scholarly journals Individual risk assessment tool for school-age asthma prediction in UK birth cohort

2019 ◽  
Vol 49 (3) ◽  
pp. 292-298 ◽  
Author(s):  
Ran Wang ◽  
Angela Simpson ◽  
Adnan Custovic ◽  
Phil Foden ◽  
Danielle Belgrave ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Birth Cohort ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Individual Risk ◽  
School Age ◽  
Individual Risk Assessment

scholarly journals Initial Experience in Predicting the Risk of Hospitalization of 496 Outpatients with COVID-19 Using a Telemedicine Risk Assessment Tool

2020 ◽  
Author(s):  
James B O'Keefe ◽  
Elizabeth J Tong ◽  
Thomas H Taylor ◽  
Ghazala D Datoo O'Keefe ◽  
David C Tong
Keyword(s):  
Risk Assessment ◽  
Assessment Tool ◽  
Cox Regression ◽  
Risk Assessment Tool ◽  
Individual Risk ◽  
Tier 2 ◽  
Tier 1 ◽  
Risk Patients ◽  
Tier 3

Objective: To determine whether a risk prediction tool developed and implemented in March 2020 accurately predicts subsequent hospitalizations. Design: Retrospective cohort study, enrollment from March 24 to May 26, 2020 with follow-up calls until hospitalization or clinical improvement (final calls until June 19, 2020) Setting: Single center telemedicine program managing outpatients from a large medical system in Atlanta, Georgia Participants: 496 patients with laboratory-confirmed COVID-19 in isolation at home. Exclusion criteria included: (1) hospitalization prior to telemedicine program enrollment, (2) immediate discharge with no follow-up calls due to resolution. Exposure: Acute COVID-19 illness Main Outcome and Measures: Hospitalization was the outcome. Days to hospitalization was the metric. Survival analysis using Cox regression was used to determine factors associated with hospitalization. Results: The risk-assessment rubric assigned 496 outpatients to risk tiers as follows: Tier 1, 237 (47.8%); Tier 2, 185 (37.3%); Tier 3, 74 (14.9%). Subsequent hospitalizations numbered 3 (1%), 15 (7%), and 17 (23%) and for Tiers 1-3, respectively. From a Cox regression model with age ≥ 60, gender, and self-reported obesity as covariates, the adjusted hazard ratios using Tier 1 as reference were: Tier 2 HR=3.74 (95% CI, 1.06-13.27; P=0.041); Tier 3 HR=10.87 (95% CI, 3.09-38.27; P<0.001). Tier was the strongest predictor of time to hospitalization. Conclusions and Relevance: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified both low-risk and high-risk patients with better performance than individual risk factors alone. This approach may be appropriate for optimum allocation of resources.

scholarly journals Using the Biopsychosocial Model for Identifying Subgroups of Detained Juveniles at Different Risk of Re-offending in Practice: a Latent Class Regression Analysis Approach

2021 ◽  
Author(s):  
Esther Laura de Ruigh ◽  
Samantha Bouwmeester ◽  
Arne Popma ◽  
Robert Vermeiren ◽  
Lieke van Domburgh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Regression Analysis ◽  
Juvenile Offenders ◽  
Juvenile Justice ◽  
Latent Class ◽  
Assessment Tool ◽  
Individual Risk ◽  
Class Membership ◽  
Latent Class Regression ◽  
Individual Risk Assessment

Abstract Background: Juvenile delinquents constitute a heterogeneous group, which complicates decision-making based on risk assessment. Various psychosocial factors have been used to define clinically relevant subgroups of juvenile offenders, while neurobiological variables have not yet been integrated in this context. Moreover, translation of neurobiological group differences to individual risk assessment has proven difficult. We aimed to identify clinically relevant subgroups associated with differential youth offending outcomes, based on psychosocial and neurobiological characteristics, and to test whether the resulting model can be used for risk assessment of individual cases. Methods: A group of 263 detained juveniles from juvenile justice institutions was studied. Latent class regression analysis was used to detect subgroups associated with differential offending outcome (recidivism at 12 month follow-up). As a proof of principle, it was tested in a separate group of 76 participants whether individual cases could be assigned to the identified subgroups, using a prototype ‘tool’ for calculating class membership. Results: Three subgroups were identified: a ‘high risk – externalizing’ subgroup, a ‘medium risk – adverse environment’ subgroup, and a ‘low risk – psychopathic traits’ subgroup. Within these subgroups, both autonomic nervous system and neuroendocrinological measures added differentially to the prediction of subtypes of reoffending (no, non-violent, violent). The ‘tool’ for calculating class membership correctly assigned 92.1% of participants to a class and reoffending risk. Conclusions: The LCRA approach appears to be a useful approach to integrate neurobiological and psychosocial risk factors to identify subgroups with different re-offending risk within juvenile justice institutions. This approach may be useful in the development of a biopsychosocial assessment tool and may eventually help clinicians to assign individuals to those subgroups and subsequently tailor treatment based on their re-offending risk.

scholarly journals Exploration of the risk factors contained within the UK’s existing domestic abuse risk assessment tool (DASH): do these risk factors have individual predictive validity regarding recidivism?

