Documented Penicillin Allergies on Antibiotic Selection at Pediatric Emergency Department Visits.

BACKGROUND: Penicillin or amoxicillin are the recommended treatments for the most common pediatric bacterial illnesses. Allergies to penicillin are commonly reported among children but rarely true. We evaluated the impact of reported penicillin allergies on broad-spectrum antibiotic use overall and for the treatment of common respiratory infections among treat-and-release pediatric emergency department (ED) visits. METHODS: Retrospective cohort study of pediatric patients receiving antibiotics during a treat-and-release visit at a large, pediatric ED in the northeast from 2014 to 2016. Study exposure was a reported allergy to penicillin in the electronic medical record. Study outcomes were the selection of broad-spectrum antibiotics and alternative (second-line) antibiotic therapy for the treatment of acute otitis media (AOM) and group A streptococcus (GAS) pharyngitis. We used unadjusted and adjusted generalized estimating equation models to analyze the impact of reported penicillin allergies on the selection of broad-spectrum antibiotics. We used unadjusted and adjusted logistic regression models to determine the probability of children with a documented penicillin allergy receiving alternative antibiotic treatments for AOM and GAS. RESULTS: Among 12,987 pediatric patients, 810 (6.2%) had a documented penicillin allergy. Penicillin allergies increased the odds of children receiving a broad spectrum versus narrow spectrum antibiotic (adjusted odds ratio, 13.55; 95% confidence interval (CI), 11.34-16.18). In our adjusted logistic regression model, the probability of children with a documented penicillin allergy receiving alternative antibiotic treatment for AOM was 0.97 (95% CI, 0.94-0.99) and for GAS was 0.97 (95% CI, 0.92-0.99). CONCLUSIONS: Antibiotic stewardship efforts in pediatric EDs may consider the delabeling of penicillin allergies particularly among children receiving antibiotics for an acute respiratory infection as a target for intervention.

Peripheral B cells repress B-cell regeneration in aging through a TNF-α/IGFBP-1/IGF-1 immune-endocrine axis.

Loss of B lymphocyte regeneration in the bone marrow (BM) is an immunologic hallmark of advanced age, which impairs the replenishment of peripheral B-cell subsets and results in impaired humoral responses, thereby contributing to immune system dysfunction associated with aging. A better understanding of the mechanism behind this loss may suggest ways to restore immune competence and promote healthy aging. In this study, we uncover an immune-endocrine regulatory circuit that mediates cross-talk between peripheral B cells and progenitors in the BM, to balance B-cell lymphopoiesis in both human and mouse aging. We found that tumor necrosis factor α (TNF-α), which is increasingly produced by peripheral B cells during aging, stimulates the production of insulin-like growth factor-binding protein 1 (IGFBP-1), which binds and sequesters insulin-like growth factor 1 (IGF-1) in the circulation, thereby restraining its activity in promoting B-cell lymphopoiesis in the BM. Upon B-cell depletion in aging humans and mice, circulatory TNF-α decreases, resulting in increased IGF-1 and reactivation of B-cell lymphopoiesis. Perturbation of this circuit by administration of IGF-1 to old mice or anti-TNF-α antibodies to human patients restored B-cell lymphopoiesis in the BM. Thus, we suggest that in both human and mouse aging, peripheral B cells use the TNF-α/IGFBP-1/IGF-1 axis to repress B-cell lymphopoiesis. This trial was registered at www.clinicaltrials.govas#NCT00863187.

Rheumatoid arthritis patients treated with Janus kinase inhibitors show reduced humoral immune responses following BNT162b2 vaccination.

