Health-related vulnerability to climate extremes in homoclimatic zones of Amazonia and Northeast region of Brazil

Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient

Climate change is one of the most relevant challenges that the world has to deal with. Studies that aim to understand the behavior of environmental and atmospheric variables and the way they relate to each other can provide helpful insights into how the climate is changing. However, such studies are complex and rarely found in the literature, especially in dealing with data from the Brazilian territory. In this paper, we analyze four environmental and atmospheric variables, namely, wind speed, radiation, temperature, and humidity, measured in 27 Weather Stations (the capital of each of the 26 Brazilian states plus the federal district). We use the detrended fluctuation analysis to evaluate the statistical self-affinity of the time series, as well as the cross-correlation coefficient ρDCCA to quantify the long-range cross-correlation between stations, and a network analysis that considers the top 10% ρDCCA values to represent the cross-correlations between stations better. The methodology used in this paper represents a step forward in the field of hybrid methodologies, combining time series and network analysis that can be applied to other regions, other environmental variables, and also to other fields of research. The application results are of great importance to better understand the behavior of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production.

Profiling sociodemographic attributes and extreme precipitation events as mediators of climate-induced disasters in municipalities in the state of Minas Gerais, Brazil

Introduction

Data indicate an increase in the number of natural disasters in Brazil, with a large share of these events occurring in the state of Minas Gerais. This study examines precipitation-related natural disasters recorded between 1991 and 2016 in Minas Gerais by identifying municipality profiles (encompassing the number of droughts, flash floods, and flooding events), their sensitivity to geophysical and extreme climatic exposure, and their relation to sociodemographic and infrastructure characteristics.

Methods

We combine climate data on seven extreme rainfall indices with elevation data for each municipal seat. We obtained data on droughts, flash floods, and floods from the Center for Engineering and Civil Defense Research and Studies. Population and socio-sanitary characteristics were obtained from the 2010 Brazilian Demographic Census. First, we modeled the climatic-geo-socio-sanitary data using latent class analysis as a pure latent cluster model (LCM) without covariates on seven extreme precipitation indices coupled with altitude data. Subsequently, the LCM was used to identify precipitation-related disaster clusters, including clusters from the 1S-LCM as an active covariate (2S-LCM). Finally, we utilized sociodemographic and infrastructure variables simultaneously with the clusters from the 2S-LCM on an LCM without active covariates (3S-LCM).

Results

Our results show an increase in precipitation-related disasters in Minas Gerais, with municipalities located in the northern part of the state being particularly affected. The state registered 5,553 natural disasters in this period, with precipitation-related disasters representing 94.5% of all natural disasters. The 1S-LCM identified four homoclimatic zones, encompassing a low-altitude dry zone, a relatively low-altitude intermediately wet zone, a relatively high-altitude intermediately wet zone, and a high-altitude wet zone. The 2S-LCM produced four precipitation-related disaster classes, denominated low risk, high risk of excess precipitation, intermediate risk of precipitation deficit and excess, and high risk of precipitation deficit.

Discussion

Cities with better infrastructure and sociodemographic profiles in semi-arid regions are more resilient to droughts. In richer areas, floods are still a concern where incomplete urbanization transitions may undermine resilience to these events as they increase in intensity with the advance of climate change.