Causal models and causal modelling in obesity: foundations, methods and evidence

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

A multi-satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1-2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System

Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system's coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem is to build mechanisms to selectively send out communications, which allow distributors to send information at certain moments and key states. The distributors in the power grid are modeled as reinforcement learning agents, and the communication bandwidth in the power grid can be reduced by optimizing the communication frequency between agents. Therefore, in this paper, we propose a model for deciding whether to communicate based on the causal inference method, Causal Inference Communication Model (CICM). CICM regards whether to communicate as a binary intervention variable, and determines which intervention is more effective by estimating the individual treatment effect (ITE). It offers the optimal communication strategy about whether to send information while ensuring task completion. This method effectively reduces the communication frequency between grid distributors, and at the same time maximizes the power distribution effect. In addition, we test the method in StarCraft II and 3D environment habitation experiments, which fully proves the effectiveness of the method.

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study

BACKGROUND

Existing diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation.

METHODS

BN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline [73% women; mean (standard deviation) age = 61.0 (6.9) years]. Participants were randomly assigned to a training (n = 12,748) set and a validation (n = 3,186) set. Model performances were assessed using area under the receiver operating characteristic curve (AUC).

RESULTS

During an average follow-up of 4.1 (interquartile range = 3.3-4.5) years, 1,302 (8.17%) participants developed diabetes. The constructed BN model showed the associations (direct, indirect, or no) among 24 risk factors, and only hypertension, impaired fasting glucose (IFG; fasting glucose of 5.6-6.9 mmol/L), and greater waist circumference (WC) were directly associated with new-onset diabetes. The risk prediction model showed that the post-test probability of developing diabetes in participants with hypertension, IFG, and greater WC was 27.5%, with AUC of 0.746 [95% confidence interval CI) = 0.732-0.760], sensitivity of 0.727 (95% CI = 0.703-0.752), and specificity of 0.660 (95% CI = 0.652-0.667). This prediction model appeared to perform better than a logistic regression model using the same three predictors (AUC = 0.734, 95% CI = 0.703-0.764, sensitivity = 0.604, and specificity = 0.745).

CONCLUSIONS

We have first reported a BN model in predicting new-onset diabetes with the smallest number of factors among existing models in the literature. BN yielded a more comprehensive figure showing graphically the inter-relations for multiple factors with diabetes than existing regression models.

Survival Analysis of Training Methodologies and Other Risk Factors for Musculoskeletal Injury in 2-Year-Old Thoroughbred Racehorses in Queensland, Australia

