Urban Versus Rural Disparities in Alcoholic Liver Disease Mortality: A Comprehensive 22-Year Analysis of Trends and Demographic Influences in the United States
Bibliographic record
Abstract
Introduction Alcoholic liver disease (ALD) encompasses a spectrum of liver conditions that result from excessive alcohol consumption. Disparities in mortality rates of ALD between urban and rural populations have become a growing concern, with rural areas experiencing a faster rise in ALD-related deaths. Understanding these disparities is crucial for developing targeted public health interventions to address this escalating concern. Methodology Disparities in mortality rates from 1999 to 2020 were analyzed using data from the CDC-WONDER (Centers for Disease Control and Prevention, Wide-ranging Online Data for Epidemiologic Research) database. Regions were categorized into urban (large central, large fringe, medium, and small metro) and rural (micropolitan and non-core) categories based on the 2013 Metropolitan classification. The data were stratified by age (10-year intervals), gender, and race. The binomial proportion test was used for comparison, and a p-value < 0.05 was considered significant. Results Rural mortality rates increased sharply post-2015, peaking in 2020, while urban rates rose more gradually. While urban areas experienced higher total mortality, rural regions exhibited a steeper rise in mortality rates, especially post-2015, highlighting a growing public health crisis. Notably, younger age groups (15-64 years) exhibited higher mortality in rural areas, particularly among those aged 25-34 years, while urban areas showed higher rates for older populations (65+ years). American Indian or Alaska Native individuals in rural areas had notably high rates (24.1 per 100,000). Rural males experienced higher mortality rates (9.04 per 100,000) compared to urban males (7.82 per 100,000, p < 0.001). Similarly, rural females also exhibited higher mortality (3.48 per 100,000) compared to urban females (3.08 per 100,000, p < 0.001), highlighting a consistent rural disadvantage across both genders. Conclusions The findings of this study indicate that males and middle-aged adults in rural settings are disproportionately affected. Targeted public health interventions aimed at improving healthcare access can significantly reduce ALD mortality rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".