cobafdek
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Posts posted by cobafdek
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The mean age of the patient subsample was 79.5 years (standard deviation [sD], 8.1), of whom,
only 601 (30.0%) were women.
Of all patients who died, 117 (30%) had ischemic heart disease,
126 (35.5%) had diabetes,
72 (20.3%) had cancer,
87(24.5%) had atrial fibrillation,
24 (6.8%) had dementia, and
34 (9.6%) had had a stroke.
The mean number of comorbidities was 2.7 (standard deviation, 1.6).
Only 3 patients (0.8%) had no underlying diseases,
89 (25.1%) had one, 91 (25.6%) had two, and 172 (48.5%) had three or more.
The patients were sicker than I thought before.
They had 2.7 comorbidities on average including comorbidities like cancer and stroke.
Only 0.8% with no underlying disease.
The underlying scientific article says following:
https://jamanetwork.com/journals/jama/fullarticle/2763667
Definition of COVID-19–Related Deaths
A second possible explanation for the high Italian case-fatality rate may be how COVID-19–related deaths are identified in Italy.
Case-fatality statistics in Italy are based on defining COVID-19–related deaths as those occurring in patients who test positive for SARS-CoV-2 via RT-PCR, independently from preexisting diseases that may have caused death. This method was selected because clear criteria for the definition of COVID-19–related deaths is not available.
.......
Electing to define death from COVID-19 in this way may have resulted in an overestimation of the case-fatality rate.
.........
The presence of these comorbidities might have increased the risk of mortality independent of COVID-19 infection.
Best post on this thread so far.
Thanks
Sorry guys:
The presence of these comorbidities might have increased the risk of mortality independent of COVID-19 infection.
This is incorrect.
Are you suggesting that people with preexisting conditions are not at a higher risk of dying?
Not at all. They are definitely at a higher risk of dying.
Without Covid-19, they would die at an expected rate. Right now in Italy, there is one exit door in a crowded theater that has suddenly caught on fire.
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The mean age of the patient subsample was 79.5 years (standard deviation [sD], 8.1), of whom,
only 601 (30.0%) were women.
Of all patients who died, 117 (30%) had ischemic heart disease,
126 (35.5%) had diabetes,
72 (20.3%) had cancer,
87(24.5%) had atrial fibrillation,
24 (6.8%) had dementia, and
34 (9.6%) had had a stroke.
The mean number of comorbidities was 2.7 (standard deviation, 1.6).
Only 3 patients (0.8%) had no underlying diseases,
89 (25.1%) had one, 91 (25.6%) had two, and 172 (48.5%) had three or more.
The patients were sicker than I thought before.
They had 2.7 comorbidities on average including comorbidities like cancer and stroke.
Only 0.8% with no underlying disease.
The underlying scientific article says following:
https://jamanetwork.com/journals/jama/fullarticle/2763667
Definition of COVID-19–Related Deaths
A second possible explanation for the high Italian case-fatality rate may be how COVID-19–related deaths are identified in Italy.
Case-fatality statistics in Italy are based on defining COVID-19–related deaths as those occurring in patients who test positive for SARS-CoV-2 via RT-PCR, independently from preexisting diseases that may have caused death. This method was selected because clear criteria for the definition of COVID-19–related deaths is not available.
.......
Electing to define death from COVID-19 in this way may have resulted in an overestimation of the case-fatality rate.
.........
The presence of these comorbidities might have increased the risk of mortality independent of COVID-19 infection.
Best post on this thread so far.
Thanks
Sorry guys:
The presence of these comorbidities might have increased the risk of mortality independent of COVID-19 infection.
This is incorrect.
If only the known comorbidities were present, the Italian hospital/ICU/ERs would be in normal shape. They are clearly not in normal shape. You cannot look at the situation independent of Covid-19.
Covid-19 MUST be included in the cause of death.
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There is so much information out there regarding Covid19. It’s hard to distinguish signal from noise. I believe the following information is signal. Hopefully this is useful.
I am emotionally sympathetic to this, and I hope it is true. I have speculated in the main Coronavirus thread that widespread serological testing might bring the CFR way lower.
