Evidence map›Paper›PMID 34370885›Full record

ArticleDepression and anxiety2021

Gender-specificity of resilience in major depressive disorder.

Roy H Perlis, Katherine Ognyanova, Alexi Quintana, Jon Green, Mauricio Santillana, Jennifer Lin, James Druckman, David Lazer, Matthew D Simonson, Matthew A Baum and 1 more

Abstract read
In one paragraph

Article in Depression and anxiety, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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  15. Biomarkers of Post-COVID Depression.Journal of clinical medicine · 2021
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Roy H PerlisCenter for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, USA.
Katherine OgnyanovaDepartment of Communication, Rutgers University, New Brunswick, New Jersey, USA.
Alexi QuintanaNetwork Science Institute, Northeastern University, Boston, Massachusetts, USA.
Jon GreenNetwork Science Institute, Northeastern University, Boston, Massachusetts, USA.
Mauricio SantillanaComputational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
Jennifer LinDepartment of Political Science, Northwestern University, Evanston, Illinois, USA.
James DruckmanDepartment of Political Science and Institute for Policy Research, Northwestern University, Evanston, Illinois, USA.
David LazerNetwork Science Institute, Northeastern University, Boston, Massachusetts, USA.
Matthew D SimonsonNetwork Science Institute, Northeastern University, Boston, Massachusetts, USA.
Matthew A BaumJohn F. Kennedy School of Government, Harvard University, Cambridge, Massachusetts, USA.
Hanyu ChweNetwork Science Institute, Northeastern University, Boston, Massachusetts, USA.

Funding

Data-driven subtyping in major depressive disorderR01MH123804 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI PERLIS, ROY H. · 2021 to 2024
$3.1M
1/2 Leveraging electronic health records for pharmacogenomics of psychiatric disordersR01MH116270 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI PERLIS, ROY H. · 2019 to 2022
$1.7M
Machine learning for data-driven subtyping of major depressionR56MH115187 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI PERLIS, ROY H. · 2018 to 2018
$596k
NIMH NIH HHS 1R56MH115187NIMH NIH HHS R01 MH116270NIMH NIH HHS R01MH116270NIMH NIH HHS R01 MH123804NIMH NIH HHS R56 MH115187
6 · The paper itself

Abstract

introductionThe major stressors associated with the COVID-19 pandemic provide an opportunity to understand the extent to which protective factors against depression may exhibit gender-specificity.

methodThis study examined responses from multiple waves of a 50 states non-probability internet survey conducted between May 2020 and January 2021. Participants completed the PHQ-9 as a measure of depression, as well as items characterizing social supports. We used logistic regression models with population reweighting to examine association between absence of even mild depressive symptoms and sociodemographic features and social supports, with interaction terms and stratification used to investigate sex-specificity.

resultsAmong 73,917 survey respondents, 31,199 (42.2%) reported absence of mild or greater depression-11,011/23,682 males (46.5%) and 20,188/50,235 (40.2%) females. In a regression model, features associated with greater likelihood of depression-resistance included at least weekly attendance of religious services (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.16) and greater trust in others (OR: 1.04 for a 2-unit increase, 95% CI: 1.02-1.06), along with level of social support measured as number of social ties available who could provide care (OR: 1.05, 95% CI: 1.02-1.07), talk to them (OR: 1.10, 95% CI: 1.07-1.12), and help with employment (OR: 1.06, 95% CI: 1.04-1.08). The first two features showed significant interaction with gender (p < .0001), with markedly greater protective effects among women.

conclusionAspects of social support are associated with diminished risk of major depressive symptoms, with greater effects of religious service attendance and trust in others observed among women than men.

Indexed as

COVID-19Major Depressive DisorderCross-Sectional StudiesDepressionFemaleHumansMalePandemicsSARS-CoV-2depressionmajor depressive disorderresilienceresilientSARS-CoV2survey

Identifiers

PMID34370885
PMCPMC9544406

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.