MODELLING THE RELATION BETWEEN URBAN MALARIA AND ENVIRONMENTAL INEQUALITY\nA CASE STUDY OF CHILDREN, IN ABIA, NIGERIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The risk of malaria falls heavily on under five aged children. Sub-Saharan Africa region bears the heaviest burden of malaria compared to other regions in the world (WHO,2015). This makes children from this region more vulnerable to malaria. Nigeria is one of the 6 countries in Sub-Saharan Africa that have about a quarter of the reported cases in the region. The record of the National Population Commission (2010) shows that malaria in the country, leads to 15 to 17% of fever cases in children and 300,000 child deaths per year. \n\nTraditional malaria theories proof that children living in rural areas are more vulnerable to malaria than those living in urban areas. However, the studies of Austin (2014) & Fobil et al. (2014) are drawing emphasis on malaria epidemic in urban centres due to poor environmental conditions present in urban areas that could favour the breeding of mosquitoes. The exploratory analysis conducted in this research show children in the urban areas of the study area; Ugwunagbo and Aba South to have higher cases of malaria than those in the rural areas. This analysis also shows female children to have higher cases of malaria compared to male children.\nThe relation between poverty and malaria is a recurring finding in malaria research. The association between these two factors points to the relation between malaria and environmental inequality. This relation based on the report of WHOEurope (2010) has not been fully researched. This research therefore, uses the environmental inequality framework of Kruize et al. (2014) to define the relation between malaria and environmental inequality. From this framework, a model was derived - the multi-level differential malaria (MDM) model. This model groups the factors (vector based and host based factors) of urban malaria in 3 aggregation levels: macro level, ward level and the individual level.\n\nAnalysis was conducted on two levels (ward and individual level) to test the derived model. At the ward level; cluster analysis and Ordinary Least Square (OLS) regression were performed. The result of the cluster analysis identified four hot spot areas of malaria for the period of 2013 – 2015: Aba River, Aba Townhall, Igwebuike and Mosque. The OLS result showed houses that are within 1000 metres of the river and 200 metres of artificial water surfaces to be correlated with malaria.\n At the individual level analysis, data derived from the household survey; conducted during field work were used to conduct three regression analyses: Ordinal logistic regression, binary logistic regression and the OLS/spatial regression. The combined result of these analyses show: the use of preventive measures; room occupancy rate; income level and educational level to be strongly correlated with child malaria occurrence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it