County-Level Political, Economic, and Social Statistics for New York State: 1962-1978

1984 ◽  
Author(s):  
Frank L. Schepps
Keyword(s):  
New York ◽  
New York State ◽  
County Level ◽  
York State ◽  
Social Statistics ◽  
Political Economic

scholarly journals Structural Racism and Severe Maternal Morbidity in New York State

2019 ◽  
Vol 12 ◽  
pp. 1179562X1985477 ◽  
Author(s):  
Sze Yan Liu ◽  
Christina Fiorentini ◽  
Zinzi Bailey ◽  
Mary Huynh ◽  
Katharine McVeigh ◽  
...  
Keyword(s):  
New York ◽  
Educational Attainment ◽  
Maternal Morbidity ◽  
New York State ◽  
Female Employment ◽  
Severe Maternal Morbidity ◽  
County Level ◽  
Hospital Level ◽  
Structural Racism ◽  
York State

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.

scholarly journals 467. Spatial-Temporal Prediction Model of COVID-19 Mortality across 62 Counties in New York State

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S300-S300
Author(s):  
Yunyu Xiao ◽  
Chengbo Zeng
Keyword(s):  
New York ◽  
Population Density ◽  
Large Population ◽  
New York State ◽  
County Level ◽  
Mixed Effect ◽  
Cumulative Mortality ◽  
York State ◽  
Temporal Prediction ◽  
Public Data

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

From Nucleated Villages to Dispersed Networks

2018 ◽  
pp. 174-191
Author(s):  
Kurt A. Jordan
Keyword(s):  
New York ◽  
Political Economy ◽  
Lived Experience ◽  
New York State ◽  
Economic Conditions ◽  
Large Core ◽  
York State ◽  
Documentary Sources ◽  
Dynamic Time ◽  
Political Economic

Members of the Seneca Nation of the Haudenosaunee (Iroquois) Confederacy resided in a surprising variety of settlement forms during the seventeenth and eighteenth centuries. Seneca communities in what is now western New York State lived in sequentially occupied sites that ranged from nucleated to fully dispersed, with and without defensive palisades. The regional Seneca settlement pattern also changed from one with two large core sites and surrounding satellites to a network of evenly spaced smaller sites arrayed across their territory. While earlier scholars viewed these transformations as decline away from a precontact cultural climax, the changes were non-linear and corresponded quite tightly to the dynamics of the regional political economy known in detail from documentary sources. This chapter reviews the details of 1669-1779 changes in Seneca community forms, and examines the lived experience of community relocation as a dynamic time for negotiation, reimagination, assessment of political-economic conditions, and the exercise of power.

scholarly journals 431. Geospatial prediction model of COVID-19 outbreak in New York State

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S282-S283
Author(s):  
Chengbo Zeng ◽  
Yunyu Xiao
Keyword(s):  
New York ◽  
Prediction Model ◽  
New York State ◽  
Critical Time ◽  
Spatial Variations ◽  
County Level ◽  
Lasso Regression ◽  
Mixed Effect ◽  
York State ◽  
Temporal Prediction

Abstract Background More than 360,000 people infected with COVID-19 in New York State (NYS) by the end of May 2020.The spatial variations of prevalence across the counties in NYS suggested that variations in county-level factors might contribute to the statewide COVID-19 outbreak. However, no study to date investigates such variations and the relevant predictors. We leveraged multiple public datasets and machine learning approaches to construct the county-level spatial-temporal prediction model of COVID-19 in NYS. Findings generating from this study help identify counties with high prevalence, county-level predictors, and promising next steps for policy efforts to control the second wave of statewide COVID-19 transmission. Methods Cumulative confirmed case rates (CCCR) of COVID-19 by county in NYS were extracted from the US Health Data system at four critical time points including March 17th (state of emergency, 4.40 per 100,000 people), April 18th (coronavirus peak, 310.10 per 100,000 people), April 25th (expand testing, 393.90 per 100,000 people), and May 11th (daily increased rate back to the level in March, 505.30 per 100,000 people. A total of 28 county-level predictors were used to construct the prediction model, and the generalized linear mixed effect least absolute shrinkage and selection operator (LASSO) regression was employed to select the predictors of COVID-19 outbreak across the counties in NYS with adjusting for time effect. Results The CCCR by the final timepoint was 1,850.3 per 100,000 people. Rockland County had the highest CCCR than any other counties, with a rate of 3,856.82 per 100,000 people, while Chautauqua and Franklin counties had the lowest CCCR (0.03 per 100,000 people). LASSO regression revealed counties with a larger proportion of non-citizen (β=9537.97, p=0.02) had a higher CCCR of COVID-19 across the time. In contrast, counties with a lower proportion of people with at least high school education (β=-6157.89, p=0.025) and a larger proportion of houses with less than 3 people (β=-5995.79471, p=0.01) had lower CCCR. Conclusion We identified immigrant status, education level and household type influenced the spatial variations of COVID-19 outbreak in NYS. Future interventions shall target on areas with greater density of non-citizens to prevent transmission. Disclosures All Authors: No reported disclosures

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