The researchers believe that the decrease in childbirth has other, more likely explanations. Those who are now in their 30s, an age when it is most common to have children, were born in the second half of the 1990s. This was a time of financial difficulties and declining childbirth rates in Sweden. In other words, the group of current potential parents has become smaller due to declining birthrates 30 years earlier. Also, factors linked to the pandemic may have reduced childbirth, such as health and economic concerns and changed behavior during lockdown.

One of the strengths of the study is that it examines pregnancy outcomes in a large group that is representative of the country's population. The researchers have taken into account that the woman's age is a possible factor that could hide a possible effect of vaccines on childbirth, and they have therefore compensated for age in their analysis.

Dennis Nordvall et al, COVID-19 vaccination carries no association with childbirth rates in Sweden, Communications Medicine (2026). DOI: 10.1038/s43856-026-01396-x

Part 2

Extreme temperature changes increase number of out-of-hospital cardiac arrests, model finds
Extreme temperature fluctuations and higher relative humidity are associated with increased out-of-hospital cardiac arrest (OHCA) incidence. A machine learning model analyzing over 190,000 cases identified 17 environmental and social risk factors, accurately predicting OHCA patterns up to seven days in advance, even in previously unstudied regions. Social factors may amplify environmental risks.

Out-of-hospital cardiac arrest, or OHCA, is a leading cause of mortality worldwide and 90% of cases are fatal. Patients lose cardiac function and circulation, and every minute they remain untreated decreases the likelihood of a good outcome.

In a study published in npj Digital Medicine, a team of researchers developed a machine learning model that identified 17 environmental and social factors that can influence the risk of OHCA.

The researchers used patient data from the Cardiac Arrest Registry to Enhance Survival, or CARES, the largest national system that tracks OHCA. They built their model using more than 190,000 cases from 2013 to 2017 and identified 17 factors that can predict OHCA risk.

Mean ambient temperature, including both colder days and extremely warm ones, as well as higher relative humidity, influenced the number of OHCA incidents. Social factors, such as including poverty and race, may also amplify their impact.

The risks associated with cardiovascular events were mostly based on the individual risk factors, including hypertension. This new prediction model is the first to show that external environmental factors also influence risk.

In addition to its high prediction accuracy, the model was able to predict OHCA patterns up to seven days in advance.

Takahiro Nakashima et al, Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors, npj Digital Medicine (2025). DOI: 10.1038/s41746-025-02235-4

China awards its first ‘practical PhDs’

A cohort of PhD candidates in China have become the first in the country to be assessed on the basis of practical achievements instead of theses. This new class of PhDs is part of the Chinese government’s broader education reforms to cultivate ‘elite engineers’ that can help boost the country’s innovation. As part of their PhD defence, candidates have to make prototypes and prove that their inventions can be used at scale in the real world. Examples so far include the equipment for a new welding technique and a fire-fighting system for a large seaplane.

First ‘practical PhDs’ awarded in China — for products rather than ...