ACA Medicaid Expansion Association With Racial Disparity Reductions in Timely Cancer Treatment

ABSTRACT

Objectives: Racial disparities in cancer care and outcomes remain a societal challenge. Medicaid expansion through the Affordable Care Act was intended to improve health care access and equity. This study aimed to assess whether state Medicaid expansions were associated with a reduction in racial disparities in timely treatment among patients diagnosed with advanced cancer.

Study Design: This difference-in-differences study analyzed deidentified electronic health record–derived data. Patients aged 18 to 64 years with advanced or metastatic cancers diagnosed between January 1, 2011, and January 31, 2019, and receiving systemic therapy were included.

Methods: The primary end point was receipt of timely treatment, defined as first-line systemic therapy starting within 30 days after diagnosis of advanced or metastatic disease. Racial disparity was defined as adjusted percentage-point (PP) difference for Black vs White patients, adjusted for age, sex, practice setting, cancer type, stage, insurance marketplace, and area unemployment rate, with time and state fixed effects.

Results: The study included 30,310 patients (12.3% Black race). Without Medicaid expansion, Black patients were less likely to receive timely treatment than White patients (43.7% vs 48.4%; adjusted difference, –4.8 PP; P < .001). With Medicaid expansion, this disparity was diminished and lost significance (49.7% vs 50.5%; adjusted difference, –0.8 PP; P = .605). The adjusted difference-in-differences estimate was a 3.9 PP reduction in racial disparity (95% CI, 0.1-7.7 PP; P = .045).

Conclusions: Medicaid expansion was associated with reduced Black-White racial disparities in receipt of timely systemic treatment for patients with advanced or metastatic cancers.

Am J Manag Care. 2021;27(7):274-281. https://doi.org/10.37765/ajmc.2021.88700

Takeaway Points

Medicaid expansion was associated with a reduction in the Black-White racial disparity in timely treatment of patients diagnosed with advanced cancer in the United States.

• Findings of prior studies support the association between insurance coverage and access to earlier-stage diagnosis and guideline-recommended cancer care.
• This study included data obtained after the most recent series of Medicaid expansions.
• This study contributes important new information to the literature concerning the role of the Affordable Care Act in improving access to quality cancer care and reducing racial disparities in health care access.

Racial disparities in cancer outcomes and care, including prevention, screening, diagnosis, and access, remain a societal challenge.^1-10 Timeliness is a critical dimension of high-quality care, as treatment delays can cause anxiety, stress, and inferior clinical outcomes in certain contexts.^11-20 However, information is limited about treatment timeliness and potential racial disparities in contemporary cancer practice in the United States.

The Affordable Care Act (ACA) was designed to improve health care access, equity, and outcomes and to reduce costs in the United States. One mechanism was increasing insurance coverage by expanding Medicaid eligibility to nonelderly adults with incomes below 138% of the federal poverty level.²¹ As of February 2019, expansions were implemented in 34 states and Washington, DC.²² Within 2 years of ACA implementation, the percentage of cancer survivors who were uninsured dropped from 12.4% pre–ACA implementation to 7.7%.^23,24 Although reports of the ACA’s effects on socioeconomic disparities in health care utilization and outcomes have been mixed, Medicaid expansion has been associated with increased health insurance coverage and earlier disease-stage diagnosis among patients with cancer.^25-30

Because Black patients are historically more likely to be uninsured and lack of insurance has been correlated with cancer treatment delays,^31-33 we hypothesized that Medicaid expansion would differentially benefit Black patients, affecting racial differences in treatment access. Timely systemic treatment initiation was selected as the primary outcome for this study, acknowledging that the clinical implications for patients with advanced cancers are not fully defined.^17,20,34 To our knowledge, no studies have included recent data assessing the impact of the latest state Medicaid expansions. The overall goal of this study was to determine whether Medicaid expansion was associated with racial disparity reduction in timely systemic cancer treatment.

PATIENTS AND METHODS

Study Design

Using a difference-in-differences approach, we examined whether state Medicaid expansion status (expanded vs not) was associated with changes in racial disparity in timely treatment among nonelderly adults (aged < 65 years) with an advanced or metastatic cancer diagnosis. Timely treatment was defined as initiation of first-line systemic therapy within 30 days of advanced or metastatic cancer diagnosis. Racial disparity was defined as the difference in probability of timely treatment for Black patients compared with White patients. Because of the small sample size and heterogeneity of Asian and other race/ethnicity categories, we report only results for the Black-White comparison. The study time horizon was from January 1, 2011, to February 28, 2019. Post–Medicaid expansion intervention periods were state specific, according to implementation dates (eAppendix Section 1.1 [eAppendix available at ajmc.com]), allowing each state’s preexpansion or never-expanded time to contribute to the “not-expanded” control group.

