Category: Not Economics

It has been drilled into our heads by the media and by politicians over the past six months that wearing masks to prevent the spread of Covid is based on “the science.” But is that really true? Or is the so-called science supportive of masks really pseudo-science or junk science?

A little while ago, I started to write a post on the case against masks. It seemed natural to start by examining the scientific support for widespread mask wearing. I began with what seemed to be the most widely cited (as least in the media) pro-mask article, published in the prestigious medical journal, The Lancet (June 27, 2020 issue), and funded by the World Health Organization (WHO). This is an article entitled “Physical distance, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis” authored by Chu et al.  and is a meta-analysis of previously published articles on SARS, MERS and Covid-19 respiratory viruses.

This study (henceforth referred to as the “Lancet study” or “Lancet meta-study”) concludes that mask-wearing as well as physical distancing and eye protection in both public and healthcare settings would result in a large reduction in the risk of Covid infection (though the authors judge the certainty of the effectiveness of both mask wearing and eye protection as “low.”

I read and analyzed each of the 29 studies referenced by the Lancet on the topic of mask wearing (I ignored the studies that focused on physical distancing and eye protection). What I found was shocking. In short, the Lancet meta-study should properly be considered junk science based on junk science that even if true, has no relevance to widespread community mask wearing. Based on my own analysis, I believe the Lancet study should be retracted.

Poor quality of the underlying studies

Let’s start by discussing the poor quality of the underlying studies. A number of the studies are non-peer reviewed and unpublished. Not a single study was based on a randomized control trial. All are observational studies based on questionnaires or interviews.

Because of the nature of observational studies, all of these articles suffer from bias. The most obvious type of bias is recall bias. As stated above, these observational studies are based on questionnaires or interviews given in most cases months (and in one study more than a year) after events took place. To quote from one study, “We encountered difficulty in our study with obtaining precise exposure history from subjects, some of whom had tended more than one patient, and all of whom had imperfect recall of an extremely stressful period” (Teleman).

Even more important than recall bias, however, are the psychological biases to which nearly all of us are prone. The first of which is telling an interviewer what they want to hear. For example, “not only was it difficult for respondents to recall behaviors during specific periods within the previous 2 months, but respondents may have been concerned that results could be used to evaluate their performance” (Ha). It is distinctly possible that healthcare workers who are trained to wear masks might feel pressure to disclose to interviewers that they wore masks even if they did not. Obviously, this acts to overstate mask wearing. An additional type of bias is to project one’s historical actions on whether or not the subject became infected. In other words, healthcare workers that subsequently got sick are more likely to say they did not wear masks (“If I got sick I must have forgotten to wear a mask”) and healthcare workers who did not get sick are more likely to say they did wear mask (“I didn’t get sick so I must have always worn a mask”). Together these biases render questionnaire-based studies likes these much less reliable, to the point of uselessness.

In addition to biases, nearly all of these studies suffer from what is known in statistics as multicollinearity, when there exists significant correlation between two or more independent variables. Most of these studies claim that mask wearing is protective. However, there is likely a strong correlation among healthcare workers between say, mask wearing and glove wearing or mask wearing and gown wearing or mask wearing and hand washing. In these instances, it is impossible to determine whether, say, mask wearing is the protective factor or hand washing. Moreover, it is highly likely that subjects (and especially healthcare workers) who voluntarily were masks when not required are excessively cautious and take other preventative precautions. Similarly, those subjects not wearing masks (either when required or not required) might take fewer precautions and/or more risks when interacting with symptomatic patients. For instance, in one very small study (Kim & Jung), the only one of nine healthcare workers exposed who got sick was a security guard (almost certainly less trained in medical precautions than doctors and nurses).

This problem of multicollinearity is compounded by the fact that most of these 29 studies reflect univariate analyses. That is, they make no attempt to separate the effect of masks from other potentially protective measures (i.e. other PPE, handwashing, face touching, etc.) using regression analysis. Lastly, the Lancet meta-study, as we will see in the next section, takes only the univariate data from these 29 studies even for the handful of studies that do perform multivariate analysis.

Poor quality of the meta-study

The Lancet meta-study examines the 29 individual studies, and for each study calculates how many exposed people who wore face masks were infected with SARS/MERS/COVID and compared those figures to how many exposed people who did not wear face masks were infected. As we have discussed, the Lancet study is a textbook example of “garbage in, garbage out.” But it gets worse.

The first problem with the meta-study itself is that it is riddled with data errors. Specifically, the authors miscalculated the figures and make one or more errors interpreting the data for at least eight of the studies (Scales, Heinzerling, Reynolds, Seto, Alraddadi, Peck, Burke, Ha) (details below). Four of the studies may have contained data errors as I was unable to replicate the Lancet’s summary data (Pei, Ki, Kim & Choi, Lau). Six of the studies reflected exceptionally weak, biased or poor design and should not have been included (Kim & Jung, Nishiyama, Loeb, Wang & Pan, Wu, Tuan). At least four of the studies showed results that were not statistically significantly regarding masks (Yin, Heinzerling, Nishiura, Alraddadi). Finally, for two of the studies I was not able to access more than 1 page abstract so I could not verify the quality of study or data (Yin, Park)

Even more importantly, an additional eight of the studies should not have been included in the Lancet meta-study because they did not reflect a true comparison of the Mask Group vs. the No Mask Group (Liu, Wang & Huang, Ho, Teleman, Wilder-Smith, Kim & Choi, Ryu, Pek). Most of these eight studies compared only a full PPE group with a not-full PPE group, rather than a mask group with a non-mask group. For instance, a healthcare worker in the not-full PPE group might still having been wearing a mask but no gown or glove or goggles.  

The third problem with the meta-study is the various inconsistencies from study to study. In some studies, the mask group represents healthcare workers who “always” wore masks while in other studies the Mask Group reflects mask wearing “sometimes” or “most of the time.” Correspondingly, the No Mask Group could reflect “never” wearing masks or “sometimes” wearing masks. Another glaring inconsistency from study to study is what is considered a positive case. Some studies consider positive cases only if the subject tested positive with a PCR or serology testing regardless of the exposed subject having symptoms (including fever). Other studies do the opposite and consider a positive case if the subject was exposed to a patient and was symptomatic regardless of whether or not they were tested for the virus. Moreover, a few studies tested subjects for antibodies weeks or months after the events, and thus almost certainly undercounted cases.

Finally, there is the question of the six studies that had zero positive cases in both the Mask and Non-Mask groups. Convention says to ignore these studies in a meta-study, which is what the Lancet authors do. However, this decision seems questionable given that some of the studies which were included also had very few positive cases. For instance, one study (Kim, Jung) had only one positive case (out of 9 subjects) and seven other studies had fewer than 10 positive cases (Scales, Park, Heinzerling, Loeb, Ho, Ki, Tuan).

Following is a table summarizing the 29 studies (studies are listed in order of Lancet Table on P. 1981, Figure 4). Full details of my analysis of each study are further below.

1	Scales	incorrect data
2	Liu	should not include, not mask vs. no mask
3	Pei	cannot replicate data
4	Yin	cannot verify data
5	Park	small study, unclear results, not statistically significant
6	Kim, Jung	tiny study, obvious flaws
7	Heinzerling	incorrect data, not statistically significant
8	Nishiura	not statistically significant
9	Nishiyama	weak study, questionnaire long after event
10	Reynolds	incorrect data
11	Loeb	Very small, weak study
12	Wang, Pan	weak study
13	Seto	incorrect data
14	Wang, Huang	should not include, not mask vs. no mask
15	Alraddadi	incorrect data, not statistically significant
16	Ho	should not include, not mask vs. no mask
17	Teleman	should not include, not mask vs. no mask (only N95 vs non-N95)
18	Wilder-Smith	should not include, not mask vs. no mask (only N95 vs non-N95), and redundant data with Teleman
19	Ki	cannot replicate data
20	Kim, Choi	cannot replicate data, should not include, not mask vs. no mask
21	Hall	zero positive cases so not included in Lancet summary
22	Ryu	zero positive cases so not included in Lancet summary, should not include, not mask vs. no mask (full PPE vs. not full PPE)
23	Park	zero positive cases so not included in Lancet summary
24	Peck	incorrect data, should not include because not mask vs. no mask, zero positive cases so not included in Lancet summary
25	Burke	incorrect data, zero positive cases so not included in Lancet summary
26	Ha	incorrect data, zero positive cases so not included in Lancet summary
27	Lau	cannot replicate data
28	Wu	weak study, high possibility of bias
29	Tuan	weak study

Irrelevance of the meta-study to community mask-wearing

We have already established that the Lancet meta-study is weak science based on weak science. But even if it were a quality meta-study of quality studies, its conclusions would still be irrelevant to the matter of the effectiveness of widespread mask wearing among the general public.

