risk factors, psychology, and communication · – fostering transparent risk communication •...

Hansjörg Neth Risk Management in Pharmacotherapy ISoP 2015, Prague | Oct. 30 2015

Risk factors, psychology, and communication

Abstract (ISoP 2015) ISOP-0055: AS01 - Risk Management in Pharmacotherapy

Risk factors, psychology and communication Hansjörg Neth Social Psychology and Decision Sciences Department of Psychology, University of Konstanz, Germany

•  Statistical illiteracy in health––the inability to understand health statistics––is widespread among the general public and among medical experts. For many people, it is generally hard to accept uncertainty, and even if they do, to understand basic numerical information. The problem is aggravated when it comes to evaluating the benefits and harms of treatment options or to understanding test outcomes, which is a severe obstacle to an informed risk management strategy.

•  Statistical illiteracy reflects not just a lack of education but often results from non-transparent framing of information that may be unintentional, but can also be a deliberate effort to manipulate people. Non-transparent framing of information seems to be the rule rather than the exception in health care: Patients have difficulties finding reliable and comprehensible information, be it online, in brochures on screening procedures, medical pamphlets, or media reports.

•  Yet all these obstacles do not imply that nothing can be done. The most important mean of improvement consists in teaching the public statistical thinking, combined with training health care workers and journalists in transparent framing. Knowing what questions to ask, which information is missing, and how to translate non-transparent statistics into transparent ones can enable informed risk management strategies. More generally, a better understanding of risks will allow citizens to develop a more relaxed attitude towards health and render the hopes and anxieties of an informed society less manipulable.

Ideal vs. real health care

Desiderata: Status quo: evidence-based medicine eminence-based medicine

informed and shared decision-making

low numeracy, lack of statistical and graphical literacy

transparency in information and communication

biased reporting with persuasive intent

informed risk management: coping with risks

misunderstanding risks, aiming to avoid uncertainty

(Gigerenzer & Gray, 2011; Bodemer & Gaissmaier, 2012)

Goals of risk communication

Persuasion: Education/Information:

(Bodemer & Gaissmaier, 2012, Fig. 24.1, p. 626 ) . Fig. 24.1

Two different ways to inform women about mammography screening. The flyer on the left side by the American Cancer Society (Retrieved from www.

nysut.org/files/makingstrikes_070921_poster.pdf in April 2011) encourages women to participate in regular mammography screening without

providing information about benefits and harms of the screening program. It states that ‘‘mammograms save lives – there’s no doubt about it (. . .) Hope

for a cancer-free future starts with you.’’ The facts box on right side (Retrieved from www. http://www.harding-center.com/fact-boxes/

mammography-screening in April 2011) summarizes the most important results based on the current scientific evidence and informs rather than

persuades. It contrasts 2,000women aged 40 and olderwhoparticipate inmammography screening over 10 yearswith 2,000 of the same agewhodonot.

Besides the benefits of the screening program, the facts box also includes information about potential harms like overtreatment

626

4 sins of risk communication…

Relative risks Survival rates

Absolute risks Mortality rates

Single event probabilities Conditional probabilities

Reference class Natural frequencies

Official announcement: “Contraceptive pills double the risk of venous thromboembolism!”

(UK Committee on the Safety of Medicines, 1995)

“Contraceptive pills increase the risk of venous thromboembolism by 100%.”

Transparent format: “Contraceptive pills increase the risk of venous thromboembolism from 1 to 2 women out of every 7,000 women.”

160,000

170,000

180,000

190,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Abt

reib

unge

n in

Eng

land

und

Wal

es

Jahr

"Antibaby-Pille erhöht Risiko einer Thromboembolie

um 100%"

Gigerenzer, Gaissmaier, Kurz-Milcke, Schwartz & Woloshin (2007). Psychological Science in the Public Interest

Num

ber o

f abo

rtio

ns in

Eng

land

and

Wal

es

“… double the risk”

Year

4 sins of risk communication




Reference classes Natural frequencies

Misleading statistics & anecdotes Diagnostic certainty

Fact boxes & icon arrays Managing uncertainty

“I had prostate cancer, five, six years ago. My chances of surviving prostate cancer—and thank God I was cured of it— in the United States, 82 percent. My chances of surviving prostate cancer in England, only 44 percent under socialized medicine.”

Rudy Giuliani New Hampshire Radio advertisement, October 29, 2007

Survival vs. mortality rates

Gigerenzer et al. (2007); Bodemer & Gaissmaier (2012)

Five-year survival rate ‘‘The 5-year survival rate for people diagnosed with prostate cancer is 98% in the USA vs. 71% in Britain.’’ Annual mortality rate ‘‘There are 26 prostate cancer deaths per 100,000 American men vs. 27 per 100,000 men in Britain.’’

