Tuesday, June 6, 2017

Important Tuna Recall


This morning I was looking at the news and found an article about a tuna recall. As you know people, especially pregnant women, need to keep an eye on the news for public health and food issues. 

Today the FDA has recalled some frozen tuna because it has hepatitis A in it. The FDA recommends that people exposed to this fish get a Hepatitis A vaccine if they are between 1-40 years of age, or a immunoglobulin shot if they are outside of that range. 

Frozen Tuna is in sushi. And you can get it in restaurants. This tuna was shipped to restaurants in Texas, Oklahoma, and California. It was sent to New York State but not distributed there yet. 

This tuna comes from Philippines and Vietnam. It comes via a Hawaii based imported called Hilo Fish Company. 

Once again I wouldn't want to impugn the Hilo Fish company. They should be awarded for checking the fish and cooperating with the recall. It is the fish companies that don't test that we need to be alert for.  I have seen really giant tunas that caught by recreational fishers. These big tunas get distributed immediately to sushi shops. That kind of distribution is what we want to worry about. 

So for now, don't eat any frozen or uncooked tuna, and check the FDA's web site for further news and lot numbers, etc. This tuna was dated April and October apparently. 

I am not aware if cooking the tuna adequately will sterilize the Hepatitis A, but certainly it cannot be eaten raw or undercooked. Unfortunately I really like sushi. 

Thanks 

Dr John Marcus 

89 North Maple Ave 
Ridgewood NJ. 

Blog at doctorjohnmarcus.blogspot.com


Sunday, April 2, 2017

Medical Science and Statistics


One thing that most people don't understand is that most medical science is very weak from a statistical and mathematical point of view. 

Another thing that doctors don't understand at all is the concept of Predictive Value of a test result. 

Lets go through both here. 

The medical journals are voluminous. There are many studies done. They are mostly done in a fairly uniform format. (such as Title, Abstract, Methods, Summary, Conclusion).  There is a ton of complicated language that is familiar only to the professors who read and write these studies.  

The biggest problem with medical studies is the issue of bias. There are a ton of problems with bias. Most studies have bias. If a scientist wishes to prove something, then they will attempt to make a study that proves their point. It is rare for a study to be done that doesn't have bias. Because a truly neutral scientist is not going to be motivated to produce a study and article at all. Pharmaceutical companies are biased because they want to make money. And meds are worth billions of dollars. 

Here is a publication that describes much of the bias in medical science: 

http://fhs.mcmaster.ca/surgery/documents/HandoutGrimesAssociationforResearch2of07Oct2009.pdf

The same article is here: 

http://thelancet.com/journals/lancet/article/PIIS0140-6736(02)07451-2/abstract

The best way to avoid bias is to gather a study group of people, and randomize them. This produces the least selection bias. Then the two or more groups of people are treated differently and someone analyzes if the outcomes are different. If the study is "Double Blinded and Randomized" then there is strong evidence that the different treatments created different outcomes, and the knowledge base of the human race has increased. 

But even the best randomized trials still have bias. One of the strongest biases is publication bias. You see, the studies are designed to create a "P Value" of 5 percent. This p value means that there is only a 5 percent chance that the outcome is due to chance variation. This kind of p value is easy to calculate for people who are trained in this kind of statistical work. There is software to calculate p values based on simple input numbers, such as study size, expected effect, etc. 

Here is a summary of how to calculate p value: 

http://www.wikihow.com/Calculate-P-Value

It looks really complicated and confusing. But it makes a ton of sense to people who know how to do it. I don't suggest you try to learn the details. Just know that the p value tells you how likely the research results are true, and not chance. P value of .05 is the standard and that means there is only a 5 percent chance the study results are just randomness. 

