Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons.

This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 µm (µm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011-2020 wildfire season (June-July-August-September-October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value < 0.05) correlation (r) between RSDxPBI (r RSD = 0.58) and PNEUxPBI (r PNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (r RSD = 0.48), PNEUxPBI (r PNEU = 0.47), COPDxPBI (r COPD = 0.45), CSDxAPI (r CSD = 0.70), RSDxAPI (r CSD = 0.71), PNEUxAPI (r PNEU = 0.49), and COPDxAPI (r PNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.

Imputation of precipitation data in northeast Brazil.

This article evaluates four statistical methods of multiple imputation to fill in the missing data of daily precipitation in Northeast Brazil (NEB). We used a daily database collected by 94 rain gauges distributed in NEB from January 1, 1986 to December 31, 2015. The methods were: random sampling from the observed values; predictive mean matching, Bayesian linear regression; and bootstrap expectation maximization algorithm (BootEm). To compare these methods, missing data from the original series were initially excluded. The next step was to create three scenarios for each method, in which 10\%, 20\% and 30\% of the data were removed at random. The BootEM method presented the best statistical results. With the average bias between the complete series and the imputed series values ranging between -0.91 and 1.30 mm/day. The values of the Pearson correlation ranging between 0.96, 0.91 and 0.86 respectively for 10\%, 20\% and 30\% missing data. We conclude that this is an adequate method for the reconstruction of historical precipitation data in NEB.

Summary of the first Brazilian Symposium on Human Biometeorology.

This brief background highlights Brazil as a 'climate-health hotspot', i.e. a country where climate affects local populations negatively through multiple pathways (Di Napoli et al. BMC Public Health 22(1):1-8, 2022). Knowledge gaps still need to be filled concerning the various climaterelated dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers in the country (Krüger et al. Int J Biometeorol 66(7):1297-1315, 2022). Motivated by this, the first Brazilian Symposium on Human Biometeorology (Simpósio Brasileiro de Biometeorologia Humana 2022) was organized and held at the Federal University of Rio Grande do Norte (UFRN) in Natal, northeastern Brazil, between July 4 and 8, 2022. The symposium was organized as a hybrid event by a committee composed of researchers acting in different regions of the country, and who had an ongoing research collaboration on matters related to human biometeorology. The event was partly sponsored by the ISB and partly self-supported by the organizers and institutions involved. The symposium aimed to promote the development of the research area on human biometeorology in Brazil in facing challenges imposed by a globally and locally changing climate. To achieve this, the symposium focused on five main topics of discussion: a) climate-driven diseases; b) thermal comfort, urban and architectural biometeorology; c) atmospheric pollution and health; d) climate change; e) climate, health and climate change. This summary highlights the main findings, future research directions, and policy implications in each topic from the presentations and panel discussions.

A first approach to human biometeorology research in Brazil: a systematic review and meta-analysis.

This systematic review aims to give an overview of the diversity of research areas related to human biometeorology in Brazil. The main focus of this paper addresses research trends, represented by published papers with national and international authorship, main contributions and shortcomings, as well as challenges and prospects of research in this area of study. An extensive literature search was conducted in the Scopus, Web of Science, and Science Direct databases so as to identify relevant publication output up to July 2021 related to the research area. The screening resulted in 96 studies chosen for full-text reading. Overall, results indicated a reduced amount of articles on the subject matter published internationally, with noticeable gaps in research in some regions of the country, such as the Amazon region and in the Brazilian Midwest region. Research gaps in relevant areas have been identified with limited output in the climate dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers. Such gaps should further encourage researchers to engage in research focused on those areas.

An epidemiological index for drought vulnerability in the Rio Grande do Norte State, Brazil.