OBJECTIVES: The mRNA-based COVID-19 vaccines are now employed globally and have shown high efficacy in preventing SARS-CoV-2 infection. However, less is known about the vaccine efficacy in immune-suppressed individuals. This study sought to explore whether humoral immunity to the COVID-19 vaccine BNT162b2 is altered in RA patients treated with Janus kinase inhibitors by analysing their antibodies titre, neutralization activity and B cell responses. METHODS: We collected plasma samples from 12 RA patients who were treated with Janus kinase inhibitors and received two doses of the BNT162b2 vaccine, as well as 26 healthy individuals who were vaccinated with the same vaccine. We analysed the quantity of the anti-spike IgG and IgA antibodies that were elicited following the BNT162b2 vaccination, the plasma neutralization capacity and the responsiveness of the B-lymphocytes. We used ELISA to quantify the antibody titres, and a plasma neutralization assay was used to determine the virus neutralization capacity. Alteration in expression of the genes that are associated with B cell activation and the germinal centre response were analysed by quantitative PCR. RESULTS: Reduced levels of anti-spike IgG antibodies and neutralization capacity were seen in the RA patients who were treated with JAK inhibitors in comparison with healthy individuals. Furthermore, B cell responsiveness to the SARS-CoV-2 spike protein was reduced in the RA patients. CONCLUSION: RA patients who are treated with JAK inhibitors show a suppressed humoral response following BNT162b2 vaccination, as revealed by the quantity and quality of the anti-spike antibodies.

A pilot RCT of a school nurse delivered intervention to reduce student anxiety.

The goal of this study was to evaluate the feasibility and impact of brief school-nurse-administered interventions for reducing anxiety. Thirty school nurses in Connecticut and Maryland were randomly assigned to deliver the Child Anxiety Learning Modules (CALM; n = 14) or CALM-Relaxation only (CALM-R; n = 16). Students (N = 54) were ages 5-12 (M age = 8; 84.9% White; 68.5% female) with elevated anxiety symptoms and/or anxiety disorders. Feasibility was assessed based on recruitment, retention, attendance, training and intervention satisfaction, and intervention adherence. Multiple informants, including independent evaluators (IEs), completed measures of clinical improvement at postintervention and at a 3-month follow-up. Of nurses in CALM and CALM-R, 62% and 81%, respectively, enrolled a student and completed an average of 6 sessions. Youth retention was 85% and 94% in CALM and CALM-R, respectively. Training and intervention satisfaction were high. At postintervention and follow-up, youth in both groups showed significant reductions in anxiety and related symptoms and improvements in functioning. Within-group effect sizes were medium to large, and between-group effect sizes were small. Task shifting responsibility for delivering brief mental health interventions to school nurses is feasible and shows promise for reducing anxiety and related impairment. This approach may also be integrated within a response to intervention model used in schools.Public Health Significance: Brief school-nurse-administered anxiety reduction interventions were shown to be feasible and had a positive impact on student anxiety and related impairment highlighting that school nurses can be an important school resource.

Pharmacist eConsult service for primary care medication optimization and safety.

BACKGROUND: There is a critical need in primary care to proactively prevent, identify, and resolve poor medication-related outcomes. However, more than 80% of primary care practices do not have clinical pharmacists as members of expanded care teams. The emergence of eConsult services in primary care settings presents an opportunity for primary care providers (PCPs) to consult with clinical pharmacists as "on-demand" pharmacotherapy specialists. OBJECTIVES: The objectives were to (1) determine the use of a clinical pharmacist in an existing eConsult network, (2) characterize the use and type of clinical pharmacist eConsults sent by PCPs, and (3) measure the implementation percentage of pharmacist recommendations by PCPs. METHODS: The study was conducted in a federally qualified health center using an existing eConsult platform. A clinical pharmacist was contracted to receive eConsults and was expected to respond within 2 business days. PCPs were introduced to the pharmacist eConsult service through presentations that reviewed the clinical pharmacist's education/training, suitable pharmacist eConsult topics, and workflow for sending a pharmacist eConsult. RESULTS: A total of 57 eConsults containing 123 individual questions were answered. Advanced practice nurses (APRNs) sent 3 times the number of eConsults and individual questions per eConsult compared with physicians (P < 0.0001). Most eConsult questions from APRNs related to adverse drug events/drug interactions (44%), drug or dosage changes to reach therapeutic goals (18%), and renal/hepatic dosage adjustments (13%). However, physician eConsult questions were primarily targeted on patient-specific drug or dosage adjustments (62%) and comprehensive medication regimen reviews (17%). A total of 74% of the pharmacist eConsult responses had at least 50% of the recommendations implemented by PCPs. CONCLUSION: This study revealed the use of a pharmacist eConsult service for medication-related questions in an existing eConsult network for PCPs. As more practices enroll in value-based plans, pharmacist eConsults can improve the quality and safety of prescribing and chronic medication management.

