Pain Doesn’t Belong on a Scale of Zero to 10

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Over the past two years, a simple but baffling request has preceded most of my encounters with medical professionals: “Rate your pain on a scale of zero to 10.”

I trained as a physician and have asked patients the very same question thousands of times, so I think hard about how to quantify the sum of the sore hips, the prickly thighs, and the numbing, itchy pain near my left shoulder blade. I pause and then, mostly arbitrarily, choose a number. “Three or four?” I venture, knowing the real answer is long, complicated, and not measurable in this one-dimensional way.

Pain is a squirrely thing. It’s sometimes burning, sometimes drilling, sometimes a deep-in-the-muscles clenching ache. Mine can depend on my mood or how much attention I afford it and can recede nearly entirely if I’m engrossed in a film or a task. Pain can also be disabling enough to cancel vacations, or so overwhelming that it leads people to opioid addiction. Even 10+ pain can be bearable when it’s endured for good reason, like giving birth to a child. But what’s the purpose of the pains I have now, the lingering effects of a head injury?

The concept of reducing these shades of pain to a single number dates to the 1970s. But the zero-to-10 scale is ubiquitous today because of what was called a “pain revolution” in the ’90s, when intense new attention to addressing pain — primarily with opioids — was framed as progress. Doctors today have a fuller understanding of treating pain, as well as the terrible consequences of prescribing opioids so readily. What they are learning only now is how to better measure pain and treat its many forms.

About 30 years ago, physicians who championed the use of opioids gave robust new life to what had been a niche specialty: pain management. They started pushing the idea that pain should be measured at every appointment as a “fifth vital sign.” The American Pain Society went as far as copyrighting the phrase. But unlike the other vital signs — blood pressure, temperature, heart rate, and breathing rate — pain had no objective scale. How to measure the unmeasurable? The society encouraged doctors and nurses to use the zero-to-10 rating system. Around that time, the FDA approved OxyContin, a slow-release opioid painkiller made by Purdue Pharma. The drugmaker itself encouraged doctors to routinely record and treat pain, and aggressively marketed opioids as an obvious solution.

To be fair, in an era when pain was too often ignored or undertreated, the zero-to-10 rating system could be regarded as an advance. Morphine pumps were not available for those cancer patients I saw in the ’80s, even those in agonizing pain from cancer in their bones; doctors regarded pain as an inevitable part of disease. In the emergency room where I practiced in the early ’90s, prescribing even a few opioid pills was a hassle: It required asking the head nurse to unlock a special prescription pad and making a copy for the state agency that tracked prescribing patterns. Regulators (rightly) worried that handing out narcotics would lead to addiction. As a result, some patients in need of relief likely went without.

After pain doctors and opioid manufacturers campaigned for broader use of opioids — claiming that newer forms were not addictive, or much less so than previous incarnations — prescribing the drugs became far easier and were promoted for all kinds of pain, whether from knee arthritis or back problems. As a young doctor joining the “pain revolution,” I probably asked patients thousands of times to rate their pain on a scale of zero to 10 and wrote many scripts each week for pain medication, as monitoring “the fifth vital sign” quickly became routine in the medical system. In time, a zero-to-10 pain measurement became a necessary box to fill in electronic medical records. The Joint Commission on the Accreditation of Healthcare Organizations made regularly assessing pain a prerequisite for medical centers receiving federal health care dollars. Medical groups added treatment of pain to their list of patient rights, and satisfaction with pain treatment became a component of post-visit patient surveys. (A poor showing could mean lower reimbursement from some insurers.)

But this approach to pain management had clear drawbacks. Studies accumulated showing that measuring patients’ pain didn’t result in better pain control. Doctors showed little interest in or didn’t know how to respond to the recorded answer. And patients’ satisfaction with their doctors’ discussion of pain didn’t necessarily mean they got adequate treatment. At the same time, the drugs were fueling the growing opioid epidemic. Research showed that an estimated 3% to 19% of people who received a prescription for pain medication from a doctor developed an addiction.

Doctors who wanted to treat pain had few other options, though. “We had a good sense that these drugs weren’t the only way to manage pain,” Linda Porter, director of the National Institutes of Health’s Office of Pain Policy and Planning, told me. “But we didn’t have a good understanding of the complexity or alternatives.” The enthusiasm for narcotics left many varietals of pain underexplored and undertreated for years. Only in 2018, a year when nearly 50,000 Americans died of an overdose, did Congress start funding a program — the Early Phase Pain Investigation Clinical Network, or EPPIC-Net — designed to explore types of pain and find better solutions. The network connects specialists at 12 academic specialized clinical centers and is meant to jump-start new research in the field and find bespoke solutions for different kinds of pain.

