Socio-Economic Disparities in Under-Five Child Malnutrition in Nigeria
Bibliographic record
Abstract
INTRODUCTION: Recent attention over the health, wellbeing and nutritional intake of children below five years of age has grown tremendously. This is mainly because these years are crucial to a child’s survival, growth and development; and if not handled properly could unfavorably affect the well-being status and efficiency of the child in later adult life. The study focused on malnutrition of children under the age of five in relation to their socio-economic status. It was measured by stunting, wasting and underweight. METHODOLOGY: Data from the Living Standards Measurement Study (LSMS)/General Household Survey (GHS) 2015/2016 Nigeria was used for analysis. Malnutrition was measured using the three anthropometric measures which are expressed in terms of Z-scores namely: Stunting: height-for-age (HAZ), Wasting: weight-for-height (WHZ) and Underweight: weight-for-age (WAZ). The socioeconomic disparities in malnutrition were checked according to gender, place of residence and geo-political zones in Nigeria. While the concentration index and curves were used to check for the magnitude of inequality in malnutrition ascribable to the socio-economic status. RESULTS: The percentage of children stunted was the highest with 37.8 percent, followed by the percentage of children underweight to be 20.25 and children wasted was 9.63 percent. The percentage of stunting, wasting and underweight were considerably greater in male children compare to the female children. Stunting and underweight were responsive to the household socioeconomic status. A higher percentage of children below five years of age who were stunted, wasted and underweight lived in the rural areas of Nigeria compare to the children living in the urban areas. The rate of stunting was highest in the North West with a 52.91 percent, followed by North East with 43 percent, and lowest in the South South with 20.67 percent. The concentration indices analysis revealed that stunting, wasting and underweight all had negative signs signifying concentration among the poor household children. Finally, as one moves up the ladder of the socioeconomic status, a significant fall in the rate of stunting is witnessed. Therefore, increasing the income of the poorest in a society is a sound strategy to curb the high rates of stunting in the socio-economically deprived segments of the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".