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Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors.
To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions.
Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title.
In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7–8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0–22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1–10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors.
Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.
The movement of healthcare professionals (HCPs) induces an indirect contact network: touching a patient or the environment in one area, then again elsewhere, can spread healthcare-associated pathogens from 1 patient to another. Thus, understanding HCP movement is vital to calibrating mathematical models of healthcare-associated infections. Because long-term care facilities (LTCFs) are an important locus of transmission and have been understudied relative to hospitals, we developed a system for measuring contact patterns specifically within an LTCF. Methods: To measure HCP movement patterns, we used badges (credit-card–sized, programmable, battery-powered devices with wireless proximity sensors) worn by HCPs and placed in 30 locations for 3 days. Each badge broadcasts a brief message every 8 seconds. When received by other badges within range, the recipients recorded the time, source badge identifier, and signal strength. By fusing the data collected by all badges with a facility map, we estimated when and for how long each HCP was in any of the locations where instruments had been installed. Results: Combining the messages captured by all of our devices, we calculated the dwell time for each job type (eg, nurses, nursing assistants, physical therapists) in different locations (eg, resident rooms, dining areas, nurses stations, hallways, etc). Although dwell times over all job and area types averaged ∼100 seconds, the standard deviation was large (115 seconds), with a mean of maximums by job type of ∼450 seconds. For example, nursing assistants spent substantially more time in resident rooms and transitioned across rooms at a much higher rate. Overall, each distribution exhibits a power-law–like characteristic. By aggregating the data from devices with location data extracted from the floor plan, we were able to produce an explicit trace for each individual (identified only by job type) for each day and to compute cross-table transition probabilities by area for each job type. Conclusions: We developed a portable system for measuring contact patterns in long-term care settings. Our results confirm that frequent interactions between HCPs and LTC residents occur, but they are not uniform across job types or resident locations. The data produced by our system can be used to better calibrate mathematical models of pathogen spread in LTCs. Moreover, our system can be easily and quickly deployed to any healthcare settings to similarly inform outbreak investigations.
Disclosures: Scott Fridkin reports that his spouse receives a consulting fee from the vaccine industry.
Background: Certain nursing home (NH) resident care tasks have a higher risk for multidrug-resistant organisms (MDRO) transfer to healthcare personnel (HCP), which can result in transmission to residents if HCPs fail to perform recommended infection prevention practices. However, data on HCP-resident interactions are limited and do not account for intrafacility practice variation. Understanding differences in interactions, by HCP role and unit, is important for informing MDRO prevention strategies in NHs. Methods: In 2019, we conducted serial intercept interviews; each HCP was interviewed 6–7 times for the duration of a unit’s dayshift at 20 NHs in 7 states. The next day, staff on a second unit within the facility were interviewed during the dayshift. HCP on 38 units were interviewed to identify healthcare personnel (HCP)–resident care patterns. All unit staff were eligible for interviews, including certified nursing assistants (CNAs), nurses, physical or occupational therapists, physicians, midlevel practitioners, and respiratory therapists. HCP were asked to list which residents they had cared for (within resident rooms or common areas) since the prior interview. Respondents selected from 14 care tasks. We classified units into 1 of 4 types: long-term, mixed, short stay or rehabilitation, or ventilator or skilled nursing. Interactions were classified based on the risk of HCP contamination after task performance. We compared proportions of interactions associated with each HCP role and performed clustered linear regression to determine the effect of unit type and HCP role on the number of unique task types performed per interaction. Results: Intercept-interviews described 7,050 interactions and 13,843 care tasks. Except in ventilator or skilled nursing units, CNAs have the greatest proportion of care interactions (interfacility range, 50%–60%) (Fig. 1). In ventilator and skilled nursing units, interactions are evenly shared between CNAs and nurses (43% and 47%, respectively). On average, CNAs in ventilator and skilled nursing units perform the most unique task types (2.5 task types per interaction, Fig. 2) compared to other unit types (P < .05). Compared to CNAs, most other HCP types had significantly fewer task types (0.6–1.4 task types per interaction, P < .001). Across all facilities, 45.6% of interactions included tasks that were higher-risk for HCP contamination (eg, transferring, wound and device care, Fig. 3). Conclusions: Focusing infection prevention education efforts on CNAs may be most efficient for preventing MDRO transmission within NH because CNAs have the most HCP–resident interactions and complete more tasks per visit. Studies of HCP-resident interactions are critical to improving understanding of transmission mechanisms as well as target MDRO prevention interventions.
