Impact of living with an adult with depressive symptoms among households in the United States
Bibliographic record
Abstract
BACKGROUND: The effect of depressive symptoms on individuals has been widely studied but their impact on households remains less explored. This study assessed the humanistic and economic impact of living with an adult with depressive symptoms on adults without depressive symptoms among households in the United States (US). METHODS: The Medical Expenditure Panel Survey (MEPS) Household Component database was used to identify adults without depressive symptoms living in households with ≥1 adult with depressive symptoms (depression household) and adults without depressive symptoms living in households without an adult with depressive symptoms (no-depression household). Weighted generalized linear models with clustered standard errors were used to compare total income (USD 2020), employment status, workdays missed, quality of life (QoL), and healthcare resource utilization (HRU) between cohorts. RESULTS: Adults without depressive symptoms living in a depression household (n = 1699) earned $4720 less in total annual income (representing 11.3% lower than the average income of $41,634 in MEPS), were less likely to be employed, missed more workdays per year, and had lower QoL than adults without depressive symptoms living in a no-depression household (n = 15,286). Differences in total annual healthcare costs and for most types of HRU, except for increased outpatient mental health-related visits, were not significant. LIMITATIONS: Data is subject to reporting bias, misclassification, and other inaccuracies. Causal inferences could not be established. CONCLUSION: The economic and humanistic consequences of depressive symptoms may extend beyond the affected adults and impact other adult members of the household.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".