Musculoskeletal injuries remain a global problem for the Thoroughbred racing industry and there is conflicting evidence regarding the effect of age on the incidence of injuries. The ideal time to commence race training is strongly debated, with limited supporting literature. There is also conflicting evidence regarding the effect of high-speed exercise on musculoskeletal injuries. There is a strong interest in developing training and management strategies to reduce the frequency of injuries. The types of musculoskeletal injuries vary between 2-year-old and older horses, with dorsal metacarpal disease the most common injury in 2-year-old horses. It is likely that risk factors for injury in 2-year-old horses are different than those for older horses. It is also likely that the risk factors may vary between types of injury. This study aimed to determine the risk factors for musculoskeletal injuries and dorsal metacarpal disease. We report the findings of a large scale, prospective observational study of 2-year-old horses in Queensland, Australia. Data were collected weekly for 56-weeks, from 26 trainers, involving 535 2-year-old Thoroughbred racehorses, 1, 258 training preparations and 7, 512-weeks of exercise data. A causal approach was used to develop our statistical models, to build on the existing literature surrounding injury risk, by incorporating the previously established causal links into our analyses. Where previous data were not available, industry experts were consulted. Survival analyses were performed using Cox proportional hazards or Weibull regression models. Analysis of musculoskeletal injuries overall revealed the hazard was reduced with increased exposure to high-speed exercise [Hazard ratio (HR) 0.89, 95% Confidence Interval (CI) 0.84, 0.94, p < 0.001], increased number of training preparations (HR 0.58, 95% CI 0.50, 0.67, p < 0.001), increased rest before the training preparation (HR 0.89, 95% CI 0.83, 0.96, p = 0.003) and increased dam parity (HR 0.86, 95% CI 0.77, 0.97, p = 0.01). The hazard of injury was increased with increasing age that training commenced (HR 1.13, 95% CI 1.06, 1.19, p < 0.001). Analyses were then repeated with the outcome of interest dorsal metacarpal disease. Factors that were protective against dorsal metacarpal disease and musculoskeletal injuries overall included: increased total cumulative distance (HR 0.89, 95% CI 0.82, 0.97, p = 0.001) and total cumulative days exercised as a gallop (HR 0.96, 95% CI 0.92, 0.99, p = 0.03), the number of the training preparations (HR 0.43, 95% CI 0.30, 0.61, p < 0.001). The age that training commenced was harmful for both dorsal metacarpal disease (HR 1.17, 95% CI 1.07, 1.28, p < 0.001 and overall musculoskeletal injuries.). The use of non-ridden training modalities was protective for dorsal metacarpal disease (HR 0.89, 95% CI 0.81, 0.97, p = 0.008), but not musculoskeletal injuries overall. The male sex increased the hazard of DMD compared to females (HR 2.58, 95% CI 1.20, 5.56, p = 0.02), but not MSI overall. In summary, the hazard of musculoskeletal injury is greatest for 2-year-old horses that are born from uniparous mares, commence training at a later age, are in their first training preparation, have undertaken little high-speed exercise or had limited rest before their training preparation. The hazard of dorsal metacarpal disease is greatest for 2-year-old horses that are males, commence training at a later age, are in their first training preparation, have undertaken little high-speed exercise or had limited use of non-ridden training modalities. Close monitoring of these high-risk horses during their training program could substantially reduce the impact of MSI. Furthermore, an understanding of how training methodologies affect the hazard of MSI facilitates modification of training programs to mitigate the risk impact of injury. The strengths of this study include a large sample size, a well-defined study protocol and direct trainer interviews. The main limitation is the inherent susceptibility to survival bias.

Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures

Prognostic biomarkers can have an important role in the clinical practice because they allow stratification of patients in terms of predicting the outcome of a disorder. Obstacles for developing such markers include lack of robustness when using different data sets and limited concordance among similar signatures. In this paper, we highlight a new problem that relates to the biological meaning of already established prognostic gene expression signatures. Specifically, it is commonly assumed that prognostic markers provide sensible biological information and molecular explanations about the underlying disorder. However, recent studies on prognostic biomarkers investigating 80 established signatures of breast and prostate cancer demonstrated that this is not the case. We will show that this surprising result is related to the distinction between causal models and predictive models and the obfuscating usage of these models in the biomedical literature. Furthermore, we suggest a falsification procedure for studies aiming to establish a prognostic signature to safeguard against false expectations with respect to biological utility.

Making Decision-Making Visible-Teaching the Process of Evaluating Interventions

Significant efforts in the past decades to teach evidence-based practice (EBP) implementation has emphasized increasing knowledge of EBP and developing interventions to support adoption to practice. These efforts have resulted in only limited sustained improvements in the daily use of evidence-based interventions in clinical practice in most health professions. Many new interventions with limited evidence of effectiveness are readily adopted each year-indicating openness to change is not the problem. The selection of an intervention is the outcome of an elaborate and complex cognitive process, which is shaped by how they represent the problem in their mind and is mostly invisible processes to others. Therefore, the complex thinking process that support appropriate adoption of interventions should be taught more explicitly. Making the process visible to clinicians increases the acquisition of the skills required to judiciously select one intervention over others. The purpose of this paper is to provide a review of the selection process and the critical analysis that is required to appropriately decide to trial or not trial new intervention strategies with patients.

Use of Comprehensive Participatory Planning and Evaluation in Rural Patient Engagement

Comprehensive participatory planning and evaluation (CPPE), a model used in community engagement research, has not been applied to patient engagement in research. We describe our methodology and interim results using CPPE in a project focused on improving research engagement of rural and distant patients and stakeholders. Specifically, we describe our development of a causal map and the subsequent use of the map to guide patient and stakeholder-driven evaluation.

A causal model on assertiveness, stress coping, and workplace environment: Factors affecting novice nurses' burnout

Aim

This study aimed to develop and test a causal model focused on assertiveness, stress coping, and workplace environment as factors affecting burnout among novice nurses.

