S1150 A Remarkable Increase in Alcohol-Related Liver Disease in the Setting of COVID-19
Bibliographic record
Abstract
Introduction: COVID-19 has had a profound effect on everyday life. In one survey, 30.8% of respondents reported “drinking a lot more than normal” during the pandemic. We examined the incidence of alcohol related liver diseases from 2015 to the present to determine whether there was a significant increase of these diseases in the setting of the COVID-19 pandemic. Methods: The number of patients at a large academic center with a first occurrence of alcoholic cirrhosis and alcoholic hepatitis were extracted for each quarter between 2015 and 2021. A quarter was defined as a three-month period with the first quarter (Q1) being the first three months of the year, the second quarter (Q2) the next three months, and so on. Data was further broken down by whether the diagnosis was made inpatient, outpatient, or in the emergency department. This data was compared to the number of outpatient visits for all liver related illnesses in each quarter to standardize the data to a rate per 1000 visits. Diagnoses and inpatients per 1000 visits from Q2 2019 to Q1 2020 of alcoholic cirrhosis and alcoholic hepatitis was compared to that of Q2 2020 to Q1 2021. A Fisher’s exact test was done to determine if there was a significant difference. Results: Diagnoses of alcoholic cirrhosis per quarter fluctuated between 55 and 90 from Q1 2016 to Q1 2020 before increasing to a peak of 267 in Q1 2021. The same trend was found in diagnoses per 1000 visits. (Figure 1) New cases and diagnoses per 1000 visits of alcoholic hepatitis have generally increased from Q1 2016 to Q1 2020 with a more precipitous increase thereafter. In the 12-month period encompassing Q2 2019 to Q1 2020, diagnoses of alcoholic cirrhosis per 1000 visits were 12.3, increasing to 20.5 in the next 12-month period with inpatients per 1000 visits increasing from 5.4 to 8. Both differences have a p-value of < 0.001. When comparing these periods for alcoholic hepatitis, there was an increase in diagnoses per 1000 visits from 5.8 to 7.5 (P = 0.014) and from 4.2 to 5.4 for inpatients per 1000 visits (P = 0.047). Conclusion: There have been more diagnoses and inpatient visits of alcohol related liver diseases since Q2 2020, the first full quarter affected by COVID-19. This could be due to worsening of pre-existing mental health issues or lack of social support during this period. Regardless of the cause, this data is useful for understanding public health needs as we recover from the current pandemic and in future pandemics.Figure 1.: Kaplan-Meier Plot on All-cause Mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".