Trends in risk factor control in patients with gout: data from the National Health and Nutrition Examination Survey, 2007–2018
Bibliographic record
Abstract
OBJECTIVES: To explore trends in risk factor control (hypertension, diabetes mellitus, hyperlipidaemia) in patients with gout and medication use among those whose risk factor control targets were not achieved. METHODS: We used the data from National Health and Nutrition Examination Survey (NHANES) between 2007-2008 and 2017-2018 for analyses. The study samples were weighted so that they could be representative of the non-institutionalized US population. We conducted a cross-sectional analysis to assess trends in risk factor control and medication use, and employed logistic regression analyses to explore patient characteristics associated with risk factor control. RESULTS: The prevalence of participants in whom blood pressure control target was achieved decreased from 64.6% in 2007-2008 to 55.3% in 2017-2018 (P-value for trend = 0.03). The percentage of participants whose glycaemic, lipid or all three risk factor control targets were achieved remained stable temporally (P > 0.05). Some patient characteristics were significantly related to risk factor control, including age 45-64, age ≥65, Asian Americans, non-Hispanic Blacks, higher family income, and being overweight and obese. A trend towards increased use of glucose-lowering medication was found (from 71.0% in 2007-2008 to 94.7% in 2017-2018, P < 0.01), while the prevalence of taking blood pressure-lowering and lipid-lowering medications remained stable (P > 0.05). CONCLUSION: Based on NHANES data, a significant trend towards decreased blood pressure control was observed in patients with gout, while glycaemic and lipid control levelled off. These findings emphasize that more endeavours are needed to improve management of cardiovascular risk factors in patients with gout.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".