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BACKGROUND: We evaluated patient safety within a randomized crossover trial comparing electronic directly observed therapy (eDOT) to in-person DOT (ipDOT) in persons undergoing TB treatment in New York City, NY, USA. METHODS: Participant symptoms, symptom severity, and clinical management were documented. We assessed adverse event reports (AERs) by DOT method during the two-period crossover. Using Cox proportional-hazards mixed-effects models, we estimated the adjusted hazard ratio (aHR) of participants reporting an adverse event (AE) vs. not reporting an AE. RESULTS: Of 211 participants, 57 (27.0%) reported AEs during the two-period crossover; of these, 54.4% (31/57) were reported while using eDOT vs. 45.6% (26/57) while using ipDOT. Controlling for study group and period, the aHR for eDOT vs. ipDOT was 0.98 (95% CI 0.49-1.93). Although statistically not significant, the wide confidence intervals suggest that a significant association cannot be entirely ruled out. Gastrointestinal symptoms were most frequently reported (42.1%, 24/57). AER types and severity did not differ significantly by DOT method. Days from symptom onset to medical attention was similar across DOT methods (median: 1.0 day, IQR 0.0-2.0). No participants switched DOT methods due to AERs or monitoring concerns. CONCLUSION: Further evaluation to ascertain whether AERs differ when patients use eDOT vs. ipDOT is warranted.

Importance: Electronic directly observed therapy (DOT) is used increasingly as an alternative to in-person DOT for monitoring tuberculosis treatment. Evidence supporting its efficacy is limited., Objective: To determine whether electronic DOT can attain a level of treatment observation as favorable as in-person DOT., Design, Setting, and Participants: This was a 2-period crossover, noninferiority trial with initial randomization to electronic or in-person DOT at the time outpatient tuberculosis treatment began. The trial enrolled 216 participants with physician-suspected or bacteriologically confirmed tuberculosis from July 2017 to October 2019 in 4 clinics operated by the New York City Health Department. Data analysis was conducted between March 2020 and April 2021., Interventions: Participants were asked to complete 20 medication doses using 1 DOT method, then switched methods for another 20 doses. With in-person therapy, participants chose clinic or community-based DOT; with electronic DOT, participants chose live video-conferencing or recorded videos., Main Outcomes and Measures: Difference between the percentage of medication doses participants were observed to completely ingest with in-person DOT and with electronic DOT. Noninferiority was demonstrated if the upper 95% confidence limit of the difference was 10% or less. We estimated the percentage of completed doses using a logistic mixed effects model, run in 4 modes: modified intention-to-treat, per-protocol, per-protocol with 85% or more of doses conforming to the randomization assignment, and empirical. Confidence intervals were estimated by bootstrapping (with 1000 replicates)., Results: There were 173 participants in each crossover period (median age, 40 years [range, 16-86 years]; 140 [66%] men; 80 [37%] Asian and Pacific Islander, 43 [20%] Black, and 71 [33%] Hispanic individuals) evaluated with the model in the modified intention-to-treat analytic mode. The percentage of completed doses with in-person DOT was 87.2% (95% CI, 84.6%-89.9%) vs 89.8% (95% CI, 87.5%-92.1%) with electronic DOT. The percentage difference was -2.6% (95% CI, -4.8% to -0.3%), consistent with a conclusion of noninferiority. The 3 other analytic modes yielded equivalent conclusions, with percentage differences ranging from -4.9% to -1.9%., Conclusions and Relevance: In this trial, the percentage of completed doses under electronic DOT was noninferior to that under in-person DOT. This trial provides evidence supporting the efficacy of this digital adherence technology, and for the inclusion of electronic DOT in the standard of care., Trial Registration: ClinicalTrials.gov Identifier: NCT03266003.

The TEACH Project obtained subjects' time-location information as part of its assessment of personal exposures to air toxics for high school students in two major urban areas. This report uses a longitudinal modeling approach to characterize the association between demographic and temporal predictors and the subjects' time-location behavior for three microenvironments--indoor-home, indoor-school, and outdoors. Such a longitudinal approach has not, to the knowledge of the authors, been previously applied to time-location data. Subjects were 14- to 19-year-old, self reported non-smokers, and were recruited from high schools in New York, NY (31 subjects: nine male, 22 female) and Los Angeles, CA (31 subjects: eight male, 23 female). Subjects reported their time-location in structured 24-h diaries with 15-min intervals for three consecutive weekdays in each of winter and summer-fall seasons in New York and Los Angeles during 1999-2000. The data set contained 15,009 observations. A longitudinal logistic regression model was run for each microenvironment where the binary outcome indicated the subject's presence in a microenvironment during a 15-min period. The generalized estimating equation (GEE) technique with alternating logistic regressions was used to account for the correlation of observations within each subject. The multivariate models revealed complex time-location patterns, with subjects predominantly in the indoor-home microenvironment, but also with a clear influence of the school schedule. The models also found that a subject's presence in a particular microenvironment may be significantly positively correlated for as long as 45 min before the current observation. Demographic variables were also predictive of time-location behavior: for the indoor-home microenvironment, having an after school job (OR=0.67 [95% confidence interval: 0.54:0.85]); for indoor-school, living in New York (0.42 [0.29:0.59]); and for outdoor, being 16-year-old (0.80 [0.67:0.96]), 17-year-old (0.71 [0.54:0.92]), and having an after school job (1.29 [1.07:1.56]).