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Record W4407652101 · doi:10.6000/1929-6029.2025.14.06

Prevalence of Depression among Women Using Hormonal Contraceptive Use: Insights from a Hospital-Based Cross-Sectional Study

2025· article· en· W4407652101 on OpenAlexvenueno aff
Ali Hassan Khormi

Bibliographic record

VenueInternational Journal of Statistics in Medical Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyDepression (economics)Hormonal contraceptionMedicineDemographyPsychologyClinical psychologyPsychiatryObstetricsEnvironmental healthPopulationFamily planningResearch methodologySociology

Abstract

fetched live from OpenAlex

Background: Hormonal contraceptives (HC) serve as a key component in managing premenopausal symptoms and controlling birth rates. However, mood-related side effects, ranging from minor disturbances to severe clinical depression, are the primary reasons for discontinuation. Objective: To assess the prevalence of depression among women who use hormonal contraceptive methods. Additionally, the study aims to explore the association between specific types of contraceptives—such as oral pills, implants, injectables—and the prevalence of depression. Methods: From October 2023 to October 2024, a total of 1500 women between the ages of 21 and 45 who currently take hormonal contraception participated in this hospital-based cross-sectional study, which was carried out at the tertiary care hospital at King Fahd Central Hospital's outpatient gynecology clinic. Results: The most frequent age categories were from 26 to 40 years (85.7%). The majority of the studied cases were non-lean (82.6%). Most of the cases had parity from 1 to 4 (97.1%). Women were mainly of a low social class (77.1%). Social problems were found in (21.8%). Hypertension and diabetes mellitus were in 4.9% and 3.2% respectively. The most frequent contraceptive method were OCPs (40.3%), followed by POPs (31.2%), then subdermal implants (16.3%), injectable (8.6%), hormonal IUD (2.2%) and patches (1.4%). Most of the studied women used such method from 3 to 6 years (88.2%). Prevalence of depression among the studied cases was (8.7%; CI: 7.3%–10.2%). Obese individuals demonstrated a significantly higher prevalence of depression (11.5%) compared to overweight (8.5%) and lean individuals (5.0%), with a statistically significant association (p=0.015). Additionally, obese participants were more likely to have diabetes mellitus (27.1%), face social issues (21.8%), and belong to a low socioeconomic class (77.1%). Regarding contraceptive types, depression was notably less common among women using combined oral contraceptives (COCs) and progesterone-only pills (POPs), with rates of 4.6% and 4.5%, respectively. In contrast, higher rates of depression were observed in users of subdermal implants (19.2%), injectables (18.6%), hormonal IUDs (18.2%), and hormonal patches (19.0%) (p<0.001). The duration of contraceptive use also played a significant role, with depression rates increasing progressively from 2.8% for women using contraceptives for 1–2 years to 3.7% for 3–4 years and 12.7% for 5–6 years. The highest rate of depression, 37.7%, was observed among women using hormonal contraceptives for seven or more years (p<0.001) Conclusion: Given the observed associations between certain hormonal contraceptives, prolonged use, and elevated depression rates, clinicians should adopt a proactive approach in assessing patients’ mental well-being, especially for women with additional risk factors like high BMI, socioeconomic challenges, or chronic conditions such as diabetes. Screening tools like the PHQ-9 should be routinely used during consultations to monitor for early signs of depression, allowing for timely intervention if needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.468
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueInternational Journal of Statistics in Medical ResearchSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207