Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time

Hallowell, Benjamin D; Chambers, Laura C; Rhodes, Jason; Basta, Melissa; Viner-Brown, Samara; Lasher, Leanne

doi:10.1177/00333549211018989

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…Similarly, with the deployment of the National Emergency Medical Services Information System, timely information on Emergency Medical Service requests is also available. 49 Aggregate event data at the county level from these or similar sources, if made publicly available every week, could support the development of nowcast systems. Such models are in use for numerical weather prediction, 50 macroeconomic analyses, 51 and influenza surveillance, 52 , 53 among other domains.…”

Section: Discussionmentioning

confidence: 99%

County-level estimates of suicide mortality in the USA: a modelling study

Kandula¹,

Martínez-Alés²,

Rutherford³

et al. 2023

The Lancet Public Health

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Section: Discussionmentioning

confidence: 99%

County-level estimates of suicide mortality in the USA: a modelling study

Kandula¹,

Martínez-Alés²,

Rutherford³

et al. 2023

The Lancet Public Health

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“…With the forthcoming launch of a national suicide prevention number (9-8-8) in mid-2022, a unified system with wide coverage is at hand. Similarly, with the deployment of the National Emergency Medical Services Information System (NEMSIS), timely information on EMS requests is also available (63,64). Aggregate event data at county resolution from these and/or similar sources, if made publicly available every week, could support the development of nowcast systems.…”

Section: Discussionmentioning

confidence: 99%

Modeling suicide mortality in US counties using population socioeconomic indicators

Kandula

Martínez-Alés

Rutherford

et al. 2022

Preprint

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Background: Suicide is one of the leading causes of death in the United States and population risk prediction models can inform the type, location, and timing of public health interventions. Here, we report the development of a prediction model of suicide risk using population characteristics. Methods: All suicide deaths reported to the Nation Vital Statistics System between 2005-2019 were identified, and age, sex, race, and county-of-residence of the decedents were extracted to calculate baseline risk. County-wise annual measures of socioeconomic predictors of suicide risk (unemployment, weekly wage, poverty prevalence, median household income, and population density), along with two state-wise measures of prevalence of major depressive disorder and firearm ownership were compiled from public sources. Conditional autoregressive (CAR) models, which account for spatiotemporal autocorrelation in response and predictors, were used to estimate county-level risk. Results: Estimates derived from CAR models were more accurate than from models not adjusted for spatiotemporal autocorrelation. Inclusion of suicide risk/protective covariates further reduced errors. Suicide risk was estimated to increase with each standard deviation increase in firearm ownership (2.8%), prevalence of major depressive episode (1%) and unemployment (2.8%). Conversely, risk was estimated to decrease by 4.3% for each standard deviation increase in both median household income and population density. Increased heterogeneity of risk across counties was also noted. Conclusions: Area-level characteristics and the CAR model structure can estimate population-level suicide risk and thus inform decisions on resource allocation and focused interventions during outbreaks.

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“…We defined opioid overdose-related EMS runs using a case definition previously developed by RIDOH. 11 We defined an EMS run as nonfatal opioid overdose-related if it met 1 of 5 criteria: (1) the primary or secondary impression was overdose related and naloxone was given, (2) the primary or secondary impression was overdose related and terms for both naloxone and unresponsive were in the narrative report, (3) naloxone was in the dropdown field for medication given and medication response was improved, (4) the terms for both naloxone and unresponsive were in the narrative report and medication response was undocumented (excludes values of no change or worse), or (5) naloxone was given before EMS arrival and the person who administered it was not a null value. 11 We compared overdose decedents (1) with and without any prior EMS run and (2) with and without a prior opioid overdose-related EMS run in the 2 years before death by sociodemographic characteristic (age, sex, race and ethnicity, county) and year of death (2018, 2019, 2020) using Pearson χ 2 tests and Fisher exact tests (when cell counts were <10).…”

Section: Methodsmentioning

confidence: 99%

“…First, we obtained data from the RIDOH Office of State Medical Examiners to identify all accidental opioid-involved fatal drug overdoses that occurred in Rhode Island among Rhode Island residents from January 1, 2018, through December 31, 2020. Second, we used data from the Rhode Island EMS Information System 11 to identify all EMS runs that occurred in Rhode Island from January 1, 2016, through December 31, 2020. For each database, we generated a unique identifier based on the first 5 letters of the last name, the first 3 letters of the first name, and the date of birth.…”

Section: Methodsmentioning

confidence: 99%

Prior Emergency Medical Services Utilization Among People Who Had an Accidental Opioid-Involved Fatal Drug Overdose—Rhode Island, 2018-2020

et al. 2023

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Objective: To help understand whether decreased emergency medical services (EMS) utilization due to the COVID-19 pandemic contributed to increased accidental fatal drug overdoses, we characterized recent EMS utilization history among people who had an accidental opioid-involved fatal drug overdose in Rhode Island. Methods: We identified accidental opioid-involved fatal drug overdoses among Rhode Island residents that occurred from January 1, 2018, through December 31, 2020. We linked decedents by name and date of birth to the Rhode Island EMS Information System to obtain EMS utilization history. Results: Among 763 people who had an accidental opioid-involved fatal overdose, 51% had any EMS run and 16% had any opioid overdose–related EMS run in the 2 years before death. Non-Hispanic White decedents were significantly more likely than decedents of other races and ethnicities to have any EMS run ( P < .001) and any opioid overdose–related EMS run ( P = .05) in the 2 years before death. Despite a 31% increase in fatal overdoses from 2019 through 2020, corresponding with the onset of the COVID-19 pandemic, EMS utilization in the prior 2 years, prior 180 days, or prior 90 days did not vary by time frame of death. Conclusion: In Rhode Island, decreased EMS utilization because of the COVID-19 pandemic was not a driving force behind the increase in overdose fatalities observed in 2020. However, with half of people who had an accidental opioid-involved fatal drug overdose having an EMS run in the 2 years before death, emergency care is a potential opportunity to link people to health care and social services.

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Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time

Cited by 13 publications

References 10 publications

County-level estimates of suicide mortality in the USA: a modelling study

County-level estimates of suicide mortality in the USA: a modelling study

Modeling suicide mortality in US counties using population socioeconomic indicators

Prior Emergency Medical Services Utilization Among People Who Had an Accidental Opioid-Involved Fatal Drug Overdose—Rhode Island, 2018-2020

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