N diagnosed with X & alive 5 years later 5-year survival rate =

N diagnosed with X in study population

N who die from X over 1 year annual mortality rate =

N in study population

Misleading survival rates


WITHOUT SCREENING

WITH SCREENING

Cancer starts

Cancer starts

Cancer diagnosed because ofsymptoms at age 67

Dead at age 70

Dead at age 70

Cancer diagnosed becauseof screening at age 60

5-year survial = 0 %


1,000 patientswith

progressivetumors

1,000 patientswith non-

progressivetumors

500 dead

500 dead

1,500 alive

500 alive

1,000 patientswith

progressivetumors

5 years later

5 years later

5 - years survival = 500 = 50 %1000

5 - years survival = 1500 = 75 %2000

a

b

. Fig. 24.2

Shortcomings of 5-year survival rates: The figure illustrates the two potential biases of 5-year

survival rates (modified from Gigerenzer et al. 2007). (a) Lead-time bias: The arrows illustrate the

course from the beginning of a disease to death. In the group without screening, cancer is

diagnosed at age 67, in the screening group at age 60. However, in both groups, patients die at

the same age (age 70). Whereas in the non-screening group the 5-year survival rate is 0%, it is

100% in the screening group. (b) Overdiagnosis bias: (1) A group of 1,000 patients with

progressive tumors is monitored over 5 years. After 5 years, 500 are still alive; the survival rate is

50%. (2) The same group of 1,000 patients with progressive tumors is monitored over 5 years.

Additionally, the screening detects patients with nonprogressive, indolent tumors. Again, after

5 years 500 patients died (500 out of 1,000 with progressive cancer). However, in the calculation

of the 5-year survival rate, those 1,000 with nonprogressive tumors are also included hence the

5-year survival rate is 75%

643 1) lead-time bias


WITHOUT SCREENING

WITH SCREENING

Cancer starts

Cancer starts

Cancer diagnosed because ofsymptoms at age 67

Dead at age 70

Dead at age 70

Cancer diagnosed becauseof screening at age 60



1,000 patientswith

progressivetumors

1,000 patientswith non-

progressivetumors

500 dead

500 dead

1,500 alive

500 alive

1,000 patientswith

progressivetumors

5 years later

5 years later

5 - years survival = 500 = 50 %1000

5 - years survival = 1500 = 75 %2000

a

b

. Fig. 24.2

Shortcomings of 5-year survival rates: The figure illustrates the two potential biases of 5-year

survival rates (modified from Gigerenzer et al. 2007). (a) Lead-time bias: The arrows illustrate the

course from the beginning of a disease to death. In the group without screening, cancer is

diagnosed at age 67, in the screening group at age 60. However, in both groups, patients die at

the same age (age 70). Whereas in the non-screening group the 5-year survival rate is 0%, it is

100% in the screening group. (b) Overdiagnosis bias: (1) A group of 1,000 patients with

progressive tumors is monitored over 5 years. After 5 years, 500 are still alive; the survival rate is

50%. (2) The same group of 1,000 patients with progressive tumors is monitored over 5 years.

Additionally, the screening detects patients with nonprogressive, indolent tumors. Again, after

5 years 500 patients died (500 out of 1,000 with progressive cancer). However, in the calculation

of the 5-year survival rate, those 1,000 with nonprogressive tumors are also included hence the

5-year survival rate is 75%

643

2) overdiagnosis bias

Misleading survival rates

Single-event probabilities require reference classes


Example: Side effects of taking Prozac Single-event probability: ‘‘The probability that you will experience sexual problems is 30–50% (or: 3 to 5 chances out of 10).’’ Frequency statement with reference class: ‘‘Out of every 10 of my patients, 3–5 experience a sexual problem.’’

~10%

10 cancer

990 no cancer

9 positive

89 positive

1 negative

901 negative

1000 women

Natural frequencies:

p( cancer | positive )

= 9 9 + 89

Conditional probabilities:

.01 x .90 .01 x .90 + .99 x .09

p( cancer | positive )

=

Prevalence: p( breast cancer ) = 1%

Sensitivity:

p( positive | cancer ) = 90% False alarm rate:

p( positive | no cancer ) = 9%

Probability ( breast cancer | positive mammogram )?

… vs. transparent representations







General remedies?

mind environment

(Gigerenzer et al., 1999; Gigerenzer & Gaissmaier, 2011; Todd et al., 2014; Neth & Gigerenzer, 2015)

match?