Every single one of the 29 studies is a study of whether the mask wearer or non-mask wearer got sick (or was virus positive) having being exposed to symptomatic carriers. Nearly all of these studies (27/29) examined healthcare workers (or in one case, visitors) in a healthcare settings (i.e. hospitals). Moreover, the majority of interactions between the study subjects (mask wearers or non-mask wearers) and the infected index patients occurred with extended contact in close indoor quarters.

However, this study has been used by politicians, health officials and the media to justify widespread mask wearing by the asymptomatic general public, often outdoors, in order to protect not the wearer of the mask but others (“source control”). Not a single study detailed in the Lancet meta-study discusses whether masks protect the general population from asymptomatic spread. Moreover, nearly all of the subjects of the 29 studies were healthcare workers, trained to correctly wear masks and provided with clean masks which they presumably did not reuse and disposed of properly. It is simply nonsensical and unscientific to extrapolate studies of the protectiveness of masks wearing protecting the wearer to studies of masks on the carrier protecting the general population, untrained in proper mask wearing and who reuse dirty masks for days or weeks on end, and constantly fiddle with them. Moreover, at least two studies (Wilder-Smith, Peck) note the fact that asymptomatic spread seems to be limited or nonexistent, further weakening the case for widespread mask wearing.

Conclusion

I believe the Lancet meta-study should be retracted. It is riddled with data errors and contains studies that should not have been included. Most of the rest of the studies included are very small, of exceptionally poor design, or report weak and statistically insignificant results. In summary, the Lancet study shows, at best, weak and circumstantial evidence that masks (most notably, properly fitted N95 masks) may be protective of healthcare workers exposed to symptomatic coronaviruses patients in a healthcare setting (in close quarters for extended contact).  But even if the science was valid, this meta-study has no relevancy whatsoever to widespread mask wearing by the general public and should not be used to justify mandated masks.

The remainder of this article summarizes my finding for each of the 29 studies pertaining to masks that are listed in the Lancet meta-study (in the same order of Lancet Table on P. 1981, Figure 4):

1. Scales et al. 2003

• Lancet Assumption:
  • Face Mask Group: 3/16 (positive/total)
  • No Face Mask Group: 4/15 (positive/total)
• SARS study in Toronto of 31 healthcare workers who had direct exposure to a single symptomatic patient; data via questionnaire
• Lancet data appears incorrect – should have included 6 total positive cases, not 7 (6 probable, 1 suspected); Corrected data is FM Group: 3/13, No FM Group: 3/18
• No Mask group includes healthcare workers who “sometimes” wore mask
• “SARS developed in one healthcare worker despite the fact that the worker wore an N-95 mask, gown and gloves.”
• “Our study involved a small number of cases, and definitive conclusions cannot be drawn from a report of this size.”
• This was a small study that showed no statistically significant difference between the mask and no mask groups.

2. Liu et al. 2009

• Lancet Assumption:
  • Face Mask Group: 8/123
  • No Face Mask Group: 43/345
• SARS study in Beijing among hospital healthcare workers exposed to symptomatic patients; data via questionnaire
• Cannot replicate Lancet figures
• Lancet misrepresents the data – seems to have taken 12-layer group as the mask group and the non-12 layer group as the non-mask group when the non-12 layer group includes 16-layer, N95 and disposable masks
• Interestingly, this study showed no statistically significance for the effectiveness of N95 masks versus other types
• “Another possible bias is that the case group attributed their infection to some high risky performance (e.g. performing intubation) and less efficient protection (wearing only one layer of mask while attending patients), while the control group did the opposite.”
• Study concludes multilayer masks helpful but those people might be OCD and use other precautions
• This study, as I understand it, should not have been included because there is no data on NO masks, only on different types of masks

3. Pei et el. 2006

• Lancet Assumption:
  • Face Mask Group: 11/98
  • No Face Mask Group: 61/115
• SARS study of healthcare workers in China in hospitals; data via questionnaire
• Cannot replicate Lancet data
• Face mask event in Lancet summary represents double 12 layer cotton masks but NOT general cotton masks; if both types used then FM event should be 86/328
• No data given on no mask so no idea where Lancet got 61/115, however seems implausible since article states that “98% of healthcare workers wore masks…”
• “In multivariate analysis the masks as factor didn’t enter logistic regression model…”

4. Yin et al. 2004

• Lancet Assumption:
  • Face Mask Group: 46/202
  • No Face Mask Group: 31/55
• SARS study of healthcare workers in Guangdong, China caring for severe SARS patients; data via questionnaire
• Cannot find full study in English; only have abstract so cannot verify data

5. Park et al. 2016

• Lancet Assumption:
  • Face Mask Group: 3/24
  • No Face Mask Group: 2/4
• MERS study among Korean hospital of healthcare workers and patients who interacted with single symptomatic MERS patient
• 1 page summary only; no full text so cannot verify quality of study or data
• Only 1 out of 5 positive cases were confirmed; 4 were probable
• Unclear if not wearing surgical masks means no mask or means other type of mask
• Mask results not statistically significant

6. Kim, Jung et al. 2016

• Lancet Assumption:
  • Face Mask Group: 0/7
  • No Face Mask Group: 1/2
• MERS study of healthcare workers exposed to a single symptomatic patient in South Korea
• Single healthcare worker who got sick was security guard, not a doctor or nurse and the study discusses fact that security guard possibly contracted MERS elsewhere
• This is a tiny “study” that is limited relevancy

7. Heinzerling et al. 2020

• Lancet Assumption:
  • Face Mask Group: 0/31
  • No Face Mask Group: 3/6
• Covid-19 study in California of healthcare workers exposed to a single symptomatic patient; data via interview
• Lancet completely misinterprets data; correct figures are:
  • Face Mask Group: 0/3
  • No Face Mask Group: 3/34
• Of 3 positive cases in the no-face mask group, 1 individual wore face masks “most of the time”
• 121 healthcare workers were exposed and 43 had symptoms (including fever, cough, shortness of breath, or sore throat), but only 3 tested positive with PCR tests
• Study assumes that 40 w/ symptoms were Covid negative but that seems unlikely especially given February 2020 timeframe
• No data on the mask use of the 121 exposed (43 with symptoms) as there was no Covid testing for non-symptomatic patients
• In addition to “recall bias”, and “the low number of cases which limit the ability to detect statistically significant differences,” “additional infections might have occurred among asymptomatic exposed HCP who were not tested…”
• This study reflects very weak science
• Mask results not statistically significant

8. Nishiura et al. 2005

• Lancet Assumption:
  • Face Mask Group: 8/43
  • No Face Mask Group: 17/72
• SARS study at Vietnam hospital of healthcare workers and relatives exposed to confirmed cases; data based on survey conduced 1 year after onset of epidemic
• Minimal difference in % positive from Face Mask Group vs % positive from No Face Mask Group (19% vs 24%) – not statistically significant
• “Put simply, the use of masks alone was shown to be insufficient to contain the epidemic.”
• Significant bias and limitation to the study: “mask usages…is vulnerable to recall bias,” “…the estimates of the protective effect of masks…may include the effects of other concomitant changes…”

9. Nishiyama et al. 2008

• Lancet Assumption:
  • Face Mask Group: 17/61
  • No Face Mask Group: 14/18
• SARS study at 3 Vietnam hospitals of people exposed to SARS patients; data by questionnaire survey 7 months after epidemic for 1 hospital and 14 months for other 2 hospitals
• Lancet ignores “sometimes” mask use data
• Very simplistic study – no discussion of other prevention measures (e.g. gloves, gowns) except handwashing
• Limited information in “short communication,” not full scientific study

10. Reynolds et al. 2006

• Lancet Assumption:
  • Face Mask Group: 8/42
  • No Face Mask Group: 14/25
• SARS study in Vietnam hospital of healthcare workers exposed to single patient; data via questionnaire
• Study reports two different types of activity: 1) exposed healthcare workers who “talked to or touched index patient without mask” and 2) “came within 1 meter of index patient without mask”
• Lancet used latter group, which shows somewhat stronger pro-mask results
• However, If one “touched” as patient, they must have been within 1 meter, so it appears correct interpretation should have used the other set which is weaker and shows non-statistically significant results (data shown for “talked to or touched”):
  • Face Mask Group: 15/51
  • No Face Mask Group: 7/16
• No analysis of other types of PPE use
• Significant bias and limitations, including, “small sample size,” inability to assess “duration, or the intensity of potential exposure,” “selection bias favoring enrollment of persons with less opportunity for direct contact with the index patient.”
• Very simplistic and poorly designed study

