Negative thinking

Negative thinking linked to dementia in later life, but you can learn to be more positive – CNN

(CNN)Are you a pessimist by nature, a “glass half empty” sort of person? That’s not good for your brain.

A new study found that repetitive negative thinking in later life was linked to cognitive decline and greater deposits of two harmful proteins responsible for Alzheimer’s disease.
“We propose that repetitive negative thinking may be a new risk factor for dementia,” said lead author Dr. Natalie Marchant, a psychiatrist and senior research fellow in the department of mental health at University College London, in a statement.
Negative thinking behaviors such as rumination about the past and worry about the future were measured in over 350 people over the age of 55 over a two-year period. About a third of the participants also underwent a PET (positron emission tomography) brain scan to measure deposits of tau and beta amyloid, two proteins which cause Alzheimer’s disease, the most common type of dementia.
The scans showed that people who spent more time thinking negatively had more tau and beta amyloid buildup, worse memory and greater cognitive decline over a four-year period compared to people who were not pessimists.
The study also tested for levels of anxiety and depression and found greater cognitive decline in depressed and anxious people, which echos prior research.
But deposits of tau and amyloid did not increase in the already depressed and anxious people, leading researchers to suspect repeated negative thinking may be the main reason why depression and anxiety contribute to Alzheimer’s disease.
“Taken alongside other studies, which link depression and anxiety with dementia risk, we expect that chronic negative thinking patterns over a long period of time could increase the risk of dementia,” Marchant said.
“This is the first study showing a biological relationship between repetitive negative thinking and Alzheimer’s pathology, and gives physicians a more precise way to assess risk and offer more personally-tailored interventions,” said neurologist Dr. Richard Isaacson, founder of the Alzheimer’s Prevention Clinic at NYork-Presbyterian and Weill Cornell Medical Center, who was not involved in the study.
“Many people at risk are unaware about the specific negative impact of worry and rumination directly on the brain,” said Isaacson, who is also a trustee of the McKnight Brain Research Foundation, which funds research to better understand and alleviate age-related cognitive decline.
“This study is important and will change the way I care for my patients at risk.”

More study needed

It is “important to point out that this isn’t saying a short-term period of negative thinking will cause Alzheimer’s disease,” said Fiona Carragher, who is chief policy and research officer at the Alzheimer’s Society in London. “We need further investigation to understand this better.”
“Most of the people in the study were already identified as being at higher risk of Alzheimer’s disease, so we would need to see if these results are echoed within the general population,” she said, “and if repeated negative thinking increases the risk of Alzheimer’s disease itself.”
The researchers suggest that mental training practices such as meditation might help promoting positive thinking while reducing negative thoughts, and they plan future studies to test their hypothesis.
“Our thoughts can have a biological impact on our physical health, which might be positive or negative, said coauthor Dr. Gael Chételat of Inserm/ Université de Caen-Normandie.
“Looking after your mental health is important, and it should be a major public health priority, as it’s not only important for people’s health and well-being in the short term, but it could also impact your eventual risk of dementia,” Chételat said.

Looking on the bright side

Previous research supports their hypothesis. People who look at life from a positive perspective have a much better shot at avoiding death from any type of cardiovascular risk than pessimistic people, according to a 2019 study. In fact, the more positive the person, the greater the protection from heart attacks, stroke and any cause of death.
It’s not just your heart that’s protected by a positive outlook. Prior research has found a direct link between optimism and other positive health attributes, such as healthier diet and exercise behaviors, a stronger immune system and better lung function, among others.
That’s probably because optimists tend to have better health habits, said cardiologist Dr. Alan Rozanski, a professor of medicine at the Icahn School of Medicine at Mount Sinai, who studies optimism’s health impacts. They’re more likely to exercise, have better diets and are less likely to smoke.
“Optimists also tend to have better coping skills and are better problem-solvers,” Rozanski told CNN in a prior interview. “They are better at what we call proactive coping, or anticipating problems and then proactively taking steps to fix them.”

