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Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Democracy relies on a shared body of knowledge among citizens, for example trust in elections and reliable knowledge to inform policy-relevant debate. We review the evidence for widespread disinformation campaigns that are undermining this shared knowledge. We establish a common pattern by which science and scientists are discredited and how the most recent frontier in those attacks involves researchers in misinformation itself. We list several ways in which psychology can contribute to countermeasures.

Event sequence data is a special type of time-dependent data that captures information about the order in which discrete events occur. The time-dimension is one of the factors that makes event sequence data hard to understand. Other factors that contribute to this complexity are the dimensionality in terms of the amount of events and attributes, the frequency of events in terms of consistency and density, varying durations, and parallel occurrences. When end-users need to compare event sequences, all these characteristics need to be considered and justified. In this state-of-the-art report we review visualization techniques for the event sequence comparison task. We focus specifically on comparison, in contrast to general event sequence visualization, and review how different aspects, such as the data attributes or granularity, affect the comparison. We define a taxonomy based on five dimensions: Scale, Comparison Type, Size, Data, and Visualization & Interaction. Based on these dimensions, we provide an overview of the literature of the past 17 years. This overview is accompanied with an analysis of the strengths and weaknesses of the proposed techniques. Furthermore, we identify current gaps and research challenges in the literature.

The Eurotransplant Senior Program (ESP) has expedited the chance for elderly patients with kidney failure to receive a timely transplant. This current study evaluated survival parameters of kidneys donated after brain death with or without matching for HLA-DR antigens. This cohort study evaluated the period within ESP with paired allocation of 675 kidneys from donors 65 years and older to transplant candidates 65 years and older, the first kidney to 341 patients within the Eurotransplant Senior DR-compatible Program and 334 contralateral kidneys without (ESP) HLA-DR antigen matching. We used Kaplan-Meier estimates and competing risk analysis to assess all cause mortality and kidney graft failure, respectively. The log-rank test and Cox proportional hazards regression were used for comparisons. Within ESP, matching for HLA-DR antigens was associated with a significantly lower five-year risk of mortality (hazard ratio 0.71; 95% confidence interval 0.53-0.95) and significantly lower cause-specific hazards for kidney graft failure and return to dialysis at one year (0.55; 0.35-0.87) and five years (0.73; 0.53-0.99) post-transplant. Allocation based on HLA-DR matching resulted in longer cold ischemia (mean difference 1.00 hours; 95% confidence interval: 0.32-1.68) and kidney offers with a significantly shorter median dialysis vintage of 2.4 versus 4.1 yrs. in ESP without matching. Thus, our allocation based on HLA-DR matching improved five-year patient and kidney allograft survival. Hence, our paired allocation study suggests a superior outcome of HLA-DR matching in the context of old-for-old kidney transplantation.

In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives.

Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated by five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients.

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

Both compact and dispersed green cities are considered sustainable urban forms, yet some developments accompanied with these planning paradigms seem problematic in times of urban growth. A compact city might lose urban green spaces due to infill and a dispersed-green city might lose green in its outskirts through suburbanisation. To study these storylines, we introduce an operationalised concept of contrasting changes in population density (shrinkage or growth) with vegetation density (sealing or greening) over time. These trends are ascribed to different land use classes and single urban development projects, to quantify threads and pathways for urban green in a densifying city. We mapped the development in vegetation density over 30 years as subpixel fractions based on a Landsat remote sensing time series (for 2015: MAE 0.12). The case study city Berlin, Germany, developed into a city that is both gaining in vegetation–greening–and population–growing–in recent years but featured highly diverse trends for both compact and green city districts before that. Pathways to achieve a greening-growing scenario in a compact city include green roofs, brownfield and industrial revitalisation, and bioswales in predominantly green city districts. A threat for compact cities pose infill developments without greening measures. A threat for dispersed-green cities is microsealing in private residential gardens–gravel gardens–or car parking infrastructure. We conclude that neither a compact nor a dispersed-green city form concept logically leads to a development towards more environmental quality–here vegetation density–in times of densification but rather context specific urban planning.

Low public support for government interventions in health, environment and other policy domains can be a barrier to implementation. Communicating evidence of policy effectiveness has been used to influence attitudes towards policies, with mixed results. This review provides the first systematic synthesis of such studies. Eligible studies were randomized controlled experiments that included an intervention group that provided evidence of a policy's effectiveness or ineffectiveness at achieving a salient outcome, and measured policy support. From 6498 abstracts examined, there were 45 effect sizes from 36 eligible studies. In total, 35 (N = 30 858) communicated evidence of effectiveness, and 10 (N = 5078) communicated evidence of ineffectiveness. Random effects meta-analysis revealed that communicating evidence of a policy's effectiveness increased support for the policy (SMD = 0.11, 95% CI [0.07, 0.15], p < 0.0001), equivalent to support increasing from 50% to 54% (95% CI [53%, 56%]). Communicating evidence of ineffectiveness decreased policy support (SMD = −0.14, 95% CI [−0.22, −0.06], p < 0.001), equivalent to support decreasing from 50% to 44% (95% CI [41%, 47%]). These findings suggest that public support for policies in a range of domains is sensitive to evidence of their effectiveness, as well as their ineffectiveness.