Exemplar Abstract for Lactobacillus minor (ex Abo-Elnaga and Kandler 1965) Kandler et al. 1983 and Weissella minor (Kandler et al. 1983) Collins et al. 1994.

Taylor, Dorothea

doi:10.1601/ex.5402

Cited by 100 publications

(111 citation statements)

References 2 publications

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“…Data were collected using a standardized case record form adapted from the World Health Organization 18 and entered into REDCap (Research Electronic Data Capture) electronic data capture tools by trained staff at a later date. 19,20 Severe COVID-19 was defined as having an oxygen saturation level below 90%, having a respiratory rate greater than 30 breaths/min, or needing oxygen therapy. 21 HIV status JAMA Network Open | Global Health was self-reported, and patients with unknown status or who were deemed eligible and consented for HIV testing were tested at admission.…”

Section: Covid-19 Clinical Outcomes Studymentioning

confidence: 99%

“…The end of a wave was defined as the date on which a decreasing number of daily SARS-CoV-2-positive test results following a peak transitioned to a stable number of daily positive test results. The increase in cases shown on the epidemic curve between April 29, 2020, and June 8, 2020, was not considered a wave because COVID- 19 had not yet spread nationally in Zambia (a high proportion of the SARS-CoV-2 testing conducted during that time was related to an outbreak in the rural Nakonde District). Data on relative variant genome frequency by region from the Global Initiative on Sharing Avian Influenza Data (GISAID) were used to define the SARS-CoV-2 variant that was circulating during each wave.…”

Section: National Testing Data Wave Definitions and Circulating Variantsmentioning

confidence: 99%

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Clinical Characteristics and Outcomes of Patients Hospitalized With COVID-19 During the First 4 Waves in Zambia

et al. 2022

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ImportanceFew epidemiologic studies related to COVID-19 have emerged from countries in Africa, where demographic characteristics, epidemiology, and health system capacity differ from other parts of the world.ObjectivesTo describe the characteristics and outcomes of patients admitted to COVID-19 treatment centers, assess risk factors for in-hospital death, and explore how treatment center admissions were affected by COVID-19 waves in Zambia.Design, Setting, and ParticipantsThis retrospective cohort study assessed patients admitted to COVID-19 treatment centers in 5 Zambian cities between March 1, 2020, and February 28, 2022.ExposuresRisk factors for in-hospital mortality, including patient age and severity of COVID-19, at treatment center admission.Main Outcomes and MeasuresPatient information was collected, including inpatient disposition (discharged or died). Differences across and within COVID-19 waves were assessed. Mixed-effects logistic regression models were used to assess associations between risk factors and in-hospital mortality as well as between characteristics of admitted patients and timing of admission.ResultsA total of 3876 patients were admitted during 4 COVID-19 waves (mean [SD] age, 50.6 [19.5] years; 2103 male [54.3%]). Compared with the first 3 waves (pooled), the proportion of patients who were 60 years or older admitted during wave 4, when the Omicron variant was circulating, was significantly lower (250 of 1009 [24.8%] vs 1116 of 2837 [39.3%]; P &lt; .001). Factors associated with in-hospital mortality included older age (≥60 vs &lt;30 years; adjusted odds ratio [aOR], 3.55; 95% CI, 2.34-5.52) and HIV infection (aOR, 1.39; 95% CI, 1.07-1.79). Within waves, patients who were admitted during weeks 5 to 9 had significantly higher odds of being 60 years or older (aOR, 2.09; 95% CI, 1.79-2.45) or having severe COVID-19 at admission (aOR, 2.49; 95% CI, 2.14-2.90) than those admitted during the first 4 weeks.Conclusions and RelevanceThe characteristics of admitted patients during the Omicron wave and risk factors for in-hospital mortality in Zambia reflect data reported elsewhere. Within-wave analyses revealed a pattern in which it appeared that admission of higher-risk patients was prioritized during periods when there were surges in demand for health services during COVID-19 waves. These findings support the need to expand health system capacity and improve health system resiliency in Zambia and other countries with resource-limited health systems.

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Section: Covid-19 Clinical Outcomes Studymentioning

confidence: 99%

Section: National Testing Data Wave Definitions and Circulating Variantsmentioning

confidence: 99%

Clinical Characteristics and Outcomes of Patients Hospitalized With COVID-19 During the First 4 Waves in Zambia

et al. 2022

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“…Data were collected at baseline (before randomization) and 12-week follow-up (after intervention) using REDCap (Research Electronic Data Capture). 38,39 Surveys were self-administered online or collected over the telephone with a trained data collector (F.P. and M.R.M.).…”

Section: Discussionmentioning

confidence: 99%

Effect of a Virtual Home-Based Behavioral Intervention on Family Health and Resilience During the COVID-19 Pandemic