11. Loeb et al. 2004

• Lancet Assumption:
  • Face Mask Group: 3/23
  • No Face Mask Group: 5/9
• SARS study in Toronto hospital of nurses exposed to symptomatic patients; data via interview
• 5/9 No Mask Group is “non consistently wearing mask”, not necessarily wearing no masks
• 2/16 SARS positive individuals always wore N95 mask and 1/4 SARS positive individuals always wore surgical mask
• “Difference for SARS infection for nurses who consistently wore N95 masks and those who consistently worse surgical masks was not significant.”
• Small weak study, for example, single nurse with the most number of shifts (most exposure by far to index patient) had “inconsistent” use of N95 mask (and was included in No Face Mask Group)

12. Wang, Pan et al. 2020

• Lancet Assumption:
  • Face Mask Group: 0/278
  • No Face Mask Group: 10/215
• Covid-19 study of healthcare works in hospital in Wuhan, China
• Mask group equals “wore N95 respirators, and disinfected and cleaned their hands frequently”
• No mask group equals “wore no medical masks, and disinfected and cleaned hands only occasionally”
• Data does not differentiate between the effects of mask wearing and cleaning hands
• What is meant by “medical masks” – might healthcare workers have worn non-N95 masks?
• Other data table shows as strong department effect: respiratory, ICU, infectious disease departments had zero positive cases, hepatobiliary pancreatic surgery, trauma and microsurgery, urology had all of the positive cases so the difference might be type of interaction, not masks (8/10 in one department: hepatobiliary pancreatic surgery)
• “A randomized clinical trial has reported that the N95 respirators vs medical masks resulted in no significant difference in the incidence of laboratory confirmed influence.”
• This is a very weak study that should not have been included because it does not clearly define the mask group and no mask group as properly mask vs. no mask

13. Seto et al. 2003

• Lancet Assumption:
  • Face Mask Group: 0/51
  • No Face Mask Group: 13/203
• SARS study in Hong Kong hospitals of healthcare workers exposed to symptomatic patients; data via questionnaire
• Lancet seems to have misinterpreted data
• 0/51 is for surgical masks only; if we use all masks (including 2 layered paper masks, surgical and N95) then the FM = 2/169 and No FM = 11/85

14. Wang, Huang et al. 2020

• Lancet Assumption:
  • Face Mask Group: 1/1286
  • No Face Mask Group: 119/4036
• Covid-19 study of healthcare workers in China in neurosurgery departments in 107 hospitals; data via questionnaire or telephone interviews
• Lancet completely misinterprets data – conflated masks/no masks with Level 1 (119/4036) vs Level 2 (1/1286) protection
  • Level 1 includes surgical masks: “Level 1 protection: white coat, disposable hat, disposable isolation clothing, disposable gloves and disposable surgical mask (replace them every 4 h or when they are wet or contaminated)”
  • Level 2 includes N95 or higher masks, goggles, gloves, etc.: “Level 2 protection: disposable hat, medical protective mask (N95 or higher standard), goggles (anti-fog) or protective mask (anti-fog), medical protective clothing or white coats covered by medical protective clothing, disposable gloves and disposable shoe covers”
• This is level 1 vs level 2 study, not mask vs no mask study
• Proper data based on study’s Table 1 shows Face Mask group had 95 positive cases (out of 120 infected staff) and No Face Mask group had 25 cases (out of 120 infected staff); no data given on mask use for non-infected individuals
• Study also ignored 300 symptomatic healthcare workers who tested negative for Covid-19
• Significant limitations to study: “the variables of the study are relatively simple,” “protective measures adopted by the medical staff members were not fixed but changed over time. Therefore, the analysis based on protective measures might be affected by time bias.” “respondents’ descriptions might be inconsistent with the facts, which could affect the reliability of the results,” “some cases had uncertain documentation of the exposure history, and recall bias might exist…“
• Study should not have been included as not correctly mask vs no-mask

15. Alraddadi et al. 2016

• Lancet Assumption:
  • Face Mask Group: 6/116
  • No Face Mask Group: 12/101
• MERS study of healthcare workers in Saudi Arabian hospital (2 cohorts exposed to patients – explain); data via questionnaire
• Lancet misinterprets data: figures of mask group (6/116) and non-mask group (12/101) is for N95 masks, not all masks!
• Should have used the data labeled, “Covering of nose and mouth with medical mask or N95 respirator), in which case data would be:
  • Face Mask Group: 11/151
  • No Face Mask Group: 7/66
• Not statistically significant if we use correct data
• Study also does not take into account other PPE (gloves, gown, eye protection)
• The No Face Mask group “sometimes” work masks
• Study ignores symptomatic but negative tested healthcare workers: “most uninfected reported illness”

16. Ho et al. 2004

• Lancet Assumption:
  • Face Mask Group: 2/62
  • No Face Mask Group: 2/10
• SARS study of healthcare workers in hospital in Singapore; data via questionnaire
• Data is for “protected” vs. “unprotected” – no mention of masks specifically, only “full PPE” (likely “N95 masks, gowns and gloves”)
• Data shows only 4 positive cases and 72 total when there were actually 8 positive and 112 total healthcare workers exposed to symptomatic patients
• 55 healthcare workers actually were exposed and had some symptoms but only 8 tested positive
• This study should not be included because not specifically for masks

17. Teleman et al. 2004

• Lancet Assumption:
  • Face Mask Group: 3/26
  • No Face Mask Group: 33/60
• SARS study of healthcare workers at hospital in Sngapore; data via telephone interview questionnaire

• Study only measures if N95 is worn – other group is not necessarily no-masks (likely wore surgical mask)
• Study should not have been included

18. Wilder-Smith et al. 2005

• Lancet Assumption:
  • Face Mask Group: 6/27
  • No Face Mask Group: 39/71
• SARS study of healthcare workers in Singapore hospital; data via telephone interview questionnaire
• Appears to be same data as previous study (Teleman et al) – should not include both studies (same Singapore hospital – Tock Seng Hospital)
• Data is for N95 masks vs no N95 masks, not no masks
• Should be 80 study participants, not 98
• Study should be excluded for two reasons: redundant data with previous study (Teleman) and study is not reflective of masks vs no mask
• “Based on our data in Singapore, transmission from asymptomatic patients appears to play no or only a minor role” (remember, the point of mask mandates is to protect wearer against asymptomatic individuals)

19. Ki et al. 2019

• Lancet Assumption:
  • Face Mask Group: 0/218
  • No Face Mask Group: 6/230
• MERS study from hospital in South Korea of hospital healthcare workers and patients exposed to a single symptomatic patient; data via video data and interview
• Possible bias because patients who are less likely to wear masks than healthcare workers are also less likely to maintain other safe behaviors
• Hand washing sems more important than masks especially since 2/11 patients had no direct contact with index patient – don’t touch face which regular (non healthcare-trained) people seem to do with masks on
• Study gives data on % people who wore surgical masks but no data if infected patients wore or did not wear masks
• Study data shows 4 positive patients with mask data (Table 2 of study) while Lancet states there are 6 – no idea where Lancet data comes from
• Cannot replicate Lancet data

20. Kim, Choi et al. 2016

• Lancet Assumption:
  • Face Mask Group: 1/444
  • No Face Mask Group: 16/308
• MERS study of healthcare workers in South Korean hospitals with direct contact with MERS patients; data via questionnaire survey
• Cannot replicate data; study says at least 2 cases wore N95 and were infected (Lancet says only 1)
• “Appropriate PPE was defined as use of all of the following: (a) N95 respirator or powered air-purifying respirator (PAPR), (b) isolation gown (coverall), (c) goggles or face shield and (d) gloves). If any part of the PPE was missing, it was considered to be exposure without appropriate PPE.”
• This is a study of full PPE (described above) vs. non-full PPE, not mask vs. no-mask. Hence, study should not be included

21. Hall et al. 2004

• Lancet Assumption:
  • Face Mask Group: 0/42
  • No Face Mask Group: 0/6
• MERS study of healthcare workers in one hospital in Saudi Arabia of healthcare workers exposed to a single patient; data via questionnaire
• Nobody got sick – 0 cases, though some had symptoms and tested negative
• Typical recall bias, since questionnaire was 4 months after event
• 87% of healthcare workers worse surgical masks, though not necessarily 100% compliance
• 33% of healthcare works used N95
• Study not included in Lancet summary data due to zero positive cases in both groups

22. Ryu et al. 2019

• Lancet Assumption:
  • Face Mask Group: 0/24
  • No Face Mask Group: 0/10
• MERS study in South Korea of people exposed to MERS patients; data via interview, 7 months after events
• No differentiation between PPE (gown, N95 mask, glasses, gloves) and only masks
• 1 person had fever and wore full PPE but wasn’t tested for MERS at the time
• Face mask group (24 people) is Grade 3 and Grade 4 = Full PPE
• Non-face mask group (10 people) is Grade 1 and Grade 2 = without full PPE (but could include mask)
• Significant study limitations: bias as questionnaire was 7 months after event; also study might have “missed some mild or asymptomatic cases,” “serological tests were performed several months post-exposure, pre-existing MERS antibodies may have decreased or disappeared in the interval, potentially leading to underestimation,” “number of participants was relatively small and may not be representative or generalizable.”
• Study should not be included because Grade 1 and 2 versus Grade 3 and 4 is not mask/no-mask
• Study not included in Lancet summary data due to zero positive cases in both groups.

