Análisis de costos de atención médica para esquizofrenia y depresión en México para el periodo 2005-2013
Bibliographic record
Abstract
The study aimed to analyze the costs of medical care for mental disorders in the Mexican health system. This was a retrospective cross-sectional evaluation study. As markers for the problem, the study selected two of the principal psychological processes in mental disorders in recent years: depression and schizophrenia. Annual accumulated incidence was identified based on epidemiological reporting by type of institution in 2005-2013. The mean annual case management cost was determined with the instrumentation and consensus technique, identifying the production functions, types of inputs, costs, and amounts of inputs ordered, concentrated in the mean case matrix. Finally, an econometric adjustment factor was applied to control the inflationary effect for each year in the study period. Mean annual case management cost was USD 2,216.00 for schizophrenia and USD 2,456.00 for depression. All the institutions in the Mexican health system showed upward and constant epidemiological and economic trends. The total cost for the two disorders in the last year of the period (2013) was USD 39,081,234.00 (USD 18,119,877.00 for schizophrenia and USD 20,961,357.00 for depression). The largest impact for the two disorders combined was in institutions serving the population without health insurance (USD 24,852,321.00) versus the population with private insurance (USD 12,891,977.00). The cost of meeting the demand for services for the two disorders differs considerably between institutions that treat the population with private health service versus the population without, and is higher in the latter. The study's epidemiological and economic indicators provide evidence for decision-making in the use and allocation of healthcare resources for these two disorders in the coming years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".