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Several recent reports have raised concern that infected co-workers may be an important source of SARS-CoV-2 acquisition by healthcare personnel. In a suspected outbreak among emergency department personnel, sequencing of SARS-CoV-2 confirmed transmission among co-workers. The suspected 6-person outbreak included 2 distinct transmission clusters and 1 unrelated infection.
Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.
Background: In the United States, carbapenemases are rarely the cause of carbapenem resistance in Pseudomonas aeruginosa. Detection of carbapenemase production (CP) in carbapenem-resistant P. aeruginosa (CRPA) is critical for preventing its spread, but testing of many isolates is required to detect a single CP-CRPA. The CDC evaluates CRPA for CP through (1) the Antibiotic Resistance Laboratory Network (ARLN), in which CRPA are submitted from participating clinical laboratories to public health laboratories for carbapenemase testing and antimicrobial susceptibility testing (AST) and (2) laboratory and population-based surveillance for CRPA in 8 sites through the Emerging Infection Program (EIP). Objective: We used data from ARLN and EIP to identify AST phenotypes that can help detect CP-CRPA. Methods: We defined CRPA as P. aeruginosa resistant to meropenem, imipenem, or doripenem, and we defined CP-CRPA as CRPA with molecular identification of carbapenemase genes (blaKPC, blaIMP, blaNDM, or blaVIM). We applied CLSI break points to 2018 ARLN CRPA AST data to categorize isolates as resistant, intermediate, or susceptible, and we evaluated the sensitivity and specificity of AST phenotypes to detect CP among CRPA; isolates that were intermediate or resistant were called nonsusceptible. Using EIP data, we assessed the proportion of isolates tested for a given drug in clinical laboratories, and we applied definitions to evaluate performance and number needed to test to identify a CP-CRPA. Results: Only 203 of 6,444 of CRPA isolates (3%) tested through AR Lab Network were CP-CRPA harboring blaVIM (n = 123), blaKPC (n = 53), blaIMP (n = 16), or blaNDM (n = 13) genes. Definitions with the best performance were resistant to ≥1 carbapenem AND were (1) nonsusceptible to ceftazidime (sensitivity, 93%; specificity, 61%) (Table 1) or (2) nonsusceptible to cefepime (sensitivity, 83%; specificity, 53%). Most isolates not identified by definition 2 were sequence type 111 from a single-state blaVIM CP-CRPA outbreak. Among 4,209 CRPA isolates identified through EIP, 80% had clinical laboratory AST data for ceftazidime and 96% had clinical laboratory AST data for cefepime. Of 967 CRPA isolates that underwent molecular testing at the CDC, 7 were CP-CRPA; both definitions would have detected all 7. Based on EIP data, the number needed to test to identify 1 CP-CRPA would decrease from 135 to 42 for definition 1 and to 50 using definition 2. Conclusions: AST-based definitions using carbapenem resistance combined with ceftazidime or cefepime nonsusceptibility would rarely miss a CP-CRPA and would reduce the number needed to test to identify CP-CRPA by >60%. These definitions could be considered for use in laboratories to decrease the testing burden to detect CP-CRPA.
Disclosures: In the presentation we will discuss the drug combination aztreonam-avibactam and acknowledge that this drug combination is not currently FDA approved.
The COVID-19 pandemic has placed unprecedented demands on health systems, where hospitals have become overwhelmed with patients amidst limited resources. Disaster response and resource allocation during such crises present multiple challenges. A breakdown in communication and organization can lead to unnecessary disruptions and adverse events. The Federal Emergency Management Agency (FEMA) promotes the use of an incident command system (ICS) model during large-scale disasters, and we hope that an institutional disaster plan and ICS will help to mitigate these lapses. In this article, we describe the alignment of an emergency department (ED) specific Forward Command structure with the hospital ICS and address the challenges specific to the ED. Key components of this ICS include a hospital-wide incident command or Joint Operations Center (JOC) and an ED Forward Command. This type of structure leads to a shared mental model with division of responsibilities that allows institutional adaptations to changing environments and maintenance of specific roles for optimal coordination and communication. We present this as a model that can be applied to other hospital EDs around the country to help structure the response to the COVID-19 pandemic while remaining generalizable to other disaster situations.
To outline features of the neurologic examination that can be performed virtually through telemedicine platforms (the virtual neurological examination [VNE]), and provide guidance for rapidly pivoting in-person clinical assessments to virtual visits during the COVID-19 pandemic and beyond.
