Frontiers | COVID-19: Rethinking the Lockdown Groupthink | Public Health (frontiersin.org) 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. 1). In this narrative review I explain why I changed my mind. 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. Third, I describe how reality started sinking in, with information on significant collateral damage from the response to the pandemic, and on the number of deaths in context. Fourth, I present a cost-benefit analysis of the response to COVID-19. I close with some suggestions for moving forward. An important point must be emphasized. The COVID-19 pandemic has caused much morbidity and mortality. This morbidity and mortality have been, and continue to be, tragic. This narrative review aims to take these tragic outcomes of the pandemic seriously, and to also consider the tragic outcomes of the public health response to the pandemic. After all, lockdowns are a public health response undertaken with the goal of improving population health outcomes from the pandemic. Given adverse effects of lockdowns on many millions of people, with increased poverty, food insecurity, loneliness, unemployment, school closures, and interrupted healthcare, a cost-benefit analysis of lockdowns is necessary. We face terrible choices, but the response of lockdowns can be predicted to cause far more loss of population wellbeing than COVID-19 itself can. Table 1). Kissler et al. predicted the need for intermittent lockdowns occurring for a total of 75% of the time, even after July 2022, to avoid “overwhelming critical care capacity (page 866 in ref. 2)” (2–4). In their discussion they wrote that the response “is likely to have profoundly negative economic, social, and educational consequences… We do not take a position on the advisability of these scenarios given the economic burden… (page 868)” (2). On March 16, 2020, the Imperial College COVID-19 Response Team published modeling of the impact of non-pharmaceutical interventions (NPI) to reduce COVID-19 mortality and healthcare demand in the United States (US) and United Kingdom (UK) (5). They wrote that suppression “needs to be in force for the majority [>2/3 of the time] of the 2 years of the simulation (page 11),” without which there would be 510,000 deaths in Great Britain and 2.2 million deaths in the United States by mid-April, surpassing ICU demand by 30 times (5). In their discussion they wrote that “we do not consider the ethical or economic implications (page 4)… The social and economic effects of the measures which are needed to achieve this policy goal will be profound (page 16)…” (5). The Imperial College COVID-19 Response Team extended this to the global impact of the pandemic on March 26, 2020, and estimated that without lockdowns there would be “7.0 billion infections and 40 million deaths globally this year (page 1)” (6). In their discussion they wrote “we do not consider the wider social and economic costs of suppression, which will be high and may be disproportionately so in lower income settings (page 2)” (6). In a later publication, this group modeled that “across 11 countries [in Europe, since the beginning of the epidemic to May 4], 3.1 (2.8–3.5) million deaths have been averted owing to [NPI] interventions… (page 260)” (7). Another group similarly claimed that, in 5 countries (China, South Korea, Iran, France, US), NPIs “prevented or delayed [to April 6] on the order of 61 million confirmed cases (page 262)” (8). TABLE 1 Table 1. Initial modeling predictions that induced fear and crowd-effects. How It Took Off: Crowd Effects (Groupthink) There ensued a contagion of fear and policies across the world (9–12). Social media spread a growing sense of panic (13). Popular media focused on absolute numbers of COVID-19 cases and deaths independent of context, with a “sheer one-sided focus (page 4)” on preventing infection (12). There was an appeal of group hysteria; “everyone got a break from their ambitions and other burdens carried in normal life,” and became united in crowds, which have a numbing effect (9). There was talk of “acting together against a common threat,” “about seeming to reduce risks of infection and deaths from this one particular disease, to the exclusion of all other health risks or other life concerns,” with virtue signaling to the crowd, of “something they love to hate and be seen to fight against” (9). A war effort analogy is apt, with the “unquestioning presumption that the cause is right, that the fight will be won, that naysayers and non-combatants [e.g., not wearing a mask] are basically traitors, and that there are technical solutions [e.g., vaccine and drugs] that will quickly overcome any apparent problem or collateral damage” (9). This was associated with a “disregard and disinterest on the part of individuals in the enormity of the collateral damage, either to their own kids, people in other countries, their own futures…” (9). The crisis was framed as a “war against an invisible enemy (page 5),” presenting the false choice between “lives and livelihood (page 5),” spreading fear and anxiety while ignoring the costs of the measures taken - this resulted in conformity and obedience (12, 13). There has been a strong positive association between new daily and total confirmed COVID-19 cases in a country and support for the heads of government, reflecting the “rally ‘round the flag’” effect [“the perception that one's group is under attack and hence unity is required to defend the group (page 25429)”] (14). The NPIs spread to ~80% of OECD countries within a 2-week period in March 2020 (15). A main predictor of a country implementing NPIs was prior adoptions of a policy among spatially proximate countries, i.e., the number of earlier adopters in the same region (15). Variables not predicting adoption of NPIs included the number of cases or deaths, population >65 years old, or hospital beds per capita in the country (15). It seems we were all “stuck in this emotional elevation of COVID-19 deaths and suffering above everything else that could possibly matter” (16). There was the unquestioned assumption that “there were and are no alternatives to extreme measures implemented on entire populations with little consideration of cost and consequences [externalities] (page 477)” (10). Even now, how a country “performed” is measured by COVID-19 cases and deaths without denominators, without other causes of deaths considered, without considering overall population health trade-offs “that cannot be wished away” (e.g., the future of our children from lack of education and social interaction, and “changes to our wealth-generating capacity that has to pay for future policies”) (9), and without considering how sustainable current policies are [protection is temporary and leaves us susceptible; “there is no exit from the pandemic; there is only an exit from the response to it (page 479)” (10)]. All of this, even though in October 2019 the WHO published that for any future Influenza pandemic: travel-related measures are “unlikely to be successful… are likely to have prohibitive economic consequences (page 2)”; “[measures] not recommended in any circumstances: contact tracing, quarantine of exposed individuals, border closure (page 3)”; social distancing measures (closures of workplace, avoiding crowding and closing public areas) “can be highly disruptive, and the cost of these measures must be weighed against their potential impact (page 4)”; and “border closures may be considered only by small island nations in severe pandemics… but must be weighed against potentially serious economic consequences (page 4)” (17). Referring to the 2009 influenza pandemic, Bonneux and Van Damme wrote that “the culture of fear” meant that “worst-case thinking replaced balanced risk assessment” on the part of influenza “experts” (page 539) (18). But “the modern disease expert knows a lot about the disease in question, but does not necessarily know much about general public health, health economics, health policy, or public policy, which are much more about priority setting and hence resource allocation between competing priorities [because resources are limited, wise allocation saves lives]” (19). Some of this crowd effect is related to cognitive biases, “the triumph of deeply human instincts over optimal policy” (page 303 in ref 21) (20–22). Identifiable lives bias included the identifiable victim effect (we ignore hidden “statistical” deaths reported at the population level), and identifiable cause effect (we prioritize efforts to save lives from a known cause even if more lives would be saved through alternative responses). Present bias made us prefer immediate benefits to even larger benefits in the future (steps that would prevent more deaths over the longer term are less attractive) (20–22). The proximity and vividness of COVID-19 cases (i.e., availability and picture superiority bias), and anchoring bias (we adhere to our initial hypothesis, and disregard evidence that disproves our favorite theory) affected our reasoning (21, 23). Superstitious bias, that action is better than non-action even when evidence is lacking, reduced anxiety (12). Escalation of commitment bias, investing more resources into a set course of action even in the face of evidence there are better options, made us stand by prior decisions (24). We need to take an “effortful pause,” reflecting on aspects of the pandemic that do not fit with our first impressions (25). The groupthink (the tendency for groups to let the desire for harmony and conformity prevail, resulting in dysfunctional decision-making processes and becoming less willing to alter their course of action once they settle on it) needs to be replaced by deliberative consideration of all the relevant information (24). 