State Medicaid Budgetary Implications of New Cancers

ABSTRACT

Objectives: The burden of a new cancer diagnosis on state Medicaid programs is not well understood. Our study aimed to (1) quantify Medicaid program spending, use of health care services, and enrollment patterns among Medicaid beneficiaries newly diagnosed with cancer; and (2) assess how spending and health care use differ between a new metastatic and a new nonmetastatic cancer diagnosis.

Study Design: Cross-sectional study.

Methods: This study used Transformed Medicaid Statistical Information System Analytic Files (2016-2019) to identify Medicaid beneficiaries 50 years and older who were newly diagnosed with cancer in 2017 and 2018. We provide descriptive evidence about their use of health care resources and used linked Medicare-Medicaid claims to follow enrollment patterns among dual-eligible beneficiaries. We also used generalized estimating equations to compare Medicaid program spending among those diagnosed with a new metastatic vs nonmetastatic cancer. Study measures were differences in total Medicaid spending, hospitalizations and emergency department visits per year, and Medicaid enrollment status at the end of the study (ie, continuously enrolled, disenrolled, or died) by metastatic status.

Results: We identified 291,014 new cancer diagnoses among Medicaid beneficiaries 50 years and older (18.8% metastatic vs 81.2% nonmetastatic). A metastatic cancer diagnosis was associated with higher hospitalizations, emergency department use, and spending compared with a nonmetastatic cancer diagnosis.

Conclusions: Our results quantify the cost and health care resource utilization burden borne by state Medicaid programs following a new cancer diagnosis. These findings can inform policy makers about the budgetary considerations associated with investing in reducing late-stage cancer diagnoses, such as through early detection and increased access to care.

Am J Manag Care. 2026;32(7):In Press

Takeaway Points

• In this study of Medicaid beneficiaries newly diagnosed with cancer, we observed that a new metastatic cancer diagnosis was associated with higher hospitalizations, emergency department use, and higher spending compared with a nonmetastatic cancer diagnosis.
• These findings were consistent across cancer types, suggesting an opportunity for state Medicaid programs to reduce spending and improve health outcomes through earlier detection and intervention.

Medicaid covers an underserved population of more than 80 million individuals, many of whom are working age.¹ Cancer incidence and health care spending are high within the Medicaid population,² as cancer is Medicaid’s third-largest source of spending following HIV and hepatitis C.³ Individuals with a history of cancer within the general population are more likely to use health care services and become eligible for public assistance programs like Medicaid.^4,5 However, prior research has not examined the overall state budgetary impacts of a new cancer diagnosis for an individual enrolled in Medicaid and how that impact varies with cancer stage at diagnosis.

A new cancer diagnosis is hypothesized to increase Medicaid spending through both the extensive (ie, any use of service) and intensive (ie, quantity of services) pathways. First, a new cancer diagnosis may limit an individual’s ability to gain employment and earn income,^4,6 thereby lengthening their time on Medicaid. Thus, longer enrollment time on Medicaid will mechanically increase program spending. Second, a new cancer diagnosis may also increase spending on the intensive margin by increasing the use of services,^7-9 such as hospitals, provider services, emergency departments (EDs), and pharmaceuticals. Patients with newly diagnosed cancer may also require long-term care, including nursing homes and home- and community-based services. Thus, both extensive and intensive pathways may contribute to increased Medicaid spending when a beneficiary is diagnosed with a new cancer.

Furthermore, there is likely differential Medicaid spending and use of health care services—including greater hospital admissions, higher costs, and more missed days of work—between individuals newly diagnosed with metastatic cancer compared with nonmetastatic cancer.^6,10-12 This highlights the importance of ensuring access to health insurance, such as Medicaid, to provide screening tests and detect cancer early before it progresses to an advanced stage.

In this study, we quantify the burden borne by state Medicaid programs associated with a new cancer diagnosis by examining differences in total Medicaid program spending, use of health care services, and enrollment patterns between patients diagnosed with a new metastatic vs nonmetastatic cancer. Our findings provide important estimates to inform policy makers of the budgetary considerations associated with cancer care and how efforts to reduce late-stage cancers, such as increased access to health care services and early detection tools, may impact the burden on state Medicaid programs and improve health outcomes.

