Individual-level Observations Research Articles

THERAPEUTIC POTENTIAL OF FUNCTIONAL PRODUCT BASED ON WILD EDIBLE MUSHROOMS

Food supplements have proven their effectiveness through the support offered to administer certain drugs and their ability to determine the prevention of degenerative diseases. The correlation of assay methods with individual-level observations is essential in understanding the effect of biopharmaceuticals as a useful tool for long-term homeostasis preservation. This paper aimed to present essential aspects related to using wild edible mushrooms as functional supplements in type 2 diabetes and associated cardiovascular diseases. The product RoBioMush was presented as a result of the scientific data applied within the university, and the effects recorded after the administration of the product to the target groups were highlighted. Thus, the administration of RoBioMush had as its main effect the regulation of intestinal transit, a fact demonstrated by the researched modulation process previously in an in vitro study.

How to measure mast seeding?

The periodic production of large seed crops, or masting, is a widespread phenomenon in perennial plants. This behavior can enhance the reproductive efficiency of plants, leading to increased fitness, and produce ripple effects on food webs. While variability from year to year is a defining characteristic of masting, the methods used to quantify this variability are highly debated. The commonly used coefficient of variation lacks the ability to account for the serial dependence in mast data and can be influenced by zeros, making it a less suitable choice for various applications based on individual-level observations, such as phenotypic selection, heritability, and climate change studies, which rely on individual-plant-level datasets that often contain numerous zeros. To address these limitations, we present three case studies and introduce volatility and periodicity, which account for the variance in the frequency domain by emphasizing the significance of long intervals in masting. By utilizing examples of Sorbus aucuparia, Pinus pinea, Quercus robur, Quercus pubescens, and Fagus sylvatica, we demonstrate how volatility captures the effects of variance at both high and low frequencies, even in the presence of zeros, leading to improved ecological interpretations of the results. The growing availability of long-term, individual-plant datasets promises significant advancements in the field, but requires appropriate tools for analysis, which the new metrics provide.

Prison Populations: The International Mystery

At first glance international prison rates, measured by prisoners per 100,000 population, appear to be uncorrelated with national intelligence and with racial diversity, which contradicts both a priori expectations and individual level observations that higher intelligence (IQ) generally reduces criminality, although GDP per capita shows a small positive correlation. We present a solution to this mystery by first breaking prison rates into three components: crime rates (proxied by homicide rates), effectiveness at catching and imprisoning criminals, and sentencing length. We then employ a path analysis to first estimate the effect of national IQ and racial diversity on GDP per capita and income inequality. Finally, we estimate both the direct and indirect association of intelligence and diversity with our prison rate components. Consistent with previous literature we find that national IQ increases GDP per capita and reduces income inequality, whilst racial diversity raises income inequality. GDP per capita in turn is correlated with lower homicide rates, higher effectiveness and longer sentencing length, whilst inequality is not significantly associated with any outcome variable, after controlling for racial diversity. While the association between national IQ and prison rate components appears to be entirely indirect, through the channel of national income, racial diversity is directly associated with higher homicide rates and is weakly positively correlated with sentencing length. This evidence suggests that the opposing effects of raising effectiveness and reducing homicide rates provides an explanation for the lack of correlation between national IQ and prison rates. Keywords: Prisoners, Crime, Homicide, Intelligence, GDP, Racial diversity, Income inequality, Police effectiveness

