Mortality Among Parents of Children With Major Congenital Anomalies
Bibliographic record
Abstract
BACKGROUND: A mother whose child has a chronic condition, such as a major congenital anomaly, often experiences poorer long-term health, including earlier mortality. Little is known about the long-term health of fathers of infants with a major congenital anomaly. METHODS: In this population-based prospective cohort study, we used individual-linked Danish registry data. Included were all mothers and fathers with a singleton infant born January 1, 1986, to December 31, 2015. Cox proportional hazards regression was used to generate hazard ratios for all-cause and cause-specific mortality among mothers and fathers whose infant had an anomaly and fathers of unaffected infants, relative to mothers of unaffected infants (referent), adjusted for child's year of birth, parity, parental age at birth, parental comorbidities, and sociodemographic characteristics. RESULTS: In total, 20 952 of 965 310 mothers (2.2%) and 20 655 of 951 022 fathers (2.2%) had an infant with a major anomaly. Median (interquartile range) of parental follow-up was 17.9 (9.5 to 25.5) years. Relative to mothers of unaffected infants, mothers of affected infants had adjusted hazard ratios (aHRs) of death of 1.20 (95% confidence interval [CI]: 1.09 to 1.32), fathers of unaffected infants had intermediate aHR (1.62, 95% CI: 1.59 to 1.66), and fathers of affected infants had the highest aHR (1.76, 95% CI: 1.64 to 1.88). Heightened mortality was primarily due to cardiovascular and endocrine/metabolic diseases. CONCLUSIONS: Mothers and fathers of infants with a major congenital anomaly experience an increased risk of mortality, often from preventable causes. These findings support including fathers in interventions to support the health of parental caregivers.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".