Influence of air pollution on NSTEMI incidence and acute coronary syndrome mortality - the 10-year community-level analysis including 80 million patient-years. EP-PARTICLES study

Abstract

Abstract Introduction During the last years, air pollution has emerged as an important modifiable cardiovascular risk factor. Most research focused on heavy-polluted agglomerations and big cities, neglecting its influence on cardiovascular health in smaller cities and villages. Purpose The study aimed to assess the spatio-temporal trends and the influence of air pollution on the non-ST-elevation myocardial infarction (NSTEMI) incidence and acute coronary syndrome (ACS) mortality in an urban and rural environment. Moreover, the study aimed to investigate whether there is a connection between the influence of air pollution on hospitalization and cause-specific mortality. Methods The analysis included data on 7 main air pollutants in 709 communities in Eastern Poland in 2011-2020. Mortality data were obtained from National Health Fund and daily air pollution concentrations for each community were obtained from National Public Health Institute. Time-stratified panel data Poisson regression techniques were used. The influence of each air pollutant was analysed at a single-day lag (0-6 days) and multi-day lag (0-1, 0-6, 0-30 days) and presented as an odds ratio (OR) with a 95% confidence interval (CI) for an increase in 10 μg/m3. Results Incidence of NSTEMI was associated with exposition to benzo-a-pyrene (BaP) on all analysed lag patterns (OR 1.121, 95% CI 1.075-1.168 for lag 0, OR 1.241, 95% CI 1.169-1.317 for lag 0-30) and to particulate matter with a diameter of <10 and <2.5 μm (PM10, PM2.5), CO and NO2 on the most of analysed lag patterns (OR 1.01, 95% CI 1.005-1.015 for PM10 on lag 0, OR 1.013, 95% CI 1.007-1.02 for PM2.5 on lag 0, OR 1.017, OR 1.002, 95% CI 1.001-1.003 for CO on lag 0, 95% CI 1.007-1.028 for NO2 on lag 0). O3 and SO2 had a protective influence on NSTEMI incidence on longer lags (OR 0.984, 95% CI 0.977-0.992 for O3 on lag 0-30, OR 0.978, 95% CI 0.963-0.993 for SO2 on lag 5) Mortality due to the ACS was associated with an increase in NO2 and CO concentrations on all lag patterns (OR 1.03, 95% CI 1.02-1.04 for NO2 on lag 0, OR 1.089, 95% CI 1.068-1.11 for NO2 on lag 0-30, OR 1.003, 95% CI 1.002-1.004 for CO on lag 0, OR 1.089, 95% CI 1.068-1.11 for CO on lag 0-30). PMs concentrations had a protective effect on ACS mortality on all lag patterns (OR 0.971, 95% CI 0.966-0.976 for PM10 on lag 0, OR 0.964, 95% CI 0.958-0.971 for PM2.5 on lag 0). Conclusions An increase in most of the major air pollutants concentrations have an influence on NSTEMI incidence. Different lag patterns for air pollutants suggest various mechanisms of detrimental effect. Cause-specific mortality analysis showed the results mostly opposite to incidence analysis. It suggests that a high rate of garbage codes may prevent high-quality population analysis focusing on cause-specific mortality. The development of air quality monitoring stations and the correct use of ICD-10 diagnoses may be crucial in drawing important conclusions from environmental studies.