Particulate Air Pollution and Primary Care Visits in Kosovo: A Time-Series Approach

Abstract

This study aimed to investigate the effects of particulate air pollution (PM2.5) on cardiovascular and respiratory diseases in Pristina, Kosovo, in a time-series analysis using daily primary healthcare visits to primary care institutions from 2019 to 2022. For the observation period, 6440 cardiovascular and 15,141 respiratory visits were reported, whereas the daily mean concentrations of PM2.5 ranged between 2.41 and 120.3 µg/m³. Single-lag models indicated a bi-phasic lag structure with increasing effect estimates some days after the air pollution event. In the distributed lag model with seven lags, the effect estimates for the cardiovascular cases indicated the adverse effect of air pollution. The cumulative effect estimate (summed over lag 0 to 6) for an increase of 10 µg/m³ of PM2.5 was a relative risk of 1.010 (95% confidence interval: 1.001–1.019). For respiratory cases, a different lag model (lag 4 through 10) was additionally examined. In this model, significant increases in visits were observed on lags 7 and 8. Overall, no relevant increase in visits occurred during the seven days considered. Visits to general practitioners will often not occur immediately at disease onset because patients will wait, hoping that their health status improves spontaneously. Therefore, we expected some latency in the effects.

Time series analysis in environmental epidemiology: challenges and considerations

“…But as an exception of that rule, suicides [4,5,17] and cerebral insults [18][19][20][21][22] have been successfully analyzed in TSA. In environmental epidemiology, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases like cardiovascular deaths [15], hospital admissions because of respiratory diseases [23] or GP consultations [24,25], with conventionally generalized linear models (GLMs) or generalized additive models (GAMs). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges [26].…”