health systems were defined as groups of commonly owned or managed entities that included at least 1 general acute care hospital, 10 primary care physicians, and 50 total physicians located within a single hospital referral region. They were identified using Centers for Medicare & Medicaid Services administrative data, Internal Revenue Service filings, Medicare and commercial claims, and other data. Health systems were categorized as academic, public, large for-profit, large nonprofit, or other private systems. Quality of preventive care, chronic disease management, patient experience, low-value care, mortality, hospital readmissions, and spending were assessed for Medicare beneficiaries attributed to system and nonsystem physicians. Prices for physician and hospital services and total spending were assessed in 2018 commercial claims data. Outcomes were adjusted for patient characteristics and geographic area.

FINDINGS

A total of 580 health systems were identified and varied greatly in size. Systems accounted for 40% of physicians and 84% of general acute care hospital beds and delivered primary care to 41% of traditional Medicare beneficiaries. Academic and large nonprofit systems accounted for a majority of system physicians (80%) and system hospital beds (64%). System hospitals were larger than nonsystem hospitals (67% vs 23% with >100 beds), as were system physician practices (74% vs 12% with >100 physicians). Performance on measures of preventive care, clinical quality, and patient experience was modestly higher for health system physicians and hospitals than for nonsystem physicians and hospitals. Prices paid to health system physicians and hospitals were significantly higher than prices paid to nonsystem physicians and hospitals (12%-26% higher for physician services, 31% for hospital services). Adjusting for practice size attenuated health systems differences on quality measures, but price differences for small and medium practices remained large.

CONCLUSIONS AND RELEVANCE

In 2018, health system physicians and hospitals delivered a large portion of medical services. Performance on clinical quality and patient experience measures was marginally better in systems but spending and prices were substantially higher. This was especially true for small practices. Small quality differentials combined with large price differentials suggests that health systems have not, on average, realized their potential for better care at equal or lower cost.

Uncertainty in lung cancer stage for survival estimation via set-valued classification

The difficulty in identifying cancer stage in health care claims data has limited oncology quality of care and health outcomes research. We fit prediction algorithms for classifying lung cancer stage into three classes (stages I/II, stage III, and stage IV) using claims data, and then demonstrate a method for incorporating the classification uncertainty in survival estimation. Leveraging set‐valued classification and split conformal inference, we show how a fixed algorithm developed in one cohort of data may be deployed in another, while rigorously accounting for uncertainty from the initial classification step. We demonstrate this process using SEER cancer registry data linked with Medicare claims data.

The impact of physician-hospital integration on spending and quality of oncology care

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Background: There has been increasing hospital and health system ownership of physician practices in recent years, particularly in oncology. However, relatively little is known about how this impacts care delivery for patients with cancer, who use many hospital-based services that may be impacted by integration. We evaluated the impact of physician-hospital integration in oncology on spending and quality of care for Medicare beneficiaries with cancer. Methods: We used Medicare Fee-for-Service claims from 2005-2019 linked with a unique Health System and Provider Database, developed by National Bureau of Economic Research and Harvard University researchers, to track practice ownership relationships over time. We used a stacked event study to assess outcomes for patients three years before and after oncologists move from independent practices to hospital- or system- owned practices. We compared outcomes to a control group with oncologists who shifted from independent to hospital- or system-owned practices in later years. We focused on two cohorts of patients. The first cohort included cancer patients with presumed incident or recurrent cancer based on ≥2 visits to an oncologist and no visit in the past year. For these patients, we evaluated the impact of physician-hospital integration on the likelihood of receiving chemotherapy following the visit. The second cohort included 6-month episodes for patients receiving chemotherapy. For these patients we evaluated the impact of physician-hospital integration on spending, utilization, and quality. Quality measures included receipt of timely chemotherapy (within 60 days) following surgery, inpatient readmissions, non-use of tamoxifen + strong CYPD26 inhibitors, and end-of-life intensity of care measures. Results: There was no change in the likelihood of receiving chemotherapy with an initial oncology consultation following an oncologist’s transition to hospital-based employment. Total spending during six-month chemotherapy episodes increased by $1391 (95%CI: $465, $2316). The primary contributors to this growth were increases in spending on inpatient care, chemotherapy administration, and office visits. Spending growth, where observed, was driven primarily by higher Medicare prices for care in hospital outpatient settings. We found no positive impact of physician-hospital integration on timeliness of chemotherapy initiation, readmissions, concurrent use of tamoxifen+strong CYPD26 inhibitors, or intensity of end-of-life care. Conclusions: Physician-hospital integration resulted in higher prices and thus higher spending, but had limited impact on utilization and no detectable impacts on measures of quality. These results suggest that claims of quality improvements and concerns regarding overuse associated with physician-hospital integration may be overstated. Our results also support continued movement towards site-neutral payments.

Variation of use of targeted therapies and molecular diagnostic testing by practice type for non-small cell lung cancer and colorectal cancer

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Background: Targeted therapies are important first-line treatments for many patients with non-small cell lung cancer (NSCLC) and colorectal cancer (CRC). All patients with newly-diagnosed metastatic NSCLC and CRC should undergo molecular diagnostic testing to guide treatment selection. Methods: We used 100% Medicare fee-for-service data from 2015 through 2019 to identify beneficiaries with incident metastatic NSCLC or CRC receiving systemic therapy and to assign beneficiaries to oncology practices. We then assessed for use of molecular diagnostic testing and targeted therapies in these cohorts. We used linear mixed effects models to assess patient and practice characteristics associated with molecular diagnostic testing and targeted therapy use. Results: Rates of molecular diagnostic testing increased between 2015 and 2019 for NSCLC and CRC. In 2019, rates of molecular diagnostic testing were 85% and 65% for NSCLC and CRC, respectively. Rates of targeted therapy use did not increase over time for NSCLC or CRC, and were 8% and 5%, respectively, in 2019. Compared to National Cancer Institute (NCI)-designated cancer centers, rates of molecular diagnostic testing for CRC were 3.7 percentage points lower at practices associated with non-academic hospitals and 10.6 percentage points lower at small independent practices. Rates of targeted therapy use for NSCLC were 4.8, 5.9 and 5.5 percentage points lower at academic medical centers, large independent practices and small independent practices, respectively, compared to NCI centers. Conclusions: Rates of molecular diagnostic testing for NSCLC and CRC increased in recent years, but testing rates remain below recommended levels, and targeted therapy use remains low. Substantial variation in testing and targeted therapy use by practice type suggest that the practice where a patient is treated may impact access to recommended testing and efficacious treatments. [Table: see text]

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.

Impact of the Oncology Care Model on Use of Supportive Care Medications During Cancer Treatment

PURPOSE

The Oncology Care Model (OCM) is an episode-based alternative payment model for cancer care that seeks to reduce Medicare spending while maintaining care quality. We evaluated the impact of OCM on appropriate use of supportive care medications during cancer treatment.

METHODS

We evaluated chemotherapy episodes assigned to OCM (n = 201) and comparison practices (n = 534) using Medicare claims (2013-2019). We assessed denosumab use for beneficiaries with bone metastases from breast, lung, or prostate cancer; prophylactic WBC growth factor use for beneficiaries receiving chemotherapy for breast, lung, or colorectal cancer; and prophylactic use of neurokinin-1 (NK1) antagonists and long-acting serotonin antagonists for beneficiaries receiving chemotherapy for any cancer type. Analyses used a difference-in-difference approach.