A zero-to-10 scale may make sense in certain situations, such as when a nurse uses it to adjust a medication dose for a patient hospitalized after surgery or an accident. And researchers and pain specialists have tried to create better rating tools — dozens, in fact, none of which was adequate to capture pain’s complexity, a European panel of experts concluded. The Veterans Health Administration, for instance, created one that had supplemental questions and visual prompts: A rating of 5 correlated with a frown and a pain level that “interrupts some activities.” The survey took much longer to administer and produced results that were no better than the zero-to-10 system. By the 2010s, many medical organizations, including the American Medical Association and the American Academy of Family Physicians, were rejecting not just the zero-to-10 scale but the entire notion that pain could be meaningfully self-reported numerically by a patient.

In the years that opioids had dominated pain remedies, a few drugs — such as gabapentin and pregabalin for neuropathy, and lidocaine patches and creams for musculoskeletal aches — had become available. “There was a growing awareness of the incredible complexity of pain — that you would have to find the right drugs for the right patients,” Rebecca Hommer, EPPIC-Net’s interim director, told me. Researchers are now looking for biomarkers associated with different kinds of pain so that drug studies can use more objective measures to assess the medications’ effect. A better understanding of the neural pathways and neurotransmitters that create different types of pain could also help researchers design drugs to interrupt and tame them.

Any treatments that come out of this research are unlikely to be blockbusters like opioids; by design, they will be useful to fewer people. That also makes them less appealing prospects to drug companies. So EPPIC-Net is helping small drug companies, academics, and even individual doctors design and conduct early-stage trials to test the safety and efficacy of promising pain-taming molecules. That information will be handed over to drug manufacturers for late-stage trials, all with the aim of getting new drugs approved by the FDA more quickly.

The first EPPIC-Net trials are just getting underway. Finding better treatments will be no easy task, because the nervous system is a largely unexplored universe of molecules, cells, and electronic connections that interact in countless ways. The 2021 Nobel Prize in Physiology or Medicine went to scientists who discovered the mechanisms that allow us to feel the most basic sensations: cold and hot. In comparison, pain is a hydra. A simple number might feel definitive. But it’s not helping anyone make the pain go away.

KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF—an independent source of health policy research, polling, and journalism. Learn more about KFF.

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  • For women with antenatal depression, micronutrients might help them and their babies – new study

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    Getty Images

    Julia J Rucklidge, University of Canterbury; Elena Moltchanova, University of Canterbury; Roger Mulder, University of Otago, and Siobhan A Campbell, University of Canterbury

    Antenatal depression affects 15% to 21% of pregnant women worldwide. It can influence birth outcomes and children’s development, as well as increase the risk of post-natal depression.

    Current treatments like therapy can be inaccessible and antidepressants can carry risks for developing infants.

    Over the past two decades, research has highlighted that poor nutrition is a contributing risk factor to mental health challenges. Most pregnant women in New Zealand aren’t adhering to nutritional guidelines, according to a longitudinal study. Only 3% met the recommendations for all food groups.

    Another cohort study carried out in Brazil shows that ultra-processed foods (UPF) accounted for at least 30% of daily dietary energy during pregnancy, displacing healthier options.

    UPFs are chemically manufactured and contain additives to improve shelf life, as well as added sugar and salt. Importantly, they are low in essential micronutrients (vitamins and minerals).

    The consumption of these foods is concerning because a nutrient-poor diet during pregnancy has been linked to poorer mental health outcomes in children. This includes depression, anxiety, hyperactivity, and inattention.

    Increasing nutrients in maternal diets and reducing consumption of UPFs could improve the mental health of the mother and the next generation. Good nutrition can have lifelong benefits for the offspring.

    However, there are multiple factors that mean diet change alone may not in itself be sufficient to address mental health challenges. Supplementing with additional nutrients may also be important to address nutritional needs during pregnancy.

    Micronutrients as treatment for depression

    Our earlier research suggests micronutrient supplements for depression have benefits outside pregnancy.

    But until now there have been no published randomised controlled trials specifically designed to assess the efficacy and safety of broad-spectrum micronutrients on antenatal depression and overall functioning.

