Improving the Health of Public Housing Residents Through a Housing Authority and Nursing School Partnership.

The connection between health and housing is well-established. People who are precariously housed have worse health than those who have stable housing arrangements.- Persons moving into public housing have a higher illness burden than the general population, and public housing residents engage in less healthy behaviors, which contribute to public housing residents having poorer health than persons living in other housing situations. Public housing authorities and residents can benefit from authentic and constructive relationships with academic partners; academicians and students can benefit from engaging in partnerships with housing authorities and residents to better understand the connection between housing and health. This article describes the well-established relationship between the Duke University School of Nursing (DUSON) and the Durham Housing Authority (DHA), the evolution of that relationship, our collaborative work in improving the health of DHA residents while advancing nursing education and science, and lessons learned.

Mass balance and pharmacokinetics of an oral dose of 14 C-napabucasin in healthy adult male subjects.

This phase 1, open-label study assessed14 C-napabucasin absorption, metabolism, and excretion, napabucasin pharmacokinetics, and napabucasin metabolites (primary objectives); safety/tolerability were also evaluated. Eight healthy males (18-45 years) received a single oral 240-mg napabucasin dose containing ~100 µCi14 C-napabucasin. Napabucasin was absorbed and metabolized to dihydro-napabucasin (M1; an active metabolite [12.57-fold less activity than napabucasin]), the sole major circulating metabolite (median time to peak concentration: 2.75 and 2.25 h, respectively). M1 plasma concentration versus time profiles generally mirrored napabucasin; similar arithmetic mean half-lives (7.14 and 7.92 h, respectively) suggest M1 formation was rate limiting. Napabucasin systemic exposure (per Cmax and AUC) was higher than M1. The total radioactivity (TRA) whole blood:plasma ratio (AUClast : 0.376; Cmax : 0.525) indicated circulating drug-related compounds were essentially confined to plasma. Mean TRA recovery was 81.1% (feces, 57.2%; urine, 23.8%; expired air, negligible). Unlabeled napabucasin and M1 recovered in urine accounted for 13.9% and 11.0% of the dose (sum similar to urine TRA recovered); apparent renal clearance was 8.24 and 7.98 L/h. No uniquely human or disproportionate metabolite was quantified. Secondary glucuronide and sulfate conjugates were common urinary metabolites, suggesting napabucasin was mainly cleared by reductive metabolism. All subjects experienced mild treatment-emergent adverse events (TEAEs), the majority related to napabucasin. The most commonly reported TEAEs were gastrointestinal disorders. There were no clinically significant laboratory, vital sign, electrocardiogram, or physical examination changes. Napabucasin was absorbed, metabolized to M1 as the sole major circulating metabolite, and primarily excreted via feces. A single oral 240-mg dose was generally well tolerated.

Using a Clinical Workflow Analysis to Enhance eHealth Implementation Planning: Tutorial and Case Study.

eHealth apps often fail to improve clinical outcomes due to poor integration with clinical workflow-the sequence and personnel needed to undertake a series of tasks for clinical care. Our central thesis is that eHealth interventions will be more effective if the clinical workflow is studied and taken into consideration for intervention implementation. This paper aims to provide an introductory tutorial on when and how to use a clinical workflow analysis to guide the implementation of eHealth interventions. The tutorial includes a step-by-step guide to conducting a clinical workflow analysis in planning for eHealth implementation. We began with a description of why a clinical workflow analysis is best completed before the implementation of eHealth interventions. Next, we described 4 steps needed to perform the clinical workflow analysis: the identification of discrete workflow components, workflow assessment, triangulation, and the stakeholder proposal of intervention implementation. Finally, we presented a case study of a clinical workflow analysis, which was conducted during patient visits of patients aged 11 or 12 years from 4 diverse pediatric or family medicine clinics to plan the implementation of a tablet-based app for adolescent vaccination. Investigators planning the implementation of new eHealth interventions in health care settings can use the presented steps to assess clinical workflow, thereby maximizing the match of their intervention with the clinical workflow. Conducting a prospective workflow study allows for evidence-based planning, identifying potential pitfalls, and increasing stakeholder buy-in and engagement. This tutorial should aid investigators in increasing the successful implementation of eHealth interventions.