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Innovation systems have seen diverse actors attempting to tame the Covid‐19 crisis, under varying degrees of government direction. Largely neglected in scholarly and public attention, however, are ‘bottom‐up’ solutions arising from the periphery of innovation systems. Drawing on inductive case research on a fringe doctor who invented the idea of the drive‐through testing system, and two university student teams that developed coronavirus applications, this study examines how peripheral actors generate innovative, bottom‐up solutions at speed in a time of crisis. Our findings reveal that, in a crisis situation, bottom‐up solutions transpire on the basis of three innovation drivers: (a) peripheral status, expediting the commence of innovation activities; (b) interdisciplinary collaboration, enabling access to a greater spectrum of knowledge and perspectives; and (c) prior knowledge, prescribing the direction of solution generation. We also identify that system intermediaries support the innovation activities of peripheral actors, thereby helping bottom‐up solutions to become more customer facing. Such functions of intermediaries include demand articulation, technical assistance, and promulgation of generated solutions. Our findings offer theoretical implications for the literature on innovation in a time of crisis and practical implications for governments and organizations preparing themselves for the potential second wave of coronavirus emergencies, or even a completely new form of future crisis.

Background Psychiatric rehospitalization is a complex phenomenon in need of more person-centred approaches. The current paper aimed to explore how community-based actions and daily life influence mental health and rehospitalization. Design, setting and participants The qualitative study included focus group data from six European countries including 59 participants. Data were thematically analysed following an inductive approach deriving themes and subthemes in relation to facilitators and barriers to mental health. Results Barriers consisted of subthemes (financial difficulty, challenging family circumstances and stigma), and facilitators consisted of three subthemes (complementing services, signposting and recovery). The recovery subtheme consisted of a further five categories (family and friends, work and recreation, hope, using mental health experience and meaning). Discussion Barriers to mental health largely related to social determinants of mental health, which may also have implications for psychiatric rehospitalization. Facilitators included community-based actions and aspects of daily life with ties to personal recovery. By articulating the value of these facilitators, we highlight benefits of a person-centred and recovery-focused approach also within the context of psychiatric rehospitalization. Conclusions This paper portrays how person-centred approaches and day-to-day community actions may impact psychiatric rehospitalization via barriers and facilitators, acknowledging the social determinants of mental health and personal recovery. Patient or public contribution The current study included participants with experience of psychiatric rehospitalization from six different European countries. Furthermore, transcripts were read by several of the focus group participants, and a service user representative participated in the entire research process in the original study.

Evidence-based decision-making is predicated on the ability of users to find and comprehend results from systematic review. Evidence producers have an obligation to support evidence users in this process. The Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) program-a producer of rigorous and comprehensive systematic reviews for two decades-has set a gold standard for reliability in health evidence reviews in the United States. It has recently begun a program of active support for evidence dissemination and uptake beyond mere publication of lengthy reports. This Brief Methods Note critiques the current paper-based format for systematic reviews and describes the development of a next generation (NxGen) AHRQ EPC Effective Health Care website. This redesigned platform will allow end-users of all types to find and share the evidence they need through data visualizations and other interactive displays. Several design principles guided the development of NxGen to make systematic review findings more accessible, customizable, adaptable, interactive, and shareable. NxGen will include visualizations for report results that are expressed as meta-analyses as well as those with narrative syntheses, through forest or bubble plots, respectively. Visual and interactive evidence heat maps are also planned. The NxGen version of the Effective Health Care website is planned to go live in the latter half of 2020 or early 2021.

