Tips for High-Fidelity Science Reporting with Drs. David Allison and Andrew Brown

8/12/2019 Tips for High-Fidelity Science Reporting with Drs. David Allison and Andrew Brown

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Disclosure

Dr. Allison has received funding from the National Institutes of

Health for the Nutrition Obesity Research Center (NORC) which he

heads. He holds several other NIH grants, including one of theCommon Fund's NIH Director's Transformative Research Awards

entitled "Energetics, Disparities, & Lifespan: A unified hypothesis". He

has also received funding from the National Science Foundation,

Centers for Disease Control and Prevention, the State of Alabama, theAmerican Diabetes Association, and numerous other government, not-

for-profit, and for-profit organizations. Dr. Allison has consulted with

numerous entities including the Federal Trade Commission, The

Frontiers Foundation, the Food and Drug Administration, the United

States Postal Inspectors Service, the United States Federal Bureau ofPrisons, and many other government, not-for-profit, and for-profit

organizations. He frequently serves as a consultant, especially to the

pharmaceutical industry, food and beverage industries, universities,

and litigators.


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Dr. Brown is supported by intramural funding from UAB and

government funding from the National Institutes of Health.

The University of Alabama at Birmingham has receivedunrestricted gifts and grants from numerous not-for-profit and

for-profit organizations, including Coca-Cola, the sponsor of

this webinar, and many other food, pharmaceutical, and other

companies, some of which have supported Drs. Allisons andBrowns work.

Drs. Allison and Brown do not advocate for or against any

particular policies. Drs. Allison and Brown only advocate for

science. They speak for themselves and their views do not

necessarily represent those of UAB or any other organization.

Drs. Allison and Brown had complete editorial control over the

content of this presentation and developed it themselves.


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David B. Allison, Ph.D.

Tips for High-Fidelity

Science Reporting

Andrew W Brown, Ph.D.

Office

of

Energetics


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Considerations

We will primarily focus on biomedical humanstudies (our expertise).

Our comments reflect what we think is important

from two scientists points of view.

Our comments may reflect what we think science

should be, not necessarily how it is.

We are not journalists, and we understand thatjournalists have other pressures and purposes in

writing than just translating studies in a vacuum.


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Outline

1. Science communication is important2. Fundamental characteristics of science

3. Details from a study that help to evaluate

the science4. Putting research into perspective

5. Positive example

6. Final Thoughts7. Resources


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Interest in Science

SCIENCE AND ENGINEERING INDICATORS 2014, Chapter 7

Sources Used by Public for Information


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What do people understand?

Reports of correlations are increasingly

accompanied by reminders that correlation

does not equal causation.

CorrelationCa

veats

(%

ofmentionsofc

orrelation) SCIENCE AND ENGINEERING INDICATORS 2014, Chapter 7


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Headline vs Study

For references, see: ObesityandEnergetics.org.

Headline Skipping breakfast to lose weight makes you fatter - and far more

likely to raid the vending machine.

Study Presentation at proceedings; MRI results and observations of how much

subjects ate at lunch after skipping breakfast. No body weight, no vending

machines.

Headline US Farm Subsidy Policies Contribute To Worsening Obesity Trends,

Study Finds.

Paper There is no studypaper is an authors commentary/review on farm

subsidies.

Headline Drinking 5 cups of coffee everyday may lead to obesity: study.

Headline Wrong amount of coffee could kill you.

Study: A study of mice, involving a substance found in coffee, but no coffee,showed no significant weight gain, and reported no deaths.

There is something fascinating about science. One gets such wholesale returns of

conjecture out of such a trifling investment of fact. Mark Twain


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Outline




5. Positive example



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FalsifiabilityCan the assertion be proven false?

UncertaintyHow confident are we in our conclusions?

ReproducibilityCan the phenomenon be replicated?

SkepticismAre there alternative explanations?

Fundamentals of Science


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Obesity Prevalence:

Where do the data

come from?

http://www.cdc.gov/obesity/data/adult.html Le, A., et al. The geographic distribution of obesity in the US and thepotential regional differences in misreporting of obesity. Obesity 2013.


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Outline




5. Positive example



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Who or what was studied?

Rodents:

Species

Sex

Strain (may be important

to scientists, but probably

not readers)

Humans:

Where from? Sex?

Age?

Country?

Ancestry?

:


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How many were studied?

Adapted from Brown et al. 2013AJCN

IncreasingSampleSi

ze

Importance of Sample Size


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Exposure of Interest

vs

Exposure Investigated

How much exposure did the sample experience?

Top 10% vs bottom 10% of meat consumers

Animal dosed with 10x the expected amount a human would receive

Exposure is the purported causal factor explaining an effect.

