250,000 Jehovah's Witnesses have died refusing blood

by nicolaou 739 Replies latest watchtower medical

• 

  Finkelstein

  Ms. Barrick's tweet was horribly irresponsible because her contention is so easily attacked. When people tweet things that can be easily attacked, it brings both their credibility and subsequent claims into question and makes them appear to be overly-emotional apostates. That does nothing but strengthen the WTBTS's hand.

  Well said Justitia Themis

  Be truthful and have evidence to back yourself up when your going to oppose something to its dire wrong.

  Fabricating something out of the air particularly on a matter that is as serious as this weakens the very position that your indirectly trying to make.

  Typically religious leaders like the WTS. fabricate information to support their own expressed doctrines, you can see that throughout

  the history of the WTS.
• 

  Simon

  What about the opinion that the Watchtower bloodban has caused the wrongful death of 250,000 people from 1961-2013? You don't seem to allow that one.

  Erm ... sorry? This whole topic is about that opinion. The fact that you're reading it is proof that the opinion is allowed.

  The answer is sometimes less useful than the discussion.
• 

  Marvin Shilmer

  -

  “Is there any indication how they got to determine the cause of death to be 'refusal of blood transfusion' for those patients, 5-15 years after the fact?

  “Does the article describe how they reached that conclusion? Do they claim it was noted as the cause of death in the medical records?”

  A patient profile was determined and rigorously adhered to. Patients meeting this profile were included in the study whether JW or not. Exclusions were made where to do otherwise would skew results. For example 2 JW palliative care cancer patients who otherwise met the profile were excluded from the data set.

  The method was to compare a matched patient profile with the sole differential acceptance or rejection of blood transfusion in patients suffering severe anemia. The result was “We have shown that compared with JW patients, ARBC transfusion in anaemic patients was associated with a 10 times reduced mortality.”

  The relevant statistic shows that in a given demographic of 2 regions within New Zealand’s healthcare system over a 10-year period 19 JWs died over and beyond what should have compared with the same/similar patient presentation when blood products were not refused. This is a hard number that can be used to statistically extrapolate the number of JWs who died that shouldn’t have for one reason: refusal of blood product.

  I recommend reading the entire article. A side-by-side comparison of patients is provided in great detail. Severely anemic patients who die from lack of red cell transfusion die from generalized hypoxia.

  Marvin Shilmer
• 

  Finkelstein

  The topic flowed into discussion of the probability of 250,000 being correct or even close.

  I think most people would draw a conclusion that its way too high.

  The attempt to verify it was interesting nevertheless.

  Thats the beauty and value of an open forum like this, anyone can Post an opinion and have the opportunity to debate on that opinion.

  Personal attacks should be restrained though, for its nothing but worthless diminishing noise to the resulting cause, in my opinion.

  If its 50,000 or 250,000 its still way too much, just like 1 person is too much.
• 

  Marvin Shilmer

  -

  “Yes, the sample is just way too small and the outcome of a few cases would have a dramatic effect on the figures which makes it difficult to have too much faith in especially to try and extrapolate it out. If one patient survived or died then what effect would that have on the final number? 500? 5,000? more?”

  That statement confuses sample with population.

  In this case the sample size is the number of JWs in a given healthcare region of New Zealand for years 1998-2007, and the population of that sample size in the same given healthcare region of New Zealand.

  The sample size of JWs is 12,700 (avg for each of the years between 1998-2007). The statistical population of that sample size is 19 over the 10-year period of 1998-2007.

  You’re correct that a higher or lower statistical population would raise or lower the ultimate extrapolated value or deaths, but that’s what statistics are for; to let a given population of a sample tells us about, what is in the case, a given demographic who refuses blood transfusion.

  Marvin Shilmer
• 

  Simon

  A patient profile was determined and rigorously adhered to. Patients meeting this profile were included in the study whether JW or not. Exclusions were made where to do otherwise would skew results. For example 2 JW palliative care cancer patients who otherwise met the profile were excluded from the data set.

  The method was to compare a matched patient profile with the sole differential acceptance or rejection of blood transfusion in patients suffering severe anemia. The result was “We have shown that compared with JW patients, ARBC transfusion in anaemic patients was associated with a 10 times reduced mortality.”

  The relevant statistic shows that in a given demographic of 2 regions within New Zealand’s healthcare system over a 10-year period 19 JWs died over and beyond what should have compared with the same/similar patient presentation when blood products were not refused. This is a hard number that can be used to statistically extrapolate the number of JWs who died that shouldn’t have for one reason: refusal of blood product.

  Does it say if it was done 'blind'? i.e. were unidentifiable patient outcomes compared and then the groups identified or were they put into groups and then the outcomes compared? One is open to considerable bias whether consciously or not.

  Still, given the interest in the outcome by the people conducting the study and the miniscule size of the data-set, even if the study was well conducted I think there are question marks over what can be interpreted by it (beyond 'getting medical treatment is a good idea') and we're still left with the problem of how that is extrapolated out to a much larger population.

  You didn't answer my question about the number of witnesses in NZ: If instead of 12k the WTS had either 10k or 20k members, would the deaths of 19 out of 103 anemia patients result in different figures using your methodology? If it would then your methodology is faulty.
• 

  Finkelstein

  Sorry I don't agree with Marvin's calculating methodology for there too many possible health issues where a BT would be an effective

  medical treatment and there too many countries where those illnesses would be treated with differing variance.

  Its a theory that holds little weight toward probability in my opinion
• 

  Marvin Shilmer

  -

  “Marvin you "constructed a calculator"? You crack me up! What the hell are you talking about?”

  I’m talking about crunching numbers for statistical purposes. This is what a calculator is used for. Try building one.

  “Can you explain in plain English why my calculation above is wrong?”

