scholarly journals COVID-19: Rethinking the Lockdown Groupthink

2021 ◽  
Vol 9 ◽  
Author(s):  
Ari R. Joffe
Keyword(s):  
Public Health ◽  
High Risk ◽  
Cost Benefit Analysis ◽  
Health Care Systems ◽  
Herd Immunity ◽  
Cost Benefit ◽  
Risk Groups ◽  
School Closures ◽  
Trade Offs ◽  
Care Systems

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease 2019 (COVID-19) worldwide pandemic in 2020. In response, most countries in the world implemented lockdowns, restricting their population's movements, work, education, gatherings, and general activities in attempt to “flatten the curve” of COVID-19 cases. The public health goal of lockdowns was to save the population from COVID-19 cases and deaths, and to prevent overwhelming health care systems with COVID-19 patients. In this narrative review I explain why I changed my mind about supporting lockdowns. The initial modeling predictions induced fear and crowd-effects (i.e., groupthink). Over time, important information emerged relevant to the modeling, including the lower infection fatality rate (median 0.23%), clarification of high-risk groups (specifically, those 70 years of age and older), lower herd immunity thresholds (likely 20–40% population immunity), and the difficult exit strategies. In addition, information emerged on significant collateral damage due to the response to the pandemic, adversely affecting many millions of people with poverty, food insecurity, loneliness, unemployment, school closures, and interrupted healthcare. Raw numbers of COVID-19 cases and deaths were difficult to interpret, and may be tempered by information placing the number of COVID-19 deaths in proper context and perspective relative to background rates. Considering this information, a cost-benefit analysis of the response to COVID-19 finds that lockdowns are far more harmful to public health (at least 5–10 times so in terms of wellbeing years) than COVID-19 can be. Controversies and objections about the main points made are considered and addressed. Progress in the response to COVID-19 depends on considering the trade-offs discussed here that determine the wellbeing of populations. I close with some suggestions for moving forward, including focused protection of those truly at high risk, opening of schools, and building back better with a economy.

scholarly journals COVID-19: Rethinking the Lockdown Groupthink

2020 ◽  
Author(s):  
Ari Joffe
Keyword(s):  
Public Health ◽  
Cost Benefit Analysis ◽  
Health Care Systems ◽  
Herd Immunity ◽  
Cost Benefit ◽  
Risk Groups ◽  
Health Goal ◽  
Public Health Goal ◽  
Care Systems ◽  
Work Education

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease 2019 (COVID-19) worldwide pandemic in 2020. In response, most countries in the world implemented lockdowns, restricting their population’s movements, work, education, gatherings, and general activities in attempt to ‘flatten the curve’ of COVID-19 cases. The public health goal of lockdowns was to save the population from COVID-19 cases and deaths, and to prevent overwhelming health care systems with COVID-19 patients. In this narrative review I explain why I changed my mind about supporting lockdowns. First, I explain how the initial modeling predictions induced fear and crowd-effects [i.e., groupthink]. Second, I summarize important information that has emerged relevant to the modeling, including about infection fatality rate, high-risk groups, herd immunity thresholds, and exit strategies. Third, I describe how reality started sinking in, with information on significant collateral damage due to the response to the pandemic, and information placing the number of deaths in context and perspective. Fourth, I present a cost-benefit analysis of the response to COVID-19 that finds lockdowns are far more harmful to public health than COVID-19 can be. I close with some suggestions for moving forward.

scholarly journals COVID-19: Rethinking the Lockdown Groupthink

2020 ◽  
Author(s):  
Ari Joffe
Keyword(s):  
Public Health ◽  
Cost Benefit Analysis ◽  
Health Care Systems ◽  
Herd Immunity ◽  
Cost Benefit ◽  
Risk Groups ◽  
Health Goal ◽  
Public Health Goal ◽  
Care Systems ◽  
Work Education

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease 2019 (COVID-19) worldwide pandemic in 2020. In response, most countries in the world implemented lockdowns, restricting their population’s movements, work, education, gatherings, and general activities in attempt to ‘flatten the curve’ of COVID-19 cases. The public health goal of lockdowns was to save the population from COVID-19 cases and deaths, and to prevent overwhelming health care systems with COVID-19 patients. In this narrative review I explain why I changed my mind about supporting lockdowns. First, I explain how the initial modeling predictions induced fear and crowd-effects [i.e., groupthink]. Second, I summarize important information that has emerged relevant to the modeling, including about infection fatality rate, high-risk groups, herd immunity thresholds, and exit strategies. Third, I describe how reality started sinking in, with information on significant collateral damage due to the response to the pandemic, and information placing the number of deaths in context and perspective. Fourth, I present a cost-benefit analysis of the response to COVID-19 that finds lockdowns are far more harmful to public health than COVID-19 can be. Controversies and objections about the main points made are considered and addressed. I close with some suggestions for moving forward.