METHODS

Data Sources

This cross-sectional study used Transformed Medicaid Statistical Information System Analytic Files (TAF) from 2016 to 2019. The TAF contains demographic information for Medicaid beneficiaries in all 50 states as well as use and spending data for inpatient hospital services claims, long-term care claims, prescription drug pharmacy claims, and other services (including outpatient services) claims.¹³ We also used the Master Beneficiary Summary File (MBSF) to identify enrollment and death dates among beneficiaries dually eligible for both Medicaid and Medicare (2017-2019).

Study Population

We included Medicaid beneficiaries 50 years and older in this study. We started the cohort at age 50 years because cancer risk increases substantially at this age¹⁴ and national screening guidelines for cancer often begin at this age.¹⁵ We also included individuals who were dually eligible because Medicaid is responsible for cost sharing and payments for other services, including long-term services and supports.

To identify new cancer cases, we required at least 1 inpatient claim or 2 outpatient claims, with a gap of 31 to 365 days, with International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) cancer diagnosis codes in 2017 or 2018. ICD-10-CM cancer diagnosis codes for benign tumors and in situ cancers (except for bladder) were excluded.¹⁶ The date corresponding to the first claim for the cancer diagnosis was defined as the incident date. For each cancer type, we identified beneficiaries with metastatic cancer based on ICD-10-CM diagnosis codes for secondary carcinoid tumors, secondary malignant neoplasms, or 2 different primary cancer diagnoses within 31 days of the incident cancer date.¹⁷ We categorized beneficiaries with a secondary code but missing a primary site as “secondary, no primary site reported.” We used 2016 data as a washout period to exclude those with a preexisting cancer diagnosis and used 2019 data to allow adequate follow-up among beneficiaries diagnosed with an incident cancer at the end of 2018. All beneficiaries were required to have continuous Medicaid coverage for at least 6 months prior to their incident cancer diagnosis.

Measures

Demographic characteristics measured at the time of the incident cancer diagnosis included sex, race and ethnicity (White, Black, Hispanic, or other/unknown), age, dual-eligibility status, Charlson Comorbidity Index score at the time of their index cancer diagnosis, and geographic information. We also identified enrollment characteristics related to the amount of time a beneficiary was continuously enrolled in Medicaid prior to and after their incident cancer diagnosis.

The main outcome of interest was the difference in total Medicaid spending between patients diagnosed with a new metastatic vs nonmetastatic cancer. We captured total Medicaid spending based on the overall Medicaid amount paid for fee-for-service (FFS) claims, wraparound payments associated with a specific beneficiary above the negotiated per-service rate, and Medicaid spending on Medicare deductibles or coinsurance for dual-eligible beneficiaries for services recorded in the TAF inpatient hospital, other services, long-term care, and pharmacy files, starting from the date of the incident cancer diagnosis. We limited our spending analyses to beneficiaries enrolled in state Medicaid programs with an FFS payment system (Alabama, Alaska, Connecticut, Idaho, Maine, Montana, North Carolina, Oklahoma, and South Dakota)¹⁸ because capitated payments in states that contracted with comprehensive managed care organizations for their Medicaid enrollment do not reflect costs of the service provided. We also examined patterns in spending within 6 months and greater than 6 months prior to the incident cancer diagnosis. All spending measures were annualized based on the length of Medicaid enrollment associated with each spending period and inflation-adjusted to 2019 US$ using the Medical Care Consumer Price Index.

