Learning debiased graph representations from the OMOP common data model for synthetic data generation.

BACKGROUND: Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention. METHODS: Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method. For this study, data pipelines were built which extract data from OMOP, convert them into time series format, learn temporal rules by 2 statistical algorithms (Markov chain, TARM) and 3 algorithms of causal discovery (DYNOTEARS, J-PCMCI+, LiNGAM) and map the outputs into Synthea graphs. The graphs are evaluated quantitatively by their individual and relative complexity and qualitatively by medical experts. RESULTS: The algorithms were found to learn qualitatively and quantitatively different graph representations. Whereas the Markov chain results in extremely large graphs, TARM, DYNOTEARS, and J-PCMCI+ were found to reduce the data dimension during learning. The MultiGroupDirect LiNGAM algorithm was found to not be applicable to the problem statement at hand. CONCLUSION: Only TARM and DYNOTEARS are practical algorithms for real-world data in this use case. As causal discovery is a method to debias purely statistical relationships, the gradient-based causal discovery algorithm DYNOTEARS was found to be most suitable.

Pathways linking nutrient enrichment, habitat structure, and parasitism to host-resource interactions.

Human activities simultaneously alter nutrient levels, habitat structure, and levels of parasitism. These activities likely have individual and joint impacts on food webs. Furthermore, there is particular concern that nutrient additions and changes to habitat structure might exacerbate the size of epidemics and impacts on host density. We used a well-studied zooplankton-fungus host-parasite system and experimental whole water column enclosures to factorially manipulate nutrient levels, habitat structure (specifically: mixing), and presence of parasites. Nutrient addition increased infection prevalence, density of infected hosts, and total host density. We hypothesized that nutrients, mixing, and parasitism were linked in multiple ways, including via their combined effects on phytoplankton (resource) abundance, and we used structural equation modeling to disentangle these pathways. In the absence of the parasite, both nutrients and mixing increased abundance of phytoplankton, whereas host density was negatively related to phytoplankton abundance, suggesting a mixture of bottom-up and top-down control of phytoplankton. In the presence of the parasite, nutrients still increased phytoplankton abundance but mixing no longer did, and there was no longer a significant relationship between host density and phytoplankton. This decoupling of host-resource dynamics may have resulted from reduced grazing due to illness-mediated changes in feeding behavior. Overall, our results show that the impact of one human activity (e.g., altered habitat structure) might depend on other human impacts (e.g., parasite introduction). Fortunately, carefully designed experiments and analyses can help tease apart these multifaceted relationships, allowing us to understand how human activities alter food webs, including interactions between hosts and their parasites and resources.

Pathway analysis between dental caries and oral health-related quality of life in the transition from childhood to adolescence: a 10-year cohort study.

PURPOSE: To evaluate the direct and indirect pathways between cavitated carious lesions and oral health-related quality of life (OHRQoL) during the transition from childhood to adolescence. METHODS: A prospective cohort study followed an initial sample of 639 children (one to five years old), in southern Brazil for ten years. In addition to the baseline (T1), two subsequent reassessments were conducted after seven (T2) and ten years (T3). OHRQoL was measured through the Early Childhood Oral Health Impact Scale (ECOHIS) and Child Perceptions Questionnaire (CPQ8-10 and CPQ11-14), according to the age group. Dental caries was evaluated using the International Caries Detection and Assessment System (ICDAS) in the three waves. Socioeconomic variables and other factors related to oral health (frequency of toothbrushing and toothache) were also collected. Structural equation modeling was used to estimate the direct and indirect effects among the variables over ten years. RESULTS: A total of 449 and 429 children were reevaluated at T2 and T3 (positive response rates of 70.3% and 67.1%, respectively). Dental caries in T1 and T2 directly predicted the occurrence of a worse OHRQoL in the respective follow-up periods. Dental caries at T3 indirectly predicted a worse OHRQoL (T3), via toothache. Dental caries at T1 and T2 directly predicted the occurrence of dental caries at T3, while a worse OHRQoL at T2 directly predicted a worse OHRQoL at T3. CONCLUSION: Dental caries negatively impacts, directly and indirectly, the OHRQoL from early childhood to adolescence.