A predictive model of pcsk-9 inhibitors eligibility in coronary and dyslipidemic patients

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
I Dima ◽  
D Soulis ◽  
D Terentes-Printzios ◽  
I Skoumas ◽  
K Aznaouridis ◽  
...  
Keyword(s):  
Health Care ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Predictive Model ◽  
Health Care Systems ◽  
Clinical Status ◽  
Risk Groups ◽  
Funding Source ◽  
Private Company ◽  
Care Systems

Abstract Purpose Dyslipidemia is a major cardiovascular risk factor and treatment is mostly based on statins and ezetimibe. PCSK-9 inhibitors are monoclonal antibodies that reduce LDL-c levels and have shown significant reduction of cardiovascular risk in high risk patients. Data regarding potential eligibility for PCSK-9, is limited especially when referring to the recent guidelines. Methods Eligibility was calculated using a proprietary adjustable software, which stores data and patient information and thus by using different criteria it can determine potential candidates for PCSK-9 inhibitors. For this purpose, 2000 patients were enrolled prospectively. Our study population was comprised of inpatients diagnosed either with acute coronary syndromes (ACS) or with chronic coronary disease (cCAD) and outpatients from Lipids' Clinic (OLC) (n=407, n=1087, n=506, respectively). In order to test eligibility, three different LDL thresholds were used in our model for high and very high risk groups: a) 70mg/dl and 55mg/dl, respectively, as recommended by the recently updated 2019 ESC/EAS Guidelines for Dyslipidaemia b) 100mg/dl and 70mg/dl, respectively, as recommended by the 2016 ESC/EAS Guidelines for Dyslipidaemias and c) 130mg/dl and 100mg/dl respectively, as mandated by our National Health Care system but also applicable in other countries. Results The eligible percentages for the three thresholds were 18.85%, 9.75% and 2.15%, in the total population (TP) respectively and it varied according to clinical status. Subgroup analysis of eligible population revealed the trends in each group (Figure 1). The increase toward more recent guidelines was mostly attributed to the increasing number of coronary patients who become eligible as our criteria become stricter. Conclusions Our predictive model provides a realistic estimation of PCSK-9 inhibitors potential eligibility in coronary and dyslipidaemic patients and thus it can become a useful tool for the use of PCSK-9 in health care systems. Figure 1 Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Amgen Hellas LTD

scholarly journals Learning from a Class Imbalanced Public Health Dataset: a Cost-based Comparison of Classifier Performance

2017 ◽  
Vol 7 (4) ◽  
pp. 2215 ◽  
Author(s):  
Rohini R Rao ◽  
Krishnamoorthi Makkithaya
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Cost Benefit Analysis ◽  
Health Care Systems ◽  
Class Imbalance ◽  
Cost Benefit ◽  
Public Health Care ◽  
Bayesian Classifiers ◽  
Classifier Ensembles ◽  
Classifier Performance

Public health care systems routinely collect health-related data from the population. This data can be analyzed using data mining techniques to find novel, interesting patterns, which could help formulate effective public health policies and interventions. The occurrence of chronic illness is rare in the population and the effect of this class imbalance, on the performance of various classifiers was studied. The objective of this work is to identify the best classifiers for class imbalanced health datasets through a cost-based comparison of classifier performance. The popular, open-source data mining tool WEKA, was used to build a variety of core classifiers as well as classifier ensembles, to evaluate the classifiers’ performance. The unequal misclassification costs were represented in a cost matrix, and cost-benefit analysis was also performed.  In another experiment, various sampling methods such as under-sampling, over-sampling, and SMOTE was performed to balance the class distribution in the dataset, and the costs were compared. The Bayesian classifiers performed well with a high recall, low number of false negatives and were not affected by the class imbalance. Results confirm that total cost of Bayesian classifiers can be further reduced using cost-sensitive learning methods. Classifiers built using the random under-sampled dataset showed a dramatic drop in costs and high classification accuracy.

scholarly journals Comparing bad apples to orange soda: Flaws and Errors in an Estimation of Years of Life Lost Associated With School Closures and COVID-19 deaths by Christakis, Van Cleve, and Zimmerman

2020 ◽  
Author(s):  
Gideon Meyerowitz-Katz ◽  
Ilya Kashnitsky
Keyword(s):  
Public Health ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Evidence Base ◽  
Years Of Life Lost ◽  
Public Attention ◽  
School Closures ◽  
Social Good ◽  
Viral Spread ◽  
Analytical Tools

