Long COVID and the Increased Risk of Food Insecurity Among Participants in Arizona CoVHORT: A matched cohort study

Chidera M. Ejike; Laura P Falk; Kristen Pogreba Brown; Kacey Ernst

doi:10.1101/2025.11.14.25340219

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Long COVID and the Increased Risk of Food Insecurity Among Participants in Arizona CoVHORT: A matched cohort study

Chidera M. Ejike, Laura P Falk, Kristen Pogreba Brown, Kacey Ernst

Unknown Journal (2025)

Empirical-ClinicalObservational-CohortPDF AvailableGrade Eligible⚠️ Moderate Risk Flags

Overall Assessment

Limited Methodological Quality

Assessment created by PaperScorers Medical AI v0.1.0 on Dec 14, 2025

D+

50/100

Key Takeaways

•Matched cohort (n=2,415) shows long COVID associated with higher food insecurity risk.
•Adjusted OR 1.47 (95% CI 1.06–2.04) using conditional logistic regression.
•Balance on matched covariates achieved; residual confounding and selection bias remain.
•Transparency weak: no preregistration, data, or code availability.

Conclusion

Long COVID is independently associated with increased odds of food insecurity in this Arizona cohort; integrate screening and support while improving study transparency.

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Quality Dimensions

Bias & Integrity

COI handling, outcome switching, selective reporting

C60

Methods Rigour

Protocol clarity, sampling, controls

C62

External Validity

Generalisability, setting, population

D45

Transparency & Reproducibility

Data/code/materials availability, preregistration

F15

Significance & Novelty

Contribution vs prior art; context if known

C-58

Statistical Validity

Models, assumptions, multiplicity, CI priority

C-55

Integrity & Transparency

Integrity checks

P-hacking risk?

?

None suspected

Signs of selective hypothesis testing or analysis choices that could inflate false positive rates.

Outcome switching?

?

Review recommended

Evidence that primary outcomes were changed during or after the study, potentially distorting results.

Conflict of interest?

?

None suspected

Authors have financial or other relationships that could bias research findings.

Data integrity issues?

?

None suspected

Concerns about the accuracy, completeness, or authenticity of the reported data.

Open science signals

Research data

?

Not shared

Raw data used in the analyses is publicly available or accessible upon request.

Analysis code

?

Not shared

Code or scripts used to analyze the data are shared for reproducibility.

Study materials

?

Not shared

Protocols, questionnaires, and other materials are publicly available.

Premise

Primary Research Question

Does long COVID increase the odds of food insecurity compared with non-long COVID adults in Arizona?

Hypothesis

Adults with long COVID have higher odds of food insecurity than matched non-long COVID adults.

Hypothesis is Falsifiable

✓Yes

Testable association using matched cohort and conditional logistic regression.

PICO Framework

Population✓Yes

Arizona CoVHORT adults with prior SARS-CoV-2 infection meeting follow-up criteria.

Intervention✓Yes

Exposure: long COVID per NASEM definition; symptom survey ≥6 months.

Comparison✓Yes

Matched non-long COVID by age, gender, income, timing.

Outcome✓Yes

Food insecurity via validated 2-item screener; 15–33 months window.

Literature Positioning

Literature Review Balanced

Well covered

Cites NASEM, NHIS, CDC, Brazilian cohort, CoVHORT design.

Evidence: Refs 1–12; intro/discussion cite prior US and Brazil findings (p.3, p.6, p.8–9).

Research Gap Clearly Stated

Well covered

Whether long COVID increases food insecurity risk is unclear; cohort designs lacking.

Evidence: “we do not know whether LC independently increases risk… first to analyse matched cohort” (p.2, lines 32–36).

Stated Contribution Clear

Well covered

Reports 47% higher odds of food insecurity in long COVID vs matched controls.

Evidence: “47% higher odds… after adjusting for confounders” (p.2, lines 41–46).

Study Provenance

Not peer reviewedFunding disclosedNo conflicts declared5 affiliations listed

Authors & Affiliations

Dept. of Epidemiology & Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ (p.1).

Funding Statement

Arizona Biomedical Research Centre RFGA2022-010-14 via ADHS; views are authors’ (p.7).

Conflicts of Interest

Authors report no conflicts of interest (p.7).

Peer Review

✗medRxiv preprint (not peer reviewed).

Evidence: “preprint… not certified by peer review” (p.1).

Methodological Assessment

Observational StudySTROBEObservational

12 applicable checklist items.

Yes 6 · No 2 · Unclear 4

Study Design & Definitions

✓

Participant Selection Described

Recruitment, eligibility, and survey cadence clearly described.

Evidence: Cohort description and eligibility (p.4, lines 87–103).

✓

Variable Definitions Clear

LC and food insecurity definitions provided; validated 2-item FIS.

Evidence: Outcome and exposure definitions (p.4, lines 109–116; p.3 lines 63–66).

✓

Confounding Addressed

Matching plus adjustment for gender, income, education.

Evidence: Matching vars (p.4, lines 103–107); adjusted model (p.4–5, lines 114–116, 134–135).

✓

Temporality Established

Outcome measured 15–33 months post-infection; LC assessed ≥6 months.

Evidence: Timing windows (p.4, lines 101–103, 109–116).

✓

Exposure Definition Anchored

LC anchored to post-infection status per NASEM, assessed via symptom surveys.

Evidence: Definition and timing (p.3, lines 63–66; p.4, lines 101–103).