But I think we're still in a fog-of-war situation. With the passage of time, the fog is only starting to lift, and optimistic information is beginning to emerge, which you've summarized nicely.
I thought this was a terrible article, both in the timing of publication and what Ioannidis was missing. He is an influential academic and has had a distinguished career. His article was widely circulated among influential people, and it might have done even worse damage if it came earlier. It was denounced by several of us in the main Coronavirus thread on it's very publication date (March 17).
Like a typical academic, he calls for more data. This would be appropriate in any other sedate research setting, like a university lab. It is completely inappropriate in an interconnected, complex, dynamic situation like a pandemic that plays out in real-time in the real world involving real people.
In the beginning of a potentially deadly event, it is natural to hope for the best, but wiser to prepare for the worst. Ioannidis doesn't seem to get that. There is no sense of urgency in his writing. It is as if he is positioning himself for the future when, if things turn out o.k., he can cite his article to point out how prescient he was. Watch for his media appearances in the future.
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People in NYC are still walking around without mask
A lot of People in grocery stores, none had mask and only some wear gloves.
Home Depot is packed with people.
Do standard (non-N95) masks aid healthy people avoid infection, prevent sick people from spreading, or both?
Standard masks catch coughs and sneezes. They prevent sick people from spreading. They do not protect healthy people from the virus aerosols in the air. The mask fit is not tight enough and the pores are too big.
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I hope everyone by now has noticed the resemblance between modeling in the finance world (for example structured finance) and the modeling being done by epidemiologists today in our world. we have epidemiologists with models and assumptions, telling the country what we should be doing in response to a novel virus. resembles to me a bad trade in the finance world. cant we fire the epidemiologists like we can some wayward financial analyst?
Not sure I understand this. A month ago, no one was dead in Italy. Now 3000+ plus are dead with an additional 400-500 coming in daily despite the fact the entire country has been shut down for 9 days.
Similar numbers in the US would be 15,000 dead in 2 weeks time with a full shutdown - but we haven't done that yet. LA just announced it. Nowhere else has and we have over 100k confirmed cases w/o testing.
This is already on course to be way worse than the 30k annually from the flu even w/ the shutdown which hasn't been implemented yet. I'm not trying to fear monger - just extrapolating the data that's available
real simple. if you build a model and it tells you to do something stupid but you do it anyhow because you trust the model and have no common sense, then you do something stupid.
Please share your model with us.
You have no idea what you are talking about, cherzeca
cherzeca is correct:
"A contentious exchange during the March 11 House Homeland Security Committee hearing on the coronavirus response revealed this reality. During the hearing, acting Deputy Secretary of Homeland Security Ken Cuccinelli explained he had advised the president to ban travel from China even though “the academic model suggested not to do that.” Cuccinelli further stressed that “the president was well aware” that the existing models recommended against a China travel ban but that Trump nonetheless instituted the ban."
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Any details about potential attributes among the 20-44 group, e.g., higher percentage of smokers, etc.?
Unfortunately the article does not provide much from a scientific perspective, only a few anecdotes.
The reason may be a paradoxical one. It may be because they are healthier. They therefore have healthier immune systems, and in response to a novel infection they mount an extremely vigorous immune response known as the cytokine release syndrome, or "cytokine storm." Most of the symptoms you experience from a viral infection is actually your immune systems fighting the virus.
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Here's my one hopeful observation that the Covid-19 pandemic may not really be as much of a pandemic as it feels like right now. It has to do with the testing issue.
All the testing we hear about in the news is about a screening test. RT-PCR is a screening test. That's the only testing available so far. It is not a diagnostic test. All "diagnoses" and "cases" reported in the news (worldwide 188,609 cases to date as of this post) is based on a screening test.
Many PCRs for many different infectious diseases are known to have unacceptably high false positive rates. This can only be determined by a gold standard test, such as a viral culture. I've searched and so far have found no information about viral cultures or other confirmatory testing for Covid-19.
