Trends in Prescription Medication Use for Depression Symptoms: An Analysis of National Health Interview Survey (NHIS) Data From 2019 to 2023
Bibliographic record
Abstract
Background: Depression is a major public health concern, and antidepressant medication is commonly prescribed for its management. Understanding trends in antidepressant use across socio-demographic groups is crucial for targeted interventions. Objective: To analyze trends in antidepressant medication use between 2019 and 2023 using data from the National Health Interview Survey (NHIS), focusing on demographic factors such as gender, age, race, and social vulnerability. Method: Data from the NHIS (2019-2023) were analyzed to assess trends in antidepressant use by sociodemographic variables. Descriptive statistics and trends were evaluated using prevalence estimates with confidence intervals. Result: Overall, antidepressant use increased from 9.8% (95% CI: 9.4-10.3) in 2019 to 11.4% (95% CI: 11.0-11.9) in 2023 (p-trend <0.001). The most notable increases were observed among females (13.3% in 2019 to 15.3% in 2023; p-trend <0.05), individuals aged 45-64 years (p-trend <0.05), and those with higher social vulnerability (p-trend =0.004). Racial disparities persisted, with White individuals showing the highest use of antidepressants (11.2% in 2019 to 13.2% in 2023; p-trend <0.05). Use was significantly higher among those with disabilities compared to those without (27.7% vs. 8.1% in 2019; 28.2% vs. 9.7% in 2023; p <0.001). Conclusion: The study reveals a steady increase in antidepressant use from 2019 to 2023, particularly among females, older adults, and individuals with higher social vulnerability or disabilities. Racial disparities in antidepressant use persist, with White individuals showing the highest prevalence. These findings highlight the ongoing need for targeted mental health interventions, especially for vulnerable groups, and underscore the importance of addressing disparities in access to mental health care. Future research should focus on the factors driving these trends and their implications for public health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".