Background People with SMI have often been excluded in advocacy efforts focused on physical health, health care and health and social policy. Objective Following a Photovoice project focused on barriers to healthy eating and physical activity in urban neighbourhoods, participant‐researchers were invited to present their insights in community advocacy settings. The purpose of this study was to explore the feasibility and participant–researchers’ experience of these community advocacy activities. Design We held four focus groups with the eight participant‐researchers after each community advocacy activity to explore their experience with public speaking, presenting their experiences and advocating. Setting and Participants People with serious mental illness who were overweight/obese living in supportive housing. Analysis approach Qualitative analysis of the focus group transcripts, using a modified grounded theory approach followed by structured coding focused on empowerment, participation and non‐discrimination. Results Participant‐researchers gave three oral presentations of their photographs at a variety of community‐based programmes and settings and participated in a rally to advocate for SNAP benefits. Two themes emerged from analysis: (a) Empowerment (the level of choice, influence and control that users of mental health services can exercise over events in their lives) and (b) Barriers to Empowerment (obstacles to participation and well‐being). Conclusions This evaluation strengthens the evidence that it is feasible for participant‐researchers in Photovoice projects to engage in robust advocacy activities, such as presentations and discussions with local policymakers. During focus groups, participant‐researchers demonstrated realistic optimism towards their roles as change agents and influencers in spite of acknowledged systemic barriers.

Objectives To describe and reflect on the methods and influence of involvement of young people with lived experience within a complex evidence synthesis. Study design and setting Linked syntheses of quantitative and qualitative systematic reviews of evidence about interventions to improve the mental health of children and young people (CYP) with long‐term physical conditions (LTCs). Methods Involvement was led by an experienced patient and public involvement in research lead. Young people with long‐term physical conditions and mental health issues were invited to join a study‐specific Children and Young People's Advisory Group (CYPAG). The CYPAG met face to face on four occasions during the project with individuals continuing to contribute to dissemination following report submission. Results Eight young people joined the CYPAG. Their views and experiences informed (a) a systematic review evaluating the effectiveness of interventions intended to improve the mental health of CYP with LTCs, (b) a systematic review exploring the experiences of interventions intended to improve the mental well‐being of CYP with LTCs and (c) an overarching synthesis. The CYPAG greatly contributed to the team's understanding and appreciation of the wider context of the research. The young people found the experience of involvement empowering and felt they would use the knowledge they had gained about the research process in the future. Conclusion Creating an environment that enabled meaningful engagement between the research team and the CYPAG had a beneficial influence on the young people themselves, as well as on the review process and the interpretation, presentation and dissemination of findings.

Background Deeply divided ideological positions challenge collaboration when engaging youth with mental disorders, caregivers and providers in mental health research. The integrative dynamics (ID) approach can restructure relationships and overcome ‘us vs them’ thinking. Objective To assess the extent to which an experience‐based co‐design (EBCD) approach to patient and family engagement in mental health research aligned with ID processes. Methods A retrospective case study of EBCD data in which transitional‐aged youth (n = 12), caregivers (n = 8) and providers (n = 10) co‐designed prototypes to improve transitions from child to adult services. Transcripts from focus groups and a co‐design event, co‐designed prototypes, the resulting model, evaluation interviews and author reflections were coded deductively based on core ID concepts, while allowing for emergent themes. Analysis was based on pattern matching. Triangulation across data sources, research team, and youth and caregiver reflections enhanced rigour. Findings The EBCD focus group discussions of touchpoints in experiences aligned with ID processes of acknowledging the past, by revealing the perceived identity mythos of each group, and allowing expression of and working through emotional pain. These ID processes were briefly revisited in the co‐design event, where the focus was on the remaining ID processes: building cross‐cutting connections and reconfiguring relationships. The staged EBCD approach may facilitate ID, by working within one's own perspective prior to all perspectives working together in co‐design. Conclusion Researchers can augment patient engagement approaches by applying ID principles with staged integration of groups to improve relations in mental health systems, and EBCD shows promise to operationalize this.