Design

Cross‐sectional study was conducted with novice nurses of 17 hospitals.

Methods

The Novice Nurse Assertiveness Scale and the Japanese version of Maslach Burnout Inventory.

Results

Data from 645 female novice nurses were analysed. The mean age, Novice Nurse Assertiveness Scale and Maslach Burnout Inventory were 22.6 ± 3.0, 67.4 ± 10.3 and 13.7 ± 2.5. For the final model, the study adopted a model that includes indirect influences; inappropriate assertiveness and inappropriate coping affected the dissatisfaction with the job and then affected the burnout. The goodness of fit index was GFI = 0.94, AGFI = 0.91, RMSEA = 0.66, and R² was .86. The findings validated this as a causal model of assertiveness, stress coping, and the work environment as factors affecting burnout for novice nurses.

Motivational interviewing for social anxiety disorder: An examination of the technical hypothesis

ABSTRACT Background and objective: Motivational interviewing (MI) was originally developed to treat problematic drinking but is increasingly integrated into treatment for anxiety disorders. A causal model has been proposed which suggests technical and relational factors may account for the efficacy of MI. The technical hypothesis suggests that therapist MI-consistent behaviours are related to client change talk, and change talk is linked to treatment outcome. Research examining the technical hypothesis has typically been conducted in MI for substance use; therefore, the current study aimed to explore the technical hypothesis in MI for social anxiety disorder (SAD). Method: Participants diagnosed with SAD (n = 85) each received MI prior to receiving group cognitive-behavioural therapy (CBT). MI sessions were coded for behaviours relevant to the MI technical hypothesis. Results: The proportion of MI-consistent therapist behaviours and reflections of change language significantly predicted the proportion of change talk by the client during MI sessions; however, therapist and client behaviours did not predict treatment outcome. Conclusion: The findings support one path of the MI causal model in the context of social anxiety, though indicate that the occurrence of these behaviours during an MI pre-treatment may not extend to predict treatment outcome following CBT.

Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review

Mental health services and systems (MHSS) are characterized by their complexity. Causal modelling is a tool for decision-making based on identifying critical variables and their causal relationships. In the last two decades, great efforts have been made to provide integrated and balanced mental health care, but there is no a clear systematization of causal links among MHSS variables. This study aims to review the empirical background of causal modelling applications (Bayesian networks and structural equation modelling) for MHSS management. The study followed the PRISMA guidelines (PROSPERO: CRD42018102518). The quality of the studies was assessed by using a new checklist based on MHSS structure, target population, resources, outcomes, and methodology. Seven out of 1847 studies fulfilled the inclusion criteria. After the review, the selected papers showed very different objectives and subjects of study. This finding seems to indicate that causal modelling has potential to be relevant for decision-making. The main findings provided information about the complexity of the analyzed systems, distinguishing whether they analyzed a single MHSS or a group of MHSSs. The discriminative power of the checklist for quality assessment was evaluated, with positive results. This review identified relevant strategies for policy-making. Causal modelling can be used for better understanding the MHSS behavior, identifying service performance factors, and improving evidence-informed policy-making.

How foraging works: Uncertainty magnifies food-seeking motivation

Food uncertainty has the effect of invigorating food-related responses. Psychologists have noted that mammals and birds respond more to a conditioned stimulus that unreliably predicts food delivery, and ecologists have shown that animals (especially small passerines) consume and/or hoard more food and can get fatter when access to that resource is unpredictable. Are these phenomena related? We think they are. Psychologists have proposed several mechanistic interpretations, while ecologists have suggested a functional interpretation: The effect of unpredictability on fat reserves and hoarding behavior is an evolutionary strategy acting against the risk of starvation when food is in short supply. Both perspectives are complementary, and we argue that the psychology of incentive motivational processes can shed some light on the causal mechanisms leading animals to seek and consume more food under uncertainty in the wild. Our theoretical approach is in agreement with neuroscientific data relating to the role of dopamine, a neurotransmitter strongly involved in incentive motivation, and its plausibility has received some explanatory and predictive value with respect to Pavlovian phenomena. Overall, we argue that the occasional and unavoidable absence of food rewards has motivational effects (called incentive hope) that facilitate foraging effort. We show that this hypothesis is computationally tenable, leading foragers in an unpredictable environment to consume more food items and to have higher long-term energy storage than foragers in a predictable environment.

