Inferring skin-brain-skin connections from infodemiology data using dynamic Bayesian networks.

The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average R 2 for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions.

Path Analysis Models Integrating Psychological, Psycho-physical and Clinical Variables in Individuals With Tension-Type Headache.

Tension type headache (TTH) is a prevalent but poorly understood pain disease. Current understanding supports the presence of multiple associations underlying its pathogenesis. Our aim was to compare competing multivariate pathway models that explains the complexity of TTH. Headache features (intensity, frequency, or duration - headache diary), headache-related disability (Headache Disability Inventory-HDI), anxiety/depression (Hospital Anxiety and Depression Scale), sleep quality (Pittsburgh Sleep Quality Index), widespread pressure pain thresholds (PPTs) and trigger points (TrPs) were collected in 208 individuals with TTH. Four latent variables were formed from the observed variables - Distress (anxiety, depression), Disability (HDI subscales), Severity (headache features), and Sensitivity (all PPTs). Structural equation modelling (SEM) and Bayesian network (BN) analyses were used to build and compare a theoretical (modeltheory) and a data-driven (modelBN) latent variable model. The modelBN (root mean square error of approximation [RMSEA] = 0.035) provided a better statistical fit than modeltheory (RMSEA = 0.094). The only path common between modelbn and modeltheory was the influence of years with pain on TrPs. The modelBN revealed that the largest coefficient magnitudes were between the latent variables of Distress and Disability (ß=1.524, P = .006). Our theoretical model proposes a relationship whereby psycho-physical and psychological factors result in clinical features of headache and ultimately affect disability. Our data-driven model proposes a more complex relationship where poor sleep, psychological factors, and the number of years with pain takes more relevance at influencing disability. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in TTH. PERSPECTIVE: A theoretical model proposes a relationship where psycho-physical and psychological factors result in clinical manifestations of headache and ultimately affect disability. A data-driven model proposes a more complex relationship where poor sleep, psychological factors, and number of years with pain takes more relevance at influencing disability.

Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study.

OBJECTIVES: To understand the physical, activity, pain, and psychological pathways contributing to low back pain (LBP) -related disability, and if these differ between subgroups. METHODS: Data came from the baseline observations (n = 3849) of the "GLA:D Back" intervention program for long-lasting nonspecific LBP. 15 variables comprising demographic, pain, psychological, physical, activity, and disability characteristics were measured. Clustering was used for subgrouping, Bayesian networks (BN) were used for structural learning, and structural equation model (SEM) was used for statistical inference. RESULTS: Two clinical subgroups were identified with those in subgroup 1 having worse symptoms than those in subgroup 2. Psychological factors were directly associated with disability in both subgroups. For subgroup 1, psychological factors were most strongly associated with disability (ß = 0.363). Physical factors were directly associated with disability (ß = -0.077), and indirectly via psychological factors. For subgroup 2, pain was most strongly associated with disability (ß = 0.408). Psychological factors were common predictors of physical factors (ß = 0.078), pain (ß = 0.518), activity (ß = -0.101), and disability (ß = 0.382). CONCLUSIONS: The importance of psychological factors in both subgroups suggests their importance for treatment. Differences in the interaction between physical, pain, and psychological factors and their contribution to disability in different subgroups may open the doors toward more optimal LBP treatments.

A tutorial on bayesian networks for psychopathology researchers.

Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using Bayesian Networks to Investigate Psychological Constructs: The Case of Empathy.

Network analysis is an emerging field for the study of psychopathology that considers constructs as arising from the interactions among their constituents. Pairwise effects among psychological components are often investigated by using this framework. Few studies have applied Bayesian networks, models that include directed interactions to perform causal inference on psychological constructs. Directed graphical models may be less straightforward to interpret in case the construct at hand does not contain symptoms but instead psychometric items from self-report measures. However, they may be useful in validating specific research questions that arise while using standard pairwise network models. In this study, we use Bayesian networks to investigate a well-known psychological construct, empathy from the Interpersonal Reactivity Index, in large two samples of 1973 university students from Belgium. Overall, our results support the hypotheses emphasizing empathic concern (i.e., sympathy) as causally important in the construct of empathy, and overall attribute the primacy of emotional components of empathy over their intellectual counterparts. Bayesian networks help researchers identify the plausible causal relationships in psychometric data, to gain new insight on the psychological construct under examination, help generate new hypotheses and provide evidence relevant to old ones.

