Changes in the prevalence of maternal chronic conditions during pregnancy: A nationwide age–period–cohort analysis
Bibliographic record
Abstract
OBJECTIVE: To estimate temporal changes in the prevalence of pre-existing chronic conditions among pregnant women in Sweden and evaluate the extent to which secular changes in maternal age, birth cohorts and obesity are associated with these trends. DESIGN: Population-based cross-sectional study. SETTING: Sweden, 2002-2019. POPULATION: All women (aged 15-49 years) who delivered in Sweden (2002-2019). METHODS: An age-period-cohort analysis was used to evaluate the effects of age, calendar periods, and birth cohorts on the observed temporal trends. MAIN OUTCOME MEASURES: Pre-existing chronic conditions, including 17 disease categories of physical and psychiatric health conditions recorded within 5 years before childbirth, presented as prevalence rates and rate ratios (RRs) with 95% confidence intervals (CIs). Temporal trends were also adjusted for pre-pregnancy body mass index (BMI) and the mother's country of birth. RESULTS: The overall prevalence of at least one pre-existing chronic condition was 8.7% (147 458 of 1 703 731 women). The rates of pre-existing chronic conditions in pregnancy increased threefold between 2002-2006 and 2016-2019 (RR 2.82, 95% CI 2.77-2.87). Rates of psychiatric (RR 3.80, 95% CI 3.71-3.89), circulatory/metabolic (RR 1.62, 95% CI 1.55-1.71), autoimmune/neurological (RR 1.69, 95% CI 1.61-1.78) and other (RR 2.10, 95% CI 1.99-2.22) conditions increased substantially from 2002-2006 to 2016-2019. However, these increasing rates were less pronounced between 2012-2015 and 2016-2019. No birth cohort effect was evident for any of the pre-existing chronic conditions. Adjusting for secular changes in obesity and the mother's country of birth did not affect these associations. CONCLUSIONS: The burden of pre-existing chronic conditions in pregnancy in Sweden increased from 2002 to 2019. This increase may be associated with the improved reporting of diagnoses and advancements in chronic condition treatment among women, potentially enhancing their fecundity.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".