Medicaid Associated with Improved Composite Risk Factor Control in Diabetes and Cardiovascular Disease

Medicaid Associated with Improved Composite Risk Factor Control in Diabetes and Cardiovascular Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Medicaid Associated with Improved Composite Risk Factor Control in Diabetes and Cardiovascular Disease Richard Bailey, Hridhay Karthikeyan, Caroline Gee, Grace Schoenhoff, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9361524/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background: Atherosclerotic cardiovascular disease, a major contributor to healthcare expenditures, is the leading complication in adults with diabetes mellitus. Composite risk factor control, defined as hemoglobin A1c < 7%, blood pressure < 130/80 mmHg, low density lipoprotein cholesterol < 55 mg/dL, improves atherosclerotic cardiovascular disease outcomes in this population but disparities persist. Methods: Cross-sectional study from 7/1/2023-6/30/2024 utilizing the University of California Health Data Warehouse of 6 health systems. Results: 29,671 patients (mean age 71.0 ± 11.6 years; 37.7% female) were included: 11.4% of patients achieved composite risk factor control, more likely for those on Medicaid (OR 1.13 [1.01-1.26]). Composite risk factor controlwas less likely among Non-Hispanic Black adults (OR 0.62 [0.52-0.74]) and in moderate (OR 0.88 [0.81-0.96]) and most (OR 0.86 [0.78-0.95]) disadvantaged area deprivation index tertiles. Conclusions: Decreased composite risk factor control disproportionately affects disadvantaged groups and racial/ethnic minorities, predisposing to major adverse cardiovascular events. Medicaid offers protective effects and enables crucial preventive care. These findings support policies advocating for sustained Medicaid access to promote health equity, which will decrease health system costs by reducing catastrophic health expenditures related to cardiovascular disease. Diabetes Mellitus Atherosclerotic Cardiovascular Disease Composite Risk Factor Control Health Equity Social Determinants of Health Figures Figure 1 Figure 2 Research Insights What is currently known about this topic? Atherosclerotic cardiovascular disease affects a third of adults with type 2 diabetes mellitus and is the leading cause of morbidity and mortality, accounting for approximately 50% of deaths. Previous studiesshow that composite risk factor control including Hemoglobin A1c <7%, low-density lipoprotein cholesterol <55 mg/dL for patients with both diabetes mellitus and atherosclerotic cardiovascular disease, and blood pressure <130/80 mmHg is strongly associated with Major Adverse Cardiovascular Event risk reduction and improved survival. Patients who achieve composite risk factor control had little to no excess risk of death, myocardial infarction, or stroke as compared to the general population. What is the key research question? Is there a difference in achieving composite risk factor control among adults aged ≥ 18 years with established diabetes mellitus and atherosclerotic cardiovascular disease based on socioeconomic factors, racial/ethnic and insurance factors? What is new? Among Medicaid enrollees with diabetes mellitus and atherosclerotic cardiovascular disease, fewer than one in eight patients in a major academic healthcare system achieve control for HbA1c, LDL-C, and BP. 70.3% were either Non-Hispanic Black or Hispanic, and disparities by race/ethnicity and socioeconomic status exist. However, Medicaid demonstrated increased odds of composite risk factor control. The use of area deprivation index allows quantitative measurement of socioeconomic factors in large health datasets and helps identify health equity disparities. How might this study influence clinical practice? (max. 1 highlight) With millions at risk of becoming uninsured with recent federal legislation cutting Medicaid funding, ongoing advocacy to reduce disparities is important to prevent catastrophic healthcare expenditures due to preventable major adverse cardiovascular events. Background Atherosclerotic cardiovascular disease (ASCVD) affects a third of adults with type 2 diabetes mellitus (T2DM) and is the leading cause of morbidity and mortality, accounting for approximately 50% of deaths 1,2,3 . Evidence from previous studies 4,5,6 shows that composite risk factor control (CRFC) based on American Diabetes Association (ADA) 7,8 and American Heart Association (AHA) 9 guidelines, including glycemic control (HbA1c <7%), low-density lipoprotein cholesterol (LDL-C) <55 mg/dL (for those with both DM and ASCVD), blood pressure (BP) <130/80 mmHg, is strongly associated with Major Adverse Cardiovascular Event (MACE) risk reduction and improved survival. In fact, patients with T2DM who achieve CRFC had little to no excess risk of death, Myocardial Infarction (MI), or stroke as compared to the general population 4,10, . Despite the established significance of CRFC 4,5,6 , estimates remain alarmingly low with less than a quarter of patients with diabetes mellitus (DM) achieving all three goals 1,3,6 . Furthermore, persistent racial, ethnic, and socioeconomic status (SES) disparities exist in MACE and mortality among adults with DM 6,11 . The One Big Beautiful Bill Act modifies federal Medicaid funding estimated by the Congressional Budget Office (CBO) to increase the number of people without health insurance by 7.8 million in 2034 relative to baseline projections under current law 12 , which may significantly worsen existing disparities in CRFC and MACE 13 . We conducted a cross-sectional, multi-institutional analysis in a large university healthcare system to investigate disparities in CRFC among adults with DM and ASCVD, including the potential role of Medicaid insurance in mitigating disparities. Research Design and Methods We conducted a cross-sectional analysis from 7/1/2023-6/30/2024 with the University of California Health Data Warehouse (UCHDW), a dataset containing deidentified electronic health record data from University of California (UC) Health systems (Davis, San Francisco, Los Angeles, Irvine, Riverside, and San Diego). UCHDW spans 18 health professional schools, six medical centers, and 10 hospitals, providing comprehensive data for 8.7 million patients with over 378 million patient encounters and more than 5.2 billion vital sign and test result measurements. Our study utilizes de-identified data qualified as non-human subjects research and was exempt from Institutional Review Board review. Adults aged ≥ 18 with DM (ICD-10: E08-E14) with established ASCVD comorbidity (Prior acute myocardial