RESULTS

After its launch in 2016, OCM led to a relative reduction in the use of denosumab for beneficiaries with bone metastases receiving bone-modifying medications (eg, 5.0 percentage point relative reduction in breast cancer episodes [90% CI, -7.1 to -2.8]). There was no OCM impact on use of prophylactic WBC growth factors during chemotherapy with high or low risk for febrile neutropenia. Among beneficiaries receiving chemotherapy with intermediate febrile neutropenia risk, OCM led to a 7.6 percentage point reduction in the use of prophylactic WBC growth factors during breast cancer episodes (90% CI, -12.6 to -2.7); there was no OCM impact in lung or colorectal cancer episodes. Among beneficiaries receiving chemotherapy with high or moderate emetic risk, OCM led to reductions in the prophylactic use of NK1 antagonists and long-acting serotonin antagonists (eg, 6.0 percentage point reduction in the use of NK1 antagonists during high emetic risk chemotherapy [90% CI, -9.0 to -3.1]).

CONCLUSION

OCM led to the reduced use of some high-cost supportive care medications, suggesting more value-conscious care.

The Oncology Care Model and Adherence to Oral Cancer Drugs: A Difference-in-Differences Analysis

BACKGROUND

Adherence to oral cancer drugs is suboptimal. The Oncology Care Model (OCM) offers oncology practices financial incentives to improve the value of cancer care. We assessed the impact of OCM on adherence to oral cancer therapy for chronic myelogenous leukemia (CML), prostate cancer, and breast cancer.

METHODS

Using 2014-2019 Medicare data, we studied chemotherapy episodes for Medicare fee-for-service beneficiaries prescribed tyrosine kinase inhibitors (TKIs) for CML, antiandrogens (ie, enzalutamide, abiraterone) for prostate cancer, or hormonal therapies for breast cancer in OCM-participating and propensity-matched comparison practices. We measured adherence as the proportion of days covered and used difference-in-difference (DID) models to detect changes in adherence over time, adjusting for patient, practice, and market-level characteristics.

RESULTS

There was no overall impact of OCM on improved adherence to TKIs for CML (DID = -0.3%, 90% confidence interval [CI] = -1.2% to 0.6%), antiandrogens for prostate cancer (DID = 0.4%, 90% CI = -0.3% to 1.2%), or hormonal therapy for breast cancer (DID = 0.0%, 90% CI = -0.2% to 0.2%). Among episodes for Black beneficiaries in OCM practices, for whom adherence was lower than for White beneficiaries at baseline, we observed small improvements in adherence to high cost TKIs (DID = 3.0%, 90% CI = 0.2% to 5.8%) and antiandrogens (DID = 2.2%, 90% CI = 0.2% to 4.3%).

CONCLUSIONS

OCM did not impact adherence to oral cancer therapies for Medicare beneficiaries with CML, prostate cancer, or breast cancer overall but modestly improved adherence to high-cost TKIs and antiandrogens for Black beneficiaries, who had somewhat lower adherence than White beneficiaries at baseline. Patient navigation and financial counseling are potential mechanisms for improvement among Black beneficiaries.

Use of Germline BRCA Testing in Patients With Ovarian Cancer and Commercial Insurance

This cross-sectional study evaluates changes in the rate of germline BRCA testing among patients with ovarian cancer between 2008 and 2018 and analyzes factors associated with testing rates.

Coding-Driven Changes In Measured Risk In Accountable Care Organizations

Claims data, which form the foundation of risk adjustment in payment for health care services, may reflect efforts to capture more-or more severe-clinical conditions rather than true changes in health status. This can distort payments. We quantify this in the context of Medicare's accountable care organization (ACO) program by comparing risk scores derived from two different measurement approaches. One approach uses diagnoses coded on claims based on Centers for Medicare and Medicaid Services Hierarchical Condition Categories (HCC), and the other uses self-reported, survey-based health data from the Consumer Assessment of Healthcare Providers and Systems (CAHPS). During 2013-16 HCC-based risk scores grew faster than CAHPS-based risk scores (2.1 percent versus 0.3 percent annually), and the gap in HCC- and CAHPS-based risk score growth varied widely across ACOs. The average gap in risk score growth appears to be the result primarily of HCC coding practices rather than poor performance of the CAHPS model, suggesting that coding practices (not necessarily driven by ACO contracts) may account for most of the observed risk score growth for ACO beneficiaries.

Association of Participation in the Oncology Care Model With Medicare Payments, Utilization, Care Delivery, and Quality Outcomes

IMPORTANCE

In 2016, the US Centers for Medicare & Medicaid Services initiated the Oncology Care Model (OCM), an alternative payment model designed to improve the value of care delivered to Medicare beneficiaries with cancer.

OBJECTIVE

To assess the association of the OCM with changes in Medicare spending, utilization, quality, and patient experience during the OCM's first 3 years.

DESIGN, SETTING, AND PARTICIPANTS

Exploratory difference-in-differences study comparing care during 6-month chemotherapy episodes in OCM participating practices and propensity-matched comparison practices initiated before (January 2014 through June 2015) and after (July 2016 through December 2018) the start of the OCM. Participants included Medicare fee-for-service beneficiaries with cancer treated at these practices through June 2019.

EXPOSURES

OCM participation.

MAIN OUTCOMES AND MEASURES

Total episode payments (Medicare spending for Parts A, B, and D, not including monthly payments for enhanced oncology services); utilization and payments for hospitalizations, emergency department (ED) visits, office visits, chemotherapy, supportive care, and imaging; quality (chemotherapy-associated hospitalizations and ED visits, timely chemotherapy, end-of-life care, and survival); and patient experiences.

RESULTS

Among Medicare fee-for-service beneficiaries with cancer undergoing chemotherapy, 483 319 beneficiaries (mean age, 73.0 [SD, 8.7] years; 60.1% women; 987 332 episodes) were treated at 201 OCM participating practices, and 557 354 beneficiaries (mean age, 72.9 [SD, 9.0] years; 57.4% women; 1 122 597 episodes) were treated at 534 comparison practices. From the baseline period, total episode payments increased from $28 681 for OCM episodes and $28 421 for comparison episodes to $33 211 for OCM episodes and $33 249 for comparison episodes during the intervention period (difference in differences, -$297; 90% CI, -$504 to -$91), less than the mean $704 Monthly Enhanced Oncology Services payments. Relative decreases in total episode payments were primarily for Part B nonchemotherapy drug payments (difference in differences, -$145; 90% CI, -$218 to -$72), especially supportive care drugs (difference in differences, -$150; 90% CI, -$216 to -$84). The OCM was associated with statistically significant relative reductions in total episode payments among higher-risk episodes (difference in differences, -$503; 90% CI, -$802 to -$204) and statistically significant relative increases in total episode payments among lower-risk episodes (difference in differences, $151; 90% CI, $39-$264). The OCM was not significantly associated with differences in hospitalizations, ED visits, or survival. Of 22 measures of utilization, 10 measures of quality, and 7 measures of care experiences, only 5 were significantly different.