Exposure of Interest Measured Exposure

Occupational heavy metal exposure Bioaccumulation in toe nails and hair

Dietary Intake Recollection of food consumption

Physical activity energy expenditure Distance and speed traveled

Stress Cortisol


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Can we adequately extrapolate from the measured outcome to

the effect of interest?

Example: Measure single meal food intake to estimate changes

in obesity prevalence

Measured Outcome Effect of Interest

Blood Cholesterol Cardiovascular Disease

Cancer Antigen 125 Ovarian CancerSelf-reported ancestry Migration patterns of populations

Cortisol Stress

Outcome of Interest

vs

Outcome Investigated


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What is the study design?

Schwitzer 2010. Covering Medical Research: A Guide for Reporting on Studies

Not Evidence

Not Human

No Comparison

Observational

Gold Standard

ComprehensiveSummary of Evidence


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How long is the study?

www.daviddarling.info/images/life_span_of_animals.jpg

How long is the study compared

to the outcome of interest?

HydraImmortal

Shah et al 2006JCEM


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Outline




5. Positive example



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How do the results fit into what we know?

If a scientist says he or she is the first to discover or demonstrate

something you have heard of, it likely means:

1) Lots of conditions and caveats.

2) The scientist is wrong (less common).

In the absence of replication efforts, one is left with unconfirmed

(genuine) discoveries and unchallenged fallacies. John IoannidisWhy Science Is Not Necessarily Self-CorrectingPerspectives on Psychological Science2012

Winners curse vs Gratuitous Replication


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Levine et al. Cell Metabolism

2014 Mar;19(3):407-17

Headline:Eating

lots of meat and

cheese in middleage is as deadly as

SMOKINGSource:

www.dailymail.co.uk/health/article-

2573088/Eating-lots-meat-cheese-middle-

age-deadly-SMOKING.html

Exaggeration

The Human Element:


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The Human Element:For interest, not evidence

ScienceOnly three things matter:

1) The data

2) The methods used to generate

the data

3) The logic connecting the data

to conclusions

Human AudiencesInterest matters:

We love stories

We remember stories

We particularly like stories

about other humans

Ali Almossawi https://bookofbadarguments.com/

All else is tangentialAd hominemarguments

are not evidence

The human element is useful for embellishment and interest,

not for scientific conclusions.
http://www.amazon.com/gp/product/1400064287?ie=UTF8&tag=matost-20&linkCode=as2&camp=1789&creative=9325&creativeASIN=1400064287


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Parroting Professors or Press-releases

Spin perpetuates

throughout the reporting

Spin: specific reporting

strategies, intentional or

unintentional, emphasizing

the beneficial effect of the

experimental treatment

Scientists and press

releases dont

always get it right


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Outline




5. Positive example



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Where did the story come from?

What kind of research was this?

What did the research involve?

What were the basic results?

How did the researchers interpret the results?

Conclusion

Introduction

Subheadings include:


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5. Positive example


Outline


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Utility Analysis: Scientific Knowledge is a Necessary butInsufficient Basis for Logical Decision Making

Table A Table B

Probability of Black 0.6 0.4

Probability of Red 0.4 0.6

Two Roulette Tables

Black: You Win

Red: I win


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Table A Table B



Value of Black $100 $1,000

Value of Red -$1,000 -$100

Utility -$340 $340

Two Roulette Tables

Black: You Win

Red: I win


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Table A Table B



Value of Black $100 $1,000

Value of Red -$1,000 -$100

Utility -$340 $340

Two Roulette Tables

Two Public Health Policies (clinical treatments, etc.)

Action A Action BProbability of Desired Outcome 0.3 0.7

Probability of Undesired Outcome 0.7 0.3

Value of Desired Outcome

Value of Undesired Outcome

Utility ??? ???

Science

Values

Science can only

ascertain what is, but not

what should be, and

outside of its domain

value judgments of all

kinds remain necessary.

Albert Einstein


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Outline




5. Good Examples

6. Positive example7. Final Thoughts

8. Resources

Resources


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Resources

www.senseaboutscience.org

http://www.nhs.uk/News/Pages/NewsIndex.aspx

https://bookofbadarguments.com/

www.healthnewsreview.org

Latest obesity and energetics research:

www.obesityandenergetics.comhttp://nationalpress.org/
http://www.senseaboutscience.org/http://www.nhs.uk/News/Pages/NewsIndex.aspxhttp://www.healthnewsreview.org/http://www.obesityandenergetics.com/http://www.obesityandenergetics.com/http://www.healthnewsreview.org/http://www.nhs.uk/News/Pages/NewsIndex.aspxhttp://www.senseaboutscience.org/


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Oak Mountain State Park, ALCare of Michelle M Bohan Brown, PhD

let us take this

path through the

woods

~ Jean-Jacques

Rousseau

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Tips for High-Fidelity Science Reporting with Drs. David Allison and Andrew Brown