Communicating climate change and health to specific target groups.

Background: The German status report on climate change and health 2023 identifies numerous health risks that are caused or exacerbated by climate change. One recommendation arising from the report is to strengthen education, information, and communication in the field. This article aims to serve as a basis for this. Methods: Based on four survey waves (2022/2023) of the PACE study (Planetary Health Action Survey, n=3,845, online), the status of risk perception as well as the Readiness to Act against climate change in the adult population in Germany is examined and a target group analysis is carried out. Results: Some health risks due to the climate crisis are perceived as comparatively low (e.g. mental health problems). People with higher risk perception show a higher Readiness to Act. Younger people, men, people with low education, and those living in smaller communities are identified as relevant target groups as they have a lower Readiness to Act. One third state that they never or hardly ever seek out specific information on climate change. Media use differs depending on target group. Conclusions: Target group-specific communication can help to educate people about the health impacts of the climate crisis. In the discussion of this article, implications from existing literature are discussed in detail, which offer practical guidance for effective climate change communication.

Secondary data for global health digitalisation.

Substantial opportunities for global health intelligence and research arise from the combined and optimised use of secondary data within data ecosystems. Secondary data are information being used for purposes other than those intended when they were collected. These data can be gathered from sources on the verge of widespread use such as the internet, wearables, mobile phone apps, electronic health records, or genome sequencing. To utilise their full potential, we offer guidance by outlining available sources and approaches for the processing of secondary data. Furthermore, in addition to indicators for the regulatory and ethical evaluation of strategies for the best use of secondary data, we also propose criteria for assessing reusability. This overview supports more precise and effective policy decision making leading to earlier detection and better prevention of emerging health threats than is currently the case.

Fact boxes that inform individual decisions may contribute to a more positive evaluation of COVID-19 vaccinations at the population level.

OBJECTIVE: For an effective control of the SARS-CoV-2 pandemic with vaccines, most people in a population need to be vaccinated. It is thus important to know how to inform the public with reference to individual preferences-while also acknowledging the societal preference to encourage vaccinations. According to the health care standard of informed decision-making, a comparison of the benefits and harms of (not) having the vaccination would be required to inform undecided and skeptical people. To test evidence-based fact boxes, an established risk communication format, and to inform their development, we investigated their contribution to knowledge and evaluations of COVID-19 vaccines. METHODS: We conducted four studies (1, 2, and 4 were population-wide surveys with N = 1,942 to N = 6,056): Study 1 assessed the relationship between vaccination knowledge and intentions in Germany over three months. Study 2 assessed respective information gaps and needs of the population in Germany. In parallel, an experiment (Study 3) with a mixed design (presentation formats; pre-post-comparison) assessed the effect of fact boxes on risk perceptions and fear, using a convenience sample (N = 719). Study 4 examined how effective two fact box formats are for informing vaccination intentions, with a mixed experimental design: between-subjects (presentation formats) and within-subjects (pre-post-comparison). RESULTS: Study 1 showed that vaccination knowledge and vaccination intentions increased between November 2020 and February 2021. Study 2 revealed objective information requirements and subjective information needs. Study 3 showed that the fact box format is effective in adjusting risk perceptions concerning COVID-19. Based on those results, fact boxes were revised and implemented with the help of a national health authority in Germany. Study 4 showed that simple fact boxes increase vaccination knowledge and positive evaluations in skeptics and undecideds. CONCLUSION: Fact boxes can inform COVID-19 vaccination intentions of undecided and skeptical people without threatening societal vaccination goals of the population.

Germany's fourth COVID-19 wave was mainly driven by the unvaccinated.

