Birth outcomes, pregnancy complications, and postpartum mental health after the 2013 Calgary flood: A difference in difference analysis
Bibliographic record
Abstract
BACKGROUND: In June 2013, the city of Calgary, Alberta and surrounding areas sustained significant flooding which resulted in large scale evacuations and closure of businesses and schools. Floods can increase stress which may negatively impact perinatal outcomes and mental health, but previous research is inconsistent. The objectives of this study are to examine the impact of the flood on pregnancy health, birth outcomes and postpartum mental health. METHODS: Linked administrative data from the province of Alberta were used. Outcomes included preterm birth, small for gestational age, a new diagnoses of preeclampsia or gestational hypertension, and a diagnosis of, or drug prescription for, depression or anxiety. Data were analyzed using a quasi-experimental difference in difference design, comparing flooded and non-flooded areas and in affected and unaffected time periods. Multivariable log binomial regression models were used to estimate risk ratios, adjusted for maternal age. Marginal probabilities for the difference in difference term were used to show the potential effect of the flood. RESULTS: Participants included 18,266 nulliparous women for the pregnancy outcomes, and 26,956 women with infants for the mental health analysis. There were no effects for preterm birth (DID 0.00, CI: -0.02, 0.02), small for gestational age (DID 0.00, CI: -0.02, 0.02), or new cases of preeclampsia (DID 0.00, CI: -0.01, 0.01). There was a small increase in new cases of gestational hypertension (DID 0.02, CI: 0.01, 0.03) in flood affected areas. There were no differences in postpartum anxiety or depression prescriptions or diagnoses. CONCLUSION: The Calgary 2013 flood was associated with a minor increase in gestational hypertension and not other health outcomes. Universal prenatal care and magnitude of the disaster may have minimized impacts of the flood on pregnant women.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".