by Dr.Harald Wiesendanger– Klartext
What the mainstream media is hiding
Before we take medicine, start therapy, or undergo medical examination, we should always ask about the NNT, the “Number Needed To Treat”: How many people have to undergo treatment for it to be beneficial to one person? Does the doctor owe us an answer? Then be careful! Does he know it? Then all too often it gives rise to justified doubts.
“60% of all patients who receive an antidepressant feel better as a result”: for someone who is chronically depressed, this sounds like a strong argument for getting such a drug prescribed.
We realize that appearances are deceiving as soon as we convert the impressive improvement rate into the “Number Needed to Treat” (NNT): the “number of treatments needed” to help one patient. To do this, we first need to know how many depressed patients feel better after receiving a placebo: 50%. The NNT is calculated as the reciprocal of the risk difference: it is 1 divided by (60% – 50%) = 1/10, reciprocal 10.
NNT = 10? This means that out of 10 patients who take an antidepressant, only one benefits from it. For the other nine, it makes no difference whether they receive the drug or not – apart from side effects and costs.
In reality, however, the benefit is usually much lower. This is because the percentages used to calculate the NNT are usually taken from studies conducted by the pharmaceutical industry. These studies are often not only poorly blinded, but also designed from the outset to include test subjects from precisely the patient group that is most likely to respond to the drug being tested. And if this trickery is not enough to deliver the desired results? No problem: the smaller the difference between the treatment and placebo groups, the more likely the study will remain unpublished.
A damning indictment of supposed “preventive care.”
How effective are drugs as preventative measures – how likely are they to prevent disease? Here, the NNT is usually much higher.
Let’s take a look at statins, for example: drugs that are supposed to prevent heart attacks and strokes by lowering cholesterol levels. (More about this marketing fairy tale here ») In a study published by the venerable journal Lancet, high-risk patients – who already suffered from angina pectoris and/or had suffered a heart attack before the study began – were given the statin simvastatin for five years. (1) In the end, it turned out that one in thirty participants was prevented from dying as a result – which some readers may find impressive. However, they should also consider that although the subjects were clearly in mortal danger, only one in three received aspirin during the five years of the study – a downright immoral omission, because in “secondary prevention” for those already affected, ASA can reduce the relative risk of recurrent heart attacks and ischemic strokes by 50 to 70%.
In addition, one in four continued to smoke – known to be one of the worst cardiovascular risk factors. Couldn’t the study participants’ lives have been saved much more cheaply and no less effectively by simply giving them aspirin instead of statins and helping them quit smoking?
Hired mouthpieces for the pharmaceutical industry even praise statins for healthy people. This seems to be supported by a meta-analysis of eight studies, according to which cholesterol-lowering drugs reduced overall mortality by 16%. This figure is impressive, but says almost nothing about the preventive benefits as long as the mortality rate among untreated patients remains unknown. In the eight studies considered, it was 2.8%. What NNT corresponds to this ratio? 223. This means that in order to prevent a single death, 223 people would have to be treated with statins (2) – a rather poor cost-benefit ratio.
With an NNT of 25 to 100, beta blockers administered after a heart attack to prevent premature death do not exactly inspire confidence either.
Vaccinations also perform anything but brilliantly: among the over-65s, up to 71 people have to be “jabbed” against influenza in order to spare a single person hospitalization. In the same age group, 3,600 to 5,000 people would have to receive the PCV13 vaccine against pneumococci to prevent a single case of invasive infection. “Impressively effective” looks different.
What good are the COVID “vaccines”? The highly respected Greek-American physician, epidemiologist, and statistician John Ioannidis assumes a ratio of 1 death prevented per 5,400 vaccine doses; he is probably overestimating here because he is basing his calculations on questionable models and secondary data, not on actual observed cases. For people under 30, Ioannidis estimates 1 life saved per 100,000 doses: a ratio that, given the nasty side effects—from myocarditis to thrombosis and allergic shock to neurological deficits—sheds a telling light on the unspeakable coronavirus vaccination campaign.
