250,000 Jehovah's Witnesses have died refusing blood

by nicolaou 739 Replies latest watchtower medical

  • Simon
    Simon

    Also, have you tried more underlining?

    Maybe if you said I am just right then we'd all accept it.

  • Marvin Shilmer
    Marvin Shilmer

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    “And that is why your application of the results to ALL JWs is giving you an unbelievable answer.

    “You are only looking at results from a small subset of the JW population.”

    Simon,

    Wrong. Records of more than 3,000 JW patients were found by Beliaev. 103 of these fit the model for his study of costs. It just happens to be the case that the model he pulled for his review is the best possible model for determining deaths (not a statistical mortality rate but, rather, statistical deaths!) among JWs for refusing blood because a primary criterion was hemoglobin concentration of =/< 8 grams per dL.

    It is the statistical number of deaths over and above the norm among these patients who refused blood that establishes a statistical death per capita value for JWs in New Zealand during years 1998 to 2007. It was 1 JW for every 3848 JWs.

    “That means nothing. Are you claiming it's a model? Most likely from what I've seen so far all it really does is automate the calculation.

    “Why even do that? I mean, if you have the numbers and the numbers are so hard then you wouldn't be re-calculating anything, ever ...

    “Unless you want to play around and do "what ifs" until you get the answer you want. A round 50,000. Nice.”

    The process is much simpler than you think.

    To extract the number of statistical deaths over and above the norm during years 1998-2007 of patients who refused blood versus those who accepted blood in Beliaev’s study requires a calculation. This calculation gives an aggregate value for years 1998-2007 among JWs in New Zealand. That value is 19.

    To determine an aggregate value to compare these deaths against requires establishing a fixed method of determining the number of JWs serviced by the same facilities whose patient records were part of the study. It does not matter the method so long as it is used equally to establish the number of JWs in New Zealand and, later on, the number of JWs in the world. By “method” I mean a rule or set of rules. The method I used was to use the published average number of JWs in New Zealand by Watchtower. To build this as an aggregate value requires a calculation. This calculation gives an aggregate value for years 1998-2007. That value is 126,989.

    One problem with the comparison of the 19 with the 126,989 is that the 19 value is gathered from patient records in a minority of healthcare institutions in New Zealand whereas the 126,989 value is of JWs in all of New Zealand. Hence another method (rule) is necessary to make these dissimilar comparisons similar. An examination of demographics in New Zealand and its distribution of healthcare facilities against Beliaev’s data set shows a regionalization that is useful for this purpose. New Zealand has 4 regions (Northern, Midland, Central and Southern) of population and each region has sets of healthcare facilities serving the population. In Beliaev’s study the 4 institutions’ patient records he used were trauma centers. 2 of the 4 had advanced trauma services, 1 of the 4 had district trauma services and 1 of the 4 had basic trauma services. But all 4 were hospitals with trauma services. Hence the rule (method) I applied ignored any and all hospitals in New Zealand that did not have trauma services by assuming none of those hospitals had any deaths of JWs refusing blood. Though the 4 hospitals in Beliaev’s study represented a disproportionately small number of trauma service centers in the 2 healthcare regions in my method (rule) assumed the other trauma centers in the same regions had no deaths at all among JWs for refusing blood. The final rule (method) for finding the corollary of JWs with the hospital patient records used by Beliaev was to use the regional percentage of total population as an indicator of JWs serviced in each region. This is the 57% factor you find in my blog presentation. This factor gives a direct method of adjusting the value of 19 deaths in 2 regions of New Zealand to compare against the total population of JWs in New Zealand in all 4 of its regions. This requires a calculation, and it gives an aggregate value for years 1998-2007 among JWs in New Zealand. That value is 33.

    Once you have 2 relevantly similar values determining the per capita deaths is simple division. You can annualize the result to get an annual per capita value. The annual number of statistical deaths among JWs in New Zealand during years 1998-2007 is 1 JW per every 3848 JWs.

    Marvin Shilmer

  • Marvin Shilmer
    Marvin Shilmer

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    “So you think they have a big wall or something? They don't have planes? That other countries allow hoardes of blood-refusing-near-death JWs to flood across their borders each day? Pretty much all nations run themselves so I'm not sure what that 'sovereign' part has to do with the price of milk.”

