The normative underpinnings of population-level alcohol use: An individual-level simulation model

Background. By defining what is “normal,” appropriate, expected, and unacceptable, social norms shape human behavior. However, the individual-level mechanisms through which social norms impact population-level trends in health-relevant behaviors are not well understood. Aims. To test the ability of social norms mechanisms to predict changes in population-level drinking patterns. Method. An individual-level model was developed to simulate dynamic normative mechanisms and behavioral rules underlying drinking behavior over time. The model encompassed descriptive and injunctive drinking norms and their impact on frequency and quantity of alcohol use. A microsynthesis initialized in 1979 was used as a demographically representative synthetic U.S. population. Three experiments were performed in order to test the modelled normative mechanisms. Results. Overall, the experiments showed limited influence of normative interventions on population-level alcohol use. An increase in the desire to drink led to the most meaningful changes in the population’s drinking behavior. The findings of the experiments underline the importance of autonomy, that is, the degree to which an individual is susceptible to normative influence. Conclusion. The model was able to predict theoretically plausible changes in drinking patterns at the population level through the impact of social mechanisms. Future applications of the model could be used to plan norms interventions pertaining to alcohol use as well as other health behaviors

Multiobjective genetic programming can improve the explanatory capabilities of mechanism-based models of social systems

The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science

An integrated dual process simulation model of alcohol use behaviours in individuals, with application to US population-level consumption, 1984–2012

Introduction The Theory of Planned Behaviour (TPB) describes how attitudes, norms and perceived behavioural control guide health behaviour, including alcohol consumption. Dual Process Theories (DPT) suggest that alongside these reasoned pathways, behaviour is influenced by automatic processes that are determined by the frequency of engagement in the health behaviour in the past. We present a computational model integrating TPB and DPT to determine drinking decisions for simulated individuals. We explore whether this model can reproduce historical patterns in US population alcohol use and simulate a hypothetical scenario, “Dry January”, to demonstrate the utility of the model for appraising the impact of policy interventions on population alcohol use. Method Constructs from the TPB pathway were computed using equations from an existing individual-level dynamic simulation model of alcohol use. The DPT pathway was initialised by simulating individuals’ past drinking using data from a large US survey. Individuals in the model were from a US population microsimulation that accounts for births, deaths and migration (1984–2015). On each modelled day, for each individual, we calculated standard drinks consumed using the TPB or DPT pathway. In each year we computed total population alcohol use prevalence, frequency and quantity. The model was calibrated to alcohol use data from the Behavioral Risk Factor Surveillance System (1984–2004). Results The model was a good fit to prevalence and frequency but a poorer fit to quantity of alcohol consumption, particularly in males. Simulating Dry January in each year led to a small to moderate reduction in annual population drinking. Conclusion This study provides further evidence, at the whole population level, that a combination of reasoned and implicit processes are important for alcohol use. Alcohol misuse interventions should target both processes. The integrated TPB-DPT simulation model is a useful tool for estimating changes in alcohol consumption following hypothetical population interventions

Introducing CASCADEPOP: an open-source sociodemographic simulation platform for US health policy appraisal

Largescale individual-level and agent-based models are gaining importance in health policy appraisal and evaluation. Such models require the accurate depiction of the jurisdiction’s population over extended time periods to enable modeling of the development of non-communicable diseases under consideration of historical, sociodemographic developments. We developed CASCADEPOP to provide a readily available sociodemographic micro-synthesis and microsimulation platform for US populations. The micro-synthesis method used iterative proportional fitting to integrate data from the US Census, the American Community Survey, the Panel Study of Income Dynamics, Multiple Cause of Death Files, and several national surveys to produce a synthetic population aged 12 to 80 years on 01/01/1980 for five states (California, Minnesota, New York, Tennessee, and Texas) and the US. Characteristics include individuals’ age, sex, race/ethnicity, marital/employment/parental status, education, income and patterns of alcohol use as an exemplar health behavior. The microsimulation simulates individuals’ sociodemographic life trajectories over 35 years to 31/12/2015 accounting for population developments including births, deaths, and migration. Results comparing the 1980 micro-synthesis against observed data shows a successful depiction of state and US population characteristics and of drinking. Comparing the microsimulation over 30 years with Census data also showed the successful simulation of sociodemographic developments. The CASCADEPOP platform enables modelling of health behaviors across individuals’ life courses and at a population level. As it contains a large number of relevant sociodemographic characteristics it can be further developed by researchers to build US agent-based models and microsimulations to examine health behaviors, interventions, and policies

