Importance: The Patient Protection and Affordable Care Act broadened insurance coverage, partially through voluntary state-based Medicaid expansion.
Objective: To determine whether patients with higher-risk prostate cancer residing in Medicaid expansion states were more likely to receive treatment after expansion compared with patients in states electing not to pursue Medicaid expansion.
Design, Setting, And Participants: This population-based cohort study included 15 332 patients diagnosed with higher-risk prostate cancer (ie, grade group >2; grade group 2 with prostate-specific antigen levels >10 ng/mL; or grade group 1 with prostate-specific antigen levels >20 ng/mL) from January 2010 to December 2016 aged 50 to 64 years who were candidates for definitive treatment. Patients residing in states that partially expanded Medicaid coverage before 2010 (ie, California and Connecticut) and those with diagnosis not confirmed by histology were excluded. Data were collected from the Surveillance, Epidemiology, and End Results Program. Data were analyzed between August and December 2019.
Exposure: State-level Medicaid expansion status.
Main Outcomes And Measures: Insurance status before and after expansion, treatment with prostatectomy or radiation therapy (including brachytherapy), treatment trends over time.
Results: Of 15 332 patients, 7811 (50.9%) lived in expansion states (mean [SD] age, 59.1 [3.8] years; 5532 [71.9%] non-Hispanic White), and 7521 (49.1%) lived in nonexpansion states (mean [SD] age, 59.0 [3.9] years; 3912 [52.1%] non-Hispanic White). Residence in an expansion state was associated with higher pre-expansion levels of Medicaid coverage (292 [8.1%] vs 161 [3.8%]; odds ratio [OR], 2.12; 95% CI, 1.78 to 2.53) and lower likelihood of being uninsured (136 [3.2%] vs 38 [1.1%]; OR, 0.28; 95% CI, 0.15 to 0.54). After expansion, there was no difference in trends in treatment receipt between expansion and nonexpansion states (change, -0.39%; 95% CI, -0.11% to 0.28%; P = .25). Patients with private or Medicare coverage were more likely to receive treatment vs those with Medicaid or no coverage across racial/ethnic groups (eg, Black patients with coverage: OR, 2.30; 95% CI, 1.68 to 3.10; Black patients with no coverage: OR, 1.48; 95% CI, 1.09 to 2.00; P < .001). Medicaid patients were not more likely to be treated compared with those without insurance (737 [78.8%] vs 435 [79.5%]; OR, 0.97; 95% CI, 0.76 to 1.25).
Conclusions And Relevance: In this cohort study, state-level expansion of Medicaid was associated with increased Medicaid coverage for men with higher-risk prostate tumors but did not appear to affect treatment patterns at a population level. This may be related to the finding that Medicaid coverage was not associated with increased treatment rates compared with those without insurance.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.15198 | DOI Listing |
Cureus
January 2025
Clive O. Callender Outcomes Research Center, Howard University College of Medicine, Washington, D.C., USA.
Introduction: Prostate cancer stands as one of the most diagnosed malignancies among men worldwide. With the recent expansion of Medicaid under the Affordable Care Act (ACA), millions more Americans now have health insurance coverage, potentially influencing healthcare access and subsequent outcomes for various illnesses, including prostate cancer. Yet, the direct correlation between Medicaid expansion and cancer-specific survival among individuals with prostate cancer remains an area warranting comprehensive exploration.
View Article and Find Full Text PDFBackground: Historically, access to high-quality care has been a central challenge for Medicaid programs. Prior single-year analyses demonstrated that Medicaid beneficiaries account for disproportionately high patient volumes at low-quality hospitals. Given major Medicaid shifts including expansion and increased managed care, we examined recent trends in low-quality hospital use for Medicaid beneficiaries.
View Article and Find Full Text PDFJAMA Health Forum
January 2025
Department of Internal Medicine, University of Michigan, Ann Arbor.
Importance: The Affordable Care Act (ACA) expanded Medicaid and Marketplace insurance to nonelderly adults in 2014, but whether these policies improved outcomes later in life is unknown.
Objective: To examine whether exposure to ACA expansions during middle age (50-64 years) was associated with changes in health, utilization, and spending after these adults entered Medicare at 65 years of age.
Design, Setting, And Participants: This serial analysis of the Health and Retirement Study cohort linked to Medicare enrollment and claims data from January 1, 2010, to December 31, 2018.
Health Aff Sch
January 2025
The Mullan Institute for Health Workforce Equity, Department of Health Policy and Management, The Milken Institute for Public Health, The George Washington University, 2175K Street, NW, Suite 250, Washington, DC 20037, United States.
Despite the recognized value of Community Health Workers (CHWs) in improving health outcomes, the integration of CHWs into Medicaid continues to be a challenge. This study examines the trends in CHW billing for Medicaid services across states from 2016 to 2020. We conducted an exploratory descriptive analysis of the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) 2016-2020 to identify trends in direct billing for CHW services, including beneficiaries served, total services rendered, payment type, place of service, and procedure codes used for services billed by CHWs.
View Article and Find Full Text PDFDrug Alcohol Depend
January 2025
University of Miami Miller School of Medicine, Department of Public Health Sciences, United States.
Introduction: Prevalence estimates of opioid use disorder (OUD) at local levels are critical for public health planning and surveillance, yet largely unavailable across the US especially at the local county level.
Methods: We used a Bayesian evidence synthesis approach to estimate the prevalence of OUD for 57 counties across New York State for 2017-2019 and compare rates of OUD across counties as well as assess the extent of undiagnosed OUD. We developed a generative model to assess conditional probabilistic relations between different subgroups of the OUD population defined by diagnosis, treatment, and overdose fatality.
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