But here is the kicker, and it is a huge problem: 

Studies that prove nothing, with elevated p values, don't generally get published. Those "worthless results" might be considered a waste of time. But if you do the study 20 times, then one of them will give a false result. (one in 20 is 5 percent, or p value of 0.05); The result will appear excellent, but it is wrong, fake, biased, incorrect, garbage, dangerous. You might think that this is a stupid criticism of necessary science. But I can tell you that it is huge. There are studies that are done where the data is looked at hundreds of different ways. Not all of those ways are published. Only the significant results are published. So if a study looks at the same data one hundred different ways, and the p value is 0.05, then 5 results will show a fake but convincing effect. Worse yet, some studies do "early look" at the data. This should be condemned entirely. An early look at the data erodes the quality tremendously. Not only is there less data to look at, but it more than doubles the risk of a false p value. There will be a more than 10 percent chance of a false finding. And if one looks at the data 10 different ways, then it becomes very likely that there will be a false presentation of statistical effect. The study will show a false truth. This certainly happened with the "Woman's Health Initiative". There was an early look and there was a possible false finding of a cause of breast cancer. That study cost many millions of dollars. And it turned the previous data on its head. There was ultimately one table in that study that will show the possible truth, The "life table" analysis, which showed the incidence of breast cancer in the hormone group vs the nonhormone group, across time. If you look at that table, the incidence of cancer was higher in the early data in the estrogen group. But the incidence lines were about to cross, consistent with older data, at the 2 year early look. Despite the fact that the data was poor, the study was cancelled. The p value declared secure. And people believed that estrogen, a natural normal female hormone, is toxic. It might take another hundred years before someone does this study properly. The early look and the multiple analysis gigantically eroded the value of the data. And in any case, the effect of estrogen was a few cases in 10,000. It became easy to vilify estrogen to the point of wrecking woman's lives. 

Also, there is the effect of "study group". A study that is done in one setting will not apply in another setting. A study done by midwives can be perfect scientifically, but it will not apply to obstetricians. Because obstetricians treat their patients differently. A study done on men might not apply to women. A study done in a poor area of Chicago might not apply to Mormons in Utah. For instance, lets say you are doing a vitamin D study and your group is in far north Canada. They might not get sunshine for half the year. This will certainly not apply in Ecuador (which is named for being on the Equator), and has near vertical sunshine year round. (Vitamin D is created by sunshine on the skin). There are a lot of vitamin D studies. One should look carefully at the study population to see if the study applies to yourself or your population. 

One example of study population affecting results is C Section closures. In a university, C Section closure techniques were studied. Staples vs Subcu dissolvable. In this study the results were proven to be equivalent. Staples and sub cu had the same scar outcome. But, the kicker is, this university also published a very high surgical skin infection rate. If I remember correctly, it was as high as 15 percent. This study cannot apply to me, because my surgical infection rate might be 15 times lower than that. I practice at a hospital that has infection control procedures down pat, with highly experienced personnel, laminar flow operating room air, etc. So that study simply doesn't apply to me. I have to make my own decisions about C Section closures, unless I do my own study.  The bottom line is that I will close a C Section in a way that the patient finds best, In other words, the patient will help decide. Some don't like staples. Some have had very good results with staples. Some want dis-solvable stitches, even though those stitches takes weeks to months to fully dissolve, if ever. 

Now lets move from statistical medicine to the doctor patient interaction. 

Predictive Value:

Lets say that I ordered a pregnancy test. And it is positive. But, the test was done on a boy, or a virginal gay woman, or a virginal nun. What is the value of that test result? It will not be valid. It will of course lead to a lot of stress, maybe recriminations, and some terrible feelings. But the value of that test is nearly nil. The predictive value of a positive pregnancy test depends on the study population. Lets say for the sake of argument that this particular test is 99 percent accurate. That leaves a lot of room for error. Because there are women who cannot get pregnant. If we test them, all of the results are inaccurate. Or at least misleading. A test can be inaccurate for a number of reasons: tumors, ovulation, HCG injections for a number of indications. I've even had patients who were ALWAYS POSITIVE. They've never had a negative pregnancy test in their life. Sorting that out is a challenge. Lets hope a 14 year old is not disowned by her father while figuring that out. We might figure that there was a tiny bit of placenta left over from her own fetal days, stuck somewhere in her body. Wherever it was, it did not seem to harm her and she wasn't worried.