Train to be an optimist

You can tell where you stand on the glass half-full or empty concept by answering a series of statements called the “life orientation test.”
The test includes statements such as, “I’m a believer in the idea that ‘every cloud has a silver lining,'” and, “If something can go wrong for me, it will.” You rate the statements on a scale from highly agree to highly disagree, and the results can be added up to determine your level of optimism or pessimism.
Prior research has shown it’s possible to “train the brain” to be more optimistic, sort of like training a muscle. Using direct measures of brain function and structure, one study found it only took 30 minutes a day of meditation practice over the course of two weeks to produce a measurable change in the brain.
One of the most effective ways to increase optimism, according to a meta-analysis of existing studies, is called the “Best Possible Self” method, where you imagine or journal about yourself in a future in which you have achieved all your life goals and all of your problems have been resolved.
Another technique is to practice gratefulness. Just taking a few minutes each day to write down what makes you thankful can improve your outlook on life. And while you’re at it, list the positive experiences you had that day, which can also raise your optimism.
“And then finally, we know that cognitive behavioral therapies are very effective treatments for depression; pessimism is on the road toward depression,” Rozanski said.
“You can apply the same principles as we do for depression, such as reframing. You teach there is an alternative way to think or reframe negative thoughts, and you can make great progress with a pessimist that way.”

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False Negative

False Negative Tests for SARS-CoV-2 Infection — Challenges and Implications –


There is broad consensus that widespread SARS-CoV-2 testing is essential to safely reopening the United States. A big concern has been test availability, but test accuracy may prove a larger long-term problem.

While debate has focused on the accuracy of antibody tests, which identify prior infection, diagnostic testing, which identifies current infection, has received less attention. But inaccurate diagnostic tests undermine efforts at containment of the pandemic.

Diagnostic tests (typically involving a nasopharyngeal swab) can be inaccurate in two ways. A false positive result erroneously labels a person infected, with consequences including unnecessary quarantine and contact tracing. False negative results are more consequential, because infected persons — who might be asymptomatic — may not be isolated and can infect others.

Given the need to know how well diagnostic tests rule out infection, it’s important to review assessment of test accuracy by the Food and Drug Administration (FDA) and clinical researchers, as well as interpretation of test results in a pandemic.

The FDA has granted Emergency Use Authorizations (EUAs) to commercial test manufacturers and issued guidance on test validation.1 The agency requires measurement of analytic and clinical test performance. Analytic sensitivity indicates the likelihood that the test will be positive for material containing any virus strains and the minimum concentration the test can detect. Analytic specificity indicates the likelihood that the test will be negative for material containing pathogens other than the target virus.

Clinical evaluations, assessing performance of a test on patient specimens, vary among manufacturers. The FDA prefers the use of “natural clinical specimens” but has permitted the use of “contrived specimens” produced by adding viral RNA or inactivated virus to leftover clinical material. Ordinarily, test-performance studies entail having patients undergo an index test and a “reference standard” test determining their true state. Clinical sensitivity is the proportion of positive index tests in patients who in fact have the disease in question. Sensitivity, and its measurement, may vary with the clinical setting. For a sick person, the reference-standard test is likely to be a clinical diagnosis, ideally established by an independent adjudication panel whose members are unaware of the index-test results. For SARS-CoV-2, it is unclear whether the sensitivity of any FDA-authorized commercial test has been assessed in this way. Under the EUAs, the FDA does allow companies to demonstrate clinical test performance by establishing the new test’s agreement with an authorized reverse-transcriptase–polymerase-chain-reaction (RT-PCR) test in known positive material from symptomatic people or contrived specimens. Use of either known positive or contrived samples may lead to overestimates of test sensitivity, since swabs may miss infected material in practice.1

Designing a reference standard for measuring the sensitivity of SARS-CoV-2 tests in asymptomatic people is an unsolved problem that needs urgent attention to increase confidence in test results for contact-tracing or screening purposes. Simply following people for the subsequent development of symptoms may be inadequate, since they may remain asymptomatic yet be infectious. Assessment of clinical sensitivity in asymptomatic people had not been reported for any commercial test as of June 1, 2020.

