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Maternal mortality remains a large challenge in global health. Learning from the experience of similar countries can help to accelerate progress. In this analysis we develop a typology of country groupings for maternal health and provide guidance on how policy implications vary by country typology. We used estimates from the Global Maternal Health (GMatH) microsimulation model, which was empirically calibrated to a range of fertility, process, and mortality indicators and provides estimates for 200 countries and territories. We used the 2022 estimates of the maternal mortality ratio (MMR) and lifetime risk of maternal death (LTR) and used a k-means clustering algorithm to define groups of countries based on these indicators. We estimated the means of other maternal indicators for each group, as well as the mean impact of different policy interventions. We identified 7 groups (A-G) of country typologies with different salient features. High burden countries (A-B) generally have MMRs above 500 and LTRs above 2%, and account for nearly 25% of global maternal deaths. Countries in these groups are estimated to benefit most from improving access to family planning and increasing facility births. Middle burden countries (C-E) generally have MMRs between 100-500 and LTRs between 0.5%-3%. Countries in these groups account for 55% of global maternal deaths and would benefit most from increasing facility births and improving quality of care. Low burden countries (F-G) generally have MMRs below 100 and LTRs below 0.5%, account for 20% of global maternal deaths, and would benefit most from improving access to family planning and community-based interventions and linkages to care. Indicators vary widely across groups, but also within groups, highlighting the importance of considering multiple indicators when assessing progress in maternal health. Policy impacts also differ by country typology, providing policymakers with information to help prioritize interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ward et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Rationale: Prior research suggests that belief in conspiracy theories can reduce the willingness of individuals to get vaccinated during the COVID-19 pandemic. Examining factors that may moderate this negative effect is an important area of research., Objectives: The objective of this study was to examine the relationship between vaccine uptake and two types of conspiracy beliefs (COVID-19 and vaccine-related) and the moderating role of direct and indirect experiences with the coronavirus., Methods: We draw on nationally representative survey data collected in Hungary in January 2022 (N=1000, 47% male, 53% female; mean age 49.6 years). Structural equation models and multi-group analysis were performed., Results: Conspiracy beliefs were strongly associated with vaccine uptake, however, both direct and indirect experiences with the virus moderated the effect of conspiracy beliefs. Individuals who experienced a serious infection or reported a close person being infected by the virus developed severe symptoms or even died were less likely to take conspiracy theories seriously when deciding about their own vaccination. In two out of the four tested moderation effects, a negative experience with the virus reduced the negative effect of conspiracy beliefs., Discussion: Our findings demonstrate that personal or close real-life experience with severe COVID-19 infection can significantly mitigate the impact of conspiracy beliefs on vaccine hesitancy, highlighting the importance of real-life evidence in overcoming misinformation and increasing vaccine uptake. Nevertheless, it is important to mention that our results are preliminary, and future studies need to replicate the findings and test their robustness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Maternal mortality remains a challenge in global health, with well-known disparities across countries. However, less is known about disparities in maternal health by subgroups within countries. The aim of this study is to estimate maternal health indicators for subgroups of women within each country., Methods: In this simulation-based analysis, we used the empirically calibrated Global Maternal Health (GMatH) microsimulation model to estimate a range of maternal health indicators by subgroup (urban/rural location and level of education) for 200 countries/territories from 1990 to 2050. Education levels were defined as low (less than primary), middle (less than secondary), and high (completed secondary or higher). The model simulates the reproductive lifecycle of each woman, accounting for individual-level factors such as family planning preferences, biological factors (e.g., anemia), and history of maternal complications, and how these factors vary by subgroup. We also estimated the impact of scaling up women's education on projected maternal health outcomes compared to clinical and health system-focused interventions., Findings: We find large subgroup differences in maternal health outcomes, with an estimated global maternal mortality ratio (MMR) in 2022 of 292 (95% UI 250-341) for rural women and 100 (95% UI 84-116) for urban women, and 536 (95% UI 450-594), 143 (95% UI 117-174), and 85 (95% UI 67-108) for low, middle, and high education levels, respectively. Ensuring all women complete secondary school is associated with a large impact on the projected global MMR in 2030 (97 [95% UI 76-120]) compared to current trends (167 [95% UI 142-188]), with especially large improvements in countries such as Afghanistan, Chad, Madagascar, Niger, and Yemen., Interpretation: Substantial subgroup disparities present a challenge for global maternal health and health equity. Outcomes are especially poor for rural women with low education, highlighting the need to ensure that policy interventions adequately address barriers to care in rural areas, and the importance of investing in social determinants of health, such as women's education, in addition to health system interventions to improve maternal health for all women., Funding: John D. and Catherine T. MacArthur Foundation, 10-97002-000-INP., Competing Interests: We declare no competing interests., (© 2024 The Author(s).)

