scholarly journals Failure of Concentric Regulatory Zones to Halt the Spread of COVID-19 in South Brooklyn, New York: October-November 2020

2021 ◽  
Author(s):  
Jeffrey Harris
Keyword(s):  
New York ◽  
Confidence Interval ◽  
Regression Model ◽  
Reproduction Number ◽  
High Mobility ◽  
General Analysis ◽  
Location Tracking ◽  
Regulatory Scheme ◽  
Access Controls ◽  
Zip Code

We relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in South Brooklyn in the fall of 2020. The regulatory scheme imposed differential controls on access to eating places, schools, houses of worship, large gatherings and other businesses within the three zones, but without restrictions on mobility. Within the central red zone, COVID-19 incidence temporarily declined from 131.2 per 100,000 population during the week ending October 3 to 62.5 per 100,000 by the week ending October 31, but then rebounded to 153.6 per 100,000 by the week ending November 28. Within the intermediate orange and peripheral yellow zones combined, incidence steadily rose from 28.8 per 100,000 during the week ending October 3 to 109.9 per 100,000 by the week ending November 28. Data on device visits to pairs of eating establishments straddling the red-orange boundary confirmed compliance with access controls. More general analysis of device movements showed stable patterns of mobility between and beyond zones unaffected by the Governor's orders. A geospatial regression model of COVID-19 incidence in relation to device movements across zip code tabulation areas identified a cluster of five high-mobility ZCTAs with estimated reproduction number 1.91 (95% confidence interval, 1.27-2.55). In the highly populous area of South Brooklyn, controls on access alone, without restrictions on mobility, were inadequate to halt an advancing COVID-19 outbreak.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals A large-scale retrospective study of opioid poisoning in New York State with implications for targeted interventions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Chen ◽  
Wei Hou ◽  
Sina Rashidian ◽  
Yu Wang ◽  
Xia Zhao ◽  
...  
Keyword(s):  
New York ◽  
Race And Ethnicity ◽  
Large Scale ◽  
New York State ◽  
Temporal Trends ◽  
Opioid Overdose ◽  
York State ◽  
Targeted Interventions ◽  
Level Data ◽  
Zip Code

AbstractOpioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

scholarly journals Caring for Pregnant Patients with COVID-19: Practical Tips Getting from Policy to Practice

2020 ◽  
Vol 37 (08) ◽  
pp. 850-853 ◽  
Author(s):  
Viktoriya London ◽  
Rodney McLaren ◽  
Janet Stein ◽  
Fouad Atallah ◽  
Nelli Fisher ◽  
...  
Keyword(s):  
New York ◽  
Pregnant Women ◽  
National Standards ◽  
Key Points ◽  
High Prevalence ◽  
Psychosocial Challenges ◽  
Novel Coronavirus ◽  
Hospital Protocols ◽  
Zip Code

Novel coronavirus disease 2019 (COVID-19) is a pandemic with most American cases in New York. As an institution residing in a high-prevalence zip code, with over 8,000 births annually, we have cared for over 80 COVID-19-infected pregnant women, and have encountered many challenges in applying new national standards for care. In this article, we review how to change outpatient and inpatient practices, develop, and disseminate new hospital protocols, and we highlight the psychosocial challenges for pregnant patients and their providers. Key Points

Clinical and Demographic Risk Factors for COVID-19 during Delivery Hospitalizations in New York City

2021 ◽  
Author(s):  
Desmond Sutton ◽  
Timothy Wen ◽  
Anna P. Staniczenko ◽  
Yongmei Huang ◽  
Maria Andrikopoulou ◽  
...  
Keyword(s):  
Risk Factors ◽  
New York ◽  
New York City ◽  
Logistic Regression ◽  
York City ◽  
Demographic Data ◽  
Outcome Data ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Zip Code

Objective This study was aimed to review 4 weeks of universal novel coronavirus disease 2019 (COVID-19) screening among delivery hospitalizations, at two hospitals in March and April 2020 in New York City, to compare outcomes between patients based on COVID-19 status and to determine whether demographic risk factors and symptoms predicted screening positive for COVID-19. Study Design This retrospective cohort study evaluated all patients admitted for delivery from March 22 to April 18, 2020, at two New York City hospitals. Obstetrical and neonatal outcomes were collected. The relationship between COVID-19 and demographic, clinical, and maternal and neonatal outcome data was evaluated. Demographic data included the number of COVID-19 cases ascertained by ZIP code of residence. Adjusted logistic regression models were performed to determine predictability of demographic risk factors for COVID-19. Results Of 454 women delivered, 79 (17%) had COVID-19. Of those, 27.9% (n = 22) had symptoms such as cough (13.9%), fever (10.1%), chest pain (5.1%), and myalgia (5.1%). While women with COVID-19 were more likely to live in the ZIP codes quartile with the most cases (47 vs. 41%) and less likely to live in the ZIP code quartile with the fewest cases (6 vs. 14%), these comparisons were not statistically significant (p = 0.18). Women with COVID-19 were less likely to have a vaginal delivery (55.2 vs. 51.9%, p = 0.04) and had a significantly longer postpartum length of stay with cesarean (2.00 vs. 2.67days, p < 0.01). COVID-19 was associated with higher risk for diagnoses of chorioamnionitis and pneumonia and fevers without a focal diagnosis. In adjusted analyses, including demographic factors, logistic regression demonstrated a c-statistic of 0.71 (95% confidence interval [CI]: 0.69, 0.80). Conclusion COVID-19 symptoms were present in a minority of COVID-19-positive women admitted for delivery. Significant differences in obstetrical outcomes were found. While demographic risk factors demonstrated acceptable discrimination, risk prediction does not capture a significant portion of COVID-19-positive patients. Key Points