Convergence of the dimensional assessment of personality pathology (DAPP-BQ) and the five-factor personality inventory for the international classification of diseases 11th edition (FFiCD) in the context of the five-factor model and personality disorders.

The current manuscript presents the convergence of the Dimensional Assessment of Personality Pathology (DAPP-BQ), using its short form the DAPP-90, and the Five-Factor Personality Inventory for International Classification of Diseases (ICD-11), the FFiCD, in the context of the five-factor personality model and the categorical approach of personality disorders (PDs). The current manuscript compares the predictive validity of both the FFiCD and the DAPP-90 regarding personality disorder scales and clusters. Results demonstrate a very high and meaningful convergence between the DAPP-90 and the FFiCD personality pathology models and a strong alignment with the FFM. The DAPP-90 and the FFiCD also present an almost identical predictive power of PDs. The DAPP-90 accounts for between 18% and 47%, and the FFiCD between 21% and 47% of PDs adjusted variance. It is concluded that both DAPP-90 and FFiCD questionnaires measure strongly similar pathological personality traits that could be described within the frame of the FFM. Additionally, both questionnaires predict a very similar percentage of the variance of personality disorders.

Predictive models for perceived convenience of accessing outdoor activities among elderly with physical disabilities in rural China.

BACKGROUND: The elderly, especially those with physical disabilities, often encounter barriers that prevent them from accessing outdoor activities. Their perceptions of the convenience of accessing outdoor activities may be influenced by various factors including their health, the social context, and/or planned behavior. This study aimed to develop predictive models that identify the principal determinants of perceived convenience among this demographic, and it also examined the disparities observed between genders. METHODS: This was a cross-sectional survey of 1216 community-dwelling older people with physical disabilities in rural China. Grounded on the rehabilitation concepts and the theory of planned behavior, structural equation models integrated health and social behavior factors were constructed to predict perceived convenience of accessing outdoor activities. The standardized coefficients explained the contributions of various factors to the variance. RESULTS: The final structural models demonstrated good fit for both female and male participants. Perceptions of the convenience of accessing outdoor activities among both women and men were directly impacted by their physical functioning and their intention to participate, and indirectly by medical expenditure, subjective norms, pain, and role limitation in emotional interactions. Positive mental health was more influential for women, while men were more influenced by subjective norms. CONCLUSIONS: Structural equation models have effectively predicted the self-reported convenience of accessing outdoor activities, underscoring the importance of functional and behavioral rehabilitation. Furthermore, gender-sensitive rehabilitation programs are advised to promote engagement in outdoor activities among elderly individuals with physical disabilities.

Econometric Causality: The Central Role of Thought Experiments.

This paper examines the econometric causal model and the interpretation of empirical evidence based on thought experiments that was developed by Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two currently popular causal frameworks: the Neyman-Rubin causal model and the Do-Calculus. The Neyman-Rubin causal model is based on the language of potential outcomes and was largely developed by statisticians. Instead of being based on thought experiments, it takes statistical experiments as its foundation. The Do-Calculus, developed by Judea Pearl and co-authors, relies on Directed Acyclic Graphs (DAGs) and is a popular causal framework in computer science and applied mathematics. We make the case that economists who uncritically use these frameworks often discard the substantial benefits of the econometric causal model to the detriment of more informative analyses. We illustrate the versatility and capabilities of the econometric framework using causal models developed in economics.

phylosem: A fast and simple R package for phylogenetic inference and trait imputation using phylogenetic structural equation models.

Phylogenetic comparative methods (PCMs) can be used to study evolutionary relationships and trade-offs among species traits. Analysts using PCM may want to (1) include latent variables, (2) estimate complex trait interdependencies, (3) predict missing trait values, (4) condition predicted traits upon phylogenetic correlations and (5) estimate relationships as slope parameters that can be compared with alternative regression methods. The Comprehensive R Archive Network (CRAN) includes well-documented software for phylogenetic linear models (phylolm), phylogenetic path analysis (phylopath), phylogenetic trait imputation (Rphylopars) and structural equation models (sem), but none of these can simultaneously accomplish all five analytical goals. We therefore introduce a new package phylosem for phylogenetic structural equation models (PSEM) and summarize features and interface. We also describe new analytical options, where users can specify any combination of Ornstein-Uhlenbeck, Pagel's-δ and Pagel's-λ transformations for species covariance. For the first time, we show that PSEM exactly reproduces estimates (and standard errors) for simplified cases that are feasible in sem, phylopath, phylolm and Rphylopars and demonstrate the approach by replicating a well-known case study involving trade-offs in plant energy budgets.

