New York State Counties Research Articles

Site Location Optimization Based on Genetic Algorithm

With the goal of providing food to people in need, the FBST serves nearly 4,000 square miles in six counties in New York State. The MFP program is among the main activities of FBST, using an MFP truck loaded with food to serve 70 sites in New York State. An effective and fair schedule for visiting all 70 sites is necessary to optimize the MFP plan. In order to describe the problem, a triad {G, R, C} of goal, resource and constraint is established according to the decision theory. Based on the established triad, this paper establishes a multi-objective optimization model with the goal of improving validity and fairness, a genetic algorithm has been adopted to solve the problem. The time schedule for visiting sites is finally obtained after the iteration of 2000 times, which took 156.40 seconds. Considering that customers can go to further sites, the number of service sites and the location of sites can be optimized. The K-means clustering algorithm based on the improved demand centroid solution method is used to solve the site location selection. After optimizing the location of the site, the performance of seniorsite and regular-site systems is improved by 30.755% and 36.413% respectively.

Exceptional upward mobility sin papeles: the case of Familia Arias and their fútbol team in New York City

In this paper I analyze cases of Mexican families in New York, which have experienced exceptional upward mobility while being headed by long-term undocumented immigrants. The focus is on Familia Arias, from Tlaxcala. They are part of a larger group of families who I identified in an amateur football team based in New York. I explain that some of these families are exceptional because out of 53 family-households headed by undocumented or DACA immigrants from Mexico in New York City and Long Island, only half a dozen reported an annual family income above $70,000 USD. Along the paper I explain how individual and group factors in each case combine to promote this upward socio-economic mobility. These mechanisms or processes that promote mobility for long term unauthorized immigrants, strongly interacting around amateur football teams, include (i) transferring binational labour skills, (ii) long-time tenure in jobs or prosperous self-employment, and (iii) positive intra-family dynamics. Stories such as the ones of Marcela and Claudio Arias are part of 92 individual ethnographic cases I developed and collected between the fall of 2014 and the summer of 2017 in two urban counties – the Bronx and Queens – and suburban towns in Suffolk County, Long Island. Almost all of these cases were incorporated into the Mexican Initiative for Deferred Action project, initiated in the summer of 2015, which includes other areas, regions, and counties in New York State and the metro area (screening over 1,700 individuals). The 92 cases studied and developed are mainly short biographies, labour and migratory trajectories of foreign-born Mexicans, most of them unauthorized immigrants, who have been living in New York for an average of 17 years with an estimated median family-household income of $43,000 USD annually. Fútbol, for participants in Queen, in New York City, was a key activity to develop strong local networks in the city.

A Novel Spatiotemporal Method for Predicting Covid-19 Cases

Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sucient supply of testing kits, hospital beds and medical personnel. This paper experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid- 19 pandemic cases. These ten methods include three types of neural networks and extrapola- tion methods based on best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method proposed by the second author. We also consider the Kriging and inverse distance weighting spatial interpolation methods. We also develop a novel spatiotemporal prediction method by combining temporal and spatial prediction methods. The experiments show that among these ten prediction methods, the spatiotemporal method has the smallest root mean square error and mean absolute error on Covid-19 cumulative data for counties in New York State between May and July, 2020.

Network effects in influenza spread: The impact of mobility and socio-economic factors

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016-2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.

Abstract 20: Patterns of Stroke Center Certification and the Impact on Stroke Mortality in Counties of New York State

