Is high availability of fruit and vegetable beneficial for children with anemia? A cross-sectional study in two peri-urban communities from Pakistan
Bibliographic record
Abstract
Introduction: In developing countries about half of young children are affected by anemia with 54% children aged under five suffering from moderate to severe anemia in Pakistan. The aim of this study was to investigate the community, household, and individual level factors associated with anemia among children aged 1-5 years and to estimate the prevalence of anemia. Methods: A community-based cross-sectional study was conducted among children in two peri urban communities of Karachi Pakistan. Systematic sampling method was used. A structured questionnaire was used to collect information on independent variables. The dependent variable was anemia which was measured by Hemacue machine. Binary multilevel logistic regression was used to analyze the data. Results: The prevalence of mild, moderate and severe anemia in 1-5 year old childrenwas 17.6%, 57.7% and 14.8%, respectively. The community level factors found to be negatively associated with anemia were living in neighborhoods with high availability of fruit [Adjusted Odds Ratio (AOR) = 0.3, 95% Confidence Interval (CI): 0.1-0.6], and residing in neighborehoods with high number of meat and dairy product and vegetable shops (AOR=0.4, 95% CI: 0.2-0.9). The household and individual level factors found to be positively associated with anemia were mothers with 4 or more children (AOR=1.9, 95% CI: 1.2-3.1), younger age (AOR=2.0, 95% CI:1.3-3.1) and child not being vaccinated (AOR=1.9, 95% CI: 1.0-3.6). Conclusion: We found a high prevalence of anemia in children living in two peri urban communities. The public health measures call for improvements in nutrition facilities in the neighborhoods, vaccination of child and reduction in the number of family members.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".