Given the range and reach of psychosocial support (PSS) interventions in humanitarian settings, within the continuum of mental health and psychosocial support services, evaluation of their impact is critical. Understanding stakeholders' perspectives on which PSS interventions of unknown effectiveness warrant rigorous evaluation is essential to identify research priorities. This project aimed to facilitate a process with stakeholders to reach consensus on PSS interventions that are of high priority for further research based on existing evidence and stakeholders' opinions.

Interviews with 109 stakeholders working on PSS programming in humanitarian settings served as the foundation for two in-person regional meetings and four webinars. Nominal Group Technique (NGT) was used to develop a priority PSS program list. The top five priorities from each meeting were combined for a final online survey distributed globally.

Seventy participants across six meetings contributed to the prioritization process. Eighty-seven individuals completed the final online survey. ‘Community based PSS’ was the top-ranked research priority, followed by PSS integrated into basic services, providing PSS to caregivers to improve child wellbeing, PSS-focused gender-based violence programming, and classroom-based PSS interventions.

NGT and online surveys were effective methods to engage stakeholders in a priority setting exercise to development a research agenda. Information from this stage of the project will be combined with findings from a concurrent systematic review to form the base of a second phase of work, which will include the development and implementation of a research strategy to strengthen the evidence base for those prioritized interventions.

Abnormal effort-based decision-making represents a potential mechanism underlying motivational deficits (amotivation) in psychotic disorders. Previous research identified effort allocation impairment in chronic schizophrenia and focused mostly on physical effort modality. No study has investigated cognitive effort allocation in first-episode psychosis (FEP).

Cognitive effort allocation was examined in 40 FEP patients and 44 demographically-matched healthy controls, using Cognitive Effort-Discounting (COGED) paradigm which quantified participants’ willingness to expend cognitive effort in terms of explicit, continuous discounting of monetary rewards based on parametrically-varied cognitive demands (levels N of N-back task). Relationship between reward-discounting and amotivation was investigated. Group differences in reward-magnitude and effort-cost sensitivity, and differential associations of these sensitivity indices with amotivation were explored.

Patients displayed significantly greater reward-discounting than controls. In particular, such discounting was most pronounced in patients with high levels of amotivation even when N-back performance and reward base amount were taken into consideration. Moreover, patients exhibited reduced reward-benefit sensitivity and effort-cost sensitivity relative to controls, and that decreased sensitivity to reward-benefit but not effort-cost was correlated with diminished motivation. Reward-discounting and sensitivity indices were generally unrelated to other symptom dimensions, antipsychotic dose and cognitive deficits.

This study provides the first evidence of cognitive effort-based decision-making impairment in FEP, and indicates that decreased effort expenditure is associated with amotivation. Our findings further suggest that abnormal effort allocation and amotivation might primarily be related to blunted reward valuation. Prospective research is required to clarify the utility of effort-based measures in predicting amotivation and functional outcome in FEP.

Better understanding of interplay among symptoms, cognition and functioning in first-episode psychosis (FEP) is crucial to promoting functional recovery. Network analysis is a promising data-driven approach to elucidating complex interactions among psychopathological variables in psychosis, but has not been applied in FEP.

This study employed network analysis to examine inter-relationships among a wide array of variables encompassing psychopathology, premorbid and onset characteristics, cognition, subjective quality-of-life and psychosocial functioning in 323 adult FEP patients in Hong Kong. Graphical Least Absolute Shrinkage and Selection Operator (LASSO) combined with extended Bayesian information criterion (BIC) model selection was used for network construction. Importance of individual nodes in a generated network was quantified by centrality analyses.

Our results showed that amotivation played the most central role and had the strongest associations with other variables in the network, as indexed by node strength. Amotivation and diminished expression displayed differential relationships with other nodes, supporting the validity of two-factor negative symptom structure. Psychosocial functioning was most strongly connected with amotivation and was weakly linked to several other variables. Within cognitive domain, digit span demonstrated the highest centrality and was connected with most of the other cognitive variables. Exploratory analysis revealed no significant gender differences in network structure and global strength.

Our results suggest the pivotal role of amotivation in psychopathology network of FEP and indicate its critical association with psychosocial functioning. Further research is required to verify the clinical significance of diminished motivation on functional outcome in the early course of psychotic illness.

This is a copy of the slides presented at the meeting but not formally written up for the volume.