Two studies from Wuhan Province, China, arouse concern about false negative RT-PCR tests in patients with apparent Covid-19 illness. In a preprint, Yang et al. described 213 patients hospitalized with Covid-19, of whom 37 were critically ill.2 They collected 205 throat swabs, 490 nasal swabs, and 142 sputum samples (median, 3 per patient) and used an RT-PCR test approved by the Chinese regulator. In days 1 through 7 after onset of illness, 11% of sputum, 27% of nasal, and 40% of throat samples were deemed falsely negative. Zhao et al. studied 173 hospitalized patients with acute respiratory symptoms and a chest CT “typical” of Covid-19, or SARS-CoV-2 detected in at least one respiratory specimen. Antibody seroconversion was observed in 93%.3 RT-PCR testing of respiratory samples taken on days 1 through 7 of hospitalization were SARS-CoV-2–positive in at least one sample from 67% of patients. Neither study reported using an independent panel, unaware of index-test results, to establish a final diagnosis of Covid-19 illness, which may have biased the researchers toward overestimating sensitivity.

In a preprint systematic review of five studies (not including the Yang and Zhao studies), involving 957 patients (“under suspicion of Covid-19” or with “confirmed cases”), false negatives ranged from 2 to 29%.4 However, the certainty of the evidence was considered very low because of the heterogeneity of sensitivity estimates among the studies, lack of blinding to index-test results in establishing diagnoses, and failure to report key RT-PCR characteristics.4 Taken as a whole, the evidence, while limited, raises concern about frequent false negative RT-PCR results.

If SARS-CoV-2 diagnostic tests were perfect, a positive test would mean that someone carries the virus and a negative test that they do not. With imperfect tests, a negative result means only that a person is less likely to be infected. To calculate how likely, one can use Bayes’ theorem, which incorporates information about both the person and the accuracy of the test (recently reviewed5). For a negative test, there are two key inputs: pretest probability — an estimate, before testing, of the person’s chance of being infected — and test sensitivity. Pretest probability might depend on local Covid-19 prevalence, SARS-CoV-2 exposure history, and symptoms. Ideally, clinical sensitivity and specificity of each test would be measured in various clinically relevant real-life situations (e.g., varied specimen sources, timing, and illness severity).

Assume that an RT-PCR test was perfectly specific (always negative in people not infected with SARS-CoV-2) and that the pretest probability for someone who, say, was feeling sick after close contact with someone with Covid-19 was 20%. If the test sensitivity were 95% (95% of infected people test positive), the post-test probability of infection with a negative test would be 1%, which might be low enough to consider someone uninfected and may provide them assurance in visiting high-risk relatives. The post-test probability would remain below 5% even if the pretest probability were as high as 50%, a more reasonable estimate for someone with recent exposure and early symptoms in a “hot spot” area.

But sensitivity for many available tests appears to be substantially lower: the studies cited above suggest that 70% is probably a reasonable estimate. At this sensitivity level, with a pretest probability of 50%, the post-test probability with a negative test would be 23% — far too high to safely assume someone is uninfected.

Chance of SARS-CoV-2 Infection, Given a Negative Test Result, According to Pretest Probability.

The blue line represents a test with sensitivity of 70% and specificity of 95%. The green line represents a test with sensitivity of 90% and specificity of 95%. The shading is the threshold for considering a person not to be infected (asserted to be 5%). Arrow A indicates that with the lower-sensitivity test, this threshold cannot be reached if the pretest probability exceeds about 15%. Arrow B indicates that for the higher-sensitivity test, the threshold can be reached up to a pretest probability of about 33%. An interactive version of this graph is available at

The graph shows how the post-test probability of infection varies with the pretest probability for tests with low (70%) and high (95%) sensitivity. The horizontal line indicates a probability threshold below which it would be reasonable to act as if the person were uninfected (e.g., allowing the person to visit an elderly grandmother). Where this threshold should be set — here, 5% — is a value judgment and will vary with context (e.g., lower for people visiting a high-risk relative). The threshold highlights why very sensitive diagnostic tests are needed. With a negative result on the low-sensitivity test, the threshold is exceeded when the pretest probability exceeds 15%, but with a high-sensitivity test, one can have a pretest probability of up to 33% and still, assuming the 5% threshold, be considered safe to be in contact with others.

