Population Density and Cancer Mortality Differentials in New York State, 1978–1982

BackgroundJail incarceration rates are positively associated with mortality at the county level. However, incarceration rates vary within counties, limiting the generalisability of this finding to neighbourhoods, where incarceration may have the greatest effects.MethodsWe performed a cross-sectional analysis of census tract-level state imprisonment rates in New York State (2010) and life expectancy data from the US Small-area Life Expectancy Estimates Project (2010–2015). We modelled fixed-effects for counties and controlled for tract-level poverty, racial makeup, education, and population density from the American Community Survey (2010–2014), and violent crime data from the New York City Police Department (2010). We also examined interactions between incarceration rate and poverty, racial makeup, and population density on life expectancy.ResultsLife expectancy at the highest quintile of incarceration was 5.5 years lower than in the lowest quintile, and over 2 years lower in a fully-adjusted model. Census tract-level poverty and racial makeup both moderated the association between incarceration and life expectancy.ConclusionCensus tract-level incarceration is associated with lower life expectancy. Decarceration, including alternatives to incarceration, and release of those currently incarcerated, may help to improve life expectancy at the neighbourhood level.

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Cancer mortality in a higher-income black population in New York state. Comparison with rates in the United States as a whole

Cancer ◽

10.1002/1097-0142(19901001)66:7<1654::aid-cncr2820660734>3.0.co;2-i ◽

1990 ◽

Vol 66 (7) ◽

pp. 1654-1660 ◽

Cited By ~ 22

Author(s):

Anthony P. Polednak

Keyword(s):

United States ◽

New York ◽

Cancer Mortality ◽

New York State ◽

Black Population ◽

The United States ◽

York State

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

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.660 ◽

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

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Opting Out in the Empire State: A Geographic Analysis of Opting Out in New York, Spring 2015 & 2016

Teachers College Record ◽

10.1177/016146812012200206 ◽

2020 ◽

Vol 122 (2) ◽

pp. 1-24

Author(s):

Kathryn P. Chapman ◽

Lydia Ross ◽

Sherman Dorn

Keyword(s):

New York ◽

Population Density ◽

Geographically Weighted Regression ◽

Child Poverty ◽

New York State ◽

District Level ◽

Weighted Regression ◽

York State ◽

Opting Out ◽

Opt Out

Background Recently, states have experienced widely varying participation in annual assessments, with the opt-out movement concentrated in New York State and Colorado. Geographic variation between and within states suggests that the diffusion of opting out is multilayered and an appropriate phenomenon to explore geographic dimensions of social movements in education. Purpose The study analyzes the geographic patterns of opting out from state assessments in school districts in New York State. Research Design We conducted linear regression and geographically weighted regression on district-level proportions of third- through eighth-grade students in local public school districts for 2015 and 2016 (n = 623), excluding New York City and charter schools. Independent variables included the district-level proportion of students with disabilities, identified as English Language Learners, and identified as White; census-based small-area child poverty estimates for the districts; and the geographic population density of the district. Linear regressions excluded racial and ethnic dummy variables to reduce collinearity problems, and geographically weighted regression limited geographically varying coefficients to child poverty and population density based on preliminary analyses. Findings The unweighted ordinary least squares (OLS) of district-level opting out in both spring 2015 and spring 2016 are weakly predictive as a whole (adjusted R2 < .20). In both years, population density was a statistically significant but low-magnitude predictor of change in opt-out behavior using OLS. The proportion of students with Individualized Education Plans was positively associated with opt-out behavior, and district-level child poverty was negatively associated with opt-out behavior. The proportion of White students was a statistically significant positive predictor of opt-out behavior in spring 2015 but not statistically significant for 2016, though with a coefficient in the same direction (positive). Analyzing the same data with geographically weighted regression more than doubled the adjusted R2 for each year and demonstrated that there were areas of New York State where the coefficients associated with child poverty and population density reversed direction, with suburban Long Island and the western upstate region as areas with a magnified negative association between district-level child poverty and opting-out percentages. Conclusions In the past five years, social networks have enabled the long-distance organizing of social and political movements in education, including opting-out and teacher walkouts. However, the long-distance transmission of ideas does not explain intrastate variations. In this study, geographically weighted regression revealed the local variations in relationships between opting-out and two key variables. Local networks still matter critically to social organizing around education.

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