Data Sources

The primary data source was a deidentified electronic health record (EHR)–derived database from Flatiron Health. This longitudinal, demographically and geographically diverse database included patient information from more than 280 cancer clinics (~800 sites of care) in the United States. Demographic characteristics of patients were generally representative in comparison with US cancer registries.³⁵ The patient-level data included information from structured EHR fields (eg, race, ethnicity, demographics) and “unstructured” data (eg, diagnosis date, oral therapies) collected through chart abstraction of oncologist notes and other documents (eg, radiology or pathology reports). We obtained average annual state unemployment rates from the US Bureau of Labor Statistics.³⁶ State Medicaid expansion status and implementation dates were obtained from the Kaiser Family Foundation.²²

Study Population

We included patients aged 18 to 64 years (Medicare enrollees were ineligible for expansion), diagnosed between January 1, 2011, and January 31, 2019, with advanced non–small cell lung cancer (NSCLC), gastric/esophageal cancer, or urothelial cancer; or metastatic colorectal, breast, melanoma, renal cell, or prostate cancers (reflecting, as a convenience sample, all solid tumor types with evaluable EHR-derived data during the study period), and who received at least 1 line of systemic therapy (for additional detail, see eAppendix Section 1.2). We excluded patients missing both race and ethnicity information, with unreported diagnosis date, who died less than 30 days after diagnosis, or with potentially incomplete treatment history (no documented office visit or medication order within 90 days following diagnosis date, suggesting that the patient received treatment elsewhere). Sensitivity analyses were performed excluding patients with first-line systemic therapy documented as starting at least 1 year after diagnosis and by restricting the population to only patients with stage IV disease at initial diagnosis.

Exposure to ACA Medicaid Expansion

We assigned expansion status based on whether the patient’s state had implemented Medicaid expansion as of their diagnosis date (eAppendix Section 1.1). The binary “expanded” variable was equal to 1 for individuals living in a state that implemented Medicaid expansion before their advanced or metastatic diagnosis date, and equal to 0 otherwise. In our main policy assignments, we did not account for pre-2014 Medicaid expansions through 1115 waivers³⁷ or early ACA expansions, because most did not cover adults without dependent children (a population that tends to be older and experience a greater cancer incidence). Sensitivity analyses excluded those states entirely.

Outcomes

“Timely treatment” was a binary variable defined as initiating first-line systemic treatment within 30 days of advanced or metastatic cancer diagnosis. Systemic treatment included intravenous and oral cancer therapies, the most common treatment modalities for patients with advanced or metastatic disease. In the absence of an established metric, the 30-day window was selected by expert coauthor consensus as a measure generalizable across cancer types. Sensitivity analyses examined alternative definitions with time windows of 14 or 60 days.

Variable Measurement

Structured race and ethnicity EHR data were combined to create a hierarchical categorical measure with White (“White”), Black or African American (“Black”), Asian, and other race/ethnicity. Patients reporting Hispanic or Latino ethnicity but not reporting race were grouped with “other.” Advanced or metastatic cancer diagnosis served as the index date for each patient. Covariates included age, sex, practice setting (community or academic), cancer type, and stage at initial cancer diagnosis. Annual unemployment rates were assigned based on state and index year.³⁶ The model included state and time at index (quarter-year) fixed effects. We created indicators for insurance coverage reported within 30 days before or after advanced or metastatic diagnosis, including Medicaid, commercial, or “other.” Because of potential measurement error regarding insurance type and coverage dates, analyses using the insurance measures were considered exploratory.

Statistical Analysis

Sample characteristics (means and proportions) were compared by state Medicaid expansion status at the time of diagnosis, race, and cancer type. Multivariable linear regression modeling compared Medicaid expansion–associated changes in racial disparity between Black and White patients. The regression specification, a difference-in-differences analysis, is described in eAppendix Section 4.1. The effect on disparity was evaluated via the interaction between race and Medicaid expansion status, controlling for age, sex, practice setting, cancer type, stage at initial diagnosis, unemployment rate, ACA implementation of the insurance marketplace, quarter-year fixed effects, and state-level fixed effects. Insurance status was determined to be on the causal pathway and was not adjusted for in the model. We assessed the assumption of parallel trends in the preexpansion period and conducted a falsification test. In sensitivity analyses, we dropped practice setting from the models.

