An assessment of Individual, community and state-level factors associated with inadequate iodised salt consumption among pregnant and lactating women in Nigeria
Bibliographic record
Abstract
Abstract Background Iodine deficiency is the most common cause of thyroid disease, and in its severe form can result in cretinism; the impairment of the brain development of a child. Pregnant and breastfeeding women daily iodine requirement is elevated due to physiological changes in iodine metabolism, requiring up to double the iodine intake of other women. Despite the introduction of salt iodisation in many countries to control iodine deficiency disorders, adverse effects of inadequate iodine intake continue to be a problem.Methods This study utilised the Multiple Indicator Cluster Survey to assess factors associated with inadequate iodised salt consumption among pregnant women and breastfeeding mothers in Nigeria. The descriptive analysis was presented using frequencies and percentages. The prevalence of adequate and inadequate iodised salt consumption with their 95% confidence interval were computed. Several multi-level mixed effect log-binomial logistic regressions was used to explore the factors associated with inadequate iodised salt consumption. The Loglikelihood, Akaike Information Criterion and Bayesian Information Criterion were used to assess the goodness of fit of the models. All analyses were adjusted for the complex survey design and analysed using Stata 15.0 at p < 0.05.Results Our findings revealed that pregnant and breastfeeding women living in most deprived communities, with no formal education, poor wealth status, and those residing in the North West and South West region were more likely to consume salt with inadequate iodine.Conclusions There is a need to enhance women’s economic opportunities and empowerment as well as sensitisation on their nutritional requirements during pregnancy and breastfeeding.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".