We defined measures to capture the use of health care services associated with the incident cancer diagnosis, including the number of hospitalizations per year from the inpatient hospitalization file and the number of ED visits per year based on the place of service code (23) from the other services file. We also assessed patterns of disenrollment from Medicaid by categorizing each beneficiary’s enrollment status at the end point of the study as being due to mortality, continuously enrolled at the end point (December 2019), or disenrolled during the study (with the potential to reenroll in Medicaid prior to December 2019). Because of concerns about the completeness of the TAF data to capture mortality, we then linked patients who were dually eligible to the MBSF to identify whether some patients whom we observed as disenrolling from Medicaid appeared in the Medicare file with a date of death recorded in the MBSF, but not in the TAF, or appeared to lose their Medicaid coverage and were enrolled in Medicare only.

Statistical Analysis

Our objectives were to quantify the financial burden of new cancer diagnoses on state Medicaid programs and to describe associated patterns of health care utilization and enrollment. We first characterized the study population by examining demographic characteristics, enrollment patterns, and cancer diagnosis rates. We stratified these descriptive analyses by metastatic vs nonmetastatic cancer.

Because Medicaid claims do not capture spending for patients enrolled in capitated managed care programs, our spending analyses were restricted to patients residing in states with FFS Medicaid programs. Within this FFS subsample, we estimated differences in total Medicaid spending associated with metastatic vs nonmetastatic cancer. We used generalized estimating equations with a log link and γ distribution¹⁹ to model percentage changes in spending, adjusting for age at diagnosis, sex, race/ethnicity (White, Black, Hispanic, or other/unknown), dual eligibility at diagnosis, Charlson Comorbidity Index score, state, and baseline Medicaid spending in the 6 months prior to cancer diagnosis.

To enable comparisons between FFS and non-FFS states, we then examined utilization measures that could be consistently observed across all states. Specifically, we compared the annualized rates of hospitalizations and ED visits following cancer diagnosis between FFS and non-FFS beneficiaries.

Finally, we used linked Medicaid-Medicare data to assess patterns of disenrollment among dual-eligible beneficiaries and categorized their status at the end of the study period as being due to mortality identified in the Medicaid TAF, remaining enrolled in Medicaid, disenrolling from Medicaid with and without Medicaid reenrollment, mortality identified in the MBSF but not in the TAF, or disenrolling from Medicaid but remaining enrolled in Medicare.

Because of concerns about the data quality in the TAF, we excluded certain states in each analysis.²⁰ All state exclusions are listed in eAppendix Table 1 (eAppendix available at ajmc.com). This study was deemed to be exempt from review by the Harvard Medical School Institutional Review Board. All analyses were conducted in Stata 18 (StataCorp LLC).

RESULTS

We identified 291,014 new cancer diagnoses among Medicaid beneficiaries (18.8% metastatic vs 81.2% nonmetastatic) (Table 1). The incidence of new cancer in our sample of Medicaid beneficiaries 50 years and older was 733 per 100,000 person-years (291,014 new cancer diagnoses per 39.7 million person-years at risk), representing 2.3% of Medicaid beneficiaries being newly diagnosed with cancer during the sample period. The overall Medicaid cancer cohort was 44.8% female and 46.7% White, with a mean age at diagnosis of 66.5 years. At diagnosis, 55.4% were dually eligible, and the mean length of Medicaid enrollment prior to their diagnosis was 20.5 months.

Characteristics were similar between those diagnosed with metastatic and nonmetastatic cancer, with larger differences observed in the length of Medicaid enrollment post cancer diagnosis (14.0 months for metastatic vs 18.6 months for nonmetastatic cancer). In our analysis of Medicaid beneficiaries enrolled in the 9 states with FFS Medicaid programs, we identified 20,877 new cancer diagnoses (19.9% metastatic vs 80.1% nonmetastatic) (eAppendix Table 2). We observed regional variation, with 66.8% of the FFS sample residing in the South US census region. Annualized mean spending was $15,251 in the more than 6 months prior to the cancer diagnosis and increased to $18,640 in the 6 months leading up to the cancer diagnosis (data not shown).

The most common cancer types among those newly diagnosed with metastatic cancer were lung (15.6%) and breast (8.0%), with a significant fraction (23.5%) having no primary site reported (Table 2). Similarly, the most common cancer types among those with nonmetastatic cancer were breast (17.2%), prostate (13.5%), and hematologic (9.9%). The most common cancer types in FFS states were similar to the overall sample: lung cancer (19.2% metastatic vs 14.9% nonmetastatic cancer) and breast cancer (7.8% metastatic vs 16.9% nonmetastatic cancer), with 22.8% having no primary site reported (eAppendix Table 3).

