Evolution of infant mortality in the Paris region over the past two decades
Bibliographic record
Abstract
Abstract Background Infant mortality is one of the major health indicators to appreciate the population health and the organization of the health system. Since the 1990s, the infant mortality rate (IMR) declines less rapidly in France as compared to other Western countries, taking the country from 7th to 27th place in 2017. As the Paris region recorded a quarter of the under one French mortality, we aimed to analyze the evolution of the regional IMR over the last two decades. Methods Using 2000 to 2019 data from the National Institute of Statistic and Economic Study on births and deaths, we ran joinpoint regressions model to analyze the evolution of mortality of infant under one. We also examined IMR by age at death subgroups (early neonatal [Day 0-D6], late neonatal [D7-27], and post-neonatal[D28-364]). We also analyzed territorial disparities. Results Over 20 years, 13,401 deaths and 3,389,048 live births were recorded among children under one in the Paris region, IMR: 3.,93 deaths per 1000 live births, an average of 18% higher than the national IMR. Data from evolution shows that Paris region’ IMR was 4.51‰ in 2001 and reached its lowest level in 2013 with 3.65‰ before rising to 3.99‰ in 2019. The joinpoint regression model shows a decline in the IMR between 2000 and 2003 (-3.78% annually), followed by a slow and steady decline between 2003 and 2011 (-1.57%). However, from 2011 to 2019 a significant increase was observed in IMR (+1.48% annually). The analysis of deaths by age-group shows that the IMR increase was mainly driven by the increase in early neonatal mortality and a little less by late neonatal deaths while the post-neonatal mortality continued to decrease. Additional analyses showed territorial disparities in global and subgroups IMR with higher IMR in poorer areas. Conclusions These results are of a higher importance and should alert French authorities. Further studies, considering risk factors of infant mortality are needed to understand the reason of such increase. Key messages • We showed anhistoric and worrying increase in infant mortality in the Paris region. • We showed territorial disparities in global and subgroups IMR with higher IMR in poorer of the Paris region areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".