    The NUTRIMUM trial, which ran between 2017 and 2022, recruited 88 women in their second trimester of pregnancy who reported moderate depressive symptoms. They were randomly allocated to receive either 12 capsules (four pills, three times a day) of a broad-spectrum micronutrient supplement or an active placebo containing iodine and riboflavin for a 12-week period.

    Micronutrient doses were generally between the recommended dietary allowance and the tolerable upper level.

    Based on clinician ratings, micronutrients significantly improved overall psychological functioning compared to the placebo. The findings took into account all noted changes based on self-assessment and clinician observations. This includes sleep, mood regulation, coping, anxiety and side effects.

    Pregnant woman looking out a window
    Adding micronutrients to the diet of pregnant women with antenatal depression significantly improved their overall psychological functioning. Getty Images

    Both groups reported similar reductions in symptoms of depression. More than three quarters of participants were in remission at the end of the trial. But 69% of participants in the micronutrient group rated themselves as “much” or “very much” improved, compared to 39% in the placebo group.

    Participants taking the micronutrients also experienced significantly greater improvements in sleep and overall day-to-day functioning, compared to participants taking the placebo. There were no group differences on measures of stress, anxiety and quality of life.

    Importantly, there were no group differences in reported side effects, and reports of suicidal thoughts dropped over the course of the study for both groups. Blood tests confirmed increased vitamin levels (vitamin C, D, B12) and fewer deficiencies in the micronutrient group.

    Micronutrients were particularly helpful for women with chronic mental health challenges and those who had taken psychiatric medications in the past. Those with milder symptoms improved with or without the micronutrients, suggesting general care and monitoring might suffice for some women.

    The benefits of micronutrients were comparable to psychotherapy but with less contact. There are no randomised controlled trials of antidepressant medication to compare these results.

    Retention in the study was good (81%) and compliance excellent (90%).

    Beyond maternal mental health

    We followed the infants of mothers enrolled in the NUTRIMUM trial (who were therefore exposed to micronutrients during pregnancy) for 12 months, alongside infants from the general population of Aotearoa New Zealand.

    This second group of infants from the general population contained a smaller sub-group who were exposed to antidepressant medication for the treatment of antenatal depression.

    We assessed the neuro-behavioural development of each infant within the first four weeks of life, and temperament up to one year after birth.

    These observational follow-ups showed positive effects of micronutrients on the infants’ ability to regulate their behaviour. These results were on par with or better than typical pregnancies, and better than treatments with antidepressants.

    Baby eats fruits and berries with their hand
    Micronutrients during pregnancy improved the neurological and behavioural development of infants. Getty Images

    Infants exposed to micronutrients during pregnancy were significantly better at attending to external stimuli. They were also better able to block out external stimuli during sleep. They showed fewer signs of stress and had better muscle tone compared to infants not exposed to micronutrients.

    They also displayed greater ability to interact with their environment. They were better at regulating their emotional state and had fewer abnormal muscle reflexes than infants exposed to antidepressant medication in pregnancy.

    Reassuringly, micronutrients had no negative impact on infant temperament.

    These findings highlight the potential of micronutrients as a safe and effective alternative to traditional medication treatments for antenatal depression.

    The prenatal environment sets the foundation for a child’s future. Further investigation into the benefits of micronutrient supplementation would gives us more confidence in their use for other perinatal (from the start of pregnancy to a year after birth) mental health issues. This could provide future generations with a better start to life.

    We would like to acknowledge the contribution of Dr Hayley Bradley to this research project.

    Julia J Rucklidge, Professor of Psychology, University of Canterbury; Elena Moltchanova, Professor of Statistics, University of Canterbury; Roger Mulder, Professor of Psychiatry, University of Otago, and Siobhan A Campbell, Intern Psychologist, Researcher – Te Puna Toiora (Mental Health and Nutrition Research Lab), University of Canterbury

    This article is republished from The Conversation under a Creative Commons license. Read the original article.

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  • Flexible Dieting – by Alan Aragon

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    This is the book from which we were working, for the most part, in our recent Expert Insights feature with Alan Aragon. We’ll re-iterate here: despite not being a Dr. Aragon, he’s a well-published research scientist with decades in the field of nutritional science, as well as being a personal trainer and fitness educator.

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  • Glycemic Index vs Glycemic Load vs Insulin Index

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    How To Actually Use Those Indices

    Carbohydrates are essential for our life, and/but often bring about our early demise. It would be a very conveniently simple world if it were simply a matter of “enjoy in moderation”, but the truth is, it’s not that simple.