Bias Mitigation

✓

Balance Diagnostics Acceptable

Matched groups balanced; no significant differences on matched vars.

Evidence: “excellent balance… no statistically significant differences” (p.5, lines 123–126).

?

Selection Bias Addressed

Volunteer cohort; no formal assessment of selection bias.

Evidence: Recruitment channels; no bias analysis (p.4, lines 89–95).

?

Misclassification Assessed

Validated screener used; no quantitative misclassification assessment.

Evidence: 2-item screener cited (p.4, lines 109–116; Ref 11).

?

Immortal Time Bias Mitigated

Timing matched (±6 months), but no explicit immortal time handling.

Evidence: Timing match and windows (p.4–5, lines 105–116, 128–131).

?

Time-varying Confounding Handled

No methods for time-varying confounding described.

Evidence: Analysis section lacks such methods (p.4–5).

Robustness

✗

Negative Controls or E-value

No negative controls or E-value reported.

Evidence: Methods/Results (p.4–6).

✗

Model Diagnostics Reported

No model diagnostics or sensitivity analyses reported.

Evidence: Results/Methods omit diagnostics (p.4–6).

Modules without applicable checklist items (9)

These designs were fully marked as not applicable but remain available for reference.

Abstract

Objectives To assess whether individuals with long COVID face a higher risk of food insecurity compared with those without long COVID. Methods We used data from the Arizona CoVHORT, which is a prospective longitudinal study of SARS-CoV-2 health outcomes initiated in May 2020. Participants with confirmed infections who completed a symptom survey ≥6 months post-infection were eligible (n = 2415). We matched participants with long COVID to participants who did not have long COVID by gender, age group, income bracket, and date of first assessment of food insecurity (±6 months). We estimated the association between long COVID and food insecurity using a conditional logistic regression analysis. Results Participants with long COVID had significantly greater odds of food insecurity (adjusted odds ratio = 1.47; 95% confidence interval = 1.06 - 2.04). Conclusions Long COVID significantly increases vulnerability to food insecurity. This highlights the need for integrated health and social interventions for individuals with long COVID. WHAT IS ALREADY KNOWN ON THIS TOPIC Food Insecurity has been shown to be an important risk factor for long COVID, but we do not know whether long COVID independently increases the risk of food insecurity, nor have studies adopted a cohort design approach to examine the relationship between long COVID and food insecurity. This study is the first to analyze the association between long COVID and food Insecurity using a matched cohort data within the United States. WHAT THIS STUDY ADDS This study uniquely adds to the current knowledge base by providing evidence that long COVID is independently associated with a significantly increased risk of food insecurity among adults in Arizona, after adjusting for confounders. We found that adults with long COVID in the Arizona CoVHORT had 47% higher odds of experiencing food insecurity compared to matched controls without long COVID. This study provides robust statistical support for a direct association between long COVID and food insecurity and advances the field by evidencing a previously under-explored dimension of the pandemic’s long-term impact, thus, highlighting an urgent need for integrated screening and intervention strategies at the intersection of chronic illness and nutrition insecurity. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY Our study provides evidence that long COVID not only affects health of individuals but also social and economic well-being, including food access and stability. Healthcare and public health systems should integrate routine screening for food insecurity in outpatient care, primary care, and long COVID clinics. Early detection and referral to nutritional or financial assistance programs may reduce the combined effects of chronic illness and economic strain. These interventions are critical for groups disproportionately impacted by the pandemic’s longer-term effects, including low-income households, women, and communities of color. Expanding safety net programs and tracking social consequences of infectious diseases are vital for public health preparedness.

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Study Overview

Study Design

Observational Cohort

Primary classification used to evaluate this research.

Population

Arizona CoVHORT adults ≥18 with lab-confirmed SARS-CoV-2; ≥1 symptom survey ≥6 months post-infection; completed 2024 supplemental survey; matched by age, gender, income, timing.

Brief summary of who or what was studied.

Sample Size

2,415

Number of participants included in the study analyses.

Study Arms

2 arms

Total number of intervention or comparison groups.

Pre-registration

Not registered

Indicates whether the protocol or trial was registered before data collection.

Randomisation

None reported

How participants were allocated to groups: None (no randomisation), Simple (random order), Blocked (groups balanced in blocks), Stratified (balanced within subgroups), Adaptive (allocation adjusted based on previous enrollments).

Blinding

None

Who was unaware of group assignments: None (everyone knew), Single blind (participants didn't know their group), Double blind (participants and researchers didn't know), Triple blind (also outcome assessors didn't know).

Analysis Set

Other

Which participants were included in analysis: ITT (all randomized, regardless of adherence), mITT (modified ITT, excluding some for valid reasons), Per protocol (only those who completed as planned), As treated (grouped by actual treatment received), Other (alternative approach).

Follow-up

15–33 months post-infection

Duration of observation or follow-up for outcomes.

Primary Outcome

Primary outcome was pre-specified in the protocol or registry.

Effect Size

OR: Adjusted OR 1.47 (95% CI 1.06–2.04)

Reviewer Notes

Prospective matched cohort (long COVID vs non-long COVID); observational; conditional logistic regression.

Publication Details

DOI

10.1101/2025.11.14.25340219

Published

November 17, 2025

Citations

0 citations

External Resources

DOI Link OpenAlex

Disclaimer: This assessment is generated by AI and should not be the sole basis for clinical or research decisions. Always review the original paper and consult with domain experts.

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