Background: There is limited evidence of genuine equal partnership where power is shared with young people with mental health difficulties throughout all research stages, particularly in data collection and analysis.\ud Objective: To describe how our qualitative study, exploring young peoples’ perceptions on the feasibility of using technology to detect mental health deterioration, was co- produced using principles of co-production, whilst reflecting on impact, challenges and recommendations.\ud Methods: Young people with experience of mental health difficulties were appointed and then worked with researchers throughout all research stages. The study was evaluated against the five principles of co-production. Reflections from researchers and young people were collected throughout.\ud Results: Seven young people formed an initial Young People's Advisory Group (YPAG); three became co-researchers. Reflection was key throughout the process. Sharing power became easier and more evident as trust, confidence and mutual re- spect grew over time, particularly after a safe space was established. The safe space was crucial for open discussions, and our WhatsApp group enabled continual com- munication, support and shared decision-making. The resulting co-produced topic guide, coding framework, thematic map, papers and presentations demonstrated significant impact.\ud Conclusions: To our knowledge, this is the first qualitative mental health study to be co-produced using the principles of co-production. Our rigorous assessment can be utilized as an informative document to help others to produce meaningful co- produced future research. Although co-production takes time, it makes significant impact to the research, researchers and co-researchers. Flexible funding for spon- taneous suggestions from co-researchers and more time for interview training is recommended.

We formulate a simple susceptible‐infectious‐recovery (SIR) model to describe the spread of the coronavirus under strict social restrictions. The transmission rate in this model is exponentially decreasing with time. We find a formula for basic reproduction function and estimate the maximum number of daily infected individuals. We fit the model to induced death data in Italy, United States, Germany, France, India, Spain, and China over the period from the first reported death to August 7, 2020. We notice that the model has excellent fit to the disease death data in these countries. We estimate the model's parameters in each of these countries with 95% confidence intervals. We order the strength of social restrictions in these countries using the exponential rate. We estimate the time needed to reduce the basic reproduction function to one unit and use it to order the quality of social restrictions in these countries. The social restriction in China was the strictest and the most effective and in India was the weakest and the least effective. Policy‐makers may apply the Chinese successful social restriction experiment and avoid the Indian unsuccessful one. [ FROM AUTHOR] Copyright of Mathematical Methods in the Applied Sciences is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

RATIONALE The wide chemical diversity and complex matrices inherent to metabolomics still pose a challenge to current analytical approaches for metabolite screening. Although dedicated front-end separation techniques combined with high-resolution mass spectrometry set the benchmark from an analytical point of view, the increasing number of samples and sample complexity demand for a compromise in terms of selectivity, sensitivity and high-throughput analyses. METHODS Prior to low-field drift tube ion mobility (IM) separation and quadrupole time-of-flight mass spectrometry (QTOFMS) detection, rapid ultrahigh-performance liquid chromatography separation was used for analysis of different concentration levels of dansylated metabolites present in a yeast cell extract. For identity confirmation of metabolites at the MS2 level, an alternating frame approach was chosen and two different strategies were tested: a data-independent all-ions acquisition and a quadrupole broad band isolation (Q-BBI) directed by IM drift separation. RESULTS For Q-BBI analysis, the broad mass range isolation was successfully optimized in accordance with the distinctive drift time to m/z correlation of the dansyl derivatives. To guarantee comprehensive sampling, a broad mass isolation window of 70 Da was employed. Fragmentation was performed via collision-induced dissociation, applying a collision energy ramp optimized for the dansyl derivatives. Both approaches were studied in terms of linear dynamic range and repeatability employing ethanolic extracts of Pichia pastoris spiked with 1 μM metabolite mixture. Example data obtained for histidine and glycine showed that drift time precision (

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modeling provides a computationally efficient approach for estimating population‐average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present a methodology for design selection, make comparisons to existing approaches in the literature, and assess the robustness of the designs to modeling assumptions.

In the current article, we aim to study in detail a novel coronavirus (2019-nCoV or COVID-19) mathematical model for different aspects under Caputo fractional derivative. First, from analysis point of view, existence is necessary to be investigated for any applied problem. Therefore, we used fixed point theorem's due to Banach's and Schaefer's to establish some sufficient results regarding existence and uniqueness of the solution to the proposed model. On the other hand, stability is important in respect of approximate solution, so we have developed condition sufficient for the stability of Ulam-Hyers and their different types for the considered system. In addition, the model has also been considered for semianalytical solution via Laplace Adomian decomposition method (LADM). On Matlab, by taking some real data about Pakistan, we graph the obtained results. In the last of the manuscript, a detail discussion and brief conclusion are provided.

COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.

The Covid‐19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country‐specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid‐19 related parameters in the long‐term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well‐suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long‐term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real‐time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

Zoonotic diseases—human diseases of animal origin—represent one of the world's greatest health challenges, both today and in the past. Since the Neolithic, zoonotic diseases have been one of the major factors shaping and influencing human adaptation. Archaeology is ideally situated to provide the long view on human–animal–pathogen relationships through combining cultural, environmental and biological datasets, yet long‐term studies of linked human and animal records have often been overlooked and undertheorized. The seven papers in this special issue “Zoonotic diseases: New directions in human–animal pathology” cover a range of diseases caused by bacterial, viral, and parasitic pathogens, from case studies drawn from across Europe, Asia, Africa and the Americas. They speak to the diversity of human–animal–environment interactions that shaped disease emergence and transmission. They also review methodological advancements relating to disease identification and interpretation and discuss interdisciplinary approaches to effectively investigate these complex processes in the past. This introduction highlights their key themes and outcomes and identifies research priorities moving forward.

COVID-19 has been the most important pandemic that hit the world in the last century An overwhelming number of initiatives aimed to save lives and protect humanity came out especially at the beginning of the health crisis, following an outside the box approach to address relevant R&D problems Digital makers, not surprisingly, have been part of the game Being masters in using digital technologies for customized products, and being strategically fitted to cope with the imperative to be fast, many of the players on the front line to fight the pandemic have looked at them as the ‘providers next to the door’ able to offer fast solutions to critical problems Based on these premises and adopting frugal innovation as theoretical base, the aim of the study is to analyze how digital makers have taken part in R&D processes to find solutions to the problems created by an exogenous and unexpected sanitary problem Through an in-depth case study, we provide insights on if and how frugal approach to innovation is adapt to cope with the extraordinary pressure coming from event like the COVID-19 The results of the study highlight the power of connecting frugal innovation principles, agile mindset of makers and open innovation strategies in providing effective solutions on a local scale and eventually exploit their potential global impact Furthermore, we highlight implications and pathways for future researches © 2021 RADMA and John Wiley & Sons Ltd

The COVID-19 pandemic has caused unprecedented public health and economic crises. As a response to face the current emergency, science and innovation communities are realizing a fundamental contribution to tackle the crisis. During the past few months, we have witnessed an impressive number of initiatives to encourage networking opportunities, to foster interactions between the different stakeholders involved (health care, industry, governments, academics, ordinary people), and to develop innovative solutions and collaborative infrastructures in support of the health sector. Adopting an open and collaborative approach and joining forces is essential in the fight against the COVID-19 crisis. Also, the involvement of crowds as innovation partners can be of great support. Therefore, our work aims to review and classify those initiatives, based on the crowdsourcing model, that have been put into place to face the emergency generated by the novel coronavirus pandemic. We illustrate the 16 crowdsourcing initiatives devoted to the SARS-CoV-2 outbreak that we identified, detailing their development and implementation. Then, we propose a classification of them, along two dimensions: type of crowdsourcing configuration and kind of tasks, being able to find a relationship between these two aspects. Evidence from the analyzed projects suggests that across disparate domains, crowdsourcing can be an effective strategy in the response to the COVID-19 pandemic. To conclude, we suggest some important implications for innovation best practices and lessons that can be learned for the future: crowdsourcing, harnessing the power of crowds and online communities, can help tackle the COVID-19 pandemic, by providing original, actionable, quick, and low-cost solutions to the challenges of the current health and economic crisis.