Introduction: To establish a statewide stroke system in March 2019, New York State (NYS) created the Stroke Designation Program. Stroke centers (SCs) must be certified by a state-approved certifying organization (CO), which is tasked with initial designation and ongoing re-certification. Previous research has found an association at the national level between socioeconomic status and access to higher levels of acute stroke care. Objective: This study characterizes the relationship between socioeconomic status of NYS populations and stroke care level access by comparing median household income and wealth in counties with and without certified SCs. Methods: Population and median household income from the U.S. Census (2010), stroke epidemiological data from the Center for Disease Control, and Area Deprivation Index (ADI) data (ranked within NYS) from the Neighborhood Atlas, a project that quantifies disadvantage by census tract, were collected and averaged for each county. Income has been used to assess local wealth and ADI to analyze community health risks. Certification data were mined from quality check databases for The Joint Commission and Det Norske Veritas, the most commonly used COs. Student’s t-tests compared income and ADI in counties with at least one certified SC to those without. Linear regression characterized the relationship between income and ADI with number of certified SCs, stroke incidence and stroke mortality. Results: All 62 counties in NYS were investigated to yield 40 certified SCs. Counties with at least one certified SC had a significantly higher income ($68,183.63 vs. $57,155.12; p=0.03) and lower ADI (5.90 vs. 7.37; p=0.004) compared to counties with no certified SC. Higher income (p<0.001) and lower ADI (p<0.001) were also associated with more certified SCs. Counties with fewer certified SCs had significantly higher stroke mortality (p<0.001) despite having similar stroke incidence. Conclusion: Socioeconomic heterogeneity in NYS counties is correlated to differential access to certified SCs and quality stroke care, as fewer centers are found in lower-income and disadvantaged communities. Although populations with less access experience stroke at similar rates, this study finds higher death rates in these counties.

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State.

Background:Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.Objective:We examined the potential association between long-term exposure to particulate matter in aerodynamic diameter [fine particulate matter ()] and disease aggravation in Alzheimer’s (AD) and Parkinson’s (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.Methods:We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000–2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual concentrations estimated by a prediction model at a resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y exposure. We allowed for nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders.Results:We found a positive nonlinear association that plateaued above (, 95% CI: 1.04, 1.14 for a increase from 8.1 to ). We also found a linear positive association (, 95% CI: 1.01, 1.09 per increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.Conclusion:Our findings suggest that annual increase in county-level concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual concentration in our study was , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425

Increased risk of multiple pregnancy complications following large-scale power outages during Hurricane Sandy in New York State

Large-scale power outages (PO) are increasing in the context of climate change. Although some research has been conducted into the adverse health impacts of POs, significant gaps remain regarding whether POs would affect the health of pregnant women. We investigated the association between ED visits due to pregnancy complications and the occurence, intensity, and duration of large-scale POs in eight Sandy-affected counties in New York State (NYS). In this cross-sectional study, daily ED visits for pregnancy complications and large-scale PO data in eight counties in NYS from October to December in 2005-2014 were collected. Using time-series analysis, we estimated the relative increase in ED visits for pregnancy complications during POs compared with non-PO periods at lag 0-7days. Short-term health impacts of PO intensity and PO duration were investigated. Estimations were also stratified by sociodemographic characteristics and disease subtypes including threatened or spontaneous abortion, threatened or early labor, hypertension complications, infections of genitourinary tract, renal diseases, gestational diabetes mellitus, mental illnesses, and cardiovascular diseases during pregnancy. From October to December in 2005-2014, there were 307,739 ED visits for pregnancy complications in the eight counties. We found significant increases in ED visits for overall pregnancy complications (16.6%, 95% confidence interval [CI]: 10.3%, 23.2%) during the Hurricane-PO period at lag 0-7days. The ED visits increased by 8.8% per level increase in PO intensity and 1.4% per day increase in PO duration. Specifically, threatened/early delivery and gestational diabetes mellitus during the PO period increased by 26.7% (95% CI: 8.2%, 48.4%) and 111.8% (95% CI: 16.7%, 284.4%), respectively. Young adult, Black, Hispanic, and uninsured individuals were at higher risk of complications. POs may adversely impact pregnancy, especially for certain pregnancy complications and among low sociodemographic women.

