Equity and seeking treatment for young children with fever in Nigeria: a cross-sectional study in Cross River and Bauchi States
Bibliographic record
Abstract
BACKGROUND: Poor children have a higher risk of contracting malaria and may be less likely to receive effective treatment. Malaria is an important cause of morbidity and mortality in Nigerian children and many cases of childhood fever are due to malaria. This study examined socioeconomic factors related to taking children with fever for treatment in formal health facilities. METHODS: A household survey conducted in Bauchi and Cross River states of Nigeria asked parents where they sought treatment for their children aged 0-47 months with severe fever in the last month and collected information about household socio-economic status. Fieldworkers also recorded whether there was a health facility in the community. We used treatment of severe fever in a health facility to indicate likely effective treatment for malaria. Multivariate analysis in each state examined associations with treatment of childhood fever in a health facility. RESULTS: 43% weighted (%wt) of 10,862 children had severe fever in the last month in Cross River, and 45%wt of 11,053 children in Bauchi. Of these, less than half (31%wt Cross River, 44%wt Bauchi) were taken to a formal health facility for treatment. Children were more likely to be taken to a health facility if there was one in the community (OR 2.31 [95% CI 1.57-3.39] in Cross River, OR 1.33 [95% CI 1.0-1.7] in Bauchi). Children with fever lasting less than five days were less likely to be taken for treatment than those with more prolonged fever, regardless of whether there was such a facility in their community. Educated mothers were more likely to take children with fever to a formal health facility. In communities with a health facility in Cross River, children from less-poor households were more likely to go to the facility (OR 1.30; 95% CI 1.07-1.58). CONCLUSION: There is inequity of access to effective malaria treatment for children with fever in the two states, even when there is a formal health facility in the community. Understanding the details of inequity of access in the two states could help the state governments to plan interventions to increase access equitably. Increasing geographic access to health facilities is needed but will not be enough.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".