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Record W4321610021 · doi:10.1016/s2468-2667(23)00001-4

Effect of covering perinatal health-care costs on neonatal outcomes in Switzerland: a quasi-experimental population-based study

2023· article· en· W4321610021 on OpenAlexaff
Adina Mihaela Epure, Émilie Courtin, Philippe Wanner, Arnaud Chioléro, Stéphane Cullati, Cristian Carmeli

Bibliographic record

VenueThe Lancet Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsMcGill University
FundersMedical Research CouncilNCCR CatalysisBundesamt für GesundheitSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicinePregnancyPopulationPediatricsRegression discontinuity designGestational agePublic healthLow birth weightInfant mortalityObstetricsEnvironmental healthNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Low birthweight and preterm birth are associated with an increased risk of neonatal death and chronic conditions across the life course. Reducing these adverse birth outcomes is a global public health priority and requires strategies to improve health care during pregnancy. We aimed to assess the effect of a Swiss health policy expansion fully covering illness-related costs during pregnancy on health outcomes in newborn babies. METHODS: We implemented a quasi-experimental difference in regression discontinuity design to assess the effect of expansion of Swiss health insurance (on March 1, 2014), to fully cover health-care costs during pregnancy and 8 weeks postpartum, on neonatal outcomes. Before this reform, only costs specific to the standard monitoring of a normal pregnancy were covered. Babies born before March 1, 2014, and their mothers were assigned to the unexposed group, and babies born on or after March 1, 2014, and their mothers were assigned to the exposed group. We included nearly all children born 2011-19 in Switzerland within a period of 9 months around the date March 1, 2014, and control years 2012, 2016, and 2018. Outcomes were birthweight, low birthweight, very low birthweight, gestational age, preterm or extremely preterm birth, and neonatal death. We estimated the intention-to-treat effect of the policy using parametric regression models. FINDINGS: 61 910 children were born 9 months before and 63 991 were born 9 months after March 1, 2014. 382 861 children were born in the same time period around the three control dates. In the period before policy implementation, mean birthweight was 3289 g, gestational age was 275 days, and 6·5% of children had low birthweight, 1·0% very low birthweight, 7·1% were preterm, 0·4% were extremely preterm, and 0·3% died within the first 28 days of life. After initiation of the policy (vs before) mean birthweight increased by 23 g (95% CI 5 to 40) and the predicted proportion of low birthweight births decreased by 0·81% (0·14 to 1·48) and of very low birthweight births decreased by 0·41% (0·17 to 0·65). The effect on very low birthweight was not robust in sensitivity analyses. The policy had a negligible effect on gestational age (mean difference 1 day, 95% CI 0 to 1) and no clear effects on the other examined outcomes. The change in predicted proportion for preterm births was -0·39% (95% CI -1·2 to 0·38), for extremely preterm births was -0·09% (-0·27 to 0·08), and for neonatal death was -0·07% (-0·2 to 0·07). INTERPRETATION: Free access to prenatal care in Switzerland reduced the risk of some adverse health outcomes in newborn babies. Expanding health-care coverage is a relevant health system intervention to reduce the risk of adverse health outcomes in the newborn baby and, potentially, across the life course. FUNDING: Swiss National Science Foundation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.406
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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