CONCLUSIONS AND RELEVANCE

In this exploratory analysis, the OCM was significantly associated with modest payment reductions during 6-month episodes for Medicare beneficiaries receiving chemotherapy for cancer in the first 3 years of the OCM that did not offset the monthly payments for enhanced oncology services. There were no statistically significant differences for most utilization, quality, and patient experience outcomes.

US Trends in Opioid Access Among Patients With Poor Prognosis Cancer Near the End-of-Life

PURPOSE

Heightened regulations have decreased opioid prescribing across the United States, yet little is known about trends in opioid access among patients dying of cancer.

METHODS

Among 270,632 Medicare fee-for-service decedents with poor prognosis cancers, we used part D data to examine trends from 2007 to 2017 in opioid prescription fills and opioid potency (morphine milligram equivalents per day [MMED]) near the end-of-life (EOL), defined as the 30 days before death or hospice enrollment. We used administrative claims to evaluate trends in pain-related emergency department (ED) visits near EOL.

RESULTS

Between 2007 and 2017, the proportion of decedents with poor prognosis cancers receiving ≥ 1 opioid prescription near EOL declined 15.5% (relative percent difference [RPD]), from 42.0% (95% CI, 41.4 to 42.7) to 35.5% (95% CI, 34.9 to 36.0) and the proportion receiving ≥ 1 long-acting opioid prescription declined 36.5% (RPD), from 18.1% (95% CI, 17.6 to 18.6) to 11.5% (95% CI, 11.1 to 11.9). Among decedents receiving opioids near EOL, the mean daily dose fell 24.5%, from 85.6 MMED (95% CI, 82.9 to 88.3) to 64.6 (95% CI, 62.7 to 66.6) MMED. Overall, the total amount of opioids prescribed per decedent near EOL (averaged across those who did and did not receive an opioid) fell 38.0%, from 1,075 morphine milligram equivalents per decedent (95% CI, 1,042 to 1,109) to 666 morphine milligram equivalents per decedent (95% CI, 646 to 686). Simultaneously, the proportion of patients with pain-related ED visits increased 50.8% (RPD), from 13.2% (95% CI, 12.7 to 13.6) to 19.9% (95% CI, 19.4 to 20.4). Sensitivity analyses demonstrated similar declines in opioid utilization in the 60 and 90 days before death or hospice, and suggested that trends in opioid access were not confounded by secular trends in hospice utilization.

CONCLUSION

Opioid use among patients dying of cancer has declined substantially from 2007 to 2017. Rising pain-related ED visits suggests that EOL cancer pain management may be worsening.

Using Consistently Low Performance to Identify Low-Quality Physician Groups

Importance

There has been a growth in the use of performance-based payment models in the past decade, but inherently noisy and stochastic quality measures complicate the assessment of the quality of physician groups. Examining consistently low performance across multiple measures or multiple years could potentially identify a subset of low-quality physician groups.

Objective

To identify low-performing physician groups based on consistently low performance after adjusting for patient characteristics across multiple measures or multiple years for 10 commonly used quality measures for diabetes and cardiovascular disease (CVD).

Design, Setting, and Participants

This cross-sectional study used medical and pharmacy claims and laboratory data for enrollees ages 18 to 65 years with diabetes or CVD in an Aetna health insurance plan between 2016 and 2019. Each physician group's risk-adjusted performance for a given year was estimated using mixed-effects linear probability regression models. Performance was correlated across measures and time, and the proportion of physician groups that performed in the bottom quartile was examined across multiple measures or multiple years. Data analysis was conducted between September 2020 and May 2021.

Exposures

Primary care physician groups.

Main Outcomes and Measures

Performance scores of 6 quality measures for diabetes and 4 for CVD, including hemoglobin A1c (HbA1c) testing, low-density lipoprotein testing, statin use, HbA1c control, low-density lipoprotein control, and hospital-based utilization.

Results

A total of 786 641 unique enrollees treated by 890 physician groups were included; 414 655 (52.7%) of the enrollees were men and the mean (SD) age was 53 (9.5) years. After adjusting for age, sex, and clinical and social risk variables, correlations among individual measures were weak (eg, performance-adjusted correlation between any statin use and LDL testing for patients with diabetes, r = -0.10) to moderate (correlation between LDL testing for diabetes and LDL testing for CVD, r = .43), but year-to-year correlations for all measures were moderate to strong. One percent or fewer of physician groups performed in the bottom quartile for all 6 diabetes measures or all 4 cardiovascular disease measures in any given year, while 14 (4.0%) to 39 groups (11.1%) were in the bottom quartile in all 4 years for any given measure other than hospital-based utilization for CVD (1.1%).

Conclusions and Relevance

A subset of physician groups that was consistently low performing could be identified by considering performance measures across multiple years. Considering the consistency of group performance could contribute a novel method to identify physician groups most likely to benefit from limited resources.

Measuring disparities in quality of oncology care across oncology practices

6533

Background: Equity is now recognized as an essential aspect of health care quality. Racial inequities in clinical performance diminish overall system performance. We assessed the feasibility and reliability of practice-level measures of racial disparities in chemotherapy-associated emergency department (ED) visits and hospitalizations. Methods: Using fee-for-service Medicare data, we identified 1,196,970 Black or White fee-for-service Medicare beneficiaries with cancer receiving chemotherapy in 2016-2019, who were attributed to 5511 oncology practices that treated at least 1 Black and 1 White beneficiary (96.4% of all beneficiaries). We studied two CMS quality measures: chemotherapy associated ED visits and chemotherapy associated hospitalizations. For each outcome, we estimated multi-level models with separate practice-level random intercepts for Black and White patients to quantify practice-level Black-White disparities in adjusted rates of these measures and assess the associations of these rates with the proportion of Black patients in the practice. Results: Overall, 108,177 Black and 966,381 White beneficiaries with cancer were treated at 1321 practices with reliable estimates (reliability ≥70%) of Black-White differences in rates of chemotherapy-associated ED visits; 101,411 Black and 915,895 White beneficiaries were treated at 1,012 practices with reliable estimates of chemotherapy-associated hospitalizations. These practices treated 80% or more of all Black and White beneficiaries; 10% of these practices treated 75% of Black beneficiaries. The median adjusted Black-White rate difference across practices was +8.9% [interquartile interval (IQI) +5.0%, +12.8%; 5th, 95th percentile -1.8 to +19.2%] for chemotherapy associated ED visits and +4.4% [IQI +1.3%, +7.7%; 5th, 95th percentile -3.5% to +13.5%] for chemotherapy associated hospitalizations. Chemotherapy-associated ED visit rates were 3.2 percentage points higher for Black vs White patients (P <.001) at the practice with the mean % of Black patients, but the difference was smaller in practices with more Black patients (0.4 percentage points less for each 10% increase in Black share, P <.001). Chemotherapy-associated hospitalization rates were 0.6 percentage points lower for Black vs White patients (P =.01) but did not vary by practice racial composition. Conclusions: Using data from more than 1000 practices over 4 years, we calculated reliable estimates of practice-level racial disparities in chemotherapy-associated ED visits and hospitalizations. Practice-level performance for these quality measures was generally lower for Black versus White beneficiaries. Measuring and providing feedback on practice-level Black-White disparities in oncology performance measures may be one effective tool for advancing racial equity in care quality for cancer patients receiving chemotherapy.