23. Park et al. 2004

• Lancet Assumption
  • Face Mask Group: 0/60
  • No Face Mask Group: 0/45
• SARS study in United States of healthcare workers exposed to SARS patients in 8 healthcare facilities; data via questionnaire
• 17 healthcare workers developed symptoms but zero tested positive
• Study not included in Lancet data due to zero positive cases in both groups

24. Peck et al. 2004

• Lancet Assumption
  • Face Mask Group: 0/13
  • No Face Mask Group: 0/19
• SARS study in United States of people exposed to single SARS patient; study comparing individuals exposed pre-diagnosis to the index patient and post-diagnosis; data via questionnaire
• Of pre-diagnosis contacts, 11/26 contacts had symptoms but all tested negative for SARS; pre-diagnosis contacts included household contacts
• Cannot replicate Lancet figures
• Correct data as per study’s Table:
  • Face Mask Group: 0/26
  • No Face Mask Group: 0/30
• Not mask vs. no-mask but Full PPE (N95 respirator, gown, gloves worn “every interaction”) vs. not-full PPE – study should not be included
• “To date, no asymptomatic SARS-CoV infection or transmission before onset of symptoms has been definitively documented.”
• Study not included in Lancet data due to zero positive cases in both groups

25. Burke et al. 2020

• Lancet Assumption:
  • Face Mask Group: 0/64
  • No Face Mask Group: 0/13
• Covid-19 study in United States of close contacts of positive cases; data via interview
• Lancet has incorrect  data (76, not 77 total individuals in study’s data table). Correct data should be:
  • Face Mask Group: 0/63
  • No Face Mask Group: 0/13
• 25/163 healthcare workers had suspected Covid, but these were not apparently among the 76 with interview data
• Study not included in Lancet data due to zero positive cases in both groups

26. Ha et al. 2004

• Lancet Assumption
  • Face Mask Group: 0/61
  • No Face Mask Group: 0/1
• SARS study of healthcare workers in one hospital in Vietnam exposed to SARS patients; data via questionnaire
• ~23% of healthcare workers had symptoms but zero tested positive for SARS
• While “all 62 SARS ward workers reported wearing masks during the outbreak,” “only 56 reported ‘always’ or ‘usually’ using a mask while in SARS patients’ rooms.” (after first week of patient care). Hence correct data should be:
  • Face Mask Group: 0/56
  • No Face Mask Group: 0/6
• Study limitations include, “subject to recall and reporting bias, because not only was it difficult for respondents to recall behaviors during specific periods within the previous 2 months, but respondents may have been concerned that results could be used to evaluate their performance. Estimates of SARS exposures and the frequency of personal protective equipment use among SARS ward workers are therefore probably inflated.”
• Study not included in Lancet data due to zero positive cases in both groups

27. Lau et al. 2004

• Lancet Assumption
  • Face Mask Group: 12/89
  • No Face Mask Group: 25/98
• SARS study of household members exposed to SARS patients in Hong Kong; data via telephone interview/questionnaire
• Cannot replicate Lancet’s data
• This study is listed in the Lancet article as a study in a “Non-health-care setting” (meaning, a study of mask-wearing in the community, not healthcare setting). However, this is not correct. While the study analyzes family members of SARS patients (non-healthcare workers), the mask data is of those family members during hospital visits. Therefore, the study should more correctly be listed as a “health-care setting.”
• Of all the Lancet mask studies, this is the only one that has any data on mask wearing by symptomatic patients, rather than mask wearing by the non-infected. Study only reports during a hospital visit whether neither visitor nor patient was wearing a mask, both were wearing masks, or one was wearing mask (no reporting is made between whether the SARS patient or the visitor is the one wearing a mask).
• 128 cases with data, 32 visited, 8 both had masks, 7 with one wearing mask, 17 no masks
• 2121 controls with data, 242 visited, 85 both masks, 76 with one wearing mask, 81 no masks
• Study limitations: “no way to confirm that the probable secondary infection of household members actually came from the index patient. Nosocomial infections, rather than secondary infections, may also have occurred in some of the household members during hospital visits to the index patient, but it is not possible to distinguish the two scenarios.”  “The case definition of SARS coronavirus was nonspecific…it is possible that some of the cases were in fact pneumonia rather than SARS.”

28. Wu et al. 2004

• Lancet Assumption:
  • Face Mask Group: 25/146
  • No Face Mask Group: 69/229
• SARS study of community cases and control group in Beijing; control group had no close contact with SARS patients; data via questionnaire
• No face mask group includes people that “sometimes” wore face masks
• Study limitations include low participation rate, recall bias, “those who agreed to participate may have self-selected for unknown reasons that could have biased our findings. For instance, several patients responding to the open-ended comment section mentioned that they were certain their illness was not ‘SARS’’
• Figures dependent on the number of the control group, which is totally the choice of the study.
• Confirmed cases equals people with symptoms, not serology testing (many other studies are the opposite – only positive if tested positive even if symptoms)

29. Tuan et al. 2007

• Lancet Assumption:
  • Face Mask Group: 0/9
  • No Face Mask Group: 7/154
• SARS study in Vietnam of household and community contacts exposed to SARS patients; data via questionnaire/interview
• Face Mask Group cases is defined as wearing mask “sometimes/most times” (not necessarily always) and the No Face Mask Group is defined as “Never” wearing a mask. This is inconsistent with nearly all other studies in Lancet
• Very simplistic univariate analysis
• “There have been no conclusive reports of transmission occurring from SARS cases in the pre-symptomatic phase and we also found no evidence of transmission occurring prior to onset of symptoms.”

The hysteria over Covid-19 is out of control. It never ceases to amaze me how the vast majority of quote unquote smart people can act in such a sheepish, thoughtless manner. Politicians I get. But that the only pushback to excessive, expanding and lengthening lockdowns are coming from the utterly thoughtless extreme right is absurd. And enormously damaging.

Studies around the U.S. and around the world are very consistently showing that the number of people exposed to coronavirus (that is, who test positive for antibodies) vastly exceeds scientists’ and doctors’ initial estimates. And by vastly, I mean by orders of magnitude. The true number of people exposed will wind up being somewhere between 10x and 1000x the number of people who have, to date, tested positive. This has been found in New York City, in Miami, in Massachusetts, in California, in Japan, and elsewhere.

This is incredibly good news, yet the mainstream media and politicians have either completely ignored these findings, or have started to view this information as instead, dire. Using the lower and most conservative estimate of 10x means that the actual mortality rate from being exposed is one tenth what the science community first thought, what the mainstream media continues to report, and what the general population continues to believe. In other words, the death rate isn’t the reported 3-5% of Wuhan or the assumed 12% of Northern Italy but, at worst, 0.3%-1.2%.

A more likely exposure rate of 20-50x compared with the number of people who have tested positive leads to an overall mortality rate somewhere in between approximately as fatal as the seasonal flu and one to three times more fatal than flu. I’ll venture to guess that when all is said and done, the overall fatality rate will wind up no more than 0.2%. Perhaps twice the 0.1% of the flu. Deadly yes. Reason to be hysterical, no. And while it is very helpful for science to confirm these numbers, this state of affairs should have been obvious to the well-informed from the beginning, since we knew how few people we were testing. Instead, the media and the politicians both benefited from grasping onto high death figures. And now they won’t let go.

