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Abstract

Aims
To demonstrate a novel method for presenting and exploring data in systematic reviews of the alcohol literature.


Methods
Harvest plots are a graphical method for displaying data on the overall pattern of evidence from a systematic review. They can display the direction of effects and risk of bias within studies for multiple outcomes in a single graphical chart. Using data from our previous meta-analysis on the association between personality disorder and alcohol treatment outcome, we extended the application of harvest plots by developing an interactive online harvest plot application.


Results
Studies included in the review were heterogeneous in design. There were many different primary outcomes, and similar outcomes were often defined differently across studies. The interactive harvest plot allows readers to explore trends in the data across multiple outcomes, including the impact of within-study bias and year of publication. In contrast, meta-analysis on the same data was hampered by a lack of consistency in the way outcomes were measured, and incomplete reporting of effect sizes and their variance. This meant many studies included in the systematic review could not be meta-analysed.


Conclusions
Interactive harvest plots are a novel graphical method to present data from systematic reviews. They can supplement or even replace meta-analysis when the studies included in a systematic review use heterogeneous designs and measures, as is often the case in the alcohol literature.

Background: Perceptions of tobacco, cannabis, and electronic nicotine delivery systems (ENDS) are continually evolving in the United States. Exploring these characteristics through user generated text sources may provide novel insights into product use behavior that are challenging to identify using survey-based methods. The objective of this study was to compare the topics frequently discussed among Reddit members in cannabis, tobacco, and ENDS-specific subreddits. Methods: We collected 643,070 posts on the social media site Reddit between January 2013 and December 2018. We developed and validated an annotation scheme, achieving a high level of agreement among annotators. We then manually coded a subset of 2,630 posts for their content with relation to experiences and use of the three products of interest, and further developed word cloud representations of the words contained in these posts. Finally, we applied Latent Dirichlet Allocation (LDA) topic modeling to the 643,070 posts to identify emerging themes related to cannabis, tobacco, and ENDS products being discussed on Reddit. Results: Our manual annotation process yielded 2,148 (81.6%) posts that contained a mention(s) of either cannabis, tobacco, or ENDS with 1,537 (71.5%) of these posts mentioning cannabis, 421 (19.5%) mentioning ENDS, and 264 (12.2%) mentioning tobacco. In cannabis-specific subreddits, personal experiences with cannabis, cannabis legislation, health effects of cannabis use, methods and forms of cannabis, and the cultivation of cannabis were commonly discussed topics. The discussion in tobacco-specific subreddits often focused on the discussion of brands and types of combustible tobacco, as well as smoking cessation experiences and advice. In ENDS-specific subreddits, topics often included ENDS accessories and parts, flavors and nicotine solutions, procurement of ENDS, and the use of ENDS for smoking cessation. Conclusion: Our findings highlight the posting and participation patterns of Reddit members in cannabis, tobacco, and ENDS-specific subreddits and provide novel insights into aspects of personal use regarding these products. These findings complement epidemiologic study designs and highlight the potential of using specific subreddits to explore personal experiences with cannabis, ENDS, and tobacco products.

Commercial operations of uncrewed aerial vehicles (UAVs or drones) are expanding, with medical logistics using UAVs as part of health service supply chains being targeted. The ability to transport cargos that include items classified as Dangerous Goods (DG) is a significant factor in enabling UAV logistics to assist medical supply chains, but DG regulations for air transport have developed from the perspective of crewed aircraft and not UAVs. This paper provides an important audit of the current DG regulations, best practice in their application and the development of much-needed new governance that will be required to fully exploit UAVs for the safe transport of DG in medical logistics. Findings from the audit provide a summary of the circumstances and potential challenges resulting from the application of DG regulations as they stand to UAV operations, particularly for medical logistics, and convenient guidance on the practical implications of DG regulations for UAV operators. The main conclusion is that this is an under-researched domain, not yet given full consideration in a holistic way by regulators, governments, industry bodies, practitioners or academia.

Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations that are driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses on the task of minority representations in degree-based rankings. By analysing synthetic and real networks with varying homophily levels and group sizes, we find that the effect of introducing systematic edge errors depends on both the type of edge error and the level of homophily in the system: in heterophilic networks, minority representations in rankings are very sensitive to the type of systematic edge error. In contrast, in homophilic networks we find that minorities are at a disadvantage regardless of the type of error present. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks.

AbstractSiblings of children with neurodevelopmental disorders (ND) are at increased risk of mental health problems. The burden on families of children with ND is exacerbated in low-income countries with limited health services and dependency on informal care systems. Yet, there is little research on family impacts of ND in non-Western settings, and no evidence-based interventions for siblings. We examined initial outcomes and feasibility of a manual-based intervention for siblings and parents of children with ND, called SIBS, delivered in Cambodia. SIBS has promising evidence from an open trial in Norway. We delivered eight groups for 52 siblings (M age = 12.7 years, SD = 2.7; 44.0% female) and 56 caregivers (M age = 43.5 years, SD = 8.5; 61.1% mothers) of 54 children with ND at the only public child mental health clinic in Cambodia. We aimed to improve sibling and parent mental health and family communication. The SIBS intervention comprises three separate sibling/parent group sessions and two joint sibling-parent dialogue sessions. Parent-reported mental health scores for siblings were higher than sibling self-report. Parent mental health problems at baseline were high, with no difference between mothers and fathers. There was significant improvement in parent mental health and parent-rated mental health for siblings from baseline to 4-month post-intervention (effect sizes d = 0.44 to 0.52). There was no change in sibling-reported mental health or family communication. Sibling- and parent-rated user satisfaction was high. We conclude that the SIBS intervention showed promise in Cambodia. However, revision of the communication component is needed.

Surface acoustic wave (SAW) is one of the most promising technology in the field of gas sensing at low concentrations. Field deployable portable SAW detectors are, however, prone to noise, there by limiting the detection at low concentrations. To meet the current requirements of gas detection at low concentrations, the readout methodology needs to be based on minimal hardware and better noise management. In this paper we describe a readout scheme for portable SAW gas detectors incorporating a field programmable gate array (FPGA). The developed readout system includes a modified reciprocal frequency counter for differential SAW sensor, median noise filtering and moving averages smoothing for noise management, peak detection and interfacing with external display, all implemented in FPGA. The developed readout was tested against VOCs using a lab developed vapour generator and the results have been presented in the paper. The readout system is compact, low power consuming and expandable through software thus ideal for portable handheld applications.

AbstractReducing global warming will require enacting strong climate policies, which is unlikely to happen without public support. While prior research has identified varied predictors of climate change policy support, it is unclear which predictors are strongest for the American electorate as a whole, and which predictors are strongest for Democrats and Republicans. In a nationally representative sample of registered voters (n = 2063), we use relative weight analysis to identify the strongest predictors of public climate policy support. We find that, among registered voters in the USA, the five most important predictors of climate policy support are: worry about global warming; risk perceptions; certainty that global warming is happening; belief that global warming is human-caused; and general affect toward global warming. Collectively, these five variables account for 51% of the variance in policy support. Results split by political party indicate that pro-climate injunctive norms and global warming risk perceptions are the variables that differ most between Republicans and Democrats, accounting for significantly more variance in policy support among Republicans. These findings can inform policymakers and advocates seeking to build public support for climate action.