Heart Rate Recovery After Exercise Is Associated With Arrhythmic Events in Patients With Catecholaminergic Polymorphic Ventricular Tachycardia

BACKGROUND: Risk stratification in catecholaminergic polymorphic ventricular tachycardia remains ill defined. Heart rate recovery (HRR) immediately after exercise is regulated by autonomic reflexes, particularly vagal tone, and may be associated with symptoms and ventricular arrhythmias in patients with catecholaminergic polymorphic ventricular tachycardia. Our objective was to evaluate whether HRR after maximal exercise on the exercise stress test (EST) is associated with symptoms and ventricular arrhythmias. METHODS: In this retrospective observational study, we included patients ≤65 years of age with an EST without antiarrhythmic drugs who attained at least 80% of their age- and sex-predicted maximal HR. HRR in the recovery phase was calculated as the difference in heart rate (HR) at maximal exercise and at 1 minute in the recovery phase (ΔHRR1'). RESULTS: We included 187 patients (median age, 36 years; 68 [36%] symptomatic before diagnosis). Pre-EST HR and maximal HR were equal among symptomatic and asymptomatic patients. Patients who were symptomatic before diagnosis had a greater ΔHRR1' after maximal exercise (43 [interquartile range, 25-58] versus 25 [interquartile range, 19-34] beats/min; P<0.001). Corrected for age, sex, and relatedness, patients in the upper tertile for ΔHRR1' had an odds ratio of 3.4 (95% CI, 1.6-7.4) of being symptomatic before diagnosis (P<0.001). In addition, ΔHRR1' was higher in patients with complex ventricular arrhythmias at EST off antiarrhythmic drugs (33 [interquartile range, 22-48] versus 27 [interquartile range, 20-36] beats/min; P=0.01). After diagnosis, patients with a ΔHRR1' in the upper tertile of its distribution had significantly more arrhythmic events as compared with patients in the other tertiles (P=0.045). CONCLUSIONS: Catecholaminergic polymorphic ventricular tachycardia patients with a larger HRR following exercise are more likely to be symptomatic and have complex ventricular arrhythmias during the first EST off antiarrhythmic drug

Meta-analysis of exome array data identifies six novel genetic loci for lung function

Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P<2·8x10-7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs (SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk.

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies

Importance of fullerenic active sites in surface modification of carbon black by plasma polymerisation

Carbon black is widely used as an active filler in rubber to improve the physical properties. The surface energy of carbon black is high compared to that of various elastomers like Styrene–Butadiene rubber, Butadiene rubber and Ethylene–Propylene Diene rubber. Reducing the surface energy and matching its surface chemistry will aid in compatibilising carbon black with various elastomers. Surface modification of carbon black by plasma polymerisation has been attempted earlier in order to reduce the surface energy of carbon black. These studies have shown that for effective surface modification of carbon black, there should be available a sufficient number of surface active sites. The present paper looks into the possibilities of utilizing the surface activity of a by-product of the production of fullerene, the fullerene soot for its use in a plasma modification process. Thermogravimetric analysis, wetting behaviour with various liquids of known surface tension, time of flight secondary ion mass spectrometry and transmission electron microscopy are used to characterise the carbon black before and after surface modification. The study shows that the fullerenic type structures present on the surface of fullerenic soot act as very active growth sites for the plasma polymer

Plasma processing of carbon materials

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Continuous production of Fullerenes and other carbon nanomaterials on a semi-industrial scale using plasma technology

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