Noise emerges as the most consistent environmental link to cardiovascular emergencies in Germany

A nationwide analysis of more than 3.7 million hospital-reported cardiovascular emergencies in Germany has found that most environmental factors that appeared to raise the risk of acute events lost their significance once socioeconomic and spatial confounders were taken into account. Only environmental noise remained consistently associated with higher risks across the full range of statistical adjustments, standing out against air pollution, temperature extremes, artificial light at night and vegetation cover, all of which showed far weaker or unstable relationships after careful modelling.

The study, published in Discover Public Health, was a retrospective ecological analysis covering all German districts between 2018 and 2023. Researchers led by Agnes Zwick of the University of Würzburg compiled hospital quality reports submitted annually to the Federal Joint Committee, identifying 3,784,737 acute cardiovascular emergencies across six diagnostic categories: ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), acute limb ischaemia, acute stroke, acute pulmonary embolism and aortic rupture. Only hospitals reporting at least five cases for any relevant ICD-10 code were included, in line with registry privacy rules that suppress small counts.

Alongside the hospital data, the team assembled sixteen environmental indicators for each district, drawing on open-source satellite data for vegetation, artificial light at night and land surface temperature, air and noise pollution data from the European Environment Agency, and climate information from the German Weather Service. Exposures were averaged primarily within inhabited areas, using 100 by 100 metre census grid cells, with district-wide averages calculated as a comparison. This design choice mattered: for several exposures, including artificial light at night and daytime land surface temperature, values were systematically higher when only populated areas were considered, because uninhabited countryside can dilute district-wide averages.

To relate these exposures to emergency case counts, the researchers used generalized additive models with a negative binomial distribution, modelling each exposure with a natural cubic spline to allow for non-linear relationships. The models adjusted for population size as an offset, calendar year, seven socioeconomic covariates from the INKAR database such as income composition, employment rate and share of foreign nationals, three land-cover variables, and a spatial Markov random field smooth that accounts for dependencies between neighbouring districts. Relative risks were estimated by comparing predicted incidence at the 95th versus the 5th percentile of each exposure distribution, with false discovery rate correction applied across the 96 exposure-outcome combinations tested.

The headline result concerns what survived this gauntlet. In unadjusted models, 51 of the 96 census-level exposure-outcome combinations were statistically significant after FDR correction. After simultaneous adjustment for socioeconomic and land-cover confounders plus spatial smoothing, only 12 remained significant. Noise exposure was the only environmental variable showing robust associations across adjustment levels, remaining significant for acute limb ischaemia (relative risk 2.47, 95 percent confidence interval 1.53 to 3.96), aortic rupture (RR 1.47, 95 percent CI 1.12 to 1.91) and pulmonary embolism (RR 1.27, 95 percent CI 1.02 to 1.58) after full adjustment. In unadjusted models, noise had been significantly associated with all six outcomes, with relative risks ranging from 1.67 to 3.36.

The fate of the other exposures tells a cautionary tale about ecological epidemiology. Artificial light at night, for example, was associated with increased relative risk across all six outcomes in unadjusted models, ranging from 1.93 for NSTEMI to 3.36 for aortic rupture. Elevated particulate matter concentrations, higher land surface temperatures, more frequent tropical nights and reduced vegetation cover were also consistently linked to higher case counts before adjustment. Yet adjustment profoundly altered these relationships, in many cases reversing their direction. Vegetation cover for acute limb ischaemia flipped from an apparent protective effect (RR 0.66) to a risk factor (RR 1.22) after full adjustment, and maximum air temperature for aortic rupture flipped from an increased risk (RR 1.49) to a protective estimate (RR 0.56). The authors note that these reversals are compatible with Simpson’s paradox, in which associations between two variables can reverse when an additional confounding variable is considered.

Correlation analyses offered a possible explanation. Environmental exposures such as artificial light at night (r = 0.87), tropical nights (r = 0.79) and nitrogen dioxide (r = 0.61) were strongly correlated with artificial land surfaces, suggesting they may act as proxies for urbanization rather than independent hazards. At the same time, cardiovascular incidence correlated positively with artificial surfaces (r = 0.48 to 0.58), potentially reflecting the concentration of healthcare infrastructure in cities rather than underlying disease burden. Land-cover variables and employment rate were the primary drivers of direction changes, occurring in 63 and 61 of the 96 combinations respectively. The authors emphasize that these findings should be regarded as exploratory rather than confirmatory, given the large number of combinations examined and the possibility of residual false positives even after FDR correction.

The study also documented the incidence patterns of the emergencies themselves. Population-weighted incidence declined across all conditions between 2018 and 2023: STEMI by 31 percent, NSTEMI by 34 percent, stroke by 27 percent, pulmonary embolism by 23 percent, acute limb ischaemia by 25 percent and aortic rupture by 28 percent, with the sharpest drops in 2020. Spatially, incidence was relatively homogeneous across Germany, with no pronounced north-south or east-west gradient, though the highest rates were predominantly observed in independent cities, with surrounding rural districts generally showing lower rates. The authors compared their pre-pandemic estimates with published German epidemiological data, noting broad consistency in trends, although their stroke estimate of 454 per 100,000 was considerably higher than earlier federal figures, likely reflecting differences in case definitions and data ascertainment.

Several important limitations temper the interpretation of the noise findings. Environmental exposures were assigned based on the district of the hospital location rather than patient residence, so the analyses describe area-level associations and should not be interpreted as individual-level risk relationships. Because patients can choose hospitals freely and specialized clinics are concentrated in urban areas, these facilities frequently receive patients from surrounding rural districts, which can inflate recorded case numbers. Residual confounding by hospital density, specialization and referral patterns therefore cannot be excluded and may have contributed to the observed associations, including those involving noise. Individual exposure is further influenced by commuting and mobility patterns, adding uncertainty. Noise exposure posed a particular challenge: owing to methodological changes in strategic noise mapping, only the 2017 noise dataset was available and was treated as time-invariant throughout the study period, an assumption that may have introduced misclassification as traffic patterns and mitigation measures changed.

The authors argue that the study’s principal lesson is methodological. When environmental exposures are aggregated at the district level, confounding by socioeconomic status and urbanization characteristics can substantially bias or even reverse observed associations, exemplifying Simpson’s paradox in ecological epidemiology. They call for environmental health studies using aggregated data to employ rigorous confounder adjustment and transparent reporting of both unadjusted and adjusted estimates, and for future research to integrate individual-level residential addresses with health outcomes where data protection permits, while accounting for mobility patterns and healthcare infrastructure. In the meantime, environmental noise stands as the one exposure in this nationwide dataset whose association with acute vascular emergencies, particularly acute limb ischaemia, aortic rupture and pulmonary embolism, withstood every statistical challenge the researchers could mount.

Subject of Research: Associations between district-level environmental exposures and acute cardiovascular emergencies across German districts

Article Title: Assessing the relationship of major cardiovascular emergencies and environmental factors in Germany

Article References: Zwick, A., Erbertseder, T., Behrendt, C.-A., Taubenböck, H., & Friesen, J. (2026). Assessing the relationship of major cardiovascular emergencies and environmental factors in Germany. Discover Public Health, 23(1), Article 1726. https://doi.org/10.1186/s12982-026-02560-3

Image Credits: AI Generated

DOI: 10.1186/s12982-026-02560-3

Keywords: cardiovascular emergencies, environmental noise, air pollution, artificial light at night, Germany, ecological study, generalized additive models, Simpson's paradox, confounding, hospital registry data, acute limb ischaemia, pulmonary embolism