Suffocation Injuries in the United States

Patient Characteristics and Factors Associated With Mortality

Roula Sasso, MD; Rana Bachir, MPH; Mazen El Sayed, MD, MPH

Disclosures

Western J Emerg Med. 2018;19(4):707-714. 

In This Article

Methods

Study Design

This retrospective cross-sectional study used the 2013 public release U.S National Emergency Department Sample (NEDS). NEDS is the largest all-payer (ED) database available in the U.S. and is part of the Healthcare Utilization Project (HCUP), which is supported by the Agency of Healthcare and Research Quality.[19] The NEDS database contains data from approximately 30 million ED visits each year.[20] In 2013, the NEDS database collected data for 134,869,015 ED visits from 947 hospitals across 30 states, representing an approximate 20% stratified sample of U.S. hospital-based EDs. The NEDS dataset is released three years after its collection.

All members of the research team who were involved in using the NEDS database completed the HCUP data use agreement training course and signed the Nationwide Data Use Agreement. The institutional review board (IRB) at the American University of Beirut provided IRB exemption for the use of the NEDS public release dataset.

We identified ED visits for patients with suffocation injury using diagnosis codes (International Classification of Disease - 9 - Clinical Modification [ICD-9-CM]) listed in Table 1. These encompassed injuries by accidental mechanical suffocation, intentional and unintentional injuries by hanging, strangulation and suffocation, injury by inhalation and aspiration of foreign bodies or food.

Variables available from the NEDS database included patient characteristics and comorbidities, type of injury and injury intent, patient disposition, admission rates, hospital length of stay and cost. Clinical outcome was defined as mortality in ED or during hospital stay (yes/no).

Statistical Analysis

We performed statistical analysis with SPSS (version 24) statistic software package. The description of the sociodemographic, clinical and administrative characteristics was presented as frequencies, percentages, and 95% confidence interval (CI) for the categorical variables and mean and 95% CI for the continuous variables. We used the Rao-Scott chi-square test for complex sample design to determine the significance of the statistical association between the independent variables and mortality (yes/no), the dependent variable. All variables that were found to be statistically significant in the bivariate level were included in a logistic regression model to determine the factors significantly associated with mortality. We presented results of the multivariate analysis as odds ratio (OR) along with the corresponding 95% CI. Convenient methods including CSDESCRIPTIVES, CSTABULATE, and CSLOGISTIC for complex survey design were performed to calculate accurate estimates. A value was considered statistically significant at a p-value less than or equal to 0.05.

Comments

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