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Background: Treatment failure (TF) in uncomplicated urinary tract infection (uUTI) increases disease burden and risk of antimicrobial resistance. Identification of risk factors for TF could inform empiric treatment decisions and reduce suboptimal outcomes., Objective: To evaluate the incidence of TF to empirically prescribed oral antibiotics and identify risk factors for TF in females with uUTI in the United States (US)., Design: This retrospective cohort study used Optum's de-identified Electronic Health Record dataset (January 2017-September 2022)., Patients: Eligible female patients aged ≥ 12 years had ≥ 1 diagnosis of urinary tract infection (UTI) in an outpatient ambulatory/emergency department (ED) setting, ≥ 1 empiric oral antibiotic prescription, and no evidence of complicated UTI (cUTI)., Main Measures: TF was defined as having a new/repeat oral antibiotic prescription, IV antibiotic administration or acute UTI diagnosis ≤ 28 days following initial empiric oral antibiotic prescription​. Risk factors of TF were selected using LASSO and reported using adjusted risk ratios (aRR) and 95% CIs., Key Results: Of 376,004 patients with uUTI, 62,873 (16.7%) experienced TF. Incidence of TF was highest in patients with history of antibiotic TF (33.9%) or fosfomycin prescription (30.1%). Significant risk factors of TF included ≥ 3 prior antibiotic prescriptions (aRR [95% CI]: 1.60 [1.56-1.64]); fosfomycin prescription (1.60 [1.38-1.86]); uUTI diagnosis in ED (1.49 [1.46-1.52]), Southern US residence (1.37 [1.35-1.40]), age ≥ 75 years (1.35 [1.29-1.41]), recurrent UTI (1.12 [1.10-1.14]) and obesity (1.06 [1.04-1.08])., Conclusions: Incidence of TF to empirically prescribed oral antibiotics for uUTI is considerable. Prior infections requiring antibiotic prescription and location of care are key risk factors for TF in female outpatients with uUTI. Knowledge of these TF risk factors can inform shared-decision making and supplement existing guidance on uUTI treatment., (© 2024. The Author(s).)

Abstract: Patients with chronic lymphocytic leukemia (CLL) and non-Hodgkin lymphoma (NHL) can develop hypogammaglobulinemia, a form of secondary immune deficiency (SID), from the disease and treatments. Patients with hypogammaglobulinemia with recurrent infections may benefit from immunoglobulin replacement therapy (IgRT). This study evaluated patterns of immunoglobulin G (IgG) testing and the effectiveness of IgRT in real-world patients with CLL or NHL. A retrospective, longitudinal study was conducted among adult patients diagnosed with CLL or NHL. Clinical data from the Massachusetts General Brigham Research Patient Data Registry were used. IgG testing, infections, and antimicrobial use were compared before vs 3, 6, and 12 months after IgRT initiation. Generalized estimating equation logistic regression models were used to estimate odds ratios, 95% confidence intervals, and P values. The study population included 17 192 patients (CLL: n = 3960; median age, 68 years; NHL: n = 13 232; median age, 64 years). In the CLL and NHL cohorts, 67% and 51.2% had IgG testing, and 6.5% and 4.7% received IgRT, respectively. After IgRT initiation, the proportion of patients with hypogammaglobulinemia, the odds of infections or severe infections, and associated antimicrobial use, decreased significantly. Increased frequency of IgG testing was associated with a significantly lower likelihood of severe infection. In conclusion, in real-world patients with CLL or NHL, IgRT was associated with significant reductions in hypogammaglobulinemia, infections, severe infections, and associated antimicrobials. Optimizing IgG testing and IgRT are warranted for the comprehensive management of SID in patients with CLL or NHL., (© 2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Background: High negative affect, low positive affect, and limited physical activity figure prominently in psychopathology, but little is known about the interrelatedness of affect and physical activity in emotional disorders., Methods: We combined ecological momentary assessment data with a network approach to examine the dynamic relations among positive affect, negative affect, and smartphone-based estimates of physical activity in 34 participants with anxiety and depressive disorders over a 2-week period., Results: In the contemporaneous networks, the positive affect nodes exhibited greater overall strength centrality than negative affect nodes. The temporal networks indicated that the negative affect node 'sadness' exhibited the greatest out-strength centrality. Furthermore, physical activity was unconnected to the affect nodes in either the temporal or contemporaneous networks., Conclusions: Whereas positive affect plays a greater role in the contemporaneous experience of emotions, negative affect contributes more so to future affective states., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Temperature-taking behaviors vary with levels of circulating infectious illness; however, little is known about how these behaviors differ by demographic characteristics. Populations with higher perceived risks of illness are more likely to adopt protective health behaviors., Objective: We investigated differences in temperature-taking frequency and the proportion of readings that were feverish among demographic groups (age, gender, urban/rural status) over influenza