Entrepreneurship and social capital: a multi-level analysis

PurposeThe purpose of this paper is to examine the relationship between an individual's social capital context and entrepreneurship using a multi-level modelling framework.Design/methodology/approachThis paper uses data from 87,007 individual level observations across 428 regions in 37 countries. The data comes from the 2010 and 2016 Life in Transition Surveys. The paper uses a principal component analysis to identify the different dimensions of an individual's social capital context. Subsequently, a multi-level model is employed examining the relationship between the components of an individual's social capital context and entrepreneurship (which is proxied by an individual's attempt to set up a business), whilst controlling for both country and regional effects.FindingsGreater levels of networking, informal connections and tolerance of others have a significant positive relationship with entrepreneurial activity. Trust of institutions and others have a negative relationship with entrepreneurial activity. Regional and country differences are also important for entrepreneurship, demonstrating the importance of the multi-level and social contextual environment for business development.Originality/valueFirstly, the authors present a broad, but comprehensive social contextual framework incorporating many measures of social capital when examining the importance of social capital for business development. Secondly, the work provides interesting results on the “bright and dark sides of trust” for entrepreneurship, answering calls for improved understandings on the positive and negative relationships between social capital and entrepreneurial activity. Thirdly, the paper extends the burgeoning but limited number of studies that examine the multi-level contextual environment of entrepreneurial activities.

Foetal and neonatal exposure prediction and dosing evaluation for ampicillin using a physiologically-based pharmacokinetic modelling approach.

Ampicillin is frequently used in neonates for the treatment of sepsis and as an intrapartum prophylaxis option for Group B Streptococcus. Pharmacokinetic data to guide ampicillin dosing in neonates and during the intrapartum period are limited. The objective of this study was to build a physiologically-based pharmacokinetic (PBPK) model to characterize the disposition of ampicillin in neonates and foetuses and to inform corresponding optimal dosing regimens. An adult ampicillin PBPK model was first developed using the Simcyp® simulator. The adult model was then scaled to neonates by accounting for maturational changes in physiological parameters and age-dependent drug disposition or extended to a pregnancy model for mothers and foetuses. Models were verified using collected mean or individual-level concentration data from the literature. The developed adult PBPK model included elimination via glomerular filtration, OAT3-mediated tubular secretion and biliary excretion as well as hepatic metabolism, and 89.8% of the observed mean concentrations in adults were within a 2-fold range of model mean predictions. Most of the observed individual-level observations in neonates (78.4%) and foetuses (about 65% in two studies) were within the 90% prediction intervals. The recommended 50 mg/kg every 8 h (q8h) ampicillin regimen achieved the 75% fraction time of total drug concentration above minimum inhibitory concentration (T > MIC) target for an MIC ≤8mg/L in >90% virtual neonates, and 1g ampicillin for pregnant women provided adequate foetal exposure (>0.25 mg/L) for 4h prior to delivery. A PBPK model was developed to characterize ampicillin's disposition in neonates, pregnant women, and foetuses, and the model supported optimal dosing evaluation in these vulnerable populations.

How the internet and social media reduce government approval: empirical evidence from Russian regions

ABSTRACT In this paper we study the effect of the internet and social media on government approval. On the one hand, the internet exposes people to independent information, which makes them possibly more critical of the government. On the other, many countries use the internet for propaganda, which might increase support for the government. We study these effects via the example of Russia. We utilize data from an existing survey: the resulting dataset contains data on 17,824 individual-level observations from 64 regions in Russia, 2010–2019. We find that more intensive internet use and access to social media are associated with a decrease in government approval. Yet, the influence of social media is more nuanced. The Russian-language homegrown network VKontakte increases public approval of the government. To partially account for self-selection bias, we use the propensity score matching method. Our results remain robust and allow us to make causal inferences.

Changes in unrelated variety and climbing the poverty ladder: a U-shaped relationship

ABSTRACT The purpose of this paper is to study how changes in unrelated variety influence individuals’ poverty alleviation. Drawing on the LiTS III database, we employed the Oprobit model to test 5007 individual-level observations from 23 regions in four former Yugoslavian countries. All results imply that the changes in unrelated variety have a U-shaped relationship with individuals’ poverty alleviation. Our findings enrich the unrelated variety research by providing micro-level evidence and offer practical insights for governments, organizations and individuals aiming to reduce poverty.

Job satisfaction dynamics: how do they impact employment mode choice for people with a disability?