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors.

Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient's sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction.

Do Short-Term Effects Predict Long-Term Improvements in Women Who Receive Manual Therapy or Surgery for Carpal Tunnel Syndrome? A Bayesian Network Analysis of a Randomized Clinical Trial.

OBJECTIVE: The purpose of this study was to develop a data-driven Bayesian network approach to understand the potential multivariate pathways of the effect of manual physical therapy in women with carpal tunnel syndrome (CTS). METHODS: Data from a randomized clinical trial (n = 104) were analyzed comparing manual therapy including desensitization maneuvers of the central nervous system versus surgery in women with CTS. All variables included in the original trial were included in a Bayesian network to explore its multivariate relationship. The model was used to quantify the direct and indirect pathways of the effect of physical therapy and surgery on short-term, mid-term, and long-term changes in the clinical variables of pain, related function, and symptom severity. RESULTS: Manual physical therapy improved function in women with CTS (between-groups difference: 0.09; 95% CI = 0.07 to 0.11). The Bayesian network showed that early improvements (at 1 month) in function and symptom severity led to long-term (at 12 months) changes in related disability both directly and via complex pathways involving baseline pain intensity and depression levels. Additionally, women with moderate CTS had 0.14-point (95% CI = 0.11 to 0.17 point) poorer function at 12 months than those with mild CTS and 0.12-point (95% CI = 0.09 to 0.15 point) poorer function at 12 months than those with severe CTS. CONCLUSION: Current findings suggest that short-term benefits in function and symptom severity observed after manual therapy/surgery were associated with long-term improvements in function, but mechanisms driving these effects interact with depression levels and severity as assessed using electromyography. Nevertheless, it should be noted that between-group differences depending on severity determined using electromyography were small, and the clinical relevance is elusive. Further data-driven analyses involving a broad range of biopsychosocial variables are recommended to fully understand the pathways underpinning CTS treatment effects. IMPACT: Short-term effects of physical manual therapy seem to be clinically relevant for obtaining long-term effects in women with CTS.

How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data.

PURPOSE: Individualised physiotherapy is an effective treatment for low back pain. We sought to determine how this treatment works by using randomised controlled trial data to develop a Bayesian Network model. METHODS: 300 randomised controlled trial participants (153 male, 147 female, mean age 44.1) with low back pain (of duration 6-26 weeks) received either individualised physiotherapy or advice. Variables with potential to explain how individualised physiotherapy works were included in a multivariate Bayesian Network model. Modelling incorporated the intervention period (0-10 weeks after study commencement-"early" changes) and the follow-up period (10-52 weeks after study commencement-"late" changes). Sequences of variables in the Bayesian Network showed the most common direct and indirect recovery pathways followed by participants with low back pain receiving individualised physiotherapy versus advice. RESULTS: Individualised physiotherapy directly reduced early disability in people with low back pain. Individualised physiotherapy exerted indirect effects on pain intensity, recovery expectations, sleep, fear, anxiety, and depression via its ability to facilitate early improvement in disability. Early improvement in disability, led to an early reduction in depression both directly and via more complex pathways involving fear, recovery expectations, anxiety, and pain intensity. Individualised physiotherapy had its greatest influence on early change variables (during the intervention period). CONCLUSION: Individualised physiotherapy for low back pain appears to work predominately by facilitating an early reduction in disability, which in turn leads to improvements in other biopsychosocial outcomes. The current study cannot rule out that unmeasured mechanisms (such as tissue healing or reduced inflammation) may mediate the relationship between individualised physiotherapy treatment and improvement in disability. Further data-driven analyses involving a broad range of plausible biopsychosocial variables are recommended to fully understand how treatments work for people with low back pain. TRIALS REGISTRATION: ACTRN12609000834257.

Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy.

BACKGROUND: The mechanisms of action that facilitate improved outcomes after conservative rehabilitation are unclear in individuals with cervical radiculopathy (CR). This study aims to determine the pathways of recovery of disability with different exercise programs in individuals with CR. METHODS: We analysed a dataset of 144 individuals with CR undergoing conservative rehabilitation. Eleven variables collected at baseline, 3, 6 and 12 months follow-up were used to build a Bayesian Network (BN) model: treatment group (neck-specific vs. general exercises), age, sex, self-efficacy, catastrophizing, kinesiophobia, anxiety, neck-arm pain intensity, headache pain intensity and disability. The model was used to quantify the contribution of different mediating pathways on the outcome of disability at 12th months. RESULTS: All modelled variables were conditionally independent from treatment groups. A one-point increase in anxiety at 3rd month was associated with a 2.45-point increase in 12th month disability (p <.001). A one-point increase in head pain at 3rd month was associated with a 0.08-point increase in 12th month disability (p <.001). Approximately 83% of the effect of anxiety on disability was attributable to self-efficacy. Approximately 88% of the effect of head pain on disability was attributable to neck-arm pain. CONCLUSIONS: No psychological or pain-related variables mediated the different treatment programs with respect to the outcome of disability. Thus, the specific characteristics investigated in this study did not explain the differences in mechanisms of effect between neck-specific training and prescribed physical activity. The present study provides candidate modifiable mediators that could be the target of future intervention trials. SIGNIFICANCE: Psychological and pain characteristics did not differentially explain the mechanism of effect that two exercise regimes had on disability in individuals with cervical radiculopathy. In addition, we found that improvements in self-efficacy was approximately five times more important than that of neck-arm pain intensity in mediating the anxiety-disability relationship. A mechanistic understanding of recovery provides candidate modifiable mediators that could be the target of future intervention trials. TRIALS REGISTRATION: ClinicalTrials.gov identifier: NCT01547611.

Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders.

PURPOSE: To evaluate whether a set of pre-accident demographic, accident-related, post-accident treatment and psychosocial factors assessed in people with acute/subacute whiplash-associated disorders (WAD) mediate the association between pain intensity and: (1) pain interference and (2) expectations of recovery, using Bayesian networks (BNs) analyses. This study also explored the potential mediating pathways (if any) between different psychosocial factors. METHODS: This was a cross-sectional study conducted on a sample of 173 participants with acute/subacute WAD. Pain intensity, pain interference, pessimism, expectations of recovery, pain catastrophizing, and self-efficacy beliefs were assessed. BN analyses were conducted to analyse the mediating effects of psychological factors on the association between pain intensity and pain-related outcomes. RESULTS: The results revealed that self-efficacy beliefs partially mediated the association between pain intensity and pain interference. Kinesiophobia partially mediated the association between self-efficacy and pain catastrophizing. Psychological factors did not mediate the association between pain intensity and expectations of recovery. CONCLUSION: These results indicate that individuals with acute/subacute WAD may present with lesser pain interference associated with a determined pain intensity value when they show greater self-efficacy beliefs. As the cross-sectional nature of this study limits firm conclusions on the causal impact, researchers are encouraged to investigate the role that patient's self-efficacy beliefs play in the transition to chronic WAD via longitudinal study designs.

Network Structures of Symptoms From the Zung Depression Scale.

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 ("My mind is as clear as it used to be") is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

An Interdisciplinary Examination of Stress and Injury Occurrence in Athletes.

