scholarly journals A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus

2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Public Health ◽  
Cardiovascular Diseases ◽  
General Population ◽  
Risk Stratification ◽  
Large Scale ◽  
Chronic Respiratory Disease ◽  
Health Policies ◽  
Public Health Policies ◽  
Infected People ◽  
The U.S

Estimating the hospitalization risk for people with certain comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance based on risk stratification. Traditional biostatistical methods require knowing both the number of infected people who were hospitalized and the number of infected people who were not hospitalized. However, the latter may be undercounted, as it is limited to only those who were tested for viral infection. In addition, comorbidity information for people not hospitalized may not always be readily available for traditional biostatistical analyses. To overcome these limitations, we developed a Bayesian approach that only requires the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. By applying our approach to two different large-scale datasets in the U.S., our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.

scholarly journals A Bayesian framework for estimating the risk ratio of hospitalization for people with comorbidity infected by SARS-CoV-2 virus

2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Public Health ◽  
New York ◽  
General Population ◽  
Risk Ratio ◽  
Bayesian Approach ◽  
Large Scale ◽  
The United States ◽  
Chronic Respiratory Disease ◽  
Infected People ◽  
Public Health Agencies

Abstract Objective Estimating the hospitalization risk for people with comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance. Traditional biostatistical methods for risk estimations require: (i) the number of infected people who were not hospitalized, which may be severely undercounted since many infected people were not tested; (ii) comorbidity information for people not hospitalized, which may not always be readily available. We aim to overcome these limitations by developing a Bayesian approach to estimate the risk ratio of hospitalization for COVID-19 patients with comorbidities. Materials and Methods We derived a Bayesian approach to estimate the posterior distribution of the risk ratio using the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. We applied our approach to 2 large-scale datasets in the United States: 2491 patients in the COVID-NET, and 5700 patients in New York hospitals. Results Our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively. Discussion Our approach only needs (i) the number of hospitalized COVID-19 patients and their comorbidity information, which can be reliably obtained using hospital records, and (ii) the prevalence of the comorbidity of interest in the general population, which is regularly documented by public health agencies for common medical conditions. Conclusion We developed a novel Bayesian approach to estimate the hospitalization risk for people with comorbidities infected with the SARS-CoV-2 virus.

A Critique of O’Byrne’s Understanding of Ethnography and the Politics of Public Health Research

2018 ◽  
Vol 29 (5) ◽  
pp. 739-746 ◽  
Author(s):  
Julien Brisson
Keyword(s):  
Public Health ◽  
General Population ◽  
Health Services ◽  
Health Research ◽  
Public Health Research ◽  
Health Policies ◽  
Main Argument ◽  
Cultural Groups ◽  
Public Health Policies ◽  
Marginalized Communities

Patrick O’Byrne criticizes the use of ethnography in public health research focused on cultural groups. His main argument is that ethnography disciplines marginalized populations that do not respect the imperative of health. In this article, I argue that O’Byrne has an erroneous understanding of ethnography and the politics of scientific research. My main argument is that a methodology itself cannot discipline individuals. I argue that if data are used as a basis to develop problematic public health policies, the issue is the policies themselves and not the methodology used to collect the data. While O’Byrne discourages researchers from conducting health research like ethnography focused on cultural groups, I argue the exact opposite. This has to do with justice and equity for marginalized communities and the obligation to tailor health services for their specific needs, which may not be the same as those of the general population.

scholarly journals Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246772 ◽  
Author(s):  
Jungsik Noh ◽  
Gaudenz Danuser
Keyword(s):  
Public Health ◽  
New York ◽  
Time Course ◽  
Test Results ◽  
Public Health Policies ◽  
Infected People ◽  
Learning Framework ◽  
Time Courses ◽  
Definition Of ◽  
The U.S

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true size of the currently infected population, which is arguably the most critical number for public health policy decisions. We developed a machine learning framework to estimate time courses of actual new COVID-19 cases and current infections in all 50 U.S. states and the 50 most infected countries from reported test results and deaths. Using published epidemiological parameters, our algorithm optimized slowly varying daily ascertainment rates and a time course of currently infected cases each day. Severe under-ascertainment of COVID-19 cases was found to be universal across U.S. states and countries worldwide. In 25 out of the 50 countries, actual cumulative cases were estimated to be 5–20 times greater than the confirmed cases. Our estimates of cumulative incidence were in line with the existing seroprevalence rates in 46 U.S. states. Our framework projected for countries like Belgium, Brazil, and the U.S. that ~10% of the population has been infected once. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population was estimated to be currently infected, as of September 3, 2020, while in New York this fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases.

