Citation

Sarmiento Suarez R, Osorio Garcia S, Pineda Lozano JC, Alvis Zakzuk NJ, Carrasquilla Sotomayor M, Veillard J. ISEE Conf. Abstr. 2024; 2024(1): e0843.

Copyright

(Copyright © 2024, The author(s) or International Society for Environmental Epidemiology, Publisher EHP Publishing)

DOI

10.1289/isee.2024.0843

PMID

unavailable

Abstract

ISEE 2024: 36th Annual Conference of the International Society of Environmental Epidemiology

BACKGROUND AND AIM[|]Due to its geographical diversity, Colombia is exposed to both heat and cold impacts associated with climate change but no previous research have analysed such effects at subnational level. The aim of this study was to estimate the burden of mortality attributable to non-optimal temperature by Colombia's departments from 2010 to 2019.[¤]METHOD[|]Data sources comprised daily estimates of the satellite temperature provided by ERA-5 database, gridded population from WorldPop and mortality data from the National Vital Statistics between 2010 and 2019. We obtained the average of each department by the interpolation of every temperature pixels weighted by population. In addition, we calculated RR and PAF by cause using the exposure response curves and the TMREL provided by the GBD Study for Colombia. Finally, we calculated exposure response functions by mortality cause and location as well as YLL attributable to heat and cold for each cause and department.[¤]RESULTS[|]The average temperature in Colombia was 23.1ºC (SD: 3.92), with the highest mean in Sucre (28.18 ºC, SD: 1.26) and the lowest registered in Bogota (13.08 ºC, SD: 2.52). Death rates attributable to cold (15.4 per million) were higher than those associated with heat (5 per million). Regarding YLL, cold accounted for 109,313 and heat for 63,557 years. Outcomes with the highest PAF associated with cold were drowning, COPD, and lower respiratory infection whereas the main causes of heat-related burden were homicides, traffic accidents and ischemic heart disease. Regarding geographic distribution, the Caribbean region had the highest burden due to heat and the Central Andean region had the highest burden due to cold.[¤]CONCLUSIONS[|]These findings allowed us to identify hotspots of vulnerability and regional burden due to non-optimal temperature exposure. Also, serves as a baseline to carry out health impact assessments of adaptation and mitigation measures according to different climate change scenarios.[¤]