BackgroundTo enhance economic participation for people with a disability in the labor market, a better understanding of how job satisfaction influences employment mode decisions is needed. MethodsThis study uses data collected from 8,345 People with Disability(PWD) workers from waves 3 to 19 of the Household, Income and Labour Dynamics in Australia (HILDA) survey to investigate the relationship between six domains of job satisfaction and employment mode (employee, solo self-employed, and employer) and covariates related to personal, health, socio-economic and employment-related attributes. ResultsAnalysis of 25,169 individual-level observations show important differences in the level of job satisfaction across the different employment modes and for different covariates. PWD entrepreneurs reported higher levels of satisfaction with their work, flexibility, and overall satisfaction, whereas employees report higher levels of contentment with salary, job security, and work hours. The findings also highlight interesting differences due to personal, health, socio-economic and employment related attributes. ConclusionsCollectively, these insights can guide policymakers to enhance entrepreneurial pathways for people with a disability.

Digital infrastructure and entrepreneurial action-formation: A multilevel study

This study investigates how country-level digital infrastructure shapes the relationships between the action-formation mechanisms of socio-cognitive traits, i.e., entrepreneurial self-efficacy, fear of failure, and opportunity recognition, and entrepreneurial action. We amalgamate the agent-centric social cognitive theory with the external enabler framework and apply mechanism-based theorizing to explain how access-related mechanisms provided by digital infrastructure influence entrepreneurial action-formation. Based on a multilevel analysis of 344,265 individual-level observations from 46 countries and an additional robustness analysis of 391,119 individuals from 53 countries, we find that an individual's proclivity to starting a new venture is contingent upon the level of the digital infrastructure of a country. The empirical results show that a country's digital infrastructure is an external enabler that moderates the relationship between socio-cognitive traits and entrepreneurial action.

COVID-19 risk factors amongst 14,786 care home residents: an observational longitudinal analysis including daily community positive test rates of COVID-19, hospital stays and vaccination status in Wales (UK) between 1 September 2020 and 1 May 2021.

BackgroundCOVID-19 vaccinations have been prioritised for high risk individuals.AimDetermine individual-level risk factors for care home residents testing positive for SARS-CoV-2.Study designLongitudinal observational cohort study using individual-level linked data from the Secure Anonymised Information Linkage (SAIL) databank.SettingFourteen thousand seven hundred and eighty-six older care home residents (aged 65+) living in Wales between 1 September 2020 and 1 May 2021. Our dataset consisted of 2,613,341 individual-level daily observations within 697 care homes.MethodsWe estimated odds ratios (ORs [95% confidence interval]) using multilevel logistic regression models. Our outcome of interest was a positive SARS-CoV-2 PCR test. We included time-dependent covariates for the estimated community positive test rate of COVID-19, hospital inpatient status, vaccination status and frailty. Additional covariates were included for age, sex and specialist care home services.ResultsThe multivariable regression model indicated an increase in age (OR 1.01 [1.00,1.01] per year), community positive test rate (OR 1.13 [1.12,1.13] per percent increase), hospital inpatients (OR 7.40 [6.54,8.36]), and residents in care homes with non-specialist dementia care (OR 1.42 [1.01,1.99]) had an increased odds of a positive test. Having a positive test prior to the observation period (OR 0.58 [0.49,0.68]) and either one or two doses of a vaccine (0.21 [0.17,0.25] and 0.05 [0.02,0.09], respectively) were associated with a decreased odds.ConclusionsCare providers need to remain vigilant despite the vaccination rollout, and extra precautions should be taken when caring for the most vulnerable. Minimising potential COVID-19 infection for care home residents when admitted to hospital should be prioritised.

Do long-life customers pay more in pay-what-you-want pricing? Evidence from live streaming

Pay-what-you-want (PWYW) pricing is a participative pricing strategy that has emerged in many industries. A prominent example is the voluntary gift-sending in live streaming. Prior research has examined factors driving viewers’ gifting decisions in a live streaming session. In this paper, we focus on the dynamics of payment behavior over time under PWYW. Based on individual-level observations of more than 60,000 viewers over a period of 165 days since their registration on a live streaming platform, we find a declining pattern in PWYW amount with the increase in individual tenure. We then propose and test several potential explanations, including variety seeking, alternative ways to interact, and substitution effect. The empirical results are consistent with the substitution effect that the cumulative spending in the past could crowd out the current PWYW amount, because viewers may believe that they have already contributed a significant amount. We further investigate several moderating factors for the overall negative effect of past spending. Besides contributing to the PWYW literature, our research would help firms understand the dynamics of individual PWYW behavior and design mechanisms to incentivize long-life customers to pay.