    Simon,

    What you raise is an important factor to examine. It goes to the question of “What is the population of JWs serviced by healthcare facilities in New Zealand?” This value is important in my extrapolation because it’s a direct corollary with the number of statistical deaths due to refusing blood.

    A large part of my research on this article was about this very issue. I didn’t share all this research in order to avoid burying the main point (numbers of needless deaths) in a lot of tedious background information. When I began this project my impression was that surrounding island communities were probably serviced by the New Zealand healthcare system with the effect that I should include these JWs in my calculation. The information I gathered suggested my impression was not the case, and was probably not much of a factor anyway.

    What I found was that most JWs in regions surrounding New Zealand who needed healthcare where the “no blood” position was likely to be an issue were not being serviced in New Zealand but, rather, either in their own communities or else Australia. When I looked at the number of JWs in the vicinity of New Zealand that were not being serviced in Australia, the number was so low that it wouldn’t have made much difference anyway. In the end, as stated in a post above, I used the number of JWs in New Zealand to compare against statistical deaths among JWs in New Zealand due to refusing blood.

    Marvin Shilmer

  • Simon
    Simon

    On the large island of Marvinia near the land of Oz lived a people called the Shilmers. No one knows how they got their name but that is not important to this story.

    There were 12,500 Shilmers left on the island and many many more lived on the mainland. Some claimed as many as 7.5 million throughout all the land of Oz.

    Some of the Shilmers had mental problems and believed in fairies. They claimed that the fairies said that if they were wounded they were not to clean or wash the wound with the magic cleaning potion and instead trust that the fairy queen would heal them with her dreams. If they died, then it must mean they had been masturbating or something, which the fairies also disliked.

    Now the ones who believed in the fairy story were a minority. Maybe it is because the others better were educated, had functioning brains or they just liked to jerk off once in a while (dirty little Shilmers) or perhaps it was because when it came down to it, they just didn't want to die.

    Anyway, over one particular decade several thousand Shilmers had been injured and 103 of them had taken a firm stand and decided not to clean their wounds with the magical potion. 19 of them had died as a result. Many more than regular Shilmers who were more fairy-lite and snook some of the magic cleaning potion when no one was looking.

    One of the ex-Shilmers who had left the island and read about this alerted the Wizard and told him that the Shilmers faced grave danger and any of the 7.5 million that lived on the mainland were in peril. He had used his divination machine to estimate that 50,000 of them would die based on the 12,500 Shilmers on the island and how many of them had died.

    "No, no, no", said the Wizard, "Are you freakin' nuts?! Not all Shilmers are at risk ... only the few with mental problems that don't accept the wound cleaning potion are ever really in more danger". And in fact, there was now a new "I can't believe it's not magical wound cleaning potion" available at Fairy-mart which was acceptable and most of them used that.

    The ex-Shilmer protested saying he had hard numbers. The munchkins all tried to explain too but he wouldn't listen and just kept repeating his claim again, and again, and again often with underlined ink. The Wizard pointed out that it was crazy to imagine that just because some brain damaged Shilmer on an island far far away decided not to clean his wounds that it would cause more Shilmers on the mainland to die. Then he turned him into a stone.

    The end.

  • Simon
    Simon

    Wrong. Records of more than 3,000 JW patients were found by Beliaev. 103 of these fit the model for his study of costs.

    Does he say what the outcomes for the 3,000 patients were? How 'increased' was the risk to JWs when they were taken into account? Aren't you talking about 19 out of 3,000 and not 103?

    It sounds like his model was perfectly designed to give the answer he was looking for which is basically that patients with severe anemia that are urgently in need of a transfusion are better off having a transfusion.

    This message brought to you by the Institute for Blood Transfusion.

  • LisaRose
    LisaRose

    MARVIN: Based values expressed in the Beliaev study, and after prorating based on assigning mortality values to 57% of the population of the 2 regions his data came from, I get an annual mortality of 0.0000086% of the population. This does no adjust upward for the increased rate of mortality among JWs in New Zealand but because JWs are such a small minority an adjusted value would not add much.