Comprehensive Benefit-Risk Assessment of Noninferior Treatments Using Multicriteria Decision Analysis.

OBJECTIVES: To develop a simple approach for evaluating the overall benefit-risk of a new noninferiority treatment compared with a standard of care. METHODS: We propose using multicriteria decision analysis that accounts for uncertainty associated with both clinical outcomes and patient preference data. Because patients' preferences are likely to be influenced by their baseline characteristics, we suggest carrying out a preference study at the beginning of a trial. To reduce the burden of an additional study questionnaire, preference elicitation could be done on a small sample of trial participants. To restore preferences for all trial participants, we propose using multiple imputation (MI). Using simulations, we examine whether 3 different MI procedures lead to the same benefit-risk assessment conclusion, as if all trial participant preferences were obtained. We also compare MI results to complete case analysis, where only preferences of the small sample of trial participants are considered. RESULTS: We show that the MI procedure successfully restores patients' preferences for the trial participants using different outcome criteria and preferences. For example, using 3 outcome criteria with only 10% of the trial participants providing their preferences, complete case analysis demonstrated a new noninferior treatment as favorable only 5.1% of the time, whereas MI procedures did so between 16.2% and 17.9% of the time. Given that 17.6% correspond to the fully observed weights, the MI methods demonstrate favorable results. CONCLUSIONS: The MI procedure can help facilitate a simple comprehensive benefit-risk assessment for new noninferior treatments.

Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data.

Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe's Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.

Multiple Imputation for Incomplete Data in Epidemiologic Studies.

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method as part of a collaborative challenge to assess the performance of various techniques for dealing with missing data (Am J Epidemiol. 2018;187(3):568-575). We detail the steps necessary to perform multiple imputation on a subset of data from the Collaborative Perinatal Project (1959-1974), where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy.

Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data.

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (Am J Epidemiol. 2018;187(3):568-575).

Principled Approaches to Missing Data in Epidemiologic Studies.

Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these methods remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576-584 and Am J Epidemiol. 2018;187(3):585-591), we discuss issues pertaining to missing data in the epidemiologic literature. We provide details regarding missing-data mechanisms and nomenclature and encourage the conduct of principled analyses through a detailed comparison of multiple imputation and inverse probability weighting. Data from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are used to create a masked data-analytical challenge with missing data induced by known mechanisms. We illustrate the deleterious effects of missing data with naive methods and show how principled methods can sometimes mitigate such effects. For example, when data were missing at random, naive methods showed a spurious protective effect of smoking on the risk of spontaneous abortion (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.19, 0.93), while implementation of principled methods multiple imputation (OR = 1.30, 95% CI: 0.95, 1.77) or augmented inverse probability weighting (OR = 1.40, 95% CI: 1.00, 1.97) provided estimates closer to the "true" full-data effect (OR = 1.31, 95% CI: 1.05, 1.64). We call for greater acknowledgement of and attention to missing data and for the broad use of principled missing-data methods in epidemiologic research.

A Review of Statistical Disclosure Control Techniques Employed by Web-Based Data Query Systems.

We systematically reviewed the statistical disclosure control techniques employed for releasing aggregate data in Web-based data query systems listed in the National Association for Public Health Statistics and Information Systems (NAPHSIS). Each Web-based data query system was examined to see whether (1) it employed any type of cell suppression, (2) it used secondary cell suppression, and (3) suppressed cell counts could be calculated. No more than 30 minutes was spent on each system. Of the 35 systems reviewed, no suppression was observed in more than half (n = 18); observed counts below the threshold were observed in 2 sites; and suppressed values were recoverable in 9 sites. Six sites effectively suppressed small counts. This inquiry has revealed substantial weaknesses in the protective measures used in data query systems containing sensitive public health data. Many systems utilized no disclosure control whatsoever, and the vast majority of those that did deployed it inconsistently or inadequately.

Fitting additive hazards models for case-cohort studies: a multiple imputation approach.