Statistical tests and identifiability conditions for pooling and analyzing multisite datasets

When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer's disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.

simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data

The simcausal R package is a tool for specification and simulation of complex longitudinal data structures that are based on non-parametric structural equation models. The package aims to provide a flexible tool for simplifying the conduct of transparent and reproducible simulation studies, with a particular emphasis on the types of data and interventions frequently encountered in real-world causal inference problems, such as, observational data with time-dependent confounding, selection bias, and random monitoring processes. The package interface allows for concise expression of complex functional dependencies between a large number of nodes, where each node may represent a measurement at a specific time point. The package allows for specification and simulation of counterfactual data under various user-specified interventions (e.g., static, dynamic, deterministic, or stochastic). In particular, the interventions may represent exposures to treatment regimens, the occurrence or non-occurrence of right-censoring events, or of clinical monitoring events. Finally, the package enables the computation of a selected set of user-specified features of the distribution of the counterfactual data that represent common causal quantities of interest, such as, treatment-specific means, the average treatment effects and coefficients from working marginal structural models. The applicability of simcausal is demonstrated by replicating the results of two published simulation studies.

Causal mechanisms of soil organic matter decomposition: deconstructing salinity and flooding impacts in coastal wetlands

Coastal wetlands significantly contribute to global carbon storage potential. Sea-level rise and other climate-change-induced disturbances threaten coastal wetland sustainability and carbon storage capacity. It is critical that we understand the mechanisms controlling wetland carbon loss so that we can predict and manage these resources in anticipation of climate change. However, our current understanding of the mechanisms that control soil organic matter decomposition, in particular the impacts of elevated salinity, are limited, and literature reports are contradictory. In an attempt to improve our understanding of these complex processes, we measured root and rhizome decomposition and developed a causal model to identify and quantify the mechanisms that influence soil organic matter decomposition in coastal wetlands that are impacted by sea-level rise. We identified three causal pathways: (1) a direct pathway representing the effects of flooding on soil moisture, (2) a direct pathway representing the effects of salinity on decomposer microbial communities and soil biogeochemistry, and (3) an indirect pathway representing the effects of salinity on litter quality through changes in plant community composition over time. We used this model to test the effects of alternate scenarios on the response of tidal freshwater forested wetlands and oligohaline marshes to short- and long-term climate-induced disturbances of flooding and salinity. In tidal freshwater forested wetlands, the model predicted less decomposition in response to drought, hurricane salinity pulsing, and long-term sea-level rise. In contrast, in the oligohaline marsh, the model predicted no change in response to drought and sea-level rise, and increased decomposition following a hurricane salinity pulse. Our results show that it is critical to consider the temporal scale of disturbance and the magnitude of exposure when assessing the effects of salinity intrusion on carbon mineralization in coastal wetlands. Here, we identify three causal mechanisms that can reconcile disparities between long-term and short-term salinity impacts on organic matter decomposition.

Association of Religious Service Attendance With Mortality Among Women

IMPORTANCE

Studies on the association between attendance at religious services and mortality often have been limited by inadequate methods for reverse causation, inability to assess effects over time, and limited information on mediators and cause-specific mortality.

OBJECTIVE

To evaluate associations between attendance at religious services and subsequent mortality in women.

DESIGN, SETTING, AND PARTICIPANTS

Attendance at religious services was assessed from the first questionnaire in 1992 through June 2012, by a self-reported question asked of 74 534 women in the Nurses' Health Study who were free of cardiovascular disease and cancer at baseline. Data analysis was conducted from return of the 1996 questionnaire through June 2012.

MAIN OUTCOMES AND MEASURES

Cox proportional hazards regression model and marginal structural models with time-varying covariates were used to examine the association of attendance at religious services with all-cause and cause-specific mortality. We adjusted for a wide range of demographic covariates, lifestyle factors, and medical history measured repeatedly during the follow-up, and performed sensitivity analyses to examine the influence of potential unmeasured and residual confounding.

