Objectives: Violence against vulnerable people is a phenomenon largely hidden and under-reported. Emergency Departments (ED) are ideal settings to identify victims of repeated abuse and maltreatment. Availability of sets of suspicion indicators derived from large population-based databases can be useful in healthcare settings to reinforce a suspicion arising from the observation of the patient and to integrate existing sets of suspicion indicators. Methods: The study was conducted in two Italian regions: Piedmont and Tuscany. A case-control study design was utilized. Patients of vulnerable groups (children, adult women, and older people) recorded in the ED register for assault, abuse or maltreatment occurred between 2013 and 2015 were selected as cases. Patients admitted in ED for road traffic injury in the same population groups were selected as controls. For each subject, all admissions occurred during the previous 24 months were summarized by means of indicators counting their frequency. For each group of vulnerable subjects, backward logistic regressions were implemented. Results: Tuscany’s EDs registered in each vulnerable group of population at least 4 times the number of ED- cases for violence than Piedmont. The difference could be partially explained by the adoption in Tuscan hospitals of a triage code (“pink code”) explicitly concerned with the tracking of victims of relational violence. An increase in the number of ED admissions and previous admission for violence were predictive of being victim of violence. Significant predictive factors were: foreign citizenship, age class 30-49, age <1 (in the regional models), age 5-9 (in the model with both regions), mental disorders (in all groups), neoplasm or respiratory diseases (in elderly). Discussion and conclusions: The high variability among Italian regions in violence rates can depend by both underreporting and misclassification in coding injury or disease cases. Results confirm the recurrence of violence as to the continuity characterizing maltreatment in domestic settings. The low specificity of models predictive of violence based on population-based healthcare databases implies that these models are still not sufficient alone to build effective screening tools. On the other hand, in population-based health registries the power of the sample is very high for each examined variable rendering a very accurate estimate of risk associations. Further analysis should consider the interactions among several factors, available in current registries and resulted significant in the present study.

Definition of Risk Indicators for Detection of Violence and Abuse on Vulnerable People from Population-Based Databases in Emergency Department Settings: Results from a Large Case-Control Study in Italy

Daniele P. Radicioni;
2021

Abstract

Objectives: Violence against vulnerable people is a phenomenon largely hidden and under-reported. Emergency Departments (ED) are ideal settings to identify victims of repeated abuse and maltreatment. Availability of sets of suspicion indicators derived from large population-based databases can be useful in healthcare settings to reinforce a suspicion arising from the observation of the patient and to integrate existing sets of suspicion indicators. Methods: The study was conducted in two Italian regions: Piedmont and Tuscany. A case-control study design was utilized. Patients of vulnerable groups (children, adult women, and older people) recorded in the ED register for assault, abuse or maltreatment occurred between 2013 and 2015 were selected as cases. Patients admitted in ED for road traffic injury in the same population groups were selected as controls. For each subject, all admissions occurred during the previous 24 months were summarized by means of indicators counting their frequency. For each group of vulnerable subjects, backward logistic regressions were implemented. Results: Tuscany’s EDs registered in each vulnerable group of population at least 4 times the number of ED- cases for violence than Piedmont. The difference could be partially explained by the adoption in Tuscan hospitals of a triage code (“pink code”) explicitly concerned with the tracking of victims of relational violence. An increase in the number of ED admissions and previous admission for violence were predictive of being victim of violence. Significant predictive factors were: foreign citizenship, age class 30-49, age <1 (in the regional models), age 5-9 (in the model with both regions), mental disorders (in all groups), neoplasm or respiratory diseases (in elderly). Discussion and conclusions: The high variability among Italian regions in violence rates can depend by both underreporting and misclassification in coding injury or disease cases. Results confirm the recurrence of violence as to the continuity characterizing maltreatment in domestic settings. The low specificity of models predictive of violence based on population-based healthcare databases implies that these models are still not sufficient alone to build effective screening tools. On the other hand, in population-based health registries the power of the sample is very high for each examined variable rendering a very accurate estimate of risk associations. Further analysis should consider the interactions among several factors, available in current registries and resulted significant in the present study.
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https://www.jscimedcentral.com/Psychiatry/psychiatry-9-1162.pdf
Violence prevention and control Domestic violence Public health informatics statistics & numerical data Emergency service hospital
Alessio Pitidis, Selene Bianco, Fabio Voller, Daniele P. Radicioni, Marco Dalmasso, Carlo Mamo
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1812819
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