Impact of maternal depression and anxiety on immunization status of children: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Maternal depression and anxiety can have a detrimental impact on birth outcomes and healthy child development; there is limited knowledge on its influence on immunization schedule adherence. Therefore, the objectives of this study were to determine the impact of maternal depression and anxiety in the perinatal period on prolonged vaccine delay of childhood vaccines. METHODS: In this prospective cohort study, we analyzed linked survey and administrative data of 2,762 pregnant women in Calgary, Alberta, Canada. Data were collected at two time-points: prenatal (< 25 weeks of gestation) and postpartum (4 months postpartum). We used multivariable logistic regression to examine the association between depression and anxiety with prolonged immunization delay, adjusting for covariates. RESULTS: In multivariable analysis, maternal depression at either time point was not associated with prolonged delay for DTaP-IPV-Hib (OR 1.16, 95% CI 0.74-1.82), MMR/MMRV (OR 1.03, 95% CI 0.72-1.48), or all routine childhood vaccines combined (OR 1.32, 95% CI 0.86-2.04). Maternal anxiety at either time point was also not associated with prolonged delayed for DTaP-IPV-Hib (OR 1.08, 95% CI 0.77-1.53), MMR/MMRV (OR 1.07, 95% CI 0.82-1.40), or all vaccines combined (OR 1.00, 95% CI 0.80-1.26). In both the depression and anxiety models, children of Canadian-born mothers had higher odds of prolonged delay, as did those with low-income mothers. CONCLUSION: Health care providers can be reassured that maternal depression and anxiety do not appear to influence maternal commitment to routine immunization. Findings suggested that low income and household moves may influence adherence to vaccine schedules and health care providers may want to provide anticipatory guidance to these families.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".