Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Gerber Y, Weston SA, Killian JM, Therneau TM, Jacobsen SJ, Roger VL. Neighborhood income and individual education: effect on survival after myocardial infarction. Mayo Clin Proc 2008;83:663-9. [PMID: 18533083 PMCID: PMC2650487 DOI: 10.4065/83.6.663] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]

For:	Gerber Y, Weston SA, Killian JM, Therneau TM, Jacobsen SJ, Roger VL. Neighborhood income and individual education: effect on survival after myocardial infarction. Mayo Clin Proc 2008;83:663-9. [PMID: 18533083 PMCID: PMC2650487 DOI: 10.4065/83.6.663] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]

Number

Cited by Other Article(s)

Gaona GA, Kassamali AA, Isakadze N, Martin SS. Harnessing Wearables and Digital Technologies to Decode the Cardiovascular Exposome. Methodist Debakey Cardiovasc J 2024;20:59-70. [PMID: 39525376 PMCID: PMC11545923 DOI: 10.14797/mdcvj.1459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Accepted: 08/29/2024] [Indexed: 11/16/2024] Open

Khan SU. Zip Code Health Disparities: Mapping Cardiovascular Inequities at the Neighborhood Level. Methodist Debakey Cardiovasc J 2024;20:6-14. [PMID: 39525373 PMCID: PMC11546200 DOI: 10.14797/mdcvj.1457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Accepted: 08/29/2024] [Indexed: 11/16/2024] Open

Brown L, Cambron C, Post WS, Brandt EJ. The Role of Social Determinants of Health in Atherosclerotic Cardiovascular Disease. Curr Atheroscler Rep 2024;26:451-461. [PMID: 38980573 PMCID: PMC11974476 DOI: 10.1007/s11883-024-01226-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2024] [Indexed: 07/10/2024]

Akioyamen LE, Abdel-Qadir H, Han L, Sud M, Mistry N, Alter DA, Atzema CL, Austin PC, Bhatia RS, Booth GL, Dhalla I, Ha ACT, Jackevicius CA, Kapral MK, Krumholz HM, Lee DS, McNaughton CD, Roifman I, Schull MJ, Sivaswamy A, Tu K, Udell JA, Wijeysundera HC, Ko DT. Association of Neighborhood-Level Marginalization With Health Care Use and Clinical Outcomes Following Hospital Discharge in Patients Who Underwent Coronary Catheterization for Acute Myocardial Infarction in a Single-Payer Health Care System. Circ Cardiovasc Qual Outcomes 2023;16:e010063. [PMID: 38050754 DOI: 10.1161/circoutcomes.123.010063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 10/06/2023] [Indexed: 12/06/2023]

Abstract

BACKGROUND

Canadian data suggest that patients of lower socioeconomic status with acute myocardial infarction receive less beneficial therapy and have worse clinical outcomes, raising questions regarding care disparities even in universal health care systems. We assessed the contemporary association of marginalization with clinical outcomes and health services use.

METHODS

Using clinical and administrative databases in Ontario, Canada, we conducted a population-based study of patients aged ≥65 years hospitalized for their first acute myocardial infarction between April 1, 2010 and March 1, 2019. Patients receiving cardiac catheterization and surviving 7 days postdischarge were included. Our primary exposure was neighborhood-level marginalization, a multidimensional socioeconomic status metric. Neighborhoods were categorized by quintile from Q1 (least marginalized) to Q5 (most marginalized). Our primary outcome was all-cause mortality. A proportional hazards regression model with a robust variance estimator was used to quantify the association of marginalization with outcomes, adjusting for risk factors, comorbidities, disease severity, and regional cardiologist supply.

