Estimating the cause-specific relative risks of non-optimal temperature on daily mortality: a two-part modelling approach applied to the Global Burden of Disease Study

Abstract

Background: Associations between high and low temperatures and increases in mortality and morbidity have been previously reported, yet no comprehensive assessment of disease burden has been done. Therefore, we aimed to estimate the global and regional burden due to non-optimal temperature exposure.

Methods: In part 1 of this study, we linked deaths to daily temperature estimates from the ERA5 reanalysis dataset. We modelled the cause-specific relative risks for 176 individual causes of death along daily temperature and 23 mean temperature zones using a two-dimensional spline within a Bayesian meta-regression framework. We then calculated the cause-specific and total temperature-attributable burden for the countries for which daily mortality data were available. In part 2, we applied cause-specific relative risks from part 1 to all locations globally. We combined exposure-response curves with daily gridded temperature and calculated the cause-specific burden based on the underlying burden of disease from the Global Burden of Diseases, Injuries, and Risk Factors Study, for the years 1990-2019. Uncertainty from all components of the modelling chain, including risks, temperature exposure, and theoretical minimum risk exposure levels, defined as the temperature of minimum mortality across all included causes, was propagated using posterior simulation of 1000 draws.

Findings: We included 64·9 million individual International Classification of Diseases-coded deaths from nine different countries, occurring between Jan 1, 1980, and Dec 31, 2016. 17 causes of death met the inclusion criteria. Ischaemic heart disease, stroke, cardiomyopathy and myocarditis, hypertensive heart disease, diabetes, chronic kidney disease, lower respiratory infection, and chronic obstructive pulmonary disease showed J-shaped relationships with daily temperature, whereas the risk of external causes (eg, homicide, suicide, drowning, and related to disasters, mechanical, transport, and other unintentional injuries) increased monotonically with temperature. The theoretical minimum risk exposure levels varied by location and year as a function of the underlying cause of death composition. Estimates for non-optimal temperature ranged from 7·98 deaths (95% uncertainty interval 7·10-8·85) per 100 000 and a population attributable fraction (PAF) of 1·2% (1·1-1·4) in Brazil to 35·1 deaths (29·9-40·3) per 100 000 and a PAF of 4·7% (4·3-5·1) in China. In 2019, the average cold-attributable mortality exceeded heat-attributable mortality in all countries for which data were available. Cold effects were most pronounced in China with PAFs of 4·3% (3·9-4·7) and attributable rates of 32·0 deaths (27·2-36·8) per 100 000 and in New Zealand with 3·4% (2·9-3·9) and 26·4 deaths (22·1-30·2). Heat effects were most pronounced in China with PAFs of 0·4% (0·3-0·6) and attributable rates of 3·25 deaths (2·39-4·24) per 100 000 and in Brazil with 0·4% (0·3-0·5) and 2·71 deaths (2·15-3·37). When applying our framework to all countries globally, we estimated that 1·69 million (1·52-1·83) deaths were attributable to non-optimal temperature globally in 2019. The highest heat-attributable burdens were observed in south and southeast Asia, sub-Saharan Africa, and North Africa and the Middle East, and the highest cold-attributable burdens in eastern and central Europe, and central Asia.

Interpretation: Acute heat and cold exposure can increase or decrease the risk of mortality for a diverse set of causes of death. Although in most regions cold effects dominate, locations with high prevailing temperatures can exhibit substantial heat effects far exceeding cold-attributable burden. Particularly, a high burden of external causes of death contributed to strong heat impacts, but cardiorespiratory diseases and metabolic diseases could also be substantial contributors. Changes in both exposures and the composition of causes of death drove changes in risk over time. Steady increases in exposure to the risk of high temperature are of increasing concern for health.

Funding: Bill & Melinda Gates Foundation.

Figure 1

Exposure–response curves displaying the relationship between daily mean temperature and the log RR for mortality from LRI, IHD, stroke, COPD, drowning, and self-harm by temperature zone (A) RR estimates per daily mean temperature category and estimated exposure–risk curves. (B) Estimated exposure–risk functions. The RR is referenced to the TMREL, which represents the minimum mortality temperature for death-weighted multicause curves in each mean annual temperature category. The grey solid line depicts the location of the TMREL in each mean annual temperature category, with dashed lines depicting 95% UI of the TMREL. RR=relative risk. LRI=lower respiratory infections. IHD=ischaemic heart disease. COPD=chronic obstructive pulmonary disease. TMREL=theoretical minimum-risk exposure level. UI=uncertainty interval.

Figure 1

Exposure–response curves displaying the relationship between daily mean temperature and the log RR for mortality from LRI, IHD, stroke, COPD, drowning, and self-harm by temperature zone (A) RR estimates per daily mean temperature category and estimated exposure–risk curves. (B) Estimated exposure–risk functions. The RR is referenced to the TMREL, which represents the minimum mortality temperature for death-weighted multicause curves in each mean annual temperature category. The grey solid line depicts the location of the TMREL in each mean annual temperature category, with dashed lines depicting 95% UI of the TMREL. RR=relative risk. LRI=lower respiratory infections. IHD=ischaemic heart disease. COPD=chronic obstructive pulmonary disease. TMREL=theoretical minimum-risk exposure level. UI=uncertainty interval.

Figure 2

Composition of deaths and DALYs attributable to high and low temperatures in 2019 by level 2 GBD causes The x-axis scales vary by location. High and low temperatures are defined as the temperatures above (heat) and below (cold) the location and year-specific theoretical minimum risk exposure level. Cardiovascular diseases were ischaemic heart disease, stroke, hypertensive heart disease, and cardiomyopathy and myocarditis. Chronic respiratory diseases was chronic obstructive pulmonary disease. Respiratory infections and tuberculosis was lower respiratory infections. Diabetes and kidney diseases were diabetes and chronic kidneydisease. Self-harm and interpersonal violence were homicide and suicide. Transport injuries were road injuries and other transport-related injuries. Unintentional injuries were drowning, mechanical injuries and other unintentional injuries, animal-related, and disaster-related injuries. DALYs=disability-adjusted life-years. GBD=Global Burden of Disease.

Figure 3

Time series plots from 1990 to 2019 of high and low temperature SEVs SEVs are a measure of risk-weighted prevalence for high and low temperature exposure and range from 0% to 100%. A SEV of 0% reflects no risk exposure, while 100% indicates that the entire population is exposed to the maximum possible level for that risk. The shaded areas indicate 95% UIs. SEV=summary exposure value. UI=uncertainty interval.

Figure 4

Spatial distribution of all-cause DALYs (per 100 000) attributable to high temperature (A), low temperature (B), and non-optimal temperature (C) exposure in 2019 DALYs=disability-adjusted life-years.

Figure 4

Spatial distribution of all-cause DALYs (per 100 000) attributable to high temperature (A), low temperature (B), and non-optimal temperature (C) exposure in 2019 DALYs=disability-adjusted life-years.