Dynamics of Cough Frequency in Adults Undergoing Treatment for Pulmonary Tuberculosis.

Background: Cough is the major determinant of tuberculosis transmission. Despite this, there is a paucity of information regarding characteristics of cough frequency throughout the day and in response to tuberculosis therapy. Here we evaluate the circadian cycle of cough, cough frequency risk factors, and the impact of appropriate treatment on cough and bacillary load. Methods: We prospectively evaluated human immunodeficiency virus-negative adults (n = 64) with a new diagnosis of culture-proven, drug-susceptible pulmonary tuberculosis immediately prior to treatment and repeatedly until treatment day 62. At each time point, participant cough was recorded (n = 670) and analyzed using the Cayetano Cough Monitor. Consecutive coughs at least 2 seconds apart were counted as separate cough episodes. Sputum samples (n = 426) were tested with microscopic-observation drug susceptibility broth culture, and in culture-positive samples (n = 252), the time to culture positivity was used to estimate bacillary load. Results: The highest cough frequency occurred from 1 pm to 2 pm, and the lowest from 1 am to 2 am (2.4 vs 1.1 cough episodes/hour, respectively). Cough frequency was higher among participants who had higher sputum bacillary load (P < .01). Pretreatment median cough episodes/hour was 2.3 (interquartile range [IQR], 1.2-4.1), which at 14 treatment days decreased to 0.48 (IQR, 0.0-1.4) and at the end of the study decreased to 0.18 (IQR, 0.0-0.59) (both reductions P < .001). By 14 treatment days, the probability of culture conversion was 29% (95% confidence interval, 19%-41%). Conclusions: Coughs were most frequent during daytime. Two weeks of appropriate treatment significantly reduced cough frequency and resulted in one-third of participants achieving culture conversion. Thus, treatment by 2 weeks considerably diminishes, but does not eliminate, the potential for airborne tuberculosis transmission

Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study

Abstract Background Understanding association between factors related to clinical work environment and well-being can inform strategies to improve physicians’ work experience. Objective To model and quantify what drivers of work composition, team structure, and dynamics are associated with well-being. Design Utilizing social network modeling, this cohort study of physicians in an academic health center examined inbasket messaging data from 2018 to 2019 to identify work composition, team structure, and dynamics features. Indicators from a survey in 2019 were used as dependent variables to identify factors predictive of well-being. Participants EHR data available for 188 physicians and their care teams from 18 primary care practices; survey data available for 163/188 physicians. Main Measures Area under the receiver operating characteristic curve (AUC) of logistic regression models to predict well-being dependent variables was assessed out-of-sample. Key Results The mean AUC of the model for the dependent variables of emotional exhaustion, vigor, and professional fulfillment was, respectively, 0.665 (SD 0.085), 0.700 (SD 0.082), and 0.669 (SD 0.082). Predictors associated with decreased well-being included physician centrality within support team (OR 3.90, 95% CI 1.28–11.97, P=0.01) and share of messages related to scheduling (OR 1.10, 95% CI 1.03–1.17, P=0.003). Predictors associated with increased well-being included higher number of medical assistants within close support team (OR 0.91, 95% CI 0.83–0.99, P=0.05), nurse-centered message writing practices (OR 0.89, 95% CI 0.83–0.95, P=0.001), and share of messages related to ambiguous diagnosis (OR 0.92, 95% CI 0.87–0.98, P=0.01). Conclusions Through integration of EHR data with social network modeling, the analysis highlights new characteristics of care team structure and dynamics that are associated with physician well-being. This quantitative methodology can be utilized to assess in a refined data-driven way the impact of organizational changes to improve well-being through optimizing team dynamics and work composition

Data from: Dynamics of cough frequency in adults undergoing treatment for pulmonary tuberculosis

Cough is the major determinant of tuberculosis transmission. Despite this, there is a paucity of information regarding characteristics of cough frequency throughout the day and in response to tuberculosis therapy. Here we evaluate the circadian cycle of cough; cough frequency risk factors; and the impact of appropriate treatment on cough and bacillary load. Methods: We prospectively evaluated HIV-negative adults (n=64) with a new diagnosis of culture-proven, drug-susceptible pulmonary tuberculosis immediately prior to treatment and repeatedly until treatment day 62. At each time-point, participant cough was recorded (n=670) and analyzed using the Cayetano Cough Monitor. Consecutive coughs at least 2-seconds apart were counted as separate cough episodes. Sputum samples (n=426) were tested with microscopic-observation drug-susceptibility broth culture, and in culture-positive samples (n=252) the time to culture positivity was used to estimate bacillary load. Results: The highest cough frequency occurred from 1-2 p.m., and the lowest from 1-2 a.m. (2.4 versus 1.1 cough episodes/hour, respectively). Cough frequency was higher among participants who had higher sputum bacillary load (p<0.01). Pre-treatment median cough episodes/hour was 2.3 (IQR=1.2-4.1), which at 14 treatment days decreased to 0.48 (IQR=0.0-1.4) and at the end of the study decreased to 0.18 (IQR=0.0-0.59), both reductions p<0.001. By 14 treatment days, the probability of culture conversion was 29% (95% CI=19-41%). Conclusions: Coughs were most frequent during daytime. Two weeks of appropriate treatment significantly reduced cough frequency and made one-third of participants achieve culture conversion. Thus treatment by two weeks considerably diminishes but not eliminates the potential for airborne tuberculosis transmission

Overview of participants

Data for HIV-negative participants with culture-proven drug-susceptible tuberculosis are reported here

Daily report of cough and sputum results

Daily results per participant's treatment day are reported here for cough recordings and sputum samples

Hourly report of cough recordings

Hourly report of all cough recordings per participant's treatment day are shown here

Longitudinal cough and sputum results

Daily cough and sputum results for each participant during treatment

Baseline characteristics of participants

Data for HIV-negative participants who have culture-proven drug-susceptible tuberculosis and a baseline CT scan