RESULTS

Among 53 841 patients (median age, 75 years; 39.1% female) from 20 640 neighborhoods, crude 1- and 3-year mortality rates were 7.7% and 17.2%, respectively. Patients in Q5 had no significant difference in 1-year mortality (hazard ratio [HR], 1.08 [95% CI, 0.95-1.22]), but greater mortality over 3 years (HR, 1.13 [95% CI, 1.03-1.22]) compared with Q1. Over 1 year, we observed differences between Q1 and Q5 in visits to primary care physicians (Q1, 96.7%; Q5, 93.7%) and cardiologists (Q1, 82.6%; Q5, 72.6%), as well as diagnostic testing. There were no differences in secondary prevention medications dispensed or medication adherence at 1 year.

CONCLUSIONS

In older patients with acute myocardial infarction who survived to hospital discharge, those residing in the most marginalized neighborhoods had a greater long-term risk of mortality, less specialist care, and fewer diagnostic tests. Yet, there were no differences across socioeconomic status in prescription medication use and adherence.

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Affiliation(s)

Leo E Akioyamen Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada
Husam Abdel-Qadir Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.) Women's College Hospital, Toronto, ON, Canada (H.A.-Q., J.A.U.)
Lu Han ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.)
Maneesh Sud Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.)
Nikhil Mistry ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.)
David A Alter Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.)
Clare L Atzema Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.)
Peter C Austin Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada
R Sacha Bhatia Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.)
Gillian L Booth Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, Ontario, Canada (G.L.B., I.R.,)
Irfan Dhalla Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada
Andrew C T Ha Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.)
Cynthia A Jackevicius Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Western University of Health Sciences, Pomona, CA (C.A.J.)
Moira K Kapral Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.)
Harlan M Krumholz Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (H.M.K.) Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (H.M.K.) Department of Health Policy and Management, Yale School of Public Health, New Haven, CT (H.M.K.)
Douglas S Lee Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.)
Candace D McNaughton Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.)
Idan Roifman Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.) Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, Ontario, Canada (G.L.B., I.R.,)
Michael J Schull Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.)
Atul Sivaswamy ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.)
Karen Tu Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Department of Family and Community Medicine, (K.T.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) North York General Hospital, Toronto, ON, Canada (K.T.)
Jacob A Udell Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) University Health Network, Toronto, ON, Canada (H.A.-Q., D.A.A., R.S.B., A.C.T.H., M.K.K., D.S.L., J.A.U.) Women's College Hospital, Toronto, ON, Canada (H.A.-Q., J.A.U.)
Harindra C Wijeysundera Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.)
Dennis T Ko Department of Medicine (L.E.A., H.A.-Q., D.A.A., C.L.A., R.S.B., I.D., A.C.T.H., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada Institute of Health Policy, Management, and Evaluation (H.A.-Q., M.S., D.A.A., C.L.A., P.C.A., G.L.B., I.D., C.A.J., M.K.K., D.S.L., I.R., M.J.S., K.T., J.A.U., H.C.W., D.T.K.), University of Toronto, ON, Canada ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada (H.A.-Q., L.H., M.S., N.M., D.A.A., C.L.A., P.C.A., G.L.B., C.A.J., M.K.K., D.S.L., C.D.M., I.R., M.J.S., A.S., K.T., J.A.U., H.C.W., D.T.K.) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (M.S., C.L.A., C.D.M., I.R., M.J.S., H.C.W., D.T.K.)

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Muthalaly RG, Nelson AJ, Baradi A, Mehta OH, Wilson AM, Nasis A. Socioeconomic determinants of health, traditional risk factors and cardiovascular outcomes in Australia. INTERNATIONAL JOURNAL OF CARDIOLOGY. CARDIOVASCULAR RISK AND PREVENTION 2023;17:200184. [PMID: 37131952 PMCID: PMC10149219 DOI: 10.1016/j.ijcrp.2023.200184] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/04/2023] [Accepted: 03/28/2023] [Indexed: 05/04/2023]

Abstract

Background

Cardiovascular disease burden is decreasing, but these reductions have not been distributed equally amongst socioeconomic groups.

Objectives

The aim of this study was to define the relationships between different domains of socioeconomic health, traditional cardiovascular risk factors and cardiovascular events.