2017 ◽  
Vol 9 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Louise Almond ◽  
Michelle McManus ◽  
David Brian ◽  
Daniel Peter Merrington
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Assessment Tool ◽  
Domestic Abuse ◽  
Criminal History ◽  
Risk Assessment Tool ◽  
Individual Risk ◽  
Content Type ◽  
The Individual ◽  
Individual Risk Factors

Purpose The purpose of this paper is to explore risk factors contained in the existing UK domestic abuse (DA) risk assessment tool: domestic abuse, stalking and harassment and honour-based violence (DASH) for individual predictive validity of DA recidivism using data from Devon and Cornwall Constabulary. Design/methodology/approach In total, 1,441 DA perpetrators were monitored over a 12-month period, and 270 (18.7 per cent) went on to commit a further DA offence. The individual risk factors which were associated and predictive of increased risk of recidivism were identified. Findings Only four of the individual risk factors were significantly associated with an increased risk of DA recidivism: “criminal history”, “problems with alcohol”, “separation” and “frightened”. Therefore, 21 of the risk factor items analysed could not discriminate between non-recidivist and recidivist perpetrators. Only two risk factors were able to significantly predict the recidivist group when compared to the non-recidivist group. These were identified as “criminal history” and “separated”. Of those who did commit a further DA offence in the following 12 months, 133 were violent and 137 were non-violent. The risk factors associated with these types of recidivism are identified. Practical implications The implications for UK police practice and the DASH risk assessment tool are discussed. By identifying key individual factors that can prioritise those individuals likely to recidivate and the severity of that recidivism, this could assist police decision making regarding the response and further prevention of DA incidents. The validation of association between individual factors and DA recidivism should improve the accuracy of risk levels. Originality/value This is the first large-scale validation of the individual risk factors contained within the UK’s DA risk assessment tool. It should be noted that the validity of the DASH tool itself was not examined within the current study.

scholarly journals Using the biopsychosocial model for identifying subgroups of detained juveniles at different risk of re-offending in practice: a latent class regression analysis approach

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
E. L. de Ruigh ◽  
S. Bouwmeester ◽  
A. Popma ◽  
R. R. J. M. Vermeiren ◽  
L. van Domburgh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Regression Analysis ◽  
Juvenile Offenders ◽  
Juvenile Justice ◽  
Latent Class ◽  
Assessment Tool ◽  
Individual Risk ◽  
Class Membership ◽  
Latent Class Regression ◽  
Individual Risk Assessment

Abstract Background Juvenile delinquents constitute a heterogeneous group, which complicates decision-making based on risk assessment. Various psychosocial factors have been used to define clinically relevant subgroups of juvenile offenders, while neurobiological variables have not yet been integrated in this context. Moreover, translation of neurobiological group differences to individual risk assessment has proven difficult. We aimed to identify clinically relevant subgroups associated with differential youth offending outcomes, based on psychosocial and neurobiological characteristics, and to test whether the resulting model can be used for risk assessment of individual cases. Methods A group of 223 detained juveniles from juvenile justice institutions was studied. Latent class regression analysis was used to detect subgroups associated with differential offending outcome (recidivism at 12 month follow-up). As a proof of principle, it was tested in a separate group of 76 participants whether individual cases could be assigned to the identified subgroups, using a prototype ‘tool’ for calculating class membership. Results Three subgroups were identified: a ‘high risk—externalizing’ subgroup, a ‘medium risk—adverse environment’ subgroup, and a ‘low risk—psychopathic traits’ subgroup. Within these subgroups, both autonomic nervous system and neuroendocrinological measures added differentially to the prediction of subtypes of reoffending (no, non-violent, violent). The ‘tool’ for calculating class membership correctly assigned 92.1% of participants to a class and reoffending risk. Conclusions The LCRA approach appears to be a useful approach to integrate neurobiological and psychosocial risk factors to identify subgroups with different re-offending risk within juvenile justice institutions. This approach may be useful in the development of a biopsychosocial assessment tool and may eventually help clinicians to assign individuals to those subgroups and subsequently tailor intervention based on their re-offending risk.

Risk score – valuable tool for individual risk assessment?

2008 ◽  
Vol 56 (S 1) ◽  
Author(s):  
B Osswald ◽  
G Thomas ◽  
U Tochtermann ◽  
V Gegouskov ◽  
D Badowski-Zyla ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Risk Score ◽  
Individual Risk ◽  
Individual Risk Assessment
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