An investigation of cancer survival inequalities associated with individual-level socio-economic status, area-level deprivation, and contextual effects, in a cancer patient cohort in England and Wales

BackgroundPeople living in more deprived areas of high-income countries have lower cancer survival than those in less deprived areas. However, associations between individual-level socio-economic circumstances and cancer survival are relatively poorly understood. Moreover, few studies have addressed contextual effects, where associations between individual-level socio-economic status and cancer survival vary depending on area-based deprivation.MethodsUsing 9276 individual-level observations from a longitudinal study in England and Wales, we examined the association with cancer survival of area-level deprivation and individual-level occupation, education, and income, for colorectal, prostate and breast cancer patients aged 20–99 at diagnosis. With flexible parametric excess hazard models, we estimated excess mortality across individual-level and area-level socio-economic variables and investigated contextual effects.ResultsFor colorectal cancers, we found evidence of an association between education and cancer survival in men with Excess Hazard Ratio (EHR) = 0.80, 95% Confidence Interval (CI) = 0.60;1.08 comparing “degree-level qualification and higher” to “no qualification” and EHR = 0.74 [0.56;0.97] comparing “apprenticeships and vocational qualification” to “no qualification”, adjusted on occupation and income; and between occupation and cancer survival for women with EHR = 0.77 [0.54;1.10] comparing “managerial/professional occupations” to “manual/technical,” and EHR = 0.81 [0.63;1.06] comparing “intermediate” to “manual/technical”, adjusted on education and income. For breast cancer in women, we found evidence of an association with income (EHR = 0.52 [0.29;0.95] for the highest income quintile compared to the lowest, adjusted on education and occupation), while for prostate cancer, all three individual-level socio-economic variables were associated to some extent with cancer survival. We found contextual effects of area-level deprivation on survival inequalities between occupation types for breast and prostate cancers, suggesting wider individual-level inequalities in more deprived areas compared to least deprived areas. Individual-level income inequalities for breast cancer were more evident than an area-level differential, suggesting that area-level deprivation might not be the most effective measure of inequality for this cancer. For colorectal cancer in both sexes, we found evidence suggesting area- and individual-level inequalities, but no evidence of contextual effects.ConclusionsFindings highlight that both individual and contextual effects contribute to inequalities in cancer outcomes. These insights provide potential avenues for more effective policy and practice.

Self-Employment, Gender and Cultural Traits

Though the gap between female and male entrepreneurship has been narrowing globally, it is still evident with female rates lagging behind those of males. While existing literature has identified several obstacles to female entrepreneurship, the role of cultural traits has not yet been adequately explored. More importantly, lacking from the literature is a close investigation of female entrepreneurial choices using individual level data across countries. Employing World Values Survey data, an extensive longitudinal panel study of countries over time with over 300,000 individual level observations, we look at how cultural indicators impact female entrepreneurship. Using probit limited dependent variable models, we analyze specifically how cultural indicators impact female entrepreneurship, using measures of independence, obedience, and control over one's own life as our cultural indicators. In our analysis, we control for age, marital status, social class, town size, and level of education, as these factors have an effect on the decision to be self-employed. We also conducted robustness analysis using alternate cultural indicator variables to look for any change when compared to our benchmark findings. Our findings show that being a female lowers the probability of being self-employed. This supports the existing literature that shows a gap, albeit narrowing, between male and female entrepreneurship. Our main set of results also show that the cultural traits of independence and sense of control over one's life both help alleviate the negative impact of gender (being a female) on entrepreneurial choices. As a society, valuing obedience has the opposite impact – it magnifies the negative effect of being female on the probability of being self-employed. These findings were shown to be robust when we incorporated alternate cultural indicator variables. Our findings suggest that consideration should be given to cultural traits in the context of development outcomes. Because cultural traits can be difficult to change, our results suggest that as policy makers implement interventions to promote self-employment among females, they should be aware of the relevant cultural factors of the area and how these interact with female entrepreneurship. Policy can then be tailored to the cultural environment to maximize the intended effect on female entrepreneurship rates.