Disentangling direct and indirect effects of extreme events on coastal wetland communities.

One of the primary ways in which climate change will impact coastal freshwater wetlands is through changes in the frequency, intensity, timing and distribution of extreme weather events. Disentangling the direct and indirect mechanisms of population- and community-level responses to extreme events is vital to predicting how species composition of coastal wetlands will change under future conditions. We extended static structural equation modelling approaches to incorporate system dynamics in a multi-year multispecies occupancy model to quantify the effects of extreme weather events on a coastal freshwater wetland system. We used data from an 8-year study (2009-2016) on St. Marks National Wildlife Refuge in Florida, USA, to quantify species-specific and community-level changes in amphibian and fish occupancy associated with two flooding events in 2012 and 2013. We examine how physical changes to the landscape, including potential changes in salinity and increased wetland connectivity, may have contributed to or exacerbated the effects of these extreme weather events on the biota of isolated coastal wetlands. We provide evidence that the primary effects of flooding on the amphibian community were through indirect mechanisms via changes in the composition of the sympatric fish community that may have had lethal (i.e. through direct predation) or non-lethal (i.e. through direct or indirect competitive interactions) effects. In addition, we have shown that amphibian species differed in their sensitivity to direct flooding effects and indirect changes in the fish community and wetland-specific conductance, which led to variable responses across the community. These effects led to the overall decline in amphibian species richness from 2009 to 2016, suggesting that wetland-breeding amphibian communities on St. Marks National Wildlife Refuge may not be resilient to predicted changes in coastal disturbance regimes because of climate change. Understanding both direct and indirect effects, as well as species interactions, is important for predicting the effects of a changing climate on individual species, communities and ecosystems.

The extreme wet and large precipitation size increase carbon uptake in Eurasian meadow steppes: Evidence from natural and manipulated precipitation experiments.

The distribution of seasonal precipitation would profoundly affect the dynamics of carbon fluxes in terrestrial ecosystems. However, little is known about the impacts of extreme precipitation and size events on ecosystem carbon cycle when compared to the effects of average precipitation amount. The study involved an analysis of carbon fluxes and water exchange using the eddy covariance and chamber based techniques during the growing seasons of 2015-2017 in Bayan, Mongolia and 2019-2021 in Hulunbuir, Inner Mongolia, respectively. The components of carbon fluxes and water exchange at each site were normalized to evaluate of relative response among carbon fluxes and water exchange. The investigation delved into the relationship between carbon fluxes and extreme precipitation over five gradients (control, dry spring, dry summer, wet spring and wet summer) in Hulunbuir meadow steppe and distinct four precipitation sizes (0.1-2, 2-5, 5-10, and 10-25 mm d-1) in Bayan meadow steppe. The wet spring and summer showed the greatest ecosystem respiration (ER) relative response values, 76.2% and 73.5%, respectively, while the dry spring (-16.7%) and dry summer (14.2%) showed the lowest values. Gross primary production (GPP) relative response improved with wet precipitation gradients, and declined with dry precipitation gradients in Hulunbuir meadow steppe. The least value in net ecosystem CO2 exchange (NEE) was found at 10-25 mm d-1 precipitation size in Bayan meadow steppe. Similarly, the ER and GPP increased with size of precipitation events. The structural equation models (SEM) satisfactorily fitted the data (χ2 = 43.03, d.f. = 11, p = 0.215), with interactive linkages among soil microclimate, water exchange and carbon fluxes components regulating NEE. Overall, this study highlighted the importance of extreme precipitation and event size in influencing ecosystem carbon exchange, which is decisive to further understand the carbon cycle in meadow steppes.