Beyond the clinical fatality rate is the fact that for the majority of people, being exposed to Covid-19 will prove asymptomatic. Even more good news is that children are almost always asymptomatic and have very low viral loads. Children don’t pass this on to adults, something many adults (and especially teacher’s unions) are fearful. So called grown-ups pass it on to children, who have a fatality rate of close to zero, almost certainly less deadly than a bad influenza year.

Add to the fact that deaths due to Covid are as likely, if not more likely to being overcounted in the U.S. than undercounted. Deaths are being attributed to the virus if the hospital patient or nursing home inhabitant tested positive or showed signs of the virus. However, many such people, especially those in nursing homes, would, statistically, have died anyway of other causes (nursing home inhabitants in the U.S. have a median life expectancy of about 5 months!). In other words, not every Covid-positive or Covid-assumed-to-be-postive death is a death caused by the virus. We also know that at least in Europe, a milder than normal flu season left more vulnerable people alive in the fall and early spring, leading to more deaths being attributed to coronavirus.

Totally unlike the Spanish flu of 1918-1919, Covid-19 is overwhelmingly affecting the old and the unhealthy, not the young and healthy.

So why, as I mentioned above, is the media reporting this good news (to the extent they are reporting it at all) as dire? Because they believe it means that Covid-19 spreads more rapidly than expected, and use that information to justify even more drastic lockdowns.

It is indeed possibly, perhaps likely, that Covid-19 spreads more easily than expected. It almost certainly spreads more easily than the seasonal flu given that the we have vaccines for the flu. But to reinforce the key point explained above, this means the virus is far less deadly than we feared. In addition, it is seeming more and more likely that virus has been spreading in the U.S. not since March as first thought, but since as early as November or December. It also means we are far closer to herd immunity than we thought (especially in densely populated, and hard-hit New York City).

I’ll speculate on what would be one more piece of good news. We know that receiving a high viral loads makes one much more likely to have serious complications from Covid. In fact even more so than old age, viral load may be the single highest risk factor, which would explain the stories of otherwise healthy and young healthcare workers becoming very sick and in some cases dying. It is further reasonable to assume that asymptomatic carriers have low viral loads and if and when they do spread the virus, they will spread small amounts and recipients will also likely be asymptomatic. In other words, provided society can protect the high-risk population, getting to herd immunity may be far less deadly than scientists believe.

Clearly we need a lot more data. Even more clearly, we need a lot less panic.

The undeniable truth is that the scientific news over the past month has been overwhelmingly positive. Mortality projections are significantly lower. Hard-hit areas like Italy, Spain and New York are all showing vastly improving figures, indicating the worst is both over, and much less worse than feared. Yet, the data is not consistent with perception.

Moreover, the stated goal of government ordered lockdown was to “flatten the curve” so that healthcare system would not be overwhelmed. Even in New York, the so-called “epicenter,” there are unused beds and an overcapacity of ventilators (which may in fact have done more harm than good). In most of the country, hospitals are essentially empty with few Covid cases and even fewer non-Covid cases as patients with other diseases have been scared or turned away. Hospitals around the country have resorted to layoffs and furloughs.

Instead of backing off off extreme lockdowns, politicians have instead “moved the goalposts,” insisting now that we cannot return to any semblance of normal life until we test more or until the “second wave” passes. Essentially, governments are saying we can’t resume life until there is zero risk. This is absurd.

Where I live, in New York City, the lockdown has grown more severe, restrictions on activities have increased nearly daily and the path to opening up seems less clear and more distant. Every day, people appear to be more hysterical over Covid-19 and overwhelmingly willing to imprison themselves, throw tens of millions out of work and risk something far worse than the Great Depression. Why? Let me suggest a few reasons.

Reason #1: the contented

For one, the upper-middle and upper classes are enjoying their staycations. The privileged (not to mention virtually all government employees) are getting paid by their employers to sit at home in their pajama bottoms and do little more than participate (or not) on a couple of daily Zoom calls. In short, without money worries, they have been given an unlimited hall pass from “adulting,” having become perpetual (stay-at-home) Ferris Buellers. Endless, guiltless, state-sponsored sloth. Naturally, they support the government lockdowns and will resist loosening them so they don’t have to go back to real life.

Even more important, those under lockdown are being made to feel like they are contributing to the efforts. They are helping! They are sacrificing! Let’s shame the people going outside and contributing to the little economy that still exists as horrible virus-spreaders not doing their fair share. Let’s laud those binge-watching Netflix as sacrificing and righteous!

On the other hand, the lower and middle classes that live paycheck-to-paycheck not to mention small business owners clearly have a different calculus. They, of course, have far less political power.

Reason #2: the politicians

A second reason is clearly political. Most obviously, there is the typical political mentalities of short-termism, cover-your-ass and hunger for absolute power. Much better politically to have saved a single life for which you can take credit even if it means having destroyed an economy which can be blamed on “nature.” We are at war, they say. And war requires extreme sacrifices and extreme measures. We can’t worry about costs and benefits and about collateral damage when we are at war. As a politician, it is always better to do something rather than nothing, even if that something is wrong. And it is always better to err on the side of what your constituents will view as their short-term public safety. It also seems apparent that many politicians, governors especially are enjoying the massive power they have seized during this pandemic to control the lives of their constituents.

Even more importantly, governors and mayors and county executives, especially those from blue states, are competing to contrast their efforts with the appalling federal response. The governor that can lockdown the hardest and exclaim their apathy for the economy the strongest becomes the most popular, highest revered anti-Trump. Cuomo in New York. Murphy in New Jersey. Newsom in California. Whitmer in Michigan. Who can be the great general that leads us into battle against the mighty adversary of the virus? Plus, being typical arrogant politicians, they will never be able to admit they were wrong.

Politically speaking, locking down is the easy part. Even more easy when you’ve brainwashed the populace into hysteria and your approval ratings are high. On the other hand, picking up the pieces of what is left of the economy will be much, much harder. With vastly diminished tax revenue and skyrocketing unemployment, budgets will have to be cut along with vital services. Crime and poverty and despair will dramatically rise. Those popularity ratings we mentioned won’t look so good then. I do think politicians in blue states are smart enough to know this and are scared to death of what awaits them, and us, once the lockdowns end. Which is why they will delay lifting lockdowns as long as possible, devastating cities like New York for what could be a generation.

As a side note, one must wonder if extended lockdowns in the rust belt (witness Michigan in particular) could end up costing Biden the presidential election. Democrats risk digging their own grave here as politically astute (and non-astute in every other way) Trump might wind up successful in placing the blame on Democratic leaders for what will almost certainly be a horrific economy come November.

Reason #3: the media

The third reason why lockdowns are continuing despite mounting evidence of abating danger is the media. We are witnessing the ultimate national version of “if it bleeds, it leads.” With nothing else for the sheltered-at-home to do than entertain themselves with television or the internet, the media has a captive audience. The more the scaremongering, the greater the ratings and the higher the readership. I’m not the first to call this “pandemic-porn” or “panic-porn”. People aren’t attracted to good news and dry statistics, they are attracted by videos of overrun emergency rooms, by images of grieving families, and by stories of otherwise healthy and fit moms and dads, suddenly stricken. Lastly, that the media is, like the virus, epi-centered in New York City means the rest of the country exaggerates their fears.

And yet it isn’t only the profit-oriented media at work in fomenting hysteria. The government is doing this too. Unrelenting reminders from our politicians on how dangerous our situation is. Roadsigns and street placards reminding us to social distance and “flatten the curve.” Signs on every storefront requiring masks to enter. Even trying to distract oneself and relax by listening to local radio requires enduring constant public service announcements (the only form of advertising that remains) reminding one to shelter, and if you can’t shelter then to distance, and if you can’t distance then to cover up.

Reason #4: the education, or lack thereof

The final reason I will give for why lockdowns are popular and thus persisting is because of the awful state of education in this country. I’m not talking about education for poor people or minorities or in urban areas. I’m talking about education for the privileged and the smart. Bluntly speaking, it sucks. We barely learn history and most of us learn no statistics at all.
Lacking a proper understanding of history means few know of or appreciate what history teaches: societies survive pandemics time and time again, even with horrific loss of life. Societies do not tend to survive economic collapse.

Not understanding statistics (something for which doctors and journalists and politicians are notorious) means most of us have no ability to understand, interpret or form any proper judgment on the kind of scientific studies about which we are now reading. We blindly trust the “experts” even though those experts have been hugely wrong with their models from the beginning of the pandemic. And we vastly overrate our own risk of dying just because we know somebody who knows somebody who died.