Stepped-wedge randomized controlled trial of a novel opioid court to improve identification of need and linkage to medications for opioid use disorder treatment for court-involved adults

In response to the opioid crisis in New York State (NYS), the Unified Court System developed a new treatment court model-the opioid intervention court-designed around 10 Essential Elements of practice to address the flaws of existing drug courts in handling those with opioid addiction via broader inclusion criteria, rapid screening, and linkage to medications to treat opioid use disorder (MOUD). The new court model is now being rolled out statewide yet, given the innovation of the opioid court, the exact barriers to implementation in different counties with a range of resources are largely unknown. We describe a study protocol for the development and efficacy-test of a new implementation intervention (Opioid Court REACH; Research on Evidence-Based Approaches to Court Health) that will allow the opioid court, as framed by the 10 Essential Elements, to be scaled-up across 10 counties in NYS. Using a cluster-randomized stepped-wedge type-2 hybrid effectiveness-implementation design, we will test: (a) the implementation impact of Opioid Court REACH in improving implementation outcomes along the opioid cascade of care (screening, referral, treatment enrollment, MOUD initiation), and (b) the clinical and cost effectiveness of Opioid Court REACH in improving public health (treatment retention/court graduation) and public safety (recidivism) outcomes. Opioid Court REACH has the potential to improve management of individuals with opioid addiction in the court system via widespread scale-up of the opioid court model across the U.S., should this study find it to be effective.

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

BackgroundMore 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.MethodsCumulative 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.ResultsThe 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.ConclusionWe 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

429. County-level predictors of COVID-19 testing across the 62 counties in New York State: A comparison across machine learning algorithms

BackgroundMore than 360,000 people infected with COVID-19 in New York State (NYS) by the end of May 2020. Although expanded testing could effectively control statewide COVID-19 outbreak, the county-level factors predicting the number of testing are unknown. Accurately identifying the county-level predictors of testing may contribute to more effective testing allocation across counties in NYS. This study leveraged multiple public datasets and machine learning algorithms to construct and compare county-level prediction models of COVID-19 testing in NYS.MethodsTesting data by May 15th was extracted from the Department of Health in NYS. A total of 28 county-level predictors derived from multiple public datasets (e.g., American Community Survey and US Health Data) were used to construct the prediction models. Three machine learning algorithms, including generalized linear regression with the least absolute shrinkage and selection operator(LASSO), ridge regression, and regression tree, were used to identify the most important county-level predictors, adjusting for prevalence and incidence. Model performances were assessed using the mean square error (MSE), with smaller MSE indicating a better model performance.ResultsThe testing rate was 70.3 per 1,000 people in NYS. Counties (Rockland and Westchester) closed to the epicenter had high testing rates while counties (Chautauqua and Clinton) located at the boundary of NYS and were far away from the epicenter had low testing rates. The MSEs of linear regression with the LASSO penalty, ridge regression, and regression tree was 123.60, 40.59, and 298.0, respectively. Ridge regression was selected as the final model and revealed that the mental health provider rate was positively associated with testing (β=5.11, p=.04) while the proportion of religious adherents (β=-3.91, p=.05) was inversely related to the variation of testing rate across counties.ConclusionThis study identified healthcare resources and religious environment as the strongest predictor of spatial variations of COVID-19 testing across NYS. Structural or policy efforts should address the spatial variations and target the relevant county-level predictors to promote statewide testing.Disclosures All Authors: No reported disclosures

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

BackgroundCOVID-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.MethodsMortality 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0.1ResultsNYC 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.ConclusionUnique 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

449. Disproportionate of COVID-19 Mortality across NYC: Experience from the Bronx Hospital during the First Wave of Pandemic Crisis