Associations between dimensions of empowerment and nutritional status among married adolescent girls in East Africa: a structural equation modelling study.

BACKGROUND: Empowering adolescent girls is an important component of combating malnutrition in this age group. Because empowerment is multidimensional and context specific, it can be difficult for policymakers and practitioners to target the dimensions of empowerment associated with adolescent girls' nutrition in a particular setting. This study sought to identify the empowerment dimensions significantly associated with married adolescent girls' nutritional status in East Africa; a region where malnutrition and gender inequality stubbornly persist. METHODS: We used cross-sectional Demographic and Health Survey (DHS) data from Ethiopia (2016), Kenya (2014), Tanzania (2015-16) and Uganda (2016) to construct and test theoretically informed structural equation models of the relationship between six dimensions of empowerment and BMI-for-age and haemoglobin levels for married adolescent girls aged 15-19 years. RESULTS: Our models were found to be a good fit for the data. Married adolescent girls' access to information, measured by their education level and mass media use, was directly and positively associated with their BMI-for-age (p < 0.05). Asset ownership, measured by owning a house or land alone or jointly, was directly and positively associated with haemoglobin (p < 0.05) and reduced odds of being moderately to severely anemic. Rejecting justifications for intimate partner violence, a measure of respondents' intrinsic agency, was directly and positively associated with the odds of being overweight or obese. Adolescent girls' level of empowerment across all dimensions had a direct relationship with their country of residence and household wealth. CONCLUSIONS: Our findings suggest that investment in girls' access to information through education and mass/social media and their economic empowerment may be important contributors to their overall empowerment and nutritional status. However, caution is needed as greater autonomy may contribute to increased consumption of unhealthy foods that can contribute to overweight and obesity. Strategies to empower married adolescent girls should be tailored to their specific circumstances. There is an urgent need for better data on adolescent empowerment and health, including increased research into age-, sex- and gender-appropriate empowerment measures and longitudinal data to assess causality. The use of statistical models should be complemented by robust qualitative research to further results interpretation.

The mediating role of scientifical-medical satisfaction between COVID-19 conspiracy beliefs and vaccine confidence: a two-waves structural equation model.

Vaccine confidence has emerged as one of the most relevant psychological factors implied in the worldwide affecting the fight against COVID-19-as well as public trust in doctors, medicine, and science. Indeed, the vaccine confidence is crucial to maximize the trust in vaccines and their use for prevention, with several implications for public health. This study aimed to analyse the relationships among between vaccine confidence, conspiracy beliefs about COVID-19, and satisfaction with science and medicine in handling the COVID-19 pandemic. A longitudinal observational survey was administered to a convenience sample (n = 544; mean age 52.76 y.o., SD = 15.11; females 46.69%) from the Italian general population. A two-waves mediation model-a structural equation model technique-was used. The survey was part of a larger international project ( https://osf.io/qy65b/ ). The model highlighted that the conspiracy beliefs about COVID-19 had a negative effect on the satisfaction with medicine and science (ß = - 0.13, se = 0.03, p < .001). The latter, in turn, had a positive effect on vaccine confidence (ß = 0.10, se = .05, p < .001). Interestingly, the effect of conspiracy beliefs on vaccine confidence was completely mediated by the scientifical-medical satisfaction (ß = - 0.02, se = 0.01, p < .05). These results highlight how the scientifical-medical satisfaction can fully mediate the relationship between conspiracy beliefs about COVID-19 and vaccine confidence. These findings about vaccine hesitancy and confidence and disclose have implications for psychological and social interventions that could promote vaccine confidence by targeting the satisfaction with science and medicine.

Factors associated with a cariogenic diet among adolescents: a structural equation modeling approach.