In our adjusted analyses of spending in FFS states, we identified a statistically significant increase in the odds of total Medicaid spending (ranging from 1.1 to 2.9 times higher spending for a new metastatic cancer vs new nonmetastatic cancer) (Table 3). For example, a new metastatic lung cancer diagnosis was associated with 72.0% higher annual Medicaid spending relative to a new nonmetastatic lung cancer diagnosis (P < .001). Additionally, when we decomposed total Medicaid spending into inpatient, other services, and prescription drug spending components, most of the differences in observed spending were driven by spending on other services (eg, outpatient services) (eAppendix Table 4).

Those with a metastatic cancer diagnosis had higher hospitalizations and ED visits compared with those with nonmetastatic cancer (Figure and eAppendix Table 5). Additionally, beneficiaries with newly diagnosed cancer in non-FFS states generally had slightly higher health care resource use compared with beneficiaries in FFS states for the same cancer types. For example, mean annual hospitalizations and ED visits were higher among beneficiaries in non-FFS compared with FFS Medicaid programs among beneficiaries with both metastatic pancreatic cancer (8.6 vs 5.9 hospitalizations and 11.6 vs 10.9 ED visits, respectively) and nonmetastatic pancreatic cancer (4.7 vs 2.9 hospitalizations and 7.3 vs 5.7 ED visits, respectively).

Finally, we compared patterns of Medicaid disenrollment among dually eligible beneficiaries (Table 4). Among those with metastatic cancer in the overall sample, 8.8% died, 41.8% remained alive and enrolled in Medicaid at the end of the study period, 3.3% of the sample disenrolled from Medicaid after their cancer diagnosis but reenrolled prior to the study end point, and 46.1% of the sample lost their Medicaid enrollment based on the TAF. When we linked our sample to the MBSF, we identified that a large portion of the attrition was due to mortality captured in the MBSF but not in the TAF data (41.3%), with a small percentage of beneficiaries who had Medicare but not Medicaid enrollment (0.3%) and 4.5% who could not be identified in either the Medicaid TAF or MBSF at the study end point. By comparison, we observed mortality rates of 4.5% in the TAF and 22.0% in the MBSF, with 62.7% of the sample who remained alive and enrolled in Medicaid at the study end point among those with a new nonmetastatic cancer diagnosis based on the TAF. The overall rates of those who died, reached the study end point, and disenrolled with and without Medicaid reenrollment were similar among non–dually eligible beneficiaries (eAppendix Table 6) and the entire sample (eAppendix Table 7).

DISCUSSION

In our analysis of Medicaid claims, we identified 291,014 beneficiaries newly diagnosed with cancer between 2017 and 2018, 20% of whom were diagnosed with metastatic cancer. A new metastatic diagnosis was significantly associated with increased Medicaid program spending, hospitalizations and ED visits, and rates of mortality among dually eligible beneficiaries. Importantly, we found that less than 5% of beneficiaries with metastatic cancer disenrolled from Medicaid without subsequently reenrolling. Our findings provide encouraging results that Medicaid beneficiaries diagnosed with cancer likely experience few disruptions in their coverage for care and treatment, but they suggest that most of the total cost of care for these beneficiaries will ultimately be borne by Medicaid or, among those eligible, Medicare. Thus, we estimate that annual Medicaid spending on care for beneficiaries 50 years and older with cancer in states with FFS Medicaid programs totaled nearly $1 billion. Because our FFS sample represented only 7% of the total number of new cancer cases identified in our study, these results imply more than $13 billion in Medicaid spending nationally.