  At this moment I’m answering for my work. From what I’ve read of your calculation, it assumes too many things we don’t have hard numbers for. On the other hand, my presentation is based strictly on hard numbers gathered in a meticulous data collection, and then compares this hard number with the hard number of JWs in the relevant region.

  When I have more time, and more posting ability, I’ll take a closer look at your work. In the meantime I’ll point out that 50,000 deaths due to Watchtower’s blood doctrine over a 50-year period is huge when seen in total butnearly imperceptible in real time. It is a figure that could only been seen for what it is in hindsight and with statistical compilation.

  “If you have the number of JWs being treated and their mortality rate then what does it matter which hopital they are in and where?”

  To apply a localized statistic to a larger sample you have to know the extent of the localized sample.

  “Marvin: If JWs were more or less successful in recruting new members in NZ - would that change their mortality rate under your methodology?”

  No. Regardless of recruiting success the number of JWs in New Zealand for years 1998-2007 is known and will not change. It is what it is. The same is true of the statistical number of deaths in New Zealand for years 1998-2007, the number is known and will not change. These two values represent hard numbers that enable us to calculate a statistical number of deaths suffered during the same period in the same patient profile of JWs refusing blood.

  “We're talking about STATISTICAL SIGNIFICANCE and you know it.

  “1.9 people per year in a developed country based on a survey with no clear methodology (perhaps some 'interpretation' by a group wanting a particular outcome) is NOT a convincing number.”

  The statistical significance is as the study’s authors expresses: “We have shown that compared with JW patients, ARBC transfusion in anaemic patients was associated with a 10 times reduced mortality.”

  A 10-times reduced mortality rate is statistically significant.

  Have you actually read this article by Beliaev et al?

  If not, I suggest you do so. Contrary to what you write above, the presentation offers very clear and meticulous methodology.

  Now the posting restrictions Simon has placed on my participation have kicked in and this is my last post until whenever.

  Marvin Shilmer
• 

  Finkelstein

  Sorry I don't agree with Marvin's calculating methodology for there too many possible health issues where a BT would be an effective

  medical treatment and there too many countries where those illnesses would be treated with possible differing variance.

  What and why is New Zealand singled out on it own, its like your saying that JWS only exist in New Zealand and nowhere else in the world ?

  Could you elaborate a little. ?
• 

  Marvin Shilmer

  -

  Looks like I have one more post…

  “What this thread does show is that, contrary to Mr. Shilmer's contention, Ms. Barrick's tweet was horribly irresponsible because her contention is so easily attacked.”

  The single thing of assuming deaths at all trauma centers in New Zealand are on par with the 4 whose data was mined by Dr. Beliaev et al would push the statistical number of deaths among JWs for refusing blood between 1961-2011 to over 200,000.

  - That statistical increase would still not include a higher mortality rate due to Watchtower’s pre-2000 tighter restriction on blood products.

  - That statistical increase would still not include a higher mortality rate due to a higher historical dependence on blood transfusion in patients with severe anemia (before products like EPO et al).

  - That statistical increase would still not include higher mortality rate due to healthcare systems less equipped than found in New Zealand during years 1998-2007.

  The only thing I find disturbing about the 250,000 number is that it was not offered along with a solid means to examine it for veracity by its author. Rightly that deserves criticism. On the other hand, I’ve observed skeptics in this discussion criticize on a basis of statistical hogwash. Anyone who wants to engage this subject on substantive grounds should undertake a careful analysis to find hard and reliable numbers useful as a population within a given sample. Then they should present this data and express in concise mathematical terms their conclusions. So far the only data collection I’ve seen offered comes from Dr. Beliaev et al, and it appears as though I’m the only person who actually read the collection.

  “Does it say if it was done 'blind'? i.e. were unidentifiable patient outcomes compared and then the groups identified or were they put into groups and then the outcomes compared? One is open to considerable bias whether consciously or not.”

  You can’t do a blind matched set data collection. To achieve a matched set you have to examine all records in a given patient population and let the sets align to given criterion. In this case the given patient population was all admissions to 4 particular hospital within the New Zealand healthcare system.

  “Still, given the interest in the outcome by the people conducting the study and the miniscule size of the data-set, even if the study was well conducted I think there are question marks over what can be interpreted by it (beyond 'getting medical treatment is a good idea') and we're still left with the problem of how that is extrapolated out to a much larger population.”

  This is an aspect of Dr. Beliaev et al’s data collection and analysis that is noteworthy: the review was not to figure out mortality but, rather cost effectiveness of treatment options. The authors were not mining for or against a particular finding of mortality due to refusing blood. It just happened to be that a mortality statistic lept from the data collection that deserved exposure.

  The “problem” of extrapolating the larger picture from this data set is not a problem. It’s simple math. What’s important is that the math is properly done to let values speak for themselves. In my case I held to a very conservative extrapolation for greater veracity.

  “You didn't answer my question about the number of witnesses in NZ: If instead of 12k the WTS had either 10k or 20k members, would the deaths of 19 out of 103 anemia patients result in different figures using your methodology? If it would then your methodology is faulty.”

  I don’t understand what you’re talking about. Were the number of JWs in New Zealand less than 12,000 and the same number died due to refusing blood then the extrapolated number of 50,000 deaths would be larger than 50,000. Were the number of JWs in New Zealand greater than 12,000 and the same number died due to refusing blood then the extrapolated number of 50,000 deaths would be lesser than 50,000. That is how math works.

  It is absurd for anyone to think that different values arrived based on different inputs means a value is right or wrong. That said, different inputs typically will result in different values!

  As it is we know the number of JWs in New Zealand for the years in question and we have data telling us the number of JWs who died preventable deaths in the same demographic. From that point it’s only a matter of doing the math and letting these inputs speak for whatever they say.

  Marvin Shilmer