BackgroundThe Bronx (BX) is an urban city with the most poverty, least educated population and poorest health outcomes among 62 counties in New York State. Unsurprisingly, BX has the highest rates of COVID-19 diagnoses across New York City (NYC). Lincoln Medical Center (LMC) is part of the NYC health and hospital system (NYC H+H) and has the highest COVID-19 admissions in BX and the second-highest across NYC. Herein we report our preliminary data on mortality rate (MR) of hospitalized COVID-19 patients and discuss the disproportionate of MR across NYC.MethodsOn 26 April 2020, we acquired the total number of hospitalized COVID-19 and deaths and mechanically ventilated (MV) COVID-19 and death from LMC and all other NYC H+H. Scheffe test was used to determine MR differences. The P-value (p) < 0.005 was set as a statistically significant threshold.ResultsMR of our hospitalized COVID-19 patients was 31%, which is higher than that of Man (24%, p 0.001). However, LMC has a high proportion of MV COVID-19 (local and transferred cases). Sub-group analysis of non-MV COVID-19 showed LMC MR (6%) is lower than Brooklyn (BL) (17%, p 0.00) and Queens (Qu) (17%, p 0.00) and didn’t differ from Man NYC H+H centers (8%, p 0.68). Analysis of MR among MV patients between LMC and other NYC H+H centers across four boroughs did not discover any differences.Hospitalized COVID-19 MR from all NYC H+H centers in BX (28%) did not differ from Man (24%, p 0.7) and Qu (28%, p 0.99). Interestingly, we found that MR is the highest in BL (33%). Moreover, MR of non-MV COVID-19 was higher in BL (17%) and Qu (17%) than BX (10%) and Man (8%) NYC H+H centers. We hypothesize this may result partly from the tense and shortage of health care resources in these two boroughs, especially, at the beginning of pandemic so some critical patients may not receive adequate care such as delaying intubation. Further research investigating reasons for this disproportion will help in developing the best available care plan for the ongoing crisis.Percentage of COVID-19 Mortality in each group Multiple comparison by Scheffe Test of MR of hospitalized patients with COVID-19 at LMC and across 4 NYC boroughs Multiple comparison by Scheffe Test of MR of non-MV patients with COVID-19 at LMC and across 4 NYC boroughs ConclusionDespite the high COVID-19 incidence and poor epidemiologic health risks of the population in BX, MR of hospitalized COVID-19 seemed to be the same as Man and Qu, and surprisingly lower than BL. MR of non-MV COVID-19 in BX is lower than BL and Qu. The studies determining the reasons underlying this disproportionate would be worthwhile.Disclosures All Authors: No reported disclosures

Aedes japonicus and West Nile Virus in New York.

Identifying the array of vectors that play a role in perpetuating West Nile virus (WNV) infection in endemic foci will help in controlling the disease. Aedes japonicus has the potential to be a vector in the wild of at least 3 kinds of encephalitis, including WNV. Aedes japonicus is a nonnative species in the USA that is temperature tolerant and a potential human biter. Detection of WNV in mosquito pools of this field-collected invasive species, combined with their ability to feed on humans, make this mosquito species a possible public health concern. In this study, we collected mosquito abundance data and tested them for WNV-positive mosquito samples from 3 counties in New York State. We found a significant association between the season and land demography and the likelihood of the virus in Ae. japonicus.

Sunlight and Protection Against Influenza

Recent medical literature suggests that vitamin D supplementation protects against acute respiratory tract infection. Humans exposed to sunlight produce vitamin D directly. This paper investigates how differences in sunlight, as measured over several years across states and during the same calendar week, affect influenza incidence. We find that sunlight strongly protects against getting influenza. This relationship is driven almost entirely by the severe H1N1 epidemic in fall 2009. A 10% increase in relative sunlight decreases the influenza index in September or October by 1.1 points on a 10-point scale. A second, complementary study employs a separate data set to study flu incidence in counties in New York State. The results are strongly in accord.

Predicting spatial and temporal responses to non-pharmaceutical interventions on COVID-19 growth rates across 58 counties in New York State: A prospective event-based modeling study on county-level sociological predictors.