RESULTS

Among the 74 534 women participants, there were 13 537 deaths, including 2721 owing to cardiovascular deaths and 4479 owing to cancer deaths. After multivariable adjustment for major lifestyle factors, risk factors, and attendance at religious services in 1992, attending a religious service more than once per week was associated with 33% lower all-cause mortality compared with women who had never attended religious services (hazard ratio, 0.67; 95% CI, 0.62-0.71; P < .001 for trend). Comparing women who attended religious services more than once per week with those who never attend, the hazard ratio for cardiovascular mortality was 0.73 (95% CI, 0.62-0.85; P < .001 for trend) and for cancer mortality was 0.79 (95% CI, 0.70-0.89; P < .001 for trend). Results were robust in sensitivity analysis. Depressive symptoms, smoking, social support, and optimism were potentially important mediators, although the overall proportion of the association between attendance at religious services and mortality was moderate (eg, social support explained 23% of the effect [P = .003], depressive symptoms explained 11% [P < .001], smoking explained 22% [P < .001], and optimism explained 9% [P < .001]).

CONCLUSIONS AND RELEVANCE

Frequent attendance at religious services was associated with significantly lower risk of all-cause, cardiovascular, and cancer mortality among women. Religion and spirituality may be an underappreciated resource that physicians could explore with their patients, as appropriate.

Structural model of self-care agency in patients with diabetes: A path analysis of the Instrument of Diabetes Self-Care Agency and body self-awareness

Aim

The aim of this study was to examine a causal model of self‐care agency by exploring the relationship between the structure of “body self‐awareness” and the structure of the Instrument of Diabetes Self‐Care Agency (IDSCA).

Methods

The participants were 353 patients with diabetes. The internal consistency of the six items for body self‐awareness was examined by calculating the factor structure using principal factor analysis and Cronbach's alpha. In order to examine the relationship between the seven factors in the IDSCA, a path analysis was conducted.

Results

With regard to the factor structure, the factor loading of these five items was 0.511–0.743 (α = 0.739). In the path analysis, “body self‐awareness” was influenced by the “ability to acquire knowledge” and had a direct effect (0.33) on the “motivation to self‐manage”, while “motivation to self‐manage” had an effect (−0.32) on the “ability to self‐manage”. The Goodness‐of‐Fit Index was 0.974.

Conclusion

“Body self‐awareness” plays a part in the self‐care operation process and serves as an intermediary factor to enable the performance of self‐care operations by making the most use of self‐care agency. Moreover, striking a proper balance between self‐management that is focused on the treatment of diabetes and a person's ability for self‐management of diabetes was found to be important.

Estimating Causal Associations of Fine Particles With Daily Deaths in Boston

Many studies have reported associations between daily particles less than 2.5 µm in aerodynamic diameter (PM2.5) and deaths, but they have been associational studies that did not use formal causal modeling approaches. On the basis of a potential outcome approach, we used 2 causal modeling methods with different assumptions and strengths to address whether there was a causal association between daily PM2.5 and deaths in Boston, Massachusetts (2004-2009). We used an instrumental variable approach, including back trajectories as instruments for variations in PM2.5 uncorrelated with other predictors of death. We also used propensity score as an alternative causal modeling analysis. The former protects against confounding by measured and unmeasured confounders and is based on the assumption of a valid instrument. The latter protects against confounding by all measured covariates, provides valid estimates in the case of effect modification, and is based on the assumption of no unmeasured confounders. We found a causal association of PM2.5 with mortality, with a 0.53% (95% confidence interval: 0.09, 0.97) and a 0.50% (95% confidence interval: 0.20, 0.80) increase in daily deaths using the instrumental variable and the propensity score, respectively. We failed to reject the null association with exposure after the deaths (P =0.93). Given these results, prior studies, and extensive toxicological support, the association between PM2.5 and deaths is almost certainly causal.

A novel approach for identifying causal models of complex diseases from family data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal-normal, normal-disease, and disease-disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene-environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene-environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.

Examining the causal model linking health literacy to health outcomes of asthma patients

AIMS AND OBJECTIVES

To explore health literacy status in asthma patients and to examine the causal model linking health literacy to health outcome-related factors via mediator and moderator variables.