TiMEG: an integrative statistical method for partially missing multi-omics data

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case–control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.

Individual cheating in the lab: a new measure and external validity

This paper investigates to what extent laboratory measures of cheating generalise to the field. To this purpose, we develop a lab measure that allows for individual-level observations of cheating whilst reducing the likelihood that participants feel observed. Decisions made in this laboratory task are then compared to individual choices taken in the field, where subjects can lie by misreporting their experimental earnings. We use two field variations that differ in the degree of anonymity of the field decision. According to our measure, no correlation of behaviour between the laboratory and the field is found. We then perform the same analysis using a lab measure that can only detect cheating at the aggregate level. In this case, we do find a weak correlation between the two environments. We discuss the significance and interpretation of these results.

Voting in a Pandemic: COVID-19 and Primary Turnout in Milwaukee, Wisconsin.

We report the first study of the effect of the novel coronavirus SARS-CoV-2 (COVID-19) on polling place consolidation and voting behavior. We draw upon individual-level observations from Milwaukee matched to similar observations in the surrounding municipalities to assess whether fewer polling places in the April 2020 presidential primary election decreased turnout in the city. We find polling place consolidation reduced overall turnout by about 8.7 points and reduced turnout among the Black population in the city by about 10 points. We conclude, based on these data, that polling place consolidation even accompanied by widespread absentee voting in the face of an emergency may result in disenfranchisement, particularly among Black voters.

Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes.

SummaryStepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the joint estimation of the mean and intraclass correlation coefficients (ICCs) can be computationally intensive due to large cluster-period sizes. Motivated by the need for marginal inference in SW-CRTs, we propose a simple and efficient estimating equations approach to analyze cluster-period means. We show that the quasi-score for the marginal mean defined from individual-level observations can be reformulated as the quasi-score for the same marginal mean defined from the cluster-period means. An additional mapping of the individual-level ICCs into correlations for the cluster-period means further provides a rigorous justification for the cluster-period approach. The proposed approach addresses a long-recognized computational burden associated with estimating equations defined based on individual-level observations, and enables fast point and interval estimation of the intervention effect and correlations. We further propose matrix-adjusted estimating equations to improve the finite-sample inference for ICCs. By providing a valid approach to estimate ICCs within the class of generalized linear models for correlated binary outcomes, this article operationalizes key recommendations from the CONSORT extension to SW-CRTs, including the reporting of ICCs.

Privacy preserving data visualizations

Data visualizations are a valuable tool used during both statistical analysis and the interpretation of results as they graphically reveal useful information about the structure, properties and relationships between variables, which may otherwise be concealed in tabulated data. In disciplines like medicine and the social sciences, where collected data include sensitive information about study participants, the sharing and publication of individual-level records is controlled by data protection laws and ethico-legal norms. Thus, as data visualizations – such as graphs and plots – may be linked to other released information and used to identify study participants and their personal attributes, their creation is often prohibited by the terms of data use. These restrictions are enforced to reduce the risk of breaching data subject confidentiality, however they limit analysts from displaying useful descriptive plots for their research features and findings.Here we propose the use of anonymization techniques to generate privacy-preserving visualizations that retain the statistical properties of the underlying data while still adhering to strict data disclosure rules. We demonstrate the use of (i) the well-known k-anonymization process which preserves privacy by reducing the granularity of the data using suppression and generalization, (ii) a novel deterministic approach that replaces individual-level observations with the centroids of each k nearest neighbours, and (iii) a probabilistic procedure that perturbs individual attributes with the addition of random stochastic noise. We apply the proposed methods to generate privacy-preserving data visualizations for exploratory data analysis and inferential regression plot diagnostics, and we discuss their strengths and limitations.

