• Maynard Hesselberg posted an update 6 months, 2 weeks ago

    c.Chronic back and neck pain are highly prevalent conditions that are among the largest drivers of physical disability and cost in the world. Recent clinical practice guidelines recommend use of non-pharmacologic treatments to decrease pain and improve physical function for individuals with back and neck pain. However, delivery of these treatments remains a challenge because common care delivery models for back and neck pain incentivize treatments that are not in the best interests of patients, the overall health system, or society. This narrative review focuses on the need to increase use of non-pharmacologic treatment as part of routine care for back and neck pain. First, we present the evidence base and summarize recommendations from clinical practice guidelines regarding non-pharmacologic treatments. Second, we characterize current use patterns for non-pharmacologic treatments and identify potential barriers to their delivery. Addressing these barriers will require coordinated efforts from multiple stakeholders to prioritize evidence-based non-pharmacologic treatment approaches over low value care for back and neck pain. These stakeholders include patients, health care providers, health care organizations, administrators, payers, policymakers and researchers.

    Big Five personality traits correlate with affective disorders, with neuroticism considered a risk factor, and conscientiousness and extroversion considered protective factors. However, the relationships between affective disorders and lower-order personality facets and aspects are less clear.

    A systematic review was carried out to identify studies measuring associations between lower-order personality constructs and affective disorders. Big Five facets were measured using the NEO-PI-R, and aspects using the BFAS. PsycINFO, EMBASE, MedLine and OpenGrey were searched from January 1

    , 1985 to June 30

    , 2020. Fifteen studies met criteria and reported a total of 408 associations. Data were analysed using best evidence synthesis.

    Most facets of neuroticism were positively associated with affective disorders. Positive emotion in extroversion, and competence and self-discipline in conscientiousness, were negatively associated with affective disorders. Trust in agreeableness, and actions in openness, were negneeded to investigate mediating pathways between personality facets and affective disorders.

    Mental health problems (MHP) are a relatively common consequence of deployment to war zones. RU.521 supplier Early identification of those at risk of post-deployment MHP would improve prevention efforts. However, screening instruments based on linear models have not been successful. Machine learning (ML) has shown promise for providing the methodological frame for better prognostic models.

    The study population was all Danish military personnel deployed for the first time between January 1, 1992 and December 31, 2013. From extensive registry data, 21 pre- or at-deployment predictors comprising early adversity, social, clinical and demographic variables were used to predict psychiatric contacts (psychiatric diagnosis and/or use of psychotropic medicine) occurring within 6.5 years after homecoming. Four supervised ML methods (penalized logistic regression, random forests, support vector machines and gradient boosting machines) were compared in ability to classify those with high risk of post-deployment MHP and those withoutinclude neurobiological data and deployment experiences to increase accuracy of the models.

    Available national public data are often too incomplete and noisy to be used directly to interpret the evolution of epidemics over time, which is essential for making timely and appropriate decisions. The use of compartment models can be a worthwhile and attractive approach to address this problem. The present study proposes a model compartmentalized by sex and age groups that allows for more complete information on the evolution of the CoViD-19 pandemic in Italy.

    Italian public data on CoViD-19 were pre-treated with a 7-day moving average filter to reduce noise. A time-varying susceptible-infected-recovered-deceased (SIRD) model distributed by age and sex groups was then proposed. Recovered and infected individuals distributed by groups were reconstructed through the SIRD model, which was also used to simulate and identify optimal scenarios of pandemic containment by vaccination. The simulation started from realistic initial conditions based on the SIRD model parameters, estimated from filtered and reconause of the poor quality and insufficient availability of stratified public pandemic data, ad hoc information filtering and reconstruction procedures proved essential. The time-varying SIRD model, stratified by age and sex groups, provided insights and additional information on the dynamics of CoViD-19 infection in Italy, also supporting decision making for containment strategies such as vaccination.

    Because of the poor quality and insufficient availability of stratified public pandemic data, ad hoc information filtering and reconstruction procedures proved essential. The time-varying SIRD model, stratified by age and sex groups, provided insights and additional information on the dynamics of CoViD-19 infection in Italy, also supporting decision making for containment strategies such as vaccination.

    To investigate the magnitude of racial/ethnic differences in hospital mortality after transjugular intrahepatic portosystemic shunt (TIPS) creation for acute variceal bleeding and whether hospital care processes contribute to them.

    Patients aged ≥18 years undergoing TIPS creation for acute variceal bleeding in the United States (n= 10,331) were identified from 10 years (2007-2016) available in the National Inpatient Sample. Hierarchical logistic regression was used to examine the relationship between patient race and inpatient mortality, controlling for disease severity, treatment utilization, and hospital characteristics.

    A total of 6,350 (62%) patients were White, 1,780 (17%) were Hispanic, and 482 (5%) were Black. A greater proportion of Black patients were admitted to urban teaching hospitals (Black, n= 409 (85%); Hispanic, n= 1,310 (74%); and White, n= 4,802 (76%); P < .001) and liver transplant centers (Black, n= 215 (45%); Hispanic, n= 401 (23%); and White, n= 2,267 (36%); P < .001). Being Black was strongly associated with mortality (Black, 32% vs non-Black, 15%; odds ratio, 3.

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