Publications

Export 2 results:
Sort by: Author [ Title  (Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U [V] W X Y Z   [Show ALL]
V
Benmarhnia, T, Deguen S, Kaufman JS, Smargiassi A.  2015.  Vulnerability to heat-related mortality a systematic review, meta-analysis, and meta-regression analysis. Epidemiology. 26:781-793.   10.1097/EDE.0000000000000375   AbstractWebsite

Background: Addressing vulnerability to heat-related mortality is a necessary step in the development of policies dictated by heat action plans. We aimed to provide a systematic assessment of the epidemiologic evidence regarding vulnerability to heat-related mortality.Methods: Studies assessing the association between high ambient temperature or heat waves and mortality among different subgroups and published between January 1980 and August 2014 were selected. Estimates of association for all the included subgroups were extracted. We assessed the presence of heterogeneous effects between subgroups conducting Cochran Q tests. We conducted random effect meta-analyses of ratios of relative risks (RRR) for high ambient temperature studies. We performed random effects meta-regression analyses to investigate factors associated with the magnitude of the RRR.Results: Sixty-one studies were included. Using the Cochran Q test, we consistently found evidence of vulnerability for the elderly ages >85 years. We found a pooled RRR of 0.99 (95% confidence interval [CI] = 0.97, 1.01) for male sex, 1.02 (95% CI = 1.01, 1.03) for age >65 years, 1.04 (95% CI = 1.02, 1.07) for ages >75 years, 1.03 (95% CI = 1.01, 1.05) for low individual socioeconomic status (SES), and 1.01 (95% CI = 0.99, 1.02) for low ecologic SES.Conclusions: We found strongest evidence of heat-related vulnerability for the elderly ages >65 and >75 years and low SES groups (at the individual level). Studies are needed to clarify if other subgroups (e.g., children, people living alone) are also vulnerable to heat to inform public health programs.

Benmarhnia, T, Sottile MF, Plante C, Brand A, Casati B, Fournier M, Smargiassi A.  2014.  Variability in Temperature-Related Mortality Projections under Climate Change. Environmental Health Perspectives. 122:1293-1298.   10.1289/ehp.1306954   AbstractWebsite

Background: Most studies that have assessed impacts on mortality of future temperature increases have relied on a small number of simulations and have not addressed the variability and sources of uncertainty in their mortality projections.Objectives: We assessed the variability of temperature projections and dependent future mortality distributions, using a large panel of temperature simulations based on different climate models and emission scenarios.Methods: We used historical data from 1990 through 2007 for Montreal, Quebec, Canada, and Poisson regression models to estimate relative risks (RR) for daily nonaccidental mortality in association with three different daily temperature metrics (mean, minimum, and maximum temperature) during June through August. To estimate future numbers of deaths attributable to ambient temperatures and the uncertainty of the estimates, we used 32 different simulations of daily temperatures for June-August 2020-2037 derived from three global climate models (GCMs) and a Canadian regional climate model with three sets of RRs (one based on the observed historical data, and two on bootstrap samples that generated the 95% CI of the attributable number (AN) of deaths). We then used analysis of covariance to evaluate the influence of the simulation, the projected year, and the sets of RRs used to derive the attributable numbers of deaths.Results: We found that < 1% of the variability in the distributions of simulated temperature for June-August of 2020-2037 was explained by differences among the simulations. Estimated ANs for 2020-2037 ranged from 34 to 174 per summer (i.e., June-August). Most of the variability in mortality projections (38%) was related to the temperature-mortality RR used to estimate the ANs.Conclusions: The choice of the RR estimate for the association between temperature and mortality may be important to reduce uncertainty in mortality projections.