Impact of the Oncology Care Model on use of bone supportive medications, antiemetics, and growth factors

1517

Background: The Oncology Care Model (OCM) is a voluntary, episode-based alternative payment model for cancer care launched by the Centers for Medicare & Medicaid Services in July 2016. OCM incentivizes participating practices to reduce spending during chemotherapy treatment while maintaining quality of care. We evaluated the impact of OCM on the use of costly supportive care medications. Methods: Using 100% Medicare claims (2013-2019), we evaluated use of outpatient supportive care medications during chemotherapy episodes assigned to OCM practices (n = 186) or propensity-matched comparison practices (n = 534). For bone supportive medications, we evaluated use of bisphosphonates and/or denosumab in beneficiaries with bone metastases from breast, lung, or prostate cancer. For anti-emetic drugs, we evaluated prophylactic use of neurokinin-1 (NK1) antagonists and long-acting (LA) serotonin antagonists. For white blood cell growth factors (GCSFs), we evaluated prophylactic use in beneficiaries starting chemotherapy for breast, lung, or colorectal cancer; we separately evaluated use of biosimilar (vs originator) filgrastim. Analyses employed the difference-in-differences (DID) approach, excepting the filgrastim biosimilar analysis where we assessed the adoption trend. Results: There was no OCM impact on receipt of any bone supportive medication (denosumab or bisphosphonate) among beneficiaries with bone metastases; however, OCM led to a relative decrease in use of denosumab for breast cancer (DID = -5.0 percentage points [90% CI -7.1, -2.8]), prostate cancer (-4.0 percentage points [90% CI -5.9, -2.2]), and lung cancer (-4.1 percentage points [90% CI -7.4, -0.9]). In beneficiaries starting chemotherapy regimens with high or moderate emetic risk, OCM led to reductions in prophylactic use of NK1 antagonists and LA serotonin antagonists (e.g. 6.0 percentage point reduction in use of NK1 antagonists during high emetic risk chemotherapy [90% CI -9.0, -3.1]); there was no impact on antiemetic use during low emetic risk chemotherapy. There was no OCM impact on use of prophylactic WBC growth factors among beneficiaries receiving chemotherapy with high risk for febrile neutropenia (FN). Among beneficiaries receiving chemotherapy with intermediate risk for FN, OCM led to a 7.6 percentage point reduction in prophylactic GCSF use for patients with breast cancer (90% CI -12.6, -2.7); however, there was no OCM impact on prophylactic GCSF use in patients with lung or colorectal cancer. Among beneficiaries receiving filgrastim, OCM led to faster adoption of biosimilar vs. originator filgrastim (differential trend estimate 2.6%, 90% CI 1.0, 4.4). Conclusions: OCM led to reduced use of some high cost supportive care medications, with patterns suggesting more value-conscious care. Alternative payment models have potential to drive value-based changes in medication use during cancer care.

Assessment of Patient Attribution to Care From Medical Oncologists, Surgeons, or Radiation Oncologists After Newly Diagnosed Cancer

This cohort study uses data from the Surveillance, Epidemiology, and End Results–Medicare Linked Database to assess the attribution of patients with newly diagnosed lung, breast, colorectal, or prostate cancer to care from multidisciplinary specialists—medical oncologists, surgeons, or radiation oncologists—within 6 months after diagnosis.

Evaluation of Reliability and Correlations of Quality Measures in Cancer Care

IMPORTANCE

Measurement of the quality of care is important for alternative payment models in oncology, yet the ability to distinguish high-quality from low-quality care across oncology practices remains uncertain.

OBJECTIVE

To assess the reliability of cancer care quality measures across oncology practices using registry and claims-based measures of process, utilization, end-of-life (EOL) care, and survival, and to assess the correlations of practice-level performance across measure and cancer types.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used the Surveillance, Epidemiology, and End Results (SEER) Program registry linked to Medicare administrative data to identify individuals with lung cancer, breast cancer, or colorectal cancer (CRC) that was newly diagnosed between January 1, 2011, and December 31, 2015, and who were treated in oncology practices with 20 or more patients. Data were analyzed from January 2018 to December 2020.

MAIN OUTCOMES AND MEASURES

Receipt of guideline-recommended treatment and surveillance, hospitalizations or emergency department visits during 6-month chemotherapy episodes, care intensity in the last month of life, and 12-month survival were measured. Summary measures for each domain in each cohort were calculated. Practice-level rates for each measure were estimated from hierarchical linear models with practice-level random effects; practice-level reliability (reproducibility) for each measure based on the between-measure variance, within-measure variance, and distribution of patients treated in each practice; and correlations of measures across measure and cancer types.

RESULTS

In this study of SEER registry data linked to Medicare administrative data from 49 715 patients with lung cancer treated in 502 oncology practices, 21 692 with CRC treated in 347 practices, and 52 901 with breast cancer treated in 492 practices, few practices had 20 or more patients who were eligible for most process measures during the 5-year study period. Patients were 65 years or older; approximately 50% of the patients with lung cancer and CRC and all of the patients with breast cancer were women. Most measures had limited variability across practices. Among process measures, 0 of 6 for lung cancer, 0 of 6 for CRC, and 3 of 11 for breast cancer had a practice-level reliability of 0.75 or higher for the median-sized practice. No utilization, EOL care, or survival measure had reliability across practices of 0.75 or higher. Correlations across measure types were low (r ≤ 0.20 for all) except for a correlation between the CRC process and 1-year survival summary measures (r = 0.35; P < .001). Summary process measures had limited or no correlation across lung cancer, breast cancer, and CRC (r ≤ 0.16 for all).

CONCLUSIONS AND RELEVANCE

This study found that quality measures were limited by the small numbers of Medicare patients with newly diagnosed cancer treated in oncology practices, even after pooling 5 years of data. Measures had low reliability and had limited to no correlation across measure and cancer types, suggesting the need for research to identify reliable quality measures for practice-level quality assessments.

How Do Claims-Based Measures of End-of-Life Care Compare to Family Ratings of Care Quality?

OBJECTIVES

Assess whether frequently-used claims-based end-of-life (EOL) measures are associated with higher ratings of care quality.

DESIGN

Retrospective cohort study.

SETTING/PARTICIPANTS

Deceased fee-for-service Medicare beneficiaries with cancer who underwent chemotherapy during July 2016 to January 2017 and died within 12 months and their caregiver respondents to an after-death survey (n = 2,559).

MEASUREMENTS

We examined claims-based measures of EOL care: chemotherapy 14 days or more before death; inpatient admissions, intensive care unit (ICU) use, and emergency department (ED) visits 30 days or more before death; hospice election and the timing of election before death. Primary outcomes are family ratings of "excellent" care in the last month of life and reports that hospice care began "at the right time." Associations were assessed with logistic regression, adjusted by patient characteristics.

RESULTS

Family rated EOL care as excellent less often, if within 30 days before death the cancer patient had inpatient admissions (1 hospitalization = 41.5% vs 51.5% none, adjusted difference -10.1 percentage points), ICU use (38.6% for any ICU use vs 47.4% none; adjusted difference -8.8 percentage points), ED visits (41.0% 1 visit vs 51.6% no visits; adjusted difference -10.6 percentage points), or elected hospice within 7 days before death. Among hospice enrollees, family more often reported that hospice began at the right time if it started at least 7 days before death (hospice 1-2 days before death 60.2% vs hospice 7-13 days 74.9%; adjusted difference +14.7 percentage points).