Designing for ecological rationality:

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Remedies: Fact boxes

https://www.harding-center.mpg.de/en/health-information/fact-boxes/mammography

Remedies: Icon arrays !"#$%&'($)*#"'+$",-'.#&#*/0)'!"#$%$$&'(%)*"#+,(--./.'##01$!-(+#2&(#3&$-.#%'-4#56#"-%(+#&(#&74-(#3*&#)%(8,/)%9-4#/.#+,(--./.'#2&(#:6#"-%(+#&(#$&(-#

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B B B B B B B B B B B B

https://www.harding-center.mpg.de/en/health-information/fact-boxes/mammography

Conclusion & challenges

•  Risk can be managed:

–  teaching statistical literacy –  fostering transparent risk communication

•  Uncertainty is inevitable: –  communicating uncertainty (e.g., of diagnostic tests) –  avoiding defensive decision making

You step outside, you risk your life. You take a drink of water, you risk your life. And nowadays you breathe, and you risk your life. Every moment you don‘t have a choice. The only thing you can choose is what you‘re risking it for.

Hershel, The Walking Dead

Thanks for your attention! Comments, panel discussion, feedback... Dr. Hansjörg Neth Social Psychology and Decision Sciences Tel.: +49 (0) 75 31/88 - 2972 Fax: +49 (0) 75 31/88 - 2899 [email protected] http://www.spds.uni-konstanz.de

Key references Risk perception and risk communication

•  Bodemer, N., & Gaissmaier, W. (2015). Risk perception. In H. Cho, T. Reimer, & K. A. McComas (Eds.). The Sage Handbook of Risk Communication (pp. 10–23). Thousand Oaks, CA: Sage.

•  Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8, 53–96.

Heuristic decision making

•  Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.

•  Neth, H., & Gigerenzer, G. (2015). Heuristics: Tools for an uncertain world. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences: An interdisciplinary, searchable, and linkable resource (pp. 1–18). New York, NY: Wiley Online Library.

Available at http://www.spds.uni-konstanz.de/publications/

Examples

Bodemer & Gaissmaier (2012), p. 655

lessons learned in health risk communication can be adapted to other domains as well.

Transparency and statistical literacy help people evaluate financial, environmental, and tech-

nological risks, and enable society to competently meet future challenges.

References

Allen M, Preiss R (1997) Comparing the persuasiveness

of narrative and statistical evidence using meta-anal-

ysis. Commun Res Rep 14:125–131

Ancker JS, Kaufman D (2007) Rethinking health numer-

acy: a multidisciplinary literature review. J Am Med

Inform Assoc 14:713–721

Ancker JS, Senathirajah Y, Kukafka R, Starren JB

(2006) Design features of graphs in health risk com-

munication: a systematic review. J Am Med Inform

Assoc 3:608–618

Baesler JE (1997) Persuasive effects of story and statistical

evidence. Argument Advocacy 33:170–175

. Table 24.3

Nontransparent versus transparent communication of risks: Four examples of how risks can be

communicated to mislead and misinform the public and their transparent counterparts

How to communicate risks nontransparently How to communicate risks transparently

Relative risks‘‘The new generation of the contraceptive pillincreases the risk of thrombosis by 100%.’’

Absolute risks‘‘The new generation of the contraceptive pillincreases the risk of thrombosis from 1 in 7,000to 2 in 7,000.’’

Conditional probabilities– The probability of breast cancer is 1% fora woman at age 40 who participates in routinescreening (this is the prevalence or base rate)– If a woman has breast cancer, the probability is90% that she will get a positive mammography(this is the sensitivity or hit rate)– If a woman does not have breast cancer, theprobability is 9% that she will also get a positivemammography (this is the false-positive rate)

What is the probability that a woman at age 40who had a positive mammogram actually hasbreast cancer?

P H Djð Þ ¼ 0:9$0:010:9$0:01þ0:09$0:99 ¼ 0:092

Natural frequencies– Ten out of 1,000 women at age 40 whoparticipate in mammography screening havebreast cancer (prevalence or base rate)– Of these 10 women, 9 have a positivemammogram (sensitivity or hit rate)– Out of the 990 women who do not have breastcancer, about 89 will have a positivemammogram nonetheless (false-positive rate)

Now imagine a representative sample of 1,000women age 40 who participate in breast cancerscreening. How many of these women witha positive test result actually have breast cancer?

P H Djð Þ ¼ 99þ89 ¼ 9:2

Five-year survival rate‘‘The 5-year survival rate for people diagnosedwith prostate cancer is 98% in the USA vs. 71% inBritain.’’

Annual mortality rate‘‘There are 26 prostate cancer deaths per100,000 American men versus 27 per 100,000men in Britain.’’

Single-event probability‘‘If you take Prozac, the probability that you willexperience sexual problems is 30–50% (or: 30 to50 chances out of 100).’’

Frequency statement‘‘Out of every 10 of my patients who take Prozac,3–5 experience a sexual problem.’’

655

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