Bootstrap approach to validate the performance of models for predicting mortality risk temperature in Portuguese Metropolitan Areas

Abstract

Background There has been increasing interest in assessing the impacts of extreme temperatures on mortality due to diseases of the circulatory system. This is further relevant for future climate scenarios where marked changes in climate are expected. This paper presents a solid method do identify the relationship between extreme temperatures and mortality risk by using as predictors simulated temperature data for cold and hot conditions in two urban areas in Portugal. Methods Based on the mortality and meteorological data from Porto Metropolitan Area (PMA) and Lisbon Metropolitan Area (LMA), a distributed lag nonlinear model (DLNM) was implemented to estimate the temperature effects on mortality due to diseases of the circulatory system. The performance of the models was validated via bootstrapping approaching by creating resamples with replacement from the validating data. Bootstrapping was also used to identify the best candidate model and to evaluate the sensitivity of the spline functions to the exposure-lag-response relationship. Results It is found that the model is able to reproduce the temperature-related mortality risk for two metropolitan areas. Temperature previously simulated by climate models is useful and even better than observed temperature. Although, the biases in predictions in both metropolitan areas are low, mortality risk predictions in PMA are more accurate than in LMA. Using parametric bootstrapping, we found that the overall cumulative association estimated under different bi-dimensional exposure-lag-response relationship are relatively stable, especially for the model selected by Quasi-Akaike Information Criteria (QAIC). Exposure to summer temperature conditions is best related to mortality risk. The association between winter temperature and mortality risk is somewhat less strong. Conclusions The use of QAIC to choose from several candidate models provides valid predictions and reduced the uncertainty in the estimated relative risk for circulatory disease mortality. Our findings can be applied to better understand the characteristics and facilitate the prevention of circulatory disease mortality in Porto and Lisbon Metropolitan Areas, namely if we consider the actual context of climate change. Electronic supplementary material The online version of this article (10.1186/s12940-019-0462-x) contains supplementary material, which is available to authorized users.

Projections of Temperature-Attributable Deaths in Portuguese Metropolitan Areas: A Time-Series Modelling Approach

“…However, this is not sufficient. The goal of a national health adaptation strategy or plan should be to build the resilience of the existing health system [34,[46][47][48][49]. The lack of a broad climate monitoring network has been a substantial obstacle to the creation of local climate adaptation plans to real and credible climate changes.…”

“…Specifically, average, maximum, and minimum near-surface (2-m high) daily atmospheric temperatures were extracted for each metropolitan area using the model grid-point closest to each location and for the different climates. These temperature data were previously submitted to bias correction [34] to minimize model systematic errors regarding observations.…”

Statistical Modelling of Temperature-Attributable Deaths in Portuguese Metropolitan Areas under Climate Change: Who Is at Risk?

“…The association between extreme temperatures and mortality in urban areas has been identified in previous studies [1][2][3][4][5][6][7][8][9][10][11]. Additionally, studies from Portugal report that a large proportion of such excess mortality in the hot season is caused by cardiovascular diseases, cerebrovascular diseases, and diseases of the respiratory system.…”

“…As reported by Dosio et al [65], bias correction of climate simulations is deemed necessary for climate impact studies because climate model outputs may present bias when compared with observed data [66]. Previously, the present data were submitted to bias correction to minimize model systematic errors to relative observations [9,55].…”

“…Concerning the cold season, Portugal has been mentioned over the years as having one of the highest rates of excess winter mortality in Europe [14][15][16][17], which may be related to socioeconomic conditions and population health status. Most often, this excess has been attributed to the influenza virus, mainly among the most vulnerable populations, due to the spread of respiratory infections and the decompensation caused by chronic illnesses [1,9,[18][19][20].…”