So to calculate the predictive value of a positive result, the most important factor to consider is the pre-existing chance of the problem studied for. A good test has a 80 percent "Sensitivity". This means that, in the presence of the condition tested, there is an 80 percent chance of the test showing it.

Take a look at the Wikipedia page as of today:

https://en.wikipedia.org/wiki/Sensitivity_and_specificity

There is a lot of math there. We don't need to know the math, but we have to know the idea. And if we don't, we mess up.

For instance, I might order a "Comprehensive Metabolic Panel" from the lab. This test has about 20 different natural chemicals on a person. For instance glucose and sodium (salt) are usually at the top. It is really tempting to order this test as it gives a ton of good info about a patients chemical status. The lab can print this out in minutes to hours. The problem, and it is not a big problem, is that the normal ranges are set at 95 percent normal ranges. That means that if we do the test 20 times, there will be on average one that falls outside the normal range, in an otherwise completely normal person. For instance, they ate a jelly donut and their glucose is high. That is a bad example because most people won't eat a jelly donut prior to a lab test, but in an emergency, the ER doc might not be able to ask the patient when they ate. So, on average there are 20 measurements, with a 95 percent "confidence interval", that means that a normal person has about one test outside the normal range. This is a completely false positive. And it is normal.

Notice the similarity here to the 95 percent confidence interval, or 5 percent false positive rate. This is identical to the 5 percent false positive rate assigned to medical studies. It seems that medical scientists are somewhat favorable to the 5 percent/95 percent confidence interval.

Where this gets really complicated is when we have tests that are 80 percent confident, or less. This is high for a screening test. A pap smear in the old days prior to HPV testing has a confidence of about 5-10 percent. A glucose screening test in pregnancy has about a 10 percent positive predictive value. In other words, 90 percent of positives are false. So we deal with low predictive values all the time. The tests still have a lot of value but alone mean nothing.

Low Predictive Value:

What is the chance of an 18 year old getting cervical cancer? It is very low. If we do a pap smear, the chance of a positive pap smear meaning cancer is next to nothing. That is because pap smears have a high false positive rate, in a population that is very low risk. Back when I used to do paps in 18 year old women, I only intervened when the biopsies showed severe risk. This did happen, and I kept my interventions very light, like a gentle laser surgery to remove only the surface of the worst areas. But it turns out that even that is unnecessary. The incidence of cancer is so low as to make the positive pap smear nearly worthless. The positive predictive value was near zero. So, as per the new protocols published by the ASCCP, I have stopped doing paps in women under 21 years of age. The paps simply don't help. The predictive value is low. It is like doing pregnancy tests on a boy. Or doing a vaginal sonogram on normal woman, which has been proven again and again to be worthless to dangerous. The predictive value is most likely below zero. In other words, it harms women.

18 year old women can still get checkups, or checked for problems, of course. It is just that the pap is not part of the checkup, unless there is a specific reason. (the reason might be the woman or her mother really wanted it).

But please, don't assume that sonograms themselves are worthless. In fact, woman should have more of them. They should present early and often for pelvic pains, pressures, bloating, or any other symptom. An indicated sonogram can save a life. And we all need to do better detecting ovarian cancer.

I haven't given up on medical science. But there is still a lot of room for what is called the Art of Medicine. That is keeping people healthy, preventing disease, eliminating risk and  pain. And doing it while keeping people feeling safe, comfortable, and happy. And I do that to the best of my abilities.


Comments are appreciated. And let me know if there are any errors.

Thanks
Blog at doctorjohnmarcus.blogspot.com.