Methods

This was a cross-sectional study of local government areas (LGAs) in Victoria, Australia. We used data from a population health survey combined with cardiovascular event data derived from hospital and government data. Four socioeconomic domains: educational attainment, financial wellbeing, remoteness, and psychosocial health, were generated from 22 variables. The primary outcome was a composite of non-STEMI, STEMI, heart failure and cardiovascular deaths per 10,000 persons. Linear regression and cluster analysis were used to assess the relationships between risk factors and events.

Results

Across 79 LGAs there were 33,654 interviews conducted. All socioeconomic domains were associated with burden of traditional risk factors, including hypertension, smoking, poor diet, diabetes, and obesity. Financial wellbeing, educational attainment and remoteness were all correlated with cardiovascular events on univariate analysis. After multivariate adjustment for age and sex, financial wellbeing, psychosocial wellbeing, and remoteness were associated with cardiovascular events, while educational attainment was not. After including traditional risk factors only financial wellbeing and remoteness remained correlated with cardiovascular events.

Conclusions

Financial wellbeing and remoteness independently be associated with cardiovascular events, while educational attainment and psychosocial wellbeing are attenuated by traditional cardiovascular risk factors. Poor socioeconomic health is clustered in certain areas, which have high cardiovascular event rates.

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Bullock-Palmer RP, Bravo-Jaimes K, Mamas MA, Grines CL. Socioeconomic Factors and their Impact on Access and Use of Coronary and Structural Interventions. Eur Cardiol 2022;17:e19. [PMID: 36643068 PMCID: PMC9820075 DOI: 10.15420/ecr.2022.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 06/28/2022] [Indexed: 01/18/2023] Open

Lee GB, Kim HC, Jeon YJ, Jung SJ. Association between socioeconomic status and longitudinal sleep quality patterns mediated by depressive symptoms. Sleep 2021;44:zsab044. [PMID: 33630996 PMCID: PMC8361348 DOI: 10.1093/sleep/zsab044] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 01/21/2021] [Indexed: 01/28/2023] Open

Assari S, Boyce S. Resting-State Functional Connectivity between Putamen and Salience Network and Childhood Body Mass Index. Neurol Int 2021;13:85-101. [PMID: 33806587 PMCID: PMC8006001 DOI: 10.3390/neurolint13010009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/19/2021] [Accepted: 01/22/2021] [Indexed: 12/16/2022] Open

Abstract

INTRODUCTION

Although the putamen has a significant role in reward-seeking and motivated behaviors, including eating and food-seeking, minorities' diminished returns (MDRs) suggest that individual-level risk and protective factors have weaker effects for Non-Hispanic Black than Non-Hispanic White individuals. However, limited research is available on the relevance of MDRs in terms of the role of putamen functional connectivity on body mass index (BMI).

PURPOSE

Building on the MDRs framework and conceptualizing race and socioeconomic status (SES) indicators as social constructs, we explored racial and SES differences in the associations between putamen functional connectivity to the salience network and children's BMI.

METHODS

For this cross-sectional study, we used functional magnetic resonance imaging (fMRI) data of 6473 9-10-year-old Non-Hispanic Black and Non-Hispanic White children from the Adolescent Brain Cognitive Development (ABCD) study. The primary independent variable was putamen functional connectivity to the salience network, measured by fMRI. The primary outcome was the children's BMI. Age, sex, neighborhood income, and family structure were the covariates. Race, family structure, parental education, and household income were potential moderators. For data analysis, we used mixed-effect models in the overall sample and by race.

RESULTS

Higher right putamen functional connectivity to the salience network was associated with higher BMI in Non-Hispanic White children. The same association was missing for Non-Hispanic Black children. While there was no overall association in the pooled sample, a significant interaction was found, suggesting that the association between right putamen functional connectivity to the salience network and children's BMI was modified by race. Compared to Non-Hispanic White children, Non-Hispanic Black children showed a weaker association between right putamen functional connectivity to the salience network and BMI. While parental education and household income did not moderate our association of interest, marital status altered the associations between putamen functional connectivity to the salience network and children's BMI. These patterns were observed for right but not left putamen. Other/Mixed Race children also showed a pattern similar to Non-Hispanic Black children.

