Back to Authors

Turney, Indira C.

https://orcid.org/0000-0002-5912-1953

Mayo Clinic

National Institutes of Health

Citations 2

Manifestations of Structural Racism and Inequities in Cardiovascular Health Across US Neighborhoods
Lawrence, Wayne R. , Williams, Faustine , Dyer, Zachary , Bergeron, Nyahne Q. , Crittendon, Denine R. , Jackson, Sarah S. , McGee‐Avila, Jennifer K. , Pichardo, Catherine M. , Turney, Indira C. , Lawrence, Wayne R , Hong, Hyokyoung G. , Williams, Faustine , Brewer, LaPrincess C. , Chen, Yingxi , Freedman, Neal D. , Haas, Cameron B. , Martz, Connor D. , Ormiston, Cameron K. , Rogers, Charles R. , Santiago-Rodríguez, Eduardo J. , Shariff‐Marco, Salma , Powell‐Wiley, Tiffany M. , Zhang, Wangjian , Shiels, Meredith S.

JAMA Health Forum | October 31, 2025 | Volume: 6 | Issue: 10 | Pages: e253864

10.1001/jamahealthforum.2025.3864 Get It

Importance The prevalence of cardiovascular disease differs substantially across neighborhoods. However, few studies have examined the contribution of neighborhood-level structural racism to inequities in cardiovascular health. Objective To investigate the association between neighborhood-level manifestations of structural racism and the prevalence of cardiovascular clinical and behavioral risk factors and cardiovascular diseases. Design, Setting, and Participants This cross-sectional study consisted of 71 915 US census tracts. The Structural Racism Effect Index (SREI) was appended to the 2020 Centers for Disease Control and Prevention’s PLACES dataset. Race and ethnicity were self-reported and obtained from the 2019 American Community Survey 5-year estimates. Data analysis was conducted from June 2024 to October 2024. Exposure The SREI is a composite score of census tract measures within 9 domains affected by structural racism (built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth). The SREI scores were categorized into distribution-based quintiles, where higher quintiles represented greater neighborhood structural racism. Main Outcomes and Measures Neighborhood-level prevalence of cardiovascular clinical (high blood pressure, high cholesterol, obesity, and diabetes) and behavioral (current cigarette smoking and no leisure-time physical activity) risk factors and cardiovascular disease (coronary heart disease and stroke). Multilevel-linear mixed models, adjusting for confounders, were used to estimate adjusted prevalence rate ratios (aPRR). Results Among 71 915 census tracts, neighborhoods in the highest quintiles of structural racism were more likely than neighborhoods in the lowest quintile to be in the southern region (quintile 1, 19.9% vs quintile 5, 56.8%). The proportion of the population that was Black was largest in neighborhoods in the highest quintile of structural racism (quintile 1, 2.2% vs quintile 5, 23.0%). After adjustment, compared with the lowest quintile of structural racism, the highest quintile had significantly higher prevalence rates of cardiovascular clinical risk factors, including high blood pressure (aPRR, 1.37; 95% CI, 1.37-1.38), obesity (aPRR, 1.40; 95% CI, 1.39-1.40), and diabetes (aPRR, 1.80; 95% CI, 1.79-1.81). Similarly, for cardiovascular behavioral risk factors, the highest quintile of structural racism had significantly higher prevalence rates of current cigarette smoking (aPRR, 1.84; 95% CI, 1.83-1.85) and no leisure-time physical activity (aPRR, 1.88; 95% CI, 1.87-1.89). Additionally, for cardiovascular diseases, the highest quintile of structural racism had higher prevalence rates of coronary heart disease (aPRR, 1.80; 95% CI, 1.79-1.82) and stroke (aPRR, 1.99; 95% CI, 1.98-2.00). Conclusions and Relevance In this cross-sectional study, structural racism was associated with inequities in neighborhood cardiovascular health, highlighting opportunities for place-based prevention efforts.
Approaches to Timescale Choice in Cognitive Aging Research and Potential Implications for Estimated Exposure Effects: Coordinated Analyses in 10 Cohorts of Older Adults
Hayes‐Larson, Eleanor , Andrews, Ryan M. , Kezios, Katrina , Bercu, Ariane, Rouanet, Anaïs , Helmer, Catherine , Crane, Paul K. , Klinedinst, Brandon S. , McEvoy, Linda K. , Weuve, Jennifer , Rajan, Kumar B. , Shaw, Crystal , Sims, Kendra D. , Therneau, Terry M. , Petersen, Ronald C., Bouteloup, Vincent , Gross, Alden L. , Albert, Marilyn, Morris, John C., Resnick, Susan M. , Turney, Indira C. , Vonk, Jet M. J. , Avila‐Rieger, Justina , Weigand, Alexandra J. , Chen, Ruijia , Wang, Jingxuan , Mayeda, Elizabeth Rose , Klinedinst, Brandon S , McEvoy, Linda K , Weuve, Jennifer , Mez, Jesse , Sims, Kendra D , Therneau, Terry , Albert, Marilyn S. , Masters, Colin L , Resnick, Susan M , Maruff, Paul , Manly, Jennifer J , Turney, Indira C , Vonk, Jet M J , Avila-Rieger, Justina , Weigand, Alexandra , Mayeda, Elizabeth Rose , Gibbons, Laura E. , Nichols, Emma , Rajan, Kumar Bharat , Hwang, Phillip H , Mez, Jesse , Farina, Mateo P. , Morris, John C. , Masters, Colin L. , Maruff, Paul , Manly, Jennifer J. , Proust‐Lima, Cécile

Epidemiology | March 31, 2025 | Volume: 36 | Issue: 4 | Pages: 560-571

10.1097/ede.0000000000001859 Get It

Background: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods: Using a coordinated analytic approach in 10 cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of apolipoprotein-E (APOE) ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and nonlinear time specifications for both timescales. Results: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower, and the average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. The current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multicohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.