How to end the lockdowns

By far, the most important aspect of ending the lockdowns is to reduce the level of hysteria amongst the general population. We need to talk people off the ledge by making them understand the true mortality rates. Politicians and the media have scared the population into believing that Covid-19 is a death sentence. We need to now change the message to one that is far less dire and far more factually correct. For the vast majority of the population, Covid is no more likely to cause death than the flu. For the young, it may even be less likely. Moreover, Covid exposure for the majority of people, including the elderly, will prove asymptomatic.

In order to change public perception, politicians are going to have to admit they were wrong and that they overreacted. Politicians can blame the scientists and say that say that they trusted the early models that wound up being overly pessimistic. They can maintain the position that they followed the experts and had the safety of the population as their prime objective. They have to say that they now realize that yes, Covid is serious, but nowhere near deadly enough that it is worth destroying an economy. For most politicians this will be impossible. But perhaps a handful of brave souls will show true leadership. Naturally, it will prove even harder for the scientists to admit that their models were wrong and that they were the cause of such huge damage. Unlike the politicians, the scientists have nobody but themselves to blame.

Unfortunately, as you may have perceived, we have a bit of a chicken and egg situation. Hysteria won’t fall until politicians change their message and the fear of leaving one’s house subsides. But politicians won’t change their message until support for the lockdowns falls. Support for the lockdowns won’t fall until the fear level is reduced. Ultimately, economic distress and social unrest will force a change of both positions. But clearly, we would all be better off if the change happened sooner.

In addition to reducing hysteria, the federal and state governments need to pass legislation to indemnify businsess, schools and other organizations from the inevitable lawsuits that will brought if someone catches the virus at that establishment. We cannot have businesses and other entities fearful of lawsuits and scared to open. To be evenhanded, legislation should also ban lawsuits against the governments that forced these lockdowns in the first place, even if they were misguided. In the spirit of moving forward, let’s all agree we are much better off leaving the lawyers out of it.

Now let’s talk about what should open when lockdowns are ended. I firmly believe that the correct answer is everything. Yes, everything. But I recognize that given the widespread and pervasive level of fear, that’s not going to happen. We’re going to have to do this in stages, as other countries and some southern states have started to do in order to prove to politicians and the general public that it is safe to do so. Start with schools. Schools should reopen immediately. Children are at very low risk from coronavirus and are suffering greatly without education, without human contact and in many cases, with unlimited screentime. All outdoors spaces should also be opened immediately, including parks and playgrounds, beaches, golf courses and outdoor sports facilities. The odds of catching the virus outside, or at least inhaling enough of a virus load to become seriously sick is minuscule. The benefits of exercise, fresh air and sunlight are immeasurable.

All doctors, dentists and other healthcare providers should resume normal operations. Hospitals should also reopen and operate normally for all patients. I don’t believe there is a single hospital, even in New York City, that is, at this date, overburdened with Covid patients. Cancer treatments should resume. Elective surgeries should be re-instated. Children should be receiving vaccines. Adults should be getting their in-person (not tele-medicine) annual physicals.

All non-retail businesses should be allowed to open without restriction. Retail and restaurant establishments should be able to open, albeit with some restrictions on capacity to be phased out over a short period of time. Large gatherings will have to be phased in over time, again with increasing occupancy. To reiterate, the gradual phase-in for the reduction of the lockdowns are not for health reasons necessarily, but to de-sensitize the hysterical masses and show them that life is indeed safe, and can continue.

Locations with a large number of elderly and high-risk people, most notably nursing homes, should remain severly restricted until the threat has passed. Far more government resources should go to to protect nursing homes and the elderly. This has been an appaling governmental failure to date.

Anyone with coronavirus symptoms should obviously stay home and be self-quarantined. To the extent they have to go outside for healthcare services or essentials, they should be mandated to wear face covering. As for those asymptomatic, there is very little evidence that masks are effective in slowing the spread of virus. Anybody that comes into close contact with people should be allowed, though not mandated, to wear a mask. For the rest of the population, masks and other facial covering should be optional. Even if there is a slight benefit to wearing masks in an indoor setting (and there is unlikely any benefit in an outdoor setting), I would argue that the constant reminder of fear seeing everyone in masks, and the anti-community sentiment that comes with pervasive masks outweigh any potentially small benefit. Plus, there are public places where masks cannot be worn, such as restaurants and hair salons.

Let’s now discuss testing and data. Politicians, having moved the goal post away from “flattening the curve” are now insisting that we cannot lift lockdowns until testing is widespread. This is absurd for two reasons. For one, testing at this point is essentially useless. Second, we are many, many months away (if not more) from being able to test everyone with symptoms in a timely manner. We can’t (and shouldn’t) keep society locked down long enough to wait for widespread testing. Anybody with symptoms should assume they are positive and self-quarantine. Now, with the virus widespread, a positive test has virtually no value.

What government should be focusing on, with regards to data and testing, is twofold. First, antibodies. Let’s understand the true number of people exposed already to the virus so we can calculate the true mortality rates. Let’s also see how close we might be to herd immunity. Note also that we do not need widespread testing to accomplish this. Relatively small samples in a given area can be statistically significant. Second, the government should be coordinating hospitals and scientists to understand who is really at high risk and who is not. Why are a very small number of otherwise healthy people getting seriously sick and in some cases dying. Is it the high viral load, for example, of healthcare workers? Or some other hidden underlying factor? And if it is viral load, does it only happen in a healthcare (hospital) setting or because of some super-spreading event? We need this information to know where social distancing is helpful and where is it useless and unnecessary.

Anyone that says that the miracle answer is “testing” is either foolish or lying. In order for testing to be effective in containing the virus, we would have to test asymptomatic people each and everyday. We obviously do not have the resources to do this, nor would the vast majority of the populace allow this to happen.

Lastly, let’s talk about what else we should absolutely not do. We cannot lockdown the world until we have a vaccine, which is likely 12-18 months away (and possibly longer). One way or another we have to learn to live with the virus.

We should not mandate temperature checking in order to go to school, enter a store, dine at a restaurant or board a plane. Nor should we allow either biometric or cellphone contact tracing. The benefits are at miniscule at best, and the costs to freedom and privacy are enormous. We must not let happen what occurred after 9/11, which is to give up our liberties. Unfortunately, we seemed to be heading directly towards those same mistakes. Then it was hysteria about terrorisim, now it is Covid hysteria. If we make people fearful and stressed out when going about what should be everyday activities like we do going through TSA checkpoints, we are essentially guaranteeing economic depression. We should not give up freedom for the theatre of false security. As Benjamin Franklin famously said (and I am not the first commentator to use this quote). “They who can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety.”

How do we know the lockdowns didn’t work?

We have one last thing to talk about. If you are paying attention you should be wondering the following: first, how do we know it wasn’t the severe lockdowns that stopped the spread of Covid, and second, how do we know the spread of the virus won’t accelerate once the lockdowns are lifted. There are three sets of answers. 1) We’re starting to know, 2) stopping the spread might not have been the optimal health solution anyway, and 3) it doesn’t matter.

1 – Evidence that lockdowns were ineffective

Let’s be honest here. As of yet, we cannot with certainty prove that the lockdowns were ineffective. However, there is a significant and growing amount of circumstantial evidence that indicate this is the case. The most obvious place to start is Sweden, the black sheep of the world.

As you probably know, Sweden was one of the only countries in the world to decide not to that lockdown the country and implement only moderate (and weakly enforced) social distancing. Restaurants, bars, elementary schools, parks and hair salons all remained open. For that, they have endured ridicule from all over the world, in fact, with many openly rooting for a high death count. So far the evidence shows that Sweden’s death rate, while higher than its neighboring Scandinavian countries is about average for Europe, lower than France, Italy, the U.K. and Spain. While Sweden’s economy has been hurt, as it is significantly export oriented, the damage is much less so than in countries that have locked down. Finally, it is obvious that Sweden is much further along the path to herd immunity than nearly all other countries of the world.

Additional evidence that lockdowns were ineffective and unnecessary is that there seems to be no correlation between states (and between countries) that locked down early, and their death rates. In other words, whether a state or country locked their populations down earlier or later seems to have no impact on fatalities.

We will have much more evidence in the next few weeks as countries in Europe (Switzerland, Austria, Spain and Germany for example) as well as certain, mostly Southern, U.S. states begin to open up their economies and free their inhabitants to resume life. Early evidence from Germany and in the United States from Georgia shows no meaningful increases in new cases since the lockdowns were lifted. We also await the findings of more antibody testing surveys so we can get a better sense for how widespread is the virus is, and its true fatality rate.