CONCLUSIONS

The association between right putamen functional connectivity to the salience network and children's BMI may depend on race and marital status but not parental education and household income. While right putamen functional connectivity to the salience network is associated with Non-Hispanic White children's BMI, Non-Hispanic Black children' BMI remains high regardless of their putamen functional connectivity to the salience network. This finding is in line with MDRs, which attributes diminished effects of individual-risk and protective factors for Non-Hispanic Black children to racism, stratification, and segregation.

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Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach. BMC Public Health 2020;20:1666. [PMID: 33160324 PMCID: PMC7648288 DOI: 10.1186/s12889-020-09766-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/26/2020] [Indexed: 12/31/2022] Open

Abstract

Background

Stroke is a chronic cardiovascular disease that puts major stresses on U.S. health and economy.

The prevalence of stroke exhibits a strong geographical pattern at the state-level, where a cluster of southern states with a substantially higher prevalence of stroke has been called the stroke belt of the nation. Despite this recognition, the extent to which key neighborhood characteristics affect stroke prevalence remains to be further clarified.

Methods

We generated a new neighborhood health data set at the census tract level on nearly 27,000 tracts by pooling information from multiple data sources including the CDC’s 500 Cities Project 2017 data release. We employed a two-stage modeling approach to understand how key neighborhood-level risk factors affect the neighborhood-level stroke prevalence in each state of the US. The first stage used a state-of-the-art Bayesian machine learning algorithm to identify key neighborhood-level determinants. The second stage applied a Bayesian multilevel modeling approach to describe how these key determinants explain the variability in stroke prevalence in each state.

Results

Neighborhoods with a larger proportion of older adults and non-Hispanic blacks were associated with neighborhoods with a higher prevalence of stroke. Higher median household income was linked to lower stroke prevalence. Ozone was found to be positively associated with stroke prevalence in 10 states, while negatively associated with stroke in five states. There was substantial variation in both the direction and magnitude of the associations between these four key factors with stroke prevalence across the states.

Conclusions

When used in a principled variable selection framework, high-performance machine learning can identify key factors of neighborhood-level prevalence of stroke from wide-ranging information in a data-driven way. The Bayesian multilevel modeling approach provides a detailed view of the impact of key factors across the states. The identified major factors and their effect mechanisms can potentially aid policy makers in developing area-based stroke prevention strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-020-09766-3.

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Achieving Health Equity in Hypertension Management Through Addressing the Social Determinants of Health. Curr Hypertens Rep 2019;21:58. [PMID: 31190099 DOI: 10.1007/s11906-019-0962-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]

Medhekar AN, Adhikari S, Abdul-Al AS, Matinrazm S, Kancharla K, Bhonsale A, Jain SK, Saba S. Lower socioeconomic status is associated with increased long-term mortality after sudden cardiac arrest. Clin Cardiol 2019;42:735-740. [PMID: 31165498 PMCID: PMC6671832 DOI: 10.1002/clc.23211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 05/24/2019] [Indexed: 11/29/2022] Open

Al-Zakwani I, M Mabry R, Zubaid M, Alsheikh-Ali AA, Almahmeed W, Shehab A, Rashed W. Association between education and major adverse cardiac events among patients with acute coronary syndrome in the Arabian Gulf. BMJ Glob Health 2019;4:e001278. [PMID: 30687526 PMCID: PMC6326284 DOI: 10.1136/bmjgh-2018-001278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 11/24/2018] [Accepted: 12/03/2018] [Indexed: 01/19/2023] Open

Schultz WM, Kelli HM, Lisko JC, Varghese T, Shen J, Sandesara P, Quyyumi AA, Taylor HA, Gulati M, Harold JG, Mieres JH, Ferdinand KC, Mensah GA, Sperling LS. Socioeconomic Status and Cardiovascular Outcomes: Challenges and Interventions. Circulation 2018;137:2166-2178. [PMID: 29760227 PMCID: PMC5958918 DOI: 10.1161/circulationaha.117.029652] [Citation(s) in RCA: 892] [Impact Index Per Article: 127.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]

