Evidence map›Paper›PMID 37199857›Full record

ArticleMaternal and child health journal2023

Evaluation of Administrative Data for Identifying Maternal Opioid Use at Delivery in Florida.

Amanda L Elmore, Jason L Salemi, Russell S Kirby, William M Sappenfield, Joseph Lowry, Ashley Dixon, Heather Lake-Burger, Jean Paul Tanner

Open access · hybridAbstract read
In one paragraph

Article in Maternal and child health journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Trends in Prenatal Opioid Use Disorder in California, 2010-2022.American journal of preventive medicine · 2025
    Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Amanda L ElmoreChiles Center, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC56, Tampa, FL, 33612-3805, USA. elmorea@usf.edu.ORCID http://orcid.org/0000-0003-3316-2797
Jason L SalemiChiles Center, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC56, Tampa, FL, 33612-3805, USA.
Russell S KirbyChiles Center, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC56, Tampa, FL, 33612-3805, USA.
William M SappenfieldChiles Center, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC56, Tampa, FL, 33612-3805, USA.
Joseph LowryFlorida Department of Health, Florida Birth Defects Registry, Tallahassee, USA.
Ashley DixonChiles Center, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC56, Tampa, FL, 33612-3805, USA.
Heather Lake-BurgerFlorida Department of Health, Florida Birth Defects Registry, Tallahassee, USA.
Jean Paul TannerChiles Center, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC56, Tampa, FL, 33612-3805, USA.
University of South Florida · USFlorida Department of Health · US

Funding

Biomedical-Behavioral Interface: Prevention and Developmental Sciences TrainingT32GM081740 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI PRINZ, RONALD J · 2008 to 2022
$4.5M
Pharmaceutical Case Management and Living Well with a DisabilityR01DD000107 · DD · UNIVERSITY OF IOWA · PI CHRISCHILLES, ELIZABETH A · 2006 to 2008
$975k
NCBDD CDC HHS NU50DD000107-01NCBDD CDC HHS R01 DD000107NCIPC CDC HHS NU17CE925020-03-02NIGMS NIH HHS T32 GM081740NIGMS NIH HHS T32-GM081740
6 · The paper itself

Abstract

objectivesStudies have shown significant increases in the prevalence of maternal opioid use. Most prevalence estimates are based on unverified ICD-10-CM diagnoses. This study determined the accuracy of ICD-10-CM opioid-related diagnosis codes documented during delivery and examined potential associations between maternal/hospital characteristics and diagnosis with an opioid-related code.

methodsTo identify people with prenatal opioid use, we identified a sample of infants born during 2017-2018 in Florida with a NAS related diagnosis code (P96.1) and confirmatory NAS characteristics (N = 460). Delivery records were scanned for opioid-related diagnoses and prenatal opioid use was confirmed through record review. The accuracy of each opioid-related code was measured using positive predictive value (PPV) and sensitivity. Modified Poisson regression was used to calculate adjusted relative risks (aRR) and 95% confidence intervals (CI).

resultsWe found the PPV was nearly 100% for all ICD-10-CM opioid-related codes (98.5-100%) and the sensitivity was 65.9%. Non-Hispanic Black mothers were 1.8 times more likely than non-Hispanic white mothers to have a missed opioid-related diagnosis at delivery (aRR:1.80, CI 1.14-2.84). Mothers who delivered at a teaching status hospital were less likely to have a missed opioid-related diagnosis (p < 0.05). CONCLUSIONS FOR PRACTICE: We observed high accuracy of maternal opioid-related diagnosis codes at delivery. However, our findings suggest that over 30% of mothers with opioid use may not be diagnosed with an opioid-related code at delivery, although their infant had a confirmed NAS diagnosis. This study provides information on the utility and accuracy of ICD-10-CM opioid-related codes at delivery among mothers of infants with NAS.

Indexed as

Neonatal Abstinence SyndromeOpioid-Related DisordersAnalgesics, OpioidFemaleFloridaHumansInfantInfant, NewbornMothersPregnancyAnalgesics, OpioidAccuracyMaternalNeonatal abstinence syndromeOpioidSensitivity

Identifiers

PMID37199857
PMCPMC10692249
OpenAlexW4377011222

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.