Stories
Slash Boxes
Comments

SoylentNews is people

posted by janrinok on Friday September 06 2019, @12:15AM   Printer-friendly

Arthur T Knackerbracket has found the following story:

Influenza vaccination in patients with high blood pressure is associated with an 18% reduced risk of death during flu season, according to research presented today at ESC Congress 2019 together with the World Congress of Cardiology.

"Given these results, it is my belief that all patients with high blood pressure should have an annual flu vaccination," said first author Daniel Modin research associate of the University of Copenhagen, Denmark. "Vaccination is safe, cheap, readily available, and decreases influenza infection. On top of that, our study suggests that it could also protect against fatal heart attacks and strokes, and deaths from other causes."

According to previous research, the stress flu infection puts on the body may trigger heart attacks and strokes. Patients with hypertension (high blood pressure) are at raised risk of heart attack and stroke. By stopping flu infection, vaccination could also protect against cardiovascular events, but until now this had not been investigated.

The study used Danish nationwide healthcare registers to identify 608,452 patients aged 18 to 100 years with hypertension during nine consecutive influenza seasons (2007 to 2016). The researchers determined how many patients had received a flu vaccine prior to each season. They then followed patients over each season and tracked how many died. In particular, they recorded death from all causes, death from any cardiovascular cause, and death from heart attack or stroke.

Finally, they analysed the association between receiving a vaccine prior to flu season and the risk of death during flu season. The analysis controlled for patient characteristics that could impact the likelihood of dying such as age, comorbidities, medications, and socioeconomic status.

[...] He said: "Heart attacks and strokes are caused by the rupture of atherosclerotic plaques in the arteries leading to the heart or the brain. After a rupture, a blood clot forms and cuts off the blood supply. It is thought that the high levels of acute inflammation induced by influenza infection reduce the stability of plaques and make them more likely to rupture."

-- submitted from IRC


Original Submission

 
This discussion has been archived. No new comments can be posted.
Display Options Threshold/Breakthrough Mark All as Read Mark All as Unread
The Fine Print: The following comments are owned by whoever posted them. We are not responsible for them in any way.
  • (Score: 0, Troll) by Anonymous Coward on Friday September 06 2019, @12:26PM (5 children)

    by Anonymous Coward on Friday September 06 2019, @12:26PM (#890485)

    I'm not that commentator but the alternative is called science. Maybe you have heard of it?

    You look at some observations, come up with an explanation for what you see, derive a prediction from that explanation, then compare that prediction to new data collected by multiple independent parties. Ideally someone who doesn't like the original theory or is in competition with you is one of those parties.

    Also, the more otherwise surprising the prediction the better. Predictions like "x should be correlated with y," or the slightly improved "x should be positively correlated with y" are too vague and consistent with too many explanations to be useful in distinguishing between them.

    All you have to do is look at what people were doing pre-WWII, before the US government ruined research like they do everything they touch.

    Starting Score:    0  points
    Moderation   0  
       Troll=1, Interesting=1, Total=2
    Extra 'Troll' Modifier   0  

    Total Score:   0  
  • (Score: 2) by Azuma Hazuki on Friday September 06 2019, @10:02PM (4 children)

    by Azuma Hazuki (5086) on Friday September 06 2019, @10:02PM (#890723) Journal

    Methodology, Mr. Troll. What specific method of statistical analysis would you replace null hypothesis testing with?

    --
    I am "that girl" your mother warned you about...
    • (Score: 0) by Anonymous Coward on Saturday September 07 2019, @05:26AM (1 child)

      by Anonymous Coward on Saturday September 07 2019, @05:26AM (#890853)

      Testing your hypothesis instead of a strawman hypothesis. Go ahead and do everything else the same, it doesn't really matter. Sorry that you think basic science is
      trolling.

      • (Score: 2) by Azuma Hazuki on Sunday September 08 2019, @07:54PM

        by Azuma Hazuki (5086) on Sunday September 08 2019, @07:54PM (#891380) Journal

        How is "let's start from the idea that we're completely wrong, and only if the data don't bear that out, say we have something" bad science? If anything, starting from the effect you're looking for would bias the study badly.

        --
        I am "that girl" your mother warned you about...
    • (Score: 1, Informative) by Anonymous Coward on Saturday September 07 2019, @06:56AM (1 child)

      by Anonymous Coward on Saturday September 07 2019, @06:56AM (#890866)

      Different AC here, but "p hacking" has a particular meaning where you put a bunch of variables into an analysis tool and try to find one that hits your significance level. The problem with that is that you don't need to do many tests before at least 1 comes up statistically significant by chance. That is post-hoc testing, which is a big no-no when done improperly. Now, there are ways to do it properly, such as a Bonferroni Correction or the Sheffe method or confirmation/validation samples, among others. However, doing multiple combinations of tests on the same data after collection and then just publishing the successful ones is not one of those ways.

      Now in this case, this is not p hacking. The reason is that they had the specific hypothesis that people with high blood pressure were at higher risk of death from the flu compared to "normal" people. They pre-registered this study to test if such higher risk existed. They then followed the registered protocol, came up with these results (the overall risk within group and the relative risk between group), and published them in a Cardiology journal. From the headline, this looks like classic "p hacking" because the specific finding they were looking for screams "let's combine variables until we see something significant," but it is actually an example of the science done right with the hypotheses laid out ahead of time and the experiment designed around them.

      • (Score: 0) by Anonymous Coward on Saturday September 07 2019, @09:15PM

        by Anonymous Coward on Saturday September 07 2019, @09:15PM (#891085)

        As an example, here is a p hacking function I wrote translated to Python for better readability:

        variables = [_ for _ in colnames(data)]
        found = 0
        comps = 0
        for d_var in variables:
                remainder = [_ for _ in variables if _ != d_var]
                for i_var in remainder:
                        comps += 1
                        result <- lm(d_var ~ i_var, data = data)
                        if 0.05 > coef(lm)[4]:
                                count = count + 1

        print(f"{count}/{comps})

        When run, I get this output:

        $ submit_job --nice=19 p-hack
        Optimizing chunk size...
        Checking available executors...
        Submitting work...
        Gathering results...
        Done...

        958/21289