Abstract

Socioeconomic status (SES) has a measurable and significant effect on cardiovascular health. Biological, behavioral, and psychosocial risk factors prevalent in disadvantaged individuals accentuate the link between SES and cardiovascular disease (CVD). Four measures have been consistently associated with CVD in high-income countries: income level, educational attainment, employment status, and neighborhood socioeconomic factors. In addition, disparities based on sex have been shown in several studies. Interventions targeting patients with low SES have predominantly focused on modification of traditional CVD risk factors. Promising approaches are emerging that can be implemented on an individual, community, or population basis to reduce disparities in outcomes. Structured physical activity has demonstrated effectiveness in low-SES populations, and geomapping may be used to identify targets for large-scale programs. Task shifting, the redistribution of healthcare management from physician to nonphysician providers in an effort to improve access to health care, may have a role in select areas. Integration of SES into the traditional CVD risk prediction models may allow improved management of individuals with high risk, but cultural and regional differences in SES make generalized implementation challenging. Future research is required to better understand the underlying mechanisms of CVD risk that affect individuals of low SES and to determine effective interventions for patients with high risk. We review the current state of knowledge on the impact of SES on the incidence, treatment, and outcomes of CVD in high-income societies and suggest future research directions aimed at the elimination of these adverse factors, and the integration of measures of SES into the customization of cardiovascular treatment.

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Knighton AJ. Is a Patient's Current Address of Record a Reasonable Measure of Neighborhood Deprivation Exposure? A Case for the Use of Point in Time Measures of Residence in Clinical Care. Health Equity 2018;2:62-69. [PMID: 30283850 PMCID: PMC6071897 DOI: 10.1089/heq.2017.0005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open

Abstract

Purpose: Interest is increasing in the use of geocoded patient address data to understand the effects that social determinants of health have on healthcare outcomes. Use of a patient's current address of record is often problematic given population mobility. Intragenerational economic mobility research suggests that patients will reside within neighborhoods with similar relative deprivation over time despite geographic mobility. The purpose of this study was to measure evidence of patient neighborhood deprivation persistence given a change in address of record.

Methods: A retrospective cohort study of patients receiving active care in an integrated delivery system in a high-mobility United States region. Neighborhood deprivation was measured using a block-group level area deprivation index. Neighborhood deprivation persistence was measured as the probability that an individual with an address of record change remained within a neighborhood with a similar deprivation score. Logistic regression was used to conduct multivariate analysis.

Results: Geographic mobility was highest among patients living in the most deprived neighborhoods versus least-deprived (odds ratio 1.75; 95% confidence interval: 1.71–1.79). Seventy-eight percent of all patients with a change of address did so to a neighborhood with a similar deprivation quintile. The probability that a random patient selected from the study had a change of address outside the same or neighboring quintile within a 1-year period ranged from 2% to 13%.

Conclusions: Neighborhood deprivation persistence was high among this population of patients from a high mobility region. A current address of record is a reasonable indicator of patient exposure to neighborhood deprivation within a 1–3-year timeframe that is useful in evaluating healthcare disparities.

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Bang DW, Wi CI, Kim EN, Hagan J, Roger V, Manemann S, Lahr B, Ryu E, Juhn YJ. Asthma Status and Risk of Incident Myocardial Infarction: A Population-Based Case-Control Study. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE 2016;4:917-23. [PMID: 27157653 DOI: 10.1016/j.jaip.2016.02.018] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 01/25/2016] [Accepted: 02/18/2016] [Indexed: 12/28/2022]