Objectives: To examine the associations between several measures and categories of religiosity and cognitive function across sex and European regions., Methods: We conducted a longitudinal study including 17,756 Europeans aged 50 and older who participated in the Survey of Health, Ageing and Retirement in Europe wave 1. Participants were followed for up to 15 years. Associations were analyzed using linear mixed effects models adjusted for several potential confounders., Results: Religious service attendance was consistently associated with better cognitive function (coefficient: 1.04, 95% CI 0.71; 1.37) across sex and European regions. Praying was also associated with better cognitive function but only among men (coefficient: 0.55, 95% CI 0.15; 0.96). However, individuals who received religious education from their parents had poorer cognitive function (coefficient: -0.59, 95% CI -0.93; -0.25). The association persisted in women and among both sexes in Western Europe. Comparing different religious categories to the non-religious, participants who were religious in childhood showed an inverse association with cognitive function, while persistently religious men exhibited better cognitive function., Conclusions: Our findings indicate that religious service attendance and, to a certain extent, prayer is associated with better cognitive function. However, receiving religious education in childhood may be linked to lower cognitive function.

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Minora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The long-term nature of climate policy measures requires stable social legitimacy, which other types of crises may jeopardize. This article examines the impact of the COVID-19 fear on climate change beliefs based on an autumn 2020 population survey in the Member States of the European Union and the United Kingdom. The results show that deep COVID-19 concerns increase climate change concerns, awareness, and perceived negative impacts of climate change. These effects are more robust among the lower educated Europeans. On the country level, strict governmental measures are also linked to deep climate change concerns. In contrast to the experience following the 2008 recession, the findings show that a secondary crisis can positively impact climate attitudes, which is a promising result for policy actions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier Ltd. All rights reserved.)

This work contributes to the discussion on how innovative data can support a fast crisis response. We use operational data from Facebook to gain useful insights on where people fleeing Ukraine following the Russian invasion are likely to be displaced, focusing on the European Union. In this context, it is extremely important to anticipate where these people are moving so that local and national authorities can better manage challenges related to their reception and integration. By means of the audience estimates provided by Facebook advertising platform, we analyse the flows of people fleeing Ukraine towards the European Union. At the fifth week since the beginning of the war, our results indicate an increase in the number of Ukrainian stocks derived from Ukrainian-speaking Facebook user estimates in all the European Union (EU) countries, with Poland registering the highest percentage share (33%) of the overall increase, followed by Germany (17%), and Czechia (15%). We assess the reliability of prewar Facebook estimates by comparison with official statistics on the Ukrainian diaspora, finding a strong correlation between the two data sources (Pearson's r = 0.9 , p < 0.0001 ). We then compare our results with data on refugees in EU countries bordering Ukraine reported by the UNHCR, and we observe a similarity in their trend. In conclusion, we show how Facebook advertising data could offer timely insights on international mobility during crises, supporting initiatives aimed at providing humanitarian assistance to the displaced people, as well as local and national authorities to better manage their reception and integration., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2022.)

Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.

Cooperation is a major factor in the evolution of human societies. The structure of social networks, which affects the dynamics of cooperation and other interpersonal phenomena, have common structural signatures. One of these signatures is the tendency to organize as groups. This tendency gives rise to networks with community structure, which are composed of distinct modules. In this paper, we study analytically the evolutionary game dynamics on large modular networks in the limit of weak selection. We obtain novel analytical conditions such that natural selection favours cooperation over defection. We calculate the transition point for each community to favour cooperation. We find that a critical inter-community link creation probability exists for given group density, such that the overall network supports cooperation even if individual communities inhibit it. As a byproduct, we present solutions for the critical benefit-to-cost ratio which perform with remarkable accuracy for diverse generative network models, including those with community structure and heavy-tailed degree distributions. We also demonstrate the generalizability of the results to arbitrary two-player games.