In the Northeast Brazil (NEB), the impacts of climate extreme events such as severe droughts are aggravated by poverty and poor socioeconomic conditions. In this region, such events usually result in the spread of endemic diseases, problems in water distribution, and agricultural losses, often leading to an increase in the population's vulnerability. Thus, this study aims to evaluate the microregions of the Rio Grande do Norte (RN) state, in the NEB, according to the Epidemiological Index for Drought Vulnerability (EIDV). We mapped and classified the microregions according to three dimensions of vulnerability: risk, susceptibility, and adaptive capacity. We also verified potential associations between drought risk and epidemiological vulnerability. The EIDV was calculated by considering the three dimensions of vulnerability as mutually exclusive events and applying the third axiom of probability. Then we carried out a cluster analysis in order to classify the microregions according to similarities in the EIDV. Odds ratio were also calculated in order to evaluate the odds of microregions having a high susceptibility to diseases and high vulnerability given the drought risk. Results showed that the Pau dos Ferros, Seridó Ocidental, Seridó Oriental, and Umarizal microregions were the most vulnerable, while Natal and Litoral Sul were the least vulnerable. Regarding the dimensions of vulnerability, we observed that almost the entire RN state exhibited high drought risk. Pau dos Ferros and Umarizal had the highest susceptibility and Litoral Nordeste presented the worst adaptive capacity to the effects of drought on health. The EIDV revealed that the population of the RN state needs improvements in living conditions and health, since socioeconomic status is one of the factors that most influence the vulnerability of microregions, which in turn is aggravated by drought risk.

Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia.

BACKGROUND/OBJECTIVES: Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon. METHODS: We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections. RESULTS: The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3 °C, mean incidence rates during epidemics could double. CONCLUSION: In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries.

Flight height preference for oviposition of mosquito (Diptera: Culicidae) vectors of sylvatic yellow fever virus near the hydroelectric reservoir of Simplício, Minas Gerais, Brazil.

In this study, the oviposition behavior of mosquito species exhibiting acrodendrophilic habits was investigated. The study was conducted near the Simplicio Hydroelectic Reservoir (SHR) located on the border of the states of Minas Gerais and Rio de Janeiro, Brazil. Samples were collected using oviposition traps installed in forest vegetation cover between 1.70 and 4.30 m above ground level during the months of April, June, August, October, and December of 2011. Haemagogus janthinomys (Dyar), Haemagogus leucocelaenus (Dyar and Shannon), Aedes albopictus (Skuse), and Aedes terrens (Walker) specimens were present among the collected samples, the first two of which being proven vectors of sylvatic yellow fever (SYF) in Brazil and the latter is a vector of dengue in mainland Asia. As the data set was zero-inflated, a specific Poisson-based model was used for the statistical analysis. When all four species were considered in the model, only heights used for egg laying and months of sampling were explaining the distribution. However, grouping the species under the genera Haemagogus Williston and Aedes Meigen showed a significant preference for higher traps of the former. Considering the local working population of SHR is very large, fluctuating, and potentially exposed to SYF, and that this virus occurs in almost all Brazilian states, monitoring of Culicidae in Brazil is essential for assessing the risk of transmission of this arbovirus.

A case study of the influence of local weather on Aedes aegypti (L.) aging and mortality.

The survival rate of mosquitoes is an important topic that affects many aspects of decision-making in mosquito management. This study aims to estimate the variability in the survival rate of Ae. aegypti, and climate factors that are related to such variability. It is generally assumed that the daily probability of mosquito survival is independent of natural environment conditions and age. To test this assumption, a three-year fieldwork (2005-2007) and experimental study was conducted at Fortaleza-CE in Brazil with the aim of estimating daily survival rates of the dengue vector Aedes aegypti under natural conditions in an urban city. Survival rates of mosquitoes may be age-dependent and statistical analysis is a sensitive approach for comparing patterns of mosquito survival. We studied whether weather conditions occurring on a particular day influence the mortality observed on that particular day. We therefore focused on the impact of daily meteorological fluctuations around a given climate average, rather than on the influence of climate itself. With regard to survival time, multivariate analyses using the stepwise logistic regression model, adjusted for daily temperature, relative humidity, and saturated vapor pressure deficit (SVPD), suggest that age, the seasonal factor, and the SVPD were the most dependent mortality factors. Similar results were obtained using the Cox proportional hazard model, which explores the relationships between the survival and explanatory variables.