    To take an extreme example, for the sake of clearest illustration: The person who eats an 80% whole fruit diet (and makes up the necessary protein and fats etc in the other 20%) will probably be healthier than the person who eats a “standard American diet”, despite not practising moderation in their fruit-eating activities. The “standard American diet” has many faults, and one of those faults is how it promotes sporadic insulin spikes leading to metabolic disease.

    If your breakfast is a glass of orange juice, this is a supremely “moderate” consumption, but an insulin spike is an insulin spike.

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    Glycemic Index

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    GI measures how much a carb-containing food raises blood glucose levels, also called blood sugar levels, but it’s just glucose that’s actually measured, bearing in mind that more complex carbs will generally get broken down to glucose.

    Pure glucose has a GI of 100, and other foods are ranked from 0 to 100 based on how they compare.

    Sometimes, what we do to foods changes its GI.

    • Some is because it changed form, like the above example of whole fruit (low GI) vs fruit juice (high GI).
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    • Some is because of other changes, like starches that were allowed to cool before being reheated (or eaten cold).

    Broadly speaking, a daily average GI of 45 is considered great.

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    Glycemic Load

    Glycemic Load, or GL, takes the GI and says “ok, but how much of it was there?”, because this is often relevant information.

    Refined sugar may have a high GI, but half a teaspoon of sugar in your coffee isn’t going to move your blood sugar levels as much as a glass of Coke, say—the latter simply has more sugar in, and just the same zero fiber.

    GL is calculated by (grams of carbs / 100) x GI, by the way.

    But it still misses some important things, so now let’s look at…

    Insulin Index

    Insulin Index, which does not get an abbreviation (probably because of the potentially confusing appearance of “II”), measures the rise in insulin levels, regardless of glucose levels.

    This is important, because a lot of insulin response is independent of blood glucose:

    • Some is because of other sugars, some some is in response to fats, and yes, even proteins.
    • Some is a function of metabolic base rate.
    • Some is a stress response.
    • Some remains a mystery!

    Another reason it’s important is that insulin drives weight gain and metabolic disorders far more than glucose.

    Note: the indices of foods are calculated based on average non-diabetic response. If for example you have Type 1 Diabetes, then when you take a certain food, your rise in insulin is going to be whatever insulin you then take, because your body’s insulin response is disrupted by being too busy fighting a civil war in your pancreas.

    If your diabetes is type 2, or you are prediabetic, then a lot of different things could happen depending on the stage and state of your diabetes, but the insulin index is still a very good thing to be aware of, because you want to resensitize your body to insulin, which means (barring any urgent actions for immediate management of hyper- or hypoglycemia, obviously) you want to eat foods with a low insulin index where possible.

    Great! What foods have a low insulin index?

    Many factors affect insulin index, but to speak in general terms:

    • Whole plant foods are usually top-tier options
    • Lean and/or white meats generally have lower insulin index than red and/or fatty ones
    • Unprocessed is generally lower than processed
    • The more solid a food is, generally the lower its insulin index compared to a less solid version of the same food (e.g. baked potatoes vs mashed potatoes; cheese vs milk, etc)

    But do remember the non-food factors too! This means where possible:

    • reducing/managing stress
    • getting frequent exercise
    • getting good sleep
    • practising intermittent fasting

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  • You Are the One You’ve Been Waiting For – by Dr. Richard Schwartz

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  • How Science News Outlets Can Lie To You (Yes, Even If They Cite Studies!)

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    Each Monday, we’re going to be bringing you cutting-edge research reviews to not only make your health and productivity crazy simple, but also, constantly up-to-date.

    But today, in this special edition, we want to lay out plain and simple how to see through a lot of the tricks used not just by popular news outlets, but even sometimes the research publications themselves.

    That way, when we give you health-related science news, you won’t have to take our word for it, because you’ll be able to see whether the studies we cite really support the claims we make.

    Of course, we’ll always give you the best, most honest information we have… But the point is that you shouldn’t have to trust us! So, buckle in for today’s special edition, and never have to blindly believe sci-hub (or Snopes!) again.

    The above now-famous Tumblr post that became a meme is a popular and obvious example of how statistics can be misleading, either by error or by deliberate spin.

    But what sort of mistakes and misrepresentations are we most likely to find in real research?

    Spin Bias

    Perhaps most common in popular media reporting of science, the Spin Bias hinges on the fact that most people perceive numbers in a very “fuzzy logic” sort of way. Do you?

    Try this:

    • A million seconds is 11.5 days
    • A billion seconds is not weeks, but 13.2 months!

