Maternal mental health and nutritional status of infants aged under 6 months: a secondary analysis of a cross-sectional survey
Bibliographic record
Abstract
ABSTRACT Maternal/caregivers’ mental health (MMH) and child nutrition are both poor in low- and middle-income countries. Links between the two are plausible but poorly researched. Our aim was to inform future malnutrition management programmes by better understanding associations between MMH and the nutritional status of infants aged u6m. We conducted a health facility-based cross-sectional survey of 1060 infants in rural Ethiopia, between October 2020 and January 2021. We collected data on: MMH status (exposure) measured using the Patient Health Questionnaire (PHQ-9) and infant anthropometry (outcome); length for age Z-score (LAZ), weight for age Z-score (WAZ), weight for length Z-score (WLZ), mid upper arm circumference (MUAC), head circumference for age Z-score (HCAZ) and lower leg length (LLL). Linear regression analysis was used to determine associations between exposure and outcome variables. Mean (SD) age was 13.4 (6.2) weeks. The median score for MMH problem was 0 (inter quartile range 0 - 2) and 29.5 and 11.2% reported minimal and mild to severe depression score of 1-4 and 5-25, respectively. Mean (SD) LAZ was -0.4 (1.4), WAZ -0.7 (1.3), WLZ -0.5 (1.2), MUAC 12.4 (1.3) centimetre, HCAZ 0.4 (1.3) and LLL 148 (13.9) millimetre. In adjusted analysis, minimal MMH problems was associated with infant LAZ marginally (β=-0.2; 95% CI: -0.4, 0.001) and LLL (β=-2.0; 95% CI: -3.8, -0.1), but not with other anthropometric measurements. Significant associations were not found between mild to severe depressive symptoms and infant anthropometric outcomes. Covariates positively associated with infant anthropometric measurements were higher wealth index with LAZ (β=0.08, 95% CI: 0.03, 0.13), WAZ (β=0.12, 95% CI: 0.08, 0.17), WLZ (β=0.09, 95% CI: 0.05, 0.13), MUAC (β=0.06, 95% CI: 0.02, 0.11), and HCAZ (β=0.07, 95% CI: 0.03, 0.12); higher maternal schooling with LAZ (β=0.24, 95% CI: 0.05, 0.43) and WAZ (β=0.24, 95% CI: 0.07, 0.41); female sex with WAZ (β=0.16, 95% CI: 0.01, 0.31) and HCAZ (β=0.16, 95% CI: 0.001, 0.31); higher maternal age with LLL (β= 0.29, 95% CI: 0.07, 0.52); and improved water, sanitation and hygiene status with MUAC (β=0.07, 95% CI: 0.01, 0.12) and LLL (β=0.64, 95% CI: 0.04, 1.24). Covariates negatively associated with infant anthropometric measurements include female sex with MUAC (β=-0.33, 95% CI: - 0.48, -0.18) and LLL (β=-2.51, 95% CI: -4.15, -0.87); higher household family size with WLZ (β=-0.08, 95% CI: -0.13, -0.02); exclusive breastfeeding with MUAC (β=-0.39, 95% CI: -0.55, - 0.24) and LLL (β=-7.37, 95% CI: -9.01, -5.75); and grandmother family support with WAZ (β=- 0.2, 95% CI: -0.3, -0.0001) and WLZ (β=-0.2, 95% CI: -0.4, 0.1). In conclusion, only minimal, but not mild, moderate or severe, maternal/caregivers’ depressive symptoms are associated with infant anthropometry outcomes. Whilst plausible relationship between maternal mental health problems and offspring nutritional status exist, we are not able to show this because of small number of participants with moderate to severe level of depression in our study population. Thus, further evidence to understand and establish robust relationship between maternal mental health and offspring nutritional status is required.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".