Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence

Eric L. Stulberg; Lynda Lisabeth; Andrea L.C. Schneider; Lesli Skolarus; Kiarri N. Kershaw; Alexander R. Zheutlin; Benjamin R.E. Harris; Daniel Sarpong; Ka Ho Wong; Kevin N. Sheth; Adam de Havenon

doi:10.1002/ana.27039

Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence

Eric L. Stulberg^*, Lynda Lisabeth, Andrea L.C. Schneider, Lesli Skolarus, Kiarri N. Kershaw, Alexander R. Zheutlin, Benjamin R.E. Harris, Daniel Sarpong, Ka Ho Wong, Kevin N. Sheth, Adam de Havenon

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Socioeconomic status (SES) is a multi-faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county-level SES, health care access and clinical risk factor measures with age-adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non-SES measures (correlation coefficient = 0.908, R² = 0.825; adjusted partial correlation coefficient: 0.589, R² = 0.347). ANN NEUROL 2024;96:739–744.

Original language	English (US)
Pages (from-to)	739-744
Number of pages	6
Journal	Annals of neurology
Volume	96
Issue number	4
DOIs	https://doi.org/10.1002/ana.27039
State	Published - Oct 2024

Funding

E.L.S. reports funding by the NIH/NHLBI via the Utah Stimulating Access to Research in Residency (StARR) program under award number 1R38HL167282-01. L.L. reports funding from the National Institutes of Health (R01 HL156906, R01 HL152741, R01 NS038916, and R01 NS107463). A.L.C.S. reports NIH/NINDS funding (K23NS123340) and Department of Defense (W81XWH-21-1-0590, HT9425-23-1-0981). L.S. receives funding from the National Institute on Aging, the National Institute on Minority Health and Health Disparities and the American Heart Association/American Stroke Association. K.N.S. reports National Institute of Neurological Disorders and Stroke funding (U01NS106513, R01NS110721, R01NR018335, R01EB301114, R01MD016178, U24NS107215, U24NS107136, and U24NS129500) and via the American Heart Association Bugher Award. A.d.H. reports NIH/NINDS funding (K23NS105924, UG3NS130228, and R01NS130189). R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria) and Stata version 18.0 (StataCorp, College Station, TX) were used for statistical analyses. E.L.S. reports funding by the NIH/NHLBI via the Utah Stimulating Access to Research in Residency (StARR) program under award number 1R38HL167282\u201001. L.L. reports funding from the National Institutes of Health (R01 HL156906, R01 HL152741, R01 NS038916, and R01 NS107463). A.L.C.S. reports NIH/NINDS funding (K23NS123340) and Department of Defense (W81XWH\u201021\u20101\u20100590, HT9425\u201023\u20101\u20100981). L.S. receives funding from the National Institute on Aging, the National Institute on Minority Health and Health Disparities and the American Heart Association/American Stroke Association. K.N.S. reports National Institute of Neurological Disorders and Stroke funding (U01NS106513, R01NS110721, R01NR018335, R01EB301114, R01MD016178, U24NS107215, U24NS107136, and U24NS129500) and via the American Heart Association Bugher Award. A.d.H. reports NIH/NINDS funding (K23NS105924, UG3NS130228, and R01NS130189). R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria) and Stata version 18.0 (StataCorp, College Station, TX) were used for statistical analyses.

ASJC Scopus subject areas

Neurology
Clinical Neurology

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title = "Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence",

abstract = "Socioeconomic status (SES) is a multi-faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county-level SES, health care access and clinical risk factor measures with age-adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non-SES measures (correlation coefficient = 0.908, R2 = 0.825; adjusted partial correlation coefficient: 0.589, R2 = 0.347). ANN NEUROL 2024;96:739–744.",

author = "Stulberg, {Eric L.} and Lynda Lisabeth and Schneider, {Andrea L.C.} and Lesli Skolarus and Kershaw, {Kiarri N.} and Zheutlin, {Alexander R.} and Harris, {Benjamin R.E.} and Daniel Sarpong and Wong, {Ka Ho} and Sheth, {Kevin N.} and {de Havenon}, Adam",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.",

year = "2024",

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doi = "10.1002/ana.27039",

language = "English (US)",

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T1 - Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence

AU - Stulberg, Eric L.

AU - Lisabeth, Lynda

AU - Schneider, Andrea L.C.

AU - Skolarus, Lesli

AU - Kershaw, Kiarri N.

AU - Zheutlin, Alexander R.

AU - Harris, Benjamin R.E.

AU - Sarpong, Daniel

AU - Wong, Ka Ho

AU - Sheth, Kevin N.

AU - de Havenon, Adam

PY - 2024/10

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N2 - Socioeconomic status (SES) is a multi-faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county-level SES, health care access and clinical risk factor measures with age-adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non-SES measures (correlation coefficient = 0.908, R2 = 0.825; adjusted partial correlation coefficient: 0.589, R2 = 0.347). ANN NEUROL 2024;96:739–744.

AB - Socioeconomic status (SES) is a multi-faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county-level SES, health care access and clinical risk factor measures with age-adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non-SES measures (correlation coefficient = 0.908, R2 = 0.825; adjusted partial correlation coefficient: 0.589, R2 = 0.347). ANN NEUROL 2024;96:739–744.

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Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence

Abstract

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ASJC Scopus subject areas

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