Interactions at magnetic interfaces are central to the operation of virtually all magnetic heterostructures. When the interface is between two magnetic materials, the exchange interaction between spins at the interface is often a dominant force, and can dramatically change the magnetic response of the overall heterostructure. In ferromagnet (FM)/antiferromagnet (AFM) heterostructures, this interaction is often referred to as exchange anisotropy or bias and it has been widely used over the past decade in a wide array of applications such as magnetic recording heads, MRAMs, etc. The powerful implications of interactions between an AFM and a FM have been realized in a wide range of thin film heterostructure with both metallic and oxide constituents. There is, however, much less work on oxide-oxide FM/AFM systems. On the other hand, the development and understanding of functional oxide materials, especially multifunctional materials like BiFeO3 (BFO), have piqued the interest of researchers worldwide with the promise of coupling between order parameters such as ferroelectricity and antiferromagnetism. Recent research suggests that there is exchange coupling and anisotropy between the metallic ferromagnet Co0.9Fe0.1 (CoFe) and the multiferroic, antiferromagnet BFO, showing the possibility to create highly desirable multifunctional systems with new possibilities for device design. Such a result provides the driving force to create multifunctional oxide-oxide systems where exchange interactions could be much stronger then in metal/oxide structures due the added epitaxial nature of the interface. In this study, we use La0.7Sr0.3MnO3(LSMO)/BFO thin film heterostructures as a model system to explore the exchange interaction at an oxide interface. The heterostructures are grown on various vicinal cuts of SrTiO3 single crystal substrates using laser MBE. Structural analysis using x-ray diffraction, transmission electron microscopy and Rutherford backscattering spectrometry reveals high quality films with the pristine interfaces required for exchange coupling. First results from photoemission electron microscope (PEEM) studies reveal that the magnetic LSMO domain structure mimics underneath ferroelectric BFO domain structure, i..e, it is strongly pinned by the underlying AFM structure. The coupling behavior is being characterized by magnetic measurements (SQUID, VSM), which shows a strong enhancement in the coercivity of the LSMO layer, suggesting the existence of exchange bias coupling. We are probing the strength of this coupling using a combination of careful laser MBE growth experiments and physical property measurements. In this paper, we will report results of experiments in which the LSMO layer has been grown by laser MBE in the thickness range of 2-50nm on a [001] BFO layer.

The present study evaluates the use of multiple correspondence analysis (MCA), a type of exploratory factor analysis designed to reduce the dimensionality of large categorical data sets, in identifying behaviours associated with measures of overweight/obesity in Vanuatu, a rapidly modernizing Pacific Island country.

Starting with seventy-three true/false questions regarding a variety of behaviours, MCA identified twelve most significantly associated with modernization status and transformed the aggregate binary responses of participants to these twelve questions into a linear scale. Using this scale, individuals were separated into three modernization groups (tertiles) among which measures of body fat were compared and OR for overweight/obesity were computed.

Vanuatu.

Ni-Vanuatu adults (n 810) aged 20–85 years.

Among individuals in the tertile characterized by positive responses to most of or all the twelve modernization questions, weight and measures of body fat and the likelihood that measures of body fat were above the US 75th percentile were significantly greater compared with individuals in the tertiles characterized by mostly or partly negative responses.

The study indicates that MCA can be used to identify individuals or groups at risk for overweight/obesity, based on answers to simply-put questions. MCA therefore may be useful in areas where obtaining detailed information about modernization status is constrained by time, money or manpower.

The Pain Catastrophizing Scale (PCS) measures three aspects of catastrophic cognitions about pain—rumination, magnification, and helplessness. To facilitate assessment and clinical application, we aimed to (a) develop a short version on the basis of its factorial structure and the items’ correlations with key pain-related outcomes, and (b) identify the threshold on the short form indicative of risk for depression.

Cross-sectional survey.

Social centers for older people.

664 Chinese older adults with chronic pain.

Besides the PCS, pain intensity, pain disability, and depressive symptoms were assessed.

For the full scale, confirmatory factor analysis showed that the hypothesized 3-factor model fit the data moderately well. On the basis of the factor loadings, two items were selected from each of the three dimensions. An additional item significantly associated with pain disability and depressive symptoms, over and above these six items, was identified through regression analyses. A short-PCS composed of seven items was formed, which correlated at r=0.97 with the full scale. Subsequently, receiver operating characteristic (ROC) curves were plotted against clinically significant depressive symptoms, defined as a score of ≥12 on a 10-item version of the Center for Epidemiologic Studies-Depression Scale. This analysis showed a score of ≥7 to be the optimal cutoff for the short-PCS, with sensitivity = 81.6% and specificity = 78.3% when predicting clinically significant depressive symptoms.

The short-PCS may be used in lieu of the full scale and as a brief screen to identify individuals with serious catastrophizing.

Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.

We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.

Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.

Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.

Immunization data are vital to support responses to vaccine-preventable disease outbreaks. The Oregon Immunization Program developed a unique prototype instrument—the Rapid Response Tool (RRT)—that provides population data to local responders within 2 hours of a request. Data outputs include vaccination coverage by age group and zip code; percentages of students with nonmedical exemptions to vaccination requirements, by school; and current, comprehensive lists of local vaccination providers.

The RRT was demonstrated to staff at 7 Oregon counties and feedback was solicited via comments and a structured survey.

The RRT received strong support. Attendees identified several uses for RRT data, including outbreak response and ongoing intervention efforts, and they pointed to areas for further development.

The success of the RRT demonstrations illustrates that a well-populated immunization information system can contribute to preparedness work well beyond current standards. (Disaster Med Public Health Preparedness. 2019;13:682–685)