Global, regional, and national burden of elderly myocarditis (1992–2021) and projections of future disease burden trends
Bibliographic record
Abstract
OBJECTIVE: Geriatric myocarditis represents a significant public health concern, directly influencing overall health and potentially leading to various cardiac diseases. This study seeks to quantify the burden of geriatric myocarditis over three decades (1992-2021) and provide forecasts for future disease burden. METHODS: Data on geriatric myocarditis from 1992 to 2021 was obtained from the Global Burden of Disease study, offering insights into the incidence of the condition, categorized by gender. The Joinpoint regression model was utilized to identify shifts in epidemiological trends, while decomposition analysis helped identify the underlying factors contributing to these trends. To project future incidence, deaths and DALYs (Disability-Adjusted Life Years), the Norpred and Bayesian Age-Period-Cohort (BAPC) models were employed. RESULTS: In 2021, the global ASIR (per 100,000) of elderly myocarditis was 47.57 (27.52-73.08), with 505,147 (292,319-774,561) cases. Age-standardized deaths(per 100,000) were 2.07 (1.55-2.51), totaling 20,718 (15,525-25,085) deaths, and age-standardized DALYs(per 100,000) were 29.77 (22.60-35.81), with 308,101 (234,226-370,674) DALYs. Greenland, Canada, and Austria had the highest ASIR(per 100,000), while Romania, Kazakhstan, and Croatia had the highest age-standardized deaths(per 100,000), and Romania, Kazakhstan, and Guyana had the highest age-standardized DALYs(per 100,000). Joinpoint Regression analysis revealed a recent upward trend in global incidence after a previous decline, consistent across genders and SDI regions. Deaths and DALYs showed declining trends globally, though male deaths recently increased. Decomposition analysis identified population growth and aging as key drivers of increased cases, deaths, and DALYs. Based on the nordpred model, by 2045, the global ASIR(per 100,000) is predicted to be 47.27, with 1,005,593 cases, age-standardized deaths(per 100,000) of 2.02, totaling 48,501 deaths, and age-standardized DALYs(per 100,000) of 26.21, with 595,694 DALYs. The BAPC model predicts a global ASIR(per 100,000) of 51.82, with 1,091,195 cases, age-standardized deaths(per 100,000) of 3.67, totaling 87,145 deaths, and age-standardized DALYs(per 100,000) of 49.09, with 1,084,738 DALYs. CONCLUSION: As of 2021, the ASIR(per 100,000) of myocarditis in the elderly population showed a decline compared to 1992; however, a recent upward trend has been identified. Considering ongoing population growth, the number of myocarditis cases among the elderly is anticipated to increase.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".