Publication Date
9-26-2024
Journal
Injury Epidemiology
DOI
10.1186/s40621-024-00529-x
PMID
39327624
PMCID
PMC11426074
PubMedCentral® Posted Date
9-26-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Syndromic surveillance, Drowning burden, Drowning trends, Accuracy
Abstract
INTRODUCTION: A drowning definition is available for use with National Syndromic Surveillance Program (NSSP) data. However, its accuracy in capturing drowning emergency department and urgent care visits at the regional level is unknown. We tested the ability of the syndromic surveillance (SS) definition in capturing unintentional and undetermined intent drowning (UUID) and describe UUID SS visit trends in a large metropolitan area.
METHODS: We applied the drowning definition to NSSP data from 2016 to 2022 for the 8-county metropolitan Houston region. We queried the dataset for UUID ICD-10-CM codes and manually reviewed the chief complaint (CC) and discharge diagnosis (DD) for SS visits. True-positives were calculated by dividing the number of UUID cases identified by UUID ICD-10-CM codes and CC/DD review by the total visits captured by the SS definition. Demographics and trends of UUID visits were calculated from 2018 to 2022 due to limited data from 2016 to 2017 in NSSP.
RESULTS: 2,759 visits were captured by the SS definition. After case review, 2,019 (73.2%) had ICD-10-CM drowning codes of any intent; and 2,015 of those (99.8%) were classified as UUID. Of the remaining 740 cases with no ICD-10-CM codes that were pulled by the SS definition, 690 (93.2%) had a CC/DD diagnosis of drowning/submersion/underwater related to aquatic exposure. Taken together, 2,705 (98.0%) were true-positive UUID visits based on the SS drowning definition.. Children (aged < 18 years) constituted 79% of UUID visits. Black, White and Asian/Pacific Islander persons comprised 17%, 60% and 4% of UUID visits respectively. Rates of UUID visits were lowest in 2020.
CONCLUSION: Syndromic surveillance is a novel and accurate method to conduct real-time drowning surveillance in a large metropolitan region.