Table 1 Literature studying associations between the neighborhood risk factors and stroke at individual-level or neighborhood-level

Individual-level

Osypuk TL, Ehntholt A, Moon JR, Gilsanz P, Glymour MM. Neighborhood Differences in Post-Stroke Mortality. Circ Cardiovasc Qual Outcomes. 2017;10 (2):e002547.

Cox proportional hazard models (All individual-level variables)

Neighborhood characteristics (Race, income, age) predict post-stroke mortality, but most effects are similar for individuals without stroke.

Menec VH, Shooshtari S, Nowicki S, Fournier S. Does the relationship between neighborhood socioeconomic status and health outcomes persist into very old age? A population-based study. J Aging Health. 2010; 22:27–47.

Multilevel logistic regressions (individual level variable and neighborhood level variable)

Relative to individuals living in the most affluent areas, those in the poorest areas had significantly higher odds of having stroke. Significant neighborhood income effects tended to be evident among individuals age 65 to 75 as well as those age 75 + .

Brown P, Guy M, Broad J. Individual socio-economic status, community socio-economic status and stroke in new zealand: A case control study. Soc Sci Med. 2005; 61:1174–1188.

Stepwise logistic regression (all individual level variables)

Individual income and average household income are significant predictors of onset of stroke both independently and after controlling for behavioural and medical risk factors.

Brown AF, Liang L-J, Vassar SD, Stein-Merkin S, Longstreth WT, Ovbiagele B, Yan T, Escarce JJ. Neighborhood disadvantage and ischemic stroke: The cardiovascular health study (chs). Stroke. 2011; 42:3363–3368.

Race-stratified multilevel Cox proportional hazard models (individual level variable and neighborhood level variable)

Higher risk of incident ischemic stroke was observed in the most disadvantaged neighborhoods among whites, but not among Blacks.

Engström G, Jerntorp I, Pessah-Rasmussen H, Hedblad B, Berglund G, Janzon L. Geographic distribution of stroke incidence within an urban population: Relations to socioeconomic circumstances and prevalence of cardiovascular risk factors. Stroke. 2001; 32:1098–1103

Direct standardization with the equivalent average rate method

Socioeconomic score correlated significantly with area-specific stroke rates among men and women. Incidence of stroke was significantly associated with cardiovascular risk score for each area.

Lisabeth L, Diez Roux A, Escobar J, Smith M, Morgenstern L. Neighborhood environment and risk of ischemic stroke: The brain attack surveillance in corpus christi (basic) project. Am J Epidemiol. 2007; 165:279–287.

Poisson regression (individual level)

In Poisson regression analyses comparing the 90th percentile of neighborhood score (median annual household income, education, occupation, housing price) with the 10th, the relative risk of stroke was 0.49 (95% confidence interval: 0.41, 0.58).

Clark CJ, Guo H, Lunos S, Aggarwal NT, Beck T, Evans DA, Mendes de Leon C, Everson-Rose SA. Neighborhood cohesion is associated with reduced risk of stroke mortality. Stroke. 2011; 42:1212–1217

Marginal Cox proportional hazard models (individual level)

Neighborhood-level social cohesion was independently protective against stroke mortality. Research is needed to further examine observed race differences and pathways by which cohesion is health-protective.

Brown AF, Liang L-J, Vassar SD, Merkin SS, Longstreth WT, Ovbiagele B, Yan T, Escarce JJ. Neighborhood socioeconomic disadvantage and mortality after stroke. Neurology. 2013; 80:520–527.

Multilevel Cox proportional hazard models (individual level variable and neighborhood level variable)

Living in a socioeconomically disadvantaged neighborhood is associated with higher mortality hazard at 1 year following an incident stroke.

Aslanyan S, Weir CJ, Lees KR, Reid JL, McInnes GT. Effect of area-based deprivation on the severity, subtype, and outcome of ischemic stroke.

Stepwise linear and logistic regression (individual level)

Tackling health inequalities in stroke should focus on stroke primary prevention by tackling deprivation, including promoting changes in lifestyle.

Gerber Y, Weston SA, Killian JM, Therneau TM, Jacobsen SJ, Roger VL: Neighborhood income and individual education: Effect on survival after myocardial infarction. Mayo Clinic Proceedings. 2008, 83 (6): 663–669. https://doi.org/10.4065/83.6.663.

Cox proportional hazards models

Poor neighborhood income was a powerful predictor of mortality even after controlling for a variety of potential confounding factors.

Neighborhood-level

Hu, L., Ji, J., Li, Y. et al. Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence. J Urban Health (2020). https://doi.org/10.1007/s11524-020-00478-y

Quantile Regression Forests

Neighborhoods with a larger share of non-Hispanic blacks, older adults or people with insufficient sleep tended to have a higher prevalence of stroke, whereas neighborhoods with a higher socio-economic status in terms of income and education had a lower prevalence of stroke.

Hu L, Ji J, Liu B, Li Y. Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level. J Am Heart Assoc. 2020; 00: e016745. https://doi.org/10.1161/JAHA.120.016745.

BART, Bayesian linear regression model

Of the five most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non-Hispanic blacks and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence.

Morgenstern LB, Escobar JD, Sánchez BN, Hughes R, Zuniga BG, Garcia N, Lisabeth LD. Fast food and neighborhood stroke risk. Ann Neurol. 2009; 66:165–170.

Poisson regression and generalized estimating equations

Controlling for demographic and SES factors, there was a significant association between fast food restaurants and stroke risk in neighborhoods in this community-based study.

Pickle LW, Mungiole M, Gillum RF: Geographic variation in stroke mortality in blacks and whites in the United States. Stroke. 1997, 28 (8): 1639–1647. https://doi.org/10.1161/01.STR.28.8.1639.

Multilevel regressions

Mortality rates in the Southeast also remain high, especially for Blacks.

Howard G, Howard VJ, Katholi C, Oli MK, Huston S: Decline in US stroke mortality - An analysis of temporal patterns by sex, race, and geographic region. Stroke. 2001, 32 (10): 2213–2218. https://doi.org/10.1161/hs1001.096047.

Logistics regression (analyses were performed at the county level)

White men have experienced the largest decline in stroke mortality, and black men have seen the smallest. Generally, stroke mortality appears to still be slowly declining for blacks but not for whites. Geographic differences in stroke mortality are predicted to persist.

Hu L, Liu B, Li Y. Ranking sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health: A Bayesian machine learning approach. Preventive Medicine. 2020;141:106240.

BART

Neighborhood behavioral factors such as the proportions of people who are obese, do not have leisure-time physical activity, and have binge drinking emerged as top five predictors for most of the neighborhood cardiovascular health outcomes.