Is neighbourhood income inequality associated with maternal mental health? A longitudinal analysis of pregnant and new mothers living in Calgary, Alberta
Bibliographic record
Abstract
OBJECTIVES: Rising income inequality is a potential risk factor for poor mental health, however, little work has investigated this link among mothers. Our goal was to determine if neighbourhood-level income inequality was associated with maternal mental health over time. DESIGN: Secondary data analysis using a retrospective cohort study design. SETTING AND PARTICIPANTS: Data from the All Our Families (AOF) ongoing cohort study in the city of Calgary (Canada) were used, with our sample including 2461 mothers. Participant data were collected at six time points from 2008 to 2014, corresponding to <25 weeks of pregnancy to 3 years post partum. AOF mothers were linked to 196 geographically defined Calgary neighbourhoods using postal code information and 2006 Canada Census data. MAIN OUTCOME MEASURES: Anxiety symptoms measured using the Spielberger State Anxiety Inventory, and depressive symptoms measured using the Edinburgh Postnatal Depression Scale and the Centre for Epidemiologic Studies-Depression Scale. RESULTS: Multilevel regression modelling was used to quantify the associations between neighbourhood-level income inequality and continuous mental health symptoms over time. For anxiety symptoms, the interaction term between neighbourhood Gini and time was significant (β=0.0017, 95% CI=0.00049 to 0.0028, p=0.005), indicating an excess rate of change over time. Specifically, a SD increase in Gini (Z-score) was associated with an average monthly rate increase in anxiety symptom scores of 1.001% per month. While depressive symptom scores followed similar longitudinal trajectories across levels of income inequality, we did not find significant evidence for an association between inequality and depressive symptoms. There was no evidence of a cross-level interaction between inequality and household income on either outcome. CONCLUSION: Income inequality within neighbourhoods appears to adversely impact the mental health trajectories of pregnant and new mothers. Further research is needed to understand the mechanisms that explain this relationship, and how interventions to reduce income inequality could benefit mental health.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".