et al. 2022

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ImportanceVirtual home-based interventions may bolster protective factors, such as family health and resilience, during stressors such as the COVID-19 pandemic; however, their effectiveness is unknown.ObjectiveTo examine the effectiveness of a virtual health coaching intervention on family health and resilience during the pandemic.Design, Setting, and ParticipantsIn this parallel-group, single-site randomized clinical trial, 123 parents and their 2- to 8-year-old children were enrolled at a pediatric clinic or community partner site in Tennessee from March 10 to August 11, 2021. Follow-up surveys were completed between June 29 and November 11, 2021.InterventionsAll participants received 11 weekly cooking videos and associated home-delivered groceries. The intervention group also received 12 weekly, 30-minute virtual health coach sessions.Main Outcomes and MeasuresThe primary outcome was the validated 6-item (range, 6-30) Family Healthy Lifestyle Subscale (FHLS) scores. The secondary outcome was the validated 6-item (range, 0-6) Family Resilience and Connection Index (FRCI) scores. Outcomes were determined a priori and evaluated at baseline and 12-week follow-up. A priori independent t tests and multivariable tobit regression models assessed intervention effects, and post hoc, secondary interaction models assessed whether effects differed over baseline outcomes.ResultsAmong the 123 enrolled families, 110 (89%) were included in the primary analyses (parent mean [SD] age, 35.1 [8.2] years; 104 [95%] female; 55 [50%] non-Hispanic Black; child mean [SD] age, 5.2 [1.7] years; 62 [56%] male). Intervention-control group mean differences were nonsignificant for follow-up FHLS scores (0.7; 95% CI, −0.6 to 2.0; P = .17) and FRCI scores (0.1; 95% CI, −0.5 to 0.6; P = .74). Tobit regression model intervention effects were nonsignificant for FHLS scores (0.9; 95% CI, −0.3 to 2.2; P = .15) and FRCI scores (0.4; 95% CI, −0.2 to 1.1; P = .17). Post hoc, secondary models found no significant interaction for FHLS scores (1.3 increase per 5-point decrease; 95% CI, −0.2 to 2.7; P = .09), with significant intervention associations for baseline scores of 6 to 23. The interaction was significant for FRCI scores (0.4 increase per 1-point decrease; 95% CI, 0.01 to 0.8; P = .047), with significant intervention associations for baseline scores of 0 to 3.Conclusions and RelevanceIn this randomized clinical trial of families with young children, weekly virtual health coaching did not detectably improve family health and resilience. Post hoc, secondary results provided preliminary evidence of potential effectiveness among families with low baseline scores.Trial RegistrationClinicalTrials.gov Identifier: NCT05328193

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“…in small blocks (2:2) via prespecified tables implemented in REDCap. 27,28 Randomization was stratified within self-reported sex (an a priori hypothesized moderator, men vs women) and race and ethnicity (non-Hispanic White vs all other racial and ethnic groups); both variables were collected per National Institutes of Health policy. 29…”

Section: Randomizationmentioning

confidence: 99%

Effect of Extending the Duration of Prequit Treatment With Varenicline on Smoking Abstinence

et al. 2022

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ImportanceEven with varenicline, the leading monotherapy for tobacco dependence, smoking abstinence rates remain low. Preliminary evidence suggests that extending the duration of varenicline treatment before quitting may increase abstinence.ObjectiveTo test the hypotheses that, compared with standard run-in varenicline treatment (1 week before quitting), extended run-in varenicline treatment (4 weeks before quitting) reduces smoking exposure before the target quit date (TQD) and enhances abstinence, particularly among women.Design, Setting, and ParticipantsThis double-blind, randomized, placebo-controlled clinical trial enrolled participants from October 2, 2017, to December 9, 2020, at a single-site research clinic in Buffalo, New York. Of 1385 people screened, 320 adults reporting smoking 5 or more cigarettes per day (CPD) were randomized and followed up for 28 weeks. Data were analyzed from August 2021 to June 2022.InterventionsIn the pre-TQD period (weeks 1-4), the extended run-in group received 4 weeks of varenicline; the standard run-in group received 3 weeks of placebo followed by 1 week of varenicline. Both groups received open-label varenicline during weeks 5 to 15 and brief quit counseling at 6 clinic visits.Main Outcomes and MeasuresThe primary outcome consisted of cotinine-verified (at end of treatment [EOT]) self-reported continuous abstinence from smoking (in CPD) during the last 4 weeks of treatment. Secondary outcomes included bioverified self-report of continuous abstinence at the 6-month follow-up and percentage of reduction in self-reported smoking rate during the prequit period (week 1 vs week 4).ResultsA total of 320 participants were randomized, including 179 women (55.9%) and 141 men (44.1%), with a mean (SD) age of 53.7 (10.1) years. Continuous abstinence during the final 4 weeks of treatment (weeks 12-15; EOT) was not greater in the extended run-in group (64 of 163 [39.3%]) compared with the standard run-in group (57 of 157 [36.3%]; odds ratio [OR], 1.13 [95% CI, 0.72-1.78]), nor was the hypothesized group × sex interaction significant (OR, 0.52 [95% CI, 0.21-1.28]). Similar nonsignificant results were obtained for continuous abstinence at the 6-month follow-up. The mean (SE) decrease in self-reported smoking rate during the prequit period was greater in the extended run-in group (−38.8% [2.8%]) compared with the standard run-in group (−17.5% [2.7%]).Conclusions and RelevanceAmong adult daily smokers, extending the duration of prequit varenicline treatment beyond the standard 1-week run-in period reduced prequit smoking exposure but, more importantly, did not significantly improve continuous abstinence rates.Trial RegistrationClinicalTrials.gov Identifier: NCT03262662

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Exemplar Abstract for Lactobacillus minor (ex Abo-Elnaga and Kandler 1965) Kandler et al. 1983 and Weissella minor (Kandler et al. 1983) Collins et al. 1994.

Cited by 100 publications

References 2 publications

Clinical Characteristics and Outcomes of Patients Hospitalized With COVID-19 During the First 4 Waves in Zambia

Clinical Characteristics and Outcomes of Patients Hospitalized With COVID-19 During the First 4 Waves in Zambia

Effect of a Virtual Home-Based Behavioral Intervention on Family Health and Resilience During the COVID-19 Pandemic

Effect of Extending the Duration of Prequit Treatment With Varenicline on Smoking Abstinence

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