The full neurologic examination is described with attention to components that can be performed virtually.
A screening VNE is outlined that can be performed on a wide variety of patients, along with detailed descriptions of virtual examination maneuvers for specific scenarios (cognitive testing, neuromuscular and movement disorder examinations).
During the COVID-19 pandemic, rapid adoption of virtual medicine will be critical to provide ongoing and timely neurological care. Familiarity and mastery of a VNE will be critical for neurologists, and this article outlines a practical approach to implementation.
The existence of long memory in mortality data improves the understandings of features of mortality data and provides a new approach for establishing mortality models. The findings of long-memory phenomena in mortality data motivate us to develop new mortality models by extending the Lee–Carter (LC) model to death counts and incorporating long-memory model structure. Furthermore, there are no identification issues arising in the proposed model class. Hence, the constraints which cause many computational issues in LC models are removed. The models are applied to analyse mortality death count data sets from three different countries divided according to genders. Bayesian inference with various selection criteria is applied to perform the model parameter estimation and mortality rate forecasting. Results show that multivariate long-memory mortality model with long-memory cohort effect model outperforms multivariate extended LC cohort model in both in-sample fitting and out-sample forecast. Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans.
Every year, there are larger and more severe disasters and health organizations are struggling to respond with services to keep public health systems running. Making decisions with limited health information can negatively affect response activities and impact morbidity and mortality. An overarching challenge is getting the right health information to the right health service personnel at the right time. As responding agencies engage in social media (eg, Twitter, Facebook) to communicate with the public, new opportunities emerge to leverage this non-traditional information for improved situational awareness. Transforming these big data is dependent on computers to process and filter content for health information categories relevant to health responders. To enable a more health-focused approach to social media analysis during disasters, 2 major research challenges should be addressed: (1) advancing methodologies to extract relevant information for health services and creating dynamic knowledge bases that address both the global and US disaster contexts, and (2) expanding social media research for disaster informatics to focus on health response activities. There is a lack of attention on health-focused social media research beyond epidemiologic surveillance. Future research will require approaches that address challenges of domain-aware, including multilingual language understanding in artificial intelligence for disaster health information extraction. New research will need to focus on the primary goal of health providers, whose priority is to get the right health information to the right medical and public health service personnel at the right time.
Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.
In April 2019, only six weeks after Cyclone Idai devastated Mozambique’s central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth’s landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.
The model prior to Kenneth’s arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model’s utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.
Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.
We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.
16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).
PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.
Large-scale mass-sporting events are increasingly requiring greater prehospital event planning and preparation to address inherent event-associated medical conditions in addition to incidents that may be unexpected. The Bank of America Chicago Marathon (Chicago, Illinois USA) is one of the largest marathons in the world, and with the improvement of technology, the use of historical patient and event data, in conjunction with environmental conditions, can provide organizers and public safety officials a way to plan based on injury patterns and patient demands for care by predicting the placement and timing of needed medical support and resources.
During large-scale events, disaster planning and preparedness between event organizers, Emergency Medical Services (EMS), and local, state, and federal agencies is critical to ensure participant and public safety.
This study looked at the Bank of America Chicago Marathon, a significant endurance event, and took a unique approach of reviewing digital runner data retrospectively over a five-year period to establish patterns of medical demand geographically, temporally, and by the presenting diagnoses. Most medical complaints were musculoskeletal in nature; however, there were life-threatening conditions such as hyperthermia and cardiac incidents that highlight the need for detailed planning, coordination, and communication to ensure a safe and secure event.
The Chicago Marathon is one of the largest marathons in the world, and this study identified an equal number of participants requiring care on-course and at the finish line. Most medical complaints were musculoskeletal in nature; however, there were life-threatening conditions such as hyperthermia and cardiac incidents that highlight the need for detailed planning, multi-disciplined coordination, and communication to ensure a safe and secure event. As technology has evolved, the use, analysis, and implementation of historical digital data with various environmental conditions can provide organizers and public safety officials a map to plan injury patterns and patient demands by predicting the placement and timing of needed medical support, personnel, and resources.
More than half of the world’s youth live in the Asia Pacific region, yet efforts to reduce disaster risk for adolescents are hindered by an absence of age-specific data on protection, health, and engagement.
