Factors associated with suicide in people who use drugs: a scoping review.

BACKGROUND: Suicide is a significant contributor to global mortality. People who use drugs (PWUD) are at increased risk of death by suicide relative to the general population, but there is a lack of information on associated candidate factors for suicide in this group. The aim of this study was to provide a comprehensive overview of existing evidence on potential factors for death by suicide in PWUD. METHODS: A scoping review was conducted according to the Arksey and O'Malley framework. Articles were identified using Medline, CINAHL, PsycINFO, SOCIndex, the Cochrane Database of Systematic Reviews and the Campbell Collaboration Database of Systematic Reviews; supplemented by grey literature, technical reports, and consultation with experts. No limitations were placed on study design. Publications in English from January 2000 to December 2021 were included. Two reviewers independently screened full-text publications for inclusion. Extracted data were collated using tables and accompanying narrative descriptive summaries. The review was reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. RESULTS: The initial search identified 12,389 individual publications, of which 53 met the inclusion criteria. The majority (87%) of included publications were primary research, with an uncontrolled, retrospective study design. The most common data sources were drug treatment databases or national death indexes. Eleven potential factors associated with death by suicide among PWUD were identified: sex; mental health conditions; periods of heightened vulnerability; age profile; use of stimulants, cannabis, or new psychoactive substances; specific medical conditions; lack of dual diagnosis service provision; homelessness; incarceration; intravenous drug use; and race or ethnicity. Opioids, followed by cannabis and stimulant drugs were the most prevalent drugs of use in PWUD who died by suicide. A large proportion of evidence was related to opioid use; therefore, more primary research on suicide and explicit risk factors is required. CONCLUSIONS: The majority of studies exploring factors associated with death by suicide among PWUD involved descriptive epidemiological data, with limited in-depth analyses of explicit risk factors. To prevent suicide in PWUD, it is important to consider potential risk factors and type of drug use, and to tailor policies and practices accordingly.

Birth Weight and Gestational Age as Modifiers of Rehospitalization after Neonatal Intensive Care Unit Admission.

OBJECTIVE: This study aimed to assess interaction effects between gestational age and birth weight on 30-day unplanned hospital readmission following discharge from the neonatal intensive care unit (NICU). STUDY DESIGN: This is a retrospective study that uses the study site's Children's Hospitals Neonatal Database and electronic health records. Population included patients discharged from a NICU between January 2017 and March 2020. Variables encompassing demographics, gestational age, birth weight, medications, maternal data, and surgical procedures were controlled for. A statistical interaction between gestational age and birth weight was tested for statistical significance. RESULTS: A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge. Statistical interaction between birth weight and gestational age was statistically significant, indicating that the odds of readmission among low birthweight premature patients increase with increasing gestational age, whereas decrease with increasing gestational age among their normal or high birth weight peers. CONCLUSION: The effect of gestational age on odds of hospital readmission is dependent on birth weight. KEY POINTS: · Population included patients discharged from a NICU between January 2017 and March 2020.. · A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge.. · The effect of gestational age on odds of hospital readmission is dependent on birth weight..

Epidemiology of Neonatal COVID-19 in the United States.

OBJECTIVES: Data on coronavirus disease 2019 (COVID-19) infections in neonates are limited. We aimed to identify and describe the incidence, presentation, and clinical outcomes of neonatal COVID-19. METHODS: Over 1 million neonatal encounters at 109 United States health systems, from March 2020 to February 2021, were extracted from the Cerner Real World Database. COVID-19 diagnosis was assessed using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) laboratory tests and diagnosis codes. Incidence of COVID-19 per 100 000 encounters was estimated. RESULTS: COVID-19 was diagnosed in 918 (0.1%) neonates (91.1 per 100 000 encounters [95% confidence interval 85.3-97.2]). Of these, 71 (7.7%) had severe infection (7 per 100 000 [95% confidence interval 5.5-8.9]). Median time to diagnosis was 14.5 days from birth (interquartile range 3.1-24.2). Common signs of infection were tachypnea and fever. Those with severe infection were more likely to receive respiratory support (50.7% vs 5.2%, P < .001). Severely ill neonates received analgesia (38%), antibiotics (33.8%), anticoagulants (32.4%), corticosteroids (26.8%), remdesivir (2.8%), and COVID-19 convalescent plasma (1.4%). A total of 93.6% neonates were discharged home after care, 1.1% were transferred to another hospital, and discharge disposition was unknown for 5.2%. One neonate (0.1%) with presentation suggestive of multisystem inflammatory syndrome in children died after 11 days of hospitalization. CONCLUSIONS: Most neonates infected with SARS-CoV-2 were asymptomatic or developed mild illness without need for respiratory support. Some had severe illness requiring treatment of COVID-19 with remdesivir and COVID-19 convalescent plasma. SARS-CoV-2 infection in neonates, though rare, may result in severe disease.

Identification of Prescribing Errors in an Electronic Health Record Using a Retract-and-Reorder Tool: A Pilot Study.