This paper adopts a novel, interdisciplinary approach to explore the relationship between stress-related psychosocial factors, physiological markers and occurrence of injury in athletes using a repeated measures prospective design. At four data collection time-points, across 1-year of a total 2-year data collection period, athletes completed measures of major life events, the reinforcement sensitivity theory personality questionnaire, muscle stiffness, heart rate variability and postural stability, and reported any injuries they had sustained since the last data collection. Two Bayesian networks were used to examine the relationships between variables and model the changes between data collection points in the study. Findings revealed muscle stiffness to have the strongest relationship with injury occurrence, with high levels of stiffness increasing the probability of sustaining an injury. Negative life events did not increase the probability of injury occurrence at any single time-point; however, when examining changes between time points, increases in negative life events did increase the probability of injury. In addition, the combination of increases in negative life events and muscle stiffness resulted in the greatest probability of sustaining an injury. Findings demonstrated the importance of both an interdisciplinary approach and a repeated measures design to furthering our understanding of the relationship between stress-related markers and injury occurrence.

A Machine Learning Approach to Relationships Among Alexithymia Components.

BACKGROUND: The aim of this paper is to explore the network structures of alexithymia components and compare results with relevant prior literature. SUBJECTS AND METHODS: In a large sample of university students, undirected and directed network structures of items from the Bermond Vorst Alexithymia Questionnaire form B are estimated with state-of-the-art network analysis and structure learning tools. Centrality estimates are used to address the topic of item redundancy and select relevant alexithymia components to study. RESULTS: Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are identified as central items in the network. CONCLUSIONS: The undirected network structure of alexithymia components reports new features with respect to prior literature, and the directed network structures offers new insight on the construct.

Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using Bayesian networks.

BACKGROUND: Rehabilitation approaches should be based on an understanding of the mechanisms underpinning functional recovery. Yet, the mediators that drive an improvement in post-surgical pain-related disability in individuals with cervical radiculopathy (CR) are unknown. The aim of the present study is to use Bayesian networks (BN) to learn the probabilistic relationships between physical and psychological factors, and pain-related disability in CR. METHODS: We analysed a prospective cohort dataset of 201 post-surgical individuals with CR. In all, 15 variables were used to build a BN model: age, sex, neck muscle endurance, neck range of motion, neck proprioception, hand grip strength, self-efficacy, catastrophizing, depression, somatic perception, arm pain intensity, neck pain intensity and disability. RESULTS: A one point increase in a change of self-efficacy at 6 months was associated with a 0.09 point decrease in a change in disability at 12 months (t = -64.09, p < .001). Two pathways led to a change in disability: a direct path leading from a change in self-efficacy at 6 months to disability, and an indirect path which was mediated by neck and arm pain intensity changes at 6 and 12 months. CONCLUSIONS: This is the first study to apply BN modelling to understand the mechanisms of recovery in post-surgical individuals with CR. Improvements in pain-related disability was directly and indirectly driven by changes in self-efficacy levels. The present study provides potentially modifiable mediators that could be the target of future intervention trials. BN models could increase the precision of treatment and outcome assessment of individuals with CR. SIGNIFICANCE: Using Bayesian Network modelling, we found that changes in self-efficacy levels at 6-month post-surgery directly and indirectly influenced the change in disability in individuals with CR. A mechanistic understanding of recovery provides potentially modifiable mediators that could be the target of future intervention trials.

Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash-associated Disorders Using Bayesian Networks.

OBJECTIVES: The present study's objective was to understand the causal mechanisms underpinning the recovery of individuals with whiplash-associated disorders (WAD). We applied Bayesian Networks (BN) to answer 2 study aims: (1) to identify the causal mechanism(s) of recovery underpinning neck-specific exercise (NSE), and (2) quantify if the cyclical pathway of the fear-avoidance model (FAM) is supported by the present data. MATERIALS AND METHODS: We analyzed a prospective cohort data set of 216 individuals with chronic WAD. Fifteen variables were used to build a BN model: treatment group (NSE with or without a behavioral approach, or general physical activity), muscle endurance, range of motion, hand strength, neck proprioception, pain catastrophizing, fear, anxiety, depression, self-efficacy, perceived work ability, disability, pain intensity, sex, and follow-up time. RESULTS: The BN model showed that neck pain reduction rate was greater after NSE compared with physical activity prescription (ß=0.59 points per month [P<0.001]) only in the presence of 2 mediators: global neck muscle endurance and perceived work ability. We also found the following pathway of variables that constituted the FAM: anxiety, followed by depressive symptoms, fear, catastrophizing, self-efficacy, and consequently pain. CONCLUSIONS: We uncovered 2 mediators that explained the mechanisms of effect behind NSE, and proposed an alternative FAM pathway. The present study is the first to apply BN modelling to understand the causal mechanisms of recovery in WAD. In doing so, it is anticipated that such analytical methods could increase the precision of treatment of individuals with chronic WAD.

