High temperatures could have caused more than 70,000 additional deaths in Europe in 2022

The burden of heat-related mortality during the summer of 2022 in Europe may have exceeded 70,000 deaths, according to a study led by the Barcelona Institute for Global Health (ISGlobal).

The authors of the study, published in The Lancet Regional Health – Europe, has revised upwards the first estimates of mortality linked to record temperatures in 2022 on the European continent. The study is entitled “The effect of aggregating temporal data to assess the impact of temperature changes in Europe: an epidemiological modeling study”.

In a previous study, published in Natural medicinethe same team used epidemiological models applied to weekly temperature and mortality data in 823 regions in 35 European countries and estimated the number of premature heat-related deaths in 2022 at 62,862.

In this study, the authors acknowledged that using weekly data would likely underestimate heat-related mortality and emphasized that daily time series data are needed to accurately estimate the impact of high temperatures on mortality.

The goal of the new study was to develop a theoretical framework capable of quantifying errors resulting from the use of aggregated data, such as weekly and monthly temperature and mortality time series. Models based on temporally aggregated data are useful because aggregated data are available in real time from institutions such as Eurostat, making it easier to quantify health risk within days of its onset.

To develop a theoretical framework, the research team pooled daily temperature and mortality records from 147 regions in 16 European countries. They then analyzed and compared estimates of heat- and cold-related mortality at different levels of aggregation: daily, weekly, bi-weekly and monthly.

The analysis revealed differences in epidemiological estimates depending on the aggregation time scale. In particular, the weekly, biweekly and monthly models were found to underestimate the effects of heat and cold compared to the daily model, and the degree of underestimation increased with the length of the aggregation period .

Specifically, for the period 1998-2004, the daily model estimated annual cold- and heat-related mortality of 290,104 and 39,434 premature deaths, respectively, while the weekly model underestimated these figures by 8. 56% and 21.56%, respectively.

“It is important to note that the differences were very small during periods of extreme cold and heat, such as summer 2003, when the model’s underestimation of weekly data was only 4.62%,” explains Joan Ballester Claramunt, ISGlobal researcher. leads the European Research Council project EARLY-ADAPT.

The team used this theoretical framework to revise the mortality burden attributed to record temperatures recorded in 2022 in their earlier study. According to calculations made using the new methodological approach, this study underestimated heat-related mortality by 10.28%, which would mean that the actual burden of heat-related mortality in 2022, estimated at using the daily data model, was 70,066 deaths, not 62,862 deaths as initially estimated.

Use weekly data to analyze short-term temperature effects

“In general, we do not find models based on aggregated monthly data useful for estimating the short-term effects of ambient temperatures,” says Ballester.

“However, models based on weekly data provide sufficient precision in mortality estimates to be useful in the real-time practice of epidemiological surveillance and to inform public policies such as, for example, the activation of health plans. “urgently to reduce the impact of heat waves and cold snaps.”

It is an advantage in this area of ​​research to be able to use weekly data, because investigators often face bureaucratic obstacles that make it difficult or impossible to design large-scale epidemiological studies based on daily data.

According to Ballester, when daily data is not available, the use of weekly data, easily accessible for Europe in real time, is a solution that can offer “a good approximation of the estimates obtained using the data model daily”.