Background: While the majority of the German population was fully vaccinated at the time (about 65%), COVID-19 incidence started growing exponentially in October 2021 with about 41% of recorded new cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. So far, it remained elusive how significant this contribution was and whether targeted non-pharmaceutical interventions (NPIs) may have stopped the amplification of the crisis. Methods: We develop and introduce a contribution matrix approach based on the next-generation matrix of a population-structured compartmental infectious disease model to derive contributions of respective inter- and intragroup infection pathways of unvaccinated and vaccinated subpopulations to the effective reproduction number and new infections, considering empirical data of vaccine efficacies against infection and transmission. Results: Here we show that about 61%-76% of all new infections were caused by unvaccinated individuals and only 24%-39% were caused by the vaccinated. Furthermore, 32%-51% of new infections were likely caused by unvaccinated infecting other unvaccinated. Decreasing the transmissibility of the unvaccinated by, e. g. targeted NPIs, causes a steeper decrease in the effective reproduction number R than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Reducing contacts between vaccinated and unvaccinated individuals serves to decrease R in a similar manner as increasing vaccine uptake. Conclusions: A minority of the German population-the unvaccinated-is assumed to have caused the majority of new infections in the fall of 2021 in Germany. Our results highlight the importance of combined measures, such as vaccination campaigns and targeted contact reductions to achieve temporary epidemic control.

Empirical evidence to understand the human factor for effective rapid testing against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapid antigen point-of-care and home tests are available to laypeople. In four cross-sectional mixed-methods data collections conducted between December 2020 and March 2021 (n = 4,026), we showed that a majority of subjects were willing to test despite mistrust and ignorance regarding rapid tests' validity. Experimental evidence shows that low costs and access to events could increase testing intentions. Mandatory reporting and isolation after positive results were not identified as major barriers. Instead, assuming that testing and isolation can slow down the pandemic and the possibility to protect others were related to greater willingness to get tested. While we did not find evidence for risk compensation for past tests, experimental evidence suggests that there is a tendency to show less mask wearing and physical distancing in a group of tested individuals. A short communication intervention reduced complacent behavior. The derived recommendations could make rapid testing a successful pillar of pandemic management.

The similarity-updating model of probability judgment and belief revision.

People often take nondiagnostic information into account when revising their beliefs. A probability judgment decreases due to nondiagnostic information represents the well-established "dilution effect" observed in many domains. Surprisingly, the opposite of the dilution effect called the "confirmation effect" has also been observed frequently. The present work provides a unified cognitive model that allows both effects to be explained simultaneously. The suggested similarity-updating model incorporates two psychological components: first, a similarity-based judgment inspired by categorization research, and second, a weighting-and-adding process with an adjustment following a similarity-based confirmation mechanism. Four experimental studies demonstrate the model's predictive accuracy for probability judgments and belief revision. The participants received a sample of information from one of two options and had to judge from which option the information came. The similarity-updating model predicts that the probability judgment is a function of the similarity of the sample to the options. When one is presented with a new sample, the previous probability judgment is updated with a second probability judgment by taking a weighted average of the two and adjusting the result according to a similarity-based confirmation. The model describes people's probability judgments well and outcompetes a Bayesian cognitive model and an alternative probability-theory-plus-noise model. The similarity-updating model accounts for several qualitative findings, namely, dilution effects, confirmation effects, order effects, and the finding that probability judgments are invariant to sample size. In sum, the similarity-updating model provides a plausible account of human probability judgment and belief revision. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

[Risk communication in the containment of the COVID-19 pandemic: challenges and promising approaches]. / Risikokommunikation bei der Eindämmung der COVID-19-Pandemie: Herausforderungen und Erfolg versprechende Ansätze.