Graham G, Xiao YYK, Rappoport D, Siddiqi S. Population-level differences in revascularization treatment and outcomes among various United States subpopulations. World J Cardiol 2016;8:24-40. [PMID: 26839655 PMCID: PMC4728105 DOI: 10.4330/wjc.v8.i1.24] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 08/29/2015] [Accepted: 11/04/2015] [Indexed: 02/06/2023] Open

Abstract

Despite recent general improvements in health care, significant disparities persist in the cardiovascular care of women and racial/ethnic minorities. This is true even when income, education level, and site of care are taken into consideration. Possible explanations for these disparities include socioeconomic considerations, elements of discrimination and racism that affect socioeconomic status, and access to adequate medical care. Coronary revascularization has become the accepted and recommended treatment for myocardial infarction (MI) today and is one of the most common major medical interventions in the United States, with more than 1 million procedures each year. This review discusses recent data on disparities in co-morbidities and presentation symptoms, care and access to medical resources, and outcomes in revascularization as treatment for acute coronary syndrome, looking especially at women and minority populations in the United States. The data show that revascularization is used less in both female and minority patients. We summarize recent data on disparities in co-morbidities and presentation symptoms related to MI; access to care, medical resources, and treatments; and outcomes in women, blacks, and Hispanics. The picture is complicated among the last group by the many Hispanic/Latino subgroups in the United States. Some differences in outcomes are partially explained by presentation symptoms and co-morbidities and external conditions such as local hospital capacity. Of particular note is the striking differential in both presentation co-morbidities and mortality rates seen in women, compared to men, especially in women ≤ 55 years of age. Surveillance data on other groups in the United States such as American Indians/Alaska Natives and the many Asian subpopulations show disparities in risk factors and co-morbidities, but revascularization as treatment for MI in these populations has not been adequately studied. Significant research is required to understand the extent of disparities in treatment in these subpopulations.

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Lewis MW, Khodneva Y, Redmond N, Durant RW, Judd SE, Wilkinson LL, Howard VJ, Safford MM. The impact of the combination of income and education on the incidence of coronary heart disease in the prospective Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study. BMC Public Health 2015;15:1312. [PMID: 26715537 PMCID: PMC4696109 DOI: 10.1186/s12889-015-2630-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 12/16/2015] [Indexed: 11/10/2022] Open

Abstract

BACKGROUND

We investigated the association between income-education groups and incident coronary heart disease (CHD) in a national prospective cohort study.

METHODS

The REasons for Geographic And Racial Differences in Stroke study recruited 30,239 black and white community-dwelling adults between 2003 and 2007 and collected participant-reported and in-home physiologic variables at baseline, with expert adjudicated CHD endpoints during follow-up. Mutually exclusive income-education groups were: low income (annual household income <$35,000)/low education (< high school), low income/high education, high income/low education, and high income/high education. Cox models estimated hazard ratios (HR) for incident CHD for each exposure group, examining differences by age group.

RESULTS

At baseline, 24,461 participants free of CHD experienced 809 incident CHD events through December 31, 2011 (median follow-up 6.0 years; interquartile range 4.5-7.3 years). Those with low income/low education had the highest incidence of CHD (10.1 [95% CI 8.4-12.1]/1000 person-years). After full adjustment, those with low income/low education had higher risk of incident CHD (HR 1.42 [95% CI: 1.14-1.76]) than those with high income/high education, but findings varied by age. Among those aged <65 years, compared with those reporting high income/high education, risk of incident CHD was significantly higher for those reporting low income/low education and low income/high education (adjusted HR 2.07 [95% CI 1.42-3.01] and 1.69 [95% CI 1.30-2.20], respectively). Those aged ≥ 65 years, risk of incident CHD was similar across income-education groups after full adjustment.

CONCLUSION

For younger individuals, low income, regardless of education, was associated with higher risk of CHD, but not observed for ≥ 65 years. Findings suggest that for younger participants, education attainment may not overcome the disadvantage conferred by low income in terms of CHD risk, whereas among those ≥ 65 years, the independent effects of income and education are less pronounced.