BACKGROUND

Understanding how low health literacy may influence health outcomes is important.

DESIGN

This is a cross-sectional survey study.

METHODS

A total of 326 asthma patients aged 20 years and older (average: 51 ± 18·3 years) were recruited by purposive sampling from pulmonary medicine outpatient departments at three medical centres and a regional teaching hospital in northern Taiwan. Data were collected via structured questionnaires, including measures of socio-demographic and disease characteristics; medical decision-making; asthma knowledge, attitudes and self-efficacy; healthcare experience and health outcome-related factors (metered-dose inhaler/dry-powder inhaler usage proficiency, medical use, self-management behaviour). Three hundred patients who met the inclusion criteria and completed the questionnaire survey were analysed.

RESULTS

Overall, 217 subjects (72·3%) had adequate functional health literacy, 42 (14%) had inadequate functional health literacy, and 41 (13·7%) had marginal functional health literacy. Subjects' average asthma knowledge, attitudes and self-efficacy scores were 7·23 ± 2·69, 51·46 ± 6·18 and 58·31 ± 8·10, respectively. Health literacy correlated positively with asthma knowledge (r = 0·605), attitudes (r = 0·192) and medical decision-making (r = 0·413).

CONCLUSIONS

Health literacy is positively associated with proficiency in metered-dose inhaler usage, asthma knowledge, attitudes and medical decision-making, but is not significantly associated with medical care use and self-management behaviour. Health literacy had an indirect effect on self-management behaviour through the mediation effect of asthma attitudes. No moderator was found for the effect of health literacy on health outcome-related factors.

RELEVANCE TO CLINICAL PRACTICE

Results of this study may help to develop adequate intervention strategies to improve the health outcomes of asthma patients.

Changes in fish consumption in midlife and the risk of coronary heart disease in men and women

Without data from randomized trials, the long-term effects of fish consumption on coronary heart disease (CHD) need to be inferred from observational studies. We estimated CHD risk under different hypothetical interventions on fish consumption during mid- and later life in 2 prospective US cohorts of 25,797 men in the Health Professionals Follow-Up Study and 53,772 women in the Nurses' Health Study. Participants provided information on risk factors and disease every 2 years and on diet every 4 years. We adjusted for baseline and time-varying risk factors for CHD by using the parametric g-formula (where g stands for "generalized"). We observed 1,865 incident CHD cases among men (in 1990-2008) and 1,891 CHD cases among women (in 1986-2008). The risk ratios for CHD when comparing the risk if everyone had consumed at least 2 servings of fish per week with the risk if no one consumed fish during the follow-up periods were 1.03 (95% confidence interval: 0.90, 1.15) for men and 0.87 (95% confidence interval: 0.76, 0.98) for women. Our results suggest that increasing fish consumption to at least 2 servings per week in mid- or later life may lower CHD risk in women but not in men. Our analytical approach allowed us to explicitly specify hypothetical interventions and to assess the effectiveness of dietary changes in midlife.

Using causal models to distinguish between neurogenesis-dependent and -independent effects on behaviour

There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past 15 years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenesis mediates the effect of an intervention on behaviour. Previous studies may have incorrectly attributed changes in behavioural performance to neurogenesis because the role of known (or unknown) neurogenesis-independent mechanisms was not formally taken into consideration during the analysis. Causal models can tease apart complex causal relationships and were used to demonstrate that the effect of exercise on pattern separation is via neurogenesis-independent mechanisms. Many studies in the neurogenesis literature would benefit from the use of statistical methods that can separate neurogenesis-dependent from neurogenesis-independent effects on behaviour.

Approaching avoidance. A step essential to the understanding of craving

Craving is only one component of the mental processes that influence drinking behavior. Alcohol-related cues (ARCs) can set in motion a dynamic competition between inclinations to approach drinking and inclinations to avoid drinking. Craving can thus be integrated into a comprehensive model of decision-making in which ambivalence or conflict is a key element. The relative strength of each component of the ARC reaction can fluctuate over time as well as in response to both subjective states and environmental circumstances. Simultaneously and independently evaluating these opposing responses puts clinicians in a better position to influence the relative weight that the patient assigns to the positive and negative outcomes of alcohol consumption.