Non-pharmaceutical interventions (NPIs) have been implemented in the New York State since the COVID-19 outbreak on March 1, 2020 to control the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Socioeconomic heterogeneity across counties closely manifests differences in the post-NPIs growth rate of incidence, which is a crucial indicator to guide future infectious control policy making. Few studies, however, examined the geospatial and sociological variations in the epidemic growth across different time points of NPIs. To guide a more effective reopening plan while controlling the transmission, the current study aims at 1) identifying hotspots of the growth rate of COVID-19 incidence among the 57 counties and New York City in NYS over time, and 2) examining the association of COVID-19 growth rates after eight critical NPIs time points and most relevant county-level sociological predictors. County-level COVID-19 incidence rates were retrieved from the Social Explorer Website between March 7, 2020 to June 22, 2020. 5-day moving average growth rates of COVID-19 incidence were calculated for 16 selected time points, including the dates of eight NPIs and their respective 14-day-lag-behind time points. A total of 36 county-level indicators were extracted from multiple public datasets. Geospatial mapping and heatmap were used to analyze spatial and temporal heterogeneity of county-level COVID-19 outbreak over selected NPIs-related dates. Generalized mixed effect least absolute shrinkage and selection operator (LASSO) regression, controlling for the 5-day moving average growth rates of COVID-19 testing rates, was employed to identify significant county-level predictors related to the changes of county-level COVID-19 growth rates over time. COVID-19 infection increased and peaked by the end of March (η=22.50%). Growth rates of COVID-19 decreased by 50.48% after implementing NPIs such as closures of schools, non-essential businesses, parks, and subways. There was a geospatial shift in the region with the highest growth rates from New York metropolitan area towards Western and Northern regions over time. Proportions of population aged 45 years and above (β=3.25 [0.17-6.32]), living alone at residential houses (β=3.31 [0.39--6.22]), and proportion of crowd residential houses (β=6.15 [2.15-10.14]) were positively associated with the growth rate of COVID-19 infection. In contrast, living alone at rental houses (β=-2.47 [-4.83--0.12]) and rate of mental health providers (β=-1.11 [-1.95--0.28]) were negatively associated with COVID-19 growth rate across all 16 time points. There are geospatial differences in COVID-19 incidence after implementing different NPIs. Socioeconomic, racial/ethnic, and healthcare resource disparities at the structural and historical levels across counties need to be considered in infection control policymaking to narrow the unequal health impact on vulnerable populations effectively.

Quebec’s Fracking Restrictions: Squandered Opportunity or Prudent Conservation?

ABSTRACT This article first reviews key developments in the Quebec natural gas fracking timeline from initial exploratory well fracking in 2007 to the 2011 moratorium and the 2018 fracking prohibition. Next, the article estimates potential jobs and wealth losses from the prohibition in Quebec via a comparison between intensive fracking in northeastern Pennsylvania (which is similar in size to Quebec’s core potential fracking region) and adjacent counties in New York state where fracking is prohibited. Quebec’s job loss from fracking prohibition is estimated to be quite modest. Moreover, the estimated wealth effect range shows that allowing development of Quebec’s shale gas reserves may well have led to net wealth losses rather than gains for its citizens because of large declines in natural gas prices. While future shale gas development in Quebec is a possibility, given the preferences of Quebec’s citizens, the weak profitability prospects of the shale gas sector, and the ongoing cost reductions for solar and wind power, it seems most likely that Quebec’s Utica Shale gas reserves will remain undeveloped and that this will be a sound policy decision.

Association Between Home Health Services and Facility Admission in Older Adults With and Without Alzheimer's Disease

To evaluate the association between home health (HH) services, including skilled nursing (SN), physical therapy (PT), occupational therapy, social work (SW), and homemaking aide assistance with the hazard of unplanned facility admissions among Medicare patients with and without Alzheimer's disease and related dementias (ADRD). Analysis of the Outcome and Assessment Information Set and billing records. A not-for-profit HH agency serving multiple counties in New York State. Adults ≥65years old who received HH from January 1, 2017 to December 31, 2017. Outcome was time from HH start of care to an unplanned facility admission of any type, including the hospital, nursing home, and rehabilitation facility. Independent variables included weekly intensity (visits/week, hours/week) of SN, PT, occupational therapy, SW, and, homemaking aide assistance separately. ADRD was identified by diagnosis (International Classification of Diseases, Tenth Revision codes in billing records) and cognitive impairment assessment (Outcome and Assessment Information Set). Of the sample (N=6153), 14.9% had an unplanned facility admission. In multivariable Cox proportional hazard models that adjusted for time-varying effects of HH intensity and covariates, receiving the highest intensity of SN (3.3 visits of 2.78hours per week) and PT (2.5 visits of 2hours per week) was related to up to a 54% and 86% decrease, respectively, in the hazard of unplanned facility admission among patients with ADRD (n=1525), and decreases of 56% and 90%, respectively, among patients without ADRD (n=4628). Receiving any SW was related to 40% decreased in the hazard of facility admission in patients without ADRD only. Other HH services were not consistently related to the risk of facility admission. Receiving a higher intensity of SN and PT was associated with reduced hazards of unplanned facility admission among HH patients with and without ADRD. Policies should ensure that patients with ADRD receive a sufficient amount and appropriate mix of HH services.

