Racial and Ethnic Disparities in Pandemic‐Onset Disorders of Gut–Brain Interaction: Results From a Nationwide Survey
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, the prevalence of disorders of gut-brain interaction (DGBI) in the US increased. The pandemic also led to health inequities among racial and ethnic minorities. Here, we conducted monthly national surveys during the pandemic to examine the association between race/ethnicity and pandemic-onset DGBI. METHODS: From March 2021 to May 2022, we recruited a nationally representative sample of adults in the US to complete an online survey with Rome IV questionnaires (9 gastroduodenal and bowel DGBI) along with demographic and comorbidity questions. Participants with a DGBI were asked whether their cardinal symptoms started before or after the COVID-19 pandemic began in the US (March 2020). Our primary outcome was the prevalence of pandemic-onset DGBI. Multivariable logistic regression models identified factors associated with pandemic-onset DGBI. We also tested for an interaction between race/ethnicity and COVID-19 positivity to assess whether the relationship between pandemic-onset DGBI and race/ethnicity varied by COVID-19 status. RESULTS: Among 71,547 respondents, 26,103 (36.5%) had ≥ 1 DGBI. Across most DGBI, non-Hispanic Blacks and Hispanics had higher odds for pandemic-onset DGBI (e.g., irritable bowel syndrome, functional dyspepsia, functional bloating) versus non-Hispanic Whites. When including interaction terms between race/ethnicity and COVID-19 positivity, most were not significant (p > 0.05), showing that the relationship between pandemic-onset DGBIs and race/ethnicity did not vary by COVID-19 status. CONCLUSIONS: In this US survey, racial/ethnic minorities had higher odds of reporting pandemic-onset DGBI. This association was independent of COVID-19 positivity, suggesting that differences in pandemic-onset DGBI among groups may be related to psychosocial challenges faced by racial/ethnic minorities rather than direct effects of SARS-CoV-2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".