Risk communication plays a central role in public health emergencies: it must enable informed decisions, promote protective or life-sustaining behaviour, and maintain trust in public institutions. In addition, uncertainties in knowledge must be named transparently; irrational fears and rumours must be refuted. Success factors for risk communication are the participation of citizens as well as the continuous recording of risk perception and risk competence in population groups. The current COVID-19 (corona virus disease 2019) pandemic poses specific challenges for risk communication.The state of knowledge on many important aspects concerning COVID-19 was and is often uncertain or preliminary, e.g. on transmission, symptoms, long-term effects and immunity. Communication is characterised by scientific language and an array of figures and statistics, which can render the content difficult to understand. Alongside the official announcements and statements by experts, COVID-19 is widely communicated on social media, spreading misinformation and speculation; this "infodemic" can complicate risk communication.Various national and international scientific projects will help tailor risk communication on COVID-19 to target groups and thereby render it more effective. These projects include explorative studies on how people deal with COVID-19-related information; the COVID-19 Snapshot Monitoring (COSMO) project, a regularly conducted online survey on risk perception and protective behaviour; and an interdisciplinary qualitative study that compares the design, implementation and effectiveness of risk communication strategies in four countries.

How to Determine When SARS-CoV-2 Antibody Testing Is or Is Not Useful for Population Screening: A Tutorial.

Extensive testing lies at the heart of any strategy to effectively combat the SARS-COV-2 pandemic. In recent months, the use of enzyme-linked immunosorbent assay-based antibody tests has gained a lot of attention. These tests can potentially be used to assess SARS-COV-2 immunity status in individuals (e.g., essential health care personnel). They can also be used as a screening tool to identify people that had COVID-19 asymptomatically, thus getting a better estimate of the true spread of the disease, gain important insights on disease severity, and to better evaluate the effectiveness of policy measures implemented to combat the pandemic. But the usefulness of these tests depends not only on the quality of the test but also, critically, on how far disease has already spread in the population. For example, when only very few people in a population are infected, a positive test result has a high chance of being a false positive. As a consequence, the spread of the disease in a population as well as individuals' immunity status may be systematically misinterpreted. SARS-COV-2 infection rates vary greatly across both time and space. In many places, the infection rates are very low but can quickly skyrocket when the virus spreads unchecked. Here, we present two tools, natural frequency trees and positive and negative predictive value graphs, that allow one to assess the usefulness of antibody testing for a specific context at a glance. These tools should be used to support individual doctor-patient consultation for assessing individual immunity status as well as to inform policy discussions on testing initiatives.

Emergency department patients with weakness or fatigue: Can physicians predict their outcomes at the front door? A prospective observational study.

BACKGROUND: Generalized weakness and fatigue are underexplored symptoms in emergency medicine. Triage tools often underestimate patients presenting to the emergency department (ED) with these nonspecific symptoms (Nemec et al., 2010). At the same time, physicians' disease severity rating (DSR) on a scale from 0 (not sick at all) to 10 (extremely sick) predicts key outcomes in ED patients (Beglinger et al., 2015; Rohacek et al., 2015). Our goals were (1) to characterize ED patients with weakness and/or fatigue (W|F); to explore (2) to what extent physicians' DSR at triage can predict five key outcomes in ED patients with W|F; (3) how well DSR performs relative to two commonly used benchmark methods, the Emergency Severity Index (ESI) and the Charlson Comorbidity Index (CCI); (4) to what extent DSR provides predictive information beyond ESI, CCI, or their linear combination, i.e., whether ESI and CCI should be used alone or in combination with DSR; and (5) to what extent ESI, CCI, or their linear combination provide predictive information beyond DSR alone, i.e., whether DSR should be used alone or in combination with ESI and / or CCI. METHODS: Prospective observational study between 2013-2015 (analysis in 2018-2020, study team blinded to hypothesis) conducted at a single center. We study an all-comer cohort of 3,960 patients (48% female patients, median age = 51 years, 94% completed 1-year follow-up). We looked at two primary outcomes (acute morbidity (Bingisser et al., 2017; Weigel et al., 2017) and all-cause 1- year mortality) and three secondary outcomes (in-hospital mortality, hospitalization and transfer to ICU). We assessed the predictive power (i.e., resolution, measured as the Area under the ROC Curve, AUC) of the scores and, using logistic regression, their linear combinations. FINDINGS: Compared to patients without W|F (n = 3,227), patients with W|F (n = 733) showed higher prevalences for all five outcomes, reported more symptoms across both genders, and received higher DSRs (median = 4; interquartile range (IQR) = 3-6 vs. median = 3; IQR = 2-5). DSR predicted all five outcomes well above chance (i.e., AUCs > ~0.70), similarly well for both patients with and without W|F, and as good as or better than ESI and CCI in patients with and without W|F (except for 1-year mortality where CCI performs better). For acute morbidity, hospitalization, and transfer to ICU there is clear evidence that adding DSR to ESI and/or CCI improves predictions for both patient groups; for 1-year mortality and in-hospital mortality this holds for most, but not all comparisons. Adding ESI and/or CCI to DSR generally did not improve performance or even decreased it. CONCLUSIONS: The use of physicians' disease severity rating has never been investigated in patients with generalized weakness and fatigue. We show that physicians' prediction of acute morbidity, mortality, hospitalization, and transfer to ICU through their DSR is also accurate in these patients. Across all patients, DSR is less predictive of acute morbidity for female than male patients, however. Future research should investigate how emergency physicians judge their patients' clinical state at triage and how this can be improved and used in simple decision aids.

