ABSTRACT
PURPOSE: The few studies that have explored self-harm presentation times at hospital emergency departments (EDs) - an important factor that can determine if a patient receives a mental health assessment - primarily focus on adult samples. This study examined the times of self-harm presentations to EDs, self-harm methods used, mental health assessments, and admission data across different age-groups. METHODS: Using data from the National Self-Harm Registry Ireland over a 13-year timeframe (2007-2019), this study compared times, days, seasons, methods of self-harm, and admission data for children (8-12 years), adolescents (13-17 years), young adults (18-25 years) and adults (> 25 years). RESULTS: The majority of the 152,474 self-harm presentations (78.6%) for all ages occurred out-of-hours (outside the standard working hours or in-hours times of 09:00-17:00, Monday-Friday). The four hours before midnight had the highest proportions of self-harm presentations for adolescents (27.9%) and adults (23.1%), whereas the four hours after midnight had the highest proportion of self-harm presentations for young adults (22.9%). The 16:00-midnight timeframe had highest proportion of self-harm presentations in children (52.3%). Higher proportions of patients received a mental health assessment in-hours compared to out-of-hours among young adults (78.2% vs. 73.3%) and adults (76.1% vs. 72.0%). Self-harm presentations were lowest during summer months in children and adolescents. DISCUSSION: Hospitals should ensure that adequate resources are available for individuals presenting with self-harm, especially in the case of overcrowded EDs, and protocols need to be designed for those presenting with self-harm due to intoxication. In line with national policy, protocols for patients presenting during out-of-hours should be designed that can incorporate services from allied health multidisciplinary teams, social work, addiction services and counselling organisations. Given the lower rates of self-harm during school holidays for children and adolescents, the school environment must be considered in the context of mental health and self-harm public health prevention interventions.
Subject(s)
Emergency Service, Hospital , Registries , Self-Injurious Behavior , Humans , Adolescent , Emergency Service, Hospital/statistics & numerical data , Self-Injurious Behavior/epidemiology , Registries/statistics & numerical data , Child , Young Adult , Male , Adult , Female , Ireland/epidemiology , Time Factors , SeasonsABSTRACT
PURPOSE: Clinical trials play a pivotal role in assessing the safety and efficacy of medical therapies. Addressing sex distribution among enrollees in clinical trials of radiologic contrast agents is essential for ensuring the generalizability of trial outcomes. Previous research has highlighted the influence of demographic factors, particularly sex, on treatment responses, emphasizing the need for equitable representation in clinical trials. Our study aim was to determine the sex distribution of enrollees in clinical trials of radiologic contrast agents. METHODS: Our retrospective study included a total of 65 clinical trials conducted between 1990 and 2017 identified on clinicaltrials.gov after a comprehensive review including searching individually for all FDA approved contrast agents. Data collected included the year of FDA approval, the number of participants, sex distribution, trial location, trial phase, and study type. Inter-rater validation ensured data accuracy. RESULTS: Our analysis revealed fluctuations in sex distribution of trial enrollees. Enrollment of males exceeded females in most years, with a shift towards a more equitable representation in recent trials. Trials conducted in the United States had a higher rate of enrollment by females. Phase I trials had the most balanced representation, whereas Phase IV trials had the highest sex disparity. CONCLUSION: Across all trials, females made up 47.3 % of enrollees [3316 out of 7016 total enrollees]. Enrollment of males exceeded females in 44 of the 65 trials studied, females outnumbered males in 19 trials, and enrollment was equal between the sexes in 2 trials. While the sex distribution observed across all trials represents an equitable representation of enrollees, the wide variance of sex distribution at the level of individual trials has the potential to limit the generalizability of results.
ABSTRACT
BACKGROUND: Self-harm is a major public health concern in young people and is governed by a complex interaction of different risk factors. While many studies have identified these risk factors, less is known about how they tend to co-occur together. METHODS: A latent class analysis was conducted using risk factors for self-harm from two waves at ages 13 and 17 from the Growing Up in Ireland (GUI) study their associations with self-harm were examined longitudinally at ages 17 and 20, respectively. RESULTS: At age 13, there was a 'peer problems' group with higher probabilities of bullying, being unpopular and internalising problems and a 'school and substance use problems' group with higher probabilities of substance use, truancy or excessive absence from school and violence. Both of these two groups had over a two-fold risk of self-harm at age 17 in comparison to the low risk factor group. At age 17, there was a group with depression that was diagnosed by a medical professional with the highest relative risk (RR:13.9 (95 % CI 10.2-19.0)) of self-harm at age 20. Two other groups with undiagnosed depression had high probabilities of being bullied, and either high or low probabilities of substance use that had a 9.4 (95 % CI 6.8-13.1) and 7.4 (95 % CI 5.5-10.0) relative risk of self-harm at age 20, respectively. CONCLUSIONS: Identifying hidden sub-groups using risk factors for self-harm in young people can inform potential public health interventions by clinicians and other professionals who work with young people.