OBJECTIVE: To investigate the factors directly and indirectly associated with a cariogenic diet among southern Brazilian adolescents. MATERIALS AND METHODS: This cross-sectional study included 15-19-year-old students attending high schools in Santa Maria, southern Brazil. The participants completed a questionnaire on sociodemographic and behavioral variables. The Oral Health Impact Profile-14 was applied to collect data on oral health-related quality of life (OHRQoL). Clinical examination was used to assess the dental caries status (decayed, missing, and filled teeth index). A cariogenic diet was considered a latent variable measured by the self-perception of a healthy diet and the frequency of consumption of sugary foods and drinks. Structural equation modeling was used to analyze the direct and indirect pathways to a cariogenic diet. RESULTS: A total of 1197 adolescents were included. Low toothbrushing frequency (standardized coefficient (SC), 0.10; p < 0.05), cigarette smoking (SC, 0.15; p < 0.01), and alcoholic beverages (SC, 0.14; p < 0.01) were directly linked to a cariogenic diet, which, in turn, was directly linked to untreated dental caries (SC, 0.18; p < 0.01) and poor OHRQoL (SC, 0.16; p < 0.01). In addition, household income (via toothbrushing frequency) and age (via alcoholic beverages) were indirectly linked to a cariogenic diet. CONCLUSION: A cariogenic diet was consistently associated with a range of unhealthy behaviors during adolescence as well as poor household income. CLINICAL RELEVANCE: Health promotion strategies to restrict sugar consumption and encourage healthier lifestyles should be aware of the synergism observed among unhealthy behaviors during adolescence.

Multiple-trait and structural equation modelling approaches to infer genetic relationships between tail length and weight traits in Merinoland sheep.

Tail docking is routinely conducted in long-tailed sheep breeds to prevent flystrike infections, but it is not in agreement with legal guidelines and animal welfare issues. Selection on short tails is a sustainable alternative in this regard, but side effects on other breeding goal traits are unclear. In consequence, the present study aimed to estimate genetic parameters for tail length (TL) at birth, birth weight (BW), weaning weight (WW) and postweaning weight (PWW) at the slaughtering date considering single-trait (STM), multiple-trait (MTM) and structural equation models (SEM) with different random effects, and accordingly, different covariance structures. The SEM considered time-lagged recursive relationships among response variables in three different pathways. The first path pertained to the effect of TL on WW and of WW on PWW. The second path reflected the effect of BW on WW and of WW on PWW. The third path was the recursive effect of TL on PWW. The phenotypic data consisted of 2803 records for TL, 13,042 records for BW, 1556 records for WW and 3986 records for PWW from Merinoland lambs. Lambs were born in the period from 1995 to 2021 and kept at the university Gießen research station, Germany, with their naturally long tails. Genetic statistical model evaluation based on Bayesian and Akaike's information criteria suggested models simultaneously considering direct genetic, maternal genetic and maternal permanent environmental effects and respective covariances. For statistical models including the same random effects and covariance structures, SEM were superior over MTM. The direct heritability for TL from the best-fitting STM was 0.60 ± 0.08, indicating the potential for genetic reduction of tail length within a few generations. For growth traits, the direct heritabilities ranged from 0.16 ± 0.03 for BW to 0.31 ± 0.09 for PWW. The maternal heritabilities were 0.03 ± 0.03 for TL, 0.12 ± 0.02 for BW, 0.04 ± 0.03 for WW and 0.07 ± 0.03 for PWW, reflecting small, but the non-significant influence of uterine characteristics on the tail development. The direct genetic correlations between TL and all weight traits were positive and very similar to MTM and SEM but reflected antagonistic genetic relationships from a breeding perspective. Oppositely, the structural equation coefficients reflecting trait associations phenotypically were negative (favourable) for the time-lagged effects of TL on WW and on PWW. As an explanation, lambs with long and woolly tails have an increased risk for contamination with dirt and dust causing infections, which in turn impairs the body weight development. In conclusion, breeding on short tails should consider trait-associated environmental risk factors, for example, disease susceptibility, which can be mimicked via SEM approaches.

A Structural Equation Model to Explain Gambling Problem Severity in Adolescents with an Integrated Perspective.

