Dataset Information

Boosting the Understanding and Approval of Anti-Corona Measures-Reducing Exponential Growth Bias and its Effects through Educational Nudges.

ABSTRACT: One major problem of compliance with anti-coronavirus measures originates from the so-called exponential growth bias, i.e. the cognitive distortion of systematically underestimating exponential growth and its consequences. We replicate an Amazon MTurk experiment regarding the spread of SARS-CoV-2 that was conducted in the general US population during the first wave of the pandemic in March 2020 dealing with this bias. Using a least-likely-design-approach, we find a similarly strong bias in our sample of German students in November 2020. Nevertheless, this bias can be reduced by one simple educational intervention. Furthermore, participants who received these educational nudges showed a considerably higher approval rating for contact restrictions. This effect is robust to different analytical techniques and the inclusion of controls. Complementing political statements about the exponential spread of the virus - which often only mention the name of the phenomenon - with simple educational nudges could help the public better understand the need for encroachments on personal liberties.

SUBMITTER: Jackle S

PROVIDER: S-EPMC8662229 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Boosting the Understanding and Approval of Anti-Corona Measures-Reducing Exponential Growth Bias and its Effects through Educational Nudges.

Jäckle Sebastian S Ettensperger Felix F

Schweizerische Zeitschrift fur Politikwissenschaft = Revue suisse de science politique = Swiss political science review 20210929 4

One major problem of compliance with anti-coronavirus measures originates from the so-called exponential growth bias, i.e. the cognitive distortion of systematically underestimating exponential growth and its consequences. We replicate an Amazon MTurk experiment regarding the spread of SARS-CoV-2 that was conducted in the general US population during the first wave of the pandemic in March 2020 dealing with this bias. Using a least-likely-design-approach, we find a similarly strong bias in our s ...[more]

PMID: 35923648

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Project description:ObjectiveWe define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB.RationaleThe scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols.MethodTo collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others' behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold.ResultsRespondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB.ConclusionBehavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors.

Project description:ImportanceInvesting in health care staffs' education can change the scope of action and improve care. The effectiveness of staff education remains inconclusive.ObjectiveTo examine whether an evidence-based educational intervention for nurses decreases the use of seclusion rooms in psychiatric hospitals compared with usual practice.Design, setting, and participantsIn this pragmatic, 2-arm parallel, stratified cluster randomized clinical trial, 28 wards in 15 psychiatric hospitals in Finland were screened for eligibility and randomly allocated (1:1). Nurses joined on either intervention (n = 13) or usual practice (n = 15) wards. The intervention was performed from May 1, 2016, to October 31, 2017. The follow-up data for January 1 to December 31, 2017, were collected from hospital registers in 2018. Data analysis was performed October 27, 2021.InterventionsEvidence-based education delivered during 18 months, including 8 months of active education, followed by a 10-month maintenance period.Main outcomes and measuresThe primary outcome was the occurrence of patient seclusion (events per total number of patients).ResultsOf 28 psychiatric hospital wards screened (437 beds and 648 nurses), 27 wards completed the study. A total of 8349 patients were receiving care in the study wards, with 53% male patients and a mean (SD) age of 40.6 (5.7) years. The overall number of seclusions was 1209 (14.5%) in 2015 and 1349 (16.5%) in 2017. In the intervention group, the occurrence rate of seclusion at the ward level decreased by 5.3% from 629 seclusions among 4163 patients (15.1%) to 585 seclusions among 4089 patients (14.3%) compared with a 34.7% increase from 580 seclusions among 4186 patients (13.9%) to 764 seclusions among 4092 patients (18.7%) in the usual practice group. The adjusted rate ratio was 0.86 (95% CI, 0.40-1.82) in 2015 and 0.66 (95% CI, 0.31-1.41) in 2017 (P = .003). However, the number of forced injections increased in the intervention group from 317 events among 4163 patients (7.6%) in 2015 to 486 events among 4089 patients (11.9%) in 2017 compared with an increase in the usual practice group from 414 events among 4186 patients (9.9%) in 2015 to 481 events among 4092 patients (11.8%) in 2017. Seven adverse events were reported.Conclusions and relevanceIn this randomized clinical trial, the educational intervention had a limited effect on the change of occurrence rate of patient seclusion, whereas the use of forced injections increased. More studies are needed to better understand the reasons for these findings.Trial registrationClinicalTrials.gov Identifier: NCT02724748.