There are many coronaviruses out there. It's possible there is significant cross-reactivity between some of these other coronaviruses, or other non-corona respiratory viruses, with the Covid-19 RT-PCR. In other words, many of the cases currently being identified may actually be identifying something else.
This situation is obviously natural early in the outbreak of a novel pathogen. The fact that the Chinese rapidly developed a test so early is amazing. But a rapidly available test means you sacrifice accuracy. For epidemiological screening, you need to identify possible cases quickly with a rapid test that that has a high sensitivity rate. But a test with high sensitivity means you're sacrificing specificity. Epidemiologists will get a better handle on specificity over time as confirmatory tests become available.
cobafdek, how big of a deal is this?
A serological assay to detect SARS-CoV-2 seroconversion in humans
https://www.medrxiv.org/content/10.1101/2020.03.17.20037713v1
Methods: Here we describe serological enzyme-linked immunosorbent assays (ELISA) that we developed using recombinant antigens derived from the spike protein of SARS-CoV-2. These assays were developed with negative control samples representing pre-COVID 19 background immunity in the general population and samples from COVID19 patients.
Results: The assays are sensitive and specific, allowing for screening and identification of COVID19 seroconverters using human plasma/serum as early as 3 days post symptom onset. Importantly, these assays do not require handling of infectious virus, can be adjusted to detect different antibody types and are amendable to scaling.
It's a huge development.
This study says they've identified antibodies to SARS-CoV-2. That means we will soon be able to get a better handle on how many people exposed/infected sometime in the past but remained asymptomatic. Therefore, there is major potential for the case fatality rate to be estimated downward, possibly by a lot. If it can be shown to be similar to influenza or better, current draconian measures might be able to be lifted sooner rather than later (and we will see the mother of all bear market reversals).
Identifying the different types of antibodies (IgM, IgGs, etc.) gives clinicians an alternative way to diagnose SARS-CoV-2, and to distinguish acute infections (IgM+) from convalescent or remote infections (IgGs). They report detection is possible within 3 days of exposure (IgM). And these antibodies can be detected with a blood test, so that lab personnel may not need the expensive personal protective equipment needed to collect samples needed for the prior available test (RT-PCR), which required collecting naso- and oropharyngeal samples, not really practical for large population-wide screening.
What remains to be determined is whether these antibodies confer immunity (and if so, for how long), or whether they are merely markers for the infection. If they reliably confer immunity, we can know which medical personnel are safer to employ in evaluating and treating these patients, thereby protecting their other vulnerable medical colleagues.
Trials are already underway in China where they are using the serum of convalescent patients to treat active cases. This research might allow fine tuning which antibody or antibodies are the effective ones.
And, of course, this will aid in the development of a vaccine, possibly much sooner than the 12-18 months surmised by Fauci.
Bad news is that they found very little cross-reactivity to existing human coronaviruses, which means the published R0 estimates are probably pretty accurate, indicating it is highly transmissible.
(Also bad news, for me, is that it exposed my ignorance of the field, since ordinary doctors like me are just barely amateur microbiologists or virologists. In my original post, I called the RT-PCR a "screening" test. Reading this latest article, I now understand that the RT-PCR is considered the diagnostic test for the acute infection. Time and clinical experience will clarify things the proper sequencing of testing - I anticipate the antibody blood tests will become the initial screening tests, and results will be confirmed with the RT-PCR to reduce false positives.)
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interesting well-argumented contrarian opinion piece from an epidemiology professor on why fatality ratio of this virus might be much lower than we think:
minten, thank you for posting. This explains why testing i.e. accurate information is a critical factor in early stage viral outbreaks.
For all we know, widespread testing could show the severity of this virus is totally overblown as critics on this thread have suggested, and therefore the pandemic responses are unwarranted. Our portfolios and emotions could have been spared much stress.
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The first, we can obtain accurate (i.e. representative) information with widespread testing.
True, but only after-the-fact, usually LONG after the fact. Accuracy of a test can be determined only retrospectively. By then it may be too late.
Case 1- We do know that we are dealing with a high-magnitude pandemic.
