Factors Associated with Stunting among Children Aged 0 to 59 Months in Harare City, Zimbabwe
Bibliographic record
Abstract
Background: Child malnutrition is a major public health problem in Zimbabwe and it has been one of the leading causes of morbidity and mortality in children under five years of age in the City of Harare. Methods: An analytic cross sectional survey was conducted on 342 mothers – child pairs of children aged 0 – 59 months in Harare’s suburbs between July and August 2014.The aim of the study was to identify factors associated with the high levels of stunting among children age 0 – 59 months. Bivariate and multivariate analyses were used to establish the risk factors for U5 years stunting. Findings: A total of 29.2% of the children were stunted with (19.3% being moderately stunted and 9.9% severely stunted). More females (32.9%) were stunted compared to (25.9%) males. The main contributing factors for stunting among the children 0 - 59 months were found to be lack of mother’s education adjusted pOR=0.49 (95%CI: 1.04-3.27); being unemployed pOR=1.22 (95%CI: 1.08-2.03); residing in high density suburbs pOR=2.14 (95%CI: 1.61-7.55); child ever being hospitalized pOR=1.04 (95%CI: 1.01-2.14); breastfed after > 1 hour after birth pOR=1.11 (95%CI: 1.09-1.80); complementary feeds < 6 months pOR=1.30 (95%CI: 1.17-2.21); low birth weight pOR=1.46 (95%CI: 1.29-3.51) and having a poor dietary diversity pOR=1.07 (95%CI: 1.01-1.84). Conclusions: Lack of mother’s education is the principal risk factor for stunting among the under five children in Harare exacerbated by unemployment of either the father or mother or relative, area of residence, feeding practices, birth weight, illness and infections and household dietary diversity. Stunting is on the increase in Harare. The findings also revealed a high level of in-equalities in social determinants of health since stunting is related to socio-economic factors. Recommendations: Any nutrition intervention has to use strategies that will comprehensively address the social determinants of health and improve the standard of living of households in the high density areas so as to have a ripple effect on the nutritional status of U5s
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".