Community emergency response teams and the wealth of communities in New Jersey

2006 ◽  
Vol 4 (6) ◽  
pp. 11
Author(s):  
Scott Phelps, JD, MPH, CEM, CBCP, Paramedic

This study examined median household income (MHI) of communities with community emergency response teams (CERTs). Preliminary data from New York City showed that in three of five counties, the mean MHI in CERT communities exceeded countywide MHI by up to $19,000. The research was then expanded to New Jersey, where, of 18 counties with CERTs, the mean MHI exceeded the countywide MHI in 15 counties (83 percent of the time). In counties where the mean CERT-community MHI was higher, it exceeded the county MHI by $6,060. Mean CERT-community MHI also exceeded the state’s MHI by over $5,000 ($60,745 versus $55,146). Given recent examples of the vulnerability of poor and working-class communities, emergency management agencies at all levels need to target CERT resources based on need, not on demand.

Author(s):  
Richard S. Whittle ◽  
Ana Diaz-Artiles

AbstractBackgroundNew York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the number of detected COVID-19 cases.MethodsData were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome and a set of 10 representative economic, demographic, and health-care associated ZCTA-level parameters as potential predictors. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion.ResultsMultiple different regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent (under 18 or 65+ years old) population, male to female ratio, and median household income. In the final model, we found that an increase of only 1% in dependent population is associated with a 2.5% increase in detected COVID-19 cases (95% confidence interval (CI): 1.6% to 3.4%, p < 0.0005). An increase of 1 male per 100 females is associated with a 1.0% (95% CI: 0.6% to 1.5%, p < 0.0005) increases in detected cases. A decrease of $10,000 median household income is associated with a 2.5% (95% CI: 1.0% to 4.1% p = 0.002) increase in detected COVID-19 cases.ConclusionsOur findings indicate associations between neighborhoods with a large dependent population, those with a high proportion of males, and low-income neighborhoods and detected COVID-19 cases. Given the elevated mortality in aging populations, the study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the number of detected cases, either in terms of the true number of cases or access to testing.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 16-16
Author(s):  
Guilherme Sacchi De Camargo Correia ◽  
Sridevi Rajeeve ◽  
Lawrence Cytryn

Factor XI (FXI) deficiency is a rare bleeding disorder. In the general population, prevalence is estimated to be 1:1 million people for the homozygous presentation (PMID: 25100430). Nonetheless, in individuals of Ashkenazi and Iraqi Jewish ancestry, the prevalence of heterozygous cases is approximately 8% (PMID: 7811996). However, these numbers may be underestimates, as some patients are asymptomatic and, so, not accounted for. Pregnant women are a special population, as FXI deficiency may pose an increased risk during pregnancy and delivery. This study describes the experience of a General Hematology Outpatient Service to which pregnant women with FXI deficiency are referred. This case series aims to describe the clinical course of these patients, and any complications and interventions they may have experienced during pregnancy and delivery. This retrospective study identified a group of 49 patients with FXI deficiency who were evaluated by a single practitioner at the Hematology Outpatient Service at Mount Sinai West, in New York City, between October 2016 and February 2020. Patients were found to be FXI deficient on routine genetic screening early in their obstetric care. Their charts were reviewed, including epidemiological data, notes from Hematology and Obstetric Clinics and from the admission for delivery and laboratory results. Four patients were excluded from the final analysis: 3 who were not pregnant, and 1 who did not have FXI deficiency. Patients were seen in by the Hematology Service at least once during their pregnancy. FXI activity was measured at least twice during pregnancy: at the initial visit, and at about gestational week 37. The data were analyzed to obtain the mean and standard deviation for the most relevant clinical parameters. A comparison between FXI activity at the first visit and at last visit near term was made with a paired T-test. The included group of 45 patients presented a mean age at delivery of 34.09 years (range 26-45 years). Genetic data was available for 42 patients, with 2.38% being homozygous. Ethnicities were described for 39 patients, and 71.79% were identified as Ashkenazi Jewish. Among 39 patients who had their FXI gene (gene NM_000128.3) mutations described, the c.901T&gt;C, p.F301L mutation was present in 61.54% of them. The mean FXI activity measured in the first appointment was 60.18%, (range 4-220%), while the mean FXI activity in week 37 of pregnancy was 52.08% (range 13-118%). When comparing the FXI activity on the first appointment and around week 37, no statistically significant difference was found (p=0.17). Four patients received preventive interventions on delivery. One patient was treated with Tranexamic Acid (TXA) and Fresh Frozen Plasma (FFP) transfusion due to a FXI activity of 21% on week 37, and received general anesthesia. Two patients received transfusion of FFP alone: 1 of them due to an elevated aPTT (57.4s) on delivery date, with no anesthesia on delivery; and the other one as a preventive measure in a patient with a FXI of 45% on week 37, but who was planned for a neuraxial block. A FXI activity of 40% is the cutoff for a neuraxial block by the Anesthesiology Department at our hospital. One patient was treated with TXA due to a borderline FXI activity of 42% and a personal history of bleeding on surgical procedures. She had an opioid patient-controlled analgesia on delivery. For the detailed data regarding mean blood loss on delivery, postpartum blood loss, and complete Hematologic and Obstetric data, see tables 1 and 2, and figures 1 and 2. Figure 3 presents a data comparison between the 2 most common genotypes observed. In our case series, no patient experienced bleeding complications during pregnancy or delivery. Monitoring FXI levels and aPTT throughout pregnancy and before delivery remains as the standard medical care (PMID: 27699729). The difference between FXI levels earlier in pregnancy and near delivery was not statistically significant, as noted in previous studies (PMID: 15199489). Checking FXI activity throughout pregnancy may not be necessary, and one measurement might be enough. Further study might be able to answer this question, as the optimal management of these patients remains a work in progress. Evidence for a reliable threshold FXI activity at which neuraxial anesthesia could be safely performed will be a valuable finding. Continuation of our study will allow for further data regarding the management of FXI deficient pregnant women. Disclosures No relevant conflicts of interest to declare.