CONCLUSIONS

Claims-based measures of EOL care for cancer patients that reflect avoidance of hospital-based care and earlier hospice enrollment are associated with higher ratings of care quality by bereaved family members.

Reliability and correlations among quality measures for lung, breast, and colorectal cancer

2073

Background: Alternative payment models for oncology seek to improve quality and reduce spending. Yet the ability to measure high-quality care across oncology practices remains uncertain. We characterized quality of care for oncology practices using registry and claims-based measures of processes, utilization, end-of-life care, and survival and assessed correlations of practice-level performance across measure type and cancers. Methods: Using SEER-Medicare data, we studied individuals with newly diagnosed lung (N = 95,635), breast (N = 78,736), or colorectal (CRC, N = 51,385) cancers in 2010-2015 treated in oncology practices with ≥20 patients (502, 492, and 347 practices, respectively). We measured receipt of guideline-recommended treatment and surveillance (processes), hospitalizations or emergency department visits during 6-month chemotherapy episodes (utilization), care intensity in the last month of life (EOL), and 12-month survival (lung and CRC only). We calculated summary process, utilization, and EOL measures for each patient (number of measures met divided by the number for which the patient was eligible). We used hierarchical linear models with practice-level random effects to estimate summary measures and survival for each practice. We calculated practice-level reliability (a measurement’s reproducibility) for each measure based on the between-measure variance, within-measure variance, and sample size. Results: Few practices had ≥20 patients eligible for most measures (38%, 37%, and 31% of practices had ≥20 patients for any lung, breast, and CRC measures, respectively). Measure reliability was low. Only 13%, 7%, and 20% of measures for lung, breast, and CRC, respectively, had a median reliability across practices ≥0.7. Among practices with ≥20 patients with summary measures of each type within cancer, correlations across measure types were low (all correlation coefficients (r)≤0.21 except a weak correlation of the CRC process summary measure with 1-year CRC survival, r = 0.38, p < 0.001). Summary process measures were minimally or not correlated across cancer type (lung, breast, CRC; all correlation coefficients ≤0.16). Conclusions: Claims-based measures of care processes, utilization, EOL care, and survival are limited by small numbers of fee-for-service Medicare patients across practices, even after pooling 6 years of data. Measures have poor reliability and are poorly correlated across measure or cancer type. Additional research is needed to identify reliable quality measures for practice-level alternate payment models.

U.S. trends and racial/ethnic disparities in opioid access among patients with poor prognosis cancer at the end of life (EOL)

7005

Background: Heightened US opioid regulations may limit advanced cancer patients’ access to effective pain management, particularly for racial/ethnic minority and other vulnerable populations. We examined trends in opioid access, disparities in access, and pain-related emergency department (ED) visits among cancer patients near end of life (EOL). Methods: Using a 20% random sample of Medicare FFS beneficiaries, we identified 243,124 patients with poor prognosis cancers who died between 2007-2016. We examined trends in outpatient opioid prescription fills and pain-related ED visits near EOL (30 days prior to death or hospice enrollment), for the overall cohort and by race (white, black, other). Per-capita opioid supply by state was obtained from the federal Drug Enforcement Agency ARCOS database. Geographic fixed-effects models examined predictors of opioid use near EOL, opioid dose in morphine milligram equivalents (MMEs), and pain-related ED visits, adjusted for patient demographic and clinical characteristics, state, opioid supply, and year. Results: From 2007-2016 the proportion of patients with poor prognosis cancers filling an opioid prescription near EOL fell from 41.7% to 35.7%, with greater decrements among blacks (39.3% to 29.8%) than whites (42.2% to 36.5%) and other races (38.2% to 32.4%). The proportion of patients receiving long-acting opioids near EOL fell from 17% to 12% overall (15% to 9% among blacks). Among patients receiving EOL opioids, the median daily dose fell from 40MMEs (IQR 16.5-98.0) to 30MMEs (IQR 15.0–78.8). In adjusted analyses, blacks were less likely than whites to receive EOL opioids (AOR 0.85; 95% CI, 0.80 to 0.91) and on average received 10MMEs less per day (b -9.9; 95% CI -15.7 to -4.2). Patients of other race were also less likely to receive EOL opioids (AOR 0.92; 95% CI, 0.85-0.95), although their dose did not differ significantly from whites. Rates of pain-related ED visits near EOL increased from 13.2% to 18.8% over the study period. In adjusted analyses, blacks were more likely than whites to have pain-related ED visits (AOR 1.29, 95% CI, 1.16-1.37) near death, as were those of other races (AOR 1.30; 95% CI, 1.17-1.37). Conclusions: While lawmakers have sought to mitigate the impact of opioid regulations upon cancer patients, access to EOL opioids have decreased substantially over time with concomitant increases in pain-related ED visits. There are significant racial/ethnic disparities in opioid access, with blacks receiving fewer opioids at lower doses and having more ED-based care for pain near EOL.

Physician and facility drivers of spending variation in locoregional prostate cancer

BACKGROUND

Prostate cancer is the most common male cancer, with a wide range of treatment options. Payment reform to reduce unnecessary spending variation is an important strategy for reducing waste, but its magnitude and drivers within prostate cancer are unknown.

METHODS

In total, 38,971 men aged ≥66 years with localized prostate cancer who were enrolled in Medicare fee-for-service and were included in the Surveillance, Epidemiology, and End Results-Medicare database from 2009 to 2014 were included. Multilevel linear regression with physician and facility random effects was used to examine the contributions of urologists, radiation oncologists, and their affiliated facilities to variation in total patient spending in the year after diagnosis within geographic region. The authors assessed whether spending variation was driven by patient characteristics, disease risk, or treatments. Physicians and facilities were sorted into quintiles of adjusted patient-level spending, and differences between those that were high-spending and low-spending were examined.

RESULTS

Substantial variation in spending was driven by physician and facility factors. Differences in cancer treatment modalities drove more variation across physicians than differences in patient and disease characteristics (72% vs 2% for urologists, 20% vs 18% for radiation oncologists). The highest spending physicians spent 46% more than the lowest and had more imaging tests, inpatient care, and radiotherapy spending. There were no differences across spending quintiles in the use of robotic surgery by urologists or the use of brachytherapy by radiation oncologists.

CONCLUSIONS

Significant differences were observed for patients with similar demographics and disease characteristics. This variation across both physicians and facilities suggests that efforts to reduce unnecessary spending must address decision making at both levels.

Spending variation among ACOs in the Medicare Shared Savings Program

OBJECTIVES

Understanding variation in spending across organizations, rather than across geographic areas, is important because care is delivered by organizations and interventions increasingly focus on organizations. Accountable care organizations (ACOs) are particularly important to study given their incentives to reduce spending. Analyzing spending differences across ACOs may help identify cost savings opportunities.

STUDY DESIGN

Cross-sectional analysis of Medicare claims.

METHODS

We stratified ACOs into quartiles based on the deviation between each ACO's risk-adjusted spending and average risk-adjusted fee-for-service spending in the same market (hospital referral region). We compared spending between top- and bottom-quartile ACOs on each of 7 major service categories and 10 clinical condition groups to identify areas of potential savings. We simulated spending reductions if ACOs with high adjusted spending reduced spending to the levels of lower-spending ACOs.

