Subsidized antimalarial drugs in Dakar (Senegal): Do the poor benefit?
Bibliographic record
Abstract
Senegal opted for an antimalarial drug policy (artemisinin-based combination therapy) of partial and then full exemption from health care costs for the whole population respectively in 2008 and 2010. Has this policy reduced access inequalities in children’s health care between rich and poor households?Data were collected in Dakar between 2008 and 2009 as part of a research program on urban malaria. A survey was conducted among the population of the Dakar metropolitan area. The sample was based on a two-stage sampling. The three questionnaires used for the survey were based on validated data collection tools. Indicators were built to characterize individuals, households and neighborhoods. Bivariate analysis (chi2 test) revealed social gradients within the Dakar agglomeration and characterized health care behaviors of the poorest and richest households. Data have therefore been adjusted by a double zero-inflated Poisson model.Results show that the policy of subsidizing antimalarial drugs in Senegal has reduced health care costs, including for the poor, but without improving its distributive equity. In contrast, this policy has benefited more the richest than the poorest, without mitigating social and financial inequalities. In light of the lessons learnt by the subsidy policy for antimalarial drugs, our study recommends that universal health coverage, currently implemented in Senegal, should seek to mitigate economic inequalities in access to health care for the poorest as well as to improve the health outcomes for the whole population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".