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Improving the Neighborhood Environment for Urban Older Adults: Social Context and Self-Rated Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2015;13:ijerph13010003. [PMID: 26703659 PMCID: PMC4730394 DOI: 10.3390/ijerph13010003] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Revised: 10/16/2015] [Accepted: 11/11/2015] [Indexed: 11/24/2022]

Osler M, Prescott E, Wium-Andersen IK, Ibfelt EH, Jørgensen MB, Andersen PK, Jørgensen TSH, Wium-Andersen MK, Mårtensson S. The Impact of Comorbid Depression on Educational Inequality in Survival after Acute Coronary Syndrome in a Cohort of 83 062 Patients and a Matched Reference Population. PLoS One 2015;10:e0141598. [PMID: 26513652 PMCID: PMC4626047 DOI: 10.1371/journal.pone.0141598] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 10/09/2015] [Indexed: 11/28/2022] Open

Mathis A, Rooks R, Kruger D. Neighborhood Environment and Self-Rated Health Among Urban Older Adults. Gerontol Geriatr Med 2015;1:2333721415607314. [PMID: 28138468 PMCID: PMC5119865 DOI: 10.1177/2333721415607314] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open

Bang DW, Manemann SM, Gerber Y, Roger VL, Lohse CM, Rand-Weaver J, Krusemark E, Yawn BP, Juhn YJ. A novel socioeconomic measure using individual housing data in cardiovascular outcome research. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014;11:11597-615. [PMID: 25396769 PMCID: PMC4245632 DOI: 10.3390/ijerph111111597] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2014] [Revised: 09/25/2014] [Accepted: 10/29/2014] [Indexed: 01/09/2023]

Affiliation(s)

Duk Won Bang Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (D.W.B.); (J.R.-W.); (E.K.) Department of Internal Medicine, Division of Cardiology, Soonchunhyang University Hospital, 22, Daesagwan-gil (657 Hannam-dong), Yongsan-gu, Seoul 140-743, Korea
Sheila M. Manemann Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (S.M.M.); (Y.G.); (V.L.R.); (C.M.L.)
Yariv Gerber Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (S.M.M.); (Y.G.); (V.L.R.); (C.M.L.) Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, P.O. Box 39040, Tel Aviv 699780, Israel
Veronique L. Roger Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (S.M.M.); (Y.G.); (V.L.R.); (C.M.L.) Division of Cardiovascular Disease, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
Christine M. Lohse Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (S.M.M.); (Y.G.); (V.L.R.); (C.M.L.)
Jennifer Rand-Weaver Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (D.W.B.); (J.R.-W.); (E.K.)
Elizabeth Krusemark Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (D.W.B.); (J.R.-W.); (E.K.)
Barbara P. Yawn Department of Research, Olmsted Medical Center, 210 Ninth Street SE, Rochester, MN 55904, USA; E-Mail:
Young J. Juhn Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; E-Mails: (D.W.B.); (J.R.-W.); (E.K.) Division of Allergic Diseases, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA

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Coady SA, Johnson NJ, Hakes JK, Sorlie PD. Individual education, area income, and mortality and recurrence of myocardial infarction in a Medicare cohort: the National Longitudinal Mortality Study. BMC Public Health 2014;14:705. [PMID: 25011538 PMCID: PMC4227052 DOI: 10.1186/1471-2458-14-705] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Accepted: 06/09/2014] [Indexed: 11/25/2022] Open

Abstract

Background

The Medicare program provides universal access to hospital care for the elderly; however, mortality disparities may still persist in this population. The association of individual education and area income with survival and recurrence post Myocardial Infarction (MI) was assessed in a national sample.

Methods

Individual level education from the National Longitudinal Mortality Study was linked to Medicare and National Death Index records over the period of 1991-2001 to test the association of individual education and zip code tabulation area median income with survival and recurrence post-MI. Survival was partitioned into 3 periods: in-hospital, discharge to 1 year, and 1 year to 5 years and recurrence was partitioned into two periods: 28 day to 1 year, and 1 year to 5 years.