Our quantification of the burden of cancer care—including spending by the Medicaid program and use of health care services—raises important considerations for policy makers. These may include monetary considerations, such as comparing the costs of screening and prevention with treatment, as well as increasing health care access more broadly through expanding Medicaid enrollment among eligible low-income individuals. For example, one study estimated that individuals who gained Medicaid coverage due to the Affordable Care Act (ACA) Medicaid expansion had 15% lower odds of being diagnosed with metastatic cancer compared with those diagnosed pre–Medicaid expansion.²¹ Other work has found that increased access to Medicaid resulted in mortality reductions,²² suggesting that expanding access to health insurance can result in improved health outcomes.

State Medicaid programs can also invest in expanding access to primary care services, which are associated with reduced odds of metastatic disease at diagnosis as well as risk of cancer-related death.²³ Primary care physicians can also monitor patients for risk factors associated with an elevated cancer risk, including tobacco use, alcohol consumption, and obesity, and identify signs of early changes. This may be particularly important for cancers that may not have screenings or tests available.

For other cancers, screening and earlier cancer detection can enable early intervention and treatment and improve rates of survival.^24,25 US Preventive Services Task Force recommendations of single-site cancer screening for breast, colorectal, cervical, and lung cancer for high-risk individuals have been estimated to save more than 12 million life-years and $6.5 trillion since their introduction.²⁶ Some studies on single cancer types have found that cancer diagnosis at an early stage results in lower expenditures compared with the costs associated with treating later-stage cancer,^27,28 and new cancer screening technologies are becoming available, expanding the number of cancer types that can be detected early.²⁹ This suggests an opportunity for Medicaid programs to expand access to early detection tools as a means of reducing the burden of a new cancer diagnosis—including the potential need for high-cost treatments such as immunotherapies for late-stage cancers³⁰—in the underserved population identified in our study.

Limitations

This study has limitations. First, because of known data quality limitations with the TAF, we were not able to include all 50 states in our analyses. Second, we estimated Medicaid program spending for individuals in FFS states only because we were unable to capture the costs of services among individuals enrolled in capitated Medicaid managed care programs. Similarly, we focused our mortality analysis on dually eligible beneficiaries because we believed that there would be uncaptured mortality in the TAF data due to disenrollment from Medicaid shortly prior to death. Third, our study sample included those with 6 months of continuous Medicaid coverage prior to their incident cancer diagnosis. Thus, our results are not generalizable to individuals who may have enrolled in Medicaid at the time of or subsequently after their incident cancer diagnosis. Fourth, we did not assess whether there may be differential spending in states that did and did not expand Medicaid under the ACA. Fifth, we did not have access to cancer registry data to identify the timing and stage of cancer, so we may have misclassified some patients as having a new or metastatic diagnosis. Sixth, our estimates are likely a conservative estimate of the burden of cancer on Medicaid given that we excluded beneficiaries younger than 50 years, including younger adults and those with pediatric cancers. Finally, we were unable to measure the full social cost associated with a new cancer diagnosis, such as costs related to employment and loss of productivity or potential burden on family members and other caregivers, which may be particularly significant for the Medicaid population.

CONCLUSIONS

Our study of Medicaid beneficiaries newly diagnosed with cancer identified large increases in spending and use of health care resources, particularly for those diagnosed with metastatic cancer compared with nonmetastatic cancer. As a result, our findings have important implications for understanding the budgetary offsets potentially available when considering investment in increased access to health care, including access to early detection and screening, to provide care for those newly diagnosed with cancer and improve health outcomes in a typically underserved population.

Author Affiliations: Department of Health Care Policy, Harvard Medical School (ACC, DCG), Boston, MA; Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (ACC), Cambridge, MA; now with Department of Pharmaceutical and Health Economics, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, and Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California (ACC), Los Angeles, CA; GRAIL, LLC (ARK), Menlo Park, CA.

Source of Funding: The study was funded by GRAIL, LLC. GRAIL, LLC had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author Disclosures: Drs Chen and Grabowski were paid consultants to GRAIL, LLC in connection with the conduct of this study and the development of the article. Dr Grabowski also reports receiving consulting fees from Analysis Group, EntityRisk, and the University of Southern California unrelated to this work. Dr Kansal is a full-time employee of and holds stock in GRAIL, LLC.