RESULTS

In 2016, geographically adjusted and risk-adjusted total per-beneficiary spending for the highest-spending quartile of ACOs was 14% higher than for ACOs in the lowest quartile. Variation between high- and low-spending ACOs was greatest, at 27%, in the use of skilled nursing facilities-a service category in which ACOs have reduced spending by the greatest percentage. Inpatient care was the largest driver of absolute dollar differences in spending, however, accounting for 37% of the total spread. If spending in ACOs above median adjusted spending were brought down to the median, savings would be 3% to 4%.

CONCLUSIONS

By extending the variations literature to focus on ACOs, we illustrated that meaningful further savings opportunities exist both within and across markets.

Disparities in outpatient visits for mental health and/or substance use disorders during the COVID surge and partial reopening in Massachusetts

OBJECTIVE

To examine changes in outpatient visits for mental health and/or substance use disorders (MH/SUD) in an integrated healthcare organization during the initial Massachusetts COVID-19 surge and partial state reopening.

METHODS

Observational study of outpatient MH/SUD visits January 1st-June 30th, 2018-2020 by: 1) visit diagnosis group, 2) provider type, 3) patient race/ethnicity, 4) insurance, and 5) visit method (telemedicine vs. in-person).

RESULTS

Each year, January-June 52,907-73,184 patients were seen for a MH/SUD visit. While non-MH/SUD visits declined during the surge relative to 2020 pre-pandemic (-38.2%), MH/SUD visits increased (9.1%)-concentrated in primary care (35.3%) and non-Hispanic Whites (10.5%). During the surge, MH visit volume increased 11.7% while SUD decreased 12.7%. During partial reopening, while MH visits returned to 2020 pre-pandemic levels, SUD visits declined 31.1%; MH/SUD visits decreased by Hispanics (-33.0%) and non-Hispanic Blacks (-24.6%), and among Medicaid (-19.4%) and Medicare enrollees (-20.9%). Telemedicine accounted for ~5% of MH/SUD visits pre-pandemic and 83.3%-83.5% since the surge.

CONCLUSIONS

MH/SUD visit volume increased during the COVID surge and was supported by rapidly-scaled telemedicine. Despite this, widening diagnostic and racial/ethnic disparities in MH/SUD visit volume during the surge and reopening suggest additional barriers for these vulnerable populations, and warrant continued monitoring and research.

Multimodality cancer care and implications for episode-based payments in cancer

Most patients receiving multimodality cancer care receive care from different practices. Therefore, episode-based payments in oncology must hold multiple providers accountable for costs and quality.

Early Findings From the Oncology Care Model Evaluation

PURPOSE:

The Oncology Care Model (OCM) is an alternative payment model administered by the Centers for Medicare & Medicaid Services (CMS) that is structured around 6-month chemotherapy treatment episodes. This report describes the CMS-sponsored OCM evaluation and summarizes early evaluation findings.

METHODS:

The OCM evaluation examines health care spending and use, quality of care, and patient experience during chemotherapy treatment episodes. Because OCM participation is voluntary, the evaluation compares participating physician practices with a propensity-matched group of nonparticipating practices by using a difference-in-differences approach. This report examines 6-month episodes initiated during the first OCM performance period (July 1, 2016, through January 1, 2017).

RESULTS:

During the first OCM performance period, there was no statistically significant impact of OCM on total episode payments. There were small declines in intensive care unit (ICU) admissions (7 per 1,000 episodes) and emergency department visits (15 per 1,000 episodes); there was no statistically significant impact on hospitalizations or 30-day readmissions. Analyses of care quality and end-of-life care showed statistically significant impacts of OCM on the proportion of patients with inpatient hospitalizations in the last 30 days of life (1.5% absolute decrease) and ICU admissions in the last 30 days of life (2.1% decrease). There was no significant OCM impact on measures of hospice use.

CONCLUSION:

Early findings from the OCM evaluation demonstrate modest program-related impacts on some acute care services and no change in total episode payments. Early findings may not reflect practice redesign efforts that were phased in after the beginning of OCM.

Association Between Clinical Practice Group Adherence to Quality Measures and Adverse Outcomes Among Adult Patients With Diabetes

IMPORTANCE

Clinical practice group performance on quality measures associated with chronic disease management has become central to reimbursement. Therefore, it is important to determine whether commonly used process and disease control measures for chronic conditions correlate with utilization-based outcomes, as they do in acute disease.

OBJECTIVE

To examine the associations among clinical practice group performance on diabetes quality measures, including process measures, disease control measures, and utilization-based outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, cross-sectional analysis examined commercial claims data from a national health insurance plan. A cohort of eligible beneficiaries with diabetes aged 18 to 65 years who were enrolled for at least 12 months from January 1, 2010, through December 31, 2014, was defined. Eligible beneficiaries were attributed to a clinical practice group based on the plurality of their primary care or endocrinology office visits. Data were analyzed from October 1, 2018, through April 30, 2019.

MAIN OUTCOMES AND MEASURES

For each clinical practice group, performance on current diabetes quality measures included 3 process measures (2 testing measures [hemoglobin A1c {HbA1c} and low-density lipoprotein {LDL} testing] and 1 drug use measure [statin use]) and 2 disease control measures (HbA1c <8% and LDL level <100 mg/dL). The rates of utilization-based outcomes, including hospitalization for diabetes and major adverse cardiovascular events (MACEs), were also measured.

RESULTS

In this cohort of 652 258 beneficiaries with diabetes from 886 clinical practice groups, 42.9% were aged 51 to 60 years, and 52.6% were men. Beneficiaries lived in areas that were predominantly white (68.1%). At the clinical practice group level, except for high correlation between the 2 testing measures, correlations among different quality measures were weak (r range, 0.010-0.244). Rate of HbA1c of less than 8% had the strongest correlation with hospitalization for MACE (r = -0.046; P = .03) and diabetes (r = -0.109; P < .001). Rates of HbA1c control at the clinical practice group level were not significantly associated with likelihood of hospitalization at the individual level. Performance on the process and disease control measures together explained 3.9% of the variation in the likelihood of hospitalization for a MACE or diabetes at the individual level.

CONCLUSIONS AND RELEVANCE

In this study, performance on utilization-based measures-intended to reflect the quality of chronic disease management-was only weakly associated with direct measures of chronic disease management, namely, disease control measures. This correlation should be considered when determining the degree of financial emphasis to place on hospitalization rates as a measure of quality in treatment of chronic diseases.

Factors Contributing To Geographic Variation In End-Of-Life Expenditures For Cancer Patients

Health care spending in the months before death varies across geographic areas but is not associated with outcomes. Using data from the prospective multiregional Cancer Care Outcomes Research and Surveillance Consortium (CanCORS) study, we assessed the extent to which such variation is explained by differences in patients' sociodemographic factors, clinical factors, and beliefs; physicians' beliefs; and the availability of services. Among 1,132 patients ages sixty-five and older who were diagnosed with lung or colorectal cancer in 2003-05, had advanced-stage cancer, died before 2013, and were enrolled in fee-for-service Medicare, mean expenditures in the last month of life were $13,663. Physicians in higher-spending areas reported less knowledge about and comfort with treating dying patients and less positive attitudes about hospice, compared to those in lower-spending areas. Higher-spending areas also had more physicians and fewer primary care providers and hospices in proportion to their total population than lower-spending areas did. Availability of services and physicians' beliefs, but not patients' beliefs, were important in explaining geographic variations in end-of-life spending. Enhanced training to better equip physicians to care for patients at the end of life and strategic resource allocation may have potential for decreasing unwarranted variation in care.

Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches

PURPOSE

Cancer stage is a key determinant of outcomes; however, stage is not available in claims-based data sources used for real-world evaluations. We compare multiple methods for classifying lung cancer stage from claims data.

METHODS

Our study used the linked SEER-Medicare data. The patient samples included fee-for-service Medicare beneficiaries diagnosed with lung cancer from 2010 to 2011 (development cohort) and 2012 to 2013 (validation cohort) who received chemotherapy. Classification algorithms considered Medicare Part A and B claims for care in the 3 months before and after chemotherapy initiation. We developed a clinical algorithm to predict stage IV (v I to III) cancer on the basis of treatment patterns (surgery, radiotherapy, chemotherapy). We also considered an ensemble of claims-based machine learning algorithms. Classification methods were trained in the development cohort, and performance was measured in both cohorts. The SEER data were the gold standard for cancer stage.

RESULTS

Development and validation cohorts included 14,760 and 14,620 patients with lung cancer, respectively. Validation analyses assessed clinical, random forest, and simple logistic regression algorithms. The best performing classifier within the development cohort was the random forests, but this performance was not replicated in validation analysis. Logistic regression had stable performance across cohorts. Compared with the clinical algorithm, the 14-variable logistic regression algorithm demonstrated higher accuracy in both the development (77% v 71%) and validation cohorts (77% v 73%), with improved specificity for stage IV disease.

CONCLUSION

Machine learning algorithms have potential to improve lung cancer stage classification but may be prone to overfitting. Use of ensembles, cross-validation, and external validation can aid generalizability. Degradation of accuracy between development and validation cohorts suggests the need for caution in implementing machine learning in research or care delivery.

The Comparative Advantage of Medicare Advantage

We ascertain the degree of service-level selection in Medicare Advantage (MA) using individual level data on the 100 most frequent HCC's or combination of HCC's from two national insurers in 2012-2013. We find differences in the distribution of beneficiaries across HCC's between TM and MA, principally in the smaller share of MA enrollees with no coded HCC, consistent with greater coding intensity in MA. Among those with an HCC code, absolute differences between MA and TM shares of beneficiaries are small, consistent with little service-level selection. Variation in HCC margins does not predict differences between an HCC's share of MA and TM enrollees, although one cannot a priori sign a relationship between margin and service-level selection. Margins are negatively associated with the importance of post-acute care in the HCC. Margins among common chronic disease classes amenable to medical management and typically managed by primary care physicians are larger than among diseases typically managed by specialists. These margin differences by disease are robust against a test for coding effects and suggest that the average technical efficiency of MA relative to TM may vary by diagnosis. If so, service-level selection on the basis of relative technical efficiency could be welfare enhancing.

Abstract 224: Recent Trends in Coronary Artery Disease Quality Performance and Implications for Clinical Practice in the Era of Alternative Payment Models

Background: In light of recent shifts away from fee-for-service and toward alternative payment models (APM), national trends in quality performance for common cardiac conditions, such as CAD, are important for identifying areas for quality improvement and also for determining physician/health system reimbursement.

Methods: Using Medicare data from 2010-2013, we created a cohort of patients with CAD using a combination of chronic condition warehouse (CCW) flags, ICD-9 and CPT codes. We the determined national performance trends for the 2011 ACC/AHA CAD performance measures. For drug use metrics, we used 80% of days covered after the index event as the threshold.

Results: From 2010-2013, performance trends for testing (annual LDL) and post-MI metrics (beta blocker use, P2Y12 use and cardiac rehab) were flat (p=ns). Among patients with CAD and another comorbidity such as heart failure or diabetes, neurohormonal therapy use increased (p<0.001, Figure 1 ).

Conclusion: The rate of neurohormonal therapy use in patients with CAD and another comorbidity improved while testing and post-MI performance in patients with CAD alone changed little. The reasons for this and may relate to an increased emphasis on complex, costly patients in APMs. Whether these trends impact longer-term patient outcomes should be explored.

Social Risk Adjustment of Quality Measures for Diabetes and Cardiovascular Disease in a Commercially Insured US Population

IMPORTANCE

Patients' social risk factors may be associated with physician group performance on quality measures.

OBJECTIVE

To examine the association of social risk with change in physician group performance on diabetes and cardiovascular disease (CVD) quality measures in a commercially insured population.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study using claims data from 2010 to 2014 from a US national health insurance plan, the performance of 1400 physician groups (physicians billing under the same tax identification number) was estimated. After base adjustments for age and sex, changes in variation across groups and reordering of rankings resulting from additional adjustments for clinical, social, or both clinical and social risk factors were analyzed. In all models, only within-group associations were adjusted to distinguish the association of patients' social risk factors with outcomes while excluding physician groups' distinct characteristics that could also change observed performance. Data analysis was conducted between April and July 2018.

MAIN OUTCOMES AND MEASURES

Process measures (hemoglobin A1c [HbA1c] testing, low-density lipoprotein cholesterol [LDL-C] testing, and statin use), disease control measures (HbA1c and LDL-C level control), and use-based outcome measures (hospitalizations for ambulatory-sensitive conditions) were calculated with base adjustment (age and sex), clinical adjustment, social risk factor adjustment, and both clinical and social adjustments. Quality variance in physician group performance and changes in rankings following these adjustments were measured.

RESULTS

This study identified 1 684 167 enrollees (859 618 [51%] men) aged 18 to 65 years (mean [SD] age, 50 [10.7] years) with diabetes or CVD. Performance rates were high for HbA1c and LDL-C level testing (mean ranged from 79.5% to 87.2%) but lower for statin use (54.7% for diabetes cohort and 44.2% for CVD cohort) and disease control measures (57.9% on LDL-C control for diabetes cohort and 40.0% for CVD cohort). On average, only 8.8% of enrollees with diabetes and 1.0% of enrollees with CVD in a group were hospitalized. The addition of clinical and social risk factors to base adjustment reduced variance across physician groups for most measures (percentage change in SD ranged from -13.9% to 1.6%). Although overall agreement between performance scores with base vs full adjustment was high, there was still substantial reordering for some measures. For example, social risk adjustment resulted in reordering for disease control in the diabetes cohort. Of the 1400 physician groups, 330 (23.6%) had performance rankings for HbA1c control that increased or decreased by at least 10 percentile points after adding social risk factors to age and sex. Both clinical and social risk adjustment affected rankings on hospital admissions.

CONCLUSIONS AND RELEVANCE

Accounting for social risk may be important to mitigate adverse consequences of performance-based payments for physician groups serving socially vulnerable populations.

What drives variation in spending for breast cancer patients within geographic regions?

OBJECTIVE

To estimate and describe factors driving variation in spending for breast cancer patients within geographic region.

DATA SOURCE

Surveillance, Epidemiology, and End Results (SEER)-Medicare database from 2009-2013.

STUDY DESIGN

The proportion of variation in monthly medical spending within geographic region attributed to patient and physician factors was estimated using multilevel regression models with individual patient and physician random effects. Using sequential models, we estimated the contribution of differences in patient and disease characteristics or use of cancer treatment modalities to patient-level and physician-level variance in spending. Services associated with high spending physicians were estimated using linear regression.