The rapid emergence of the COVID‐19 crisis has challenged both private and public firms, requiring them to reshape their internal processes and external linkages in the fight against the virus, but also to survive the disrupting economic impact of the pandemic on their activities. Academic spinoffs have not been exempted from these dynamics. In this paper, we present and discuss a case study of an academic spinoff, Omnidermal, which has developed a new, efficient and easy‐to‐realize emergency life support machine for use in intensive and sub‐intensive care units. This case, apart from offering information on the best practices of how spinoffs may contribute socially to the fight against COVID‐19 and – more in general – against other exogenous shocks, also provides insights on their stages of development, evolution patterns and ability to define new solutions. The case shows that when the market needs are clear to a firm (as in the case of medical devices during the COVID‐19 crisis), the ‘legacy competences and practices’ of spinoffs (i.e., technical competences and work practices) can be fully exploited to compress the development time and to realize products demanded by the market. We also identify access to a network as being an essential boundary condition for this process. These results introduce an alternative scope for academic spinoffs. Given the ‘legacy competences and practices’ they are able to develop, they are ideal candidates to respond to the societal and economic challenges posed by a crisis over short periods of time. On the basis of these insights, we draw a series of implications for practitioners, policy makers and academics.

Most studies aiming to construct reference or standard charts use a cross‐sectional design, collecting one measurement per participant. Reference or standard charts can also be constructed using a longitudinal design, collecting multiple measurements per participant. The choice of appropriate statistical methodology is important as inaccurate centiles resulting from inferior methods can lead to incorrect judgements about fetal or newborn size, resulting in suboptimal clinical care. Reference or standard centiles should ideally provide the best fit to the data, change smoothly with age (eg, gestational age), use as simple a statistical model as possible without compromising model fit, and allow the computation of Z‐scores from centiles to simplify assessment of individuals and enable comparison with different populations. Significance testing and goodness‐of‐fit statistics are usually used to discriminate between models. However, these methods tend not to be useful when examining large data sets as very small differences are statistically significant even if the models are indistinguishable on actual centile plots. Choosing the best model from amongst many is therefore not trivial. Model choice should not be based on statistical considerations (or tests) alone as sometimes the best model may not necessarily offer the best fit to the raw data across gestational age. In this paper, we describe the most commonly applied methodologies available for the construction of age‐specific reference or standard centiles for cross‐sectional and longitudinal data: Fractional polynomial regression, LMS, LMST, LMSP, and multilevel regression methods. For illustration, we used data from the INTERGROWTH‐21st Project, ie, newborn weight (cross‐sectional) and fetal head circumference (longitudinal) data as examples.

A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision-making as required in the regulatory context. On the basis of examples, we explore the use of data-based Bayesian meta-analytic-predictive methods and compare these approaches with common frequentist and Bayesian meta-analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions.

Medical decisions for children are usually justified by the claim that they are in a child's best interests. More recently, following criticisms of the best interests standard, some advocate that the family's interests should influence medical decisions for children, although what is meant by family interests is often not made clear. I argue that at least two senses of family interests may be discerned. There is a ‘weak’ sense (as the amalgamated interests of family members) of family interests and a ‘strong’ sense (that the family itself has interests over and above the interests of individuals). I contend that there are problems with both approaches in making medical decisions for children but that the weak sense is more plausible. Despite this, I argue that claims for family interests are not helpful in making medical decisions for children.

Objective:\ud \ud The diagnosis of delirium depends on eliciting its features through mental status examination and informant history. However, there is marked heterogeneity in how these features are assessed, from binary subjective clinical judgement to more comprehensive methods supported by cognitive testing. The aim of this article is to review the neuropsychological research in delirium and suggest future directions in research and clinical practice.\ud Methods:\ud \ud We reviewed the neuropsychological literature on formal assessment and quantification of the different domains in delirium, focusing on the core feature of inattention.\ud Results:\ud \ud Few studies have characterised and quantified the features of delirium using objective methods commonly employed in neuropsychological research. The existing evidence confirms that patients with delirium usually show impairments on objective tests of attention compared with cognitively intact controls and, in most cases, compared with patients with dementia. Further, abnormal level of arousal appears to be a specific indicator of delirium. The neuropsychological evidence base for impairments in other cognitive domains in delirium, including visual perception, language and thought processes, is small.\ud Conclusions:\ud \ud Delirium diagnosis requires accurate testing for its features, but there is little neuropsychological research examining the nature of these features, or evaluating the reliability, validity and discriminatory power of existing assessment processes. More research using the neuropsychological approach has enormous potential to improve and standardise delirium assessment methods of the individual features of delirium, such as inattention, and in developing more robust reference standards to enable greater comparability between studies.