    I am sorry, but this is still confusing to me. That mortality percentage above is from the patients who died in the study, against the general population in that part of the country. But didn't you say there were other hospitals in that part of the country? If there are eighty hospitals, there must be more than four in the part of the country that has 57% of the population. So then might not the other hospitals have anemia deaths too?

  • Marvin Shilmer
    Marvin Shilmer

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    “Does he say what the outcomes for the 3,000 patients were? How 'increased' was the risk to JWs when they were taken into account? Aren't you talking about 19 out of 3,000 and not 103?”

    Simon,

    Outcomes for the 3,000? He does not give this because his study was of and only of outcomes and costs in relation to treating patients suffering severe anemia.

    Increased risk when the 3,000 are taken into account? What does that have to do with our discussion? We are talking about increased incident (not rate) of mortality of patients with severe anemia refusing blood.

    We can talk about rate or rates of mortality until the cows come home and it won’t change the number (the incident) of statistical deaths among JWs in New Zealand with severe anemia refusing blood for the years of 1998-2007.

    “It sounds like his model was perfectly designed to give the answer he was looking for which is basically that patients with severe anemia that are urgently in need of a transfusion are better off having a transfusion.”

    The authors were looking for a means and method to compare outcomes and cost between treating severe anemia patients with red cell transfusion versus not treating them with red cell transfusion. The only means and method available was to dig into patient records for individuals with severe anemia who refused blood transfusion. There was no way to conduct a controlled trial because it would pose an unethical threat to patients. This left JWs.

    Hence the authors scoured patient records from 4 trauma centers and extracted all incidents where patients suffered severe anemia and then separated these into 2 categories. 1 group that accepted red cell transfusion and 1 group that refused red cell transfusion. Will call this Group A and Group B respectively.

    Then they examined Group B (the group that refused transfusion) and used it as a model to segregate patients in Group A based on things like general characteristics (i.e., gender, ethnicity, comorbidities, hospital admission type and treatment type). Once this segregation was made the authors randomly selected from the segregated patients of Group A to achieve a matched comparison to Group B so that both groups were very close in all comparisons accept one, and that one comparison was whether the patient accepted or rejected blood.

    From that point forward it was only a matter of math, including making adjustment for things like Maori ethnicity given severe anemia was the subject.

    The thing about math is that it speaks for itself.

    This is what the authors did.

    Marvin Shilmer

  • Simon
    Simon

    Why would they 'randomly' select patients from the segregated groups? If they were dealing with such low numbers why not include them all?

    All this does is increase the chance that if you repeated the study you'd get very different results.

    Roll the dice and .... we picked a patient that died.

    That sounds a bit fishy to me. Random selection can easily be 'we picked the patients that produced the answer we wanted'.

  • Marvin Shilmer
    Marvin Shilmer

    -

    “I am sorry, but this is still confusing to me. That mortality percentage above is from the patients who died in the study, against the general population in that part of the country. But didn't you say there were other hospitals in that part of the country? If there are eighty hospitals, there must be more than four in the part of the country that has 57% of the population. So then might not the other hospitals have anemia deaths too?”

    LisaRose,

    There are loads of hospitals in the same 2 regions as the 4 whose patient records are part of this study. We’d be naïve to think none of these other hospitals in the same 2 regions had JW patients with severe anemia refusing blood who died as a result. But to keep my extrapolation conservative, for statistical purposes I assumed no such deaths at any of these many other facilities in the 2 regions.

    When I was constructing the per capita deaths of JWs in New Zealand due to refusing blood I assumed the only deaths in the 2 regions were the ones reported by Beliaev.

    Does that answer your question?

    Oh, and by the way, I’m still not sure what your earlier statistic speaks to in terms of “severe anemia” but I did find the CDC report for deaths attributed to anemia in the United States and for year 2010 the adjusted number is 4,631 of a population of 308,745,538. This represents an annual mortality of 0.0014999%, which, based on the proration I used in my presentation is more than the annual mortality among New Zealanders by a factor of 170x. This makes my extrapolation conservative by comparison.

    Marvin Shilmer

  • Scott77
    Scott77
    Why would they 'randomly' select patients from the segregated groups? If they were dealing with such low numbers why not include them all?

    I think, it might have been done to eliminate researchers' bias. By using random selection, reseachers would claim that every segregaed subject in that group had a chance to be selected without bias.

    Scott77

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