OBJECTIVES: The aims of this study were to develop and to validate an adapted Retract-and-Reorder (RAR) tool to identify and quantify near-miss/intercepted prescribing errors in an electronic health record. METHODS: This is a cross-sectional study between February and March 2021 in an Irish maternity hospital. We used the RAR tool to detect near-miss prescribing errors in audit log data. Potential errors flagged by the tool were validated using prescriber interviews. Chart reviews were performed if the prescriber was unavailable for interview. Errors were judged to be clinical decisions in chart reviews through review of narrative notes, order components, and patient's clinical history. Interviews were analyzed with reference to the London Protocol, a process of incident analysis that categorizes causes of errors into various contributory factors including patient factors, task and technology factors, and work environment. Logistic regression with robust clustered standard errors was used to determine predictors for near-miss prescribing errors. We calculated the positive predictive value of the RAR tool by dividing the number of confirmed near-miss prescribing errors by the total number of RAR events identified. RESULTS: Eighty-four RAR events were identified in 27,407 medication orders. Seventy-one events were confirmed near-miss prescribing errors, resulting in a positive predictive value of 85.0% (95% confidence interval, 75%-91%) and an estimated near-miss prescribing error rate of 259/100,000 medication orders. Duplicate prescribing errors were most common (54/71, 76.1%). No errors were reported by prescribers. Consultants were less likely to make an error than nonconsultant hospital doctors (adjusted odds ratio, 0.10; 95% confidence interval, 0.01-0.84). Factors associated with errors included workload, staffing levels, and task structure. CONCLUSIONS: Our adapted RAR tool identified a variety of near-miss prescribing errors not otherwise reported. The tool has been implemented in the study hospital as a patient safety resource. Further implementations are planned across Irish hospitals.

Impact of an electronic health record on task time distribution in a neonatal intensive care unit.

BACKGROUND: An Electronic Health Record (EHR) has been introduced to four Irish maternity units, with further implementations planned. Previous studies indicate that healthcare professionals are concerned that EHRs may increase time spent on documentation and medication-related tasks. OBJECTIVE: To determine the impact of an EHR on task time distribution in a Neonatal Intensive Care Unit (NICU). METHODS: A pre-post, time and motion study. An electronic data collection tool was used to collate time spent on direct care, professional communication, reviewing charts, documentation, and medication-related tasks. Interruptions and contact with the patient zone were quantified. Statistical significance was assessed using two-sample proportion tests, two-sample t-tests, and two-sample Wilcoxon rank-sum tests. A Bonferroni correction set significance at p ≤ 0.0025. RESULTS: 63 doctors and nurses participated, with 169.23 h of data collected. There were no significant changes to nurses' task time distribution. The proportion of time spent by doctors on professional communication increased from 15.4% to 26.0% (p < 0.001). Significant increases to median task times were seen for both doctors and nurses. Interruptions to tasks decreased post-implementation (p < 0.001), as did frequency of contact with the patient zone (p < 0.001). CONCLUSION: The EHR did not redistribute time towards documentation and medication-related tasks. A reduction in interruptions to tasks may streamline workflow. Decreased contact with the patient zone may improve patient safety through reduced potential for pathogen transmission.

The impact of health information technology on prescribing errors in hospitals: a systematic review and behaviour change technique analysis.

BACKGROUND: Health information technology (HIT) is known to reduce prescribing errors but may also cause new types of technology-generated errors (TGE) related to data entry, duplicate prescribing, and prescriber alert fatigue. It is unclear which component behaviour change techniques (BCTs) contribute to the effectiveness of prescribing HIT implementations and optimisation. This study aimed to (i) quantitatively assess the HIT that reduces prescribing errors in hospitals and (ii) identify the BCTs associated with effective interventions. METHODS: Articles were identified using CINAHL, EMBASE, MEDLINE, and Web of Science to May 2020. Eligible studies compared prescribing HIT with paper-order entry and examined prescribing error rates. Studies were excluded if prescribing error rates could not be extracted, if HIT use was non-compulsory or designed for one class of medication. The Newcastle-Ottawa scale was used to assess study quality. The review was reported in accordance with the PRISMA and SWiM guidelines. Odds ratios (OR) with 95% confidence intervals (CI) were calculated across the studies. Descriptive statistics were used to summarise effect estimates. Two researchers examined studies for BCTs using a validated taxonomy. Effectiveness ratios (ER) were used to determine the potential impact of individual BCTs. RESULTS: Thirty-five studies of variable risk of bias and limited intervention reporting were included. TGE were identified in 31 studies. Compared with paper-order entry, prescribing HIT of varying sophistication was associated with decreased rates of prescribing errors (median OR 0.24, IQR 0.03-0.57). Ten BCTs were present in at least two successful interventions and may be effective components of prescribing HIT implementation and optimisation including prescriber involvement in system design, clinical colleagues as trainers, modification of HIT in response to feedback, direct observation of prescriber workflow, monitoring of electronic orders to detect errors, and system alerts that prompt the prescriber. CONCLUSIONS: Prescribing HIT is associated with a reduction in prescribing errors in a variety of hospital settings. Poor reporting of intervention delivery and content limited the BCT analysis. More detailed reporting may have identified additional effective intervention components. Effective BCTs may be considered in the design and development of prescribing HIT and in the reporting and evaluation of future studies in this area.