scholarly journals Estimating COVID-19 cases infected with the variant alpha (VOC 202012/01): an analysis of screening data in Tokyo, January-March 2021

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hiroaki Murayama ◽  
Taishi Kayano ◽  
Hiroshi Nishiura
Keyword(s):  
Confidence Interval ◽  
Growth Model ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Hypergeometric Distribution ◽  
Actual Number ◽  
Growth Data ◽  
Exponential Growth Model

Abstract Background In Japan, a part of confirmed patients’ samples have been screened for the variant of concern (VOC), including the variant alpha with N501Y mutation. The present study aimed to estimate the actual number of cases with variant alpha and reconstruct the epidemiological dynamics. Methods The number of cases with variant alpha out of all PCR confirmed cases was estimated, employing a hypergeometric distribution. An exponential growth model was fitted to the growth data of variant alpha cases over fourteen weeks in Tokyo. Results The weekly incidence with variant alpha from 18–24 January 2021 was estimated at 4.2 (95% confidence interval (CI): 0.7, 44.0) cases. The expected incidence in early May ranged from 420–1120 cases per week, and the reproduction number of variant alpha was on the order of 1.5 even under the restriction of contact from January-March, 2021, Tokyo. Conclusions The variant alpha was predicted to swiftly dominate COVID-19 cases in Tokyo, and this has actually occurred by May 2021. Devising the proposed method, any country or location can interpret the virological sampling data.

Primary Uterine Cancer in Maryland: Impact of Distance on Access to Surgical Care at High-Volume Hospitals

2013 ◽  
Vol 23 (7) ◽  
pp. 1244-1251 ◽  
Author(s):  
Camille C. Gunderson ◽  
Ana I. Tergas ◽  
Aimee C. Fleury ◽  
Teresa P. Diaz-Montes ◽  
Robert L. Giuntoli
Keyword(s):  
Confidence Interval ◽  
Odds Ratio ◽  
Uterine Cancer ◽  
Multivariable Analysis ◽  
Community Setting ◽  
Surgical Care ◽  
High Volume ◽  
High Volume Hospital ◽  
To Receive ◽  
Zip Code

ObjectiveTo evaluate the influence of distance on access to high-volume surgical treatment for patients with uterine cancer in Maryland.MethodsThe Maryland Health Services Cost Review Commission database was retrospectively searched to identify primary uterine cancer surgical cases from 1994 to 2010. Race, type of insurance, year of surgery, community setting, and both surgeon and hospital volume were collected. Geographical coordinates of hospital and patient’s zip code were used to calculate primary independent outcomes of distance traveled and distance from nearest high-volume hospital (HVH). Logistic regression was used to calculate odds ratios and confidence intervals.ResultsFrom 1994 to 2010, 8529 women underwent primary surgical management of uterine cancer in Maryland. Multivariable analysis demonstrated white race, rural residence, surgery by a high-volume surgeon and surgery from 2003 to 2010 to be associated with both travel 50 miles or more to the treating hospital and residence 50 miles or more from the nearest HVH (allP< 0.05). Patients who travel 50 miles or more to the treating hospital are more likely to have surgery at a HVH (odds ratio, 6.03; 95% confidence interval, 4.67–7.79) In contrast, patients, who reside ≥50 miles from a HVH, are less likely to have their surgery at an HVH. (odds ratio, 0.37; 95% confidence interval, 0.32–0.42).ConclusionIn Maryland, 50 miles or more from residence to the nearest HVH is a barrier to high-volume care. However, patients who travel 50 miles or more seem to do so to receive care by a high-volume surgeon at an HVH. In Maryland, Nonwhites are more likely to live closer to an HVH and more likely to use these services.

Failure to Identify Human Immunodeficiency Virus-Seropositive Newborns: Epidemiology and Enrollment Patterns in a Predominantly White, Nonurban Setting

PEDIATRICS ◽  
1995 ◽  
Vol 96 (6) ◽  
pp. 1083-1089
Author(s):  
James Coplan ◽  
Timothy D. Dye ◽  
Kathie A. Contello ◽  
Coleen K. Cunningham ◽  
Kim Kirkwood ◽  
...  
Keyword(s):  
New York ◽  
Human Immunodeficiency Virus ◽  
Confidence Interval ◽  
Predominantly White ◽  
Pediatric Aids ◽  
Immunodeficiency Virus ◽  
Enrollment Rates ◽  
First 90 Days ◽  
Onondaga County ◽  
Hiv Seropositive