Again, by the time we "do know," it may be too late. Furthermore, Case 1 is not a scientific statement. Philosophically and technically speaking, science does not "verify" anything. It can only falsify. All scientific knowledge is provisional. Humans only "think" something is "confirmed" by science, when in fact that something is merely not falsified. Then the black swan comes, and only then we will know something for certain, that that something is false. It's the problem of induction that cannot be overcome with more information and data.
Sometime in the future, we will "know" (that is, have a better idea) how serious this pandemic is with higher accuracy. By then, we will have enough data to get to a probability level high enough to feel certain. But certainty in medicine is an illusion. Especially early in a pandemic.
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minten, thank you for posting. This explains why testing i.e. accurate information is a critical factor in early stage viral outbreaks.
For all we know, widespread testing could show the severity of this virus is totally overblown as critics on this thread have suggested, and therefore the pandemic responses are unwarranted. Our portfolios and emotions could have been spared much stress.
This is incorrect, and dangerous thinking.
It is true: accurate information is a critical factor, but it is not possible for humans to obtain such early in these viral outbreaks.
The pandemic response is warranted - the precautionary principle is the only guide that would ensure the survival of the species.
In these situations, you don't need accurate knowledge of the probabilities in order to know what to do.
Our emotions and stress are wiser guide than our intelligence in deciding how to react.
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So what you are saying is that we have a lot of false positives out there. That would mean that the denominator of cases is potentially smaller than we think it is and the mortality is higher. That would not be reassuring to me.
The numerator would also be smaller. Change in mortality rate would depend on which changes more.
So what are those people dying from? Run-of-the-mill coronavirus that typically causes common cold?
Strictly speaking, they are dying from something associated with a positive RT-PCR Covid-19 test, the test characteristics (accuracy and predictive value) of which has not been systematically evaluated.
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So what you are saying is that we have a lot of false positives out there. That would mean that the denominator of cases is potentially smaller than we think it is and the mortality is higher. That would not be reassuring to me.
The numerator would also be smaller. Change in mortality rate would depend on which changes more.
maybe you're right, but don't you think the number of false positives would be greatly exceeded by people that were never even tested at all?
i'm personally starting to come around to the exact opposite conclusion: much more people have it than we think, but don't know it. which as i think i said before, isn't necessarily bad news either.
What "it" is, is my point. What is "it"? [edit: The only way to know that more people have "it" is if we know that the positive predictive value is very high, and no one knows this number yet.]
Keep in mind I am not changing my behavior based on my speculation. I'm still running like hell.
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Here's my one hopeful observation that the Covid-19 pandemic may not really be as much of a pandemic as it feels like right now. It has to do with the testing issue.
All the testing we hear about in the news is about a screening test. RT-PCR is a screening test. That's the only testing available so far. It is not a diagnostic test. All "diagnoses" and "cases" reported in the news (worldwide 188,609 cases to date as of this post) is based on a screening test.
Many PCRs for many different infectious diseases are known to have unacceptably high false positive rates. This can only be determined by a gold standard test, such as a viral culture. I've searched and so far have found no information about viral cultures or other confirmatory testing for Covid-19.
There are many coronaviruses out there. It's possible there is significant cross-reactivity between some of these other coronaviruses, or other non-corona respiratory viruses, with the Covid-19 RT-PCR. In other words, many of the cases currently being identified may actually be identifying something else.
This situation is obviously natural early in the outbreak of a novel pathogen. The fact that the Chinese rapidly developed a test so early is amazing. But a rapidly available test means you sacrifice accuracy. For epidemiological screening, you need to identify possible cases quickly with a rapid test that that has a high sensitivity rate. But a test with high sensitivity means you're sacrificing specificity. Epidemiologists will get a better handle on specificity over time as confirmatory tests become available.
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Is it really possible to become and MD without a basic understanding of exponential growth and no understanding of statistics/sampling theory? (This is a serious question, not rhetorical, because I don't know the answer and I'm curious if such big holes are normal in doctors' education.)
