Sixteen years analysis of PM2.5 and mortality in Kuwait

Abstract

BACKGROUND AND AIM: Middle Eastern desert countries like Kuwait are notoriously known for dramatic dust storms and enormous petrochemical industries affecting ambient air pollution. However, local health authorities have not been able to assess health impacts of air pollution due to limited monitoring networks and lack of historical exposure data. We aimed to estimate the relationship between estimated historical fine particulate matter (PM2.5) and mortality in Kuwait stratified by cause, gender and age. METHODS: We developed a novel approach using machine learning and remote sensing to estimate spatially and temporally resolved daily urban PM2.5 exposures from 2001 to 2016 in the country. We then used over-dispersed generalized additive models to investigate the association with cause-specific, gender- and age-stratified mortality timeseries. RESULTS:There was a total of 70,321 deaths during the study period of 16 years. The average urban PM2.5 was estimated to be 46.2±19.8 µg/m3. A 10 µg/m3 increase in a 5-day moving average of urban PM2.5 was associated with 1.31% (95% CI: 0.56 to 2.07%) increase in all-cause mortality and a 1.15% (0.13 to 2.18%) increase in daily cardiovascular deaths. The corresponding associations among males and females were 1.23% (0.29 to 2.17%) and 1.44% (0.33 to 2.56%), respectively. For the elderly (above 65 years) there was a 1.23% (0.15 to 2.31%) increase in all-cause mortality. The associations were unchanged after adjusting for dust storm days. CONCLUSIONS:We leveraged available pollution, weather and remote sensing data to predict historical PM2.5 with high temporal and spatial resolution. Urban PM2.5 concentrations were above the international regulatory limits. Our findings suggest that, PM2.5 is associated with increased mortality across different strata of the Kuwaiti population. The approach we used can be implemented in other countries that lack historical pollution data or those with insufficient monitoring networks. KEYWORDS: Desert, Dust storm, Kuwait, Middle East, Air Pollution, Machine Learning