Wednesday, February 15, 2017

LOONEY MONTHS


I came home from work today just after sundown and I saw a full moon rising.  A family friend was over. I mentioned it offhandedly and said full moons were fun. She looked at me straight faced and asked why?  so I started thinking.  Why indeed? As an OB I of course think of the busy obstetrical unit. Full moons are busy nights right?  The ancient lore is that full moons make for very busy labor units. This has been suspected since the beginning of time. The reality is that there may be a small increase of maybe a percent or two, but the lore is very strong. Is there something to the ancient lore? May there be a reason to think that Ob units are busy on full moon nights? Is there some ancient anthropological principle involved? So I did a little investigating.


I started listing for myself why I thought the full moon was fun and interesting.  


First the full moon always rises in the east and looks very very full..  It appears to be giant when rising in the east over a distant horizon. Photos of this effect are striking.  You can easily search for these photos. Do a google search for full moon rising, switch to the images tab, and look at the striking photos.


Second a full moon always rises at sundown.  This means everyone sees it at the end of a workday when they are tired and hungry and ready for dinner.  It seems most impressive then.  When it rises most people are not thinking they’ll be looking for the full moon tonight.  It just pops up huge and bright on the horizon.  


Third a full moon strikes people's mood.  It is what is meant by loony, lunatic, lunacy etc.


Fourth, the ob unit at the hospital always groans that a lot of labors are going to come in.  The feeling is that full moons means more laboring women. We all say “uh oh get ready”.  It'll get crazy. It'll get looney around here. But the purely logical folks say that is just an old wives tales. It means nothing right? Which prediction wins, logic or lore? Will the unit be busy or is it just an old wives tale? (midwives tale might be a better term)


Fifth a full moon is the brightest night of the month.  The moon shines bright and full.  Moreover it shines brightest right at midnight. Why? A full moon is always exactly opposite the sun from the earth.  That is pure astronomical  geometry and that is why the moon is full. So the darkest part of night becomes the brightest. And it is brightest right at midnight. That is just weird at midnight. It is like it is not even night. Especially on a winter night with snow on the ground. It is astonishingly bright on some full moon midnights.


There are a ton of human endeavors that are tied to the idea of a month. Paychecks, rent checks, contracts, meetings, mortgage payments, menstrual cycles, birth control pill packages,  and tons more.


So there I am thinking why would ob units get busy on a full moon night? This is very strong old lore. Maybe there is an old truth to be discovered here.  


Now that I have thought about it I think the answer is obvious.  


But first you'll need to come along with some math again. This time we will do date and time math.


You see a human pregnancy lasts just about 40 weeks. We have pregnancy dating wheels that show exactly 40 weeks. We Ob’s always bemoan that everyone calls it 9 months. It is hard to make 9 months out of 40 weeks. We would more likely make it 10 months, if the month is defined as 4 weeks. Many months are defined as exactly 4 weeks. A cycle of birth control pills is exactly 4 weeks, or 28 days. Many paycheck cycles are 2 weekly or 4 weekly. Now think about a pregnancy of 40 weeks. 40 weeks is exactly 280 days. 40 weeks is exactly 10 months, if a month is defined as 4 weeks. This is 280 days from the first day of the last menstrual period.


But conception happens 2 weeks later. Or 14 days after the first menstrual day.


So labor happens, mostly, about 266 days after conception.


So then I'm thinking what happens on bright full moon midnights? Walks on the beach… can't sleep… too bright … romance…. conception. That is what happens. Romance and conception happens. So if conception happens on full moons… where is the moon when labor happens? Where is the moon?


So I looked up how many days happen between full moons. I found it online. 29.53 days happen between full moons. That is a lunar month. There are a ton of different kinds of months. Calendar months are obvious. But lunar months is what I am interested here.


What is 266 days divided by 29.53 days?


Hold your breath…


9.007 moons happen.