26). Among those <70 years old the median crude and corrected IFR was 0.05% (range 0.00–0.31%). He estimated that for those <45 years old the IFR was almost 0%, 45–70 years old approximately 0.05–0.30%, and ≥70 years old ≥ 1%, rising to up to 25% for some frail older people in nursing homes (27). He estimated that at that point there were likely 150–300 million infections that had occurred in the world, not the reported 13 million, most being asymptomatic or mildly symptomatic (26, 27). The WHO recently estimated that about 10% of the global population may have been already infected, which, with a world population of 7.8 billion, and 1.16 million deaths, would make a rough approximation of IFR as 0.15% (28). Even these numbers are most likely a large over-estimate of the IFR. First, in serosurveys the vulnerable (e.g., homeless, imprisoned, institutionalized, disadvantaged people), who have higher COVID-19 incidence, are more difficult to recruit. Second, there is likely a healthy volunteer bias in serosurvey studies. Third, and most importantly, there is a lack of sensitivity of serology (29–34). Many reports now document there is often a rapid loss of antibody in COVID-19 patients that were less severely ill (29–36). Moreover, at least 10% of COVID-19 patients never seroconvert, and many more may only develop a mucosal IgA response (37, 38), or only a T-cell response (which may be the case in up to 50% of mild infections) (39, 40). Finally, most data come from unusual epicenters where infection finds its way into killing predominantly older citizens in nursing homes and hospitals (26), and where “[in Italy, Spain, France] an underfunded, understaffed, overstretched and increasingly privatized and fractured healthcare system contribute to higher mortality rates… [Lombardy] has long been an experimental site for healthcare privatization (page 474–475)” (10). With “precise non-pharmacological measures that selectively try to protect high-risk vulnerable populations and settings, the IFR may be brought even lower (page 10)” (26). A serology-informed estimate of the IFR in Geneva, Switzerland put the IFR at: age 5–9 years 0.0016% (95% Credible Interval, CrI 0, 0.019), 10–19 years 0.00032% (95% CrI 0, 0.0033), 20–49 years 0.0092% (95% CrI 0.0042, 0.016), 50–64 years 0.14% (95% CrI 0.096, 0.19), and age 65+ outside of assisted care facilities 2.7% (95% CrI 1.6, 4.6), for an overall population IFR 0.32% (95% CrI 0.17, 0.56) (41). Similarly, a large study from France found an inflection point in IFR around the age of 70 years (see their Figure 2D) (42). High-Risk Groups Ioannidis et al. analyzed reported deaths from epicenters, in 14 countries and 13 states in the United States, to June 17, 2020 (43). They found that in those age <65 years the relative risk of death was 30–100X lower in Europe and Canada, and 16–52X lower in the USA, compared to those ≥65 years old (43). They estimated that those age 40–65 years old have double the risk of the overall <65 year-old group, and females have 2X lower risk than males (43). This is compatible with a steep inflection point in the IFR around the age of 70 years old. Older adults in nursing homes accounted for at least half of the COVID-19 deaths in Europe and North America, and over 80% in Canada (44, 45). In nursing homes the usual median survival is ~2.2 years, with a yearly mortality rate >30%, even without COVID-19 (46). Outbreaks of the seasonal respiratory coronavirus in adults living in long-term care facilities are common, with case-fatality rates of 8% (47). Ioannidis et al. estimated that the average daily risk of COVID-19 death for an individual <65 years old was equivalent to the risk from driving between 12–82 miles/day during the pandemic period, higher in the UK and 8 states (106–483 miles/day), and only 14 miles/day in Canada (43). By far the most important risk factor is older age (41–43). There is an approximately 1,000-fold difference in death risk for people >80 years old vs. children (43). In the largest observational study I am aware of, the OpenSAFELY population in the UK, including over 17 million people with 10,900 COVID-19 deaths, compared to those age 50–59 years old, the Hazard Ratio for death from COVID-19 ranged from 0.06 for those age 18–39 years, to >10 for those age >80 years (48). In comparison, even important co-morbidities such as severe obesity, uncontrolled diabetes, recent cancer, chronic respiratory or cardiac or kidney disease, and stroke or dementia rarely had HR approaching ≥2 (48). Those co-morbidities with HR>2, including hematological malignancy, severe chronic kidney disease, and organ transplant, affected only 0.3, 0.5, and 0.4% of the total population (48). A rapid systematic review found that only age had a “consistent and high strength association with hospitalization and death from COVID-19… strongest in people older than 65 years…(page 1)” (49). Other risk groups for mortality had either a low-moderate effect (obesity, diabetes mellites, male biological sex, ethnicity, hypertension, cardiovascular disease, COPD, asthma, kidney disease, cancer) and/or were inconsistently found to have an effect in the literature (obesity, diabetes mellites, pregnancy, ethnicity, hypertension, cardiovascular disease, COPD, kidney disease) (49). Even with these risk factors, the absolute risk may still be low, given the overall IFR in the population at that age. Objection: Is This Age Discrimination? An objection may be that singling out older people as high risk is age discrimination. This is false on two counts. First, pointing out the truly high-risk group is in older