    …just kidding, it’s actually nearly thirty-two years.

    Did the months figure seem reasonable to you, though? If so, this is the same kind of “human brains don’t do large numbers” problem that occurs when looking at statistics.

    Let’s have a look at reporting on statistically unlikely side effects for vaccines, as an example:

    • “966 people in the US died after receiving this vaccine!” (So many! So risky!)
    • “Fewer than 3 people per million died after receiving this vaccine!” (Hmm, I wonder if it is worth it?)
    • “Half of unvaccinated people with this disease die of it” (Oh)

    How to check for this: ask yourself “is what’s being described as very common really very common?”. To keep with the spiders theme, there are many (usually outright made-up) stats thrown around on social media about how near the nearest spider is at any given time. Apply this kind of thinking to medical conditions.. If something affects only 1% of the population (So few! What a tiny number!), how far would you have to go to find someone with that condition? The end of your street, perhaps?

    Selection/Sampling Bias

    Diabetes disproportionately affects black people, but diabetes research disproportionately focuses on white people with diabetes. There are many possible reasons for this, the most obvious being systemic/institutional racism. For example, advertisements for clinical trial volunteer opportunities might appear more frequently amongst a convenient, nearby, mostly-white student body. The selection bias, therefore, made the study much less reliable.

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    See This And Many More Educational Cartoons At Sketchplanations.com!

    How to check for this: Does the “method” section of the scientific article describe how they took pains to make sure their sample was representative of the relevant population, and how did they decide what the relevant population was?

    Publication Bias

    Scientific publications will tend to prioritise statistical significance. Which seems great, right? We want statistically significant studies… don’t we?

    We do, but: usually, in science, we consider something “statistically significant” when it hits the magical marker of p=0.05 (in other words, the probability of getting that result is 1/20, and the results are reliably coming back on the right side of that marker).

    However, this can result in the clinic stopping testing once p=0.05 is reached, because they want to have their paper published. (“Yay, we’ve reached out magical marker and now our paper will be published”)

    So, you can think of publication bias as the tendency for researchers to publish ‘positive’ results.

    If it weren’t for publication bias, we would have a lot more studies that say “we tested this, and here are our results, which didn’t help answer our question at all”—which would be bad for the publication, but good for science, because data is data.

    To put it in non-numerical terms: this is the same misrepresentation as the technically true phrase “when I misplace something, it’s always in the last place I look for it”—obviously it is, because that’s when you stop looking.

    There’s not a good way to check for this, but be sure to check out sample sizes and see that they’re reassuringly large.

    Reporting/Detection/Survivorship Bias

    There’s a famous example of the rise in “popularity” of left-handedness. Whilst Americans born in ~1910 had a bit under a 3.5% chance of being left handed, those born in ~1950 had a bit under a 12% change.

    Why did left-handedness become so much more prevalent all of a sudden, and then plateau at 12%?

    Simple, that’s when schools stopped forcing left-handed children to use their right hands instead.

    In a similar fashion, countries have generally found that homosexuality became a lot more common once decriminalized. Of course the real incidence almost certainly did not change—it just became more visible to research.

    So, these biases are caused when the method of data collection and/or measurement leads to a systematic error in results.

    How to check for this: you’ll need to think this through logically, on a case by case basis. Is there a reason that we might not be seeing or hearing from a certain demographic?

    And perhaps most common of all…

    Confounding Bias

    This is the bias that relates to the well-known idea “correlation ≠ causation”.

    Everyone has heard the funny examples, such as “ice cream sales cause shark attacks” (in reality, both are more likely to happen in similar places and times; when many people are at the beach, for instance).

    How can any research paper possibly screw this one up?

    Often they don’t and it’s a case of Spin Bias (see above), but examples that are not so obviously wrong “by common sense” often fly under the radar:

    “Horse-riding found to be the sport that most extends longevity”

    Should we all take up horse-riding to increase our lifespans? Probably not; the reality is that people who can afford horses can probably afford better than average healthcare, and lead easier, less stressful lives overall. The fact that people with horses typically have wealthier lifestyles than those without, is the confounding variable here.

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    In short, when you look at the scientific research papers cited in the articles you read (you do look at the studies, yes?), watch out for these biases that found their way into the research, and you’ll be able to draw your own conclusions, with well-informed confidence, about what the study actually tells us.

    Science shouldn’t be gatekept, and definitely shouldn’t be abused, so the more people who know about these things, the better!

    So…would one of your friends benefit from this knowledge? Forward it to them!

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