Examining Neighbourhood Socioeconomic Status, Anxiety and Depression during Pregnancy, and Preterm Birth
Bibliographic record
Abstract
Background: Understanding of influence of anxiety, depression, and neighbourhood socioeconomic status (SES) on the risk of preterm birth (PTB) is unclear. This doctoral research examined the ability of neighbourhood SES to predict the risk of PTB, the utility of existing anxiety scales in measuring anxiety in pregnancy, and whether neighbourhood SES modified the association between anxiety and depression during pregnancy and PTB. Methods: This study used data from two pregnancy cohort studies in Alberta, Canada (n=5,528). The data were linked to neighbourhood SES data, derived from the Canadian census. A multilevel logistic regression prediction model was developed to examine whether neighbourhood SES improves the prediction of PTB. Confirmatory factor analysis and Spearman correlation were used to examine the utility of anxiety scales in pregnancy. A multivariable logistic regression model was used to assess whether neighbourhood SES modifies the association between anxiety and/or depression and PTB. Results: Neighbourhood level variance explained PTB by 6%. Neighbourhood SES combined with maternal characteristics predicted PTB with an area under the receiver operating characteristic curve (AUC) of 0.75. Maternal characteristics alone had AUC of 0.60. The model fit of anxiety scales ranged from inadequate to adequate. The correlation between the scales was low to moderate. The presence of both anxiety and depression, but neither anxiety nor depression alone, was significantly associated with PTB (OR=1.57, 95% CI=1.07, 2.29) and had significant interaction with neighbourhood deprivation (p-value=0.014). Conclusions: This research may suggest that women’s neighbourhood SES improves overall prediction of PTB and that it modifies the effects of anxiety and depression on risk of PTB. It may also indicate that existing anxiety scales do not measure anxiety as a single dimension and they are incomparable. These findings may guide the identification of women at increased risk for PTB and future research in the field.
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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 | 0.003 |
| 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 | 0.001 |
| 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".