Depressed Women of Low Socioeconomic Status Have High Numbers of Physician Visits in the Year Before Pregnancy: Implications for Care
Bibliographic record
Abstract
BACKGROUND: There is a higher prevalence of depression in women of low socioeconomic status (SES) than other women. Further, previous depression is the best predictor of future depression. Therefore, due to the negative effects of maternal depression on the fetus and subsequent child, particularly in combination with low SES, depression is ideally treated before pregnancy. During the year before pregnancy and by SES, we aimed to assess the odds of a physician visit associated with maternal depression and the mean number of physician visits in women by depressive status. METHODS: We used population-based registry data of 243,933 women with 348,273 singleton live births in British Columbia from 1999 - 2009 and estimated family SES decile using tax-file data. Mixed effects logistic regression, adjusting for maternal age and parity, was used to calculate odds ratios and a two-sided, two-sample test was used to compare proportions. STATA 14 was used for analyses. RESULTS: Compared to women of middle SES (Decile-6), women of low SES (from Decile-1, Decile-2) had higher odds of more than 20 physician visits whether depressed (aOR = 1.46 (95% CI: (1.15, 1.86); aOR = 1.26 (95% CI: (0.98, 1.61)) or non-depressed (aOR = 1.26 (95% CI: (1.13, 1.41); aOR = 1.24 (95% CI: (1.11, 1.38)) during the year before pregnancy. During pre-pregnancy, depressed women had more than three times the mean number of physician visits than non-depressed women: (8.56 (8.38, 8.73) versus (2.59 (2.57, 2.61), P < 0.00005. CONCLUSIONS: Physicians have ample opportunities to assess women of child-bearing age for depression and to refer for appropriate treatment. It is particularly important that physicians pay extra attention to identify depression in those of lower SES who are likely to become pregnant. Further, identifying depression and providing appropriate referral for treatment in all women who are likely to become pregnant, are already pregnant or are caring for children is important. In such a way, the possible negative effects of prenatal and post-partum depression, along with the interactive effects of low SES on the child, might be reduced.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".