In addition to these studies, we can utilize what was, at one time, called “common sense.” As we’ve discussed, many more people were exposed to Covid and the exposure has been going on for much longer. We can draw three conclusions for these facts, the first two of which we have already mentioned. The mortality fate of Covid is much lower than expected and we are closer to herd immunity. The third conclusion is that the government ordered lockdowns were too late to stop the spread of the virus. Perhaps they might have been effective in December or January or even February but they were not effective in March and April.

It is also plausible, as one New York based ER Doctor has concluded, that the decline of Covid infections that we are experiencing (the so-called flattering curve) is due not to lockdowns but to the natural progression of the virus. That is to say, the pandemic would diminish in severity regardless of what governments ordered.

In a similar vein, it is reasonable to assume that the death rate would be highest at the beginning of the pandemic (a time point we have past) and would decrease significantly as it becomes more widespread. We know that in most countries and in most states, more than half of all deaths occurred in people living in nursing homes or long-term care facilities. We also now know that Covid is widespread in nursing homes. As the virus spreads, there are fewer and fewer nursing homes to infect (residents are either dead or almost certainly immune). Any further spreading of the virus including the feared “second wave” will have fewer deaths and hence, it is wrong to project total fatalities based on initial mortality rates. The same logic applies to healthcare workers, most of whom we can assume have already been exposed. Lastly, it is also wrong to extrapolate the death rate in a densely populated city such as New York to the rest of the country.

2 – Stopping the spread might have been the wrong thing to do

Next, let’s move on to the second answer about lockdowns, that stopping the spread might not having been the optimal course of action even if the goal is to minimize fatalities. Clearly lockdowns have reduced the spread of Covid to some extent, especially by virtually eliminating travel. However, the question remains whether that strategy will ultimately save lives. It is not at all clear that lockdowns are the best strategy to prevent deaths over the long-term, given the demographic of what populations are at risk. It is highly plausible that a far better strategy would have been to quarantine and protect the high-risk population, most notably those in nursing homes but let the virus spread freely amongst the majority of the population (especially children and the working population) that are at very low risk.

This strategy of protecting the high-risk and encouraging herd immunity among the majority and low-risk has even more merit if we take into account predictions that we will face a second wave of Covid in the fall at the same time as the seasonal flu, a double whammy for those at risk. Notwithstanding the fact that nearly all Covid predictions of such nature have been overly pessimistic and erroneous, if we take this idea as face value then we must conclude that getting to herd immunity in the spring and summer when the flu is mostly dormant is a superior strategy.

3 – Even if the lockdowns were effective in saving lives, the cure was still worse than the disease

The last answer is the most controversial to many people, but the most important. Even if the lockdowns were shown to be effective in saving lives in the near-term, the byproducts of these lockdowns are worse than the disease itself. This does not mean that those with this viewpoint (as I have) are insensitive to thousands of people dying. It in fact means exactly the opposite. We believe that the attempt to save lives by locking down the population will over the longer-term cost far more lives than it saves. If lockdowns (and hysteria) persist, the damage to health from these effects will greatly exceed Covid deaths. These effects are much harder to quantity than the number of deaths so it is easy for government to ignore them. But the health of the entire population including mental health must be taken into account.

We know already that the impact of the lockdowns on the health of the general population is enormous, yet governments in their destructive policies, have paid virtually no consideration to these factors. We know that people are dying of non-Covid causes because they have been unable or too afraid to seek aid in hospitals. We know that hospitals in rural areas have already closed or are at risk of closing because of lack of patients. We know that cancer patients are skipping their treatments and children are missing their scheduled vaccines. We know that there have been suicides due to joblessness and because of social distancing. We know that there are rising cases of domestic and child abuse. We know that people are drinking more alcohol at home. We know that most people locked down have virtually stopped exercising and getting fresh air. We know that screen addiction is up, especially among children. We know that stress and feelings of helplessness and loneliness are pervasive.

Those are just some of the direct effects of the lockdowns on health. The economic effects of shutting down the world economy will prove to be far, far worse. A recent widely reported study forecasted 130 million people worldwide are at risk of starving to death because of the economic disruption. In the United States, 30 million people, nearly one fifth the working population, have already filed for unemployment. The world is heading towards a depression that will rival or even exceed that of the Great Depression of the 1930s. The health effects of years of economic despair are immense

How about the ramifications of central banks printing trillions of dollars? Or the federal government running record deficits and handing out trillions with little oversight? What about state and local governments with little tax revenue and massive unemployment bills? Will they cut services and see crime dramatically rise? What about pensions? Will we finally see states file bankruptcy? And how long can any oil producing country survive with today’s oil prices? Can China placate their population with their first downturn in more than a generation? This is scary stuff. The kind of scary stuff that history shows causes wars and revolutions.

Who knows what other effects economic depression will bring. Depression in Germany in the 1930s led to Hilter’s rise and World War, with tens of millions of deaths. Could something similar happen again? Not impossible, perhaps not unlikely.

There is one other economic trend worth mentioning – income inequality. We know that the virus itself takes a harder toll on the poor and on minorities for two reasons. First, because these populations tend to be unhealthier and have higher rates of obesity, diabetes, heart disease and other risk factors. Second, because they have less access to quality healthcare services. In the near-term there is little the government can do to alleviate the Covid-related health outcomes between the more and less fortunate. However, the worst thing government can do is exacerbate the gap between the rich and poor with policy, but that is exactly what lockdowns do. Perhaps the second worst thing they can do is to further empower tech giants such as Amazon and Facebook and Google at the expense of brick-and-mortar retail, mom-and-pop businesses and local journalism.

There is a huge discrepancy between those getting paid throughout the lockdowns (mostly the well-off, white collar workers as well as government employees) and those laid off, furloughed or otherwise not allowed to earn an income. The latter group are mostly the poorer and blue collar workers. Governments are literally creating a great schism within society that is bound to cause social unrest. The unforgivable closing of schools exacerbates these inequalities. Private schools can maintain high quality online learning and wealthy and highly educated parents can supplement their children’s education. The children in public schools and of less privileged families learn little or nothing. This gap in education will never be made up.

Lastly I want to talk briefly about New York City, where I live (though most of what I write here applies to other cities as well). New York City is being decimated, and not by Covid itself, but by the government lockdowns and by the fear that politicians and the media have instilled. Imagine New York City without restaurants, without museums and art galleries, without concerts and the theater, without retail stores and Christmas windows, without tourists. Contemplate a city made for walking where people are so afraid of each other that they won’t walk on the same sidewalk. Think about social distancing requirements that preclude profitable business and make city life unbearable. And now add a reduction in local services due to massive budget cuts, a rise in crime and a mass exodus of population. What I describe is not just New York City under lockdown but the New York City that will exist for years or even decades after lockdowns are lifted. We are witnessing the death of New York City, the city that I love. Governor Cuomo, who has been lauded by many for leadership will go down in history as the man who destroyed New York City.

Conclusion

In absolute numbers, the virus has been, and will continue to be deadly for many thousands of people. This is tragic. Tragic for the dead and tragic for their surviving families. But the tragedy of perpetual lockdown and overblown hysteria will prove to be far greater.

We know now that the virus has been spreading for months longer than scientists first thought. We know now that at minimum between 10 and 50 times the number of people who have tested positive have actually been exposed to the virus, the vast majority of them completely asymptomatic. We know now that the true fatality rate is at worst, only several times that of the seasonal flu. We know now that of the people dying, over half were from nursing homes and approximately 90% had more than one underlying health risk. We know now that the risk to children is almost exactly zero.

Over the past six weeks, over 30 million Americans have filed unemployment. The Federal Reserve has printed trillions of dollars. Government deficits have skyrocketed to unprecedented levels. Oil future prices have plummeted to below zero. Meanwhile, more than a hundred million people are at risk of starving to death across the world due to economic disruption. Domestic violence has increased. Cancer patients are forgoing treatments. Children are missing scheduled vaccines.

The lockdowns were never justified to begin with. They certainly aren’t justified now. But even worse than the damage caused by the lockdowns is the damage caused by creating mass hysteria. By frightening the vast majority of the country’s, indeed the world’s population, into thinking that their lives are severely at risk if they leave their home, we have essentially guaranteed that any recovery will take years even once the lockdowns are ended. We will have caused many times the number of deaths as did Covid.