In this paper, we consider fitting semiparametric additive hazards models for case-cohort studies using a multiple imputation approach. In a case-cohort study, main exposure variables are measured only on some selected subjects, but other covariates are often available for the whole cohort. We consider this as a special case of a missing covariate by design. We propose to employ a popular incomplete data method, multiple imputation, for estimation of the regression parameters in additive hazards models. For imputation models, an imputation modeling procedure based on a rejection sampling is developed. A simple imputation modeling that can naturally be applied to a general missing-at-random situation is also considered and compared with the rejection sampling method via extensive simulation studies. In addition, a misspecification aspect in imputation modeling is investigated. The proposed procedures are illustrated using a cancer data example. Copyright © 2015 John Wiley & Sons, Ltd.

Analyzing clinical trial outcomes based on incomplete daily diary reports.

A case study is presented assessing the impact of missing data on the analysis of daily diary data from a study evaluating the effect of a drug for the treatment of insomnia. The primary analysis averaged daily diary values for each patient into a weekly variable. Following the commonly used approach, missing daily values within a week were ignored provided there was a minimum number of diary reports (i.e., at least 4). A longitudinal model was then fit with treatment, time, and patient-specific effects. A treatment effect at a pre-specified landmark time was obtained from the model. Weekly values following dropout were regarded as missing, but intermittent daily missing values were obscured. Graphical summaries and tables are presented to characterize the complex missing data patterns. We use multiple imputation for daily diary data to create completed data sets so that exactly 7 daily diary values contribute to each weekly patient average. Standard analysis methods are then applied for landmark analysis of the completed data sets, and the resulting estimates are combined using the standard multiple imputation approach. The observed data are subject to digit heaping and patterned responses (e.g., identical values for several consecutive days), which makes accurate modeling of the response data difficult. Sensitivity analyses under different modeling assumptions for the data were performed, along with pattern mixture models assessing the sensitivity to the missing at random assumption. The emphasis is on graphical displays and computational methods that can be implemented with general-purpose software. Copyright © 2016 John Wiley & Sons, Ltd.

Latent Class Analysis of Incomplete Data via an Entropy-Based Criterion.

Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.

Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.

Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic studies. Perhaps the most common approach to handling missing data is to simply drop those records with 1 or more missing values, in so-called "complete records" or "complete case" analysis. In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions. We give detailed guidance describing how the observed data can be used to judge the plausibility of these assumptions. The results mean that in large epidemiologic studies which are affected by missing data and analyzed by logistic regression, exposure associations may be estimated without bias in a number of settings where researchers might otherwise assume that bias would occur.

Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data.

Innovative video tailoring for dietary change: final results of the Good for you! cluster randomized trial.

BACKGROUND: Effective, low-cost approaches are needed to enhance dietary behavior change. While both video and tailoring technology have been effective interventions to improve diet, these approaches have never been combined to study the effectiveness of tailored videos. The purpose of this paper is to discuss the results of Good For You!, a randomized trial that tested the efficacy of innovative, individually tailored videos in helping worksite employees decrease dietary fat and increase fruit and vegetable (F&V) intake. METHODS: Worksites were matched on approximate size, type of company and workforce composition and randomized to one of three experimental conditions: Non-Tailored written information (NT) (n = 14), Tailored Written information (TW) (n = 14), or Tailored Written + Tailored Video (TW + TV) (n = 15). Evaluation was conducted at baseline, 4 and 7 months. We used the NCI Fat Screener and an adapted Food Habits Questionnaire (FHQ) to estimate fat intake and fat-related behaviors, the NCI F&V Screener and F&V Habits Questionnaire (FVHQ) to measure F&V intake and behaviors. Generalized linear models were examined for all outcome measurements. RESULTS: 2525 worksite employees were recruited. At 4 months, dietary fat intake decreased significantly more for TW (-2.95 %) and TW + TV (-3.14%) compared with NT (-2.42%). FHQ scores decreased significantly more for TW + TV than the other two groups. Fruit intake increased the most for TW + TV compared to NT and TW. Both TW (1.30 cups) and TW + TV (1.59 cups) increased F&V intake significantly more than NT (0.78 cups). TW + TV showed the largest increase in F&V behaviors on the FVFQ. At 8 months, dietary fat change continued to be significantly better for TW + TV (-3.48%) than NT (3.01%). F&V intake increased significantly more for the TW + TV group (1.38 cups) compared to the NT group (1.04 cups) and FVHQ changes were significantly greater in TW + TV and TW than for NT. CONCLUSIONS: The tailored intervention participants were more likely to decrease fat and increase F&V intake. The TW + TV group was generally the stronger of the two tailored interventions, especially at the longer term follow-up, demonstrating the promise of tailored video as an intervention to change eating habits. Future studies should explore newer channels and technologies in addition to DVDs for delivering tailored video interventions such as the internet and smart phones. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00301678.

Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: application to a smoking cessation trial.

The true missing data mechanism is never known in practice. We present a method for generating multiple imputations for binary variables, which formally incorporates missing data mechanism uncertainty. Imputations are generated from a distribution of imputation models rather than a single model, with the distribution reflecting subjective notions of missing data mechanism uncertainty. Parameter estimates and standard errors are obtained using rules for nested multiple imputation. Using simulation, we investigate the impact of missing data mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably missing data were a concern. Our method provides a simple approach for formalizing subjective notions regarding nonresponse and can be implemented using existing imputation software.

Randomized community-level HIV prevention intervention trial for men who drink in South African alcohol-serving venues.

BACKGROUND: South African alcohol-serving establishments (i.e., shebeens) offer unique opportunities to reduce HIV risks among men who drink. PURPOSE: To test an individual- and a social structural-level HIV prevention intervention for men who drink in shebeens. METHODS: Twelve matched pairs of township neighbourhoods were randomized to receive either (i) an HIV prevention intervention (guided by Social Action Theory) to reduce sexual risk and increase risk reduction communication in social networks, or (ii) an attention-matched control intervention that focused on the prevention of relationship violence. At the individual level, the interventions delivered skills building workshops focused on sexual risk reduction. At the social structural level, the intervention aimed to increase conversations about safer sex among men in the shebeens, distributed small media and implemented community educational events. Individual-level outcomes were assessed by following the workshop cohorts for 1 year (N = 984), and community-level outcomes were examined through cross-sectional community surveys conducted for 1 year in the shebeens (N = 9,678). RESULTS: Men in the HIV prevention workshops demonstrated greater condom use, more HIV prevention-oriented conversations and greater perceptions of safer sex norms than men in the comparison workshops. Changes at the community level demonstrated significant differences in condom use, although the pattern was not consistent over time. CONCLUSIONS: Multi-level interventions that target men who drink in South African shebeens may help reduce risks for HIV and other sexually transmitted infections.

Physical health and function trajectories in adults with cancer: psychosocial predictors of class membership.

PURPOSE: To prospectively examine different trajectories of recovery, across different aspects of physical health and function and to examine trajectory class membership. METHODS: This prospective study enrolled 569 recently diagnosed adult cancer patients (Mage = 58.7) between 2019 and 2022 identified through the Rapid Case Ascertainment resource of The Yale Cancer Center. Patients were diagnosed with breast (63.8%), prostate (25.3%), or colorectal cancer (10.9%) within six-months of baseline assessment. Participants completed comprehensive psychosocial and health survey measures (SF-12) through REDCap at five time points. Growth mixture modeling examined unconditional distinct trajectories for four aspects of physical health and function. We fit logistic regression and multinomial logistic regression models to estimate associations between psychosocial predictors of trajectory class membership for each of the four aspects. RESULTS: We identified distinct trajectories of physical health and function. Over one-third (38.4%) of the sample experienced low and declining scores in their ability to accomplish work/regular daily activities due to physical health. Over half (54.9%) demonstrate moderately stable general health with no improvement over time. A small but significant subset of the sample (3%, 5.7%, 5%) was in the moderate and declining groups with sharp decline in physical function, bodily pain, and general health, after treatment. Different predictors of trajectory class membership were also found. CONCLUSIONS: Our results showed heterogeneity in physical health and function trajectories and different patterns of predictors for each aspect of physical health and function. Findings have the potential to inform screening and intervention efforts to help those who may need additional support.