We quantified the effects of race, Medicaid expansion, and their interaction using average marginal effects to express how the predicted probability of timely treatment changes with a change in each of these terms.³⁸ The method, also known as recycled predictions, alternatively turns on and off the expansion and race indicators for all individuals in the study population, predicts outcomes, and compares counterfactuals.³⁹ Medicaid expansion effect was the difference between the predicted outcome (timely treatment overall, by race, and disparity between races) if all patients were diagnosed in states with vs without Medicaid expansion. We simulated 1000 clustered bootstrap replicates to estimate CIs.

For comparability with prior ACA Medicaid expansion studies, we also employed an alternative modeling approach. Most pre-post difference-in-differences analyses investigating ACA effects have focused on the early expansion period using data through 2015, classifying any state expanding Medicaid in 2014 or 2015 as an expansion state, and using a pre-post, expansion vs nonexpansion difference-in-differences analysis. In a sensitivity analysis, we excluded all states with Medicaid expansion on or after January 1, 2016, and compared the remaining expansion (2014-2015) states with nonexpansion states. Because our focus was on differences in expansion effects by race, we used a difference-in-difference-in-differences (DDD) analysis, with 2- and 3-way interactions among expansion status, post period, and race (eAppendix Section 4.1).

To better understand the relationship connecting Medicaid expansion and racial disparities in timely treatment, we analyzed the rates of insurance coverage types reported by patients within 30 days before or after advanced or metastatic diagnosis, by race and Medicaid expansion.

Data were analyzed using R version 3.3.2, with session documentation in the eAppendix.⁴⁰ Reporting aligns with STROBE guidelines.⁴¹ Institutional review board approval of the study protocol was obtained before study conduct and included a waiver of informed consent.

RESULTS

Baseline Characteristics

The study included 30,310 patients (Table 1) selected by the criteria summarized in eAppendix Figure 1. The most common cancer types were NSCLC (n = 10,647), colorectal cancer (n = 6392), and breast cancer (n = 6367). The median age was 57 years and 12.3% of patients were Black. The analysis included practices in 36 states; 23 of those states expanded Medicaid during the observation period (Figure 1). Patient characteristics, including race, differed by expansion status: Black patients accounted for only 8.7% of patients diagnosed under a Medicaid expansion vs 14.5% under not-expanded status (Table 1).

Timely Treatment

Overall, 48.6% of patients received systemic treatment within 30 days of advanced or metastatic diagnosis. For each cancer type, Figure 2 describes the unadjusted percentage with timely treatment, stratified by race. The unadjusted probability of timely treatment is higher among White patients in all cancer types except prostate cancer and renal cell carcinoma. The unadjusted rate of timely treatment under not-expanded status was 45.8% for Black patients and 48.1% for White patients. There were baseline differences in the magnitude of racial disparities in the period before Medicaid expansion (2011-2013): Among states that subsequently expanded Medicaid, timely treatment was received by 43.7% (Black) vs 45.3% (White), a difference of 1.6 percentage points (PP), whereas among states that never expanded, the baseline unadjusted rates were 46.6% (Black) vs 47.7% (White), a difference of 1.1 PP.

Difference-in-Differences Estimates

After multivariable adjustment for sociodemographic and clinical characteristics, the difference in timely treatment rate between Black and White patients in nonexpansion states was –4.8 PP (95% CI, –6.9 to –2.6; P < .001). For patients diagnosed in states that had expanded Medicaid, there was no significant difference by race in the probability of receiving timely treatment after Medicaid expansion: 49.7% for Black patients and 50.5% for White patients, with an adjusted difference of –0.8 PP (95% CI, –4.1 to 2.4; P = .605). The adjusted difference-in-differences estimate (change in race disparity associated with Medicaid expansion) was a 3.9 PP disparity decrease (95% CI, 0.1-7.7; P = .045) (Table 2). Thus, Medicaid expansion was associated with a greater increase in timely treatment among Black patients than among White patients, largely eliminating the preexisting disparity. Age and stage of diagnosis were also related to timely treatment (eAppendix Section 4.2). Results showed that the assumption of parallel trends was not violated (eAppendix Section 4.3).

Health Insurance Coverage

A greater proportion of Black patients reported Medicaid coverage compared with White patients (18.7% vs 8.1%, respectively), regardless of state expansion status (Figure 3). Both Black and White patients had higher rates of Medicaid coverage under expansion compared with not expanded. Among Black patients, the rate of Medicaid coverage at the time of advanced diagnosis was 17.8% in not-expanded and 20.9% in expansion states; among White patients, those rates were 7.7% and 8.6%, respectively. Without statistical testing, we observed a larger raw difference in rates comparing Medicaid expansion with nonexpansion for Black patients than White patients.

