Evaluation of the impact of the Family Health Program on infant mortality in Brazil, 1990–2002
Bibliographic record
Abstract
OBJECTIVE: To use publicly available secondary data to assess the impact of Brazil's Family Health Program on state level infant mortality rates (IMR) during the 1990s. DESIGN: Longitudinal ecological analysis using panel data from secondary sources. Analyses controlled for state level measures of access to clean water and sanitation, average income, women's literacy and fertility, physicians and nurses per 10,000 population, and hospital beds per 1,000 population. Additional analyses controlled for immunisation coverage and tested interactions between Family Health Program and proportionate mortality from diarrhoea and acute respiratory infections. SETTING: 13 years (1990-2002) of data from 27 Brazilian states. MAIN RESULTS: From 1990 to 2002 IMR declined from 49.7 to 28.9 per 1,000 live births. During the same period average Family Health Program coverage increased from 0% to 36%. A 10% increase in Family Health Program coverage was associated with a 4.5% decrease in IMR, controlling for all other health determinants (p<0.01). Access to clean water and hospital beds per 1,000 were negatively associated with IMR, while female illiteracy, fertility rates, and mean income were positively associated with IMR. Examination of interactions between Family Health Program coverage and diarrhoea deaths suggests the programme may reduce IMR at least partly through reductions in diarrhoea deaths. Interactions with deaths from acute respiratory infections were ambiguous. CONCLUSIONS: The Family Health Program is associated with reduced IMR, suggesting it is an important, although not unique, contributor to declining infant mortality in Brazil. Existing secondary datasets provide an important tool for evaluation of the effectiveness of health services 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 | 0.005 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".