Results

First MIs were found in 8,043 women and 7,929 men. In women and men 66-79 years of age, less than a high school education compared with a college degree or more was associated with 1-5 year mortality in both women (HRR 1.61, 95% confidence interval 1.03-2.50) and men (HRR 1.37, 1.06-1.76). Education was also associated with 1-5 year recurrence in men (HRR 1.68, 1.18-2.41, < High School compared with college degree or more), but not women. Across the spectrum of survival and recurrence periods median zip code level income was inconsistently associated with outcomes. Associations were limited to discharge-1 year survival (RR lowest versus highest quintile 1.31, 95% confidence interval 1.03-1.67) and 28 day-1 year recurrence (RR lowest versus highest quintile 1.72, 95% confidence interval 1.14-2.57) in older men.

Conclusions

Despite the Medicare entitlement program, disparities related to individual socioeconomic status remain. Additional research is needed to elucidate the barriers and mechanisms to eliminating health disparities among the elderly.

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Kirchberger I, Meisinger C, Golüke H, Heier M, Kuch B, Peters A, Quinones PA, von Scheidt W, Mielck A. Long-term survival among older patients with myocardial infarction differs by educational level: results from the MONICA/KORA myocardial infarction registry. Int J Equity Health 2014;13:19. [PMID: 24552463 PMCID: PMC3940020 DOI: 10.1186/1475-9276-13-19] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Accepted: 02/07/2014] [Indexed: 11/10/2022] Open

Zielinski A, Borgquist L, Halling A. Distance to hospital and socioeconomic status influence secondary health care use. Scand J Prim Health Care 2013;31:83-8. [PMID: 23301541 PMCID: PMC3656400 DOI: 10.3109/02813432.2012.759712] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open

Gerber Y, Myers V, Goldbourt U, Benyamini Y, Drory Y. Neighborhood socioeconomic status and leisure-time physical activity after myocardial infarction: A longitudinal study. Am J Prev Med 2011;41:266-73. [PMID: 21855740 DOI: 10.1016/j.amepre.2011.05.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2011] [Revised: 05/09/2011] [Accepted: 05/09/2011] [Indexed: 10/17/2022]

Pedigo A, Aldrich T, Odoi A. Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach. BMC Public Health 2011;11:644. [PMID: 21838897 PMCID: PMC3171373 DOI: 10.1186/1471-2458-11-644] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2011] [Accepted: 08/12/2011] [Indexed: 11/21/2022] Open

Abstract

BACKGROUND

Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.

METHODS

Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.

RESULTS

There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.

CONCLUSIONS

These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.

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Pedigo A, Seaver W, Odoi A. Identifying unique neighborhood characteristics to guide health planning for stroke and heart attack: fuzzy cluster and discriminant analyses approaches. PLoS One 2011;6:e22693. [PMID: 21829481 PMCID: PMC3145655 DOI: 10.1371/journal.pone.0022693] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2010] [Accepted: 07/04/2011] [Indexed: 11/19/2022] Open

Abstract

BACKGROUND

Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI.

METHODS AND FINDINGS

The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk.

CONCLUSIONS

Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique neighborhood health needs and is important in guiding resource allocation, service provision, and policy decisions to address neighborhood health disparities and improve population health.

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Wennerholm C, Grip B, Johansson A, Nilsson H, Honkasalo ML, Faresjö T. Cardiovascular disease occurrence in two close but different social environments. Int J Health Geogr 2011;10:5. [PMID: 21226912 PMCID: PMC3025825 DOI: 10.1186/1476-072x-10-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2010] [Accepted: 01/12/2011] [Indexed: 12/04/2022] Open

Mead H, Andres E, Ramos C, Siegel B, Regenstein M. Barriers to effective self-management in cardiac patients: the patient's experience. PATIENT EDUCATION AND COUNSELING 2010;79:69-76. [PMID: 19748205 DOI: 10.1016/j.pec.2009.08.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2008] [Revised: 07/30/2009] [Accepted: 08/06/2009] [Indexed: 05/28/2023]