High alert medications administration errors in neonatal intensive care unit: A pediatric tertiary hospital experience

This is a hospital-based descriptive cross sectional study, implemented in the NICU, at Cairo University Pediatric hospital. A convenient sample of 33 bedside NICU nurses, who agreed to participate was recruited. A valid, reliable questionnaire was used to measure NICU nurses' general and specific knowledge regarding five therapeutic HAM. An observational checklist was used to assess nurses' administration practices. Both revealed that the mean percentage score of the nurses' knowledge (76.2±11.6) was higher than the mean percentage score of their total practice (69.1±13.3). Analysis of types of nurses' errors, showed that the most common error type was the wrong dose (15%), followed by wrong drug type (13.6%). Nurses' knowledge and training are not mandatorily interpreted into improved implementation practices. Interventions highlighted for preventing HAM errors were developing specific training on HAM for nurses and establishing neonate centered, multidisciplinary teams formed of physicians, nurses, and pharmacists

Time to start of tuberculosis treatment in penitentiary system of Kyrgyz Republic: A retrospective cohort study

Background Tuberculosis burden among the incarcerated population is generally higher than that of general population. Early diagnosis and prompt initiation of treatment are key strategies to contain disease transmission. The aim of this study was to determine the time to treatment initiation among inmates with new smear or Xpert MTB/RIF positive pulmonary tuberculosis and explore risk factors associated with delayed treatment initiation in prison settings. Methods We conducted a retrospective cohort study using routine health care data from prison settings in Kzrgyz Republic on new pulmonary tuberculosis patients confirmed by smear microscopy or GeneXpert MTB/RIF during 2014–2019. We computed delay in start of treatment—days from specimen collection to treatment initiation—for exposure variables. We dichotomized treatment delay using 10-day cut-off point,and used logistic regression to identify factors associated with treatment delay. Results Among 406 cases included into analysis, the median delay to treatment initiation was 7 days [IQR: 2–16 days]. Using 10-day cut-off, 189 (46.6%) patients had delayed treatment initiation. Treatment delay was negatively associated with smear positivity [adjusted OR (aOR) = 0.44, 95% CI 0.29–0.68] compared to smear negative patients, while patients with isoniazid resistant (aOR = 2.61, 95%CI 1.49–4.56) and rifampicin resistant tuberculosis (aOR = 4.14, 95%CI 2.56–6.77) had increased delay compared to patients who were sensitive for both rifampicin and isoniazid. Conclusion Timely diagnosis and effective treatment remain the cornerstone of TB control program populations in the general and in prison settings in particular. Prison authorities need to address all potential areas of delay in TB diagnosis and treatment to strengthen their TB control efforts so that prisons remain free of TB for detainees, prison staff and visitors. These include improved supply of TB drugs, early detection of TB cases and improved collaboration with the health authorities outside the prison system. </jats:sec

Time (in days) from the date of specimen collection for microscopy or Xpert MTB/RIF) to TB treatment initiation by demographic and clinical characteristics.

Time (in days) from the date of specimen collection for microscopy or Xpert MTB/RIF) to TB treatment initiation by demographic and clinical characteristics.</p

Flowchart of study population based on exclusion criteria.

*WRD–WHO recommended rapid diagnostics.</p

Predictors of delay in TB treatment initiation by demographic and clinical characteristics.

Predictors of delay in TB treatment initiation by demographic and clinical characteristics.</p

Significant variables in sensitivity analyses for treatment delays using different cut-off points.

Significant variables in sensitivity analyses for treatment delays using different cut-off points.</p

S1 Dataset -

(XLSX)</p