1931 ◽  
Vol 25 (2) ◽  
pp. 238-251
Author(s):  
Blewett Lee

On September 15, 1930, the State Board of Commerce and Navigation of New Jersey made a ruling that aircraft would not be permitted to land on any New Jersey waters above tidewater within the jurisdiction of the state. The application had been made for permission to operate a five passenger flying boat between Nolan's Point, Lake Hopatcong, a vacation resort, and New York City, and to set off a portion of the lake to make a landing place for the hydroairplane. It was stated that other inland waters in New Jersey were being used for a similar purpose, and the ground of the refusal was that aircraft flying from water constituted a menace to surface navigation. This ruling created considerable newspaper comment and aroused vigorous protest from persons interested in aviation, and by order of October 20, 1930, the ruling was limited to Lake Hopatcong.


Geophysics ◽  
1969 ◽  
Vol 34 (2) ◽  
pp. 235-248 ◽  
Author(s):  
John T. Kuo ◽  
Mario Ottaviani ◽  
Shri K. Singh

Careful gravity measurements with La Coste‐Romberg geodetic gravimeters were carried out in tall buildings on a floor‐to‐floor basis in New York City and on the Armstrong Tower, Alpine, New Jersey. Corrections for the instrumental drift and tidal gravity variation and for the Bouguer effect, topography, mass of the buildings, and subway and basement excavations have been applied to the gravity data, which are tied to the absolute gravity value of the National Gravity Base Station of Washington, D. C. The observed gravity versus elevation curves are nonlinear, particularly near the surface of the ground; the slope of the observed gravity anomaly versus elevation curves reverses sign at an elevation of about 170 ft for the campus buildings and about 350 ft for the downtown buildings, and is nearly linear without a reversal for the Armstrong Tower. The vertical gradients vary substantially even within short distances. Comparisons of the corrected observed gradients with the theoretical gradients of gravity are made. The anomalous gradient anomalies are positive and are correlated with the positive isostatic surface gravity anomalies. Calibration of gravimeters against the observed vertical gradient of gravity to an accuracy of ±2 μgal is definitely feasible provided the gradient is predetermined to a comparable accuracy by a standard instrument.


2021 ◽  
Author(s):  
Adam T Schulman ◽  
Gyan Bhanot

The five boroughs of New York City (NYC) were early epicenters of the Covid-19 pandemic in the United States, with over 380,000 cases by May 31. High caseloads were also seen in nearby counties in New Jersey (NJ), Connecticut (CT) and New York (NY). The pandemic started in the area in March with an exponential rise in the number of daily cases, peaked in early April, briefly declined, and then, showed clear signs of a second peak in several counties. We will show that despite control measures such as lockdown and restriction of movement during the exponential rise in daily cases, there was a significant net migration of households from NYC boroughs to the neighboring counties in NJ, CT and NY State. We propose that the second peak in daily cases in these counties around NYC was due, in part, to the movement of people from NYC boroughs to these counties. We estimate the movement of people using "Change of Address" (CoA) data from the US Postal Service, provided under the "Freedom of Information Act" of 1967. To identify the timing of the second peak and the number of cases in it, we use a previously proposed SIR model, which accurately describes the early stages of the coronavirus pandemic in European countries. Subtracting the model fits from the data identified, we establish the timing and the number of cases, NCS, in the second peak. We then related the number of cases in the second peak to the county population density, P, and the excess Change of Address, ECoA, into each county using the simple model N_CS~P^α E_CoA^β which fits the data very well with α = 0.68, β = 0.31 (R^2 = 0.74, p = 1.3e-8). We also find that the time between the first and second peaks was proportional to the distance of the county seat from NY Penn Station, suggesting that this migration of households and disease was a directed flow and not a diffusion process. Our analysis provides a simple method to use change of address data to track the spread of an infectious agent, such as SARS-Cov-2, due to migrations away from epicenters during the initial stages of a pandemic.


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