offseason; influenza season; and waves 1, 2, and 3 of the COVID-19 pandemic., Methods: Using data from smart thermometers collected from May 1, 2019, to February 28, 2021, across the United States, we calculated the frequency of temperature-taking and the proportion of temperature readings that were feverish. Mixed-effects negative binomial and mixed-effects logistic regression analyses were performed to identify demographic characteristics associated with temperature-taking frequency and the proportion of feverish readings, respectively. Separate models were fit over five study periods: influenza offseason (n=122,480), influenza season (n=174,191), wave 1 of COVID-19 (n=350,385), wave 2 (n=366,489), and wave 3 (n=391,578)., Results: Both temperature-taking frequency and the proportion of feverish readings differed by study period (ANOVA P<.001) and were the highest during influenza season. During all periods, children aged 2-5 years and 6-11 years had significantly higher frequencies of temperature-taking than users aged 19-30 years, and children had the highest proportion of feverish readings of all age groups, after adjusting for covariates. During wave 1 of COVID-19, users over the age of 60 years had 1.79 times (95% CI 1.76-1.83) the rate of temperature-taking as users aged 19-30 years and 74% lower odds (95% CI 72%-75%) of a reading being feverish. Across all periods, men had significantly lower temperature-taking frequency and significantly higher odds of having a feverish reading compared to women. Users living in urban areas had significantly higher frequencies of temperature-taking than rural users during all periods, except wave 2 of COVID-19, and urban users had higher odds of a reading being feverish in all study periods except wave 1 of COVID-19., Conclusions: Temperature-taking behavior and the proportion of readings that were feverish are associated with both population disease levels and individual demographic characteristics. Differences in the health behavior of temperature-taking may reflect changes in both perceived and actual illness risk. Specifically, older adults may have experienced an increase in perceived risk during the first three waves of COVID-19, leading to increased rates of temperature monitoring, even when their odds of fever were lower than those of younger adults. Men's perceived risk of circulating infectious illnesses such as influenza and COVID-19 may be lower than that of women, since men took their temperature less frequently and each temperature had a higher odds of being feverish across all study periods. Infectious disease surveillance should recognize and incorporate how behavior impacts illness monitoring and testing., (©Jack Seifarth, Megan Pinaire, John Zicker, Inder Singh, Danielle Bloch. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.09.2022.)

Background: Poor quality of life, sleep problems, anhedonia, and negative metacognitions are common in anxiety and depression. To examine the nature of the relationship between these features and the role of metacognitions, anhedonia, and quality of life in anxiety and depression, we conducted a complex network analysis with items of self-report measures assessing quality of life, sleep, negative thinking styles, anxiety, and depression., Methods: Participants were 226 treatment seeking individuals with a primary DSM-5 diagnosis of generalized anxiety disorder. Node centrality, strength, expected influence, community, and bridge estimation were calculated using partial correlation coefficients and glasso regularization., Results: Results revealed that anhedonia was the most central node followed by quality of life nodes. Moreover, anhedonia exhibited the highest strength and expected influence, which were both stable, reliable metrics within the network. Metacognitions were not central nodes in the network, but were strong bridge symptoms between communities., Limitations: The results are limited by the cross-sectional nature of the data and the administration of self-report scales at one time-point, despite different rating anchors., Conclusion: These findings suggest that anhedonia is a crucial element for the association between quality of life, sleep problems, and negative cognitions., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14-17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and differences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA)., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Laboratory models of extinction learning in animals and humans have the potential to illuminate methods for improving clinical treatment of fear-based clinical disorders. However, such translational research often neglects important differences between threat responses in animals and fear learning in humans, particularly as it relates to the treatment of clinical disorders. Specifically, the conscious experience of fear and anxiety, along with the capacity to deliberately engage top-down cognitive processes to modulate that experience, involves distinct brain circuitry and is measured and manipulated using different methods than typically used in laboratory research. This paper will identify how translational research that investigates methods of enhancing extinction learning can more effectively model such elements of human fear learning, and how doing so will enhance the relevance of this research to the treatment of fear-based psychological disorders.