infarction, acute coronary syndrome, coronary artery disease, cerebrovascular accident, peripheral vascular disease, coronary revascularization or bypass), and at least one measure for HbA1c, LDL-C, blood pressure, and body mass index (BMI) during the study period were included. Area deprivation index (ADI), a standardized score incorporating 17 census-based measures of employment, income, housing, and education from the American Community Survey 14,15 was calculated using baseline demographic data and stratified into tertiles based on published criteria 14 : least disadvantaged (ADI 0-3), middle (ADI 4-6), and most disadvantaged (ADI 7-10) . Data extraction was performed via Structured Query Language (SQL) within Databricks. Univariate analyses between categorical variables were analyzed via Chi-square tests in SAS version 9.4. Multivariate logistic regression and subsequent analyses were performed using Python version 3.12. Results 29,671 adult patients with DM and ASCVD were evaluated, with baseline demographics summarized in Table 1 . The majority were Non-Hispanic (NH) White (46.9%), followed by Hispanic (20.1%), NH Asian/Pacific Islander (15.2%), NH Black (7.2%), and unspecified race (10.6%). Nearly all patients (99.7%) had insurance coverage, including 35.1% privately insured, 43.3% with Medicare, and 20.6% with Medicaid. The mean age was 71.0 ± 11.6 years and 37.7% of the cohort were female. Obesity prevalence was 32.9% with a mean BMI of 28 kg/m 2 . Mean HbA1c was 6.7%, highest among Hispanic patients (7.0 ± 1.7%). 68.3% of patients achieved HbA1c < 7%, 32.8% with LDL-C < 55 mg/dL, and 48% with BP < 130/80 mmHg. Only 11.4% of our total cohort with DM and ASCVD achieved CRFC for all three risk factors. Table 1 Descriptive Statistics of Sample Characteristic Total (n = 29671) NH White (n = 13925) NH Asian or Pacific Islander (n = 4499) NH Black (n = 2137) Hispanic (n = 5962) Other or Unknown (n = 3148) Age, mean (SD), years 71 (11.6) 73 (10.3) 71 (11.9) 68 (12.4) 66 (12.9) 71 (11.1) Age category, No. (%) NA NA NA NA NA NA <40 368 (1.2) 70 (0.5) 52 (1.2) 43 (2.0) 177 (3.0) 26 (0.8) 40–64 7520 (25.3) 2603 (18.7) 1100 (24.4) 744 (34.8) 2354 (39.5) 719 (22.8) ≥65 21783 (73.4) 11252 (80.8) 3347 (74.4) 1350 (63.2) 3431 (57.5) 2403 (76.3) Female sex, No. (%) 11197 (37.7) 4878 (35.0) 1787 (39.7) 1025 (48.0) 2421 (40.6) 1086 (34.5) Area deprivation index score (ADI), mean (SD) 4.7 (2.7) 4.3 (2.7) 4.2 (2.4) 5.8 (2.6) 6.0 (2.5) 4.4 (2.7) Health insurance, No. (%) NA NA NA NA NA NA No Insurance 82 (0.3) 13 (0.1) 16 (0.4) 10 (0.5) 36 (0.6) 7 (0.2) Private 10409 (35.1) 5545 (39.8) 1674 (37.2) 572 (26.8) 1498 (25.1) 1120 (35.6) Medicare 8503 (28.7) 4569 (32.8) 1252 (27.8) 526 (24.6) 1224 (20.5) 932 (29.6) Medicare Advantage 4339 (14.6) 2199 (15.8) 528 (11.7) 349 (16.3) 804 (13.5) 459 (14.6) Medicaid 6118 (20.6) 1487 (10.7) 997 (22.2) 648 (30.3) 2382 (40.0) 604 (19.2) Department of Veteran Affairs 220 (0.7) 112 (0.8) 32 (0.7) 32 (1.5) 18 (0.3) 26 (0.8) Comorbidities, No. (%) NA NA NA NA NA NA Chronic kidney disease (CKD) 8960 (30.2) 3831 (27.5) 1391 (30.9) 812 (38.0) 2103 (35.3) 823 (26.1) Heart failure (HF) 6971 (23.5) 3277 (23.5) 860 (19.1) 668 (31.3) 1502 (25.2) 664 (21.1) Obstructive sleep apnea (OSA) 882 (3.0) 416 (3.0) 131 (2.9) 67 (3.1) 170 (2.9) 98 (3.1) Obesity (BMI ≥ 30 kg/m 2 ) 9772 (32.9) 4996 (35.9) 660 (14.7) 826 (38.7) 2246 (37.7) 1044 (33.2) BMI, mean (SD), kg/m 2 28 (6.3) 29 (6.3) 25 (4.9) 29 (7.3) 29 (6.3) 28 (5.7) Blood pressure, mean (SD), mmHg NA NA NA NA NA NA Systolic 129 (20.5) 128 (19.6) 129 (20.4) 131 (22.5) 130 (21.9) 129 (20.3) Diastolic 71 (12.1) 71 (11.5) 70 (11.5) 74 (13.5) 71 (13.2) 71 (11.6) HbA1c, mean (SD), % 6.7 (1.4) 6.6 (1.3) 6.8 (1.3) 6.8 (1.7) 7 (1.7) 6.8 (1.5) LDL-C, mean (SD), mg/dL 73 (35.1) 73 (34.1) 69 (33.1) 80 (38.0) 72 (36.4) 75 (37.0) Abbreviations : SD Standard Deviation; No. Number; NH Non-Hispanic; BP Blood Pressure; HbA1c Hemoglobin A1c; LDL-C Low Density Lipoprotein Cholesterol CRFC rates by ADI tertile and race/ethnicity are presented in Fig. 1 . Overall mean ADI was 4.7, highest among Hispanics with mean ADI of 6.0. CRFC rates declined progressively across ADI tertiles: 12.6% in the least disadvantaged (ADI 0–3), 11.0% in the middle group (ADI 4–6), and 10.4% in the most disadvantaged group (ADI 7–10). Of note, for both NH Black (7.2% ADI 0–3, 6.7% ADI 4–6) and Hispanic (10.8% ADI 0–3, 10.5% ADI 4–6) participants, the most disadvantaged cohort had slightly higher CRFC rates, and middle ADI tertile actually had the lowest CRFC rates but was not a statistically significant difference. Multivariable logistic regression (Fig. 2 ) identified predictors of CRFC. Medicaid showed statistically significant higher odds of CRFC compared to private insurance (OR 1.13 [1.01–1.26], p = 0.026), as well as male sex (OR 1.45 [1.34–1.57], p < 0.001). Conversely, obesity (BMI ≥ 30 kg/m 2 ) was associated with decreased odds of CRFC (OR 0.85 [0.79–0.93], p < 0.001). Among racial/ethnic groups, NH Black participants demonstrated decreased odds of CRFC (OR 0.62 [0.52–0.74], p < 0.001) compared to NH White participants. Regarding SES, participants in moderate and most disadvantaged ADI tertiles had reduced odds of CRFC compared with least disadvantaged ADI tertile (OR 0.88 [0.81–0.96], p = 0.005 and OR 0.86 [0.78–0.95], p = 0.003, respectively). Discussion This study presents real-world data for adults with DM and ASCVD in a large academic healthcare system. Only 11.4% achieved target levels of HbA1c, LDL-C, and BP. NH Black and Hispanic patients had lowest CRFC rates across all ADI tertiles, lower rates of private insurance and higher rates of Medicaid coverage compared to other groups. Nadir CRFC rates were among middle disadvantaged ADI tertiles for NH Black and Hispanic participants. These findings align with observations from other large-scale datasets which highlights racial and ethnic disparities in CRFC 1 , 3 , 6 , which intersect with SES disadvantage. Among Medicaid enrollees, 70.3% were either NH Black or Hispanic, with our multivariable analysis demonstrating increased odds of CRFC. This highlights Medicaid’s potential role in facilitating health equity and preventive care that reduces disparities in CRFC, which can ultimately reduce MACE outcomes. One possible mechanism for this protective association is access to cardioprotective medications, such as glucagon-like peptide 1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) covered by Medicaid. Study strengths include use of ADI to quantitatively assess the independent effect of SES on health outcomes. The study utilized a