Using NNT to counter advertising tricks
All these examples illustrate why the Arznei-Telegramm considers NNT to be an important “aid for therapy decisions”: doctors are often guided by studies that base efficacy claims on relative risk reduction (RRR). This indicates the extent to which a medical intervention reduces the probability of disease in percentage terms compared to the untreated group. This RRR value can be used to make a powerful impression – but it is misleading because it depends on the initial risk.
Absolute risk reduction (ARR), on the other hand, shows the real difference in percentage points: by how much does the measure actually reduce the risk?
Let’s take, for example, a new vaccine against a disease that – as is usually the case – only affects a small number of people.
Suppose that in a study of 10,000 people, 50 fall ill without vaccination and 20 with vaccination. According to this, the vaccine reduces the relative risk of illness by 60%, which sounds like a huge success. (Pfizer & Co. used this pattern to construct outrageous efficacy promises for their Covid injections.) In reality, however, only 30 out of 10,000 vaccinated people benefit – so the absolute risk is reduced by only 0.3%. The NNT opens our eyes to this fact: in order to effectively protect a single person, 333 people had to be unnecessarily “jabbed.” “NNT,” praises the Arznei-Telegramm, “clarifies the difference between advertising gimmicks and reality,” based on a simple fact: the lower the NNT, the more likely the therapy will be successful.
NNT complements relative risk reductions, which are often misleading. That is why David Sackett (1934-2015) – the Canadian physician and epidemiologist who is considered the founder of evidence-based medicine – recommended NNT as an intuitively plausible measure: “The NNT indicates how many patients must be treated to achieve the desired result in a single patient – a number that doctors and patients can immediately understand.”
Further decision-making aids: NNH and NNS
No less informative is the “number needed to harm” (NNH): How many people must undergo treatment before, on average, one of them suffers harm, e.g., a severe side effect? With very high NNH values, the risk of this is low, while particularly low NNH values warn of a fairly high risk. For SSRI antidepressants such as sertraline and paroxetine, for example, NNHs between 5 and 20 are typical: one in five to 20 patients discontinues therapy due to side effects such as nausea, fatigue, and sexual dysfunction.
Hardly any doctor fails to remind their patients at every opportunity how important “prevention” is. By this, they usually do not mean a lifestyle that makes it unnecessary because it prevents diseases from developing in the first place, but rather billable examinations to detect diseases after they have already broken out or at least already show signs of certain abnormal parameters. How helpful such measures really are could be revealed by their NNS, the “number needed to screen.” For mammography, it is 746. This means that in order to prevent a single death from breast cancer, 746 women would have to be screened each year over a period of ten years.
For colonoscopy to prevent colon cancer? 455.
For PSA tests for prostate cancer screening? 742 to 1000.
And for bone density measurements, so that osteoporosis can be detected and treated earlier to prevent fractures? In the large British SCOOP study of women aged 70 to 85, the absolute risk of suffering a hip fracture in the following five years fell by 0.9%. This corresponds to an NNS of 111. This means that 111 older women would have to be screened—and treated if necessary—to prevent a single one of them from suffering a hip fracture in the next few years that would have occurred without screening.
What should we think of a doctor who is unaware of these revealing values, ignores them, and conceals them from his patients? Doesn’t the principle of “informed consent” oblige him to communicate them in advance? His actions are professionally and ethically questionable. NNT, NNH, and NNS make risks and benefits tangible; they are essential for informed decisions. Period.
Notes
(1) Simvastatin was launched in pharmacies in Germany in 1990 under the trade name Zocor. Global sales since its market launch, including generics after the patent expired in 2006, are estimated at 40 to 60 billion US dollars.
(2) Calculation basis: The mortality rate in the untreated group is 2.8% (risk rc = 0.028). The relative risk reduction (RRR) of 16% reduces the risk in the treated group to rt = rc × (1−0.16) = 0.028 × 0.84 = 0.02352 or 2.352%. The absolute risk reduction (ARR) is ARR = rc –rt =0.00448 or 0.448%.
NNT = (1 divided by ARR) minus (1 divided by 0.00448) is approximately equal to 223. This NNT is typical for statins in primary prevention at low baseline risk (e.g., 2.8% mortality rate), where the absolute benefit is small, while it is lower at higher risk (secondary prevention) (e.g., NNT 50–100).