Evidence map›Paper›PMID 38682155›Full record

ArticleJournal of multimorbidity and comorbidity

Conceptualizing COVID-19 syndemics: A scoping review.

Nicola Bulled, Merrill Singer

Abstract readScoping Review
In one paragraph

Article in Journal of multimorbidity and comorbidity. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

2 authors.

Nicola BulledInCHIP, University of Connecticut, Storrs, CT, USA.ORCID https://orcid.org/0000-0002-9699-3937
Merrill SingerAnthropology, University of Connecticut, Storrs, CT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19's heavy toll on human health, and its concentration within specific at-risk groups including the socially vulnerable and individuals with comorbidities, has made it the focus of much syndemic discourse. Syndemic theory recognizes that social factors create the conditions that support the clustering of diseases and that these diseases interact in a manner that worsens health outcomes. Syndemics theory has helped to facilitate systems-level approaches to disease as a biosocial phenomenon and guide prevention and treatment efforts. Despite its recognized value, reviews of syndemics literature have noted frequent misuse of the concept limiting its potential in guiding appropriate interventions. Objective: To review how the term 'syndemic' is defined and applied within peer-reviewed literature in relation to COVID-19. Design: A scoping review of definitions within COVID-19 literature published between January 1, 2020 to May 15, 2023 was conducted. Searches took place across six databases: Academic Search Premier, CINAHL, JSTOR, MEDLINE/Pubmed, PsycINFO and Scopus. PRISMA-ScR guidelines were followed. Results: Content analysis revealed that COVID-19 has varied clustered configurations of communicable-non-communicable diseases and novel communicable disease interactions. Spatial analysis was presented as a new strategy to evidence syndemic arrangements. However, syndemics continue to be regarded as universal, with continued misunderstanding and misapplication of the concept. Conclusion: This review found that current applications of syndemics remain problematic. Recommendations are made on the design of syndemic studies. A syndemic framework offers an opportunity for systems-level thinking that considers the full complexity of human-disease interactions and is useful to inform future pandemic preparations and responses.

Indexed as

comorbidityCOVID-19scoping reviewsocial determinants of healthsyndemics

Identifiers

PMID38682155
PMCPMC11055430

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.