Negative (antagonistic) connections have been of longstanding theoretical importance for social structure. In a population of 24,696 adults interacting face to face within 176 isolated villages in western Honduras, we measured all connections that were present, amounting to 105,175 positive and 16,448 negative ties. Here, we show that negative and positive ties exhibit many of the same structural characteristics. We then develop a complete taxonomy of all 138 possible triads of two-type relationships. Consistent with balance theory, we find that antagonists of friends and friends of antagonists tend to be antagonists; but, in an important empirical refutation of balance theory, we find that antagonists of antagonists also tend to be antagonists, not friends. Finally, villages with comparable levels of animosity tend to be geographically proximate. Similar processes, involving social contact, give rise to both positive and negative social ties in rural villages, and negative ties play an important role in social structure., (© 2019 The Author(s).)

In areas of human activity where performance is difficult to quantify in an objective fashion, reputation and networks of influence play a key role in determining access to resources and rewards. To understand the role of these factors, we reconstructed the exhibition history of half a million artists, mapping out the coexhibition network that captures the movement of art between institutions. Centrality within this network captured institutional prestige, allowing us to explore the career trajectory of individual artists in terms of access to coveted institutions. Early access to prestigious central institutions offered life-long access to high-prestige venues and reduced dropout rate. By contrast, starting at the network periphery resulted in a high dropout rate, limiting access to central institutions. A Markov model predicts the career trajectory of individual artists and documents the strong path and history dependence of valuation in art., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

From decentralized banking systems to digital community currencies, the way humans perceive and use money is changing 1-3 , thus creating novel opportunities for solving important economic and social problems. Here, we study Sardex, a fast-growing community currency in Sardinia (involving 1,477 businesses arrayed in a network with 48,170 transactions) using network analysis to shed light on its operation. Based on our experience with its day-to-day operations, we propose performance metrics tailored for Sardex but also to similar economic systems, introduce criteria for identifying prominent economic actors and investigate the interplay between network structure and economic robustness. Leveraging new methods for quantifying network 'cyclic density' and 'k-cycle centrality,' we show that geodesic transaction cycles, where money flows in a circle through the network, are prevalent and that certain nodes have a pivotal role in them. We analyse the transactions within cycles and find that the economic turnover of the involved firms is higher, and that excessive currency and debt accumulations are lower. We also measure a similar, but secondary, effect for nodes and edges that serve as intermediaries to many transactions. These metrics are strong indicators of the success of such mutual credit systems at individual and collective levels.

Social structure affects the emergence and maintenance of cooperation. Here, we study the evolutionary dynamics of cooperation in fragmented societies, and show that conjoining segregated cooperation-inhibiting groups, if done properly, rescues the fate of collective cooperation. We highlight the essential role of intergroup ties, which sew the patches of the social network together and facilitate cooperation. We point out several examples of this phenomenon in actual settings. We explore random and non-random graphs, as well as empirical networks. In many cases, we find a marked reduction of the critical benefit-to-cost ratio needed for sustaining cooperation. Our finding gives hope that the increasing worldwide connectivity, if managed properly, can promote global cooperation.

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens., (Published by Elsevier B.V.)

Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society's fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual's risk of getting the disease (disease attack rate) and the disruption to the system's functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.

Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure-graph surgery-affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

Interim results from the Guinea Ebola ring vaccination trial suggest high efficacy of the rVSV-ZEBOV vaccine. These findings open the door to the use of ring vaccination strategies in which the contacts and contacts of contacts of each index case are promptly vaccinated to contain future Ebola virus disease outbreaks. To provide a numerical estimate of the effectiveness of ring vaccination strategies we introduce a spatially explicit agent-based model to simulate Ebola outbreaks in the Pujehun district, Sierra Leone, structurally similar to previous modelling approaches. We find that ring vaccination can successfully contain an outbreak for values of the effective reproduction number up to 1.6. Through an extensive sensitivity analysis of parameters characterising the readiness and capacity of the health care system, we identify interventions that, alongside ring vaccination, could increase the likelihood of containment. In particular, shortening the time from symptoms onset to hospitalisation to 2-3 days on average through improved contact tracing procedures, adding a 2km spatial component to the vaccination ring, and decreasing human mobility by quarantining affected areas might contribute increase our ability to contain outbreaks with effective reproduction number up to 2.6. These results have implications for future control of Ebola and other emerging infectious disease threats., Competing Interests: The authors have declared that no competing interests exist.