To be effective in the prevention of adolescent problem gambling, it is fundamental to enhance knowledge about the antecedents of gambling problem severity and the mechanisms through which these dimensions are related to problematic gambling behavior. This study aimed at testing how selected cognitive (correct knowledge of gambling and gambling-related cognitive distortions) and affective (positive economic perception of gambling and expectation and enjoyment and arousal towards gambling) variables are related to gambling frequency and gambling problem severity. Problem gambling was conceptualized as Gambling Disorder symptoms according to the last edition of the DSM. Participants were 447 Italian high school students (68% males, mean age = 16.8, SD = 0.84). Structural Equation Models (SEMs) conducted with adolescent gamblers attested two indirect effects from knowledge to problem gambling: One through gambling-related cognitive distortions and one through gambling frequency. Overall, results confirmed that adolescent problem gambling is a complex phenomenon explained by multiple and different factors. Practical implications for preventive efforts are discussed.

A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data.

Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.

Examination of the Effect of Task Complexity and Coping Capacity on Driving Risk: A Cross-Country and Transportation Mode Comparative Study.

The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.

Socioeconomic status associates with worse asthma morbidity among Black and Latinx adults.

BACKGROUND: Asthma disproportionately affects African American/Black (AA/B) and Hispanic/Latinx (H/L) patients and individuals with low socioeconomic status (SES), but the relationship between SES and asthma morbidity within these racial/ethnic groups is inadequately understood. OBJECTIVE: To determine the relationship between SES and asthma morbidity among AA/B and H/L adults with moderate to severe asthma using multidomain SES frameworks and mediation analyses. METHODS: We analyzed enrollment data from the PeRson EmPowered Asthma RElief randomized trial, evaluating inhaled corticosteroid supplementation to rescue therapy. We tested for direct and indirect relationships between SES and asthma morbidity using structural equation models. For SES, we used a latent variable defined by poverty, education, and unemployment. For asthma morbidity, we used self-reported asthma exacerbations in the year before enrollment (corticosteroid bursts, emergency room/urgent care visits, or hospitalizations), and Asthma Control Test scores. We tested for mediation via health literacy, perceived stress, and self-reported discrimination. All models adjusted for age, sex, body mass index, ethnicity, and comorbidities. RESULTS: Among 990 AA/B and H/L adults, low SES (latent variable) was directly associated with hospitalizations (ß = 0.24) and worse Asthma Control Test scores (ß = 0.20). Stress partially mediated the relationship between SES and increased emergency room/urgent care visits and worse asthma control (ß = 0.03 and = 0.05, respectively). Individual SES domains were directly associated with asthma morbidity. Stress mediated indirect associations between low educational attainment and unemployment with worse asthma control (ß = 0.05 and = 0.06, respectively). CONCLUSIONS: Lower SES is directly, and indirectly through stress, associated with asthma morbidity among AA/B and H/L adults. Identification of stressors and relevant management strategies may lessen asthma-related morbidity among these populations.

Quantifying the direct effects of long-term dynamic land use intensity on vegetation change and its interacted effects with economic development and climate change in jiangsu, China.

Vegetation change reflects sensitive responses of ecosystem environment to global climate change as well as land use. It is well known that land use type and its transformation affect vegetation change. However, how the changes in land use intensity (LUI) within different land use types impact vegetation and the interactions with other drivers remain poorly understood. We measured the LUI of Jiangsu Province, China, within the main land use types in 1995, 2000, 2005, 2010, 2015 and 2018 by combining remote sensing-based land use data with representative county scale economic and social indicators. Structural equation models (SEMs) were built to quantify the influences of long term LUI on vegetation change interacting with economic development, climate change and topographical conditions in transformed land, cropland, rural settlements and urbanized land, respectively. Seventy percent of significant vegetation change existed in non-transformed land use types. Although the area with a vegetation greening trend is larger than that with a vegetation browning trend, the vegetation browning areas is prominent in urbanized lands and some croplands in south basins. The constructed SEMs suggested the dominant negative effect of fast economic development regardless of land use types, while LUI played important and different direct and indirect effects on affecting vegetation change significantly interacting with economic development and climate change in different land use types. The LUI increasing led a vegetation greening in cropland, and stronger than climate warming with both positive direct and indirect effects for influencing climate change. The LUI change took negative effects on vegetation change in rural and urban areas, while a positive indirect effect of LUI increasing in urbanized land signaled the positive results of human managements. We then provided some land use-specific suggestions on basin scale for land management in Jiangsu. Our results highlight the necessity of long-term LUI quantification and promote the understanding of its effects on vegetation change interacted with other drivers within different land use types. This can be very helpful for sustainable land use and managements in regions with fast economic development.