The influence of local environment on the aging and mortality of Aedes aegypti (L.): case study in Fortaleza-CE, Brazil.

It is generally assumed that the daily probability of survival of mosquitoes is independent of age. To test this assumption we have conducted a three-year experimental fieldwork study (2005-2007) at Fortaleza-CE in Brazil, determining daily survival rates of the dengue vector Aedes aegypti (L.). Survival rates of adult Ae. aegypti may be age-dependent and the statistical analysis is a sensitive approach for comparing patterns of mosquito survival. The mosquito survival data were better fit by a Weibull survival function than by the more traditionally used Gompertz or logistic survival functions. Gompertz, Weibull, or logistic survival functions often fit the survival, and the tails of the survival curves usually appear to fall between the values predicted by the three functions. We corroborate that the mortality of Ae. aegypti in semi-natural conditions may no more be considered as a constant phenomenon during the life of adult mosquitoes but varies according to the age and environmental conditions under a tropical climate. This study estimates the variability in the survival rate of Ae. aegypti and environmental factors that are related to such variability. The statistical analysis shows that the fitting ability, concerning the hazard function, was in decreasing order: Seasonal Cox, the three-parameter Gompertz, and the three-parameter Weibull, that was similar to the three-parameter logistic. The advantage of using the Cox model is that it is convenient for exploring the relationship between survival and several explanatory variables. The Cox model has the advantage of preserving the variable in its original quantitative form and of using a maximum of information. The survival analyses indicate that mosquito mortality is both age- and environment-dependent.

Estimação de sub-registros de óbitos em pequenas áreas com os métodos bayesiano empírico e algoritmo EM / Estimation of death underreporting at small areas using empiric Bayesian and EM algorithm methods / /Estimación de subregistros de fallecimientos en pequeñas áreas con los métodos bayesiano empírico y algoritmo EM

Um grande problema em estimativas demográficas no Brasil diz respeito ao nível e padrão da mortalidade. Os demógrafos que trabalham com mortalidade, no país, ainda não se sentem tão seguros sobre o real comportamento desta componente da dinâmica populacional. Por outro lado, necessita-se da disponibilidade de indicadores de mortalidade para níveis geográficos mais desagregados, sobretudo municípios. O problema é que quanto mais desagregado, mais complexo se torna o trabalho de estimar qualquer indicador social ou demográfico. Neste trabalho, objetiva-se estimar e propor correção de sub-registros de óbitos no nível municipal, segundo grupos etários, por meio de dois métodos: estimador bayesiano empírico (BE) e algoritmo EM (Expectation-Maximization). Para que os dois métodos fossem operacionalizados entre municípios semelhantes, foram realizados dois exercícios: agruparam-se os municípios segundo a mesorregião; e agruparam-se os municípios em grupos homogêneos, gerados a partir de uma análise de cluster utilizando as variáveis grau de urbanização, proporção de óbitos por causas externas e a população de cada município. Foram utilizados dados do Estado do Rio Grande do Norte, referentes a 2000. Para o total do Estado, estimou-se um sub-registro de 11% com o estimador BE e de 12,9% com o algoritmo EM. Outro resultado importante é a possibilidade de avaliar o grau de cobertura de óbitos por grupos etários em municípios e em qualquer nível de agregação a partir deste.

Level and standard of mortality are major demographic estimation problems in Brazil. Demographists dealing with mortality in Brazil still do not fell assured of the real behavior of this population dynamics component. On the other hand, there is a need for mortality indicators available for more disaggregated geographic levels, mostly municipalities. The difficulty is that the more disaggregated, the more complex is the task for estimating any social or demographic indicator. In this study, we aimed to estimate and to propose the correction of death underreporting at the municipal level, according to age, using two methods: the empiric Bayesian estimator (BE) and the EM (Expectation-Maximization) algorithm. For the two methods to be operational within comparable municipalities, two steps were performed: we grouped the municipalities according to a mesoregion; and we grouped them into two homogeneous groups, created from a cluster analysis using the variables level of urbanization, proportion of death from external causes and the population of each municipality. We used data collected in 2000 from the State of Rio Grande do Norte. For the entire State, we estimated underreporting to be 11% using the BE estimator, and 12.9 % using the EM algorithm. Another important finding was the capability to assess the level of death coverage by age groups in the municipalities and, at any level of aggregation.

