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S0216 Rates of Inflammatory Bowel Disease in Hispanics Comparable to Non-Hispanic Whites: Results of a Large Cohort Study

2020· article· en· W3093868904 on OpenAlexaboutno aff
Alex Zhornitskiy, Stacy Shen, Long Le, Brian M. Fung, Felicia Zhornitsky, Tom Z. Liang, Jenny Sauk, James H. Tabibian

Bibliographic record

VenueThe American Journal of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEpidemiologyConfidence intervalDemographyIncidence (geometry)PopulationInflammatory bowel diseaseCohortEthnic groupRate ratioGerontologyDiseaseInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Inflammatory bowel disease (IBD) has historically been believed to occur at higher rates among non-Hispanic Whites compared to other ethnicities. However, current epidemiological understanding of IBD within the United States (US) has been drawn from research consisting of primarily non-Hispanic White cohorts with scant ethnoracial diversity. Hispanics are the fastest growing and the largest minority group in the US, yet they remain grossly underrepresented in studies of IBD. With this study, we aimed to better understand the epidemiology, demographics, clinical presentation, and treatment of IBD in the Hispanic population in Los Angeles County. METHODS: Our study was based at Olive View-UCLA Medical Center (OVMC), one of three safety net hospitals that comprise the Los Angeles Department of Health Services (LADHS). We queried pathology reports from 2015 to 2018 for key terms to identify biopsy-confirmed cases of IBD (N = 170) and abstracted pertinent data from electronic health records. Incidence and prevalence for ethnicities were standardized using their respective proportions in the composition of the OVMC patient population. Statistical analyses included two-proportion t-test, analysis of variance, binomial proportion confidence interval, general linear model and logistic regression. RESULTS: Of the 170 biopsy-confirmed cases of IBD, Hispanics represented 55% of all patients. The annual incidence rate per 100,000 patients for IBD among Hispanics was 175 (95% confidence interval [CI] 127–240) vs. 113 (95% CI 62–200) among non-Hispanic Whites. The prevalence of IBD per 100,000 patient-years was 418 (95% CI 341–512) for Hispanics and 557 (95% CI 431–739) for non-Hispanic Whites. In the subset with Crohn’s disease, smoking rates were significantly lower in Hispanics as compared to non-Hispanic Whites (12.9 vs 62.9%, P = 0.033). There was no significant difference between the two ethnic groups with regard to medical treatment, IBD-related surgery, presenting symptoms, or Montreal classification of disease. CONCLUSION: To our knowledge this is the largest US-based study of Hispanics with biopsy-confirmed IBD. The findings, herein, indicate that the incidence and prevalence of IBD among Hispanics in the US is higher than previously reported and comparable to rates among non-Hispanic Whites.Table 1.: Ethnic background of patients with biopsy-confirmed IBD, Olive View-UCLA Medical Center, Los Angeles County, 2015–2018Table 2.: Demographics of Hispanic and NHW patients with biopsy-confirmed IBD, Olive View-UCLA Medical Center, Los Angeles County, 2015–2018

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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