Public Policies and Type of Insurance Are Associated With the Burden of Bladder Cancer–Related Inpatient Health Care in Chile: A Two-Decade Analysis
Bibliographic record
Abstract
ObjectiveTo quantify changes in the burden of bladder cancer (BC) inpatient health care in Chile between 2001 and 2019, focusing on the impact of public policies and the type of medical insurance (public or private) held by patients.MethodsWe retrospectively collected national data on hospital discharges and calculated raw and adjusted hospitalization rates for the period of 2001 to 2019 categorized by sex and age. Additionally, we analyzed length of hospital stays, outcomes of surgical interventions, and discharge conditions based on the type of medical insurance — public: FONASA; private: ISAPRE. We also evaluated the impact of public policies such as the GES (“garantías explícitas en salud”) program, which ensures opportunities and access to medical attention, financial protection, and quality of care for a subset of diseases.ResultsA total of 34 100 hospital discharges were analyzed. Most patients were men (71%), and median age was 69 years. Of the patients, 91.3% had some kind of medical insurance, either private or public. Within this subset, 71.3% had public medical insurance (FONASA) and 23.2% had private medical insurance (ISAPRE). Patients on FONASA had significantly higher levels of overall surgery-related mortality (0.83% vs. 0.2%) and significantly longer median hospital stays (4 days vs. 2 days) compared to patients on ISAPRE. Following the implementation of the GES program in 2013, we observed an increase in transurethral resections and a reduction in radical cystectomies among publicly insured patients.ConclusionsThe type of medical insurance has a significant impact on the burden of BC-related inpatient health care in Chile, reflecting a significant disparity in terms of health care. The implementation of public policies such as the GES program can play a key role in reducing this gap between public and private medical insurance systems, especially in underdeveloped countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".