people is only emphasizing that this is the group that requires protection from severe COVID-19 outcomes. Second, as Singer has pointed out, “what medical treatment does, if successful, is prolong lives. Successfully treating a disease that kills children and young adults is, other things being equal, likely to lead to a greater prolongation, and thus do more good, than successfully treating a disease that kills people in the 70's, 80's, and 90's” (50). In fact, when we try to stay healthy “what we are trying to do is to live as long as we can, compatibly with having a positive quality of life for the years that remain to us. If life is a good, then, other things being equal, it is better to have more of it rather than less” (50). We should count every quality adjusted life year equally, whether it is in the life of a teenager or a 90-year old (50, 51). This was also the conclusion of “The Fair Priority Model” for global vaccine allocation, prioritizing preventing premature death using a standard expected years of life lost metric (52). Veil of ignorance reasoning is a widely respected and transparent standard for adjudicating claims of fairness. A fair distribution of resources is said to be one that people would choose out of self-interest, without knowing whom among those affected they will be: what would I want if I didn't know who I was going to be? In an experimental study participants were asked to decide whether to give the last available ventilator in their hospital to the 65 year old who arrived first and is already being prepped for the ventilator, or the 25 year old who arrived moments later, assuming whoever is saved will live to age 80 years old. In the veil of ignorance condition, the participant was asked to “imagine that you have a 50% chance of being the older patient, and 50% the younger” (53). Asked if “it is morally acceptable to give the last ventilator to the younger patient,” 67% in the veil of ignorance condition vs. 53% in control answered “yes” (odds ratio 1.69; 95% Confidence Interval 1.12, 2.57); compared to younger age participants (18–30 years), older participants (odds ratio 3.98) and middle age participants (odds ratio 2.02) were more likely to agree (53). Asked if “you want the doctor to give the ventilator to the younger patient,” 77% answered “yes,” maximizing the number of life-years saved rather than the number of lives saved (53). Thus, veil of ignorance reasoning resulted in the participants counting every quality adjusted life year equally, whether it is in the life of a 25-year old or a 65-year old. The Herd Immunity Threshold The classical herd immunity level is calculated based on the basic reproduction number (Ro) as (1–1/Ro), and is the proportion of the population that must be immune to a virus before the effective reproduction number (Re) is <1, and thus the virus cannot perpetuate itself in the population. This calculation assumes a homogeneously mixing population, where all are equally susceptible and infectious. For Ro 2.5, the threshold is ~60% of the population. However, the assumption is not valid, as there is heterogeneity in social mixing and connectivity, with higher and lower levels of activity and contacts. One model incorporating heterogeneity of social mixing found the threshold, for Ro 2.5, to be 43%, and likely lower as other heterogeneity in the population was not modeled (e.g., sizes of households, attending school or big workplaces, metropolitan vs. rural location, protecting older people, etc.) (54). A model that incorporated variation in connectivity compatible with other infectious diseases found that for Ro 3, the threshold is 10–25% of the population developing immunity (55). Another model that “fit epidemiological models with inbuilt distributions of susceptibility or exposure to SARS-CoV-2 outbreaks” calculated “herd immunity thresholds around 10-20% [because]… immunity induced by infection… [contrary to random vaccination] is naturally selective (page 2)” (56). In support of this heterogeneity, it is now known that there is overdispersion of transmission of SARS-CoV-2, with 80% of secondary infections arising from just ~10% of infected people (57–59). Objection: Consider Sweden It has been claimed that Sweden's strategy of achieving herd immunity failed, with excess deaths and a suffering economy. However, that is not clear. First, cases and deaths fell consistently in later July/August, with deaths continuing at a very low level into October despite no lockdown (60). Second, serosurveys in mid-July found 14.4% of the population may be seropositive; thus, with 5,761 deaths as of August 1, in a population of 10.23 million, the crude IFR may have been 0.39%, and even lower considering the sensitivity of serology discussed above (61). Early on, Sweden did not adequately protect those in nursing homes, a failing that also inflates the IFR (62). The excess all-cause mortality per 100,000 up to July 25, 2020 in Sweden was 50.8, lower than in England and Wales, Spain, Italy, Scotland, Belgium, Netherlands, France, and the US (62, 63). Third, in a globalized world, with entangled webs of