Trends and Factors Associated With the Mortality Rate of Depressive Episodes: An Analysis of the CDC Wide-Ranging Online Data for Epidemiological Research (WONDER) Database
Bibliographic record
Abstract
Background Depressive episodes are associated with increased mortality rates across the United States. Recognizing the relationship between depression and physical health, understanding the contributing factors, and addressing disparities are critical in reducing mortality rates and improving the overall well-being of individuals experiencing depressive episodes. Continued research, public health efforts, and collaborative approaches are essential to tackle this complex public health concern effectively. Studying the mortality rate trends of depressive episodes along with other related factors will help enhance the understanding of the condition, which, in turn, will assist in reducing mortality rates in the vulnerable population. Methodology Data from the CDC Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on the Underlying Cause of Death were examined to identify individuals who experienced fatal outcomes related to depressive episodes from 1999 to 2020. The WONDER database refers to the online system used by the CDC to make its various resources accessible to the public and public health experts. CDC WONDER offers access to a broader range of information on public health. Results A total of 13,290 individuals who died from depressive episodes between 1999 and 2020 were identified. Data analysis revealed an overall mortality rate of 0.20 per 100,000 individuals during the specified period. The highest mortality rates were observed in the years 2003 (0.28), 2001 (0.27), and 1999 (0.27). The analysis revealed significant disparities in mortality rates among different demographic groups. Older adults, females, specific racial groups, including Whites and African Americans, and specific geographic areas, including the Midwest, Northeast, South, and West, exhibited higher mortality rates associated with depressive episodes. Conclusions The study identified that older individuals, females, Whites, and African Americans, as well as certain geographic regions, exhibited an increased likelihood of mortality related to depressive episodes. These findings highlight the importance of understanding the complex interplay between mental health and mortality. The findings emphasize the importance of addressing disparities in mental health outcomes among different demographic groups. Identifying vulnerable populations can inform targeted interventions and resources to address the elevated mortality risk.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".