Book Review Bioterrorism: Psychological and Public Health Interventions Edited by Robert J. Ursano, Ann E. Norwood, and Carol S. Fullerton. 363 pp., with CD-ROM. New York, Cambridge University Press, 2004. $110. 0-521-81472-3

2005 ◽  
Vol 352 (24) ◽  
pp. 2560-2560 ◽  
Author(s):  
Andrew W. Artenstein
Keyword(s):  
Public Health ◽  
New York ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Cd Rom ◽  
Cambridge University

scholarly journals Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study (Preprint)

2020 ◽  
Author(s):  
Xiaofeng Wang ◽  
Rui Ren ◽  
Michael W Kattan ◽  
Lara Jehi ◽  
Zhenshun Cheng ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
New York ◽  
New York State ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Transmission Rates ◽  
Hospital Use

BACKGROUND Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio’s transmission rates declined before the state’s “stay at home” order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS The series of public health interventions in three areas were temporally associated with the burden of COVID-19–attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

scholarly journals Changes in SARS-CoV-2 viral load and mortality during the initial wave of the pandemic in New York City

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257979
Author(s):  
Michael J. Satlin ◽  
Jason Zucker ◽  
Benjamin R. Baer ◽  
Mangala Rajan ◽  
Nathaniel Hupert ◽  
...  
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
Viral Load ◽  
Hospital Mortality ◽  
York City ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Viral Loads ◽  
The Impact

Public health interventions such as social distancing and mask wearing decrease the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they decrease the viral load of infected patients and whether changes in viral load impact mortality from coronavirus disease 2019 (COVID-19). We evaluated 6923 patients with COVID-19 at six New York City hospitals from March 15-May 14, 2020, corresponding with the implementation of public health interventions in March. We assessed changes in cycle threshold (CT) values from reverse transcription-polymerase chain reaction tests and in-hospital mortality and modeled the impact of viral load on mortality. Mean CT values increased between March and May, with the proportion of patients with high viral load decreasing from 47.7% to 7.8%. In-hospital mortality increased from 14.9% in March to 28.4% in early April, and then decreased to 8.7% by May. Patients with high viral loads had increased mortality compared to those with low viral loads (adjusted odds ratio 2.34). If viral load had not declined, an estimated 69 additional deaths would have occurred (5.8% higher mortality). SARS-CoV-2 viral load steadily declined among hospitalized patients in the setting of public health interventions, and this correlated with decreases in mortality.

scholarly journals A Bayesian approach to the g-formula

2017 ◽  
Vol 27 (10) ◽  
pp. 3183-3204 ◽  
Author(s):  
Alexander P Keil ◽  
Eric J Daza ◽  
Stephanie M Engel ◽  
Jessie P Buckley ◽  
Jessie K Edwards
Keyword(s):  
Public Health ◽  
New York ◽  
Bayesian Approach ◽  
Tobacco Smoke ◽  
Computational Approach ◽  
Health Interventions ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Small Samples ◽  
Public Health Interventions

Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4–9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally.

scholarly journals Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study

10.2196/25174 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e25174
Author(s):  
Xiaofeng Wang ◽  
Rui Ren ◽  
Michael W Kattan ◽  
Lara Jehi ◽  
Zhenshun Cheng ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
New York ◽  
New York State ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Transmission Rates ◽  
Hospital Use

Background Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. Objective We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. Methods This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. Results The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio’s transmission rates declined before the state’s “stay at home” order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. Conclusions The series of public health interventions in three areas were temporally associated with the burden of COVID-19–attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

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