All-cause mortality and risk factors in a cohort of retired military male veterans, Xi'an, China: an 18-year follow up study
Bibliographic record
Abstract
BACKGROUND: Risk factors of all-cause mortality have not been reported in Chinese retired military veterans. The objective of the study was to examine the risk factors and proportional mortality in a Chinese retired military male cohort. METHODS: A total of 1268 retired military men aged 55 or older were examined physically and interviewed using a standard questionnaire in 1987. The cohort was followed up every two years and the study censored date was June30, 2005 with a follow-up of up to 18 years. Death certificates were obtained from hospitals and verified by two senior doctors. Data were entered (double entry) by Foxbase, and analysis was carried out by SAS for Windows 8.2. Multivariate Cox proportional hazard regression model was used to compute hazard ratio (HR) and 95% confidence interval (CI). RESULTS: The total person-years of follow-up was 18766.28. Of the initial cohort of 1268 men, 491 had died, 748 were alive and 29 were lost to follow up. Adjusted mortality (adjusted for age, blood pressure, body mass index, cholesterol, triglycerides, alcohol, exercise, and existing disease) was 2,616 per 100,000 person years. The proportional mortality of cancer, vascular disease and Chronic Obstructive Pulmonary Disease (COPD) were 39.71%, 28.10% and 16.90% respectively. Multivariate analysis showed that age, cigarettes per day, systolic blood pressure, triglyceride, family history of diseases (hypertension, stroke and cancer), existing diseases (stroke, diabetes and cancer), body mass index, and age of starting smoking were associated with all-cause mortality, HR (95%CI) was1.083(1.062-1.104), 1.026(1.013-1.039), 1.009(1.003-1.015), 1.002(1.001-1.003), 1.330(1.005-1.759), 1.330(1.005-1.759), 1.444(1.103-1.890), 2.237(1.244-4.022), 1.462(1.042-2.051), 2.079(1.051-4.115), 0.963(0.931-0.996)and 0.988(0.978-0.999)respectively. Compared with never-smokers, current smokers had increased risks of total mortality [HR 1.369(1.083-1.731)], CHD [HR 1.805 (1.022-3.188)], and lung cancer [HR 2.939 (1.311-6.585)]. CONCLUSION: The three leading causes of diseases were cancer, CHD and stroke, and COPD. Aging, cigarette smoking, high systolic blood pressure, high triglyceride, family history of cancer, hypertension and stroke, existing cases recovering from stroke, diabetes and cancer, underweight, younger age of smoking were risk factors for all-cause mortality. Quitting cigarette smoking, maintaining normal blood pressure, triglyceride and weight are effect control strategies to prevent premature mortality in this military cohort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".