A Multi-Method Approach to Understand Parent Behaviors during Child Acute Pain

Parent behaviors strongly predict child responses to acute pain; less studied are the factors shaping parent behaviors. Heart rate variability (HRV) is considered a physiological correlate of emotional responding. Resting or trait HRV is indicative of the capacity for emotion regulation, while momentary changes or state HRV is reflective of current emotion regulatory efforts. This study aimed to examine: (1) parent state HRV as a contributor to parent verbal behaviors before and during child pain and (2) parent trait HRV as a moderator between parent emotional states (anxiety, catastrophizing) and parent behaviors. Children 7-12 years of age completed the cold pressor task (CPT) in the presence of a primary caregiver. Parents rated their state anxiety and catastrophizing about child pain. Parent HRV was examined at 30-second epochs at rest ( trait HRV ), before ( state HRV-warm ), and during their child\u27s CPT ( state HRV-cold ). Parent behaviors were video recorded and coded as coping-promoting or distress-promoting. Thirty-one parents had complete cardiac, observational, and self-report data. A small to moderate negative correlation emerged between state HRV-cold and CP behaviors during CPT. Trait HRV moderated the association between parent state catastrophizing and distress-promoting behaviors. Parents experiencing state catastrophizing were more likely to engage in distress-promoting behavior if they had low trait HRV. This novel work suggests parents who generally have a low (vs. high) HRV, reflective of low capacity for emotion regulation, may be at risk of engaging in behaviors that increase child distress when catastrophizing about their child\u27s pain

Bringing the commercial determinants of health out of the shadows : a review of how the commercial determinants are represented in conceptual frameworks

BACKGROUND: The term 'commercial determinants of health' (CDOH) is increasingly focussing attention upon the role of tobacco, alcohol and food and beverage companies and others-as important drivers of non-communicable diseases (NCDs). However, the CDOH do not seem to be clearly represented in the most common social determinants of health (SDOH) frameworks. We review a wide range of existing frameworks of the determinants of health to determine whether and how commercial determinants are incorporated into current SDOH thinking. METHODS: We searched for papers and non-academic reports published in English since 2000 describing influences on population health outcomes. We included documents with a formal conceptual framework or diagram, showing the integration of the different determinants. RESULTS: Forty-eight framework documents were identified. Only one explicitly included the CDOH in a conceptual diagram. Ten papers discussed the commercial determinants in some form in the text only and fourteen described negative impacts of commercial determinants in the text. Twelve discussed positive roles for the private sector in producing harmful commodities. Overall, descriptions of commercial determinants are frequently understated, not made explicit, or simply missing. The role of commercial actors as vectors of NCDs is largely absent or invisible in many of the most influential conceptual diagrams. CONCLUSIONS: Our current public health models may risk framing public health problems and solutions in ways that obscure the role that the private sector, in particular large transnational companies, play in shaping the broader environment and individual behaviours, and thus population health outcomes

Macrosocial determinants of population health in the context of globalization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55738/1/florey_globalization_2007.pd

Training Population Optimization for Genomic Selection in Miscanthus

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus x giganteus (M·g) beyond the single clone used in many programs. Germplasm from the corresponding parental speciesM. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M·g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M·g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train theseGSmodels. To overcome the drawback of having only one interspecific M·g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M·g F2 panels derived from different sets of Msi and diploidMsa parents. The results revealed that genetic architectures with common causalmutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M·g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible

Training Population Optimization for Genomic Selection in Miscanthus

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus x giganteus (M·g) beyond the single clone used in many programs. Germplasm from the corresponding parental speciesM. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M·g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M·g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train theseGSmodels. To overcome the drawback of having only one interspecific M·g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M·g F2 panels derived from different sets of Msi and diploidMsa parents. The results revealed that genetic architectures with common causalmutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M·g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible

Training Population Optimization for Genomic Selection in Miscanthus

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible

Comparing traditional and participatory dissemination of a shared decision making intervention (ADAPT-NC): a cluster randomized trial

BACKGROUND: Asthma is a common disease that affects people of all ages and has significant morbidity and mortality. Poor outcomes and health disparities related to asthma result in part from the difficulty of disseminating new evidence and care delivery methods such as shared decision making (SDM) into clinical practice. This 3-year study explores the ideal framework for rapid dissemination of an evidence-based SDM toolkit for asthma management. The study leverages a partnership between the North Carolina (NC) statewide Medicaid network and the NC Network Consortium of practice-based research networks (PBRNs). METHODS/DESIGN: This non-blinded study will randomize 30 primary care clinics in NC stratified by four PBRNs. We will test dissemination across these practices using a facilitator-led participatory approach to dissemination (FLOW), a novel method of participatory dissemination involving key principles of community-based participatory research, and a more typical “lunch and learn” dissemination method. Specifically, we will use cluster randomization to assign each of the 30 practices to one of three arms: (1) control, no dissemination; (2) traditional dissemination, one didactic session a year and distribution of educational material; and (3) FLOW dissemination. We hypothesize that at the unit of randomization, the clinic, patients in the FLOW dissemination arm will be more likely to share in their treatment decisions compared to patients in the traditional dissemination or control arms. All outcomes will be measured at the level of the clinic. Adoption of the SDM approach will be evaluated by 1) asthma exacerbations, 2) level of patient involvement in the decision making process, and 3) qualitative assessments from patients and providers. The research question is: What dissemination strategy most effectively increases practice level adoption of a shared decision making approach to asthma management? This study will provide important data to support best practices in dissemination of an evidence-based toolkit and implementation of shared decision making into primary care practices. TRIAL REGISTRATION: The trial was registered on January 27, 2014 through the United States National Institutes of Health’s ClinicalTrials.gov NCT02047929 and funded by the Patient-Centered Outcomes Research Institute (PCORI)