Project description:BackgroundHumans struggle to grasp the extent of exponential growth, which is essential to comprehend the spread of an infectious disease. Exponential growth bias is the tendency to linearize exponential functions when assessing them intuitively. Effective public health communication about the nonlinear nature of infectious diseases has strong implications for the public's compliance with strict restrictions. However, there is a lack of synthesized knowledge on the communication of the exponential growth of infectious diseases and on the outcomes of exponential growth bias.ObjectiveThis systematic review identifies, evaluates, and synthesizes the findings of empirical studies on exponential growth bias of infectious diseases.MethodsA systematic review will be conducted using the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2015 statement. Eligibility criteria include empirical studies of exponential growth bias of infectious diseases regardless of methodology. We include studies both with and without interventions/strategies. For information sources, we include the following five bibliographic databases: MEDLINE, Embase, Cochrane Library, PsychINFO, and Web of Science Core Collection. The risk of bias will be assessed using RoB 2 (Risk of Bias 2) and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology). Data synthesis will be achieved through a narrative synthesis.ResultsBy February 2022, we included 11 experimental studies and 1 cross-sectional survey study. Preliminary themes identified are the presence of exponential growth bias, the effect of exponential growth bias, and communication strategies to mitigate exponential growth bias. Data extraction, narrative synthesis, and the risk of bias assessment are to be completed by February 2023.ConclusionsWe anticipate that this systematic review will draw some lines related to how people comprehend and misperceive exponential growth and its consequences for infectious disease mitigation and communication. Furthermore, the study will conclude with the limitations of the research and suggestions for future research.International registered report identifier (irrid)DERR1-10.2196/37441.

Project description:BackgroundInappropriate polypharmacy, prevalent among older patients, is associated with substantial harms.ObjectiveTo develop measures of high-risk polypharmacy and pilot test novel electronic health record (EHR)-based nudges grounded in behavioral science to promote deprescribing.DesignWe developed and validated seven measures, then conducted a three-arm pilot from February to May 2019.ParticipantsValidation used data from 78,880 patients from a single large health system. Six physicians were pre-pilot test environment users. Sixty-nine physicians participated in the pilot.Main measuresRate of high-risk polypharmacy among patients aged 65 years or older. High-risk polypharmacy was defined as being prescribed ≥5 medications and satisfying ≥1 of the following high-risk criteria: drugs that increase fall risk among patients with fall history; drug-drug interactions that increase fall risk; thiazolidinedione, NSAID, or non-dihydropyridine calcium channel blocker in heart failure; and glyburide, glimepiride, or NSAID in chronic kidney disease.InterventionsPhysicians received EHR alerts when renewing or prescribing certain high-risk medications when criteria were met. One practice received a "commitment nudge" that offered a chance to commit to addressing high-risk polypharmacy at the next visit. One practice received a "justification nudge" that asked for a reason when high-risk polypharmacy was present. One practice received both.Key resultsAmong 55,107 patients 65 and older prescribed 5 or more medications, 6256 (7.9%) had one or more high-risk criteria. During the pilot, the mean (SD) number of nudges per physician per week was 1.7 (0.4) for commitment, 0.8 (0.5) for justification, and 1.9 (0.5) for both interventions. Physicians requested to be reminded to address high-risk polypharmacy for 236/833 (28.3%) of the commitment nudges and acknowledged 441 of 460 (95.9%) of justification nudges, providing a text response for 187 (40.7%).ConclusionsEHR-based measures and nudges addressing high-risk polypharmacy were feasible to develop and implement, and warrant further testing.

Dataset Information

Boosting the Understanding and Approval of Anti-Corona Measures-Reducing Exponential Growth Bias and its Effects through Educational Nudges.

Publications

Boosting the Understanding and Approval of Anti-Corona Measures-Reducing Exponential Growth Bias and its Effects through Educational Nudges.

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