scholarly journals Revealing the influence of national public health policies for the outbreak of the SARS-CoV-2 epidemic in Wuhan, China through status dynamic modeling

2020 ◽  
Author(s):  
Tianyi Qiu ◽  
Han Xiao
Keyword(s):  
Public Health ◽  
Real Time ◽  
Dynamic Model ◽  
Health Policies ◽  
Chinese Government ◽  
Wuhan City ◽  
Public Health Policies ◽  
Infected People ◽  
End Point ◽  
National Public Health

SummaryBackgroundThe epidemic caused by SARS-CoV-2 was first reported in Wuhan, China, and now is spreading worldwide. The Chinese government responded to this epidemic with multiple public health policies including locking down the city of Wuhan, establishing multiple temporary hospitals, and prohibiting public gathering events. Here, we constructed a new real-time status dynamic model of SEIO (MH) to reveal the influence of national public health policies and to model the epidemic in Wuhan.MethodsA real-time status dynamic model was proposed to model the population of Wuhan in status Susceptible (S), Exposed (E), Infected with symptoms (I), with Medical care (M), and Out of the system (O) daily. Model parameters were fitted according to the daily report of new infections from Jan. 27th, 2020 to Feb. 2nd, 2020. Using the fitted parameters, the epidemic under different conditions was simulated and compared with the current situation.FindingAccording to our study, the first patient is most likely appeared on Nov. 29th, 2019. There had already been 4,153 infected people and 6,536 exposed ones with the basic reproduction number R0 of 2.65 before lockdown, whereas R0 dropped to 1.98 for the first 30 days after the lockdown. The peak point is Feb. 17th, 2020 with 24,115 infected people and the end point is Jun. 17th, 2020. In total, 77,453 people will be infected. If lockdown imposed 7 days earlier, the total number of infected people would be 21,508, while delaying the lockdown by 1-6 days would expand the infection scale 1.23 to 4.94 times. A delay for 7 days would make the epidemic finally out of control. Doubling the number of beds in hospitals would decrease the total infections by 28%, and further investment in bed numbers would yield a diminishing return. Last, public gathering events that increased the transmission parameter by 5% in one single day would increase 4,243 infected people eventually.InterpretationOur model forecasted that the peak time in Wuhan was Feb. 17th, 2020 and the epidemic in Wuhan is now under control. The outbreak of SARS-CoV-2 is currently a global public health threat for all nations. Multiple countries including South Korea, Japan, Iran, Italy, and the United States are suffering from SARS-CoV-2. Our study, which simulated the epidemic in Wuhan, the first city in the world fighting against SARS-CoV-2, may provide useful guidance for other countries in dealing with similar situations.FundingNational Natural Science Foundation of China (31900483) and Shanghai Sailing program (19YF1441100).Research in contextEvidence before this studyThe epidemic of SARS-CoV-2 has been currently believed to started from Wuhan, China. The Chinese government started to report the data including infected, cured and dead since Jan 20th, 2020. We searched PubMed and preprint archives for articles published up to Feb 28th, 2020, which contained information about the Wuhan outbreak using the terms of “SARS-CoV-2”, “2019-nCoV”, “COVID-19”, “public health policies”, “coronavirus”, “CoV”, “Wuhan”, “transmission model”, etc. And a number of articles were found to forecast the early dynamics of the SARS-CoV-2 epidemic and clinical characteristics of COVID-19. Several of them mentioned the influence of city lockdown, whereas lacked research focused on revealing the impact of public health policies for the outbreak of SARS-CoV-2 through modeling study.Added value of this studyAs the first study systemically analysis the effect of three major public health policies including 1) lockdown of Wuhan City, 2) construction of temporary hospitals and 3) reduction of crowed gathering events in Wuhan city. The results demonstrated the epidemic in Wuhan from the potential first patient to the end point as well as the influence of public health policies are expected to provide useful guidance for other countries in fighting against the epidemic of SRAS-CoV-2.Implications of all the available evidenceAvailable evidence illustrated the human-to-human transmission of SARS-CoV-2, in which the migration of people in China during the epidemic may quickly spread the epidemic to the rest of the nation. These findings also suggested that the lockdown of Wuhan city may slow down the spread of the epidemic in the rest of China.

scholarly journals Estimation of Tunisia COVID-19 infected cases based on mortality rate