This is astonishing. It is not astonishing that there are 9 moons. That is ancient lore.  It is pretty cool that the ancient lore gets a boost here. But, what is astonishing, is that, on average, a baby conceived under a full moon will, on average, labor under a full moon. In fact, labor should occur within a few minutes of 9 moons later.


So there you have it. The first real explanation ever given as to why the lore of Ob/Gyn’s and midwives expects extra work on a full moon night. It is the same reason so many songs are written about romance under a full moon.


If you doubt this then think about evolution of the human species. We have been on the planet as genetically and anatomically modern humans for 250,000 years. For more than 200,000 of those years, a moonless night would have been abjectly absolutely dark. A person would not have been able to find their spouse or their baby, even if they wanted to. They would likely have not even seen their hand in front of their face. So romance, conception, and delivery of babies, would have been really difficult. And very dangerous for the baby. The new mother would have really struggled without effective assistance from any midwives that might have been there. Babies would be far more likely to die. There would have been a very strong Darwinian Evolution pressure to not deliver on dark nights. Bright moon filled nights would have been no problem. For the 200,000 years before humans had fire the human race might have arranged for biology to make conceptions on full moon nights, and labors exactly 9 moons later.


40 weeks has nothing to do with 9 months.  Maybe more like 10 months. But ancient midwives 10000 years ago might have known that a bright moon now meant labor in nine more moons. Or, if not that, they certainly would have known that full moons meant more babies were to be born. That is something we talk about even today in a modern Ob unit. We usually joke about it. But this may be the ancient history of the 9 moons connection.


This 9 moons time frame is too much coincidence for me to ignore. Babies are generally laboring to within minutes of 9 moons after they are conceived. This is crazy. This is lunacy.


Thanks for reading.  


Comments are of course welcome. And questions are welcome as well.


John W Marcus MD FACOG PC
89 North Maple Ave
Ridgewood NJ 07481


Phone 201-447-0077

Fax 201-447-3560.

Wednesday, December 7, 2016


Family Powers of Two

I have just come back from The Valley Hospital where I delivered a beautiful baby boy, a first baby, to a wonderful couple. The baby came out right onto the mothers chest and abdomen, and he was moving around, pink, and of course crying. He took his first 5 breaths, which are mostly in breaths, before he started crying from being born. I think that babies do not like being born. It is like getting evicted from a warm easy 98 degree bath that one has floated in for 9 months. I can't imaging getting evicted like that. And then babies have to do the hard work of actually breathing and digesting. It is called transition by the baby care staff. Transitioning is important, and it can be difficult for babies that are born in a stressful manner. Difficult or stressful births might be a premee or an infection. This could include influenza or strep sepsis, for instance, or a placental abruption. Transition is also sometimes a bit difficult for C Section babies that have not experienced labor. I think that the babies that don't get squished or compressed by uterine contractions have a harder time with transition. They breath harder and faster, and sometimes need oxygen, suctioning, and stimulation. They have more amniotic fluid in their lungs that needs to get expelled somehow. We call this transitioning difficulty "Transitional Tachypnea of the Newborn".  Here is the Wikipedia page as of 2 PM on December 7th. https://en.wikipedia.org/wiki/Transient_tachypnea_of_the_newborn. Notice that Wikipedia calls it transient tachypnea. That is another name.

But as a mathematician and a amateur philosopher I have been thinking about generational genetic math again. One of my patients has done the 23andMe genetics service and has found out some genetic history. This may be very valuable to someone who has no known family history. It can elucidate particular genetic risks, and will be valuable for the whole family, and her kids. (I can use the pronoun "her" because I only have female patients, as per my board certification rules, which disallow male patients under most circumstances).

Lets think about generational math. Realize that you have two parents. Your parents have two parents, meaning you have 4 grandparents. Your grandparents have two parents each. This means that you have 8 great grandparents. Notice that each generation has a power of two. Powers of two have very easy calculations, especially for a computer scientist as they deal with powers of two all the time. 2 to the 8th power is a byte, and there are 256 different bytes, starting at zero and ending at 255.

