Maternal Factors Contribute to Low Cranial Size among <i>Mam</i> ‐Mayan Infants in Guatemala
Bibliographic record
Abstract
Background In Guatemala, emerging research reveals a diversity of stressors in pregnancy and lactation impact early infant growth. Most research has focused efforts on uncovering associations with infant stunting. In comparison, rates of low cranial size, as measured by infant head circumference for age z scores (HCZ) < −2SD, and its potential causes are largely unknown. It is also unclear if a premature birth contributes to retarded infant HCZ in this population. Objectives The objectives were to: (1) quantify the prevalence of low cranial size at early (0–6 wks) and later (4–6 mo) post‐partum in a cohort of Mam ‐Mayan infants; (2) investigate the ΔHCZ from 0–6 wks to 4–6 mo; and to (3) explore the relative contribution of infant prematurity and maternal anthropometric measures to infant HCZ at both 0–6wks and 4–6 mo. Methods This study followed a cohort of Mam ‐Mayan mothers (n=81 mother‐infant dyads) in the Western Highlands of Guatemala from pregnancy through 6 months postpartum. Anthropometric measures were collected from mothers and infants at early (< 6 wks) and later (4–6 mo) postpartum. Gestational age (GA) was based on last menstrual period; infant prematurity was defined as a delivery <37wks of gestation. Rate of change for infant HCZ was calculated based on Δ HCZ from 0–6 wks to 4–6 mo divided by days postpartum. Correlations between HCZ, GA and anthropometric variables were explored. Separate multiple linear regression models for HCZ at 0–6 wks and 4–6 mo were investigated and unstandardized coefficients (B) reported. All models included the variables of GA (wks) and prematurity (yes/no). Statistical significance was set at p<0.05. Results At 0–6 wks, the prevalence of low cranial size (HCZ <‐2SD) was 16%; the median HCZ was −0.30 (mean HCZ −0.48±1.57). At 4–6 mo, the prevalence of low cranial size was 13%; the median HCZ was −0.48 (mean HZC −0.63±1.49). The mean infant Δ HCZ was −0.04±0.42/day with a mean GA of 34.49±1.4 wks; 23% of infants were premature. GA was positively correlated with infant HCZ (r=0.29, p=0.01) only at 0–6 wks. In bivariate analyses, HCZ at 0–6 wks was lower for premature (−1.31±1.82) compared to non‐premature infants (−0.25±1.42), but in unadjusted odds ratios (OR), premature birth (OR=2.64, p=0.134) was not associated with low cranial size at 0–6 wks. At 0–6 wks, multiple linear regression showed that only maternal weight during pregnancy (B=0.06, p=0.016) was positively associated with HCZ in a model that captured 16.3% of the variance while controlling for maternal height, GA, and prematurity, which were not significant. At 4–6 mo, multiple linear regression showed that infant HCZ at 0–6 wks (B=0.26, p=0.029) was positively associated with HCZ at 4–6 mo in an adjusted model that captured 11.2% of the variance and that controlled for maternal weight during pregnancy, maternal height, GA and prematurity, none of which were significant. Conclusion In contrast to our expectation, prematurity did not contribute to low cranial size when adjusting for maternal variables during pregnancy. Our study showed that maternal weight during pregnancy was associated with infant HCZ at 0–6 wks and that growth at 0–6 wks is strongly associated with HCZ at 4–6 mo. Our findings strongly suggest that both in utero and postnatal conditions contribute to low cranial size. Support or Funding Information CeSSIAM
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".