The Epidemiology of UK Autoimmune Liver Disease Varies With Geographic Latitude
Bibliographic record
Abstract
BACKGROUND & AIMS: The epidemiology of autoimmune liver disease (AILD) is challenging to study because of the diseases' rarity and because of cohort selection bias. Increased incidence farther from the Equator has been reported for multiple sclerosis, another autoimmune disease. We assessed the incidence of primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and autoimmune hepatitis (AIH) in relation to latitude. METHODS: We performed a retrospective cohort study using anonymized UK primary care records from January 1, 2002, to May 10, 2016. All adults without a baseline diagnosis of AILD were included and followed up until the first occurrence of an AILD diagnosis, death, or they left the database. Latitude was measured as registered general practice rounded down to whole degrees. RESULTS: years of follow-up evaluation from 694 practices. There were 1314 incident cases of PBC, 396 of PSC, and 1034 of AIH. Crude incidences were as follows: PBC, 2.47 (95% CI, 2.34-2.60); PSC, 0.74 (95% CI, 0.67-0.82); and AIH, 1.94 (95% CI, 1.83-2.06) per 100,000 per year. PBC incidence correlated with female sex, smoking, and deprivation; PSC incidence correlated with male sex and non-smoking; AIH incidence correlated with female sex and deprivation. A more northerly latitude was associated strongly with incidence of PBC: 2.16 (95% CI, 1.79-2.60) to 4.86 (95% CI, 3.93-6.00) from 50°N to 57°N (P = .002) and incidence of AIH: 2.00 (95% CI, 1.65-2.43) to 3.28 (95% CI, 2.53-4.24) (P = .003), but not incidence of PSC: 0.82 (95% CI, 0.60-1.11) to 1.02 (95% CI, 0.64-1.61) (P = .473). Incidence after adjustment for age, sex, smoking, and deprivation status showed similar positive correlations for PBC and AIH with latitude, but not PSC. Incident AIH cases were younger at more northerly latitude. CONCLUSIONS: We describe an association in the United Kingdom between more northerly latitude and the incidence of PBC and AIH that requires both confirmation and explanation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".