supply, demand, and beliefs, what we do here will devastate people not just here, but also elsewhere and everywhere (64). Compared to Denmark, with an economy heavily dependent on pharmaceuticals, Sweden's recession looks bad. However, compared to the European Union, Sweden looks good; the European Commission forecasts a better 2020 economic result for Sweden (GDP−5.3%) than many other comparable European countries (e.g., France−10.6%, Finland−6.3%, Austria−7.1%, Germany−6.3%, Netherlands−6.8%, Italy−11.2%, Denmark−5.2%) (65). The Exit Strategy Herd immunity appears to be the only exit from the response to COVID-19. This can be achieved naturally, or through vaccine. For the reasons given here, it is very possible that the lockdowns are only delaying the inevitable. There are problems with the natural herd immunity approach involving the currently projected and implemented waves of lockdowns. First, this will take years to occur, causing economic and social devastation. This also assumes immunity is long-lasting such that cycles of shutting down can be successful over 2 or 3 years, and without which it is more likely COVID-19 will be an annual occurrence (2). Second, the less devastating test-trace-isolation/quarantine strategy seems not feasible. In the United States it was estimated that there would be a need to train an extra 100,000 public health workers, and to do >5 million SARS-CoV-2 tests per day, necessitating the building of many new very large testing factories (66). Countries would still need to keep borders closed and maintain physical distancing (e.g., no large events) in order to make contact tracing feasible; this would be for years, during which people may become very reluctant to be tested. Modeling suggests that to be successful, because asymptomatic and pre-symptomatic individuals may account for 48–62% of transmission (even in nursing home residents) (67), contact tracing and quarantine would have to occur within 0.5 days for >75% of contacts, necessitating mobile app technology that has its own feasibility and ethical problems (68–70). Vaccine induced herd immunity involves many assumptions. First, there will be the discovery of an effective and safe vaccine that does not cause antibody-dependent (or other immune) enhancement; this, even though the problem in severe COVID-19 may be the host response, especially in older people and children (71–73). Second, the immune response will be durable, not last for only months, and have little immunosenescence (reduced response to vaccine with rapid decline of antibody levels) in older people (72, 74). Third, that mass production and delivery of the vaccine will occur very soon, and be done equitably to all humans on Earth; otherwise, there is the risk of conflict, war, and terrorism in response to gross inequity in vaccine distribution (52). In response to the 2009 pandemic of H1N1 Influenza the United States achieved a weekly vaccination rate of only 1% of the population (72). Vaccine refusers may include 30% of the population in North America and globally (72, 75), and if they have “increased contact rates relative to the rest of the population, vaccination alone may not be able to prevent an outbreak (page 817)” (72). There is already competition among high income countries, and likely crowding out of low-income countries that represent about half of the human population (76). The only globally eradicated human disease is smallpox, which took “30 years to achieve,” and the “fastest historical development of a [new] vaccine was 4 years (Merck: mumps), while most take 10 years (page 2)” (77). 78–82). The numbers involved are staggering, and in the many millions (Table 2). The response has had major detrimental effects on childhood vaccination programs, education, sexual and reproductive health services, food security, poverty, maternal and under five mortality, and infectious disease mortality (78–93). The effect on child and adolescent health will “set the stage for both individual prosperity and the future human capital of all societies (page 2)” (94). The destabilizing effects may lead to chaotic events (e.g., riots, wars, revolutions) (95, 96).
From well-known anti-lockdown advocate, Ari R. Joffe, who constantly pushes debunked nonsense. This paper is so poor he can't even find a co-author for it. Nor can he find any type of leading medical journal to accept it.
I noticed once again you went after the messenger instead of the content. gwBe-abuffoon. Particularly when it comes to making logical arguments. The guy is a doctor and a professor from what I just gathered... and he wrote and interesting paper.
Extreme liberal ET trolls love to slam Governor Ron DeSantis and President Donald Trump for their handling of the Corona Virus pandemic. Gretchen Whitmer, another extreme liberal darling showing us how Corona Virus should be handled. Just like Gavin Newsom and Andrew Cuomo. They are not held accountable for their inept and incompetent handling of Corona Virus. More proof these extreme liberal Democrat politicans have no clue. https://www.yahoo.com/news/michigan-became-hotspot-variants-rose-143318985.html