Subject(s)
Bullying , Self-Injurious Behavior , Substance-Related Disorders , Humans , Adolescent , Young Adult , Adult , Latent Class Analysis , Self-Injurious Behavior/diagnosis , Risk Factors , Substance-Related Disorders/epidemiologyABSTRACT
BACKGROUND: Pancreatic cystic lesions (PCLs) are frequent on MRI and are thought to be associated with pancreatic adenocarcinoma (PDAC) necessitating long-term surveillance based on older studies suffering from selection bias. PURPOSE: To establish the percentage of patients with PCLs on MRI with a present or future PDAC. STUDY TYPE: Systematic review, meta-analysis. POPULATION: Adults with PCLs on MRI and a present or future diagnosis of PDAC were eligible. MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Scopus were searched to April 2022 (PROSPERO:CRD42022320502). Studies limited to PCLs not requiring surveillance, <100 patients, or those with a history/genetic risk of PDAC were excluded. FIELD STRENGTH/SEQUENCE: ≥1.5 T with ≥1 T2-weighted sequence. ASSESSMENT: Two investigators extracted data, with discrepancies resolved by a third. QUADAS-2 assessed bias. PDAC was diagnosed using a composite reference standard. STATISTICAL TESTS: A meta-analysis of proportions was performed at the patient-level with 95% confidence intervals (95% CI). RESULTS: Eight studies with 1289 patients contributed to the percentage of patients with a present diagnosis of PDAC, and 10 studies with 3422 patients to the percentage with a future diagnosis. Of patients with PCLs on MRI, 14.8% (95% CI 2.4-34.9) had a PDAC at initial MRI, which decreased to 6.0% (2.2-11.3) for studies at low risk of bias. For patients without PDAC on initial MRI, 2.0% (1.1-3.2) developed PDAC during surveillance, similar for low risk of bias studies at 1.9% (0.7-3.6), with no clear trend of increased PDAC for longer surveillance durations. For patients without worrisome features or high-risk stigmata, 0.9% (0.1-2.2) developed PDAC during surveillance. Of 10, eight studies had a median surveillance ≥3 years (range 3-157 months). Sources of bias included retrospectively limiting PCLs to those with histopathology and inconsistent surveillance protocols. DATA CONCLUSION: A low percentage of patients with PCLs on MRI develop PDAC while on surveillance. The first MRI revealing a PCL should be scrutinized for PDAC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.
ABSTRACT
We conducted an umbrella review to synthesise the evidence from systematic reviews and meta-analyses that examined the risk and protective factors for self-harm in young people. We searched six different databases and used the AMSTAR-2 checklist for quality assessment. The importance of each risk and protective factor was determined based on (1) the number of times it was identified by general reviews examining any risk or protective factor, and (2) the effect sizes from meta-analyses. There were 61 systematic reviews included in this review. The most frequently identified risk factors for self-harm in young people included childhood abuse, depression/anxiety, bullying, trauma, psychiatric illnesses, substance use/abuse, parental divorce, poor family relationships, lack of friends, and exposure to self-harm behaviour in others. The risk factors with the strongest evidence for an association with self-harm were behavioural disorders, personality disorders and depression or anxiety. There was a dearth of systematic reviews examining protective factors but good family/friend relationships were most frequently identified. There was also evidence to show that non-suicidal and suicidal self-harm shared many of the same risk factors. Clinicians and other professionals who work with young people should be particularly cognisant of the psychiatric and adverse life event risk factors as well as the substance use, education-related and individual-level (e.g. being LGB) risk factors for self-harm. Knowledge of risk factors for self-harm can potentially be used to inform the design and implementation of prevention measures and further research is needed on the protective factors for self-harm.