Funding: Centers for Disease Control and Prevention (grant no. U01CK000555-01-00)
Disclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)
Molecular weight (Mw) effects in poly(vinylidene fluoride) (PVDF) influence both processability and combustion behavior in energetic Al–PVDF filaments. Results show decreased viscosity in unloaded and fuel-lean (i.e., 15 wt% Al) filaments. In highly loaded filaments (i.e., 30 wt% Al), reduced viscosity is minimal due to higher electrostatic interaction between Al particles and low Mw chains as confirmed by Fourier-transform infrared spectroscopy. Thermal and combustion analysis further corroborates this story as exothermic activity decreases in PVDF with smaller Mw chains. Differential scanning calorimetry and Thermogravimetric analysis show reduced reaction enthalpy and lower char yield in low Mw PVDF. Enthalpy reduction trends continued in nonequilibrium burn rate studies, which confirm that burn rate decreases in the presence of low Mw PVDF. Furthermore, powder X-ray patterns of post-burn products suggest that low Mw PVDF decomposition creates a diffusion barrier near the Al particle surface resulting in negligible AlF3 formation in fuel-rich filaments.
Previous genetic association studies have failed to identify loci robustly associated with sepsis, and there have been no published genetic association studies or polygenic risk score analyses of patients with septic shock, despite evidence suggesting genetic factors may be involved. We systematically collected genotype and clinical outcome data in the context of a randomized controlled trial from patients with septic shock to enrich the presence of disease-associated genetic variants. We performed genomewide association studies of susceptibility and mortality in septic shock using 493 patients with septic shock and 2442 population controls, and polygenic risk score analysis to assess genetic overlap between septic shock risk/mortality with clinically relevant traits. One variant, rs9489328, located in AL589740.1 noncoding RNA, was significantly associated with septic shock (p = 1.05 × 10–10); however, it is likely a false-positive. We were unable to replicate variants previously reported to be associated (p < 1.00 × 10–6 in previous scans) with susceptibility to and mortality from sepsis. Polygenic risk scores for hematocrit and granulocyte count were negatively associated with 28-day mortality (p = 3.04 × 10–3; p = 2.29 × 10–3), and scores for C-reactive protein levels were positively associated with susceptibility to septic shock (p = 1.44 × 10–3). Results suggest that common variants of large effect do not influence septic shock susceptibility, mortality and resolution; however, genetic predispositions to clinically relevant traits are significantly associated with increased susceptibility and mortality in septic individuals.
In this chapter, the second limitation of the human rights-based approach is examined. The chapter develops the argument that the human rights-based approach casts the personal scope of the refugee definition too widely, as anyone who is exposed to a 'sustained or systemic denial of human rights demonstrative of a failure of state protection' is, by definition, persecuted. Such a definition, however, incorporates a wide range of individuals who would not, having regard to the 'ordinary meaning' of the term, be described as 'persecuted', including people who die because of failures of the state to maintain effective disaster risk reduction infrastructure (as in the case of Budayeva v Russian Federation). The argument is advanced that this dominant definition of being persecuted misses what is fundamental to the experience of being persecuted within the meaning of the Refugee Convention. In this context, being persecuted cannot be understood without reference to the role of discrimination as a contributory cause of a person's exposure to serious denials of human rights. Employing the methodology at Articles 31-33 of the Vienna Convention on the Law of Treaties, a recalibrated definition of being persecuted is articulated.
The concluding chapter applies the recalibrated human rights-based interpretation of the refugee definition in the context of disasters and climate change. A three-step methodology for determining refugee status is set out, making clear that the methodology applies in the same manner for any kind of claim for recognition of refugee status, not only those relating to disasters and climate change.
Relying on the Vienna Convention on the Law of Treaties (VCLT), this chapter explains why international human rights law is relevant to the interpretation of Article 1A(2) of the Refugee Convention. It then describes Articles 31-33 of the VCLT, which will be relied upon to address the two limitations in the dominant human rights-based interpretation of the refugee definition identified in Chapter 3.
The introductory chapter presents the view, widely reflected by legal academics, UNHCR and senior judiciary in leading refugee law jurisdictions, that the Refugee Convention has very limited relevance to displacement across borders in the context of disasters and climate change. Some key examples of this perspective are presented and a connection is made between the expression of this view and what the book terms the ‘hazard paradigm’ to understanding disasters. The chapter notes that much of the jurisprudence reflecting the hazard paradigm has relied upon disasters as an aid to articulating the scope of the refugee definition. The argument is advanced that the critical analysis of epistemological and doctrinal assumptions underpinning this 'dominant view' helps to clarify the contours of the refugee definition in general, as well as in the specific context that is the focus of the book.
In this chapter, the first limitation identified in the human rights-based approach is considered. The chapter develops the argument that the dominant human rights-based approach reflects an 'event paradigm' that only recognises that a person is persecuted once she has become the victim of a serious denial of human rights. From this perspective, persecution is equivalent to the kinds of harm recognised by international human rights law, such as being tortured. It therefore follows that the well-founded fear criterion has come to be seen as introducing a criterion of risk assessment, determining how likely it is that 'persecution' might happen on return. This perspective casts the temporal scope of being persecuted too narrowly, and, using the methodology set out at Articles 31-33 of the Vienna Convention on the Law of Treaties, the argument is advanced that a person can accurately be described as being persecuted before being subjected to a serious denial of human rights, and that the risk of being exposed to such harm is inherent in the predicament of being persecuted. Rather than being an event, being persecuted is better understood as a condition of existence.