Chronic diseases in pregnant women: prevalence and birth outcomes based on the SNiP-study
Bibliographic record
Abstract
BACKGROUND: The subject of "pregnancy and disease" is of particular importance for maternal well-being and neonatal outcomes. The international literature has focused on acute diseases during pregnancy; however, there are only a few studies investigating chronic diseases in pregnant women. The focus of this study is on diseases of women in childbearing age that are not related to the pregnancy. The objective of the paper is to deliver population based prevalences of chronic dieases in childbearing women and compare the two groups of chronically ill women and healthy women in detail regarding sociodemography, peri- and prenatal parameters and birth outcomes. METHODS: Data of n = 5320 childbearing women were evaluated in the context of the population-based Survey of Neonates in Pomerania (SNiP). Data were obtained via face-to-face interviews, self-applied questionnaires, and abstraction from medical records at the time of giving birth. Sociodemographic and health status data were assessed, including chronic diseases that were taken out of medical records. A comprehensive set of pre- and perinatal varaiables were assessed. RESULTS: In the SNiP, every fifth pregnant woman suffers from at least one chronic disease, and higher prevalence rates have been reported in the literature. There was a significant difference between chronically ill women and healthy women in age, education and income. Prenatal complications were more frequent in the healthy group than in the chronic disease group. Women with chronic diseases delivered by Cesarean section more frequently than women in the healthy group. Every tenth woman with at least one chronic disease gave birth to a premature infant, while only one in every 13 woman in the healthy control group gave birth to a premature infant. CONCLUSIONS: This analysis is the first population-based study in which all chronic diseases could be taken into consideration. The population-based prevalences rates in the SNiP data are consistently lower than those found in the literature. There are differences between chronically ill women and healthy women in peri- and prenatal variables as well as birth outcome on the population level. However, they are less frequent than expected and further analyses are need focusing on specific diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".