Objective. To describe the epidemiology of newborn seroprevalence for human immunodeficiency virus (HIV) in a predominantly white, nonurban population, and to determine the factors associated with enrollment at a regional pediatric acquired immunodeficiency syndrome (AIDS) center serving that population. Design. Retrospective case series of children enrolled at a regional pediatric AIDS center during a 6-year period and comparison with universal blind newborn screening data collected by the state of New York during the same time interval. Setting. The Pediatric AIDS Center at State University of New York-Health Science Center at Syracuse, which serves as the only source of HIV-related pediatric care for children in a 16-county region of upstate New York totaling 1.8 million population. Results. One hundred thirty-nine HIV-seropositive infants were born in the region during the 6-year study period; complete blind screening data were available for 138. Sixty-five (47%) of these infants were white. Thirty-nine (28%) of 138 had been enrolled at the Pediatric AIDS Center within the first 90 days of life. An additional 22 (16%) were enrolled at older than 90 days of life. The remaining 77 (56%) have never been seen at the center and are presumed to be unidentified. County enrollment rates varied from 0% to 100% and correlated with percent nonwhite births (r = .58; 95% confidence interval, 0.04-0.86). Children in outlying counties were at greater risk for nonenrollment than children from Onondaga County (site of the Pediatric AIDS Center) (adjusted relative risk, 1.38; 95% confidence interval, 1.05-1.85). White infants residing outside of Onondaga County were at the greatest risk of nonenrollment; of 50 seropositive white infants residing outside of Onondaga County, only 7(14%) were enrolled at the center within the first 90 days of life. Conclusions. Local demographic factors can skew the racial distribution of HIV-seropositive infants dramatically compared with the national experience. White race and residence in counties away from the medical center each constituted risk factors for nonenrollment at the Pediatric AIDS Center. The epidemiology of HIV in this predominantly white, rural population, coupled with physician practices, probably contributed to low identification and enrollment rates. As the AIDS epidemic spreads into similar populations elsewhere, HIV infection in pregnant women or newborn infants is likely to become progressively harder to detect, unless universal screening is adopted.

scholarly journals Prediction of Hospital Mortality Rates by Admission Laboratory Tests

2006 ◽  
Vol 52 (2) ◽  
pp. 325-328 ◽  
Author(s):  
Paul Froom ◽  
Zvi Shimoni
Keyword(s):  
Internal Medicine ◽  
Logistic Regression ◽  
Confidence Interval ◽  
Regression Model ◽  
Odds Ratio ◽  
Laboratory Tests ◽  
Logistic Regression Model ◽  
Complete Blood Count ◽  
Laboratory Data ◽  
Regional Hospital

Abstract Background: The aim of this study was to explore whether electronically retrieved laboratory data can predict mortality in internal medicine departments in a regional hospital. Methods: All 10 308 patients hospitalized in internal medicine departments over a 1-year period were included in the cohort. Nearly all patients had a complete blood count and basic clinical chemistries on admission. We used logistic regression analysis to predict the 573 deaths (5.6%), including all variables that added significantly to the model. Results: Eight laboratory variables and age significantly and independently contributed to a logistic regression model (area under the ROC curve, 88.7%). The odds ratio for the final model per quartile of risk was 6.44 (95% confidence interval, 5.42–7.64), whereas for age alone, the odds ratio per quartile was 2.01 (95% confidence interval, 1.84–2.19). Conclusions: A logistic regression model including only age and electronically retrieved laboratory data highly predicted mortality in internal medicine departments in a regional hospital, suggesting that age and routine admission laboratory tests might be used to ensure a fair comparison when using mortality monitoring for hospital quality control.

scholarly journals Community factors associated with local epidemic timing of respiratory syncytial virus: A spatiotemporal modeling study

2021 ◽  
Vol 7 (26) ◽  
pp. eabd6421
Author(s):  
Zhe Zheng ◽  
Virginia E. Pitzer ◽  
Joshua L. Warren ◽  
Daniel M. Weinberger
Keyword(s):  
New York ◽  
Respiratory Syncytial Virus ◽  
The Elderly ◽  
Spatiotemporal Modeling ◽  
Geographic Proximity ◽  
Community Factors ◽  
Harmonic Regression ◽  
Syncytial Virus ◽  
Zip Code ◽  
Epidemic Timing

Respiratory syncytial virus (RSV) causes a large burden of morbidity in young children and the elderly. Spatial variability in the timing of RSV epidemics provides an opportunity to probe the factors driving its transmission, including factors that influence epidemic seeding and growth rates. Using hospitalization data from Connecticut, New Jersey, and New York, we estimated epidemic timing at the ZIP code level using harmonic regression and then used a Bayesian meta-regression model to evaluate correlates of epidemic timing. Earlier epidemics were associated with larger household size and greater population density. Nearby localities had similar epidemic timing. Our results suggest that RSV epidemics grow faster in areas with more local contact opportunities, and that epidemic spread follows a spatial diffusion process based on geographic proximity. Our findings can inform the timing of delivery of RSV extended half-life prophylaxis and maternal vaccines and guide future studies on the transmission dynamics of RSV.

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