Neighborhood Socioeconomic Status and Stroke Incidence

Kim, Yeonwoo; Twardzik, Erica; Judd, Suzanne E.; Colabianchi, Natalie

doi:10.1212/wnl.0000000000011892

Cited by 23 publications

(30 citation statements)

References 52 publications

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“…Other studies have identified social and economic neighborhood characteristics as factors that are important to health, including cardiovascular disease, stroke, and mortality ( Brown et al, 2011 ; Diez-Roux et al, 2001 ; Freedman et al, 2011 ; Pujades-Rodriguez et al, 2014 ; Smith et al, 1998 ; Steenland et al, 2004 ; Sundquist et al, 2004 ; Winkleby & Cubbin, 2003 ). Some studies revealed that residence in a socioeconomically disadvantaged neighborhood can increase risk of incident stroke ( Brown et al, 2011 ; Kim et al, 2021 ; Kleindorfer et al, 2006 ; Lisabeth et al, 2007 ), which varied by sex or race ( Brown et al, 2011 ; Grimaud et al, 2011 ; Hart et al, 2000 ; Lisabeth et al, 2007 ; Pujades-Rodriguez et al, 2014 ). Another study found that stroke incidence increased with a decrease in neighborhood socioeconomic score (nSES) ( Howard et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Matching participant address with public records database in a US national longitudinal cohort study

Brooks

Bennett

Lovasi

et al. 2021

SSM - Population Health

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Background Epidemiological studies utilize residential histories to assess environmental exposure risk. The validity from using commercially-sourced residential histories within national longitudinal studies remains unclear. Our study assessed predictors of non-agreement between baseline addresses from the commercially-sourced LexisNexis database and participants in the national longitudinal study, REasons for Geographic and Racial Differences in Stroke (REGARDS). Additionally, we assessed differences in stroke risk by neighborhood socioeconomic score (nSES) based on participant reported address compared to nSES from LexisNexis/REGARDS matched baseline address. Methods From January 2003–October 2007, REGARDS enrolled 30,239 black and white adults aged 45 and older within the continental United States and collected their baseline address. ArcGIS Desktop 10.5.1 with ESRI 2016 Business Analyst Data was used to geocode baseline addresses from LexisNexis and REGARDS. Logistic regression was used to estimate the likelihood that LexisNexis address matched REGARDS baseline address for each participant. Survival analysis was used to estimate association between nSES and incident stroke. Results Approximately 91% of REGARDS participants had a LexisNexis address. Of these geocoded addresses, 93% of REGARDS baseline addresses matched LexisNexis addresses. Odds of agreement between LexisNexis and REGARDS was higher for older-aged participants (OR = 1.02 per year, 95% CI: 1.01, 1.02), blacks compared to whites (OR = 1.16, 95% CI: 1.05, 1.29), females compared to males (OR = 1.15, 95% CI: 1.04, 1.26), participants with an income of $34k-74k compared to an income less than $20k (OR = 1.62, 95% CI: 1.39, 1.89). Odds of agreement were lower for residents in Midwest compared to residents in the south (OR = 0.82, 95% CI: 0.73, 0.94). No significant differences in nSES-stroke associations were observed between REGARDS only and LexisNexis/REGARDS matched addresses; however, differences in interactions were observed. Conclusion Agreement between LexisNexis and REGARDS addresses varied by sociodemographic groups, potentially introducing bias in studies reliant on LexisNexis alone for residential address data.

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Section: Introductionmentioning

confidence: 99%

Matching participant address with public records database in a US national longitudinal cohort study

Brooks

Bennett

Lovasi

et al. 2021

SSM - Population Health

Self Cite

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“…A model included the number of expected deaths as an offset term to adjust for the population size of each municipality. Moreover, in terms of municipality-level socioeconomic status variables, this study adopted; the ratio of people who completed college and university studies (%), the proportion of workers in primary industries (%), such as agriculture and fishing, the proportion of workers in secondary industries (%), such as manufacturing and construction, and the proportion of workers in tertiary industries (%), mainly services [14][15][16][17]. However, these socioeconomic values were impact of socioeconomic factors on stroke is a global challenge, and improving access to healthcare may reduce disparities in outcomes.…”

