Late stage melanoma in New York State: associations with socioeconomic factors and healthcare access at the county level

Author(s):  
Payal Shah ◽  
Yongzhao Shao ◽  
Alan C. Geller ◽  
David Polsky
2019 ◽  
Vol 12 ◽  
pp. 1179562X1985477 ◽  
Author(s):  
Sze Yan Liu ◽  
Christina Fiorentini ◽  
Zinzi Bailey ◽  
Mary Huynh ◽  
Katharine McVeigh ◽  
...  

Objective: We examined the association between county-level structural racism indicators and the odds of severe maternal morbidity (SMM) in New York State. Design: We merged individual-level hospitalization data from the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS) with county-level data from the American Community Survey and the Vera Institute of Justice from 2011 to 2013 (n = 244 854). Structural racism in each county included in our sample was constructed as the racial inequity (ratio of black to white population) in female educational attainment, female employment, and incarceration. Results: Multilevel logistic regression analysis estimated the association between each of these structural racism indicators and SMM, accounting for individual- and hospital-level characteristics and clustering in facilities. In the models adjusted for individual- and hospital-level factors, county-level racial inequity in female educational attainment was associated with small but statistically significant higher odds of SMM (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.47, 1.85). County-level structural racism indicators of female employment inequity and incarceration inequity were not statistically significant. Interaction terms examining potential effect measure modification by race with each structural racism indicator also indicated no statistical difference. Conclusions: Studies of maternal disparities should consider multiple dimensions of structural racism as a contributing cause to SMM and as an additional area for potential intervention.


2020 ◽  
Vol 140 (7) ◽  
pp. S88
Author(s):  
P. Shah ◽  
S. Bajaj ◽  
D. Polsky

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S300-S300
Author(s):  
Yunyu Xiao ◽  
Chengbo Zeng

Abstract Background COVID-19 pandemic has resulted in considerable morbidity and mortality. New York State (NY) is the hotspot with most coronavirus cases, while there are spatial/temporal variations. Yet, few examined county-level factors of mortality in COVID-19 patients in NY. Based on the sociological framework in health, this study links large and representative public data to understand COVID-19 mortality in NY over different stages of pandemic. Methods Mortality cases were from Mar 17 (state of emergency; 0.1 per 100,000), Apr 18 (coronavirus peak; 87.4), Apr 25 (expand testing; 108.7), and May 11 (daily reduced to original; 137.6). Three domains (compositional, contextual, and collective) and 28 county-level predictors of mortality were extracted from American Community Survey, Area Health Resources, US Crime Data, and Religious Data systems for each county. Compositional domain covered socio-demographic characteristics in local areas (e.g., age, sex, race/ethnicity, housing). Contextual domain covered include social and physical opportunities (e.g., health insurance coverage, transportation, mental health providers). Collective domain covered neighborhood safety and religious adherents. Mixed effect regression with the least absolute shrinkage selection operator (LASSO) was used to select the predictors and estimate the parameters after adjusting the time effect and cumulative prevalence of COVID-19. 有道词典 ; 0.1 per 100,000 people 详细X ;每100000人0.1 Results NYC and the nearby boroughs (i.e., Bronx, Kings, Manhattan, Queens) had the highest cumulative mortality (231.69 per 100,000 people). Counties far from New York Cities (e.g., Allegany, Cortland, Delaware) had the lowest cumulative mortality. Spatial variation showed counties with larger population density (β=.01, p=.022) and/or higher proportion of people with at least high school education (β=227.24, p=.03) were at risk of higher cumulative mortality in COVID-19. Conclusion Unique spatial clustering mortality risk of COVID-19s was detected, highlighting important but understudied roles of contextual and collective factors. Tailored policy efforts shall be designed to support counties with large population density and high levels of education to prevent the mortality related to COVID-19 infection in NY. Disclosures All Authors: No reported disclosures


Sign in / Sign up

Export Citation Format

Share Document