Socio-economic equity in access to malaria prevention and treatment in Nigeria: temporal and geographic variations
Bibliographic record
Abstract
Background: Malaria affects millions and kills over 400,000 people globally each year, two-thirds of whom are children under five years. The disease is concentrated in socio-economically vulnerable populations. Nigeria accounted for more than a quarter of the global malaria cases in 2019.\nAims: Analyse the socio-economic equity in coverage of malaria preventions and treatment among children under 5 years in Nigeria’s 37 states, comparing the years 2008 to 2018.\nMethod: Data was obtained from open access Demographic Health Surveys implemented in 2008 and 2018. Socio-economic status was defined using principal component analysis of selected household assets to construct a wealth index. Coverage of malaria prevention and treatment interventions were compared between the poorest and least poor quintiles. A concentration index was used to calculate the distribution of inequality by intervention.\nResults: Access to and use of insecticide-treated bed nets (ITNs) was more concentrated among rich in 2008 but slightly concentrated among poor in 2018, with the index closer to equality. In some states access and use of ITNs was still in favour of the wealthier household in 2008 and 2018, despite malaria prevalence being generally concentrated in poorest households. The access to care for children under 5 years that had fever and those who got\nappropriate treatment was overall still concentrated among the rich, although fever prevalence was higher in poorer households.\nConclusions: The poor are the most vulnerable and the most exposed to malaria infection and disease. Despite some improvements in 2018 compared to 2008, inequity is still high in many states in access and use of malaria preventions and treatment. Further measures are needed to reduce this inequity with better targeting of interventions to the poorer households, including expansion of community level treatment of malaria.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".