Sensitivity Analyses

The direction and magnitude of the association between Medicaid expansion and racial disparities in timely treatment were consistent across alternative model specifications and assumptions in sensitivity analyses, although less precisely estimated and no longer significant. Including only patients with stage IV disease at diagnosis, racial disparity reduction associated with expansion was magnified to 4.5 PP (95% CI, –0.6 to 9.6). Excluding patients diagnosed in a state with early expansion preceding ACA implementation attenuated the adjusted difference-in-differences estimate to 2.7 PP (95% CI, –1.7 to 7.1). Use of alternative “timely treatment” definition windows of 14 and 60 days slightly attenuated the effect, resulting in adjusted estimates of 2.8 and 2.3 PP reductions in racial disparity, respectively.

In our DDD analysis, excluding states with Medicaid expansion after December 31, 2015 (Louisiana, Maine, Montana, and Virginia), reduced the sample size from 30,310 to 27,924 patients, and this yielded a 4.2 PP (95% CI, –4.3 to 12.8) reduction in the racial disparity in timely treatment. This is comparable with the 3.9 PP estimate in our main analysis, although no longer significant (eAppendix Section 5.2 and 5.3: regression output in eAppendix Table 4, population-adjusted rates in eAppendix Table 5, and additional sensitivity analyses in eAppendix Table 6).

DISCUSSION

Results from this large observational study suggest that implementation of Medicaid expansion was associated with a significant reduction in Black-White racial disparities in timely treatment for advanced or metastatic cancer. In states without Medicaid expansion, Black patients were 4.8 PP less likely to receive timely systemic treatment compared with White patients; this difference was no longer present with Medicaid expansion. The difference-in-differences estimate indicates that Black patients benefited 3.9 PP more than White patients from the Medicaid expansions. This study contributes important new information to the literature concerning the role of the ACA in reducing racial disparities and improving overall health care access. It also illustrates a novel application of EHR data to examine the impact of policy changes on the cancer care delivery process.

Several ACA mechanisms can influence health care, and prior studies’ findings support the association between insurance coverage and access to earlier-stage diagnosis and guideline-recommended cancer care.^23-31 Recent studies using Surveillance, Epidemiology, and End Results data have shown that the dependent coverage provision of the ACA was associated with increases in insurance coverage in young adult patients with cancer and that Medicaid expansion is associated with earlier cancer diagnosis and improved access to surgery.^42,43 This study focuses on how disparities in systemic cancer treatment delivery may be modifiable through targeted health care policy interventions. Increased insurance coverage is the key mechanism through which the ACA Medicaid expansions are expected to affect timely treatment. The results of our exploratory analysis using insurance data are consistent with that causal interpretation, showing differential increases in Medicaid enrollment by Black patients under the expansions. In the equal-access US Military Health System, time to treatment for patients with colon cancer has been shown to be similar between White and Black patients.⁴⁴ Our end point of timely treatment does not address downstream cancer care processes and outcomes; factors such as socioeconomic status (education, health literacy, income, or wealth), housing vulnerability, and food insecurity may also contribute to racial disparities in cancer outcomes.⁴ In fact, the persisting disparities in some countries with universal health care⁴⁵ would indicate that equal coverage may be necessary but not sufficient to eliminate racial disparities in cancer care. As seen recently with the COVID-19 pandemic, major strains on health care systems can exacerbate already-present dysfunction and disparities; understanding how structural factors contribute to disparities can be a first step toward building a more equitable and resilient ecosystem. Future studies could home in on how trends like the one studied here may have been amplified by an event like the recent pandemic.

Limitations

Our study was based on a large sample spanning 36 states and made novel use of deidentified EHR-derived data to assess the effects of health care policy. Yet, we acknowledge limitations to data drawn from medical oncology practices, which could affect the observed results. Specifically, we lacked full information on care provided outside those practices, such as other treatment modalities. This limited our ability to capture and adjust for receipt of surgery or radiation before systemic therapy initiation. For this reason, the absolute rates of timely treatment should be interpreted with caution. We focused on timely initiation of systemic treatment among patients receiving therapy in each practice. Timely treatment is a patient-centered proxy for efficient and quality care, although its correlation with other quality domains and with clinical outcomes such as overall survival for advanced cancers is less certain.^15-20 We limited our sample to patients with advanced or metastatic cancer, for whom systemic therapy is the principal therapeutic modality; in sensitivity analyses, we restricted further to patients diagnosed with metastatic cancer and found consistent results.