If we don’t reverse the hysteria soon, we are almost certain to manufacture another Great Depression. We will see wars fought over oil and food. We will see social unrest and crime. We will see revolutions and civil wars. We will enable strongmen and dictators around the world and here in the U.S. We will give up our privacy and our freedoms in near totality. We will see great cities like New York neutered beyond recognition.

Many people have referred to our current situation as the greatest threat to the world since World War II. They are correct. But let’s be very clear about something. The great threat is not from the natural disaster of the pandemic. No, we are living through the beginnings of a man and woman-made disaster. As a society, we’ve taken a serious but manageable pandemic and through childish overreaction, turned it into something far worse. Unfortunately, we will be feeling these effects certainly for years, likely for decades and possibly for generations.

A quick note published on April 19, 2020:

I originally published the following very short piece on March 22 with the intention to expand it significantly. However, a few days later, President Trump gave a press conference using the same words, “the cure is worse than the disease.” Having not had time to explain my thoughts further and not wanting to get lumped in with Trump’s line of reasoning (or lack thereof), I pulled the post. However today I re-instate it as it was.

While I have little doubt that lockdowns have significantly reduced the spread of the Covid-19, I wholly stand by view that shutting the economy and effectively imprisoning the majority of the population will be, over the long-term, far worse than the effects of the virus. This was true when the sophisticated “models” showed a worse case scenario of over 2 million Americans dead and it is even more true now that the models predict far fewer than 100,000 dead.

I, by no means, belittle the human tragedy brought on by nature. But the human tragedy being brought on by humans is far worse.

The original post (published March 22, 2020):

History teaches us that societies survive pandemics, even with horrifying loss of life. Societies do not survive economic collapse.

I write this as the U.S. economy grounds to a halt with nearly all states issuing mandatory lockdowns. The financial markets meanwhile are melting down, worse than in the 2008-2009 financial crises. The Federal Reserve has already, in about a week, exceeded its past extraordinary monetary stimulus. Congress is negotiating a stimulus package that dwarfs 2009’s.

And there is no end in sight.

We are past the time where social distancing will be effective. Perhaps this would have worked in December, or January, or even February but it will not work in March or April or May. The virus spreads too robustly and we have no technology to detect asymptomatic carriers in sufficient numbers.

We’ve lost the battle for containment. Unless we allow the economy to restart immediately, we will lose the war for society. Losing this war will prove far more painful than even the worst-case scenario of 2 million Americans dead.

More to come…

With this post I take a break from my regularly scheduled economics programming to express some sadness. The subject of my sadness is Star Wars, specifically The Last Jedi. Star Wars is one of my few guilty pleasures. I grew up with the original trilogy, and I’m a fan. Not an Ultra Passionate Fan, as Mark Hamill would say, but a fan nonetheless. Like many, I went to see The Last Jedi on opening night. Like some, I left the movie theater disappointed, angry and sad. Admittedly, far more disappointed, angry and sad than a grown man should be after a movie. Alas, that was the power of Star Wars, or perhaps the weakness and immaturity of me.

I’d like to try to explain to all of you why some people (including myself) are so angry about this movie as I believe I am more or less representative of this demographic. For full disclosure, I am a 42 year old, clean shaven (no neckbeard), married (I do not live in my parent’s basement) white male. You can call me a fanboy or not, I have no idea what that really means.

This is not meant to be a movie review. There are countless of those out there on the internet, some made by folks far more knowledgeable about movies and far more knowledgeable about Star Wars than I. Having said that, let me begin with the following (with minor spoilers). As a standalone modern sci-fi/fantasy/action big budget blockbuster, The Last Jedi was good though certainly not great. It had some major plot issues, underdeveloped characters, a boring 2nd Act, misplaced humor and too obvious political correctness. But it had fewer issues than most other contemporary sci-fi/fantasy/action big budget blockbusters.

On the flip side, it had some terrific scenes, awesome visuals (Superman Leia notwithstanding), great music (mostly legacy melodies) and was far more thoughtful, surprising and original than most other sci-fi/fantasy/action big budget blockbusters. In a nutshell, it makes perfect sense that critics and most audience members are overwhelmingly positive. (As the middle chapter of a trilogy, there are some additional and significant issues with The Last Jedi, but they are not pertinent to this discussion.)

So why the intense hatred? It has nothing to do with Snoke’s lack of a backstory or Rey’s anonymous parents, as some have suggested. It does, however, have to do with fan expectations as others have surmised. In short, the movie mocks the Star Wars I grew up with. It makes fun of the mythology, it negates everything about the original trilogy (The Force Awakens is equally, if not more guilty here). Our heroes are back where they started, having accomplished nothing. Most importantly, The Last Jedi obliterates the character of Luke Skywalker that I’ve known for 40 years. In doing these things, it mocks, makes fun of, negates and obliterates a small part of my own life. Director Rian Johnson isn’t just saying that the Jedi are fools. Or that Luke had been a fool. Or simplistic good vs evil is foolish. He is implicitly saying that I have been a fool for being a fan. That hurts.

Now, you can tell me that I’m immature, that I need to grow up, that Star Wars is just a meaningless B-movie/space opera with cheesy dialogue. You would be right. But that doesn’t negate the fact that after seeing the The Last Jedi, I do feel an emptiness and a betrayal for having been a Star Wars fan for my entire life.

I need to explain what made Star Wars so special to so many (more special than any other movie or book series in entertainment history). There are three things: 1) the enormous world building/universe, 2) the mythology/religion that is as interesting (and of course as unrealistic) as any mythological stories here on earth and 3) the characters. The first two are obvious but let me explain the characters.

Star Wars created perhaps the greatest villain in movie history (Darth Vader), perhaps the greatest, and second greatest Wisemen (Yoda and Obi Wan) and one of the greatest AND most relatable superhero protagonists in Luke. That he is relatable is crucial. All of us were at one point (or perhaps for some readers of this site, will someday be) whiny, impatient, impulsive, bored teenagers who need to grow up. We ALL wanted to be Luke. (As a side note, this is not so for Rey and it has nothing to do with her being a woman. She is NOT relatable because she is already perfect.) Luke is a landmark and should have been treated as such.

Having said all that, I suspect that the majority of fans with similar views to my own are about my age, and grew up with the original trilogy and not the prequels. The reason is that the prequels (which were generally lousy movies) already began the process of changing (ruining) Star Wars. It hurt the mythology (midichlorians,etc.) but more importantly it damaged two of the three great characters of the original trilogy. It made Yoda (and the Jedi) seem like idiots. And it turned Vader into a whiny brat (or more accurately, turned a whiny brat into Vader for no good or comprehensible reason).

But, and this is important, it did two things right, and this is why we can forgive or ignore or even enjoy the prequels. It did not impact our real hero, Luke. And more crucially, the movies enormously expanded the Star Wars universe (not always, but mostly for the better). And this is why the prequels, as bad as they were, are still an integral part of Star Wars.

The Force Awakens (which was really a reboot, not a sequel) and more importantly The Last Jedi fail to be Star Wars, in my opinion. Or at the very least, they substantially decrease the essence of Star Wars rather than increase it. They make the universe seem small, not large. Most planets are copies of those seen in prior movies. The First Order and Resistance are essentially just smaller versions of the Empire and the Rebellion. Second, the new movies (specifically The Last Jedi as I already mentioned) blatantly destroy the mythology of Star Wars and the Jedi. Third, the characters are weaker, and less relatable copies of the originals. Lastly, Star Wars is meant to be escapism from the real world (even if it has subtle or not so subtle underpinnings of social commentary). The Last Jedi’s self awareness and cynicism were exactly the opposite.

I am sad after having seen The Last Jedi. I don’t think that’s the emotion that the director intended for many fans to feel. I do think this movie was as big of a failure for the Star Wars universe as it is a commercial success. I will probably see Episode IX out of curiosity but I have zero anticipation for it, something I never would have thought I would say after a Star Wars movie.

Finally, and for what it’s worth, I don’t place the full blame on Rian Johnson. He was dealt the hand that JJ Abrams left him, and he clearly wanted to go in a different direction and make his own movie. I get that. I do, however, blame Disney/Lucasfilm. They decided to make these movies before they had any stories. They clearly had no plan. For all the hundreds of millions of dollars invested in making these movies, and for the billions of dollars of revenue they generate in movie tickets and merchandise, Disney should have had a good story and a real plan for the trilogy. Most importantly, they should have been respectful of what came before and they never should have let fans of 30 or 40 years down the way they did. There’s simply no excuse for that.