A framework for building cognitive process models.

The term process model is widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following dimensions characterize process models: They have a scope that includes different levels of abstraction. They specify a hypothesized mental information transformation. They make predictions not only for the behavior of interest but also for processes. The models' predictions for the processes can be derived from the input, without reverse inference from the output data. Moreover, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Lastly, process models require a conceptual scope specifying levels of abstraction for the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.

Combinations of Symptoms in Emergency Presentations: Prevalence and Outcome.

The predictive power of certain symptoms, such as dyspnoea, is well known. However, research is limited to the investigation of single chief complaints. This is in contrast to patients in the emergency department (ED) presenting usually more than one symptom. We aimed to identify the most common combinations of symptoms and to report their related outcomes: hospitalisation, admission to intensive care units, and mortality. This is a secondary analysis of a consecutive sample of all patients presenting to the ED of the University Hospital Basel over a total time course of 6 weeks. The presence of 35 predefined symptoms was systematically assessed upon presentation. A total of 3960 emergency patients (median age 51, 51.7% male) were included. Over 130 combinations of two, 80 combinations of three, and 10 combinations of four symptoms occurred 42 times or more during a total inclusion period of 42 days. Two combinations of two symptoms were predictive for in-hospital mortality: weakness and fatigue (Odds ratio (OR) = 2.45), and weakness and headache (OR = 3.01). Combinations of symptoms were frequent. Nonspecific complaints (NSCs), such as weakness and fatigue, are among the most frequently reported combinations of symptoms, and are associated with adverse outcomes. Systematically assessing symptoms may add valuable information for prognosis and may therefore influence triage, clinical work-up, and disposition.

How to improve health literacy to reduce short- and long-term consequences of maternal obesity?

Aim: To demonstrate the global challenge of maternal obesity and to propose models to increase awareness and health literacy. Methods: The regional perinatal data base and the international literature were reviewed to demonstrate the rising rates of maternal overweight and obesity causing major public health problems in low and high-resourced countries. A preliminary systematic review analyzing interventions in maternal obesity and a fact box based on a recent Cochrane review on dietary interventions were performed. Results: Between 2000 and 2015, the regional rates of maternal overweight and obesity have significantly increased, and the rate of morbid maternal obesity has even doubled. Pregnant women were insufficiently informed about the health risks and international recommendations for weight gain associated with pre-pregnancy body mass index. Scientific publications and guidelines of professional boards have not yet interrupted the vicious cycle of transgenerational transfer of associated health risks for the offspring. For the first time we propose a fact box to translate the results from a Cochrane review about dietary interventions into a transparent information for health care providers and patients which could help to improve awareness. Conclusions: Improving health literacy and translating clinical science into models which are understandable by policy makers, health care providers and parents is a challenge mainly if health risks are modifiable during gestation and could prevent the increasing burden of obesity for future societies.

