Sidra Goldman-Mellor1, Yusheng Jia1, Kevin Kwan1, Jared Rutledge1. 1. Dr. Goldman-Mellor and Mr. Kwan are with the Department of Public Health, University of California, Merced. Ms. Jia is with the School of Public Health, Fudan University, Shanghai. Dr. Rutledge is with the Fresno County Department of Public Health, Fresno, California.
Abstract
OBJECTIVE: This study evaluated whether emergency department (ED) patient presentations for problems related to mental and substance use disorders could be validly monitored by a syndromic surveillance system that uses chief complaints to identify mental disorders. METHODS: The study used syndromic surveillance data on 146,315 ED visits to participating Fresno County, California, hospitals between January 1 and December 31, 2013. Free-text patient chief complaints are automatically classified into syndromes based on the developer's algorithms. Agreement was assessed between the algorithm's syndrome classification of mental health and substance abuse (MHSA) disorders and ICD-9-CM discharge diagnostic codes. Diagnosis and ED utilization patterns among patients with at least one visit with an MHSA syndrome classification were also examined. RESULTS: Approximately 8% of ED visits during the study period received an MHSA syndrome classification. Overall agreement between MHSA syndrome classification and psychiatric- or substance use-related ICD-9 discharge diagnoses was high (κ=.92, 95% confidence interval=.91-.92). Sensitivity (100%) and specificity (98.6%) of the MHSA syndrome classification were also very high. MHSA syndrome-classified patients exhibited high levels of health care and morbidity burden compared with other patients. CONCLUSIONS: ED chief complaints can be utilized to reliably and validly ascertain the incidence of patient presentations for mental and substance use disorders in contexts in which discharge diagnoses are not routinely available. Wider adoption of MHSA-related syndrome algorithms by syndromic surveillance systems could be valuable for public mental health surveillance, service delivery, and resource planning efforts.
OBJECTIVE: This study evaluated whether emergency department (ED) patient presentations for problems related to mental and substance use disorders could be validly monitored by a syndromic surveillance system that uses chief complaints to identify mental disorders. METHODS: The study used syndromic surveillance data on 146,315 ED visits to participating Fresno County, California, hospitals between January 1 and December 31, 2013. Free-text patient chief complaints are automatically classified into syndromes based on the developer's algorithms. Agreement was assessed between the algorithm's syndrome classification of mental health and substance abuse (MHSA) disorders and ICD-9-CM discharge diagnostic codes. Diagnosis and ED utilization patterns among patients with at least one visit with an MHSA syndrome classification were also examined. RESULTS: Approximately 8% of ED visits during the study period received an MHSA syndrome classification. Overall agreement between MHSA syndrome classification and psychiatric- or substance use-related ICD-9 discharge diagnoses was high (κ=.92, 95% confidence interval=.91-.92). Sensitivity (100%) and specificity (98.6%) of the MHSA syndrome classification were also very high. MHSA syndrome-classified patients exhibited high levels of health care and morbidity burden compared with other patients. CONCLUSIONS: ED chief complaints can be utilized to reliably and validly ascertain the incidence of patient presentations for mental and substance use disorders in contexts in which discharge diagnoses are not routinely available. Wider adoption of MHSA-related syndrome algorithms by syndromic surveillance systems could be valuable for public mental health surveillance, service delivery, and resource planning efforts.
Entities:
Keywords:
Diagnosis/classification (DSM); Emergency psychiatry; Mental health systems/hospitals; Public health; Syndromic surveillance
Authors: Gillian E Smith; Sally E Harcourt; Uy Hoang; Agnieszka Lemanska; Alex J Elliot; Roger A Morbey; Helen E Hughes; Iain Lake; Obaghe Edeghere; Isabel Oliver; Julian Sherlock; Richard Amlôt; Simon de Lusignan Journal: JMIR Public Health Surveill Date: 2022-08-03
Authors: Jonathan Purtle; Katherine L Nelson; Nathaniel Z Counts; Michael Yudell Journal: Annu Rev Public Health Date: 2020-01-06 Impact factor: 21.981