Authorship Information: Concept and design (ARK, DCG); acquisition of data (DCG); analysis and interpretation of data (ACC, ARK, DCG); drafting of the manuscript (ACC); critical revision of the manuscript for important intellectual content (ARK, DCG); statistical analysis (ACC); obtaining funding (ARK); and supervision (DCG).

Address Correspondence to: David C. Grabowski, PhD, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115. Email: grabowski@hcp.med.harvard.edu.

REFERENCES

1. Medicaid state fact sheets. KFF. August 14, 2024. Accessed August 21, 2024. https://www.kff.org/interactive/medicaid-state-fact-sheets/

2. Taylor K, Diaz A, Nuliyalu U, Ibrahim A, Nathan H. Association of dual Medicare and Medicaid eligibility with outcomes and spending for cancer surgery in high-quality hospitals. JAMA Surg. 2022;157(4):e217586. doi:10.1001/jamasurg.2021.7586

3. McCarthy D. Cancer spending in Medicaid: from unmanaged problem to untapped opportunity. New Century
Health. October 28, 2019. Accessed August 19, 2024. https://web.archive.org/web/20240526121008/ https://www.newcenturyhealth.com/resources/news/cancer-spending-in-medicaid-from-unmanaged- problem-to-untapped-opportunity

4. Nitecki R, Albright BB, Johnson MS, Moss HA. Employment outcomes among cancer patients in the United States. Cancer Epidemiol. 2022;76:102059. doi:10.1016/j.canep.2021.102059

5. Grabowski DC, Kansal AR, Goldman DP, Lakdawalla DN. Assessment of medical and public assistance expenditures and employment among US adults with cancer diagnoses. JAMA Netw Open. 2023;6(5):e2315823. doi:10.1001/jamanetworkopen.2023.15823

6. Cong Z, Tran O, Nelson J, Silver M, Chung K. Productivity loss and indirect costs for patients newly diagnosed with early- versus late-stage cancer in the USA: a large-scale observational research study. Appl Health Econ Health Policy. 2022;20(6):845-856. doi:10.1007/s40258-022-00753-w

7. McGarvey N, Gitlin M, Fadli E, Chung KC. Increased healthcare costs by later stage cancer diagnosis. BMC Health Serv Res. 2022;22(1):1155. doi:10.1186/s12913-022-08457-6

8. Soni A. Trends in use and expenditures for cancer treatment among adults 18 and older, U.S. civilian noninstitutionalized population, 2001 and 2011. Medical Expenditure Panel Survey statistical brief 443. Agency for Healthcare Research and Quality. June 2014. Accessed August 21, 2024. https://meps.ahrq.gov/data_files/publications/st443/stat443.shtml

9. Farjah F, Wood DE, Varghese TK, Massarweh NN, Symons RG, Flum DR. Health care utilization among surgically treated Medicare beneficiaries with lung cancer. Ann Thorac Surg. 2009;88(6):1749-1756. doi:10.1016/j.athoracsur.2009.08.006

10. Tangirala K, Appukkuttan S, Simmons S. Costs and healthcare resource utilization associated with hospital admissions of patients with metastatic or nonmetastatic prostate cancer. Am Health Drug Benefits. 2019;12(6):306-312.

11. Trogdon JG, Baggett CD, Gogate A, et al. Medical costs associated with metastatic breast cancer in younger, midlife, and older women. Breast Cancer Res Treat. 2020;181(3):653-665. doi:10.1007/s10549-020-05654-x

12. Jaber Chehayeb R, Hood A, Wang X, et al. Treatment sequencing patterns and associated direct medical costs of metastatic breast cancer care in the United States, 2011 to 2021. JAMA Netw Open. 2022;5(11):e2244204. doi:10.1001/jamanetworkopen.2022.44204

13. Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF). Medicaid.gov. Accessed August 19, 2024. https://www.medicaid.gov/medicaid/data-systems/macbis/medicaid-chip-research-files/transformed-medicaid-statistical-information-system-t-msis-analytic-files-taf/index.html