Un gran problema, en lo que se refiere a estimativas demográficas en Brasil, está relacionado con el nivel y patrón de la mortalidad. Los demógrafos que trabajan con mortalidad en el país todavía no se sienten muy seguros sobre el comportamiento real de este componente de la dinámica poblacional. Por otro lado, es necesario que se disponga de indicadores de mortalidad para niveles geográficos más desagregados, sobre todo municipios. El problema es que cuanto más desagregado, más complejo se hace el trabajo de estimar cualquier indicador social o demográfico. Este trabajo tiene por objetivo estimar y proponer una corrección de subregistros de fallecimientos en el nivel municipal, según grupos de edad, por medio de dos métodos: estimador bayesiano empírico (BE) y algoritmo EM (Expectation-Maximization). Con el objeto de que los dos métodos fueran puestos en funcionamiento entre municipios semejantes, se realizaron dos ejercicios: se agruparon los municipios según la mesorregión; y se agruparon los municipios en grupos homogéneos, generados a partir de un análisis de cluster, utilizando las variables grado de urbanización, proporción de óbitos por causas externas y la población de cada municipio. Se utilizaron datos del Estado de Río Grande do Norte, referentes al año 2000. Para el total del Estado, se estimó un subregistro de un 11% con el estimador BE y de un 12,9% con el algoritmo EM. Otro resultado importante es la posibilidad de evaluar el grado de cobertura de óbitos por grupos de edad en municipios y en cualquier nivel de agregación a partir de este nivel.

Evolução da mortalidade e dos anos potenciais e produtivos de vida perdidos por câncer de mama em mulheres no Rio Grande do Norte, entre 1988 e 2007 / Breast cancer mortality trends among women and years of potential life Lost in the State of Rio Grande do Norte, Brazil, from 1988 to 2007

Objetivos: descrever a evolução da mortalidade por câncer de mama e estimar os anos potenciais de vida perdidos (APVP) e anos produtivos de vida perdidos (APrVP). Metodologia: usou-se uma série temporal sobre óbitos de mulheres entre 20 e 70 anos cuja causa básica foi o câncer de mama; utilizou-se como fonte de dados o Sistema de Informações sobre Mortalidade (SIM); foram calculadas taxas de mortalidade brutas e padronizadas pela população mundial, APVP, APrVP e os seus respectivos coeficientes, por ano de ocorrência. Resultados: o risco de morrer por câncer de mama cresceu ao longo do período – taxa média de mortalidade ajustada de 9,7/100.000 mulheres –, sendo maior para a faixa de 50 a 59 anos de idade; perderam-se, no período estudado, 20.983,5 APVP e 10.853,5 APrVP. Conclusão: os resultados apontam à necessidadede estratégias de rastreamento e diagnóstico precoce de câncer de mama como cuidados indispensáveis às mulheres.

Objectives: to describe breast cancer mortality trends, to estimate potential years of life lost (PYLL) and potentially productive years of life lost (PPYLL). Methodology: a mortality time series comprising data on 20 to 70 year-oldwomen who died from breast cancer and the Mortality Information System (MIS) were used as database; crude and standard international mortality rates, PYLL and PPYLL were calculated by year of occurrence. Results: the risk of awoman dying from breast cancer increased during the period of analysis – the average adjusted mortality rate was9.7 per 100,000 women – being higher for the age group of 50 to 59 years; PYLL and PPYLL were 20983.5 and 10853.5 per 100,000, respectively. Conclusion: esults suggest the need for screening strategies and early diagnosis of breast cancer as priority actions related to women’s health care.