Are Mortality and Acute Morbidity in Patients Presenting With Nonspecific Complaints Predictable Using Routine Variables?

OBJECTIVES: Patients presenting to the emergency department (ED) with nonspecific complaints are difficult to accurately triage, risk stratify, and diagnose. This can delay appropriate treatment. The extent to which key medical outcomes are at all predictable in these patients, and which (if any) predictors are useful, has previously been unclear. To investigate these questions, we tested an array of statistical and machine learning models in a large group of patients and estimated the predictability of mortality (which occurred in 6.6% of our sample of patients), acute morbidity (58%), and presence of acute infectious disease (28.2%). METHODS: To investigate whether the best available tools can predict the three key outcomes, we fed data from a sample of 1,278 ED patients with nonspecific complaints into 17 state-of-the-art statistical and machine learning models. The patient sample stems from a diagnostic multicenter study with prospective 30-day follow-up conducted in Switzerland. Predictability of the three key medical outcomes was quantified by computing the area under the receiver operating characteristic curve (AUC) for each model. RESULTS: The models performed at different levels but, on average, the predictability of the target outcomes ranged between 0.71 and 0.82. The better models clearly outperformed physicians' intuitive judgments of how ill patients looked (AUC = 0.67 for mortality, 0.65 for morbidity, and 0.60 for infectious disease). CONCLUSIONS: Modeling techniques can be used to derive formalized models that, on average, predict the outcomes of mortality, acute morbidity, and acute infectious disease in patients with nonspecific complaints with a level of accuracy far beyond chance. The models also predicted these outcomes more accurately than did physicians' intuitive judgments of how ill the patients look; however, the latter was among the small set of best predictors for mortality and acute morbidity. These results lay the groundwork for further refining triage and risk stratification tools for patients with nonspecific complaints. More research, informed by whether the goal of a model is high sensitivity or high specificity, is needed to develop readily applicable clinical decision support tools (e.g., decision trees) that could be supported by electronic health records.

Inferring conjunctive probabilities from noisy samples: evidence for the configural weighted average model.

Judging whether multiple events will co-occur is an important aspect of everyday decision making. The underlying probabilities of occurrence are usually unknown and have to be inferred from experience. Using a rigorous, quantitative model comparison, we investigate how people judge the conjunctive probabilities of multiple events to co-occur. In 2 experiments, participants had to repeatedly choose between pairs of 2 conjunctive events (represented as 2 gambles). To estimate the probability that both events occur, they had access to a small sample of information. The 1st experiment consisted of a balanced set of gambles, whereas in the 2nd experiment, the gambles were constructed such that the models maximally differed in their predictions. A hierarchical Bayesian approach used for estimating the models' parameters and for testing the models against each other showed that the majority of participants were best described by the configural weighted average model. This model performed best in predicting people's choices, and it assumes that constituent probabilities are ranked by importance, weighted accordingly, and added up. The cognitive modeling approach provides an understanding of the cognitive processes underlying people's conjunctive probability judgments.

Exploring the overestimation of conjunctive probabilities.

People often overestimate probabilities of conjunctive events. The authors explored whether the accuracy of conjunctive probability estimates can be improved by increased experience with relevant constituent events and by using memory aids. The first experiment showed that increased experience with constituent events increased the correlation between the estimated and the objective conjunctive probabilities, but that it did not reduce overestimation of conjunctive probabilities. The second experiment showed that reducing cognitive load with memory aids for the constituent probabilities led to improved estimates of the conjunctive probabilities and to decreased overestimation of conjunctive probabilities. To explain the cognitive process underlying people's probability estimates, the configural weighted average model was tested against the normative multiplicative model. The configural weighted average model generates conjunctive probabilities that systematically overestimate objective probabilities although the generated probabilities still correlate strongly with the objective probabilities. For the majority of participants this model was better than the multiplicative model in predicting the probability estimates. However, when memory aids were provided, the predictive accuracy of the multiplicative model increased. In sum, memory tools can improve people's conjunctive probability estimates.