Since I'm an MD, I can answer your question. The answer is yes. Probability/statistics is hard. Furthermore, it's probably the worst taught class in medical school, with the least interest for most med students, who are understandably more interested in anatomy, physiology, biochemistry, pathology, etc. The average doctor is no more proficient in formal probability and statistical concepts than the average layman, i.e., CoBF member.
That said, and in defense of us MDs, ordinary clinical decisions are based on intuitive probability judgments that are made implicitly, and without the need for any tricky mathematics. Mathematical probability comes into play only on those rare occasions when the right answer turns out to be counterintuitive. It's not really relevant in everyday clinical practice.
After completing my residency, I did a research fellowship which involved getting an MPH degree in biostatistics. But even if an MD has a good understanding of statistics, a medical opinion is nearly useless in this current coronavirus situation. Clinical decision making is based on statistical inference, whether it's explicit or intuitive. Statistical inference is based on well-designed experiments, clean data, and a well-understood scenario, none of which characterize this pandemic. Statistical thinking is retrospective, based on past data. With coronovirus, we have a whopping 4 months of chaotic short-term data.
Probability judgments are prospective. They can be based on relative frequencies over time, relative frequencies in an actual group of patients, relative frequencies in an imaginary group of patients, or they can be subjective belief. Most of the speculation in this thread is based on the latter two. We're all guessing. Orthopa is clear on that. He is also self-aware because he admits he may be subject to the anchoring bias, having committed to a relatively controversial opinion.
My local experience parallels Orthopa's so far (except for no cases of positive coronovirus tests yet, which is because there has been hardly any testing done around here as of this weekend). I'm in Orange County, California. My hospital's ER has a tent outside for overflow cases. It went up last week and hasn't been utilized. The hospital and ICU census is the usual at this time of year. (I know it's early.) My flow of office patients has actually slowed in the past week, possibly because patients are now thinking twice about coming to the doctor or hospital. (I know it's early.)
(Cobafdek: this is why I'm at the 2% rather than the 70% number for orthopa's theory. If someone doesn't understanding even the most basic concepts of exponential growth or statistics--ideas you'd learn in your first year courses--them, when it comes to a pandemic, their hypotheses about the meaning of anything they observe are likely to be worthless.)
My opinion that Orthopa's theory has a 70% probability of being correct is pure subjective belief, partly based on my local anecdotal scene. It's just a feeling. I don't think it's any more or less valid than any of the counter-opinions in this thread. We're all fooled by randomness. And I have not expressed my opinion in my community because I think people would misinterpret it and not do the right thing, which is to use the precautionary principle.
In fact, I think it is a major mistake and dangerous to think that heavy speculative computation - seen on this thread - is at all helpful in these cases. For unknown unknowns like this novel coronovirus, the worst case scenario is bad enough. Put down the calculator and run like hell.
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I'd put the chance that Orthopa is right at less than 2%. But I've been continuing to mull the issue, since it's an interesting thought experiment--how can Orthopa's data point be reconciled with the 100 data points that contradict his claims?
One way it could be true is if the area around Orthopa previously had a coronavirus that infected people and gave them some sort of heightened immunity compared to everywhere else. Or, maybe the COVID-19 came early to his region, but was a mutated version that happens to have a much lower rate of serious consequences.
That said, I think both of these cases are super low probability, that it's much more likely that multitude of experts saying "this is a big deal" are right, and Orthopa isn't.
In fact, in my case, I view the evidence Orthopa's brought to support his argument as weakening his argument since it's showing that he's confidently making large, unwarranted leaps to support his thesis. To me, this increases the chance that he's a guy who's comfortable squeezing evidence into odd shapes in order to support his conclusions. (e.g. a few days ago, 2 cases was enough for him to extrapolate conclusions about 100,000 people infected. Today, he's saying that 140,000 tests isn't enough to extrapolate anything.)
At this point, I'm curious whether he's a troll or just completely locked into an incorrect mental model. I still lean toward the latter.
There's a simple explanation: reliance on anecdotes over objective data, narrative fallacy, etc. Happens to a lot of clinicians because they see patients and extrapolate from those handful of clinical encounters to the larger population.