A network perspective of engaging patients in specialist and chronic illness care: The 2014 International Health Policy Survey.

BACKGROUND: Patient engagement helps to improve health outcomes and health care quality. However, the overall relationships among patient engagement measures and health outcomes remain unclear. This study aims to integrate expert knowledge and survey data for the identification of measures that have extensive associations with other variables and can be prioritized to engage patients. METHODS: We used the 2014 International Health Policy Survey (IHPS), which provided information on elder adults in 11 countries with details in patient characteristics, healthcare experiences, and patient-physician communication. Patient engagement or support was measured with eight variables including patients' treatment choices, involvement, and treatment priority setting. Three types of care were identified: primary, specialist and chronic illness care. Specialists were doctors specializing in one area of health care. Chronic illness included eight chronic conditions surveyed. Expert knowledge was used to assist variable selection. We used Bayesian network models consisting of nodes that represented variables of interest and arcs that represented their relationships. RESULTS: Among 25,530 participants, the mean age was 68.51 years and 57.40% were females. The distributions of age, sex, education, and patient engagement were significantly different across countries. For chronic illness care, written plans provided by professionals were linked to treatment feasibility and helpfulness. Whether professionals contacted patients was associated with the availability of professionals they could reach for chronic illness care. For specialist care, if specialists provided treatment choices, patients were more likely to be involved and discuss about what mattered to them. CONCLUSION: The strategies to engage patients may depend on the types of care, specialist or chronic illness care. For the study on the observational IHPS data, network modeling is useful to integrate expert knowledge. We suggest considering other theory-based patient engagement in major surveys, as well as engaging patients in their healthcare by providing written plans and actively communicating with patients for chronic illnesses, and encouraging specialists to discuss and provide treatment options.

Bayesian Networks Analysis of Malocclusion Data.

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment.

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011.

BACKGROUND: There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status. METHODS: This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure. RESULTS: There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals' explanation was understandable. CONCLUSIONS: It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.

Using Genetic Distance to Infer the Accuracy of Genomic Prediction.

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Applying association mapping and genomic selection to the dissection of key traits in elite European wheat.

KEY MESSAGE: We show the application of association mapping and genomic selection for key breeding targets using a large panel of elite winter wheat varieties and a large volume of agronomic data. The heightening urgency to increase wheat production in line with the needs of a growing population, and in the face of climatic uncertainty, mean new approaches, including association mapping (AM) and genomic selection (GS) need to be validated and applied in wheat breeding. Key adaptive responses are the cornerstone of regional breeding. There is evidence that new ideotypes for long-standing traits such as flowering time may be required. In order to detect targets for future marker-assisted improvement and validate the practical application of GS for wheat breeding we genotyped 376 elite wheat varieties with 3,046 DArT, single nucleotide polymorphism and gene markers and measured seven traits in replicated yield trials over 2 years in France, Germany and the UK. The scale of the phenotyping exceeds the breadth of previous AM and GS studies in these key economic wheat production regions of Northern Europe. Mixed-linear modelling (MLM) detected significant marker-trait associations across and within regions. Genomic prediction using elastic net gave low to high prediction accuracies depending on the trait, and could be experimentally increased by modifying the constituents of the training population (TP). We also tested the use of differentially penalised regression to integrate candidate gene and genome-wide markers to predict traits, demonstrating the validity and simplicity of this approach. Overall, our results suggest that whilst AM offers potential for application in both research and breeding, GS represents an exciting opportunity to select key traits, and that optimisation of the TP is crucial to its successful implementation.