large, diverse sample of adults with DM and ASCVD with real-world data from a large academic healthcare system. Limitations include cross-sectional design, which prevents temporal analysis of variables, and reliance on accurate data collection and cataloguing. Further research can include trial emulations for CRFC to prospectively track MACE outcomes, including the impact of changes to Medicaid coverage. Conclusions In summary, our real-world study demonstrates that fewer than one in eight patients with DM and ASCVD in a major academic healthcare system achieve control for HbA1c, LDL-C, and BP. Disparities by race/ethnicity and SES disadvantage exist but are partially mitigated by Medicaid coverage. Medicaid provides critical access to preventative care including newer cardioprotective therapies for racial and ethnic minorities and socioeconomically disadvantaged Americans. With millions at risk of becoming uninsured with recent federal legislation cutting Medicaid funding, ongoing advocacy to reduce these disparities is important to prevent catastrophic healthcare expenditures due to preventable MACE outcomes. Abbreviations ASCVD Atherosclerotic Cardiovascular Disease T2DM Type 2 Diabetes Mellitus CRFC Composite Risk Factor Control ADA American Diabetes Association AHA American Heart Association HbA1c Hemoglobin A1c LDL-C low-density lipoprotein cholesterol BP Blood pressure MACE Major Adverse Cardiovascular Event MI Myocardial Infarction DM Diabetes Mellitus SES Socioeconomic Status CBO Congressional Budget Office UCHDW University of California Health Data Warehouse ADI Area Deprivation Index SQL Structured Query Language Declarations Ethics approval and consent to participate: The information within the UC Health Data warehouse is deidentified patient information which was exempt from institutional review board review. Consent for publication: All authors edited, reviewed, and consented to publication of the final version of the manuscript. Availability of data and materials: The research team will make available upon request to the corresponding author ( [email protected] ), after approval of a proposal for a specified purpose, to anyone who wishes to see the statistical/ analytic code immediately upon publication, with no end date. The datasets generated and/or analysed during the current study are available in the University of California Health Data Warehouse. Requests for participant data must be directed to administrators of this data set. Competing interests: A.N.A - Pfizer, Salix, Alexion, AstraZeneca, Bayer, Ferring, Seres, Spero, Eli Lilly, Nova Nordisk, Gilead, Renibus, GSK, Dexcom, Reprieve. Stock or Stock Options HeartRite, Aseptiscope. N.D.W. - Amgen, Novartis, Ionis, Kaneka, Heart Lung, Regeneron Funding: None Authors' contributions: R.B., H.K, C.G., T.T., M.B., Y.F, K.S., S.S., P.C., Z.Z., G.S., A.A., N.W., Q.Y., were involved in the conception, design, and conduct of the study and the analysis and interpretation of the results. C.G.,G.S. wrote the first draft of the manuscript. Nathan Wong, PhD, MPH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Acknowledgements: Kai Zheng, Timothy Hayes, and David Gonzalez who provided technical assistance for utilization of the UC Health Data Warehouse. Ethics Declaration: This research was conducted in accordance with the Declaration of Helsinki. Authors' information (optional) References Fan W, Song Y, Inzucchi SE, Sperling L, Cannon CP, Arnold SV, et al. 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Health Econ Rev. 2024 Mar 6;14(1):18. doi:10.1186/s13561-024-00486-7 Additional Declarations Competing interest reported. A.N.A - Pfizer, Salix, Alexion, AstraZeneca, Bayer, Ferring, Seres, Spero, Eli Lilly, Nova Nordisk, Gilead, Renibus, GSK, Dexcom, Reprieve. Stock or Stock Options HeartRite, Aseptiscope. N.D.W. - Amgen, Novartis, Ionis, Kaneka, Heart Lung, Regeneron Supplementary Files GraphicalAbstrac1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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23:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9361524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9361524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107619295,"identity":"aa296ea2-a519-4f07-94b1-080aa5c02b19","added_by":"auto","created_at":"2026-04-23 09:27:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":227555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComposite Risk Factor Control by Area Deprivation Index and Race/Ethnicity.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProportion of participants with: (A) CRFC, defined as meeting all three individual targets; (B) Target BP \u0026lt;130/80 mmHg; (C) Target HbA1c \u0026lt;7%; (D) Target LDL-C \u0026lt;55 mg/dL.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/u\u003e: \u003cstrong\u003eADI\u003c/strong\u003e Area deprivation index; \u003cstrong\u003eCRFC\u003c/strong\u003e Composite Risk Factor Control; \u003cstrong\u003eBP\u003c/strong\u003e Blood Pressure; \u003cstrong\u003eHbA1c\u003c/strong\u003e Hemoglobin A1c; \u003cstrong\u003eLDL-C\u003c/strong\u003eLow-Density Lipoprotein Cholesterol; \u003cstrong\u003eNH\u003c/strong\u003e Non-Hispanic; \u003cstrong\u003ePI\u003c/strong\u003e Pacific Islander; \u003cstrong\u003eref\u003c/strong\u003e Indicates subgroup belongs to overall reference category.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.0001. Reference group = NH White.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9361524/v1/23a9990dcfbb1c5be9833c71.png"},{"id":107619297,"identity":"b8e94502-83af-409f-89ce-7ccb956e4203","added_by":"auto","created_at":"2026-04-23 09:27:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335881,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot based on multivariate logistic regression.\u003c/strong\u003e \u003cu\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e ADI \u003c/strong\u003eArea deprivation index; \u003cstrong\u003eASCVD \u003c/strong\u003eAtherosclerotic cardiovascular disease; \u003cstrong\u003eCRFC \u003c/strong\u003eCardiovascular Risk Factor Control; \u003cstrong\u003eDM \u003c/strong\u003eDiabetes mellitus; \u003cstrong\u003eNH \u003c/strong\u003eNon-Hispanic; \u003cstrong\u003eOR \u003c/strong\u003eOdds ratio; \u003cstrong\u003ePI\u003c/strong\u003e Pacific Islander; \u003cstrong\u003eref\u003c/strong\u003e Indicates subgroup belongs to overall reference category.