Long-Lasting Impact of Maternal Immune Activation and Interaction With a Second Immune Challenge on Pig Behavior

The combined effects on pig behavior of maternal immune challenge during gestation followed by a second immune challenge later in life have not been studied. Porcine reproductive and respiratory syndrome virus (PRRSV) infection during gestation can elicit maternal immune activation (MIA) yet the interactions with the offspring response to a second immune challenge after birth remains unexplored. Knowledge on the response to viral challenges in rodents has been gained through the use of the viral mimetic polyinosinic-polycytidylic acid (Poly(I:C)), yet the effects of this immune stimulant on pig behavior have not been assessed. This study advances the understanding of the combined effect of MIA and a second immune challenge later in life on female and male pig behavior. Three complementary experiments enabled the development of an effective Poly(I:C) challenge in pigs, and testing the interaction between PRRSV-elicited MIA, Poly(I:C) challenge at 60 days of age, and sex on behaviors. Individual-level observations on sickness, locomotor, and social behaviors were measured 1–3 h after Poly(I:C) challenge. Vomiting, panting, lethargy, walking, laying, playing, and touching behaviors were analyzed using generalized linear mixed effect models. Results indicated that a Poly(I:C) dose of 1 mg/kg within 1 h after injection increased the incidence of laying and sickness behavior. The Poly(I:C) challenge decreased the incidence of locomotor behaviors and activity levels. Pigs exposed to MIA had lower rates of social behaviors such as playing. The combined effect of PRRSV-elicited MIA and Poly(I:C) immune challenge further sensitized the pigs to behavior disruption across sexes including changes in sternal and lateral laying, walking, lethargy, and touching incidence. Notably, the effects of Poly(I:C) immune challenge alone on behaviors tended to be more extreme in males, whereas the effects of Poly(I:C) following MIA tended to be more extreme in females. Our findings demonstrate that MIA and Poly(I:C) affected behaviors, and the viral mimetic effects shortly after injection can offer insights into the prolonged effect of postnatal viral infections on feeding, social interactions and health status. Management practices that reduce the likelihood of gestational diseases and accommodate for behavioral disruptions in the offspring can minimize the impact of MIA.

Long term pre-treatment opioid use trajectories in relation to opioid agonist therapy outcomes among people who use drugs in a Canadian setting

BackgroundOpioid agonist therapy (OAT) models are generally provided without consideration of how pre-treatment characteristics may be associated with outcome. Therefore, we aimed to first characterize longitudinal trajectories of opioid use before initiating OAT. Then we explored the impact of OAT on opioid use across these pre-treatment trajectories. MethodsData were derived from three prospective cohort studies involving people who use drugs in Vancouver, Canada. Latent class growth analysis was applied to identify opioid use trajectories based on individual-level observations three years before starting OAT. Multivariable generalized linear mixed model was used to examine whether engaging in OAT was associated with lower risk of illicit opioid use among participants with different pre-treatment opioid use trajectories. Results464 participants were included in the study between September 2005 and November 2018. Two pre-treatment opioid use trajectories were identified: high frequency users (246, 53.0%) and gradually increasing frequency users (218, 47.0%). We observed different strengths of association between OAT engagement and illicit opioid use among high frequency users (adjusted odds ratio [AOR] = 0.36, 95% Confidence Interval [CI]: 0.20 – 0.63) and gradually increasing frequency users (AOR = 0.91, 95% CI: 0.53 – 1.56). Unstable housing, any stimulant use, experiencing violence, drug dealing, sex work involvement, and incarceration were independently and positively associated with ongoing illicit opioid use. ConclusionsDistinct pre-treatment opioid use trajectories are likely to influence treatment outcomes. Research is required to determine if tailored strategies specific to people with different pre-treatment opioid use patterns have potential to improve outcomes of OAT.