Despite the growing importance of ‘social value’ as a central feature of research ethics, the term remains both conceptually vague and to a certain extent operationally rigid. And yet, perhaps because the rhetorical appeal of social value appears immediate and self‐evident, the concept has not been put to rigorous investigation in terms of its definition, strength, function, and scope. In this article, we discuss how the anthropological concept of liminality can illuminate social value and differentiate and reconfigure its variegated approaches. Employing liminality as a heuristic encourages a reassessment of how we understand the mobilization of ‘social value’ in bioethics. We argue that social value as seen through the lens of liminality can provide greater clarity of its function and scope for health research. Building on calls to understand social value as a dynamic, rather than a static, concept, we emphasize the need to appraise social value iteratively throughout the entire research as something that transforms over multiple times and across multiple spaces occupied by a range of actors.

Funding support for this study is from Singapore Ministry of Education Academic Research Fund Tier 2 (MOE2015‐T2‐1‐008); Hong Kong Research Grants Council through its General Research Fund (Project 17606815); Wellcome Trust UK (GR079946/B/06/Z and GR079946/Z/06/Z). Global circuits of migration regularly separate parents from children. How families navigate this separation has changed markedly. The sharp decline in the cost of international communication makes possible new forms of transnational parenting. In many contexts, migrants are now actively engaged parents, involved in decisions, knowledgeable of children's schooling, employment, and activities, and in some cases, even conversant face‐to‐face with children via videoconferencing. These practices, however, are not universal. We use data from surveys in 3 countries to document the frequency and variability of intensive, engaged transnational parenting in the diverse global regions of Asia, Africa, and the Americas. We then ask whether the organisation of children's lives—specifically, time allocated to school homework, leisure, and household chores—varies by the degree to which migrant parents stay connected to sending homes. The gender of the migrant parent, stay‐behind caregiver, and the gender of the child emerge as explanatory factors for engaged parenting and children's time use. However, and unexpectedly, in the Philippines, migrant mothers are less likely to practice engaged parenting. In sending households, girls in two of the three countries spend more time doing household chores than boys, but parental migration does not mitigate this difference. Although we find some evidence of more traditional gender practices, we also find exceptions that suggest potentially fruitful avenues for future research. Publisher PDF

The analysis of adverse events plays an important role in the benefit assessment of drugs. Consequently, results on adverse events are an integral part of reimbursement dossiers submitted by pharmaceutical companies to health policy decision-makers. Methods applied in the analysis of adverse events commonly include simple standard methods for contingency tables. However, the results produced may be misleading if observations are censored at the time of discontinuation due to treatment switching or noncompliance, resulting in unequal follow-up periods. In this paper, we present examples to show that the application of inadequate methods for the analysis of adverse events in the reimbursement dossier can lead to a downgrading of the evidence on a drug's benefit in the subsequent assessment, as greater harm from the drug cannot be excluded with sufficient certainty. Legal regulations on the benefit assessment of drugs in Germany are presented, in particular, with regard to the analysis of adverse events. Differences in safety considerations between the drug approval process and the benefit assessment are discussed. We show that the naive application of simple proportions in reimbursement dossiers frequently leads to uninterpretable results if observations are censored and the average follow-up periods differ between treatment groups. Likewise, the application of incidence rates may be misleading in the case of recurrent events and unequal follow-up periods. To allow for an appropriate benefit assessment of drugs, adequate survival time methods accounting for time dependencies and duration of follow-up are required, not only for time-to-event efficacy endpoints but also for adverse events. © 2016 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.

Summary For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose–response function assumes correct specification of both the GPS and the outcome‐treatment relationship. This paper introduces a machine learning method, the ‘Super Learner’, to address model selection in this context. In the two‐stage estimation approach proposed, the Super Learner selects a GPS and then a dose–response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose–response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.