The graph also highlights why efforts to reduce pretest probability (e.g., by social distancing, possibly wearing masks) matter. If the pretest probability gets too high (above 50%, for example), testing loses its value because negative results cannot lower the probability of infection enough to reach the threshold.

We draw several conclusions. First, diagnostic testing will help in safely opening the country, but only if the tests are highly sensitive and validated under realistic conditions against a clinically meaningful reference standard. Second, the FDA should ensure that manufacturers provide details of tests’ clinical sensitivity and specificity at the time of market authorization; tests without such information will have less relevance to patient care.

Third, measuring test sensitivity in asymptomatic people is an urgent priority. It will also be important to develop methods (e.g., prediction rules) for estimating the pretest probability of infection (for asymptomatic and symptomatic people) to allow calculation of post-test probabilities after positive or negative results. Fourth, negative results even on a highly sensitive test cannot rule out infection if the pretest probability is high, so clinicians should not trust unexpected negative results (i.e., assume a negative result is a “false negative” in a person with typical symptoms and known exposure). It’s possible that performing several simultaneous or repeated tests could overcome an individual test’s limited sensitivity; however, such strategies need validation.

Finally, thresholds for ruling out infection need to be developed for a variety of clinical situations. Since defining these thresholds is a value judgement, public input will be crucial.

Funding and Disclosures

Disclosure forms provided by the authors are available at

This article was published on June 5, 2020, at

Author Affiliations

From the Center for Medicine in the Media, Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH (S.W.); the Lisa Schwartz Program for Truth in Medicine, Norwich, VT (S.W.); the Program on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston (S.W., A.K.); and Yale University, New Haven, CT (N.P.).

Supplementary Material

References (5)

  1. 1. U.S. Food and Drug Administration. Emergency Use Authorization (EUA) information, and list of all current EUAs (

  2. 2. Yang Y, Yang M, Shen C, et al. Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections. February 17, 2020 ( preprint.

  3. 3. Zhao J, Yuan Q, Wang H, et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clin Infect Dis 2020 March 28 (Epub ahead of print).

  4. 4. Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, et al. False-negative results of initial RT-PCR assays for COVID-19: a systematic review. April 21, 2020 ( preprint.

  5. 5. Watson J, Whiting PF, Brush JE. Interpreting a covid-19 test result. BMJ 2020;369:m1808m1808.

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Negative Territory

CPI and Core CPI in Rare Negative Territory – TheStreet


Month-over-Month measures of the CPI are in negative territory.

The BLS reports consumer prices are in negative territory for the month with both the CPI and core CPI in negative territory. 

Year-over-year the CPI is up 1.4% and core CPI, which excluded food and energy is up 0.3% 

Year-Over-Year CPI 

Year-Over-Year CPI and Core CPI 2src2src-src5

CPI Details

  • The Consumer Price Index for All Urban Consumers (CPI-U) declined 0.8 percent in April on a seasonally adjusted basis, the largest monthly decline since December 2008.
  • A 20.6-percent decline in the gasoline index was the largest contributor to the monthly decrease in the seasonally adjusted all items index, but the indexes for apparel, motor vehicle insurance, airline fares, and lodging away from home all fell sharply as well. 
  • Food indexes rose in April, with the index for food at home posting its largest monthly increase since February 1974.
  • The index for all items less food and energy fell 0.4 percent in April, the largest monthly decline in the history of the series, which dates to 1957. 
  • The indexes for used cars and trucks and recreation also declined. 
  • The indexes for rent, owners’ equivalent rent, medical care, and household furnishings and operations all increased in April. 
  • The all items index increased 0.3 percent for the 12 months ending April, the smallest 12-month increase since October 2015. The index for all items less food and energy increased 1.4 percent over the last 12 months, its smallest increase since April 2011. 
  • The food index rose 3.5 percent over the last 12 months, its largest 12-month increase since February 2012.

Mish Personal Results

I had a photography trip that I cancelled then rescheduled for the exact same time.

  • Airline ticket dropped from $800 to $600
  • Car rental dropped from $800 to $300
  • Hotel dropped from $130 a night to $80 a night

Now is a great time to travel if you are in good health and can practice Social Distancing. The airlines require use of masks.


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