Anyway--the rule is that if lots of people's lives are on the line, you should err on the side of caution ("prepare for the worst"), not assume "everything is going to be fine". Ie. board up your house even if it is 98% likely to be out of the hurricane's direct path. That's the precautionary principle in a nutshell.
I'd put the chance that Orthopa is right at greater than 70%. (I believe if the entire population were tested, the base rate of Covid-19 is very high, and therefore the CFR is very low). I also think that his and my probability assessment of this rare pandemic event is irrelevant for deciding what to do: "panic" precautionary behavior is required.
It's like Taleb being asked whether he was bullish or bearish on the stock market. He said he was bullish, meaning 70% chance the market was going up. Yet, he had a very large short position on SP500 futures (the TV interviewer was dumbfounded).
There is no contradiction here.
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This is a book for all the anti-Trumper board members who are drop-outs from Prof. Munger's psychology class. I referenced the author, Rory Sutherland, early in my Trump Derangement Syndrome thread in the Politics section.
Here's a sample of Sutherland from the introduction:
"Now, as reasonable people, you're going to hate me saying this, and I don't feel good saying it myself. But, for all the man's faults, I think Donald Trump can solve many problems that the more rational Hillary Clinton simply wouldn't have been able to address. I don't admire him, but he is a decision maker from a different mould. For example, both candidates wanted manufacturing jobs to return to the United States. Hillary's solution was logical - engagement in tripartite trade negotiations with Mexico and Canada. But Donald simply said, 'We're going to build a wall, and the Mexicans are going to pay.'
"'Ah,' you say. 'But he's never going to build that wall.' And I agree with you - I think it highly unlikely that a wall will be built, and even less likely that the unlucky Mexicans will agree to pay for it. But here's the thing: he may not need to build the wall to achieve his trade ambitions - he just needs to people to believe that he might. Similarly, he doesn't need to repeal the North American Free Trade Agreement - he just needs to raise it as a possibility. Irrational people are much more powerful than rational people, because their threats are so much more convincing.
"For perhaps thirty years, the prevailing economic consensus meant that no American carmaker felt they owed any patriotic duty to workers in their home country; had you suggested such a thing in any of their board meetings, you would have been viewed as a dinosaur. So pervasive was the belief in untrammeled free trade - on both sides of the American political divide - that manufacturing was shifted overseas without any consideration about whether there might be a risk to losing the support of government or public opinion. All Trump needed to do was to signal that this assumption was no longer safe. No tariffs (or walls) are actually needed: the threat of them alone is enough.
"A rational leader suggests changing course to avoid a storm. An irrational one can change the weather.
"Being slightly bonkers can be a good negotiating strategy: being rational means you are predictable, and being predictable makes you weak. Hillary thinks like an economist, while Donald is a game theorist, and is able to achieve with one tweet what would take Clinton four years of congressional infighting. That's alchemy; you may hate it, but it works."
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Thanks for all the replies.
Any health problems with your cats on predominantly dry food? Especially, any problems your cat's vet attributes to diet? Any veterinarian visits and costs, beyond the basic preventive care, that might be attributed to diet?
For instance, one problem I hear about is urinary tract infections. Wet food is high in water content, and cats in the wild, according to some experts, don't naturally drink a lot free water, and dehydration is a factor in UTIs.
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FWIW I feed them this stuff:
42% protein, with 2 cats 1 bag lasts about a month, maybe more. At $30/bag you're looking at 50c/day per cat.
This looks good (for the cat, that is). I might get it for mine for a trial.
Looks like you put some deliberate thinking into this choice, it being high-protein and grain-free. Did the cost difference with cheaper mass market brands make you pause?
We always used Hill's Science Diet but about 2-3 years ago we heard from multiple people that the quality had really gone downhill. After a bit of research we found that to be correct and changed their food to Performatrin Ultra.
What was the problem with Science Diet? My sister and nephew use it for their cat, and I'll ask them about it also.
