Secular trend of gout incidence in the UK: an age-period-cohort analysis
Bibliographic record
Abstract
OBJECTIVES: The incidence of gout in the UK appears to have declined since 2013; however, whether such a trend occurred across participants born in different years (ie, birth cohort) is unknown. We aimed to examine the effects of the birth cohort on gout incidence using an age-period-cohort (APC) model. DESIGN: Cross-sectional study. SETTING: Nationwide data from the UK primary care database. PARTICIPANTS: Individuals between 30 and 89 years of age were included. We excluded individuals who had gout history when entering the database and individuals with less than 1 year of continuous follow-up between 1 January 1999 and 31 December 2019. PRIMARY AND SECONDARY OUTCOME MEASURES: Gout was identified using READ codes assigned by general practitioners. The incidence of gout between 1999-2013 and 2011-2019 was analysed with APC model. RESULTS: The incidence of gout between 1999 and 2013 increased with birth cohorts. Compared with those born in 1949-1953 (reference), the age-adjusted and period-adjusted rate ratios (RRs) of incident gout increased from 0.39 (95% CI 0.34 to 0.46) in participants born in 1910-1914 to 2.36 (95% CI 2.09 to 2.66) in participants born in 1979-1983 (p for trend <0.001). In contrast, the incidence of gout between 2011 and 2019 decreased with birth cohorts. Compared with those born in 1949-1953 (reference), the age-adjusted and period-adjusted RRs of incident gout declined from 2.75 (95% CI 2.30 to 3.28) in participants born in 1922-1926 to 0.75 (95% CI 0.65 to 0.87) in participants born in 1976-1980 but then increased slightly to 0.95 (95% CI 0.77 to 1.17) in participants born in 1985-1989. CONCLUSIONS: The gout incidence between 1999 and 2013 in the UK increased with the birth cohorts and then decreased between 2011 and 2019 except for those born after 1980. Future monitoring is needed to help identify aetiological factors and guide preventive and treatment strategies for gout.
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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".