China and Nepal have faced a recent escalation in the number of climatic and geological hazards affecting urban and rural communities. We aimed to examine disaster-related threats experienced by adolescents and their caregivers in China and Nepal, determine the scope for adolescent participation, and elicit recommendations for improving disaster risk reduction.
Sixty-nine adolescents (51% female, ages 13-19) and 72 adults (47% female, ages 22-66) participated in key informant interviews and focus group discussions in disaster-affected areas of southern China and Nepal. Using inductive content analysis, several themes were identified as key to adolescents’ needs.
Security and protection emerged as a central issue, interlinked with preparedness, timely and equitable disaster response, psychosocial support, and adolescent participation. The mental health risks emerging from trauma exposure were substantial. Adolescents made extensive contributions to disaster response including involvement in rescue efforts and delivering first aid, rebuilding homes and caring for family members. Participants forwarded a number of recommendations, including investing in psychological support, skills training, and stronger systems of protection for those at risk of family separation, trafficking, or removal from school.
The findings informed a multilevel, interconnected model for disaster risk reduction tailored to adolescents’ needs. Supporting adolescents’ recovery and long-term resilience after humanitarian crises will require coordinated efforts in preparedness, security, and mental health care.
The extensive heterogeneity both between and within the medulloblastoma (MB) subgroups underscores a critical need for variant-specific biomarkers and therapeutic strategies. We previously identified a role for the CD271/p75 neurotrophin receptor (p75NTR) in regulating stem/progenitor cells in the SHH MB subgroup. Here, we demonstrate the utility of CD271 as a novel diagnostic and prognostic marker for SHH MB using immunohistochemical analysis as well as transcriptome data across 763 primary tumors. Characterization of CD271+ and CD271- cells by RNA sequencing revealed that these two subpopulations are molecularly distinct, co-existing cellular subsets both in vitro and in vivo. MAPK/ERK signaling is upregulated in the CD271+ population and inhibiting this pathway reduced CD271 levels, stem/progenitor cell proliferation and cell survival as well as cell migration in vitro. Importantly, the MEK inhibitor selumetinib extends survival and reduces CD271 levels in vivo. Our study demonstrates the clinical utility of CD271 as both a diagnostic and prognostic tool for SHH MB tumors and reveals a novel role for MEK inhibitors in targeting CD271+ SHH MB cells.
Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.
To evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.
Data collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.
A total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).
The MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.
Declaration of interest
Drs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
The objectives of this study were (1) to evaluate the measurement structure of the Perceived Empathic and Social Self-Efficacy Scale amongst 194 individuals with serious mental illness (SMI) and (2) to establish construct validity for the Perceived Empathic and Social Self-Efficacy Scale. Confirmatory factor analysis yielded a two-factor measurement structure of the Perceived Empathic and Social Self-Efficacy Scale, which was positively associated with insight, social support, and life satisfaction. The Perceived Empathic and Social Self-Efficacy Scale is a useful measure to assess social skills amongst individuals with SMI in rehabilitation counselling.
The European Union (EU) approach to data protection consists of assessing the adequacy of the data protection offered by the laws of a particular jurisdiction against a set of principles that includes purpose limitation, transparency, quality, proportionality, security, access, and rectification. The EU's Data Protection Directive sets conditions on the transfer of data to third countries by prohibiting Member States from transferring to such countries as have been deemed inadequate in terms of the data protection regimes. In theory, each jurisdiction is evaluated similarly and must be found fully compliant with the EU's data protection principles to be considered adequate. In practice, the inconsistency with which these evaluations are made presents a hurdle to international data-sharing and makes difficult the integration of different data-sharing approaches; in the 20 years since the Directive was first adopted, the laws of only five countries from outside of the EU, Economic Area, or the European Free Trade Agreement have been deemed adequate to engage in data transfers without the need for further administrative safeguards.
This study investigated pain coping profiles using the Coping Strategies Questionnaire-24 (CSQ-24) in a sample of 171 workers’ compensation clients with chronic musculoskeletal pain from Canada. Cluster analysis identified three distinct coping profiles: mixed coping, catastrophising, and positive coping. Multivariate analysis of variance (MANOVA) results revealed that the positive coping group had lower levels of activity interference and depression as well as higher levels of quality of life than the mixed coping and catastrophising groups. Study findings indicate clients with chronic musculoskeletal pain can be categorised according to pain coping strategies, and pain coping strategies used are related to rehabilitation outcomes. The implications of these pain coping profiles for rehabilitation counselling practice are discussed.