Births in the Nordics 2021 to 2022—Pandemic fluctuation or fundamental shift?
Bibliographic record
Abstract
The effects of the COVID-19 pandemic on general and particularly birth preterm rates have been repeatedly analysed.1-3 At the recent Nordic Neonatal Meeting in Oslo, November 2023, we engaged in discussions concerning the total number of births in Denmark and Sweden. Both countries experienced an unexpected and unexplained decline in number of births in 2022. Upon delving into the data, we discovered that this phenomenon was not restricted to Denmark and Sweden but was observed across all five Nordic countries. The combined total number of births for the Nordic region declined from 288 269 in 2021 to 263 986 in 2022 (−8.4%)—marking the lowest total births in 30 years. A closer examination over time revealed a preceding rise in number of births in 2021 (Figure 1; Appendices S1 and S2), prompting us to extend our data inspection beyond the Nordic countries. This pattern was not limited to the Nordic countries. Data from national databases in France, Germany and England as well as Canada and Australia also showed an increase in births in 2021, followed by a notable decline in 2022 (Appendices S1–S3). While caution must be exercised in interpreting aggregated rate data due to the potential for ecological bias, we propose that the COVID-19 pandemic may offer one plausible explanation. Media speculation during the initial lockdowns in early 2020 anticipated a rise in birth rates in the subsequent year due to couples spending more time together in isolation. Surprisingly, it appears that this speculation can have been accurate, leading to an increase in births in 2021. Subsequently, the following year 2022 witnessed a decline in births, possibly partly as families already had a child the previous year. Sociological and demographic studies have identified fluctuations in fertility rates in multiple countries, including England and Norway, between 2020 and 2021.4, 5 These studies offer in-depth analysis of the phenomenon in single countries and suggest a causal effect of the pandemic and its mitigation strategies on fertility rates, but also highlight moderating effects of various covariates, including maternal age, parity, occupational status, socio-economic class, housing, education and ethnicity. Notably, fertility rate changes associated with these variables were already occurring in many countries pre-pandemic. Given the possible association between socio-economic factors and preterm birth risk, we propose their potential explanatory role in preterm birth rate fluctuations during the pandemic. When investigating causal relationships between the pandemic itself, its mitigation strategies (e.g., lockdowns), or derived effects (e.g., reduced overall viral burden, improved air quality) and preterm birth rates, socio-economic and demographic variables might act as confounders. Study designs should consider addressing this potential issue. Future analyses of this proposed association may prioritise the use of cohort data with longitudinal individual information, as opposed to relying solely on cross-sectional aggregated rate data. Longitudinal follow-up will determine whether 2021–2022 was just a fluctuation possibly associated with the pandemic or mitigation strategies, or a starting point for a more long-lasting decline in fertility, which warrants a deeper explanation. Jesper Padkær Petersen: Conceptualization; writing – original draft; methodology; formal analysis; writing – review and editing. Heidi Cueto: Conceptualization; methodology; writing – review and editing; formal analysis. Mikael Norman: Conceptualization; methodology; writing – review and editing; formal analysis. None. None. Appendix S1. Appendix S2. Appendix S3. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 teacher head, 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".