A cold-health watch and warning system, applied to the province of Quebec (Canada)

Burden of chronic obstructive pulmonary disease attributable to non-optimal temperature from 1990 to 2019: a systematic analysis from the Global Burden of Disease Study 2019

Chronic obstructive pulmonary disease (COPD) has been the third leading cause of death worldwide. As the traditional risk factors (like smoking and ambient air pollution) on the burden of COPD being well characterized, the burden of COPD due to non-optimal temperature has been widely concerned. In this study, we extracted the relevant burden data of COPD attributable to non-optimal temperature from GBD 2019 and adopted estimated annual percent changes, Gaussian process regression (GPR), and age-period-cohort model to evaluate the spatiotemporal patterns, relationships with socio-demographic level, and the independent effects of age, period and cohort from 1990 to 2019. In brief, the global COPD burden attributable to non-optimal temperatures showed declining trends but was still more severe in the elderly, males, Asia, and regions with low socio-demographic index (SDI). And cold had a greater burden than heat. The inverted U-shape is expected for the relationship between SDI and the burden of COPD caused by non-optimal temperatures according to the GPR model, with the inflection point around SDI 0.45. Besides, the improvements were observed in period and cohort effects but were relatively limited in low and low-middle SDI regions. Public health managers should execute more targeted programs to lessen this burden predominantly among lower SDI countries.

A global comprehensive analysis of ambient low temperature and non-communicable diseases burden during 1990-2019

Climate change and health are inextricably linked, especially the role of ambient temperature. This study aimed to analyze the non-communicable disease (NCD) burden attributable to low temperature globally, regionally, and temporally using data from the Global Burden of Disease (GBD) study 2019. Globally, in 2019, low temperature was responsible for 5.42% DALY and 7.18% death of NCDs, representing the age-standardized disability-adjusted life years (DALY) and death rates (per 100,000 population) of 359.6 (95% uncertainty intervals (UI): 306.09, 416.88) and 21.36 (95% UI:18.26, 24.74). Ischemic heart disease was the first leading cause of DALY and death resulting from low temperature, followed by stroke. However, age-standardized DALY and death rates attributable to low temperature have exhibited wide variability across regions, with the highest in Central Asia and Eastern Europe and the lowest in Caribbean and Western sub-Saharan Africa. During the study period (1990-2019), there has been a significant decrease in the burden of NCDs attributable to low temperature, but progress has been uneven across countries, whereas nations exhibiting high sociodemographic index (SDI) declined more significantly compared with low SDI nations. Notably, three nations, including Uzbekistan, Tajikistan, and Lesotho, had the maximum NCDs burden attributed to low temperature and displayed an upward trend. In conclusion, ambient low temperature contributes to substantial NCD burden with notable geographical variations.

Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach

Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events far from the mean, such as a day with an unusually high number of hospitalizations. Health peaks represent a heavy burden for the public health system; they are, however, usually studied specifically when they occur (e.g., the European 2003 heatwave). Specific analyses are needed, using appropriate statistical tools. Quantile regression can provide such analysis by focusing not only on the conditional median, but on different conditional quantiles of the dependent variable. In particular, high quantiles of a health issue can be treated as health peaks. In this study, quantile regression is used to model the relationships between conditional quantiles of cardiovascular variables and meteorological variables in Montreal (Canada), focusing on health peaks. Results show that meteorological impacts are not constant throughout the conditional quantiles. They are stronger in health peaks compared to quantiles around the median. Results also show that temperature is the main significant variable. This study highlights the fact that classical statistical methods are not appropriate when health peaks are of interest. Quantile regression allows for more precise estimations for health peaks, which could lead to refined public health warnings.

Effects of ambient temperature and fall-related injuries in Ma'anshan, Anhui Province, China: a distributed lag nonlinear analysis

Despite the significant economic cost of falls and injuries to individuals and communities, little is known about the impact of meteorological factors on the incidence of fall-related injuries (FRIs). Therefore, a time-series study was conducted to explore the effects of meteorological factors on FRIs in Ma'anshan City, East China. Injury data from 2011 to 2017 were collected from the National Injury Monitoring Station in Ma'anshan City. A distributed lag nonlinear model was used in this study to evaluate the correlation between ambient temperature and fall injuries. The results showed a significant exposure-response relationship between temperature and FRIs in Ma'anshan City. The high temperatures increased the risk of FRIs (RR = 1.110; 95% CI, 1.005-1.225; lag 0). The lag effect appeared at lag 10 (RR = 1.032; 95% CI, 1.003-1.063), and then gradually remained stable after lag 25 (RR = 1.077; 95% CI, 1.045-1.110). The effect of ambient temperature varied with age and gender. The lag effect of high temperature appeared in the male group after lag 15 (RR = 1.042; 95% CI, 1.006-1.079). In contrast, the effect of the female group appeared for the first time at lag 0 (RR = 1.187; 95% CI, 1.042-1.352). And the ≥ 60 years subgroup seemed to be more sensitive in low temperature (RR = 1.017; 95% CI, 1.004-1.031; lag 0; RR = 1.003; 95% CI, 1.000-1.007; lag 25). The cumulative result is similar to the single-day effect. From the results, this study would help the establishment of fall-related injury prediction and provide evidence for the formulation and implementation of preventive strategies and measures in the future.

A heat-health watch and warning system with extended season and evolving thresholds

BACKGROUND

Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere.

METHODS

The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality.

RESULTS

We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season.

CONCLUSIONS

This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July-August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.