Evidence map›Paper›PMID 41001594›Full record

ArticleO&G open2024

Evaluating Bias and Racism in a Risk-Factor-Based Drug Testing Policy in the Labor and Delivery Department.

Arienne Malekmadani, Leah Puglisi, Roman Starikov, Mishka Terplan

Abstract read
In one paragraph

Article in O&G open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

4 authors.

Arienne MalekmadaniScripps Health, San Diego, California; and Friends Research Institute, Baltimore, Maryland.
Leah PuglisiScripps Health, San Diego, California; and Friends Research Institute, Baltimore, Maryland.
Roman StarikovScripps Health, San Diego, California; and Friends Research Institute, Baltimore, Maryland.
Mishka TerplanScripps Health, San Diego, California; and Friends Research Institute, Baltimore, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess bias in and consent before drug testing during prenatal care and birth in a hospital system with a risk-factor-based drug testing policy.

methodsThis retrospective cohort study included all patients who delivered in a regional hospital system from January 1, 2020, to June 1, 2022. Chart abstraction identified whether urine drug testing was performed and age, race, marital status, insurance status, ZIP code, gestational age, body mass index (BMI), and delivery mode. Propensity score matching through a pregnancy risk model was used to assess the association of race, insurance status, marital status, and median income with likelihood of testing. Data were analyzed with logistic regression. Charts of 200 randomly selected patients were also evaluated for documentation of consent, testing indication, test result, and child welfare reports.

resultsAmong 20,084 patients, 1,251 (6.2%) were tested, of whom 142 (11.3%) tested positive. White patients had a higher rate of positive test results (16.6%) compared with patients of other racial groups (overall rate 11.3%). None of the patients had consent documented. The most common indication for testing was limited prenatal care (48.7%). Fewer than a third of tests (28.5%) were obtained for a medical indication. The majority (67.9%) of positive results were reported to child welfare services.

conclusionRisk-factor-based toxicology policies disproportionately target Black, low-income, single, publicly insured, and uninsured patients. Patients are typically not consented; testing is infrequently obtained for medical indications; and reports to child welfare services based on positive test results are common.

Identifiers

PMID41001594
PMCPMC12456578

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.