BACKGROUND: Particulate matter exposure (PM) is a cause of aerodigestive disease globally. The destruction of the World Trade Center (WTC) exposed first responders and inhabitants of New York City to WTC-PM and caused obstructive airways disease (OAD), gastroesophageal reflux disease (GERD) and Barrett\'s Esophagus (BE). GERD not only diminishes health-related quality of life but also gives rise to complications that extend beyond the scope of BE. GERD can incite or exacerbate allergies, sinusitis, bronchitis, and asthma. Disease features of the aerodigestive axis can overlap, often necessitating more invasive diagnostic testing and treatment modalities. This presents a need to develop novel non-invasive biomarkers of GERD, BE, airway hyperreactivity (AHR), treatment efficacy, and severity of symptoms.
METHODS: Our observational case-cohort study will leverage the longitudinally phenotyped Fire Department of New York (FDNY)-WTC exposed cohort to identify Biomarkers of Airway Disease, Barrett\'s and Underdiagnosed Reflux Noninvasively (BAD-BURN). Our study population consists of n = 4,192 individuals from which we have randomly selected a sub-cohort control group (n = 837). We will then recruit subgroups of i. AHR only ii. GERD only iii. BE iv. GERD/BE and AHR overlap or v. No GERD or AHR, from the sub-cohort control group. We will then phenotype and examine non-invasive biomarkers of these subgroups to identify under-diagnosis and/or treatment efficacy. The findings may further contribute to the development of future biologically plausible therapies, ultimately enhance patient care and quality of life.
CONCLUSIONS: Although many studies have suggested interdependence between airway and digestive diseases, the causative factors and specific mechanisms remain unclear. The detection of the disease is further complicated by the invasiveness of conventional GERD diagnosis procedures and the limited availability of disease-specific biomarkers. The management of reflux is important, as it directly increases risk of cancer and negatively impacts quality of life. Therefore, it is vital to develop novel noninvasive disease markers that can effectively phenotype, facilitate early diagnosis of premalignant disease and identify potential therapeutic targets to improve patient care.
BACKGROUND: Name of Primary Registry: \"Biomarkers of Airway Disease, Barrett\'s and Underdiagnosed Reflux Noninvasively (BADBURN)\". Trial Identifying Number: NCT05216133 . Date of Registration: January 31, 2022.

UNASSIGNED: Our study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.
UNASSIGNED: New York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.
UNASSIGNED: COVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.
UNASSIGNED: This study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

BACKGROUND: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time.
METHODS: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City.
RESULTS: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors.
CONCLUSIONS: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them.
CONCLUSIONS: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

Black and Hispanic patients have high rates of recurrent stroke and uncontrolled hypertension in the US. The effectiveness of home blood pressure telemonitoring (HBPTM) and telephonic nurse case management (NCM) among low-income Black and Hispanic patients with stroke is unknown.
To determine whether NCM plus HBPTM results in greater systolic blood pressure (SBP) reduction at 12 months and lower rate of stroke recurrence at 24 months than HBPTM alone among Black and Hispanic stroke survivors with uncontrolled hypertension.
Practice-based, multicenter, randomized clinical trial in 8 stroke centers and ambulatory practices in New York City. Black and Hispanic study participants were enrolled between April 18, 2014, and December 19, 2017, with a final follow-up visit on December 31, 2019.
Participants were randomly assigned to receive either HBPTM alone (12 home BP measurements/week for 12 months, with results transmitted to a clinician; n = 226) or NCM plus HBPTM (20 counseling calls over 12 months; n = 224).
Primary outcomes were change in SBP at 12 months and rate of recurrent stroke at 24 months. Final statistical analyses were completed March 14, 2024.
Among 450 participants who were enrolled and randomized (mean [SD] age, 61.7 [11.0] years; 51% were Black [n = 231]; 44% were women [n = 200]; 31% had ≥3 comorbid conditions [n = 137]; 72% had household income <$25 000/y [n = 234/324]), 358 (80%) completed the trial. Those in the NCM plus HBPTM group had a significantly greater SBP reduction than those in the HBPTM alone group at 12 months (-15.1 mm Hg [95% CI, -17.2 to -13.0] vs -5.8 mm Hg [95% CI, -7.9 to -3.7], respectively; P < .001). The between-group difference in SBP reduction at 12 months, adjusted for primary care physician clustering, was -8.1 mm Hg (95% CI, -11.2 to -5.0; P < .001) at 12 months. The rate of recurrent stroke was similar between both groups at 24 months (4.0% in the NCM plus HBPTM group vs 4.0% in the HBPTM alone group, P > .99).
Among predominantly low-income Black and Hispanic stroke survivors with uncontrolled hypertension, addition of NCM to HBPTM led to greater SBP reduction than HBPTM alone. Additional studies are needed to understand the long-term clinical outcomes, cost-effectiveness, and generalizability of NCM-enhanced telehealth programs among low-income Black and Hispanic stroke survivors with significant comorbidity.
Clinical Trials.gov Identifier: NCT02011685.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents\' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model\'s dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

