Evidence map›Paper›PMID 42245803›Full record

ArticleResearch square2026

Nationwide Epic Cosmos Data Reveals Antidepressant Prescription Rate Trends Across Demographic Groups.

Dmitry Scherbakov, Brandy Davis, Jeffrey Korte, Jihad S Obeid, Alexander V Alekseyenko

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In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Dmitry ScherbakovMedical University of South Carolina.
Brandy DavisAuburn University.
Jeffrey KorteMedical University of South Carolina.
Jihad S ObeidMedical University of South Carolina.
Alexander V AlekseyenkoMedical University of South Carolina.

Funding

SC Biomedical Informatics & Data Science for Health Impact (SC BIDS4Health)T15LM013977 · NLM · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Alexander V Alekseyenko, Brian Dean · 2022 to 2026
$1.2M
NLM NIH HHS T15 LM013977
6 · The paper itself

Abstract

Purpose: Sex disparities in depression and its treatment are well-documented, but less is known about how these patterns vary across the lifespan and intersect with race, neighborhood vulnerability, and prior health challenges. Methods: We analyzed data from 2.5 million patients sampled from the Epic Cosmos Electronic Health Record (EHR) database. Active antidepressant prescriptions were used as a proxy for depression diagnoses. Data were stratified by age, sex, race/ethnicity, residence, Social Vulnerability Index (SVI 2018), prior antidepressant use, and high-risk behaviors (HIV and chlamydia testing). Rates of prescriptions by sex and odds ratios (OR) for females versus males across age at time of visit were calculated. Results: The probability of receiving an antidepressant prescription rose sharply during adolescence, but more steeply for females, with female vs. male differences peaking at ages 17-18 (OR = 2). In the overall population, the sex gap narrowed in young adulthood (OR = 1.6 at ages 25-30), remained stable through mid-life, and widened again after age 55 (OR = 1.75 by age 70). However, the later-life increase was not observed uniformly across some groups, such as the unmarried, divorced, and living in vulnerable areas. The white population had noticeably higher prescription rates across the life span, while the Asian group had the highest adolescent peak (female vs. male OR = 2.75). Prior HIV and chlamydia testing strata demonstrated higher antidepressant prescribing and a larger, earlier adolescent female-to-male OR, although this factor likely explains only a small portion of the overall adolescent sex difference at the population level. Conclusions: The well-known depression sex gap in adolescents observed in the general population, and the lesser-known retirement gap, are shaped by a combination of demographic, economic, and behavioral factors, explored in this study.

Indexed as

AntidepressantsDepressionElectronic health recordsEpic CosmosLife courseSex differences

Identifiers

PMID42245803
PMCPMC13232459

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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.