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9361524/v1/3d5aa30e3c01d81a313a0808.png"},{"id":107706877,"identity":"91feb86f-2a89-4b0d-9439-7c8ccf8c0702","added_by":"auto","created_at":"2026-04-24 09:18:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":772703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9361524/v1/a501823a-4efc-405b-b756-a71aa101d572.pdf"},{"id":107619296,"identity":"f3f38729-7858-4c74-864b-1e7fb8039e73","added_by":"auto","created_at":"2026-04-23 09:27:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":639139,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstrac1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9361524/v1/001d10ade74172dc3f1d8965.docx"}],"financialInterests":"Competing interest reported. A.N.A - Pfizer, Salix, Alexion, AstraZeneca, Bayer, Ferring, Seres, Spero, Eli Lilly, Nova Nordisk, Gilead, Renibus, GSK, Dexcom, Reprieve. Stock or Stock Options HeartRite, Aseptiscope.\nN.D.W. - Amgen, Novartis, Ionis, Kaneka, Heart Lung, Regeneron","formattedTitle":"Medicaid Associated with Improved Composite Risk Factor Control in Diabetes and Cardiovascular Disease","fulltext":[{"header":"Research Insights","content":"\u003cul\u003e\n \u003cli\u003eWhat is currently known about this topic?\u003cul\u003e\n \u003cli\u003eAtherosclerotic cardiovascular disease affects a third of adults with type 2 diabetes mellitus and is the leading cause of morbidity and mortality, accounting for approximately 50% of deaths.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePrevious studiesshow that composite risk factor control including Hemoglobin A1c \u0026lt;7%, low-density lipoprotein cholesterol \u0026lt;55 mg/dL for patients with both diabetes mellitus and atherosclerotic cardiovascular disease, and blood pressure \u0026lt;130/80 mmHg is strongly associated with Major Adverse Cardiovascular Event risk reduction and improved survival. Patients who achieve composite risk factor control had little to no excess risk of death, myocardial infarction, or stroke as compared to the general population.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eWhat is the key research question?\u003cul\u003e\n \u003cli\u003eIs there a difference in achieving composite risk factor control among adults aged ≥ 18 years with established diabetes mellitus and atherosclerotic cardiovascular disease based on socioeconomic factors, racial/ethnic and insurance factors?\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eWhat is new?\u003cul\u003e\n \u003cli\u003eAmong Medicaid enrollees with diabetes mellitus and atherosclerotic cardiovascular disease, fewer than one in eight patients in a major academic healthcare system achieve control for HbA1c, LDL-C, and BP. 70.3% were either Non-Hispanic Black or Hispanic, and disparities by race/ethnicity and socioeconomic status exist. However, Medicaid demonstrated increased odds of composite risk factor control.\u003c/li\u003e\n \u003cli\u003eThe use of area deprivation index allows quantitative measurement of socioeconomic factors in large health datasets and helps identify health equity disparities.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eHow might this study influence clinical practice? (max. 1 highlight)\u003cul\u003e\n \u003cli\u003eWith millions at risk of becoming uninsured with recent federal legislation cutting Medicaid funding, ongoing advocacy to reduce disparities is important to prevent catastrophic healthcare expenditures due to preventable major adverse cardiovascular events.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eAtherosclerotic cardiovascular disease (ASCVD) affects a third of adults with type 2 diabetes mellitus (T2DM) and is the leading cause of morbidity and mortality, accounting for approximately 50% of deaths\u003csup\u003e1,2,3\u003c/sup\u003e. Evidence from previous studies\u003csup\u003e4,5,6\u003c/sup\u003e shows that composite risk factor control (CRFC) based on American Diabetes Association (ADA)\u003csup\u003e7,8\u003c/sup\u003e and American Heart Association (AHA)\u003csup\u003e9\u003c/sup\u003e guidelines, including glycemic control (HbA1c \u0026lt;7%), low-density lipoprotein cholesterol (LDL-C) \u0026lt;55 mg/dL (for those with both DM and ASCVD), blood pressure (BP) \u0026lt;130/80 mmHg, is strongly associated with Major Adverse Cardiovascular Event (MACE) risk reduction and improved survival. In fact, patients with T2DM who achieve CRFC had little to no excess risk of death, Myocardial Infarction (MI), or stroke as compared to the general population\u003csup\u003e4,10,\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite the established significance of CRFC\u003csup\u003e4,5,6\u003c/sup\u003e, estimates remain alarmingly low with less than a quarter of patients with diabetes mellitus (DM) achieving all three goals\u003csup\u003e1,3,6\u003c/sup\u003e. Furthermore, persistent racial, ethnic, and socioeconomic status (SES) disparities exist in MACE and mortality among adults with DM\u003csup\u003e6,11\u003c/sup\u003e. The \u003cem\u003eOne Big Beautiful Bill Act\u003c/em\u003e modifies federal Medicaid funding estimated by the Congressional Budget Office (CBO) to increase the number of people without health insurance by 7.8 million in 2034 relative to baseline projections under current law\u003csup\u003e12\u003c/sup\u003e, which may significantly worsen existing disparities in CRFC and MACE\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional, multi-institutional analysis in a large university healthcare system to investigate disparities in CRFC among adults with DM and ASCVD, including the potential role of Medicaid insurance in mitigating disparities.\u003c/p\u003e"},{"header":"Research Design and Methods","content":"\u003cp\u003eWe conducted a cross-sectional analysis from 7/1/2023-6/30/2024 with the University of California Health Data Warehouse (UCHDW), a dataset containing deidentified electronic health record data from University of California (UC) Health systems (Davis, San Francisco, Los Angeles, Irvine, Riverside, and San Diego). UCHDW spans 18 health professional schools, six medical centers, and 10 hospitals, providing comprehensive data for 8.7 million patients with over 378 million patient encounters and more than 5.2 billion vital sign and test result measurements. \u0026nbsp; Our study utilizes de-identified data qualified as non-human subjects research and was exempt from Institutional Review Board review.