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Record W4212968491 · doi:10.1093/jcag/gwab049.201

A202 IMPACT OF THE COVID-19 PANDEMIC ON THE EPIDEMIOLOGY OF ALCOHOL-RELATED HEPATITIS

2022· article· en· W4212968491 on OpenAlexaffabout
Alexandra Frolkis, Meredith A. Borman, Matthew D Sadler, Stephen E. Congly, Henry H. Nguyen, S Lee, Laura M. Stinton, Melanie Swain, Carla S. Coffin, Alexander I. Aspinall, Kelly W. Burak, A M Shaheen

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPandemicMedicineEpidemiologyDemographyIncidence (geometry)Coronavirus disease 2019 (COVID-19)Alcohol consumptionLiver diseaseDiseaseInternal medicineAlcoholInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Background Alcohol-related hepatitis (AH) is the most severe form of alcohol-related liver disease, with rising incidence. Stay-at-home orders for the COVID-19 pandemic were associated with increased alcohol consumption. Online sales reported a 262% increase from March 2019 to 2020. Aims The purpose of this study was to track the epidemiology of hospitalizations for AH by sex before and after the COVID-19 pandemic. We hypothesized that AH would be more severe in females and younger individuals during the pandemic. Methods Using the Discharge Abstract Database, we identified all hospitalizations in Alberta with international classification of disease-10 codes for AH between March 2018 and September 2020. We merged this dataset with provincial laboratory data to identify all inpatient lab values. We calculated Model for End-Stage Liver Disease (MELD) and Maddrey scores and validated a laboratory-based algorithm for AH. Severe AH was defined as Maddrey score > 32. Onset of the pandemic was defined as March 2020. Stratified by pandemic onset, descriptive statistics were done with Chi-squared and Kruskal Wallis tests. Inpatient mortality was assessed as a primary outcome. Binomial regression was used to assess changes in frequency of admission for AH with the denominator as all cirrhosis-related admissions over the same time-period. Results We identified 991 hospitalizations for AH prior to the pandemic (n=381, 38.5% female) and 417 during the pandemic (n=144, 34.5% female). Hospitalizations for AH significantly increased during the pandemic (p = 0.04) (Figure 1). Median Maddrey score for females (30.5) before the pandemic was significantly higher than for males (22.9), p < 0.01. During the pandemic, median Maddrey for females (28.7) was higher than males 21.4, p = 0.07. Median age at admission was significantly lower for both males and females during the pandemic (age 44 and 41, respectively) as compared to prior (age 47 and 45, respectively) p < 0.05. There was no significant difference in MELD between sexes before (13.5 for females, 14.0 for males, p = 0.15) and during the pandemic (13.3 for females, 13.0 for males, p = 0.75). Additionally, there was no significant difference in mortality between sexes before (10.4% in females, 11.5% in males, p = 0.22) and after the pandemic (9.2% in females, 9.9% in males, p = 0.67). Conclusions Hospitalizations for AH rose during the pandemic and occurred at younger ages. There was no significant difference in disease severity or mortality before and during the pandemic. Overall, females have more severe AH than males. Public health efforts should continue to be made to educate about the harms of alcohol excess and offer community support. Future studies will expand the trend through multiple pandemic waves. Funding Agencies None

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)0.001
metaresearch head score (Gemma)0.002
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 score0.358
Threshold uncertainty score0.712

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.373
Teacher spread0.280 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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