Environmental determinants of NSTEMI incidence: a population-based time-series analysis in southeast Germany

Introduction

Non-ST-elevation myocardial infarction (NSTEMI) is a multifactorial cardiovascular condition. In addition to established cardiovascular risk factors, short-term environmental exposures have been proposed as potential triggers of acute coronary events. However, the combined effects of multiple environmental factors at the population level remain insufficiently characterised.

Purpose

This study aimed to investigate the association between environmental factors and the incidence of NSTEMI within a defined population in Southeast Germany.

Methods

We conducted a retrospective population-based time-series analysis using anonymised statutory health insurance data provided by a German health insurance institute. Confirmed NSTEMI cases were identified according to ICD-10-GM criteria over the study period (2009–2023) and stratified by sex and age (<60 and ≥60 years). Daily case counts were temporally matched with region-wide environmental and meteorological data derived from a European reanalysis dataset and an atmospheric composition monitoring service.

Associations were estimated using generalised additive models with a quasi-Poisson distribution and distributed lag structures (weather ≤10 days; pollutants ≤7 days), adjusting for seasonality, long-term trends, and weekday effects.

Results

A total of 226,713 NSTEMI cases were identified (63% male, 37% female). Several environmental variables were significantly associated with changes in NSTEMI incidence, including windy days, frost days, heat waves (HW), particulate matter (PM₁₀), the wind chill index (WCI), daily mean air pressure, and heat stress wave duration and intensity.

WCI showed consistent positive associations across demographic subgroups (p values ranging from 0.001 to 0.004). Among men aged ≥60 years, both HW (p = 0.007) and WCI (p = 0.004) were significantly associated with increased NSTEMI incidence, whereas HW was not significant in women. In contrast, heat stress wave duration and intensity were primarily associated with NSTEMI incidence in women aged <60 years.

The strongest association was observed for windy days in women aged ≥60 years, with a relative risk of 1.07 (95% CI, 1.03–1.11), corresponding to a 6.8% increase in NSTEMI incidence.

Conclusion

Extreme environmental conditions appear to play a significant role in modulating the incidence of NSTEMI at the population level. The interaction of combined environmental factors may act as risk-enhancing triggers for acute coronary events. Incorporating environmental forecasting into regional healthcare management strategies may therefore contribute to improved preparedness and optimisation of cardiovascular care delivery.