We note several additional limitations to the database that may have affected our sample or analysis. One overarching limitation relates to our particular EHR-derived database, which ultimately provides a convenience sample for this type of analysis. As such, the study was limited to the tumor types available for analysis. There were 8.5% of patients missing information on race and/or ethnicity; it is unclear whether this missingness is systematic, which might result in biased estimates. Sample sizes were insufficient to estimate effects separately for each cancer type. Patient income data were not available, limiting our ability to target the analysis to those most likely to benefit from expanded Medicaid eligibility—potentially diluting the intervention effect. The database lacked uniform data on clinical factors such as functional status or comorbidities that might affect treatment selection or timeliness of therapy, hence we were unable to adjust for these in the analysis. Finally, local practice environment and time-varying factors beyond Medicaid expansion could have influenced timely treatment; we included time and state fixed effects to minimize this potential bias.

CONCLUSIONS

We found that ACA Medicaid expansion was associated with a significant reduction in Black-White racial disparities in timely systemic treatment for patients with advanced or metastatic cancers. Evaluation of health care policy impact is critical to determining if desired goals are met and to inform the design of future policy. This study also demonstrates how clinical data obtained during routine care can be a tool for real-time evaluation of health care policies. Future research is needed to fully understand the factors influencing timing of treatment initiation to ensure equity and improve access to care for all patients with cancer.

Acknowledgments

Mark Bounthavong, PharmD, PhD, health economist, Health Economics Resource Center, VA Palo Alto Healthcare System; and Anirban Basu, PhD, MS, professor of health economics, Departments of Pharmacy, Health Services, and Economics, University of Washington, Seattle, informed design of difference-in-differences study design methodology. Sharon Moon, Mariana Hernandez, and Pooja Shaw substantially contributed to the data acquisition. Tanya Elshahawi created the US map visualizations. Ian Hooley and Lura Long supported preliminary analyses. Brian Segal reviewed the statistical methods and results. Aracelis Torres reviewed the manuscript and R code used for analysis. Somnath Sarkar, Kenneth Carson, and Melisa Tucker provided feedback on a draft of the manuscript. Julia Saiz-Shimosato and Lesley Plotkin edited a draft of the manuscript. Sam Azaria provided project management support.

Author Affiliations: Flatiron Health, Inc (BJSA, ABC, ME, KM, EW, NJM), New York, NY; University of Washington (BJSA), Seattle, WA; New York University School of Medicine (ABC), New York, NY; The Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, Yale School of Medicine, Yale Cancer Center (CPG, AJD), New Haven, CT; National Clinician Scholar Program, Yale School of Medicine (CPG), New Haven, CT; Case Comprehensive Cancer Center (NJM), Cleveland, OH; Yale School of Public Health (AJD), New Haven, CT.

Source of Funding: This study was sponsored by Flatiron Health, Inc, which is an independent subsidiary of the Roche Group.

Prior Presentation: Results from this study were presented at the American Society of Clinical Oncology Annual Meeting Plenary Session on June 2, 2019, in Chicago, IL. A summary of that presentation appeared in The Cancer Letter. 2019;45(25):5-17.

Author Disclosures: Dr Adamson, Ms Estévez, Ms Magee, and Ms Williams report employment at Flatiron Health, Inc, which is an independent subsidiary of the Roche Group, and stock ownership in Roche. Dr Cohen and Dr Meropol report employment at Flatiron Health, Inc, equity ownership in Flatiron Health, and stock ownership in Roche. Dr Gross has received research funding from the National Comprehensive Cancer Network/Pfizer and funding from Johnson & Johnson through Yale University to help develop new approaches for sharing clinical trial data; he has also been reimbursed for travel and speaking by Flatiron, Inc. Dr Davidoff has received research funding from Celgene through Yale University and consults for Amgen; an immediate family member reports consulting/advisory board relationships with Celgene, Jazz Pharmaceuticals, AbbVie, Kyowa Hakko Kirin, Tolero Pharmaceuticals, and Daiichi Sankyo.

Authorship Information: Concept and design (BJSA, ABC, KM, EW, AJD); acquisition of data (BJSA); analysis and interpretation of data (BJSA, ABC, CPG, ME, KM, EW, NJM, AJD); drafting of the manuscript (BJSA, ABC, KM, NJM, AJD); critical revision of the manuscript for important intellectual content (BJSA, ABC, CPG, NJM, AJD); statistical analysis (BJSA, ABC, ME); provision of patients or study materials (BJSA); administrative, technical, or logistic support (BJSA, ME, EW); and supervision (BJSA, CPG).

Address Correspondence to: Blythe J.S. Adamson, PhD, Flatiron Health, Inc, 233 Spring St, Fifth Fl, New York, NY 10025. Email: badamson@flatiron.com.

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