Structural Equation Models Suggest That On-Farm Noncrop Vegetation Removal Is Not Associated with Improved Food Safety Outcomes but Is Linked to Impaired Water Quality.

While growers have reported pressures to minimize wildlife intrusion into produce fields through noncrop vegetation (NCV) removal, NCV provides key ecosystem services. To model food safety and environmental tradeoffs associated with NCV removal, published and publicly available food safety and water quality data from the Northeastern United States were obtained. Because data on NCV removal are not widely available, forest-wetland cover was used as a proxy, consistent with previous studies. Structural equation models (SEMs) were used to quantify the effect of forest-wetland cover on (i) food safety outcomes (e.g., detecting pathogens in soil) and (ii) water quality (e.g., nutrient levels). Based on the SEMs, NCV was not associated with or had a protective effect on food safety outcomes (more NCV was associated with a reduced likelihood of pathogen detection). The probabilities of detecting Listeria spp. in soil (effect estimate [EE] = -0.17; P = 0.005) and enterohemorrhagic Escherichia coli in stream samples (EE = -0.27; P < 0.001) were negatively associated with the amount of NCV surrounding the sampling site. Larger amounts of NCV were also associated with lower nutrient, salinity, and sediment levels, and higher dissolved oxygen levels. Total phosphorous levels were negatively associated with the amount of NCV in the upstream watershed (EE = -0.27; P < 0.001). Similar negative associations (P < 0.05) were observed for other physicochemical parameters, such as nitrate (EE = -0.38). Our findings suggest that NCV should not be considered an inherent produce safety risk or result in farm audit demerits. This study also provides a framework for evaluating environmental tradeoffs associated with using specific preharvest food safety strategies. IMPORTANCE Currently, on-farm food safety decisions are typically made independently of conservation considerations, often with detrimental impacts on agroecosystems. Comanaging agricultural environments to simultaneously meet conservation and food safety aims is complicated because farms are closely linked to surrounding environments, and management decisions can have unexpected environmental, economic, and food safety consequences. Thus, there is a need for research on the conservation and food safety tradeoffs associated with implementing specific preharvest food safety practices. Understanding these tradeoffs is critical for developing adaptive comanagement strategies and ensuring the short- and long-term safety, sustainability, and profitability of agricultural systems. This study quantifies tradeoffs and synergies between food safety and environmental aims, and outlines a framework for modeling tradeoffs and synergies between management aims that can be used to support future comanagement research.

Allostatic Load, Mobility Disability, and Viral Effects in Cancer: A Structural Equation Model.

A growing number of cancers have been linked to specific oncogenic viruses and physiological stress. Recently, two separate studies linked mobility limitations to allostatic load and four major cancer types. The objective of this study was to determine if cancer occurrence regresses on three latent domains of Allostatic Load, Level of Physical Functioning (i.e., Mobility Disability), and Viral Exposure. We compared several structural equation models using adult participant (n = 17,969) data from three National Health and Nutrition Examination Survey (NHANES) periods. The primary two-level model with three exogenous latent factors and a single Cancer endogenous latent factor demonstrated a strong fit (GFI = 0.948, RMSEA = 0.024), and the model had a non-significant Chi-Square indicative of a strong model.What is already known on this subjectAllostatic load represents how the body responds to physiological stress and is associated with increased morbidity/mortality, including cancers.Viruses are the causative agents of 15-20% of cancers and can be stress activated.People with mobility limitations experience significantly higher allostatic loads and secondary health conditions, and one recent study indicates a heightened risk for certain cancers.What this study addsThis study is original in its testing of a conceptual model that links together cancer outcomes with latent factors/variables including disability/mobility limitations, allostatic load, and viral exposure.The study indicates that there might be important associations between allostatic load, disability burden, and viral exposure/activation on the occurrence of cancer.The research suggests the need for stress reduction, preventative health interventions, and additional supports for people with disabilities and their caregivers.