2020 ◽  
Author(s):  
Ines Abdeljaoued-Tej ◽  
Marc Dhenain
Keyword(s):  
Public Health ◽  
Mortality Rate ◽  
Health Policies ◽  
Public Health Policies ◽  
Infected People ◽  
Pessimistic Scenario ◽  
The Impact

ABSTRACTEstimating the number of people affected by COVID-19 is crucial in deciding which public health policies to follow. The authorities in different countries carry out mortality counts. We propose that the mortality reported in each country can be used to create an index of the number of actual cases at a given time. The specificity of whether or not deaths are rapid or not by COVID-19 also affects the number of actual cases. The number of days between the declaration of illness and death varies between 12 and 18 days. For a delay of 18 days, and using an estimated mortality rate of 2%, the number of cases in April 2020 in Tunisia would be 5 580 people. The pessimistic scenario predicts 22 320 infected people, and the most optimistic predicts 744 (which is the number of reported cases on April 12, 2020). Modeling the occurrence of COVID-19 cases is critical to assess the impact of policies to prevent the spread of the virus.

scholarly journals Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide

2020 ◽  
Author(s):  
Jungsik Noh ◽  
Gaudenz Danuser
Keyword(s):  
New York ◽  
Health Policies ◽  
Test Results ◽  
Virus Spread ◽  
Public Health Policies ◽  
Infected People ◽  
Learning Framework ◽  
Time Courses ◽  
Definition Of ◽  
The U.S

Since the beginning of the COVID-19 pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true prevalence of the virus. A machine learning framework was developed to estimate time courses of actual new COVID-19 cases and current infections in 50 countries and 50 U.S. states from reported test results and deaths, as well as published epidemiological parameters. Severe under-reporting of cases was found to be universal. Our framework projects for countries like Belgium, Brazil, and the U.S. ~10% of the population has been once infected. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population is estimated to be currently infected, as of September 3, 2020, while in New York the fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases.

scholarly journals Public Health Workforce Burnout in the COVID-19 Response in the U.S.

2021 ◽  
Vol 18 (8) ◽  
pp. 4369
Author(s):  
Kahler W. Stone ◽  
Kristina W. Kintziger ◽  
Meredith A. Jagger ◽  
Jennifer A. Horney
Keyword(s):  
Public Health ◽  
Health Workforce ◽  
Work Experience ◽  
Large Scale ◽  
Health Workers ◽  
Public Health Workforce ◽  
Cross Sectional Survey ◽  
Public Health Response ◽  
Cross Sectional ◽  
The U.S

While the health impacts of the COVID-19 pandemic on frontline health care workers have been well described, the effects of the COVID-19 response on the U.S. public health workforce, which has been impacted by the prolonged public health response to the pandemic, has not been adequately characterized. A cross-sectional survey of public health professionals was conducted to assess mental and physical health, risk and protective factors for burnout, and short- and long-term career decisions during the pandemic response. The survey was completed online using the Qualtrics survey platform. Descriptive statistics and prevalence ratios (95% confidence intervals) were calculated. Among responses received from 23 August and 11 September 2020, 66.2% of public health workers reported burnout. Those with more work experience (1–4 vs. <1 years: prevalence ratio (PR) = 1.90, 95% confidence interval (CI) = 1.08−3.36; 5–9 vs. <1 years: PR = 1.89, CI = 1.07−3.34) or working in academic settings (vs. practice: PR = 1.31, CI = 1.08–1.58) were most likely to report burnout. As of September 2020, 23.6% fewer respondents planned to remain in the U.S. public health workforce for three or more years compared to their retrospectively reported January 2020 plans. A large-scale public health emergency response places unsustainable burdens on an already underfunded and understaffed public health workforce. Pandemic-related burnout threatens the U.S. public health workforce’s future when many challenges related to the ongoing COVID-19 response remain unaddressed.

scholarly journals Population simulations of COVID-19 outbreaks provide tools for risk assessment and continuity planning

JAMIA Open ◽  
2021 ◽  
Author(s):  
Bo Peng ◽  
Rowland W Pettit ◽  
Christopher I Amos
Keyword(s):  
Public Health ◽  
Risk Assessment ◽  
Emergency Response ◽  
Robustness Analysis ◽  
Health Policies ◽  
Command Line ◽  
Public Health Policies ◽  
Continuity Planning ◽  
Quantitative Estimates ◽  
Community Infection

Abstract Objectives We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations. Materials and methods Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations. Results The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans. Discussion Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters. Conclusion Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.

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