In this case study, we describe a well-resourced private school in New York City that implemented COVID-19 mitigation measures based on public health expert guidance and the lessons learned from this process. Avenues opened in New York City in 2012 and has since expanded, becoming Avenues: The World School, with campuses in São Paulo, Brazil; Shenzhen, China; the Silicon Valley, California; and online. It offers education at 16 grade levels: 2 early learning years, followed by a prekindergarten through grade 12. We describe the mitigation measures that Avenues implemented on its New York campus. We compare COVID-19 case prevalence at the school with COVID-19 case positivity in New York City, as reported by the New York State Department of Health. We also compare the school\'s indoor air quality to ambient indoor air quality measures reported in the literature. The school\'s mitigation measures successfully reduced the prevalence of COVID-19 among its students, staff, and faculty. The school also established a consistently high level of indoor air quality safety through various ventilation mechanisms, designed to reduce common indoor air pollutants. The school received positive parent and community feedback on the policies and procedures it established, with many parents commenting on the high level of trust and quality of communication established by the school. The successful reopening provides useful data for school closure and reopening standards to prepare for future pandemic and epidemic events.

This study reviews the impact of eligibility policies in the early rollout of the COVID-19 vaccine on coverage and probable outcomes, with a focus on New York City. We conducted a retrospective ecological study assessing age  65+, area-level income, vaccination coverage, and COVID-19 mortality rates, using linked Census Bureau data and New York City Health administrative data aggregated at the level of modified zip code tabulation areas (MODZCTA). The population for this study was all individuals in 177 MODZCTA in New York City. Population data were obtained from Census Bureau and New York City Health administrative data. The total mortality rate was examined through an ordinary least squares (OLS) regression model, using area-level wealth, the proportion of the population aged 65 and above, and the vaccination rate among this age group as predictors. Low-income areas with high proportions of older people demonstrated lower coverage rates (mean vaccination rate 52.8%; maximum coverage 67.9%) than wealthier areas (mean vaccination rate 74.6%; maximum coverage 99% in the wealthiest quintile) in the first 3 months of vaccine rollout and higher mortality over the year. Despite vaccine shortages, many younger people accessed vaccines ahead of schedule, particularly in high-income areas (mean coverage rate 60% among those 45-64 years in the wealthiest quintile). A vaccine program that prioritized those at greatest risk of COVID-19-associated morbidity and mortality would have prevented more deaths than the strategy that was implemented. When rolling out a new vaccine, policymakers must account for local contexts and conditions of high-risk population groups. If New York had focused limited vaccine supply on low-income areas with high proportions of residents 65 or older, overall mortality might have been lower.

OBJECTIVE: Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC).
METHODS: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level.
RESULTS: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC.
CONCLUSIONS: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.

The COVID-19 pandemic originated in 2019 and has become an endemic disease that we must learn to live with, similar to other strains of influenza. The Organization (WHO) declared on May 5, 2023, in Geneva, Switzerland, the end of the Public Health Emergency of International Concern regarding COVID-19. As vaccines become more widely available and the pandemic appears to be improved, our focus shifts to the challenges we still face. Understanding how external factors like temperature, air humidity, and social isolation impact the spread of the SARS-CoV-2 virus remains a crucial challenge beyond our control. In this study, potential links between the number of COVID-19 cases in São Paulo City (SPC) and New York City (NWC) were explored. Our analysis was carried out utilizing the continuous wavelet transform, alongside other tools such as cross-wavelet transform and wavelet coherence. Based on our findings, there appears to be a correlation between the variables related to low frequencies, which aligns with previous research on the topic. Particularly, our research has revealed a connection between COVID-19 cases and factors such as temperature, air humidity, and social isolation rates. Regarding the latter, our findings indicate that implementing social distancing measures was a wise public policy decision, although the correlation with daily COVID-19 cases requires careful analysis. For this study, we analyzed data from February of 2020, when the first cases were reported in the cities under investigation, SPC and NWC, up until December 31, 2022, by which time the vaccination campaign was well under way.

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