\u003c/p\u003e\n\u003cp\u003eAdults aged ≥ 18 with DM (ICD-10: E08-E14)\u0026nbsp;with established ASCVD comorbidity (Prior acute myocardial infarction, acute coronary syndrome, coronary artery disease, cerebrovascular accident, peripheral vascular disease, coronary revascularization or bypass), and at least one measure for HbA1c, LDL-C, blood pressure, and body mass index (BMI) during the study period were included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eArea deprivation index (ADI), a standardized score incorporating 17 census-based measures of employment, income, housing, and education from the American Community Survey\u003csup\u003e14,15\u003c/sup\u003e was calculated using baseline demographic data and stratified into tertiles based on published criteria\u003csup\u003e14\u003c/sup\u003e: least disadvantaged (ADI 0-3), middle (ADI 4-6), and most disadvantaged (ADI 7-10)\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Data extraction was performed via Structured Query Language (SQL) within Databricks. Univariate analyses between categorical variables were analyzed via Chi-square tests in SAS version 9.4. Multivariate logistic regression and subsequent analyses were performed using Python version 3.12.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e29,671 adult patients with DM and ASCVD were evaluated, with baseline demographics summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The majority were Non-Hispanic (NH) White (46.9%), followed by Hispanic (20.1%), NH Asian/Pacific Islander (15.2%), NH Black (7.2%), and unspecified race (10.6%). Nearly all patients (99.7%) had insurance coverage, including 35.1% privately insured, 43.3% with Medicare, and 20.6% with Medicaid. The mean age was 71.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6 years and 37.7% of the cohort were female. Obesity prevalence was 32.9% with a mean BMI of 28 kg/m\u003csup\u003e2\u003c/sup\u003e. Mean HbA1c was 6.7%, highest among Hispanic patients (7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7%). 68.3% of patients achieved HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%, 32.8% with LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;55 mg/dL, and 48% with BP\u0026thinsp;\u0026lt;\u0026thinsp;130/80 mmHg. Only 11.4% of our total cohort with DM and ASCVD achieved CRFC for all three risk factors.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics of Sample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;29671)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNH White (n\u0026thinsp;=\u0026thinsp;13925)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNH Asian or Pacific Islander (n\u0026thinsp;=\u0026thinsp;4499)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eNH Black (n\u0026thinsp;=\u0026thinsp;2137)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eHispanic (n\u0026thinsp;=\u0026thinsp;5962)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eOther or Unknown (n\u0026thinsp;=\u0026thinsp;3148)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge, mean (SD), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e71 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e73 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e71 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e68 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e66 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e71 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge category, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e368 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e70 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e52 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e43 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e177 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e26 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e40\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7520 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2603 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1100 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e744 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2354 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e719 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21783 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11252 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3347 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1350 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3431 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2403 (76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale sex, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11197 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4878 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1787 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1025 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2421 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1086 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eArea deprivation index score (ADI), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.7 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.2 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5.8 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.0 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.4 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHealth insurance, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e82 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e36 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10409 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5545 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1674 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e572 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1498 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1120 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8503 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4569 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1252 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e526 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1224 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e932 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedicare Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4339 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2199 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e528 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e349 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e804 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e459 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6118 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1487 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e997 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e648 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2382 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e604 