Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 403

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

The global prevalence of major depressive disorder (MDD) among the elderly: A systematic review and meta-analysis

Open access
Abdoli, Nasrin, Salari, Nader, Darvishi, Niloofar, Jafarpour, Sima, Solaymani, Mina, Mohammadi, Masoud, Shohaimi, Shamarina·2022·Neuroscience & Biobehavioral Reviews

Abstract

BACKGROUND Major depressive disorder is characterized by a depressed mood or feeling of sadness, loss of interest or pleasure in everyday activities. Depressed individuals have a cognitive impairment, low self-esteem, difficulty making decisions, feeling helpless and hopeless. The factors that have been associated with depression include the lack of social support, living in rural areas, suffering from chronic diseases, smoking, and alcohol abuse. This study aimed to investigate the global prevalence of major depressive disorder in the elderly. METHOD The electronic database such as Web of Science (WoS), Scopus, SID, PubMed, Google Scholar, Mag Iran, and IranDoc were systematically searched for studies reporting the prevalence of major depressive disorderin the elderly published up to March 2021. Meta-analysis was performed using Comprehensive Meta-Analysis (CMA) software. Heterogeneity between the studies was evaluated using the I2 index. Begg and Mazumdar rank correlation test was used to assess publication bias. RESULT A total of 20 studies involving 18953 participants were included in this study. The global prevalence of major depression in the elderly was 13.3 % (95 % CI: 8.4-20.3 %). In the subgroup analysis, the prevalence of major depression in elderly women was 11.9 % (95 % CI: 7.6-18.6) and men 9.7 % (95 % CI: 5.2-17.3). No comparison was made between the two sexes, but based on the confidence intervals and large overlap, the two groups are not statistically different. Among continents, Australia had the highest prevalence of major depression in the elderly at 20.1 % (CI: 14.5-27.2 %). This was followed by Europe at 12.9 % (95 % CI: 5.1-28.9 %). CONCLUSION Major depressive disorder has a growing trend in the elderly population of the world. The prevalence of major depression in the elderly depends on various clinical and demographic factors such as age and gender. Therefore, mental health and the quality of life (QoL) of the elderly are important. The present study emphasizes the importance of social support in mental health that can reduce depression in the elderly.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neubiorev.2021.10.041

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 46 references

Cited by

See all 403

Metadata

Authors
Abdoli, N., Salari, N., Darvishi, N., Jafarpour, S., Solaymani, M., Mohammadi, M., & Shohaimi, S.
Venue
Neuroscience & Biobehavioral Reviews
Publisher
Elsevier BV
Published
2022-01
Volume
132
Pages
1067-1073
ISSN
0149-7634 (ISSN-L)
OpenAlex
W3211143280
Cited by
403
References
46
FWCI
8.85
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The global prevalence of major depressive disorder (MDD) among the elderly: A systematic review and meta-analysis. https://app.cochat.ai/papers/10.1016/j.neubiorev.2021.10.041
The paper
Abdoli, N., Salari, N., Darvishi, N., Jafarpour, S., Solaymani, M., Mohammadi, M., & Shohaimi, S. (2022). The global prevalence of major depressive disorder (MDD) among the elderly: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews. https://doi.org/10.1016/j.neubiorev.2021.10.041

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.