Section: Response and Explanatory Variablesmentioning

confidence: 99%

Spatial-temporal analysis of cerebral infarction mortality in Hokkaido, Japan: an ecological study using a conditional autoregressive model

et al. 2022

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Background: Accessibility to stroke treatments is a challenge that depends on the place of residence. However, recent advances in medical technology have improved health outcomes. Nevertheless, the geographic heterogeneity of medical resources may increase regional disparities. Therefore, evaluating spatial and temporal influences of the medical system on regional outcomes and advanced treatment of cerebral infarction are important from a health policy perspective. This spatial and temporal study aims to identify factors associated with mortality and to clarify regional disparities in cerebral infarction mortality at municipality level. Methods: This ecological study used public data between 2010 and 2020 from municipalities in Hokkaido, Japan. We applied spatial and temporal condition autoregression analysis in a Bayesian setting, with inference based on the Markov chain Monte Carlo simulation. The response variable was the number of deaths due to cerebral infarction (ICD-10 code: I63). The explanatory variables were healthcare accessibility and socioeconomic status. Results: The large number of emergency hospitals per 10,000 people (relative risk (RR) = 0.906, credible interval (Cr) = 0.861 to 0.954) was associated with low mortality. On the other hand, the large number of general hospitals per 10,000 people (RR = 1.123, Cr = 1.068 to 1.178) and longer distance to primary stroke centers (RR = 1.064, Cr = 1.014 to 1.110) were associated with high mortality. The standardized mortality ratio decreased from 2010 to 2020 in Hokkaido by approximately 44%. Regional disparity in mortality remained at the same level from 2010 to 2015, after which it narrowed by approximately 5% to 2020. After mapping, we identified municipalities with high mortality rates that emerged in Hokkaido’s central and northeastern parts. Conclusion: Cerebral infarction mortality rates and the disparity in Hokkaido improved during the study period (2010–2020). This study emphasized that healthcare accessibility through places such as emergency hospitals and primary stroke centers was important in determining cerebral infarction mortality at the municipality level. In addition, this study identified municipalities with high mortality rates that require healthcare policy changes. The impact of socioeconomic factors on stroke is a global challenge, and improving access to healthcare may reduce disparities in outcomes.

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“…15 The level of deprivation in a geographical area, commonly termed neighborhood socioeconomic status (nSES), is a risk factor for general health and well-being. 16,17 However, findings concerning association between nSES and risk of dementia are inconsistent. [18][19][20][21] The aim of the present study was to investigate the associations between nSES and missed or delayed diagnosis of dementia.…”

Section: Introductionmentioning

confidence: 99%

“…The level of deprivation in a geographical area, commonly termed neighborhood socioeconomic status (nSES), is a risk factor for general health and well‐being 16,17 . However, findings concerning association between nSES and risk of dementia are inconsistent 18–21 .…”

Section: Introductionmentioning

confidence: 99%

Frequency of missed or delayed diagnosis in dementia is associated with neighborhood socioeconomic status

Holm

Jacobsen

Lony

et al. 2022

A&D Transl Res & Clin Interv

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Introduction Underdetection of dementia in areas with low socioeconomic status (SES) may interfere with findings concerning associations between SES and dementia. Methods Using administrative registers we assessed the associations between age‐ and sex‐adjusted dementia incidence and neighborhood socioeconomic status (nSES) in 94 Danish municipalities. Wealth was divided into income quartiles and other nSES variables were dichotomized into high versus low according to the median. Results High population density (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.18–1.24), higher proportion of inhabitants in higher income quartiles ( P for trend < .0001), and high educational level (OR 1.19, 95% CI 1.15–1.22) were associated with higher incidence of dementia. High proportion of residents above 65 years was associated with lower age‐adjusted dementia incidence (OR 0.86, 95% CI 0.84–0.89). Discussion Low nSES municipalities have a lower age‐adjusted incidence of dementia diagnosis. These findings corroborate prior concerns that a large number of dementia diagnoses may be missed in municipalities characterized by low SES.

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Neighborhood Socioeconomic Status and Stroke Incidence

Cited by 23 publications

References 52 publications

Matching participant address with public records database in a US national longitudinal cohort study

Matching participant address with public records database in a US national longitudinal cohort study

Spatial-temporal analysis of cerebral infarction mortality in Hokkaido, Japan: an ecological study using a conditional autoregressive model

Frequency of missed or delayed diagnosis in dementia is associated with neighborhood socioeconomic status

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