We are writing this openly-published letter to express deep concerns regarding the paper recently published in JAMA Network Open: Estimation of US Children’s Educational Attainment and Years of Life Lost Associated With Primary School Closures During the Coronavirus Disease 2019 Pandemic https://doi.org/10.1001/jamanetworkopen.2020.28786The paper by Christakis, Van Cleve, and Zimmerman (2020, abbrev. CVZ) is built upon multiple critically flawed assumptions, obvious misuse of the standard analytical tools, and clear mistakes in study design. Additionally, the analysis presented contains crucial mathematical and statistical errors that completely revert the main results, sufficient that if the estimates had been calculated according to the declared methodology, the results would completely contradict the stated conclusions and policy recommendations. These are not idle criticisms. This study has received enormous public attention, and its results immediately appeared in discussions of public health policies around schools worldwide. The central question is resolving an evidence base for the inevitable trade-off between (a) the very real harms of missed education provoked by policies that decrease viral spread vs. (b) the resumption of education as a social good which increases viral spread.This is an incredibly important public health question, and it demands careful cost-benefit analysis. To that end, this paper adds no usable evidence whatsoever.

scholarly journals Learning from health care in other countries: the prospect of comparative research

2015 ◽  
Vol 14 (1) ◽  
pp. 8-12 ◽  
Author(s):  
Edwin Van Teijlingen ◽  
Cecilia Benoit ◽  
Ivy Bourgeault ◽  
Raymond DeVries ◽  
Jane Sandall ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Comparative Research ◽  
Cultural Context ◽  
Health Care Systems ◽  
Cultural Factors ◽  
Reproductive Health Care ◽  
Care Systems ◽  
Cross National ◽  
The Way

It is widely accepted that policy-makers (in Nepal and elsewhere) can learn valuable lessons from the way other countries run their health and social services. We highlight some of the specific contributions the discipline of sociology can make to cross-national comparative research in the public health field. Sociologists call attention to often unnoticed social and cultural factors that influence the way national reproductive health care systems are created and operated. In this paper we address questions such as: ‘Why do these health services appear to be operating successfully in one country, but not another?’; ‘What is it in one country that makes a particular public health intervention successful and how is the cultural context different in a neighbouring country?’ The key examples in this paper focus on maternity care and sex education in the Netherlands and the UK, as examples to highlight the power of cross-national research. Our key messages are: a) Cross-national comparative research can help us to understand the design and running of health services in one country, say Nepal, by learning from a comparison with other countries, for example Sri Lanka or India. b) Cultural factors unique to a country affect the way that reproductive health care systems operate. c) Therefore,we need to understand why and how services work in a certain cultural context before we start trying to implement them in another cultural context.

The Illusion of Costs without Benefits

2020 ◽  
pp. 107-118
Author(s):  
Michael A. Livermore ◽  
Richard L. Revesz
Keyword(s):  
Public Health ◽  
Cost Benefit Analysis ◽  
Cost Savings ◽  
Cost Benefit ◽  
Social Benefits ◽  
Executive Order ◽  
Benefit Analysis ◽  
Compliance Costs ◽  
The Core ◽  
Trump Administration

The core of the Trump administration’s regulatory agenda is to focus on the costs of regulations while ignoring, trivializing, and mischaracterizing their benefits. The administration has made significant regulatory efforts to delay or repeal important initiatives of the Obama administration designed to protect public health and the environment. In some of these proceedings, the Trump administration has altogether ignored the benefits of the rules it seeks to eliminate or suspend, instead focusing solely on cost savings to regulated industry. For example, Trump’s Executive Order 13,771 directs agencies to control costs and eliminate two regulations for every new one. This one-sided approach makes a mockery of cost-benefit analysis. Saving regulatory costs is attractive only if the benefits forgone as a result of these savings are lower than those costs. A rule that reduces compliance costs by giving up an even larger set of social benefits is hardly an attractive proposition.

Cost–benefit analysis for applied public health economic evaluation

2019 ◽  
pp. 204-232 ◽  
Author(s):  
Emma McIntosh ◽  
Camilla Baba ◽  
Willings Botha
Keyword(s):  
Public Health ◽  
Economic Evaluation ◽  
Cost Benefit Analysis ◽  
Stated Preference ◽  
Health Economic Evaluation ◽  
Public Health Intervention ◽  
Cost Benefit ◽  
Health Interventions ◽  
Benefit Analysis ◽  
Process Outcomes

Chapter 9 introduces the reader to the stages of cost–benefit analysis (CBA) as specifically applied to public health intervention economic evaluation. The specific focus of this chapter follows on from the messages of Chapter 6 on the relevance of, and methods for, quantifying the ‘outcomes’ of public health interventions in monetary form for CBA. Two case studies focus on the use of stated preference discrete choice experiment (SPDCE) methodology for valuation of multi-attribute benefits comprising health, non-health, and process outcomes of the type likely to occur in PHIs.

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