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDepartment of Veteran Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e220 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e112 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e32 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e32 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e18 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e26 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eComorbidities, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChronic kidney disease (CKD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8960 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3831 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1391 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e812 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2103 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e823 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeart failure (HF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6971 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3277 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e860 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e668 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1502 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e664 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eObstructive sleep apnea (OSA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e882 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e416 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e131 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e67 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e170 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e98 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eObesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9772 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4996 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e660 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e826 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2246 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1044 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI, mean (SD), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e28 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e29 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e25 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e29 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e29 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e28 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBlood pressure, mean (SD), mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSystolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e129 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e128 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e129 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e131 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e130 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e129 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiastolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e71 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e71 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e70 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e74 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e71 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e71 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHbA1c, mean (SD), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.7 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.6 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.8 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.8 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.8 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLDL-C, mean (SD), mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e73 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e73 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e69 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e80 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e72 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e75 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAbbreviations\u003c/span\u003e: \u003cstrong\u003eSD\u003c/strong\u003e Standard Deviation; \u003cstrong\u003eNo.\u003c/strong\u003e Number; \u003cstrong\u003eNH\u003c/strong\u003e Non-Hispanic; \u003cstrong\u003eBP\u003c/strong\u003e Blood Pressure; \u003cstrong\u003eHbA1c\u003c/strong\u003e Hemoglobin A1c; \u003cstrong\u003eLDL-C\u003c/strong\u003e Low Density Lipoprotein Cholesterol\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eCRFC rates by ADI tertile and race/ethnicity are presented in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall mean ADI was 4.7, highest among Hispanics with mean ADI of 6.0. CRFC rates declined progressively across ADI tertiles: 12.6% in the least disadvantaged (ADI 0\u0026ndash;3), 11.0% in the middle group (ADI 4\u0026ndash;6), and 10.4% in the most disadvantaged group (ADI 7\u0026ndash;10). Of note, for both NH Black (7.2% ADI 0\u0026ndash;3, 6.7% ADI 4\u0026ndash;6) and Hispanic (10.8% ADI 0\u0026ndash;3, 10.5% ADI 4\u0026ndash;6) participants, the most disadvantaged cohort had slightly higher CRFC rates, and middle ADI tertile actually had the lowest CRFC rates but was not a statistically significant difference.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified predictors of CRFC. Medicaid showed statistically significant higher odds of CRFC compared to private insurance (OR 1.13 [1.01\u0026ndash;1.26], p\u0026thinsp;=\u0026thinsp;0.026), as well as male sex (OR 1.45 [1.34\u0026ndash;1.57], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) was associated with decreased odds of CRFC (OR 0.85 [0.79\u0026ndash;0.93], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among racial/ethnic groups, NH Black participants demonstrated decreased odds of CRFC (OR 0.62 [0.52\u0026ndash;0.74], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to NH White participants. Regarding SES, participants in moderate and most disadvantaged ADI tertiles had reduced odds of CRFC compared with least disadvantaged ADI tertile (OR 0.88 [0.81\u0026ndash;0.96], p\u0026thinsp;=\u0026thinsp;0.005 and OR 0.86 [0.78\u0026ndash;0.95], p\u0026thinsp;=\u0026thinsp;0.003, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents real-world data for adults with DM and ASCVD in a large academic healthcare system. Only 11.4% achieved target levels of HbA1c, LDL-C, and BP. NH Black and Hispanic patients had lowest CRFC rates across all ADI tertiles, lower rates of private insurance and higher rates of Medicaid coverage compared to other groups. Nadir CRFC rates were among middle disadvantaged ADI tertiles for NH Black and Hispanic participants. These findings align with observations from other large-scale datasets which highlights racial and ethnic disparities in CRFC\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, which intersect with SES disadvantage.