Background: Among the three countries most affected by the Ebola virus disease outbreak in 2014-2015, Guinea presents an unusual spatiotemporal epidemic pattern, with several waves and a long tail in the decay of the epidemic incidence., Methods: Here, we develop a stochastic agent-based model at the level of a single household that integrates detailed data on Guinean demography, hospitals, Ebola treatment units, contact tracing, and safe burial interventions. The microsimulation-based model is used to assess the effect of each control strategy and the probability of elimination of the epidemic according to different intervention scenarios, including ring vaccination with the recombinant vesicular stomatitis virus-vectored vaccine., Results: The numerical results indicate that the dynamics of the Ebola epidemic in Guinea can be quantitatively explained by the timeline of the implemented interventions. In particular, the early availability of Ebola treatment units and the associated isolation of cases and safe burials helped to limit the number of Ebola cases experienced by Guinea. We provide quantitative evidence of a strong negative correlation between the time series of cases and the number of traced contacts. This result is confirmed by the computational model that suggests that contact tracing effort is a key determinant in the control and elimination of the disease. In data-driven microsimulations, we find that tracing at least 5-10 contacts per case is crucial in preventing epidemic resurgence during the epidemic elimination phase. The computational model is used to provide an analysis of the ring vaccination trial highlighting its potential effect on disease elimination., Conclusions: We identify contact tracing as one of the key determinants of the epidemic's behavior in Guinea, and we show that the early availability of Ebola treatment unit beds helped to limit the number of Ebola cases in Guinea.

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Empirical networks of weighted dyadic relations often contain "noisy" edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges. Here, we focus on integer-weighted graphs commonly arising when weights count the occurrences of an "event" relating the nodes. We introduce a simple and intuitive null model related to the configuration model of network generation and derive two significance filters from it: the marginal likelihood filter (MLF) and the global likelihood filter (GLF). The former is a fast algorithm assigning a significance score to each edge based on the marginal distribution of edge weights, whereas the latter is an ensemble approach which takes into account the correlations among edges. We apply these filters to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph. Furthermore, compared with thresholding based on edge weight, we show that our filters extract a larger and significantly sparser giant component.

Background: The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions., Methods: We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits., Findings: Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4-76·4) were acquired in hospitals, 30·7% (14·1-46·4) in households, and 8·6% (3·2-11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits., Interpretation: The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates., Funding: US Defense Threat Reduction Agency, US National Institutes of Health., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Internet-based systems for epidemiological studies have advantages over traditional approaches as they can potentially recruit and monitor a wider range of individuals in a relatively inexpensive fashion. We studied the association between communication strategies used for recruitment (offline, online, face-to-face) and follow-up participation in nine Internet-based cohorts: the Influenzanet network of platforms for influenza surveillance which includes seven cohorts in seven different European countries, the Italian birth cohort Ninfea and the New Zealand birth cohort ELF. Follow-up participation varied from 43% to 89% depending on the cohort. Although there were heterogeneities among studies, participants who became aware of the study through an online communication campaign compared with those through traditional offline media seemed to have a lower follow-up participation in 8 out of 9 cohorts. There were no clear differences in participation between participants enrolled face-to-face and those enrolled through other offline strategies. An Internet-based campaign for Internet-based epidemiological studies seems to be less effective than an offline one in enrolling volunteers who keep participating in follow-up questionnaires. This suggests that even for Internet-based epidemiological studies an offline enrollment campaign would be helpful in order to achieve a higher participation proportion and limit the cohort attrition.

In close elections, the losing side has an incentive to obtain evidence that the election result is incorrect. Sometimes this evidence comes in the form of court testimony from a sample of invalid voters, and this testimony is used to adjust vote totals (Borders v King County, 2005; Belcher v Mayor of Ann Arbor, 1978). However, while courts may be reluctant to make explicit findings about out-of-sample data (e.g. invalid voters that do not testify), when samples are used to adjust vote totals, the court is implicitly making findings about this out-of-sample data. In this paper, we show that the practice of adjusting vote totals on the basis of potentially unrepresentative samples can lead to incorrectly voided election results. More generally, we show that given the difficulties of sampling and non-response in this context, even when frame error is minimal and if voter testimony is accurate, such testimony has limited power to detect incorrect election results without precinct level polarization or the acceptance of large Type I error rates. Therefore in U.S. election disputes, even high quality post-vote vote-choice data will often not be sufficient to resolve contested elections without modeling assumptions (whether or not these assumptions are acknowledged).