Anyway, lets continue. Two to the 16 is 65,536. This means you have 65,536 great great... 16th generation... grandparents. And so does everyone else.

Powers of two have an "exponential" growth rate. There is an astronomical amount of power in an exponential growth rate. Two to the 32 is 4, 294, 967,296.  

Here is the first kicker. 32 generations ago, was how long? If we allow 20-25 years per generation, we get 640 to 800 years. There was not 4 billion people on the planet back then. This was the European middle ages, the Ottoman empire, the Shogun's of Japan, the natives of the America's, which came over from Asia via multiple routes during the last Ice Age 10,000 or more years ago.

The Earth only got it's first 1 billion people as of the year 1800 or so. So how can you have 4 billion ancestors if there were much less than a billion people on the planet? The answer is that people share ancestors. This is another way of saying that we are all related.

Using this kind of math, we can show that in General, of the 8 billion people presently on the planet, we are no more than, maybe 64th cousins. This is a highly conservative estimate, because 2 to the 64th is 1.8 times 10 to the 20th power. This is trillions of times more than the number of people that have ever lived.

Or, another way to look at it, is that 2 to the 33 is about 8.6 billion. This is way more than the number of humans that existed 33 generations ago. Therefore, we all must share a lot of ancestors to get this high number.

How many generations of humans exist? Anthropologists and geneticists believe the humans evolved from a herd of homo sapiens consisting of about 40 woman, tracked through the mitochondrial genetics. This herd lived somewhere around 250,000 years ago, centered somewhere in Africa, and expanded from there. 250,000 years, divided by 20 years per generation, gives 12,500 generations. So your 12,500th grandfather, is most certainly the same as mine, no matter where you are in the world. The only way this could be different is if your family came from a different planet.

No matter how you do this math, the numbers will add up somewhere in the same ballpark.

We are likely no more than 35th cousin, and we are not possibly more than 12,000th cousin. No matter who you are in the world.

Can 12,000 generations evolve humans so differently? Pale skin was necessary in northern latitudes because humans would die of rickets without vitamin D. Pale skin was necessary to survive the low sunlight levels of northern latitudes.

Pale skin in equatorial Africa was similarly dangerous. A person like myself can get a sunburn in under 20 minutes of hot unmitigated sunshine. Everyday all day without coverage from the Sun and I would eventually get a tan, but I would be seriously challenged by the burn, the blisters, and the cancer that would likely ensue. A darker neighbor would most likely be healthier and have more successful reproduction. I believe that even a few generations like this would evolve humans to have different pigments. So yes, this many generations can evolve a lot of differences.

So, the bottom line, is that we are all related. All Europeans are no more than maybe 32nd cousins. All Asians similarly. All Africans similarly. But, assuming that the races of humans split up at the exact moment of creation of the species and didn't mix since, we can be no more than 12,000th cousin. I believe those conditions were not true, and that humans intermixed over history, so it would be difficult to find humans that are, say, more than a thousandth cousin.

We are all related. We are all one family.

Thanks for reading.

http://doctorjohnmarcus.blogspot.com 

Phone 201-447-0077
Fax 201-447-3560 

Member of: 

Lifeline Medical Associates at LMA_LLC.com 
Medical Society of New Jersey 
Past President of Bergen County Medical Society 
Member of Medical Justice 
Member of the U.S. Woman's Health Alliance at http://uswomenshealthalliance.com/index.php
Member of the American College of Ob/Gyn at www.acog.org 
Board Certified by American Board of Obstetrics and Gynecology at www.abog.org 
Member of The Valley Hospital Medical staff Department of Ob Gyn 
-ex Director, Associate Director, Secretary, Chief of Education, Chief of cancer committee. 
- present member of Ob Critical Care Committee 
Member of Hackensack UMC at Pascack Valley  Medical Staff at http://www.hackensackumcpv.com/