\u003c/p\u003e \u003cp\u003eAmong Medicaid enrollees, 70.3% were either NH Black or Hispanic, with our multivariable analysis demonstrating increased odds of CRFC. This highlights Medicaid\u0026rsquo;s potential role in facilitating health equity and preventive care that reduces disparities in CRFC, which can ultimately reduce MACE outcomes. One possible mechanism for this protective association is access to cardioprotective medications, such as glucagon-like peptide 1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) covered by Medicaid.\u003c/p\u003e \u003cp\u003eStudy strengths include use of ADI to quantitatively assess the independent effect of SES on health outcomes. The study utilized a large, diverse sample of adults with DM and ASCVD with real-world data from a large academic healthcare system. Limitations include cross-sectional design, which prevents temporal analysis of variables, and reliance on accurate data collection and cataloguing. Further research can include trial emulations for CRFC to prospectively track MACE outcomes, including the impact of changes to Medicaid coverage.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our real-world study demonstrates that fewer than one in eight patients with DM and ASCVD in a major academic healthcare system achieve control for HbA1c, LDL-C, and BP. Disparities by race/ethnicity and SES disadvantage exist but are partially mitigated by Medicaid coverage. Medicaid provides critical access to preventative care including newer cardioprotective therapies for racial and ethnic minorities and socioeconomically disadvantaged Americans. With millions at risk of becoming uninsured with recent federal legislation cutting Medicaid funding, ongoing advocacy to reduce these disparities is important to prevent catastrophic healthcare expenditures due to preventable MACE outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtherosclerotic Cardiovascular Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComposite Risk Factor Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Diabetes Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin A1c\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor Adverse Cardiovascular Event\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyocardial Infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocioeconomic Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCongressional Budget Office\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCHDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversity of California Health Data Warehouse\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Deprivation Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSQL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructured Query Language\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe information within the UC Health Data warehouse is deidentified patient information which was exempt from institutional review board review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors edited, reviewed, and consented to publication of the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research team will make available upon request to the corresponding author ([email protected]), after approval of a proposal for a specified purpose, to anyone who wishes to see the statistical/ analytic code immediately upon publication, with no end date. The datasets generated and/or analysed during the current study are available in the University of California Health Data Warehouse. Requests for participant data must be directed to administrators of this data set.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.N.A - Pfizer, Salix, Alexion, AstraZeneca, Bayer, Ferring, Seres, Spero, Eli Lilly, Nova Nordisk, Gilead, Renibus, GSK, Dexcom,\u0026nbsp;Reprieve. Stock or Stock Options HeartRite, Aseptiscope.\u003c/p\u003e\n\u003cp\u003eN.D.W. - Amgen, Novartis, Ionis, Kaneka, Heart Lung, Regeneron\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.B., H.K, C.G., T.T., M.B., Y.F, K.S., S.S., P.C., Z.Z., G.S., A.A., N.W., Q.Y., were involved in the conception, design, and conduct of the study and the analysis and interpretation of the results. C.G.,G.S. wrote the first draft of the manuscript. Nathan Wong, PhD, MPH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKai Zheng, Timothy Hayes, and David Gonzalez who provided technical assistance for utilization of the UC Health Data Warehouse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFan W, Song Y, Inzucchi SE, Sperling L, Cannon CP, Arnold SV, et al. Composite cardiovascular risk factor target achievement and its predictors in US adults with diabetes: The Diabetes Collaborative Registry [Internet]. doi:10.1111/dom.13625\u003c/li\u003e\n \u003cli\u003eTrends and Disparities in Cardiovascular Mortality Among U.S. Adults With and Without Self-Reported Diabetes, 1988\u0026ndash;2015 | Diabetes Care | American Diabetes Association [Internet]. [cited 2026 Mar 17]. Available from: https://diabetesjournals.org/care/article-abstract/41/11/2306/36539/Trends-and-Disparities-in-Cardiovascular-Mortality?redirectedFrom=fulltext\u003c/li\u003e\n \u003cli\u003eGolden F, Tran J, Wong ND. Composite cardiovascular risk factor control in US adults with diabetes and relation to social determinants of health: The \u003cem\u003eAll of Us\u003c/em\u003e research program. American Journal of Preventive Cardiology. 2025 Mar 1;21:100939. doi:10.1016/j.ajpc.2025.100939\u003c/li\u003e\n \u003cli\u003eBittner V, Bertolet M, Barraza Felix R, Farkouh ME, Goldberg S, Ramanathan KB, et al. Comprehensive Cardiovascular Risk Factor\u0026nbsp;Control\u0026nbsp;Improves Survival. JACC. 2015 Aug 18;66(7):765\u0026ndash;73. doi:10.1016/j.jacc.2015.06.019\u003c/li\u003e\n \u003cli\u003eEffect of a Multifactorial Intervention on Mortality in Type 2 Diabetes | New England Journal of Medicine [Internet]. [cited 2026 Mar 17]. Available from: https://www.nejm.org/doi/full/10.1056/NEJMoa0706245\u003c/li\u003e\n \u003cli\u003eCardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study | Diabetes Care | American Diabetes Association [Internet]. [cited 2026 Mar 17]. Available from: https://diabetesjournals.org/care/article-abstract/39/5/668/30791/Cardiovascular-Risk-Factor-Targets-and?redirectedFrom=fulltext\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 6. