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Record W4415600541 · doi:10.1177/07334648251391521

Neighborhood Deprivation and Symptom Burden Among Older Adults After Hospitalization for COVID-19

2025· article· en· W4415600541 on OpenAlexaboutno aff
Jason R. Falvey, Denise Acampora, Katy Araujo, Mary Geda, Thomas M. Gill, Gail McAvay, Terrence E. Murphy, Alexandra M. Hajduk, Andrew Cohen, Lauren E. Ferrante

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute on Aging
KeywordsSocioeconomic statusSocial deprivationAffect (linguistics)Burden of diseasePersistence (discontinuity)Young adultDisease burdenComorbidity

Abstract

fetched live from OpenAlex

Persistent symptoms following COVID-19 disproportionately affect older adults and may be exacerbated by neighborhood socioeconomic deprivation. We evaluated the association between neighborhood deprivation and symptom burden after COVID-19 hospitalization among 298 older adults from five Connecticut hospitals (June 2020–June 2021). Symptom burden was measured using the Edmonton Symptom Assessment System at baseline and 1, 3, and 6 months post-discharge, while neighborhood deprivation was assessed with the Area Deprivation Index. Using Bayesian linear mixed models adjusted for demographic and clinical factors, we found that residing in high-deprivation neighborhoods (ADI > 9/10; n = 28) was associated with higher symptom burden over the 6-month follow-up period. Adjusted analyses estimated a 2.2-point greater mean symptom burden (95% CI: 0.1–4.2). These findings suggest that neighborhood socioeconomic factors may significantly contribute to the persistence of COVID-19 symptoms in older adults, underscoring the need for targeted post-discharge care strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.105
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.290
Teacher spread0.283 · 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 teacher head, 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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