Subject(s)
Self-Injurious Behavior , Substance-Related Disorders , Humans , Adolescent , Young Adult , Child , Protective Factors , Systematic Reviews as Topic , Self-Injurious Behavior/epidemiology , Self-Injurious Behavior/psychology , Suicidal Ideation , Risk Factors , Substance-Related Disorders/epidemiologyABSTRACT
OBJECTIVES: The aim of this study was to determine the relative infectiousness of asymptomatic SARS-CoV-2 infected persons compared with symptomatic individuals based on a scoping review of available literature. DESIGN: Rapid scoping review of peer-reviewed literature from 1 January to 5 December 2020 using the LitCovid database and the Cochrane library. SETTING: International studies on the infectiousness of individuals infected with SARS-CoV-2. PARTICIPANTS: Studies were selected for inclusion if they defined asymptomatics as a separate cohort distinct from presymptomatics and if they provided a quantitative measure of the infectiousness of asymptomatics relative to symptomatics. PRIMARY OUTCOME MEASURES: PCR result (PCR studies), the rate of infection (mathematical modelling studies) and secondary attack rate (contact tracing studies) - in each case from asymptomatic in comparison with symptomatic individuals. RESULTS: There are only a limited number of published studies that report estimates of relative infectiousness of asymptomatic compared with symptomatic individuals. 12 studies were included after the screening process. Significant differences exist in the definition of infectiousness. PCR studies in general show no difference in shedding levels between symptomatic and asymptomatic individuals; however, the number of study subjects is generally limited. Two modelling studies estimate relative infectiousness to be 0.43 and 0.57, but both of these were more reflective of the infectiousness of undocumented rather than asymptomatic cases. The results from contact tracing studies include estimates of relative infectiousness of 0, but with insufficient evidence to conclude that it is significantly different from 1. CONCLUSIONS: There is considerable heterogeneity in estimates of relative infectiousness highlighting the need for further investigation of this important parameter. It is not possible to provide any conclusive estimate of relative infectiousness, as the estimates from the reviewed studies varied between 0 and 1.
Subject(s)
COVID-19 , SARS-CoV-2 , Cohort Studies , Contact Tracing , Humans , Mass ScreeningABSTRACT
The serial interval is the time between symptom onsets in an infector-infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector-infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. OBJECTIVE: Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. METHODS: We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review. RESULTS: The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time. DISCUSSION: Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.
Subject(s)
COVID-19/epidemiology , Models, Statistical , Pandemics/statistics & numerical data , Global Health , Humans , SARS-CoV-2 , Time FactorsABSTRACT
OBJECTIVES: The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. DESIGN: Rapid systematic review and meta-analysis of observational research. SETTING: International studies on incubation period of COVID-19. PARTICIPANTS: Searches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies. PRIMARY OUTCOME MEASURES: Parameters of a lognormal distribution of incubation periods. RESULTS: The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days. CONCLUSIONS: The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.
Subject(s)
Coronavirus Infections/transmission , Infectious Disease Incubation Period , Pneumonia, Viral/transmission , Betacoronavirus , COVID-19 , Clinical Decision-Making , Humans , Logistic Models , Pandemics , SARS-CoV-2 , SoftwareABSTRACT
OBJECTIVES: Our objective was to review the literature on the inferred duration of the infectious period of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and provide an overview of the variation depending on the methodological approach. DESIGN: Rapid scoping review. Literature review with fixed search terms, up to 1 April 2020. Central tendency and variation of the parameter estimates for infectious period in (A) asymptomatic and (B) symptomatic cases from (1) virological studies (repeated testing), (2) tracing studies and (3) modelling studies were gathered. Narrative review of viral dynamics. INFORMATION SOURCES: Search strategies developed and the following searched: PubMed, Google Scholar, MedRxiv and BioRxiv. Additionally, the Health Information Quality Authority (Ireland) viral load synthesis was used, which screened literature from PubMed, Embase, ScienceDirect, NHS evidence, Cochrane, medRxiv and bioRxiv, and HRB open databases. RESULTS: There was substantial variation in the estimates, and how infectious period was inferred. One study provided approximate median infectious period for asymptomatic cases of 6.5-9.5 days. Median presymptomatic infectious period across studies varied over <1-4 days. Estimated mean time from symptom onset to two negative RT-PCR tests was 13.4 days (95% CI 10.9 to 15.8) but was shorter when studies included children or less severe cases. Estimated mean duration from symptom onset to hospital discharge or death (potential maximal infectious period) was 18.1 days (95% CI 15.1 to 21.0); time to discharge was on average 4 days shorter than time to death. Viral dynamic data and model infectious parameters were often shorter than repeated diagnostic data. CONCLUSIONS: There are limitations of inferring infectiousness from repeated diagnosis, viral loads and viral replication data alone and also potential patient recall bias relevant to estimating exposure and symptom onset times. Despite this, available data provide a preliminary evidence base to inform models of central tendency for key parameters and variation for exploring parameter space and sensitivity analysis.