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes\u0026mdash;2024. Diabetes Care. 2023 Dec 11;47(Supplement_1):S111\u0026ndash;25. doi:10.2337/dc24-S006\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 10. Cardiovascular Disease and Risk Management: Standards of Care in Diabetes\u0026mdash;2025. Diabetes Care. 2024 Dec 9;48(Supplement_1):S207\u0026ndash;38. doi:10.2337/dc25-S010\u003c/li\u003e\n \u003cli\u003eArnold SV, Bhatt DL, Barsness GW, Beatty AL, Deedwania PC, Inzucchi SE, et al. Clinical Management of Stable Coronary Artery Disease in Patients With Type 2 Diabetes Mellitus: A Scientific Statement From the American Heart Association. Circulation. 2020 May 12;141(19):e779\u0026ndash;806. doi:10.1161/CIR.0000000000000766\u003c/li\u003e\n \u003cli\u003eRawshani A, Rawshani A, Franz\u0026eacute;n S, Sattar N, Eliasson B, Svensson AM, et al. Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes. New England Journal of Medicine. 2018 Aug 16;379(7):633\u0026ndash;44. doi:10.1056/NEJMoa1800256\u003c/li\u003e\n \u003cli\u003eBerman AN, Biery DW, Ginder C, Singh A, Baek J, Wadhera RK, et al. Association of Socioeconomic Disadvantage With Long-term Mortality After Myocardial Infarction: The Mass General Brigham YOUNG-MI Registry. JAMA Cardiol. 2021 Aug 1;6(8):880\u0026ndash;8. doi:10.1001/jamacardio.2021.0487\u003c/li\u003e\n \u003cli\u003eVintimilla, R., Seyedahmadi, A., Hall, J., Johnson, L., \u0026amp; O\u0026rsquo;Bryant, S. (2023). Association of Area Deprivation Index and hypertension, diabetes, dyslipidemia, and Obesity: A Cross-Sectional Study of the HABS-HD Cohort. Gerontology and Geriatric Medicine, 9, 23337214231182240. https://doi.org/10.1177/23337214231182240\u003c/li\u003e\n \u003cli\u003eDonohue JM, Cole ES, James CV, Jarlenski M, Michener JD, Roberts ET. The US Medicaid Program: Coverage, Financing, Reforms, and Implications for Health Equity. JAMA. 2022 Sep 20;328(11):1085\u0026ndash;99. doi:10.1001/jama.2022.14791\u003c/li\u003e\n \u003cli\u003eInformation Concerning Medicaid-Related Provisions in Title IV of H.R. 1 | Congressional Budget Office [Internet]. 2025 [cited 2026 Mar 17]. Available from: https://www.cbo.gov/publication/61510\u003c/li\u003e\n \u003cli\u003eLinde S, Egede LE. Catastrophic health expenditures: a disproportionate risk in uninsured ethnic minorities with diabetes. Health Econ Rev. 2024 Mar 6;14(1):18. doi:10.1186/s13561-024-00486-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"cardiovascular-diabetology-endocrinology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Cardiovascular Diabetology – Endocrinology Reports","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Diabetes Mellitus, Atherosclerotic Cardiovascular Disease, Composite Risk Factor Control, Health Equity, Social Determinants of Health ","lastPublishedDoi":"10.21203/rs.3.rs-9361524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9361524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Atherosclerotic cardiovascular disease, a major contributor to healthcare\u003c/p\u003e\n\u003cp\u003eexpenditures, is the leading complication in adults with diabetes mellitus. Composite risk factor\u003c/p\u003e\n\u003cp\u003econtrol, defined as hemoglobin A1c \u0026lt; 7%, blood pressure \u0026lt; 130/80 mmHg, low density lipoprotein cholesterol \u0026lt; 55 mg/dL, improves atherosclerotic cardiovascular disease outcomes in this population but disparities persist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eCross-sectional study from 7/1/2023-6/30/2024 utilizing the University of California Health Data Warehouse of 6 health systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e29,671 patients (mean age 71.0 ± 11.6 years; 37.7% female) were included: 11.4% of patients achieved composite risk factor control, more likely for those on Medicaid (OR 1.13 [1.01-1.26]). Composite risk factor controlwas less likely among Non-Hispanic Black adults (OR 0.62 [0.52-0.74]) and in moderate (OR 0.88 [0.81-0.96]) and most (OR 0.86 [0.78-0.95]) disadvantaged area deprivation index tertiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eDecreased composite risk factor control disproportionately affects disadvantaged groups and racial/ethnic minorities, predisposing to major adverse cardiovascular events. Medicaid offers protective effects and enables crucial preventive care. These findings support policies advocating for sustained Medicaid access to promote health equity, which will decrease health system costs by reducing catastrophic health expenditures related to cardiovascular disease.\u003c/p\u003e","manuscriptTitle":"Medicaid Associated with Improved Composite Risk Factor Control in Diabetes and Cardiovascular Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:27:43","doi":"10.21203/rs.3.rs-9361524/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T18:47:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T18:31:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T21:23:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159371420711957456278651650091905629562","date":"2026-04-16T20:09:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29488452355826118378884306243078064520","date":"2026-04-16T14:15:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294404346114255023617550096535742809006","date":"2026-04-15T13:33:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T12:18:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T11:11:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T11:11:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology – Endocrinology Reports","date":"2026-04-08T23:45:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"cardiovascular-diabetology-endocrinology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Cardiovascular Diabetology – Endocrinology Reports","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7e38b226-c06c-4ba8-8755-5662dc133c20","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T18:47:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T18:31:11+00:00","index":70,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T21:23:15+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T18:54:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:27:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9361524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9361524","identity":"rs-9361524","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}