Loss and damage affecting the public health sector and society resulting from flooding and flash floods in Brazil between 2010 and 2014 - based on data from national and global information systems.

This article outlines the results of a descriptive study that analyses loss and damage caused by hydrometeorological disasters in Brazil between 2010 and 2014 using the EM DAT (global) and S2iD (national) databases. The analysis shows major differences in the total number of disaster events included in the databases (EM-DAT = 36; S2iD = 4,070) and estimated costs of loss and damage (EM-DAT - R$ 9.2 billion; S2iD - R$331.4 billion). The analysis also shows that the five states most affected by these events are Santa Catarina, Rio Grande do Sul, Minas Gerais, São Paulo and Paraná in Brazil's South and Southeast regions and that these results are consistent with the findings of other studies. The costs of disasters were highest for housing, public infrastructure works, collectively used public facilities, other public service facilities, and state health and education facilities. The costs associated with public health facilities were also high. Despite their limitations, both databases demonstrated their usefulness for determining seasonal and long-term trends and patterns, and risk areas, and thus assist decision makers in identifying areas that are most affected by and vulnerable to natural disasters.

Danos materiais causados à Saúde Pública e à sociedade decorrentes de inundações e enxurradas no Brasil, 2010-2014: dados originados dos sistemas de informação global e nacional / Loss and damage affecting the public health sector and society resulting from flooding and flash floods in Brazil between 2010 and 2014 - based on data from national and global information systems

Resumo Este estudo apresenta uma análise de dados sobre danos materiais por desastres hidrológicos no Brasil, de 2010 a 2014. Trata-se de em estudo descritivo que contrasta os resultados de dois bancos de dados de desastre: EM-DAT (global) e S2iD (nacional). A análise mostra diferenças tanto no número total de ocorrências de desastres (EM-DAT = 36; S2iD = 4070), quanto nos danos materiais resultantes (EM-DAT – R$ 9,2 bilhões; S2iD – R$ 331,4 bilhões). A análise da distribuição de eventos permite concluir que os cinco estados mais afetados por tais eventos (SC, RS, MG, SP e PR) estão nas regiões sul e sudeste, condizente com achados de outros estudos. As edificações que tiverem maiores prejuízos materiais foram, nesta ordem: unidades habitacionais, obras de infraestrutura pública, instalações públicas de uso comunitário, instalações públicas prestadoras de outros serviços, instalações públicas de ensino e instalações públicas de saúde. O prejuízo a instalações pública de saúde apresentou valores elevados. Apesar das limitações de ambos, o EM-DAT e o S2iD demonstraram sua utilidade em determinar tendências sazonais e de longo prazo, bem como padrões e áreas onde se concentram riscos, auxiliando tomadores de decisão na identificação das áreas mais afetadas e vulneráveis aos desastres.

Abstract This article outlines the results of a descriptive study that analyses loss and damage caused by hydrometeorological disasters in Brazil between 2010 and 2014 using the EM DAT (global) and S2iD (national) databases. The analysis shows major differences in the total number of disaster events included in the databases (EM-DAT = 36; S2iD = 4,070) and estimated costs of loss and damage (EM-DAT – R$ 9.2 billion; S2iD – R$331.4 billion). The analysis also shows that the five states most affected by these events are Santa Catarina, Rio Grande do Sul, Minas Gerais, São Paulo and Paraná in Brazil’s South and Southeast regions and that these results are consistent with the findings of other studies. The costs of disasters were highest for housing, public infrastructure works, collectively used public facilities, other public service facilities, and state health and education facilities. The costs associated with public health facilities were also high. Despite their limitations, both databases demonstrated their usefulness for determining seasonal and long-term trends and patterns, and risk areas, and thus assist decision makers in identifying areas that are most affected by and vulnerable to natural disasters.