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I just returned from the local Walmart where I found some canned cat food, grain-free, going for about 30 cents per day for my cat's size and weight - much better than $1-2 per day, and closer to the 20 cents/day her food is now costing.
During this transition period, I'll consider other options, such as some combination of dry and wet cat foods.
So far it's not just you guys online, but friends and everyone I've spoken in person report essentially no problems with near-100% dry food for their cat's lifetime. Perhaps I've been over-influenced by the first things that pop up when I typed "cat nutrition" in Google. The first few articles I saw were quite passionate and logical about the superiority of the wet food option - a reasonable and plausible idea but unfortunately no actual hard data to support it definitively. Sort of reminds me of Taleb's tyranny of the minority concept, in which just a few people fanatically devoted to an idea have outsize influence, like cyclists who are the only ones who show up at city council meetings are able to get their bike lanes all over town.
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Just adopted a cat from the shelter about two weeks ago. She's a fully grown 8 pounder about 18 months old.
I've done a fair amount of reading about what's best to feed her. There seems to be two main camps: the dry food cat owners and vets, versus the wet food camp.
Regarding dry cat food, the advantages are cost and convenience. The disadvantages, from the wet food camp's perspective, is that dry food is not the cat's natural diet, since they are carnivores who eat 95%+ protein in the wild. Manufactured, "scientifically" designed dry cat food is high in carbohydrates. Some of the more premium brands get up to only about 40% protein. A primary disadvantage of canned wet cat food is that it is more expensive.
There are parallels in this controversy to human nutrition. Inexpensive, widely commercially available food prepared according to standard nutritional guidelines has led to long-term health problems related to obesity and the associated diabetes, heart disease, and cancer, for both cats and humans. The wet cat food advocates are analogous to the paleo diet camp for humans. That said, the quality of animal nutrition research is about as good as human nutrition research. In other words, very low quality with little long-term outcome evidence.
Decent dry cat food, such as from Trader Joe's (30% protein) would cost about 20 cents per day. Wet cat food is $1 per day and up.
The value investor in me initially went for the 80% discount. But now I am in the process of transitioning my cat to a grain-free protein wet food "more natural" cat diet.
I know there are other board members out there whose value investing decision-making principles bleed into all areas of their non-financial lives. From those cat owners out there, I'd like to hear your thinking and experience, and how you made your choices.
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I've never heard of JJ McCullough before, and I'm strictly an amateur on trade policy, but I'm sure some of the Canadians know him. Is this piece a predictable output based on his past opinions and ideology, or is he presenting the facts fairly?
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(I almost put this in the Politics section to make an analogy between Trump and mothers, but never mind.)
Don't forget guys:
Happy Mother's Day!
"Husbands buy imprudent gifts or nothing at all, arguing at their peril: But she isn't my mother.
'Every mom I know says the same thing, "I really don't need anything,"' says Norah O'Donnell, co-host of 'CBS This Morning.'
'But beware,' she says, a spouse should not take this literally . . .'"
(or access the article here:
http://online.wsj.com/public/resources/documents/print/WSJ_-A001-20180512.pdf
http://online.wsj.com/public/resources/documents/print/WSJ_-A007-20180512.pdf
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So if I'm getting what you're saying, it's totally okay for anyone who owns a construction company to burn down your house, since they will have created net positive houses in the world. It's okay for a woman who's had a bunch of kids to commit a few murders, since she's created a net positive for human life.
Oh wait, I misunderstood. What you are actually saying is that there should be no consequences to someone burning down your house because there's a chance in the future that they might build more houses.
(In a way, that's the most left-wing suggestion I've heard on these forums--anybody should be able to break the law without consequences because they might do something later that is a net positive for the world.)
So using your logic, you'd let a future Shkreli defraud you out of your money as long as he insists after defrauding you that he will ultimately make you all your money back and create a lot of value for society?
I agree with you that laws are sometimes selectively enforced. But that's not a good excuse for breaking them.
You guys are playing the role of Cathy Newman to ScottHall's Jordan Peterson.
Coronavirus
in General Discussion
Posted
My point:
Covid-19 MUST be included in the cause of death.
It's my response to