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    Pathways for socio-economic system transitions expressed as a Markov chain
    (Public Library of Science, 2023-07-31) Schweizer, Vanessa Jine; Jamieson-Lane, Alastair David; Cai, Hua; Lehner, Stephan; Smerlak, Matteo
    Cross-impact balance (CIB) analysis provides a system-theoretical view of scenarios useful for investigating complex socio-economic systems. CIB can synthesize a variety of qualitative or quantitative inputs and return information suggestive of system evolution. Current software tools for CIB are limited to identifying system attractors as well as describing system evolution from only one scenario of initial conditions at a time. Through this study, we enhance CIB by developing and applying a method that considers all possible system evolutions as transitions in a Markov chain. We investigated a simple three-variable system (27 possible scenarios) of the demographic transition and were able to generally replicate the findings of traditional CIB. Through our experiments with four possible approaches to produce CIB Markov chains, we found that information about transition pathways is gained; however, information about system attractors may be lost. Through a comparison of model results to a recent literature review on human demography, we found that low-income countries are more likely to remain stuck in a demographic trap if economic development is not prioritized alongside educational gains. Future work could test our comparative methodological findings for systems comprised of more than three variables.
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    The impact of expanded access to direct acting antivirals for Hepatitis C virus on patient outcomes in Canada
    (Public Library of Science, 2023-08-08) Chu, Cherry; Gomes, Tara; Antoniou, Tony; Wong, William W. L.; Janjua, Naveed; Guertin, Jason Robert; Schwartz, Kevin L.; Feld, Jordan; Kwong, Jeff; Tadrous, Mina
    Background Hepatitis C virus (HCV) has high global prevalence and can lead to liver complications and death. Access to direct-acting antivirals (DAAs) in Canada increased following several policy changes, however the real-world impact of expanded DAA access and increased use of these drugs is unknown. Objective We aimed to determine the early change in rates of HCV-related hospitalizations overall and HCV-related hospitalizations with hepatocellular carcinoma (HCC) after expanded DAA access. Methods We conducted a population-based time series analysis using national administrative health databases in Canada. Rates of HCV-related hospitalizations and HCV-related hospitalizations with HCC were enumerated monthly between April 2006 and March 2020. We used Autoregressive Integrated Moving Average (ARIMA) models with ramp functions in October 2014 and January 2017 to evaluate the impact of policies to expand DAA access on hospitalization outcomes. Results Rates of HCV-related hospitalizations in Canada increased between 2006 and 2014, and gradually declined thereafter. The decrease after October 2014, or the first policy change, was significant (p = 0.0355), but no further change was found after the second policy change in 2017 (p = 0.2567). HCV-related hospitalizations with HCC increased until end of 2013, followed by a plateau, before declining in 2016. No significant shifts were found after the first policy change in 2014 (p = 0.1291) nor the second policy change in 2017 (p = 0.6324). Subgroup analyses revealed that those aged 50–64 and males had observable declines in rates of HCV-related hospitalizations in the year prior to the first policy change. Conclusions Expanding DAA access was associated with a drop in HCV-related hospitalizations in the overall Canadian population coinciding with the 2014 policy change. In light of the time required for HCV-related complications to manifest, continued ongoing research examining the real-world effectiveness of DAAs is required.
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    MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenome-assembled genomes
    (Public Library of Science, 2023-08-18) Li, Wanxin; Kari, Lila; Yu, Yaoliang; Hug, Laura A.
    We propose MT-MAG, a novel machine learning-based software tool for the complete or partial hierarchically-structured taxonomic classification of metagenome-assembled genomes (MAGs). MT-MAG is alignment-free, with k-mer frequencies being the only feature used to distinguish a DNA sequence from another (herein k = 7). MT-MAG is capable of classifying large and diverse metagenomic datasets: a total of 245.68 Gbp in the training sets, and 9.6 Gbp in the test sets analyzed in this study. In addition to complete classifications, MT-MAG offers a “partial classification” option, whereby a classification at a higher taxonomic level is provided for MAGs that cannot be classified to the Species level. MT-MAG outputs complete or partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks. To assess the performance of MT-MAG, we define a “weighted classification accuracy,” with a weighting scheme reflecting the fact that partial classifications at different ranks are not equally informative. For the two benchmarking datasets analyzed (genomes from human gut microbiome species, and bacterial and archaeal genomes assembled from cow rumen metagenomic sequences), MT-MAG achieves an average of 87.32% in weighted classification accuracy. At the Species level, MT-MAG outperforms DeepMicrobes, the only other comparable software tool, by an average of 34.79% in weighted classification accuracy. In addition, MT-MAG is able to completely classify an average of 67.70% of the sequences at the Species level, compared with DeepMicrobes which only classifies 47.45%. Moreover, MT-MAG provides additional information for sequences that it could not classify at the Species level, resulting in the partial or complete classification of 95.13%, of the genomes in the datasets analyzed. Lastly, unlike other taxonomic assignment tools (e.g., GDTB-Tk), MT-MAG is an alignment-free and genetic marker-free tool, able to provide additional bioinformatics analysis to confirm existing or tentative taxonomic assignments.
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    The association between religious participation and memory among middle-aged and older adults: A systematic review
    (Public Library of Science, 2023-08-18) Nath, Bonita; Patel, Priya; Oremus, Mark
    Objectives Mounting evidence suggests religion plays an important role in maintaining cognition. No prior systematic review has focused on the specific association between religion and the memory domain of cognition in middle-aged and older adults. We carried out a systematic review to explore this association in depth. Methods We searched the PsycINFO, Scopus, and PubMed databases to identify articles assessing any means of measuring religion as the exposure and memory as the outcome. Articles had to report on studies with comparison groups to be eligible for inclusion in the review. We followed the PRISMA checklist to conduct the review (PROSPERO registration # CRD42022330389). Results Nine out of the 1648 citations retrieved in the literature search were included in the review. The majority of included articles had a moderate risk of bias. Most results showed positive associations between religion and memory. Discussion Despite consistency in the direction of association between religion and memory, the literature contained some important research gaps: the studies were cross-sectional; a lack of information existed regarding whether different faiths, sex/gender and depression affected the association; and underpowered studies prevented us from drawing firm conclusions about the direction or magnitude of effect. Longitudinal studies avoiding these issues are needed in this field.
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    Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
    (Public Library of Science, 2023-08-24) Cousins, Melanie; Parmley, E. Jane; Greer, Amy L.; Neiterman, Elena; Lambraki, Irene A.; Graells, Tiscar; Leger, Anais; Henriksson, Patrik J. G.; Troell, Max; Wernli, Didier; Jorgensen, Peter Sogaard; Carson, Carolee A.; Majowicz, Shannon E.
    Background Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context. Methods This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR. Main findings Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants’ statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context. Conclusion Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking.