Vaccine hesitancy: characteristics of the refusal of childhood vaccination in a Peruvian population.

Understanding the determinants of vaccine hesitancy is paramount to reinstate confidence in immunizations. The objective of this investigation was to explore the characteristics of the vaccination decision-making process that may result in the refusal of childhood immunization in Peru, during February-June 2020. A descriptive, cross-sectional study involving telephone interviews was executed in Peru. The Parents Attitudes about Childhood Vaccines (PACV) survey was used. A demographic analysis was done, followed by an unadjusted exploratory subgroup analysis. Out of 552 subjects, 9.8% were considered vaccine hesitant, 70.3% had purposively delayed vaccination, 88.4% thought fewer vaccines were better and 52.2% were concerned about vaccine safety. The level of hesitancy was inversely proportional to the level of education and the number of children at home. Mothers and subjects aged ≤29 years showed a greater level of vaccine hesitancy. This population displays a vaccine-hesitant conduct. Vaccine safety and the number of vaccines to administer are important determining factors. This behavior could be influenced by variables such as level of education, number of children at home, parental relationship, and age. These results help understand local vaccination behaviors. More studies are encouraged to confirm and validate these findings

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

The predominance of seafood allergy in Vietnamese adults: results from the first population-based questionnaire survey

Background: Food allergy (FA) is a serious, costly and growing health problem worldwide. FA occurs in both children and adults; however, there is a paucity of information on FA prevalence and its clinical features in the adult population, especially in Asia. We sought to assess the prevalence of FAs in Vietnamese adults and the distribution of offending food items among different regions throughout Vietnam. Methods: A nationwide, cross-sectional, population-based survey was conducted among University students aged 16-50 years. We used a structured, anonymous questionnaire, which was modified from recently published FA epidemiologic studies and based on European Academy of Allergy and Clinical Immunology (EAACI) guidelines, to collect data on FA prevalence, clinical presentations, and implicated food groups. Statistical analysis was performed to generate the prevalence of self-reported and doctor-diagnosed FA and to examine the association of key environmental factors and FA incidence in this population. Results: Of the 14,500 surveys distributed, a total of 9,039 responses were returned, resulting in a response rate of 62.4%. Among participants who reported food-induced adverse reactions, 48.0% have repeated reactions. 18.0% of the participants perceived FA symptoms, but less than half of them sought medical services for confirmation (37.9%). Stratifying for true FA symptoms, the prevalence of self-reported FA was 11.8% and of doctor-diagnosed FA, 4.6%. The most common doctordiagnosed FA was to crustacean (3.0%; 95% CI, 2.6-3.3), followed by fish (1.6%; 95% CI, 1.3-1.8), mollusk (1.3%; 95% CI, 1.0-1.5) and beef (1.0%; 95% CI, 0.8-1.2). The prevalence of doctordiagnosed FA differed among participants living in urban (6.5%) and rural regions (4.9%) (P< 0.001). Atopic family history was the strongest predictor for FA(odds ratio 8.0; 95% CI, 6.2-10.4). Conclusions: Seafood allergy among adults is predominant in Vietnam, followed by beef, milk, and egg, while peanut, soy, and tree nut allergy are much less common. Populations in rural regions have considerably less FA; however, the protective environmental factors have yet to be identified

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

Using multi-objective grammar-based genetic programming to integrate multiple social theories in agent-based modeling

Different theoretical mechanisms have been proposed for explaining complex social phenomena. For example, explanations for observed trends in population alcohol use have been postulated based on norm theory, role theory, and others. Many mechanism-based models of phenomena attempt to translate a single theory into a simulation model. However, single theories often only represent a partial explanation for the phenomenon. The potential of integrating theories together, computationally, represents a promising way of improving the explanatory capability of generative social science. This paper presents a framework for such integrative model discovery, based on multi-objective grammar-based genetic programming (MOGGP). The framework is demonstrated using two separate theory-driven models of alcohol use dynamics based on norm theory and role theory. The proposed integration considers how the sequence of decisions to consume the next drink in a drinking occasion may be influenced by factors from the different theories. A new grammar is constructed based on this integration. Results of the MOGGP model discovery process find new hybrid models that outperform the existing single-theory models and the baseline hybrid model. Future work should consider and further refine the role of domain experts in defining the meaningfulness of models identified by MOGGP

Can social norms explain long-term trends in alcohol use? Insights from inverse generative social science

Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates successfully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure's theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact

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

Cre recombinase expression cooperates with homozygous FLT3 internal tandem duplication knockin mouse model to induce acute myeloid leukemia

Murine models offer a valuable tool to recapitulate genetically defined subtypes of AML, and to assess the potential of compound mutations and clonal evolution during disease progression. This is of particular importance for difficult to treat leukemias such as FLT3 internal tandem duplication (ITD) positive AML. While conditional gene targeting by Cre recombinase is a powerful technology that has revolutionized biomedical research, consequences of Cre expression such as lack of fidelity, toxicity or off-target effects need to be taken into consideration. We report on a transgenic murine model of FLT3-ITD induced disease, where Cre recombinase expression alone, and in the absence of a conditional allele, gives rise to an aggressive leukemia phenotype. Here, expression of various Cre recombinases leads to polyclonal expansion of FLT3(ITD/ITD) progenitor cells, induction of a differentiation block and activation of Myc-dependent gene expression programs. Our report is intended to alert the scientific community of potential risks associated with using this specific mouse model and of unexpected effects of Cre expression when investigating cooperative oncogenic mutations in murine models of cancer