Environmental Determinants of Aedes albopictus Abundance at a Northern Limit of Its Range in the United States.

.Aedes albopictus is a vector of arboviruses with high rates of morbidity and mortality. The northern limit of Ae. albopictus in the northeastern United States runs through New York state (NYS) and Connecticut. We present a landscape-level analysis of mosquito abundance measured by daily counts of Ae. albopictus from 338 trap sites in 12 counties during May–September 2017. During the study period, the mean number of Ae. albopictus caught per day of trapping across all sites was 3.21. We constructed four sets of negative binomial generalized linear models to evaluate how trapping methodology, land cover, as well as temperature and precipitation at multiple time intervals influenced Ae. albopictus abundance. Biogents-Sentinel (BGS) traps were 2.78 times as efficient as gravid traps and 1.49 times as efficient as CO2-baited CDC light traps. Greater proportions of low- and medium-intensity development and low proportions of deciduous cover around the trap site were positively associated with increased abundance, as were minimum winter temperature and March precipitation. The cumulative precipitation within a 28-day time window before the date of collection had a nonlinear relationship with abundance, such that greater cumulative precipitation was associated with increased abundance until approximately 70 mm, above which there was a decrease in abundance. We concluded that populations are established in Nassau, Suffolk, and New York City counties in NYS; north of these counties, the species is undergoing population invasion and establishment. We recommend that mosquito surveillance programs monitoring the northward invasion of Ae. albopictus place BGS traps at sites chosen with respect to land cover.

Urban-Normative Reforms Missing the Mark Rural School Consolidation in New York State, USA

Abstract: For close to fifty years, rural and non-metro counties in New York State have suffered economic and demographic declines. This paper examines the impact of urbannormative educational reform efforts on one community, and the continued crisis emerging after the consolidated district closed the elementary school. Using Discourse Analysis, two sets of voices are interrogated for their narrative during the crisis. The paper uses social and traditional media in an effort to uncover often ignored or forgotten voices who argue against the public, dominate narrative concerning rural educational reforms in the United States.

The immediate and lasting impact of Hurricane Sandy on pregnancy complications in eight affected counties of New York State

The frequency and intensity of hurricane have increased greatly. However, whether hurricane exposure is associated with an increased risk of pregnancy complications is less known. To assess the immediate impact and lasting impact of Hurricane Sandy (Sandy) on pregnancy complications. Using time-series study, we estimated the relative risks (RRs) of emergency department (ED) visits for pregnancy complications in eight affected counties in New York State, based on data of 2005-2014. The immediate impact was estimated by comparing the ED visits of pregnancy complications during the Sandy period to the non-Sandy periods. For the lasting impact of Sandy, we estimated the RRs by contrasting the ED visits in the following 12 months after Sandy with the same months of other years. We found that ED visits for overall pregnancy complications increased 6.3% (95% confidence interval (CI): 2.2%, 10.5%) during the Sandy month. ED visits increased for threatened abortion (9.9%, 95% CI: 4.4%, 15.7%), threatened labor (10.1%, 95% CI: 1.9%, 18.9%), early onset of delivery (115.9%, 95% CI: 6.9%, 336.3%), renal disease (73.2%, 95% CI: 0.3%, 199.4%), and diabetes (42.3%, 95% CI: 15.0%, 76.0%). Gestational hypertension and renal disease were elevated 7-8 months after Sandy. The ED visits of mental illness increased gradually after Sandy and peaked eight months later with visits increasing 33.2%. This study suggests that hurricanes may impact pregnancy health immediately and that some negative health may last for months thereafter.