Association of Patient Sex and Pregnancy Status With Naloxone Administration During Emergency Department Visits

Abstract

Objective: To evaluate the association of sex and pregnancy status with rates of naloxone administration during opioid overdose-related emergency department (ED) visits using the Nationwide Emergency Department Sample (NEDS).Methods: A retrospective cohort study was conducted using NEDS 2016 and 2017 datasets. Eligible records included men and women, 15-49 years of age, with an opioid overdose-related ED visit; records for women were stratified by pregnancy status (ICD-10 O codes). A multivariable logistic regression model was used to assess the primary outcome of naloxone administration (CPT code: J2310). Secondary outcomes included subsequent admission and mortality. A subgroup analysis compared pregnant women who did versus did not receive naloxone.Results: Records from 443,714 men, 304,364 non-pregnant women, and 25,056 pregnant women were included. Non-pregnant women had lower odds for naloxone administration (1.70% vs 2.10%; aOR: 0.86(0.83-0.89)) and mortality (2.21% vs 2.99%; aOR: 0.71(0.69-0.73)) but higher odds of subsequent admission (30.22% vs 27.18%; aOR: 1.04(1.03-1.06)) compared with men. Pregnant women had lower odds for naloxone administration (0..27% vs 1.70%; aOR: 0.16(0.13-0.21)) and mortality (0.41% vs 2.21%; aOR: 0.28(0.23-0.35)) but higher odds of subsequent admission (40.50% vs 30.22%; aOR: 2.04(2.00-2.10)) compared with non-pregnant women. Pregnant women who received naloxone had higher odds of mortality (14% vs 0.39%; aOR: 6.30(2.11-18.78)) compared with pregnant women who did not receive naloxone. Pregnant

Comparison of emergency department throughput and process times between male and female patients: A retrospective cohort investigation by the Reducing Disparities Increasing Equity in Emergency Medicine Study Group

“…Lau et al 18 found women with flank or abdominal pain, trauma, or headache were less likely to receive opioid pain medications during an ED visit than men with similar complaints and also less likely to be given naloxone after having opioid overdose-related ED care. 19 Similar to pain scores, time is also an important metric that can inform us of the over-/underestimation of illness severity by healthcare practitioner, both physician and non-physician. When compared between sexes, our study found that all of the time-to-symptom treatment measures were longer for females than males.…”

“…Overestimation and underestimation of ESI level designations can lead to unnecessary and insufficient treatments, respectively, leading to poor clinical outcomes. Lau et al 18 found women with flank or abdominal pain, trauma, or headache were less likely to receive opioid pain medications during an ED visit than men with similar complaints and also less likely to be given naloxone after having opioid overdose‐related ED care 19 . Similar to pain scores, time is also an important metric that can inform us of the over‐/underestimation of illness severity by healthcare practitioner, both physician and non‐physician.…”

Connect the Dots—May 2021

“…The upshot of that exclusion is that pregnant women who elect to receive COVID-19 vaccines do so without the knowledge they deserve to make an informed decision. In perhaps another example of pregnancy penalizing, in this month's issue of the journal (see page 855), Forbes et al 1 raise the possibility that, at least in the emergency department, pregnant women (and to a much smaller degree, nonpregnant, reproductive aged women) may be less likely to receive naloxone than reproductive-aged men when it is indicated for opioid-related overdose. Though multiple factors other than pregnancy might account for this differential treatment, their study makes one wonder whether opioid overdose therapy is another medical realm where the insidious problem of inappropriate pregnancy-modified care is operative.…”

Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges

“…The paradox arises when an association observed in aggregated data disappears or reverses when the same data are disaggregated into their underlying subgroups. For example, if an AI-guided CDSS was to be built for naloxone administration, when testing the model, if the clinical presentation severity or opioid type is unequally distributed among groups, the Simpson paradox will likely contribute to different rates of naloxone administration [ 115 ].…”