Impact of the Family Health Program on gastroenteritis in children in Bahia, Northeast Brazil: An analysis of primary care-sensitive conditions
Bibliographic record
Abstract
In seeking to provide universal health care through its primary care-oriented Family Health Program, Brazil has attempted to reduce hospitalization rates for preventable illnesses such as childhood gastroenteritis. We measured rates of Primary Care-sensitive Hospitalizations and evaluated the impact of the Family Health Program on pediatric gastroenteritis trends in high-poverty Northeast Brazil. We analyzed aggregated municipal-level data in time-series between years 1999-2007 from the Brazilian health system payer database and performed qualitative, in-depth key informant interviews with public health experts in municipalities in Bahia. Data were sampled for Bahia's Salvador microregion, a population of approximately 14 million. Gastroenteritis hospitalization rates among children aged less than 5 years were evaluated. Declining hospitalization rates were associated with increasing coverage by the PSF (P = 0.02). After multivariate adjustment for garbage collection, sanitation, and water supply, evidence of this association was no longer significant (P = 0.28). Qualitative analysis confirmed these findings with a framework of health determinants, proximal causes, and health system effects. The PSF, with other public health efforts, was associated with decreasing gastroenteritis hospitalizations in children. Incentives for providers and more patient-centered health delivery may contribute to strengthening the PSF's role in improving primary health care outcomes in Brazil.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".