14. Age and cancer risk. National Cancer Institute. Updated May 2, 2025. Accessed August 26, 2025. https://www.cancer.gov/about-cancer/causes-prevention/risk/age

15. Recommendations by grade. US Preventive Services Task Force. Accessed August 26, 2025. https://www.uspreventiveservicestaskforce.org/webview/#!/

16. Gitlin M, McGarvey N, Shivaprakash N, Cong Z. Time duration and health care resource use during cancer diagnoses in the United States: a large claims database analysis. J Manag Care Spec Pharm. 2023;29(6):659-670. doi:10.18553/jmcp.2023.29.6.659

17. Whyte JL, Engel-Nitz NM, Teitelbaum A, Gomez Rey G, Kallich JD. An evaluation of algorithms for identifying metastatic breast, lung, or colorectal cancer in administrative claims data. Med Care. 2015;53(7):e49-e57. doi:10.1097/MLR.0b013e318289c3fb

18. Total Medicaid MCO enrollment. KFF. July 1, 2021. Accessed August 19, 2024. https://www.kff.org/other/state-indicator/total-medicaid-mco-enrollment/

19. Dodd S, Bassi A, Bodger K, Williamson P. A comparison of multivariable regression models to analyse cost data. J Eval Clin Pract. 2006;12(1):76-86. doi:10.1111/j.1365-2753.2006.00610.x

20. DQ Atlas. Medicaid.gov. Accessed August 19, 2024. https://www.medicaid.gov/dq-atlas/welcome

21. Kim U, Koroukian S, Statler A, Rose J. The effect of Medicaid expansion among adults from low-income communities on stage at diagnosis in those with screening-amenable cancers. Cancer. 2020;126(18):4209-4219. doi:10.1002/cncr.32895

22. Wyse A, Meyer BD. Saved by Medicaid: new evidence on health insurance and mortality from the universe of low-income adults. National Bureau of Economic Research working paper 33719. April 2025. doi:10.3386/w33719

23. Qiao EM, Guram K, Kotha NV, et al. Association between primary care use prior to cancer diagnosis and subsequent cancer mortality in the Veterans Affairs health system. JAMA Netw Open. 2022;5(11):e2242048. doi:10.1001/jamanetworkopen.2022.42048

24. Crosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. Science. 2022;375(6586):eaay9040. doi:10.1126/science.aay9040

25. Goddard KAB, Feuer EJ, Mandelblatt JS, et al. Estimation of cancer deaths averted from prevention, screening, and treatment efforts, 1975-2020. JAMA Oncol. 2025;11(2):162-167. doi:10.1001/jamaoncol.2024.5381

26. Philipson TJ, Durie T, Cong Z, Fendrick AM. The aggregate value of cancer screenings in the United States: full potential value and value considering adherence. BMC Health Serv Res. 2023;23(1):829. doi:10.1186/s12913-023-09738-4

27. Homan SG, Yun S, Bouras A, Schmaltz C, Gwanfogbe P, Lucht J. Breast cancer population screening program results in early detection and reduced treatment and health care costs for Medicaid. J Public Health Manag Pract. 2021;27(1):70-79. doi:10.1097/PHH.0000000000001041

28. Wilkinson AN, Mainprize JG, Yaffe MJ, et al. Cost-effectiveness of breast cancer screening using digital mammography in Canada. JAMA Netw Open. 2025;8(1):e2452821. doi:10.1001/jamanetworkopen.2024.52821

29. Hoffman RM, Wolf AMD, Raoof S, et al. Multicancer early detection testing: guidance for primary care discussions with patients. Cancer. 2025;131(7):e35823. doi:10.1002/cncr.35823

30. Shin YE, Kumar A, Guo JJ. Spending, utilization, and price trends for immune checkpoint inhibitors in US Medicaid programs: an empirical analysis from 2011 to 2021. Clin Drug Investig. 2023;43(4):289-298. doi:10.1007/s40261-023-01254-x