DATA EXTRACTION METHOD

A total of 20 818 women with a breast cancer diagnosis in 2010-2011.

PRINCIPAL FINDINGS

We observed substantial between-patient and between-provider variation in spending following diagnosis and at the end-of-life. Immediately following diagnosis, 48% of between-patient and 31% of between-physician variation were driven by differences in delivery of cancer treatment modalities to similar patients. At the end-of-life, patients of high spending physicians had twice as many inpatient days, double the chemotherapy spending, and slightly more hospice days.

CONCLUSIONS

Similar patients receive very different treatments, which yield significant differences in spending. Efforts to reduce unwanted variation may need to target treatment choices within patient-doctor discussions.

Comparison of Approaches for Aggregating Quality Measures in Population-based Payment Models

OBJECTIVE

To assess the impact of alternative methods of aggregating individual quality measures on Accountable Care Organization (ACO) overall scores.

DATA SOURCE

2014 quality scores for Medicare ACOs.

STUDY DESIGN

We compare ACO overall scores derived using CMS' aggregation approach to those derived using alternative approaches to grouping and weighting measures.

PRINCIPAL FINDINGS

Alternative grouping and weighting methods based on statistical criteria produced overall quality scores similar to those produced using CMS' approach (κ = 0.80 to 0.95). Scores derived from giving specific domains greater weight were less similar (κ = 0.51 to 0.93).

CONCLUSIONS

How measures are grouped into domains and how these domains are weighted to generate overall scores can have important implications for ACO's shared savings payments.

Synthesis Of Research On Patient-Centered Medical Homes Brings Systematic Differences Into Relief

The patient-centered medical home (PCMH) model emphasizes comprehensive, coordinated, patient-centered care, with the goals of reducing spending and improving quality. To evaluate the impact of PCMH initiatives on utilization, cost, and quality, we conducted a meta-analysis of methodologically standardized findings from evaluations of eleven major PCMH initiatives. There was significant heterogeneity across individual evaluations in many outcomes. Across evaluations, PCMH initiatives were not associated with changes in the majority of outcomes studied, including primary care, emergency department, and inpatient visits and four quality measures. The initiatives were associated with a 1.5 percent reduction in the use of specialty visits and a 1.2 percent increase in cervical cancer screening among all patients, and a 4.2 percent reduction in total spending (excluding pharmacy spending) and a 1.4 percent increase in breast cancer screening among higher-morbidity patients. These associations were significant. Identification of the components of PCMHs likely to improve outcomes is critical to decisions about investing resources in primary care.

Abstract 35: Trends in Statin Use and Adherence and the Impact of the 2013 Cholesterol Guidelines

Background: In 2013, the ACC/AHA updated the cholesterol treatment guidelines. At the time, it was estimated that an additional 13 million Americans would quality for statin therapy. To date, the real-world implications of this guideline change have not been well studied. This study aims to better understand trends in statin use and adherence, by gender, and the impact of guideline change.

Methods: This is a retrospective, observational study using medical and pharmacy claims from 2009 to 2014 from a large, national, commercial insurer. Considering all beneficiaries aged 18-65 with ≥1 year of continuous enrollment, we created annual cross sectional populations of statin-eligible patients and divided them into 3 statin benefit groups (SBG). In descending order of risk, the groups were: (1) atherosclerotic cardiovascular disease (ASCVD); (2) diabetes and (3) hyperlipidemia. Patients were assigned to the highest risk group that they qualified for.

Results: Statin use rates among those with ASCVD increased until 2012 and then plateaued (Figure 1a ). Use rates among those with diabetes, were flat until 2011 and then increased. Use rates among those with hyperlipidemia steadily rose from 2009-2014. Statin adherence rates among those with ASCVD increased from 2009-2014 ( Figure 1b ). Adherence rates among those with diabetes, decreased from 2009-2011 and then rose significantly from 2011-2014. Adherence rates among those with hyperlipidemia also rose steadily from 2009-2014. The most significant gender gap in treatment, for both use and adherence, was between men and women with ASCVD. There was with little change in this treatment gap, in any risk group, over the time period observed.

Conclusion: The 2013 cholesterol guidelines have not yet had a significant effect on statin use or adherence. Recently improving trends in statin use and adherence, especially among patients with diabetes, appear to predate the 2013 guideline change. A significant gender gap in statin treatment remains, especially among those in the highest risk group.

Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data

Research in oncology quality of care and health outcomes has been limited by the difficulty of identifying cancer stage in health care claims data. Using linked cancer registry and Medicare claims data, we develop a tool for classifying lung cancer patients receiving chemotherapy into early vs. late stage cancer by (i) deploying ensemble machine learning for prediction, (ii) establishing a set of classification rules for the predicted probabilities, and (iii) considering an augmented set of administrative claims data. We find our ensemble machine learning algorithm with a classification rule defined by the median substantially outperforms an existing clinical decision tree for this problem, yielding full sample performance of 93% sensitivity, 92% specificity, and 93% accuracy. This work has the potential for broad applicability as provider organizations, payers, and policy makers seek to measure quality and outcomes of cancer care and improve on risk adjustment methods.

An administrative stage inference algorithm for use in patients receiving chemotherapy for colorectal cancer

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Background: Claims data are often used for evaluating cancer care outcomes; however, such data lack information about cancer stage. We developed a claims-based stage inference algorithm to classify chemotherapy-treated patients with localized vs. metastatic colorectal cancer (CRC). Methods: We used the SEER-Medicare linked data (2010-‘11) to develop and validate an algorithm to predict cancer stage (localized vs. metastatic) among patients receiving chemotherapy within 6 months of CRC diagnosis. We used claims to identify treatments received (surgery, radiation, and chemotherapy agents) during the 6 months before and after the first dose of chemotherapy. The sample was split 1:1 into development and validation cohorts. After testing in the development cohort, the final algorithm was evaluated in the validation cohort. SEER data served as the gold standard for cancer stage. Results: We identified 25,258 patients with fee-for-service Medicare and a new diagnosis of CRC. 6,907 patients (27%) received chemotherapy for CRC within 6 months of diagnosis. The median age of chemotherapy-treated patients was 73, 49% were female, and 76% were white; 69% had localized cancer (AJCC stage 1-3) and 31% had metastasis at diagnosis (stage 4). Split-sample validation of the final classification algorithm demonstrated sensitivity and specificity of 87% (95% CI 86-89%) and 76% (73-78%) for localized cancer and 73% (70-75%) and 91% (90-92%) for metastatic cancer. The overall accuracy of classification was 83%. Stratified analyses demonstrated preserved algorithm performance across subgroups of age, sex, race, geography, and comorbidity. Misclassification was most common among patients with metastatic disease who were treated with surgery followed by fluoropyrimidine chemotherapy with or without oxaliplatin. 2-year overall survival was 79.8% (stage 1-3) and 35.4% (stage 4) for SEER stage groups, vs. 79.6% and 35.8% for predicted stage groups. Conclusions: A claims-based algorithm can classify extent of disease in chemotherapy-treated CRC patients with an accuracy of 83%. This algorithm will allow more clinically-relevant patient stratification for claims-based evaluations of cancer care outcomes.